WO2025183993A1

WO2025183993A1 - Methods and devices for glioma circuit mapping and neuromodulation therapy

Info

Publication number: WO2025183993A1
Application number: PCT/US2025/016809
Authority: WO
Inventors: Shawn L. HERVEY-JUMPER; Andy Daniel; Saritha KRISHNA
Original assignee: University of California Berkeley; University of California San Diego UCSD
Current assignee: University of California Berkeley; University of California San Diego UCSD
Priority date: 2024-02-29
Filing date: 2025-02-21
Publication date: 2025-09-04
Anticipated expiration: 2026-08-29

Abstract

Methods for mapping and characterizing the neuronal inputs and circuit dynamics unique to each individual glioma are provided. Novel experimental models, in conjunction with cutting edge brain circuit interrogation tools, are used to identify and characterize the role of specific tumor heterogeneities in glioma progression. Implantable electrodes are used to chronically map and characterize gliomas in order to identify electrical biomarkers of various tumor features, including tumor heterogeneities, of interest such as those associated with tumor growth, glioma progression, or treatment response. Identified electrical biomarkers are used to monitor disease states and treatment effects and, aided by an understanding of the role specific heterogeneities play in driving glioma progression, enable the development of personalized treatments for patients afflicted with malignant gliomas. In addition, methods and devices for monitoring and therapeutically treating glioma growth at a patient specific level using identified electrical biomarkers are also provided.

Description

METHODS ND DEVICES FOR GLIOMA CIRCUIT MAPPING AND NEUROMODULATION

THERAPY

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Patent Application No. 63/559,691, filed February 29, 2024, which application is incorporated herein by reference in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

This invention was made with government support under K08 NS110919 awarded by the National Institutes of Health. The government has certain rights in the invention.

INTRODUCTION

Malignant gliomas are a major cause of neurological morbidity and mortality, with high-grade gliomas (HGG) being the leading cause of brain tumor death in both children and adults alike. However, despite their relative prevalence, no effective therapies exist for treating malignant gliomas, and the average life expectancy after diagnosis remains only about a year. Recent efforts seeking to characterize the determinants of glioma cell proliferation and invasion have experienced some success. Unfortunately, despite a burgeoning understanding of the molecular drivers of glioma cell behavior, the development of new treatments has been encumbered by a limited understanding of the tumor- specific and evolving inputs driving glioma progression.

SUMMARY

Thus, there is a critical need for improved and useful tools for understanding and monitoring the processes that drive malignant glioma proliferation. The inventors discovered that tumor driven functional remodeling of brain circuitry governs glioma progression and, further, that subpopulations of glioma cells exhibiting specific characteristics or features may play an outsized role in promoting such functional remodeling. These discoveries led the inventors to a fundamentally different approach in monitoring and controlling malignant glioma circuity in patients with malignant brain tumors wherein, as malignant glioma network establishment and evolution is patient-specific, each specific tumor circuit must be mapped and characterized.

To accomplish this, new and useful methods and systems for mapping and characterizing the neuronal inputs and circuit dynamics unique to each individual malignant tumor are provided. Novel experimental models, in conjunction with cutting edge intact brain circuit interrogation tools, are used to identify and characterize the role of specific tumor heterogeneities, such as specific subpopulations of glioma cells, in malignant glioma progression. Implantable electrodes are used to chronically map and characterize malignant gliomas throughout their progression, including throughout tumor treatment, in order to identify electrical biomarkers of various tumor features (e.g., tumor heterogeneities) of interest such as those associated with tumor growth, tumor progression, and/or treatment response. Identified electrical biomarkers are used to monitor disease states and treatment effects and, aided by knowledge of the role specific heterogeneities play in driving glioma progression (provided, e.g., by the novel experimental models and brain circuit interrogation tools of the disclosure), enable the development of personalized treatments for patients afflicted with malignant brain tumors.

In addition, methods and devices for monitoring and therapeutically treating activity dependent glioma growth at a patient specific level using identified electrical biomarkers are also provided. These methods include the application of electrical stimulation through at least one stimulating electrode in contact with, e.g., depolarizing tumor cells, neurons located within a tumor infiltrated region of a patient’s brain, or neurons projecting into a tumor infiltrated region of a patient’s brain. Embodiments of the disclosed methods and systems, e.g., as described in greater detail below, find use in a variety of applications where it is desirable to characterize, monitor, and/or treat brain tumors, including malignant gliomas, in order to significantly advance disease understanding and improve patient outcomes for individuals having a variety of presently lethal brain cancers.

In one aspect, methods of identifying biomarkers of tumor features of interest for a brain tumor of a subject are provided. Aspects of the methods include: obtaining data characterizing a tumor infiltrated volume of the brain; and determining a relationship between the obtained tumor characterizing data and a tumor feature of interest. In some embodiments, the obtained tumor characterizing data is determined to be associated with the tumor feature of interest (i.c., the tumor characterizing data is a biomarkcr of the tumor feature of interest). In some embodiments, the tumor feature of interest is related to tumor growth, tumor progression, and/or treatment response. In these cases, the biomarker of the tumor feature of interest may include any characterizing data affecting tumor growth, tumor progression, and/or treatment response such as, e.g., any characterizing data determined to promote tumor growth.

In another aspect, methods of identifying electrical biomarkers of tumor features in the brain of a subject are provided. Aspects of the methods include: non-invasively mapping the brain of the subject to identify a first volume of the brain infiltrated by a first tumor; obtaining data associated with or indicative of one or more features of interest for the first tumor; positioning a first measurement electrode configured to measure electrical brain activity at a location associated with or inside of the first volume of the brain based on the non-invasive mapping; recording the electrical brain activity measured by the first measurement electrode; and identifying an electrical biomarker of a tumor feature of interest from the obtained tumor feature data and the recorded electrical brain activity. In some embodiments, the first tumor is a glioma.

In certain embodiments, the non-invasive mapping includes structural and/or functional imaging (using, e.g., magnetic resonance imaging [MRI] and/or magnetoencephalography [MEG]). In some embodiments, the non-invasive mapping includes mapping the connectivity of the brain such as, e.g., the functional connectivity of the first volume of the brain with a plurality of other volumes of the brain. In some embodiments, the non-invasive mapping includes identifying one or more neural circuits associated with a first section of the first tumor. In some embodiments, the first tumor section corresponds to the first volume of the brain.

In certain embodiments, the tumor feature data includes one or more of a subtype or classification of the first tumor (e.g., a tumor grade and/or a type of cell a population of the first tumor cells originated from or resemble), a characteristic of a microenvironment of the first tumor (e.g., the identification of a cell population and/or protein located within the first tumor microenvironment), information regarding a stimuli received by the subject (e.g., the time the stimuli was received in relation to the recorded electrical brain activity and/or a quantitative metric of stimuli magnitude or intensity), information regarding an action performed by the subject (e.g., the time the action was performed in relation to the recorded electrical brain activity and/or some quantitative metric of action magnitude or intensity or action completion), a neuronal input of the first tumor (determined, e.g., based on the region of the brain in which the first tumor is located and/or the identification of one or more cell populations located within the first tumor), and control data (e.g., data generated by a control volume of a brain). In some embodiments, obtaining the first tumor feature data includes one or more of performing a biopsy of the first tumor, performing a laboratory experiment, analyzing images of the first tumor, recording electrical brain activity within a volume of the subject’s brain not infiltrated by or associated with the first tumor, recording electrical brain activity from one or more additional subjects, recording an action performed by the subject, recording a stimuli received by the subject, and procuring data generated from previously performed experiments and/or studies. In some embodiments, the obtaining includes the step of determining that the obtained tumor feature data is associated with a tumor feature.

In certain embodiments, the first measurement electrode is positioned based on the non-invasive mapping and/or the obtained tumor feature data. In some embodiments, the first measurement electrode is positioned at a location inside of the first volume of the brain. In other embodiments, the first measurement electrode is positioned at a location functionally connected with or immediately adjacent to the first volume of the brain. In some embodiments, one or more additional measurement electrodes configured to measure electrical brain activity are positioned at a location inside of, functionally connected with, and/or immediately adjacent to the first volume of the brain. In some embodiments, the measurements of the first measurement electrode are recorded based on measurements generated by the one or more additional measurement electrodes. In some embodiments, recording the measurements of the first measurement electrode includes: monitoring the measurements generated by the one or more additional measurement electrodes for a specific pattern or event of electrical brain activity; and recording the measurements of the first measurement electrode when the specific pattern or event of electrical brain activity is detected. In some embodiments, the measurements of the one or more of additional measurement electrodes are recorded based on measurements generated by the first measurement electrode. In certain embodiments, the identified electrical biomarker occurs during a resting state and/or during the performance of a specific task or reception of a specific stimuli. In some embodiments, the identified electrical biomarker relates to hyperexcitability. In some embodiments, the identified electrical biomarker relates to the recruitment of the first volume of the brain into a specific neural circuit. In some embodiments, the identified electrical biomarker relates to a dynamic of a specific neural circuit or neural network involving the first volume of the brain or, e.g., a population of neurons of such a circuit or network.

In certain embodiments, the tumor feature data is obtained at two or more different timepoints (e.g., two or more different timepoints at least a month or more apart). In some embodiments, electrical brain activity measured by the first electrode is recorded for each timepoint. In some embodiments, the electrical brain activity measurements are continuously recorded during the time period from the first timepoint to the final timepoint. In other embodiments, one or more distinct recordings are generated for each timepoint. In some embodiments, generating the one or more distinct recordings includes: continuously monitoring the electrical brain activity measurements from each measurement electrode positioned at a location associated with or inside of the first volume of the brain during the time period from the first timepoint to the final timepoint for a specific pattern or event of electrical brain activity; and recording the measurements from one or more measurement electrodes when the specific pattern or event of electrical brain activity is detected.

In certain embodiments, the tumor feature data is associated with or indicative of tumor growth, tumor progression, and/or treatment response. In some embodiments, the tumor feature data includes the total volume of the first tumor and/or the distribution of the first tumor within the brain at the two or more different timepoints and, e.g., an electrical biomarker of tumor growth is identified using the tumor feature data and the recorded electrical brain activity measurements. In some embodiments, the tumor feature data includes a subtype or classification of the first tumor and/or data associated with or indicative of the connectivity of the first tumor at the two or more different timepoints and, e.g., an electrical biomarker of tumor progression is identified using the tumor feature data and the recorded electrical brain activity measurements.

In certain embodiments, the method further includes treating the first tumor in the subject. In some embodiments, the treatment includes surgery, radiotherapy, and/or chemotherapy. For example, the treatment may include tumor resection followed hy chemoradiation. In some embodiments, the treatment includes a pharmacological treatment such as, e.g., the application of a pharmaceutical drug to a specific region of the brain. In some embodiments, the treatment occurs during a time period in between the first timepoint and the final timepoint (i.e., for which the tumor feature data is obtained, and the electrical brain activity measurements are recorded). In some embodiments, the treatment begins after the first timepoint. In some embodiments, an electrical biomarker of treatment response is identified using the tumor feature data and the recorded electrical brain activity measurements.

In certain embodiments, the method further includes preprocessing the recorded electrical brain activity measurements. In some embodiments, the preprocessing includes reducing or filtering the recorded electrical brain activity measurements. In some embodiments, the preprocessing includes filtering the recorded electrical brain activity measurements using a high-pass, band pass, and/or notch filter. In some embodiments, the preprocessing includes extracting signal features from the recorded electrical brain activity measurements. In some embodiments, the preprocessing includes transforming the recorded electrical brain activity measurements.

In certain embodiments, the electrical biomarker is identified using a statistical model and/or a machine learning model. In some embodiments, the electrical biomarker is identified using a mixed effects regression model. In some embodiments, the electrical biomarker is identified using recursive partitioning. In some embodiments, the electrical biomarker is identified using principal component analysis (PCA). In some embodiments, the method further includes determining if the identified electrical biomarker meets a predetermined threshold of statistical significance.

In certain embodiments, the electrical biomarker is identified by: obtaining tumor feature data for one or more tumor features of interest and one or more recordings of electrical brain activity measurements for each one of a plurality of additional brain tumor afflicted subjects; processing the obtained tumor feature data and electrical brain activity recordings such that the processed tumor feature data and electrical brain activity recordings are readily comparable across subjects; and identifying an electrical biomarker of a tumor feature from the obtained tumor feature data and the recorded electrical brain activity. In some embodiments, the electrical biomarker is identified using a statistical model and/or a machine learning model. In some embodiments, the electrical biomarkcr is identified using a machine learning and the method includes: training a machine learning model to identify an electrical biomarker of a tumor feature from a first subset of the obtained tumor feature data and electrical brain activity recordings; and applying the trained machine learning model to a second subset of the electrical brain activity recordings to detect the electrical biomarker of the tumor feature in the second subset of recordings. In some embodiments, the obtained tumor feature data and electrical brain activity recordings are saved to a database, and the trained machine learning model is continuously updated using the database.

In certain embodiments, the method further includes monitoring the tumor in a subject using the identified electrical biomarker. In some embodiments, the method further includes identifying exacerbating factors of tumor growth or progression based on the monitoring. In some embodiments, the method further includes altering the monitored subject’s treatment plan based on the monitoring. In some embodiments, the monitoring is ambulatory monitoring.

In certain embodiments, the method further includes developing a therapeutic neuromodulatory treatment effective in treating a tumor in a subject using the identified electrical biomarker. In some embodiments, the identified electrical biomarker is associated with or indicative of neuronal activity that promotes tumor growth or tumor progression (e.g., hyperexcitability) and, e.g., the therapeutic neuromodulatory treatment is adjusted or refined to reduce the tumor promoting neuronal activity using the electrical biomarker. In some embodiments, the identified electrical biomarker is associated with or indicative of the present severity of the tumor and, e.g., the therapeutic neuromodulatory treatment is adjusted or refined based on changes in the severity of the tumor determined by monitoring the electrical biomarker over a period of time. In some embodiments, the period of time is at least a month. In some embodiments, the therapeutic neuromodulatory treatment includes delivering electrical stimulation to a location associated with or infiltrated by the tumor. In some embodiments, the electrical stimulation is applied to depolarizing tumor cells, neurons located within a tumor infiltrated region of the subject’s brain, or neurons projecting into a tumor infiltrated region of the subject’s brain. In some embodiments, the applied electrical stimulation is sufficient to prevent or inhibit tumor cells from repolarizing. In some embodiments, the therapeutic neuromodulatory treatment includes delivering a pharmaceutical drug to a section of the tumor (such as, c.g., gabapentin or a GABA agonist). In some embodiments, the section of the tumor includes a high functionally connected (HFC) volume of the tumor, or a specific population of neurons found to have a certain feature or characteristic (such as, e.g., reduced expression of a specific receptor). In some embodiments, the therapeutic neuromodulatory treatment includes delivering low-intensity focused ultrasound (LIFU) to a tumor infiltrated region of the subject’s brain. In some embodiments, the location the neuromodulatory treatment is applied and/or a characteristic of the neuromodulatory treatment is determined using non-invasive mapping, by performing a biopsy (and, e.g., a laboratory experiment using the biopsied cells or tissue) and/or by performing an organoid or xenograft experiment.

In certain embodiments, the method further includes administrating the developed therapeutic neuromodulatory treatment to a tumor in the brain of a subject using one or more identified electrical biomarkers. In some embodiments, the identified electrical biomarker is associated with or indicative of neuronal activity that promotes tumor growth or tumor progression. In some embodiments, the developed therapeutic neuromodulatory treatment is only delivered when the identified electrical biomarker is detected. In some embodiments, the amplitude of a delivered therapeutic electrical stimulation is adjusted based on the presence of the identified electrical biomarker. In some embodiments, the amplitude of a delivered therapeutic electrical stimulation is adjusted based on a quantitative metric of the magnitude or intensity of the identified electrical biomarker.

In another aspect, systems for performing the methods of identifying electrical biomarkers of tumor features, as well as systems for using the identified electrical biomarkers to monitor disease states, develop therapeutic neuromodulatory treatments, and administer therapeutic neuromodulatory treatments for malignant brain tumors are provided. Further, non-transitory computer readable storage media including the instructions of the memory of the systems, and kits including one or more of the system components and/or the non- transitory computer readable storage media, are also provided.

In another aspect, methods of treating a brain tumor in a subject using electrical stimulation are provided. Aspects of the methods include: positioning a stimulation electrode at a first location of the brain of the subject associated with or infiltrated by the tumor; and applying electrical stimulation to the first location via the stimulation electrode in a manner effective to treat the tumor in the subject. In some embodiments, the tumor is a glioma.

In certain embodiments, the stimulation electrode is in contact with and applies electrical stimulation to depolarizing tumor cells, neurons located within a tumor infiltrated region of the subject’s brain, and/or neurons projecting into a tumor infiltrated region of the subject’s brain. In some embodiments, the applied electrical stimulation is sufficient to prevent or inhibit tumor cells from repolarizing. In some embodiments, the location the stimulation electrode is positioned and/or one or more characteristics of the electrical stimulation (e.g., the frequency of the electrical stimulation) are determined using non- invasive mapping, by performing a biopsy (and, e.g., a laboratory experiment using the biopsied cells or tissue), and/or by performing an organoid or xenograft experiment. In some embodiments, the non-invasive mapping includes determining the functional connectivity of one or more sections of the tumor with the rest of the brain. In some embodiments, the determined functional connectivity is used to identify high functional connectivity (HFC) and/or low functional connectivity (LFC) sections of the tumor. In certain embodiments, the method further includes obtaining data associated with or indicative of one or more features of interest for the tumor. In some embodiments, the stimulation electrode is positioned and/or the electrical stimulation is applied based on the obtained tumor feature data.

In certain embodiments, the method further includes: positioning a measurement electrode configured to measure electrical brain activity at a second location of the brain of the subject associated with or infiltrated by the tumor; detecting one or more electrical biomarkers of tumor features via the measurement electrode; and applying the electrical stimulation based on the one or more detected electrical biomarkers. In some embodiments, one or more of the detected electrical biomarkers are associated with or indicative of neuronal activity that promotes tumor growth or tumor progression. In some embodiments, the amplitude of the applied electrical stimulation is adjusted based on the one or more detected electrical biomarkers. In some embodiments, the amplitude of the applied electrical stimulation is adjusted based on a quantitative metric of the magnitude or intensity of the one or more detected electrical biomarkers.

In certain embodiments, the method further includes positioning one or more additional measurement electrodes and one or more additional stimulation electrodes at additional locations of the brain of the subject. In some embodiments, the additional measurement and stimulation electrodes arc positioned based on an assessment of brain locations of the subject the tumor cells are likely to invade. In some embodiments, the additional measurement electrodes are used to monitor tumor growth and the additional stimulation electrodes are used to slow tumor growth. In some embodiments, the method further includes assessing effectiveness of the treatment in the subject. In some embodiments, an assessment of treatment efficacy is generated using the measurements of each measurement electrode.

In another aspect, systems for treating a brain tumor in a subject using electrical stimulation are provided. Aspects of the systems include: a stimulation electrode adapted for positioning at a first location of the brain of the subject associated with or infiltrated by the tumor; and a processor programmed to instruct the stimulation electrode to apply an electrical stimulation to the first location in a manner effective to treat the tumor in the subject. In some embodiments, the brain tumor is a neuronal activity-dependent malignant glioma. In some embodiments, the system further includes: a measurement electrode adapted for positioning at a second location of the brain of the subject associated with or infiltrated by the tumor, wherein the measurement electrode is configured to record an electrical signal from the second location; and memory operably coupled to the processor wherein the memory includes instructions stored thereon, which when executed by the processor, cause the processor to: receive the electrical signal from the second location of the brain of the subject via the measurement electrode; detect one or more electrical biomarkers of tumor features from the electrical signal; modulate one or more programmed electrical stimulation parameters based on the one or more detected electrical biomarkers; and apply the modulated electrical stimulation to the first location via the stimulation electrode in a manner effective to treat the tumor.

In another aspect, a non-transitory computer-readable medium is provided, the non- transitory computer-readable medium including the instructions of the memory of the systems for treating a brain tumor in a subject using electrical stimulation. In another aspect, kits including one or more of the system components and/or the non-transitory computer readable storage medium, are also provided. In another aspect, closed loop methods for treating a brain tumor in a subject are provided. Aspects of the methods include: positioning an adjustable ncuromodulating device at a first location of the brain of the subject associated with or infiltrated by the tumor, wherein the adjustable neuromodulating device is configured to modulate neuronal activity at the first location; positioning a measurement electrode configured to measure electrical brain activity at a second location of the brain of the subject associated with or infiltrated by the tumor; detecting one or more electrical biomarkers of tumor features via the measurement electrode; determining one or more parameters of the adjustable neuromodulating device based on the one or more detected electrical biomarkers; and modulating neuronal activity at the first location via the adjustable neuromodulating device in a manner effective to treat the tumor in the subject. In some embodiments, the brain tumor is a neuronal activity-dependent malignant glioma.

In certain embodiments, the adjustable neuromodulating device includes a stimulation electrode configured to apply electrical stimulation to the first location. In some embodiments, the stimulation electrode is in contact with and applies electrical stimulation to depolarizing tumor cells, neurons located within a tumor infiltrated region of the subject’s brain, and/or neurons projecting into a tumor infiltrated region of the subject’s brain. In some embodiments, the applied electrical stimulation is sufficient to prevent or inhibit tumor cells from repolarizing. In some embodiments, the method further includes obtaining data associated with or indicative of one or more features of interest for the tumor. In some embodiments, the stimulation electrode is positioned and/or the electrical stimulation is applied based on the tumor feature data.

In certain embodiments, the adjustable neuromodulating device includes an implantable drug delivery device configured to deliver one or more doses of a pharmaceutical drug. In some embodiments, the active pharmaceutical ingredient of the pharmaceutical drug is determined using the tumor feature data. In some embodiments, the pharmaceutical drug acts as an inhibitor to a specific biological mechanism (e.g., gabapentin use to inhibit TSP-1 functions) or as an agonist to a specific type of receptor (e.g., Profilin use as a GABA agonist). In some embodiments, the size of the delivered drug dose is determined based on the or more detected electrical bio markers. In some embodiments, the implantable drug delivery device is positioned based on the tumor feature data. In certain embodiments, low-intensity focused ultrasound (LIFU) is delivered to a tumor infiltrated region of the brain based on the one or more detected electrical biomarkers. In some embodiments, the method further includes assessing effectiveness of the treatment in the subject. In some embodiments, an assessment of treatment efficacy is generated based on the one or more detected electrical biomarkers.

In another aspect, closed loop systems for treating a brain tumor in a subject are provided. Aspects of the systems include: an adjustable neuromodulating device adapted for positioning at a first location of the brain of the subject associated with or infiltrated by the tumor, wherein the adjustable neuromodulating device is configured to modulate neuronal activity; a measurement electrode adapted for positioning at a second location of the brain of the subject associated with or infiltrated by the tumor, wherein the measurement electrode is configured to record an electrical signal from the second location; and memory operably coupled to the processor wherein the memory includes instructions stored thereon, which when executed by the processor, cause the processor to: receive the electrical signal from the second location of the brain of the subject via the measurement electrode; detect one or more electrical biomarkers of tumor features from the electrical signal; adjust one or more programmed neuromodulation parameters based on the one or more detected electrical biomarkers; and modulate the neuronal activity of the first location via the adjustable neuromodulating device in a manner effective to treat the tumor. In some embodiments, the brain tumor is a neuronal activity-dependent malignant glioma.

In certain embodiments, the adjustable neuromodulating device includes a stimulation electrode, wherein the one or more adjusted neuromodulation parameters includes the amplitude and/or frequency of the electrical stimulation applied by the stimulation electrode. In some embodiments, the adjustable neuromodulating device includes an implantable drug delivery device configured to deliver one or more doses of a pharmaceutical drug, wherein the one or more adjusted neuromodulation parameters includes the size of the pharmaceutical drug dose delivered by the implantable drug delivery device. In some embodiments, the system further includes a low-intensity focused ultrasound (LIFU) emitting device. In some embodiments, LIFU emitted by the LIFU emitting device is adjusted based on the one or more detected electrical biomarkers. In another aspect, a non-transitory computer- readable medium is provided, the non- transitory computer-readable medium including the instructions of the memory of the closed loop systems for treating a brain tumor in a subject. In another aspect, kits including one or more of the system components and/or the non-transitory computer readable storage medium, are also provided.

BRIEF DESCRIPTION OF THE FIGURES

FIGS. 1A to IE provide the results of experiments, performed in accordance with an embodiment of the invention, demonstrating how high-grade gliomas remodel long-range functional neural circuits.

FIGS. 2A to 2G provide the results of experiments, performed in accordance with an embodiment of the invention, illustrating that tumour-infiltrated circuits exhibit areas of synaptic remodeling characterized by glioma cells expressing synaptogenic factors.

FIGS. 3A to 3H provide the results of experiments, performed in accordance with an embodiment of the invention, demonstrating that high-grade gliomas exhibit bidirectional interactions with HFC brain regions.

FIGS. 4A to 4H provide the results of experiments, performed in accordance with an embodiment of the invention, demonstrating how intratumoural connectivity in patients with high-grade glioma is correlated with survival and TSP-1 .

FIGS. 5A to 5D demonstrate the experimental workflow used to elucidate how various glioma characteristics (i.e., heterogeneities) affect the neural circuits, cognition, and clinical outcomes of patients.

FIGS. 6A to 6D depict electrode locations and spectral data across cortically infiltrating diffuse gliomas for experiments performed in accordance with embodiments of the invention.

FIGS. 7A to 7E provide the results of experiments, performed in accordance with an embodiment of the invention, demonstrating speech initiation neural activity in the lateral prefrontal cortex (LPFC).

FIGS. 8A to 8C provide the results of experiments, performed in accordance with an embodiment of the invention, illustrating gamma power and tumour-intrinsic connectivity imaging correlations. FIGS. 9 A to 9C provide the results of experiments, performed in accordance with an embodiment of the invention, demonstrating neurogenic gene expression in glioblastoma.

FIGS. 10A to 10J provide the results of experiments, performed in accordance with an embodiment of the invention, illustrating TSP-1 expression in single-cell primary patient- derived glioblastoma.

FIGS. 11A to 11G provide the results of experiments, performed in accordance with an embodiment of the invention, illustrating TSP-1 expression, synaptic puncta colocalization in primary patient-derived glioblastoma tissues, neuron organoid-glioblastoma co-culture models, and structural synapse formation in patient-derived xenograft models.

FIGS. 12A to 12B provide the results of experiments, performed in accordance with an embodiment of the invention, illustrating the effect of neuron co-culture conditions on the proliferation of primary patient-derived glioblastoma cell monocultures.

FIGS. 13A to 13D provide the results of experiments, performed in accordance with an embodiment of the invention, demonstrating activity-dependent invasion of TSP- 1 positive HFC cells.

FIGS. 14A to 14B provide the results of cell viability and TSP-1 knockdown validation experiments performed in accordance with an embodiment of the invention.

FIGS. 15A to 15C provide the results of experiments, performed in accordance with an embodiment of the invention, illustrating the effect of tumor functional connectivity on patient survival and language task performance.

FIG. 16 provides the results of experiments, performed in accordance with an embodiment of the invention, demonstrating the anti-proliferative effects of TSP- 1 inhibition in glioblastoma.

FIG. 17 provides an example of neuron-glioma interactions of the central nervous system that may be modulated by the delivery of therapeutic stimulation in accordance with an embodiment of the invention.

FIG. 18 illustrates implanted high-density subdural electrodes overlying a glioma infiltrated region of a brain that may be used to establish electrical biomarkers of tumor progression in accordance with an embodiment of the invention.

FIG. 19 depicts chronically implanted cortical depth electrodes and high-density subdural electrodes overlying a glioma infiltrated region of a brain that may be used to monitor tumor progression and deliver depolarization currents to inhibit malignant glioma proliferation, in accordance with an embodiment of the invention.

FIGS. 20A to 20C illustrate a responsive closed-loop device for therapeutically inhibiting malignant glioma proliferation by longitudinally monitoring electrical biomarkers and delivering depolarization blocking currents, in accordance with an embodiment of the invention.

FIG. 21 demonstrates the multi-modal experimental workflow used to investigate the electrophysiologic, structural, and genomic landscape of glioma-infiltrated cortex.

FIG. 22 provides the results of experiments, performed in accordance with an embodiment of the invention, demonstrating delta-beta shifts to be an electrophysiologic hallmark of infiltrated cortex.

FIG. 23 provides the results of experiments, performed in accordance with an embodiment of the invention, demonstrating glioma burden was found to be differentially present in cortical layers 5-6.

FIGS. 24A to 24B provide the results of experiments, performed in accordance with an embodiment of the invention, demonstrating the use of spatial transcriptomics to identify conserved cortical structure.

FIGS. 25A to 25B provide the results of experiments investigating layer- specific gene changes and contributions performed in accordance with an embodiment of the invention.

FIGS. 26A to 26B provide the results of cell-cell interactions analysis performed in accordance with an embodiment of the invention.

FIG. 27 demonstrates a sensory static detection model of neuronal population tuning that includes a sensory discrimination task in accordance with an embodiment of the invention.

FIG. 28 provides the results of experiments, performed in accordance with an embodiment of the invention, demonstrating elevated sensory static detection thresholds within glioma-infiltrated cortex.

FIG. 29 provides the results of experiments, performed in accordance with an embodiment of the invention, demonstrating a reduced ability to discriminate between hand and face stimulation within local circuits of tumor- infiltrated cortex. FIG. 30 provides the results of experiments, performed in accordance with an embodiment of the invention, demonstrating functional specificity responses.

FIG. 31 provides the results of experiments, performed in accordance with an embodiment of the invention, demonstrating that IDHm oligodendrogliomas lose theta discrimination and astrocytomas lose gamma discrimination.

FIG. 32 provides the results of experiments, performed in accordance with an embodiment of the invention, demonstrating that pharmacological GABAA agonists restore neuronal specificity within hemispheric glioma infiltrated cortex.

DETAILED DESCRIPTION

Methods and systems for mapping and characterizing the neuronal inputs and circuit dynamics unique to each individual malignant tumor are provided. Novel experimental models, in conjunction with cutting edge intact brain circuit interrogation tools, are used to identify and characterize the role of specific tumor heterogeneities, such as specific subpopulations of glioma cells, in malignant tumor progression. Implantable electrodes are used to chronically map and characterize malignant tumors in order to identify electrical biomarkers of various tumor features (e.g., tumor heterogeneities) of interest such as those associated with tumor growth, tumor progression, and/or treatment response. Identified electrical biomarkers are used to monitor disease states and treatment effects and, aided by knowledge of the role specific heterogeneities play in driving tumor progression provided by the models and tools of the disclosure, enable the development of personalized treatments for patients afflicted with malignant brain tumors. In addition, methods and devices for monitoring and therapeutically treating brain tumors, including activity dependent malignant gliomas, at a patient specific level using identified electrical biomarkers are also provided.

Before the present invention is described in greater detail, it is to be understood that this invention is not limited to particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims. Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range, is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.

Certain ranges are presented herein with numerical values being preceded by the term "about." The term "about" is used herein to provide literal support for the exact number that it precedes, as well as a number that is near to or approximately the number that the term precedes. In determining whether a number is near to or approximately a specifically recited number, the near or approximating unrecited number may be a number which, in the context in which it is presented, provides the substantial equivalent of the specifically recited number.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present invention, representative illustrative methods and materials are now described.

All publications and patents cited in this specification are herein incorporated by reference as if each individual publication or patent were specifically and individually indicated to be incorporated by reference and are incorporated herein by reference to disclose and describe the methods and/or materials in connection with which the publications are cited. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided may be different from the actual publication dates which may need to be independently confirmed.

It is noted that, as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. It is further noted that the claims may be drafted to exclude any optional element. As such, this statement is intended to serve as antecedent basis for use of such exclusive terminology as “solely,” “only” and the like in connection with the recitation of claim elements, or use of a “negative” limitation.

As will be apparent to those of skill in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the present invention. Any recited method can be carried out in the order of events recited or in any other order which is logically possible.

While the apparatus and method has or will be described for the sake of grammatical fluidity with functional explanations, it is to be expressly understood that the claims, unless expressly formulated under 35 U.S.C. §112, are not to be construed as necessarily limited in any way by the construction of "means" or "steps" limitations, but are to be accorded the full scope of the meaning and equivalents of the definition provided by the claims under the judicial doctrine of equivalents, and in the case where the claims are expressly formulated under 35 U.S.C. §112 are to be accorded full statutory equivalents under 35 U.S.C. §112.

METHODS

As summarized above, methods for mapping and characterizing the neuronal inputs and circuit dynamics unique to each individual malignant brain tumor are provided. Novel experimental models, in conjunction with cutting edge intact brain circuit interrogation tools, are used to identify and characterize the role of specific tumor heterogeneities, such as specific subpopulations of glioma cells, in malignant tumor progression. Implantable electrodes are used to chronically map and characterize malignant brain tumors in order to identify electrical biomarkers of various tumor features (e.g., tumor heterogeneities) of interest such as those associated with tumor growth, tumor progression, and/or treatment response. Identified electrical biomarkers are used to monitor disease states and treatment effects and, aided by a knowledge of the role specific heterogeneities play in driving tumor progression provided, e.g., by the models and tools of the disclosure, enable the development of personalized treatments for patients afflicted with malignant brain tumors. In addition, methods for monitoring and therapeutically treating brain tumors, including activity dependent malignant gliomas, at a patient specific level using identified electrical biomarkers arc also provided.

The terms “subject”, “individual”, “patient”, and “participant” are used interchangeably herein and refer to a patient having a brain cancer or a neurodegenerative disease. The patient is preferably human, e.g., a child, an adolescent, or an adult (such as a young, middle-aged, or elderly adult) human who may benefit from the methods and systems disclosed herein, or who may facilitate understanding of the disease or condition in which they are afflicted through use of the subject methods and systems disclosed herein. In some embodiments, the subject may have a brain tumor such as, e.g., a malignant or a benign brain tumor. Malignant brain tumors may include, but are not limited to, gliomas, choroid plexus tumors, embryonal tumors, germ cell tumors, pineal tumors, meningiomas, nerve tumors, and pituitary tumors. In some embodiments, the subject may have a glioma such as, e.g., an astrocytoma, glioblastoma, oligodendroglioma, ependymoma, diffuse midline gliomas (DMG), diffuse intrinsic pontine gliomas (DIPG), etc. In some embodiments, the methods and systems disclosed herein may be applied to a subject having a neurodegenerative disease primarily affecting a single region of the brain such as, e.g., Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis (ALS), Huntington's disease, etc.

The patient or subject may belong to any demographic, and may be characterized by any number of different prognostic variables. The malignant brain tumor may be of any classification or grade. In some cases, the subject may have a malignant brain tumor that affects or decreases their cognitive abilities. In some embodiments, the tumor (e.g., malignant glioma) in which the subject methods and systems are applied may be characterized as the first occurrence of a primary tumor, the reoccurrence of a tumor at the same site as a primary tumor, or the reoccurrence of a primary tumor at another location of the brain (i.e., different from the location of the primary tumor).

Identifying and Characterizing Tumor Heterogeneities

As described above, methods of identifying and characterizing the role of specific tumor heterogeneities, such as specific subpopulations of tumor cells, in driving malignant tumor progression and/or growth arc provided. By tumor heterogeneities is meant any characteristic or feature of a tumor that differentiates it from other tumors, or that differentiates one region of a tumor from another region of the same tumor (i.e., heterogeneities may be intcrtumoral or intratumoral). In some cases, identifying and characterizing tumor heterogeneities includes identifying biomarkers of tumor features of interest for a brain tumor of a subject (i.e., identifying characterizing data associated with or indicative of a tumor features of interest). Identifying biomarkers of tumor features may include: obtaining data characterizing a tumor infiltrated volume of a brain; and determining a relationship between the obtained tumor characterizing data and a tumor feature of interest.

Tumor features of interest may vary, and may include specific features or characteristics associated with tumor growth, tumor progression, and/or treatment response. For example, a specific subpopulation of tumor cells may be demonstrated to promote tumor progression or tumor cell proliferation (i.e., the specific subpopulation may be the tumor feature of interest), and tumor characterizing data correlated with the presence of the specific subpopulation (e.g., the presence or relative concentration of a specific protein or RNA sequence) may be considered a biomarker of the tumor feature of interest. In some cases, tumor features of interest may be directly indicative of, e.g., tumor growth, tumor progression, and/or treatment response. For example, the tumor feature of interest may be high tumor growth rate, and a specific genetic mutation or protein may be correlated with increased tumor growth rates (i.e., the specific genetic mutation or protein may be considered a biomarker of tumor growth rate). In some embodiments, tumor features of interest may include the neuronal inputs of a tumor, wherein specific characterizing data may be associated with (i.e., may be a biomarker of) neurons from a specific region of the brain projecting into a tumor. In some embodiments, tumor features of interest may include features or characteristics associated with the efficacy of a specific treatment. For example, a specific tumor cell subpopulation demonstrated to promote tumor cell proliferation may have a specific genetic characteristic associated with a type of treatment providing beneficial effects, and tumor characterizing data correlated with the presence of the specific subpopulation and/or genetic characteristic may be considered a biomarker of the tumor feature of interest.

In some embodiments, characterizing data may be identified to be associated with or indicative of a first tumor feature of interest and the first tumor of interest may be identified to be associated with or indicative of a second tumor feature of interest, effectively making the characterizing data a hiomarker of the second tumor feature of interest. For example, the expression of a specific gene may be linked to a specific neuronal input and the specific neuronal input may be linked to tumor cell proliferation, effectively making the expressed gene a biomarker of tumor cell proliferation.

In some embodiments, the tumor characterizing data includes one or more of a subtype or classification of the tumor, a characteristic of a microenvironment of the tumor, the connectivity of the tumor, and a neuronal input of the tumor. The subtype or classification may include a determination of malignancy, a prognosis, a tumor grade, and/or a type of cell a population of tumor cells originated from or resemble (such as, e.g., an astrocyte, an oligodendrocyte, an oligodendrocyte precursor cell [OPC], etc.). The characteristic of the microenvironment may include the identification of one or more cell populations located within the microenvironment and/or the identification of one or more proteins located within the tumor microenvironment (such as, e.g., one or more synaptogenic proteins). In some embodiments, the characteristic of the microenvironment is related to the expression or regulation of specific genes by one or more cell populations of the microenvironment. The connectivity of the tumor may include the functional connectivity of the tumor within itself or with other regions of the brain. In some embodiments, the connectivity of the tumor includes the number and distribution of high functional connectivity (HFC) and/or low functional connectivity (LFC) sections of the tumor.

In certain embodiments, obtaining the tumor characterizing data includes one or more of performing a biopsy of the tumor, performing a laboratory experiment, and analyzing images of the tumor. In some embodiments, the tumor is biopsied and/or the tumor images are analyzed in order to determine a subtype or classification of the tumor. In some embodiments, the biopsy is a resection the tumor and, e.g., the tumor characterizing data includes the volume of the tumor at one or more timepoints. In some embodiments, the laboratory experiment is performed on cells or tissue obtained from the biopsy. In certain embodiments, the laboratory experiment includes performing one or more of transcriptomic profiling or sequencing, genetic profiling or sequencing, microscopy, mass spectrometry, flow cytometry, immunohistochemistry and/or immunofluorescence analysis, neuronal organoid model experiments, and xenograft model experiments. In some embodiments, a tumor microenvironment characteristic is obtained by performing the laboratory experiment. As discussed above, embodiments of the methods include determining a relationship between the obtained tumor characterizing data and a tumor feature of interest. In some embodiments, the obtained tumor characterizing data is determined to be associated with or indicative of a tumor feature of interest (i.e., the tumor characterizing data is determined to be a biomarker of the tumor feature of interest). The obtained tumor characterizing data may be determined to be associated with or indicative of a tumor feature of interest by performing, e.g., a neuronal organoid model experiment and/or a xenograft model experiment. For example, specific characterizing data (such as, e.g., data correlated or indicative of specific subpopulations of cells) may be associated with specific neural inputs by performing xenograft model experiments using, e.g., rabies-based monosynaptic tracing, whole cortex wide calcium imaging, and/or fiber photometry. In another example, specific characterizing data (such as, e.g., data correlated or indicative of the expression of specific synaptogenic proteins or genes and/or HFC tumor sections) may be associated with tumor growth by performing organoid model experiments and/or spheroid invasion assays.

In some embodiments, the obtained tumor characterizing data may be determined to be associated with or indicative of a tumor feature of interest by performing, e.g., cell-cell communication analysis and/or gene expression programming. In some cases, obtained tumor characterizing data relates to the expression or regulation of specific genes by one or more cell populations of the microenvironment and the specific genes may be linked to communication between different cell types (e.g., via cell-cell communication analysis) or a specific phenotype (e.g., via gene expression programming) (i.e., the specific genes are demonstrated to be biomarkers of communication or a phenotype). In these cases, the communication or phenotype may further be associated with, e.g., tumor growth, tumor progression, and/or treatment response, effectively making the specific genes a biomarker of tumor growth, tumor progression, and/or treatment response.

As described above, methods of identifying biomarkers of tumor features may include: obtaining data characterizing a tumor infiltrated volume of a brain; and determining a relationship between the obtained tumor characterizing data and a tumor feature of interest. The characterizing data may pertain to any tumor heterogeneity, and may be obtained through any number of techniques. Similarly, tumor features of interest may pertain to any tumor heterogeneity different from the characterizing data. In some embodiments, tumor features of interest include specific features or characteristics associated with or indicative of tumor growth, tumor progression, and/or treatment response. In some embodiments, the obtained tumor characterizing data is determined to be associated with or indicative of a tumor feature of interest. In these embodiments, the tumor characterizing data associated with or indicative a feature of interest may be used in the methods of identifying electrical biomarkers of tumor features discussed in greater detail below (e.g., as the obtained tumor feature data).

Identifying Electrical Biomarkers

As described above, methods using implantable electrodes to chronically map and characterize malignant tumors in order to identify electrical biomarkers of various tumor features are provided. Embodiments of the methods include: non-invasively mapping the brain of a subject to identify a volume of the brain infiltrated by a tumor; obtaining data associated with or indicative of one or more features of interest for the tumor; positioning a measurement electrode configured to measure electrical brain activity at a location associated with or inside of the tumor infiltrated volume of the brain based on the non-invasive mapping; recording the electrical brain activity measured by the measurement electrode; and identifying an electrical biomarker of a tumor feature of interest from the obtained tumor feature data and the recorded electrical brain activity. The identified electrical biomarkers may then be used to monitor disease states, develop therapeutic neuromodulatory treatments, and/or administer therapeutic neuromodulatory treatments for a subject afflicted a with brain tumor.

Non-invasive Mapping and Obtaining Tumor Feature Data

As discussed above, embodiments of the electrical biomarker identification methods include non-invasively mapping the brain of a subject to identify a volume of the brain infiltrated by a tumor. The non-invasive mapping may include structural and/or functional imaging. In some embodiments, the non-invasive mapping includes structural imaging using magnetic resonance imaging (MRI). In some embodiments, non-invasive mapping includes functional imaging using magnetoencephalography (MEG). The non-invasive mapping may include mapping the connectivity of the brain. Tn some embodiments, mapping the connectivity of the brain includes determining the functional connectivity of a volume of the brain infiltrated by the tumor with a plurality of other volumes of the brain (e.g., other tumor volumes and/or non-tumor infiltrated volumes) In some cases, the tumor is divided into a plurality of volumes or voxels, and mapping the connectivity includes determining the functional connectivity of each tumor volume with all other tumor volumes and/or with a plurality of other non-tumor infiltrated volumes of the brain. In some embodiments, the functional connectivity of each volume of the brain is determined relative to a different volume of the brain contralaterally equivalent to the volume. In some embodiments, the determined functional connectivity is used to identify high functional connectivity (HFC) and/or low functional connectivity (LFC) volumes of the tumor. In some embodiments, the functional connectivity is determined using imaginary coherence (IC) metrics and MEG.

In some embodiments, the non-invasive mapping includes identifying one or more neural circuits associated with a section of the tumor. The one or more neural circuits may be identified using domain specific knowledge of the region of the brain in which the tumor section is located. For example, the tumor section may be located within the lateral prefrontal cortex (LPFC) of the brain and the one or more identified neural circuits may be related to speech production. In some embodiments, the one or more neural circuits are identified by measuring neuronal activity in the brain of the subject while the subject receives a stimuli or performs a task associated with the neural circuit, hi some embodiments, the neuronal activity is measured using MEG or electroencephalogram (EEG).

As discussed above, embodiments of the electrical biomarker identification methods include obtaining data associated with or indicative of one or more features of interest for the tumor of the subject. The obtained data may be determined to be associated with or indicative of a tumor feature using the methods of identifying and characterizing tumor heterogeneities discussed above. In addition, the tumor feature data may be obtained using any of the methods or techniques for obtaining tumor characterizing data as discussed above. In some embodiments, the tumor feature data is associated with or indicative of two or more features of interest. In some embodiments, the tumor feature data includes separate data for two or more of the features of interest. In other embodiments, the tumor feature data includes data simultaneously indicative of or associated with two or more of the features of interest. In these cases, one tumor feature may be indicative of or associated with another tumor feature. For example, the expression of specific genes (e.g., obtained using bulk or single cell RNA sequencing of biopsy obtained tissue) may be associated or indicative of a specific subpopulation of tumor cells, and the specific subpopulation of cells may be associated with a specific neuronal input (e.g., as established using xenograft model experiments) and/or enhanced tumor growth (e.g., as established using organoid model experiments).

In some embodiments, the tumor feature data includes one or more of a subtype or classification of the tumor, a characteristic of a microenvironment of the tumor, information regarding a stimuli received by the subject, information regarding an action performed by the subject, a neuronal input of the tumor, and control data.

The information regarding the stimuli may include a time the stimuli was delivered in relation to the recorded electrical brain activity and/or a quantitative metric of stimuli magnitude or intensity. For example, the stimuli may be a visual and/or auditory stimuli associated with a specific word or phrase and the quantitative metric relates to the commonality of the word or phrase. In some embodiments, the stimuli received by the subject depends on the region of the brain in which a volume of the tumor is located and/or includes a visual stimuli, an auditory stimuli, and/or electrical neurostimulation of a volume of the brain. Similarly, the information regarding the action may include a time the action was performed in relation to the recorded electrical brain activity and/or some quantitative metric of action magnitude or intensity or action completion. In some embodiments, the action performed by the subject depends on the region of the brain in which the identified tumor infiltrated volume of the brain is located. In some embodiments, the action performed by the subject includes attempting to perform a language task or physical task and the quantitative metric relates to the successful completion of the task.

In some embodiments, the neuronal input of the tumor is determined based on a region of the brain in which a tumor volume is located, the identification of one or more cell populations located within the volume, and/or the results of one or more xenograft experiments. In some embodiments, the neuronal input is determined to promote tumor growth or progression. In some embodiments, the control data includes data generated by a control volume of a brain, wherein the control volume is located in an equivalent region of a brain as a section of the tumor. By equivalent region of the brain is meant the same region of a different control brain (c.g., wherein the control brain docs not include a tumor, or includes a tumor determined to have different tumor features or characteristics), or a volume of the subject’s brain contralaterally equivalent to the tumor section.

In certain embodiments, obtaining the tumor feature data includes one or more of performing a biopsy of the tumor, performing a laboratory experiment, performing an in silico experiment, analyzing images of the tumor, recording electrical brain activity within a volume of the subject’s brain not infiltrated by or associated with the tumor, recording electrical brain activity from one or more additional subjects, recording an action performed by the subject, recording a stimuli received by the subject, and procuring data generated from previously performed experiments and/or studies. In some embodiments, the tumor is biopsied and/or the tumor images are analyzed in order to determine a subtype or classification of the tumor. In some embodiments, the non-tumor associated electrical brain activity and/or the additional subject electrical brain activity is recorded in order to function as a control. In some embodiments, the procured previously generated data is used as a control. In some embodiments, the procured previously generated data is used to determine that the obtained tumor feature data is associated with or indicative of a tumor feature of interest.

In some embodiments, the biopsy is performed on a portion of the identified tumor infiltrated volume of the brain or on tissue adjacent to the tumor infiltrated volume. In some embodiments, the biopsy is a resection of the tumor and, e.g., the tumor feature data includes the volume of the tumor in the brain of the subject before and/or after tumor resection. In some embodiments, the measurement electrode is positioned based on tumor feature data obtained from the biopsy. For example, the obtained tumor feature data may include a neuronal input of the tumor (or, e.g., a neuronal input of the identified tumor infiltrated volume of the brain), and the measurement electrode is positioned to be in contact with neurons of the neuronal input. In some embodiments, the electrical brain activity measured by the measurement electrode is recorded before and/or after the tumor biopsy. In some embodiments, the laboratory experiment is performed on cells or tissue obtained from the biopsy. In certain embodiments, the laboratory experiment includes performing one or more of transcriptomic profiling or sequencing, genetic profiling or sequencing, microscopy, mass spectrometry, flow cytometry, immunohistochemistry and/or immunofluorescence analysis, neuronal organoid model experiments, and xenograft model experiments. In some embodiments, a characteristic of a microenvironment of the tumor is obtained by performing the laboratory experiment. In some embodiments, a microenvironment characteristic associated with the tumor is determined to be associated with or indicative of a tumor feature of interest by performing, e.g., a neuronal organoid model experiment, an xenograft model experiment, and/or an in silico experiment (such as, e.g., cell-cell communication analysis or genetic expression programming). In these instances, the tumor feature of interest may be associated with or indicative of tumor growth, tumor progression, and/or treatment response.

In certain embodiments, the recorded performed action or received stimuli is associated with the region of the brain in which the identified tumor infiltrated volume of the brain is located and the performing or receiving occurs while the electrical brain activity measured by the measurement electrode is recorded. In these instances, the non-tumor associated electrical brain activity and/or the additional subject electrical brain activity is recorded while the subject and/or the additional subject receives the stimuli or performs the action. In some embodiments, the received stimuli or performed action is selected based on the non-invasive mapping.

Electrode Positioning and Recording

As discussed above, embodiments of the electrical biomarker identification methods include positioning a measurement electrode configured to measure electrical brain activity at a location associated with or inside of the tumor infiltrated volume of the brain based on the non-invasive mapping, and recording the electrical brain activity measured by the measurement electrode. Positioning an electrode for recording brain activity at specified location(s) of the brain may be carried out using standard surgical procedures for placement of intra-cranial electrodes. As used herein, the phrases “an electrode” or “the electrode” refer to a single electrode or multiple electrodes such as an electrode array. As used herein, the term “contact” as used in the context of an electrode in contact with a location/region of the brain refers to a physical association between the electrode and the location/region. In other words, an electrode that is in contact with a location or component of the brain is physically touching the location or component of the brain. An electrode in contact with a location of the brain can be used to detect electrical signals corresponding to neuronal activity and/or deliver electrical stimulation to the location. Wherein an electrode is positioned at a region/location of the brain (or, e.g., within a volume of the brain), contact with the brain (i.e., at the location or within the volume) is assumed even if not explicitly specified. Electrodes used in the methods disclosed herein may be monopolar (cathode or anode) or bipolar (e.g., having an anode and a cathode).

The precise number of electrodes contained in an electrode array (e.g., for recording of the electrical brain activity) may vary. In certain aspects, an electrode array may include 2 or more electrodes, such as 3 or more, 10 or more, 50 or more, 100 or more, 200 or more, 250 or more, 500 or more, including 253 or more, e.g., about 6 to 12 electrodes, about 12 to 18 electrodes, about 18 to 24 electrodes, about 24 to 30 electrodes, about 30 to 48 electrodes, about 48 to 72 electrodes, about 72 to 96 electrodes, about 96 to 128 electrodes, about 128 to 196 electrodes, about 196 to 294 electrodes, about 294 to 440 electrodes, or more electrodes. The electrodes may be arranged into a regular repeating pattern (e.g., a grid, such as a grid with about 3 mm center-to-center spacing between electrodes), or no pattern. An electrode may conform to a target site for optimal recording of electrical signals from neural activity. In some embodiments, a high-density ECoG electrode array is used to record electrical signals from neural activity. For example, a high-density ECoG electrode array may include at least 100 electrodes, at least 128 electrodes, at least 196 electrodes, at least 253 electrodes, at least 294 electrodes, at least 500 electrodes, or at least 1000 electrodes, or more. The size of each electrode may also vary depending upon such factors as the number of electrodes in the array, the location of the electrodes, the material, the age of the patient, and other factors.

In some embodiments, the measurement electrode is positioned based on the obtained tumor feature data. For example, the obtained tumor feature data may include the location of neuronal inputs (e.g., input neurons distally projecting into the tumor infiltrated volume) and the measurement electrode may be positioned in contact with neurons of the neuronal inputs. In some embodiments, the measurement electrode is positioned at a location inside of the tumor infiltrated volume. In other embodiments, the measurement electrode is positioned at a location functionally connected with or immediately adjacent to the tumor infiltrated volume of the brain (c.g., as identified through the non-invasivc mapping).

In some embodiments, one or more additional measurement electrodes configured to measure electrical brain activity are positioned at a location inside of, functionally connected with, and/or immediately adjacent to the identified tumor infiltrated volume of the brain. The one or more additional measurement electrodes may be positioned based on the non-invasive mapping and/or the obtained tumor feature data. In some embodiments, the electrical brain activity measured by the one or more additional measurement electrodes is recorded. In some embodiments, the measurements of one measurement electrode may be recorded based on measurements generated by one or more other measurement electrodes. Recording the measurements of a first measurement electrode may include: monitoring the measurements generated by one or more additional measurement electrodes (i.e., not the first measurement electrode) for a specific pattern or event of electrical brain activity; and recording the measurements of the first measurement electrode when the specific pattern or event of electrical brain activity is detected. In some embodiments, the measurements of a plurality of measurement electrodes are recorded based on measurements generated by a single different measurement electrode (i.e., not of the plurality of measurement electrodes).

In some embodiments, a measurement electrode is positioned, and electrical brain activity is measured, at multiple locations within the tumor and/or one or more neuronal inputs of the tumor. Electrical brain activity in any frequency range may be measured. In some embodiments, electrical activity ranging from less than 1 Hz to over 200 Hz may be measured. In some embodiments, electrical activity in the high gamma frequency range (such as 70 Hz to 170 Hz) may be measured.

Identifying Electrical Biomarkers

As discussed above, embodiments of the electrical biomarker identification methods include identifying an electrical biomarker of a tumor feature of interest from the obtained tumor feature data and the recorded electrical brain activity. The identified electrical biomarker may occur during a resting state of the subject and/or during the performance of a specific task or reception of a specific stimuli. In some embodiments, the identified electrical biomarker can be detected using a single measurement electrode. In other embodiments, the identified electrical biomarker is identified based on a relationship between the measurements generated by two or more measurement electrodes positioned at two or more locations of the brain.

In some embodiments, the identified electrical biomarker relates to a subtype or classification of tumor, a characteristic of tumor microenvironment, or a characteristic of tumor connectivity. For example, the electrical biomarker may be indicative of a specific grade or classification of tumor, a specific subpopulation of tumor cells, or HFC volumes of tumor. In some embodiments, the identified electrical biomarker relates to hyperexcitability. In some embodiments, the electrical biomarker may include the power of the electrical brain activity at one or more specific frequency bands. In some embodiments, the one or more specific frequency bands include the frequency band ranging from 70 Hz to 170 Hz, or 70 Hz to 110 Hz. In some cases, the electrical biomarker includes a signature of relative power across a plurality of frequency bands.

In some embodiments, the identified electrical biomarker relates to the recruitment of the tumor infiltrated volume of the brain into a specific neural circuit. In these instances, the obtained tumor feature data may include control data (e.g., including information regarding the involvement of a control volume of a brain in the specific neural circuit) and at least one of a subtype or classification of the first tumor and a characteristic of the microenvironment of the first volume of the brain. In such cases, the control volume includes a subtype or classification of tumor different from the identified tumor infiltrated volume of the brain or a microenvironment characteristic different from the tumor infiltrated volume. In some embodiments, the specific neural circuit relates to the performance of a specific task or reception of a specific stimuli by the subject.

In some embodiments, the identified electrical biomarker relates to a dynamic of a specific neural circuit or neural network involving the identified tumor infiltrated volume of the brain or, e.g., a characteristic of a specific neuron population of the circuit or network. In some embodiments, the dynamic is related to the effect of the tumor on cognition. In these instances, the dynamic may include the decodability of the electrical brain activity of the specific neural circuit or neural network (such as, e.g., the selectivity of neurons to different magnitudes of a stimulation). In some embodiments, the dynamic of the specific neural circuit or neural network includes hyperexcitability associated with the specific neural circuit or neural network.

The tumor feature data may be obtained at two or more different timepoints. In some embodiments, the two or more different timepoints are at least a week apart including, e.g., a month or more apart, two months or more apart, six months or more apart, etc. In some embodiments, the tumor feature data is obtained at three or more different timepoints, such as four or more timepoints, or 5 or more, or 10 or more, or 20 or more, etc. The number of timepoints for which the tumor feature data is obtained may vary depending on the nature of the data. In some embodiments, the tumor feature data is obtained whenever the subject receives a resection of the tumor. Electrical brain activity measured by the measurement electrode (or, e.g., each positioned measurement electrode) may be recorded for each timepoint tumor feature data is obtained. In some embodiments, electrical brain activity is recorded from each measurement electrode positioned at a location associated with or inside of the identified tumor infiltrated volume of the brain for each timepoint. In some embodiments, the electrical brain activity measurements are continuously recorded during the time period from the first timepoint to the final timepoint. In other embodiments, one or more distinct recordings are generated for each timepoint. In some embodiments, generating the one or more distinct recordings includes: continuously monitoring the electrical brain activity measurements from each measurement electrode positioned at a location associated with or inside of the identified tumor infiltrated volume of the brain during the time period from the first timepoint to the final timepoint for a specific pattern or event of electrical brain activity; and recording the measurements from one or more of the measurement electrodes when the specific pattern or event of electrical brain activity is detected.

As discussed above, the tumor feature data may be associated with or indicative of tumor growth, tumor progression, and/or treatment response. In some embodiments, the tumor feature data includes the total volume of the first tumor and/or the distribution of the first tumor within the brain at the two or more different timepoints and, e.g., an electrical biomarker of tumor growth is identified using the tumor feature data and the recorded electrical brain activity measurements. In some embodiments, the tumor feature data includes a subtype or classification of the first tumor and/or data associated with or indicative of the connectivity of the tumor at the two or more different timepoints and, e.g., an electrical biomarker of tumor progression is identified using the tumor feature data and the recorded electrical brain activity measurements.

In some embodiments, the method further includes treating the tumor in the subject. The treatment may include surgery, radiotherapy, and/or chemotherapy. For example, the treatment may include tumor resection followed by chemoradiation. In some embodiments, the treatment occurs during a time period in between the first timepoint and the final timepoint (i.e., for which the tumor feature data is obtained, and the electrical brain activity measurements are recorded). In some embodiments, the treatment begins after the first timepoint. In some embodiments, an electrical biomarker of treatment response is identified using the tumor feature data and the recorded electrical brain activity measurements.

In some embodiments, the method further includes preprocessing the recorded electrical brain activity measurements. The preprocessing may include reducing or filtering the recorded electrical brain activity measurements. In some embodiments, the preprocessing includes filtering the recorded electrical brain activity measurements using a high-pass, band pass, and/or notch filter. In some embodiments, the preprocessing includes extracting signal features from the recorded electrical brain activity measurements. In some embodiments, the preprocessing includes transforming the recorded electrical brain activity measurements (such as, e.g., using the Hilbert transform).

The electrical biomarker may be identified using a statistical model and/or a machine learning model. In some embodiments, the electrical biomarker is identified using a mixed effects regression model, recursive partitioning, and/or principal component analysis (PCA). In some embodiments, the electrical biomarker is identified using a logistic regression. In some embodiments, the method further includes determining if the identified electrical biomarker meets a predetermined threshold of statistical significance. The determination of statistical significance may include any number of statistical tests such as, e.g., t-tests, the Bonferroni method, two-tailed unpaired Mann-Whitney tests, ANOVA tests, etc. In some embodiments, identified electrical biomarkers that do not meet the predetermined threshold are discarded or ignored. In some embodiments, the statistical significance of an identified electrical biomarker is used to weight the identified electrical biomarker in a control algorithm that uses multiple electrical biomarkers to make decisions (such as, e.g., a control algorithm of a closed loop neuromodulation device used to treat a tumor in a subject, as described in greater detail below). In some embodiments, the electrical biomarker is identified by: obtaining tumor feature data for one or more tumor features of interest and one or more recordings of electrical brain activity measurements for each one of a plurality of additional brain tumor afflicted subjects; processing the obtained tumor feature data and electrical brain activity recordings such that the processed tumor feature data and electrical brain activity recordings are readily comparable across subjects; and identifying an electrical biomarker of a tumor feature from the obtained tumor feature data and the recorded electrical brain activity. In some embodiments, the electrical biomarker is identified using a statistical model and/or a machine learning model. In some embodiments, the electrical biomarker is identified using machine learning. In these instances, the identifying may include: training a machine learning model to identify an electrical biomarker of a tumor feature from a first subset of the obtained tumor feature data and electrical brain activity recordings; and applying the trained machine learning model to a second subset of the electrical brain activity recordings to detect the electrical biomarker of the tumor feature in the second subset of recordings.

In some embodiments, the obtained tumor feature data and electrical brain activity recordings are saved to a database. In some embodiments, a machine learning model trained to identify one or more electrical biomarkers is continuously updated based on the tumor feature data and electrical brain activity recordings saved to the database.

Monitoring and Treating Tumors Using Electrical Biomarkers

As discussed above, embodiments of the electrical biomarker identification methods further include methods of using the identified electrical biomarkers to monitor disease states, develop therapeutic neuromodulatory treatments, and administer therapeutic neuromodulatory treatments for malignant brain tumors. In some embodiments, the method includes monitoring a tumor in a subject using the identified electrical biomarker. In some embodiments, the monitored subject is the subject from which the electrical biomarkers were originally identified, or a different subject having a tumor capable of being monitored using the identified electrical biomarker. For example, tumor feature data may be obtained from a subject from which the one or more electrical biomarkers were not originally identified in order to determine if a specific electrical biomarkers may be used to monitor the subject. In some embodiments, the electrical biomarkers are associated with or indicative of tumor growth, tumor progression, and/or treatment response. In some embodiments, the method further includes identifying exacerbating factors of tumor growth or progression based on the electrical biomarker monitoring. For example, the one or more identified electrical biomarkers may include neuronal activity associated with tumor growth, and the time of the neuronal activity may be correlated with the performance of a specific activity by the subject. In some embodiments, the method further includes altering the monitored subject’s treatment plan based on the electrical biomarker monitoring. In some embodiments, the monitoring is ambulatory monitoring.

In some embodiments, the method further includes developing a therapeutic neuromodulatory treatment effective in treating a tumor in a subject using one or more identified electrical biomarkers (i.e., as described above). In some embodiments, one or more of the identified electrical biomarkers are associated with or indicative of neuronal activity that promotes tumor growth or tumor progression (e.g., hyperexcitability) and, e.g., the therapeutic neuromodulatory treatment is developed by using the electrical biomarker to adjust or refine a neuromodulating mechanism to reduce the tumor promoting neuronal activity. In some embodiments, one or more of the identified electrical biomarkers are associated with or indicative of the present severity (e.g., size or progression) of the tumor and, e.g., the therapeutic neuromodulatory treatment is developed by adjusting or refining a neuromodulating mechanism based on changes in the severity of the tumor determined by monitoring the electrical biomarker over a period of time. In some embodiments, the period of time is at least a month.

In some embodiments, the therapeutic neuromodulatory treatment includes delivering electrical stimulation to a location associated with or infiltrated by the tumor (i.e., the neuromodulating mechanism is electrical stimulation). In some embodiments, the electrical stimulation is applied to depolarizing tumor cells, neurons located within a tumor infiltrated region of the subject’s brain, or neurons projecting into a tumor infiltrated region of the subject’s brain. In some embodiments, the applied electrical stimulation is sufficient to prevent or inhibit tumor cells from repolarizing.

The location the electrical stimulation is applied may be determined using non- invasive mapping and/or by performing an organoid or xenograft experiment. In some embodiments, one or more characteristics of the electrical stimulation are determined by performing an organoid or xenograft experiment. The one or more characteristics of the electrical stimulation may include, e.g., the frequency or the amplitude of the electrical stimulation. In some embodiments, the organoid or xenograft experiment is performed using cells derived from the treated subject. In other embodiments, the organoid or xenograft experiment is performed using cells derived from another individual (i.e., not the treated subject) having a tumor with one or more of the same tumor features as the tumor of the treated subject. In some embodiments, the electrical stimulation is applied to a HFC volume of the tumor, or a volume associated therewith.

In some embodiments, the therapeutic neuromodulatory treatment includes delivering a pharmaceutical drug to a section of the tumor (i.e., the neuromodulating mechanism is a pharmaceutical drug such as, e.g., a neuromodulating drug). In some embodiments, the section of the tumor includes a HFC volume of the tumor. In some embodiments, the section of the tumor includes a subpopulation expressing a certain gene, e.g., above a specific threshold (i.e., upregulation). In some embodiments, the location the pharmaceutical drug is applied and/or the active pharmaceutical ingredient of the pharmaceutical drug is determined using non-invasive mapping, by performing an organoid or xenograft experiment, or by obtaining tumor feature data (e.g., wherein the tumor feature is upregulation of a specific protein). In some embodiments, the organoid or xenograft experiment is performed using cells derived from the treated subject. In other embodiments, the organoid or xenograft experiment is performed using cells derived from another individual (i.e., not the treated subject) having a tumor with one or more of the same tumor features as the tumor of the treated subject. In some embodiments, the active pharmaceutical ingredient of the neuromodulatory or pharmaceutical drug inhibits neuronal activity (e.g., by inhibiting a specific receptor or by working as a receptor agonist). In some cases, the active pharmaceutical ingredient of the neuromodulatory drug may be gabapentin (GBP) or may be a GABA agonist (such as, e.g., Propofol).

In some embodiments, the therapeutic neuromodulatory treatment includes delivering low-intensity focused ultrasound (LIFU) to a tumor infiltrated region of the monitored subject’s brain (i.e., the neuromodulating mechanism is LIFU). As discussed above, in some embodiments, the method further includes administrating the developed therapeutic ncuromodulatory treatment to a tumor in the brain of a subject using one or more identified electrical biomarkers (i.e., as described above). The identified electrical biomarker used to administer the developed therapeutic ncuromodulatory treatment may be associated with or indicative of neuronal activity that promotes tumor growth or tumor progression. In some embodiments, the location wherein the therapeutic ncuromodulatory treatment is delivered is different from the location from which the identified electrical biomarker is detected. In some embodiments, the amplitude of a delivered therapeutic electrical stimulation, the size of a delivered ncuromodulatory drug dose, and/or the intensity of delivered LIFU is adjusted based on the presence of an identified electrical biomarker. In some embodiments, the amplitude of a therapeutic electrical stimulation, the size of a delivered ncuromodulatory drug dose, and/or the intensity of delivered LIFU is adjusted based on a quantitative metric of the magnitude or intensity of an identified electrical biomarker. For example, delivered therapeutic electrical stimulation may be adjusted based on a biomarker related to hyperexcitability (such as, e.g., the power of electrical brain activity measured by a specifically placed measurement electrode within the high-gamma frequency band) or decodability (such as, e.g., selectivity between different magnitudes of a stimuli).

In some embodiments, the method further includes determining that the treated tumor has one or more specific tumor features associated with the therapeutic ncuromodulatory treatment. For example, specific therapeutic neuromodulatory treatments may be developed for tumors characterized by specific tumor features. In some embodiments, the one or more therapeutic neuromodulatory treatment specific tumor features are determined by performing non-invasive mapping and/or a biopsy. In some embodiments, the one or more therapeutic neuromodulatory treatment specific tumor features are determined by detecting an electrical biomarker of the tumor features.

Electrical Stimulation Neuromodulation Therapy

As discussed above, methods for therapeutically treating brain tumors at a patient specific level, e.g., using identified electrical biomarkers are provided (and may include, e.g., any of the elements of the therapeutic neuromodulatory treatments discussed above or below). Embodiments of the methods include: positioning a stimulation electrode at a location of the brain of a subject associated with or infiltrated by a tumor; and applying electrical stimulation to the location via the stimulation electrode in a manner effective to treat the tumor in the subject. In some embodiments, the tumor is a glioma such as, e.g., an activity dependent malignant glioma.

In some embodiments, the stimulation electrode is in contact with and applies electrical stimulation to depolarizing tumor cells, neurons located within a tumor infiltrated region of the subject’s brain, and/or neurons projecting into a tumor infiltrated region of the subject’s brain. The applied electrical stimulation may be sufficient to prevent or inhibit tumor cells from repolarizing. In some embodiments, the location the stimulation electrode is positioned is determined using non-invasive mapping and/or by performing an organoid or xenograft experiment. In some embodiments, the non-invasive mapping includes determining the functional connectivity of one or more sections of the tumor with the rest of the brain. In some embodiments, the determined functional connectivity is used to identify high functional connectivity (HFC) and/or low functional connectivity (LFC) sections of the tumor.

In some embodiments, one or more characteristics of the electrical stimulation may be determined by performing an organoid or xenograft experiment. The one or more characteristics of the electrical stimulation include the frequency or the amplitude of the electrical stimulation. In some embodiments, the organoid or xenograft experiment is performed using cells derived from the subject. In other embodiments, the organoid or xenograft experiment is performed using cells derived from a different subject (i.e., not the treated subject) having a tumor with one or more of the same tumor features as the treated subject’s tumor.

In some embodiments, the method further includes obtaining data associated with or indicative of one or more features of interest for the tumor (i.e., as described above). In some embodiments, the stimulation electrode is positioned and/or the electrical stimulation is applied based on the obtained tumor feature data. In some embodiments, the tumor feature data is obtained by performing a biopsy.

In some embodiments, the method further includes: positioning a measurement electrode configured to measure electrical brain activity at a second location of the brain of the subject associated with or infiltrated by the tumor; detecting one or more electrical biomarkers of tumor features (i.e., as discussed above) via the measurement electrode; and applying the electrical stimulation based on the one or more detected electrical biomarkers. The one or more the detected electrical biomarkers may be associated with or indicative of neuronal activity that promotes tumor growth or tumor progression. In some embodiments, the application, amplitude, and/or frequency of the applied electrical stimulation is adjusted based on the one or more detected electrical biomarkers. In some embodiments, the application, amplitude, and/or frequency of the applied electrical stimulation is adjusted based on a quantitative metric of the magnitude or intensity of the one or more detected electrical biomarkers. In some embodiments, the electrical stimulation is applied at least two times based on the one or more detected electrical biomarkers, wherein the electrical stimulation is spatially and/or temporally different.

In some embodiments, the method may further include positioning one or more additional measurement electrodes and/or one or more additional stimulation electrodes. The additional measurement electrodes may be positioned in order to detect specific electrical biomarkers, and the additional stimulation electrodes may be positioned wherever they will aid in treating the tumor in the subject (e.g., wherever they will be effective in slowing tumor growth and/or progression). In some embodiments, the additional measurement and stimulation electrodes are positioned based on an assessment of brain locations of the subject the tumor cells are likely to invade. The additional measurement electrodes may be used to monitor tumor growth and the additional stimulation electrodes may be used to slow tumor growth. In some embodiments, the method further includes assessing the effectiveness of the treatment in the subject, e.g., using one or more identified electrical biomarkers or by obtaining tumor feature data as discussed above. In some embodiments, an assessment of treatment efficacy is generated using the measurements of each positioned measurement electrode.

Closed-Loop Neuromodulation Therapy

As discussed above, closed loop methods for treating a brain tumor in a subject using one or more electrical biomarkers are provided (and may include, e.g., any of the elements of the therapeutic neuromodulatory treatments described above). Embodiments of the methods include: positioning an adjustable neuromodulating device at a first location of the brain of the subject associated with or infiltrated by the tumor, wherein the adjustable ncuromodulating device is configured to modulate neuronal activity at the first location; positioning a measurement electrode configured to measure electrical brain activity at a second location of the brain of the subject associated with or infiltrated by the tumor; detecting one or more electrical biomarkers of tumor features (e.g., as described above) via the measurement electrode; determining one or more parameters of the adjustable neuromodulating device based on the one or more detected electrical biomarkers; and modulating neuronal activity at the first location via the adjustable neuromodulating device in a manner effective to treat the tumor in the subject. In some embodiments, the brain tumor is a neuronal activity-dependent malignant glioma.

In some embodiments, the adjustable neuromodulating device includes a stimulation electrode configured to apply electrical stimulation to the first location. The stimulation electrode may be in contact with, and apply electrical stimulation to, depolarizing tumor cells, neurons located within a tumor infiltrated region of the subject’s brain, and/or neurons projecting into a tumor infiltrated region of the subject’s brain. The application, amplitude, and/or frequency of the applied electrical stimulation may be adjusted based on the one or more detected electrical biomarkers. In some embodiments, the application, amplitude, and/or frequency of the applied electrical stimulation is adjusted based on the presence of an identified electrical biomarker. In some embodiments, the application, amplitude, and/or frequency of the applied electrical stimulation is adjusted based on a quantitative metric of the magnitude or intensity of an identified electrical biomarker. In some embodiments, the applied electrical stimulation is sufficient to prevent or inhibit tumor cells from repolarizing.

In some embodiments, the adjustable neuromodulating device includes an implantable drug delivery device configured to deliver one or more doses of a pharmaceutical/neuromodulating drug. The active pharmaceutical ingredient of the pharmaceutical drug may be determined using obtained tumor feature data (e.g., as described above), and the size of the delivered drug dose may be determined based on the or more detected electrical biomarkers. In some embodiments, the size of the delivered pharmaceutical drug dose is adjusted based on the presence of an identified electrical biomarker. In some embodiments, the size of the delivered pharmaceutical drug dose is adjusted based on a quantitative metric of the magnitude or intensity of an identified electrical biomarkcr.

In some embodiments, low-intensity focused ultrasound (LIFU) is delivered to a tumor infiltrated region of the brain based on the one or more detected electrical biomarkers. The intensity of the delivered LIFU may be determined based on the or more detected electrical biomarkers. In some embodiments, the intensity of delivered LIFU is adjusted based on the presence of an identified electrical biomarker. In some embodiments, the intensity of delivered LIFU is adjusted based on a quantitative metric of the magnitude or intensity of an identified electrical biomarker.

In some embodiments, the closed-loop method may include positioning a plurality of measurement electrodes and/or a plurality of neuromodulating devices. In these cases, the plurality of measurement electrodes may be used to detect one or more electrical biomarkers of tumor features and/or the plurality of neuromodulating devices may be used to modulate neuronal activity at a plurality of locations of the subject’s brain in a manner effective to treat the tumor in the subject in the same manner as described above. The plurality of measurement electrodes may be positioned in order to detect specific electrical biomarkers, and the plurality of neuromodulating devices may be positioned wherever they will aid in treating the tumor in the subject (e.g., wherever they will be effective in slowing tumor growth and/or progression). In some embodiments, the plurality of measurement electrodes and the plurality of neuromodulating devices are positioned based on an assessment of brain locations of the subject the tumor cells are likely to invade. In some cases, the plurality of measurement electrodes may be used to monitor tumor growth and the plurality of neuromodulating devices may be used to slow tumor growth. In some embodiments, the plurality of neuromodulating devices include both stimulation electrodes and implantable drug delivery devices. In some embodiments, each neuromodulating device includes both a stimulation electrode and an implantable drug delivery device.

In some embodiments, the closed-loop methods may include positioning a processor in communication with each measurement electrode and each neuromodulating device at a location of the subject’s body. The processor may be configured in a manner, and positioned at a location, such that it does not interfere with the daily life of the subject and/or is not easily visible. In some embodiments, the processor may include batteries sufficient to power itself, each measurement electrode, and each neuromodulating device such that recharging is only required twice a day or less, or once a day or less, or once every two days or less, or once a week or less. In some embodiments, the processor is configured to recharge via inductive charging.

In some embodiments, the processor may be configured to: receive an electrical signal including electrical brain activity measurements from each measurement electrode; detect one or more electrical biomarkers of tumor features from the electrical signals; adjust one or more neuromodulation parameters of each neuromodulating device based on the one or more detected electrical biomarkers; and modulate neuronal activity at each location having a neuromodulating device (i.e., via each neuromodulating device) in a manner effective to treat the tumor. In some embodiments, the processor detects the one or more electrical biomarkers and adjusts the one or more neuromodulation parameters using a closed-loop control algorithm. In some embodiments, the control algorithm may detect one or more electrical biomarkers by performing calculations. In some embodiments, the control algorithm may detect one or more electrical biomarkers using a trained machine learning model.

The control algorithm may weight detected electrical biomarkers when adjusting the one or more neuromodulation parameters using, e.g., the statistical significance of a detected electrical biomarker. In some embodiments, the statistical significance is determined during the identification of the electrical biomarker (e.g., as described above) or based on one or more tumor features obtained for the subject. In some cases, the control algorithm may weight detected electrical biomarkers based on, e.g., the location of the measurement electrode from which the electrical biomarker was detected or a quantitative metric of the magnitude or intensity of the detected electrical biomarker. The control algorithm may alert the patient when an update or a recharge is needed. In some embodiments, the processor is in communication with other devices measuring the vitals of the subject (such as, e.g., the subject’s heart rate, blood oxygen levels, pupil dilations, etc.). In some embodiments, the control algorithm detects abnormalities in one or more of the subject’s vitals resulting from delivery of the therapeutic neuromodulation. In these instances, the control algorithm may halt the delivery of the therapeutic neuromodulation and, e.g., alert the patient and/or emergency professionals depending on the detected abnormality. In some embodiments, the method further includes assessing the effectiveness of the treatment in the subject, c.g., using one or more identified electrical biomarkers or by obtaining tumor feature data as discussed above. In some embodiments, an assessment of treatment efficacy is generated using the measurements of each positioned measurement electrode.

SYSTEMS AND COMPUTER IMPLEMENTED METHODS

Aspects of the invention additionally include systems configured to perform the above-described methods of identifying electrical biomarkers of tumor features, as well as systems for using the identified electrical biomarkers to monitor disease states, develop therapeutic neuromodulatory treatments, and/or administer therapeutic neuromodulatory treatments for subjects afflicted with brain tumors.

In one aspect, systems for treating a brain tumor in a subject by applying electrical stimulation are provided. Aspects of the systems include: a stimulation electrode adapted for positioning at a first location of the brain of the subject associated with or infiltrated by the tumor; and a processor programmed to instruct the stimulation electrode to apply an electrical stimulation to the first location in a manner effective to treat the tumor in the subject. In some embodiments, the brain tumor is a neuronal activity-dependent malignant glioma. In some embodiments, the system further includes: a measurement electrode adapted for positioning at a second location of the brain of the subject associated with or infiltrated by the tumor, wherein the measurement electrode is configured to record an electrical signal from the second location; and memory operably coupled to the processor wherein the memory includes instructions stored thereon, which when executed by the processor, cause the processor to: receive the electrical signal from the second location of the brain of the subject via the measurement electrode; detect one or more electrical biomarkers of tumor features from the electrical signal; modulate one or more programmed electrical stimulation parameters based on the one or more detected electrical biomarkers; and apply the modulated electrical stimulation to the first location via the stimulation electrode in a manner effective to treat the tumor.

In another aspect, closed loop systems for treating a brain tumor in a subject are provided. Aspects of the closed loop systems include: an adjustable neuromodulating device adapted for positioning at a first location of the brain of the subject associated with or infiltrated by the tumor, wherein the adjustable ncuromodulating device is configured to modulate neuronal activity; a measurement electrode adapted for positioning at a second location of the brain of the subject associated with or infiltrated by the tumor, wherein the measurement electrode is configured to record an electrical signal from the second location; and memory operably coupled to the processor wherein the memory includes instructions stored thereon, which when executed by the processor, cause the processor to: receive the electrical signal from the second location of the brain of the subject via the measurement electrode; detect one or more electrical biomarkers of tumor features from the electrical signal; adjust one or more programmed neuromodulation parameters based on the one or more detected electrical biomarkers; and modulate the neuronal activity of the first location via the adjustable neuromodulating device in a manner effective to treat the tumor. In some embodiments, the brain tumor is a neuronal activity-dependent malignant glioma

In some embodiments, the adjustable neuromodulating device includes a stimulation electrode, wherein the one or more adjusted neuromodulation parameters includes the amplitude and/or frequency of the electrical stimulation applied by the stimulation electrode. In some embodiments, the adjustable neuromodulating device includes an implantable drug delivery device configured to deliver one or more doses of a pharmaceutical drug, wherein the one or more adjusted neuromodulation parameters includes the size of the neuro- modulatory drug dose delivered by the implantable drug delivery device. In some embodiments, the closed loop system further includes a low-intensity focused ultrasound (LIFU) emitting device. In some embodiments, LIFU emitted by the LIFU emitting device is adjusted based on the one or more detected electrical biomarkers.

In some instances the systems further include one or more computers for complete automation or partial automation of the methods described herein. In some embodiments, systems include a computer having a computer readable storage medium with a computer program stored thereon.

In embodiments, the system includes an input module, a processing module and an output module. The subject systems may include both hardware and software components, where the hardware components may take the form of one or more platforms, e.g., in the form of servers, such that the functional elements, i.e., those elements of the system that carry out specific tasks (such as managing input and output of information, processing information, etc.) of the system may be carried out by the execution of software applications on and across the one or more computer platforms represented of the system.

Systems may include a display and operator input device. Operator input devices may, for example, be a keyboard, mouse, or the like. The processing module includes a processor which has access to a memory having instructions stored thereon for performing the steps of the subject methods. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, memory storage devices, and input-output controllers, cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or it may be one of other processors that are or will become available. The processor executes the operating system and the operating system interfaces with firmware and hardware in a well-known manner, and facilitates the processor in coordinating and executing the functions of various computer programs that may be written in a variety of programming languages, such as Java, Perl, C++, Python, other high-level or low-level languages, as well as combinations thereof, as is known in the art. The operating system, typically in cooperation with the processor, coordinates and executes functions of the other components of the computer. The operating system also provides scheduling, input-output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques. The processor may be any suitable analog or digital system. In some embodiments, the processor includes analog electronics which provide feedback control, such as for example negative feedback control.

The system memory may be any of a variety of known or future memory storage devices. Examples include any commonly available random access memory (RAM), magnetic medium such as a resident hard disk or tape, an optical medium such as a read and write compact disc, flash memory devices, or other memory storage device. The memory storage device may be any of a variety of known or future devices, including a compact disk drive, a tape drive, a removable hard disk drive, or a diskette drive. Such types of memory storage devices typically read from, and/or write to, a program storage medium (not shown) such as, respectively, a compact disk, magnetic tape, removable hard disk, or floppy diskette. Any of these program storage media, or others now in use or that may later be developed, may be considered a computer program product. As will be appreciated, these program storage media typically store a computer software program and/or data. Computer software programs, also called computer control logic, typically are stored in system memory and/or the program storage device used in conjunction with the memory storage device.

In some embodiments, a computer program product is described including a computer usable medium having control logic (computer software program, including program code) stored therein. The control logic, when executed by the processor the computer, causes the processor to perform functions described herein. In other embodiments, some functions are implemented primarily in hardware using, for example, a hardware state machine. Implementation of the hardware state machine so as to perform the functions described herein will be apparent to those skilled in the relevant arts.

Memory may be any suitable device in which the processor can store and retrieve data, such as magnetic, optical, or solid-state storage devices (including magnetic or optical disks or tape or RAM, or any other suitable device, either fixed or portable). The processor may include a general-purpose digital microprocessor suitably programmed from a computer readable medium carrying necessary program code. Programming can be provided remotely to processor through a communication channel, or previously saved in a computer program product such as memory or some other portable or fixed computer readable storage medium using any of those devices in connection with memory. For example, a magnetic or optical disk may carry the programming, and can be read by a disk writer/reader. Systems of the invention also include programming, e.g., in the form of computer program products, algorithms for use in practicing the methods as described above. Programming according to the present invention can be recorded on computer readable media, e.g., any medium that can be read and accessed directly by a computer. Such media include, but are not limited to: magnetic storage media, such as floppy discs, hard disc storage medium, and magnetic tape; optical storage media such as CD-ROM; electrical storage media such as RAM and ROM; portable flash drive; and hybrids of these categories such as magnetic/optical storage media.

The processor may also have access to a communication channel to communicate with a user at a remote location. By remote location is meant the user is not directly in contact with the system and relays input information to an input manager from an external device, such as a computer connected to a Wide Area Network (“WAN”), telephone network, satellite network, or any other suitable communication channel, including a mobile telephone (i.c., smartphone).

In some embodiments, systems according to the present disclosure may be configured to include a communication interface. In some embodiments, the communication interface includes a receiver and/or transmitter for communicating with a network and/or another device. The communication interface can be configured for wired or wireless communication, including, but not limited to, radio frequency (RF) communication (e.g., Radio-Frequency Identification (RFID), Zigbee communication protocols, WiFi, infrared, wireless Universal Serial Bus (USB), Ultra Wide Band (UWB), Bluetooth® communication protocols, and cellular communication, such as code division multiple access (CDMA) or Global System for Mobile communications (GSM).

In one embodiment, the communication interface is configured to include one or more communication ports, e.g., physical ports or interfaces such as a USB port, an RS-232 port, or any other suitable electrical connection port to allow data communication between the subject systems and other external devices such as a computer terminal (for example, at a physician’s office or in hospital environment) that is configured for similar complementary data communication.

In one embodiment, the communication interface is configured for infrared communication, Bluetooth® communication, or any other suitable wireless communication protocol to enable the subject systems to communicate with other devices such as computer terminals and/or networks, communication enabled mobile telephones, personal digital assistants, or any other communication devices which the user may use in conjunction.

In one embodiment, the communication interface is configured to provide a connection for data transfer utilizing Internet Protocol (IP) through a cell phone network, Short Message Service (SMS), wireless connection to a personal computer (PC) on a Local Area Network (LAN) which is connected to the internet, or WiFi connection to the internet at a WiFi hotspot.

In one embodiment, the subject systems are configured to wirelessly communicate with a server device via the communication interface, e.g., using a common standard such as 802.11 or Bluetooth® RF protocol, or an IrDA infrared protocol. The server device may be another portable device, such as a smart phone, Personal Digital Assistant (PDA) or notebook computer; or a larger device such as a desktop computer, appliance, etc. In some embodiments, the server device has a display, such as a liquid crystal display (LCD), as well as an input device, such as buttons, a keyboard, mouse or touch-screen.

In some embodiments, the communication interface is configured to automatically or semi-automatically communicate data stored in the subject systems, e.g., in an optional data storage unit, with a network or server device using one or more of the communication protocols and/or mechanisms described above.

Output controllers may include controllers for any of a variety of known display devices for presenting information to a user, whether a human or a machine, whether local or remote. If one of the display devices provides visual information, this information typically may be logically and/or physically organized as an array of picture elements. A graphical user interface (GUI) controller may include any of a variety of known or future software programs for providing graphical input and output interfaces between the system and a user, and for processing user inputs. The functional elements of the computer may communicate with each other via system bus. Some of these communications may be accomplished in alternative embodiments using network or other types of remote communications. The output manager may also provide information generated by the processing module to a user at a remote location, e.g., over the Internet, phone or satellite network, in accordance with known techniques. The presentation of data by the output manager may be implemented in accordance with a variety of known techniques. As some examples, data may include SQL, HTML or XML documents, email or other files, or data in other forms. The data may include Internet URL addresses so that a user may retrieve additional SQL, HTML, XML, or other documents or data from remote sources. The one or more platforms present in the subject systems may be any type of known computer platform or a type to be developed in the future, although they typically will be of a class of computer commonly referred to as servers. However, they may also be a main-frame computer, a workstation, or other computer type. They may be connected via any known or future type of cabling or other communication system including wireless systems, either networked or otherwise. They may be co-located or they may be physically separated. Various operating systems may be employed on any of the computer platforms, possibly depending on the type and/or make of computer platform chosen. Appropriate operating systems include Windows, iOS, Oracle Solaris, Linux, IBM, Unix, and others.

Aspects of the present disclosure further include non-transitory computer readable storage mediums having instructions for practicing the subject methods. Computer readable storage mediums may be employed on one or more computers for complete automation or partial automation of a system for practicing methods described herein. In certain embodiments, instructions in accordance with the method described herein can be coded onto a computer-readable medium in the form of “programming”, where the term "computer readable medium" as used herein refers to any non-transitory storage medium that participates in providing instructions and data to a computer for execution and processing. Examples of suitable non-transitory storage media include a floppy disk, hard disk, optical disk, magneto-optical disk, CD-ROM, CD-R, magnetic tape, non-volatile memory card, ROM, DVD-ROM, Blue-ray disk, solid state disk, and network attached storage (NAS), whether or not such devices are internal or external to the computer. A file containing information can be “stored” on computer readable medium, where “storing” means recording information such that it is accessible and retrievable at a later date by a computer. The computer-implemented method described herein can be executed using programming that can be written in one or more of any number of computer programming languages. Such languages include, for example, Python, Java, Java Script, C, C#, C++, Go, R, Swift, PHP, as well as many others.

The non-transitory computer readable storage medium may be employed on one or more computer systems having a display and operator input device. Operator input devices may, for example, be a keyboard, mouse, or the like. The processing module includes a processor which has access to a memory having instructions stored thereon for performing the steps of the subject methods. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, memory storage devices, and input-output controllers, cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor, or it may be one of other processors that are or will become available. The processor executes the operating system and the operating system interfaces with firmware and hardware in a well-known manner, and facilitates the processor in coordinating and executing the functions of various computer programs that may be written in a variety of programming languages, such as those mentioned above, other high level or low-level languages, as well as combinations thereof, as is known in the art. The operating system, typically in cooperation with the processor, coordinates and executes functions of the other components of the computer. The operating system also provides scheduling, input-output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques.

KITS

Kits are also provided for carrying out the methods described herein. In some embodiments, the kit includes software for carrying out the computer implemented methods of identifying electrical biomarkers of tumor features, as well as the computer implemented methods of using the identified electrical biomarkers to monitor disease states, develop therapeutic neuromodulatory treatments, and/or administer therapeutic neuromodulatory treatments for subjects afflicted with brain tumors, as described herein. In some embodiments, the kit includes one or more components of a system for carrying out the computer implemented methods of identifying electrical biomarkers of tumor features, and using identified electrical biomarkers to monitor and treat subjects afflicted with brain tumors, as described herein.

In some embodiments, the kit includes software for carrying out the computer implemented methods of treating a brain tumor in a subject using electrical stimulation, as described herein. In some embodiments, the kit includes one or more components of a system for treating a brain tumor in a subject using electrical stimulation, as described herein. Such a system may include: a stimulation electrode adapted for positioning at a first location of the brain of the subject associated with or infiltrated by the tumor; and a processor programmed to instruct the stimulation electrode to apply an electrical stimulation to the first location in a manner effective to treat the tumor in the subject. In some embodiments, such a system may further include: a measurement electrode adapted for positioning at a second location of the brain of the subject associated with or infiltrated by the tumor, wherein the measurement electrode is configured to record an electrical signal from the second location; and memory operably coupled to the processor wherein the memory includes instructions stored thereon, which when executed by the processor, cause the processor to: receive the electrical signal from the second location of the brain of the subject via the measurement electrode; detect one or more electrical biomarkers of tumor features from the electrical signal; modulate one or more programmed electrical stimulation parameters based on the one or more detected electrical biomarkers; and apply the modulated electrical stimulation to the first location via the stimulation electrode in a manner effective to treat the tumor. In some embodiments, such a system may further include: a low-intensity focused ultrasound (LIFU) emitting device.

In some embodiments, the kit includes software for carrying out the computer implemented closed loop methods of treating a brain tumor in a subject using electrical biomarkers of tumor features, as described herein. In some embodiments, the kit includes one or more components of a closed loop system for treating a brain tumor in a subject, as described herein. Such a system may include: an adjustable neuromodulating device adapted for positioning at a first location of the brain of the subject associated with or infiltrated by the tumor, wherein the adjustable neuromodulating device is configured to modulate neuronal activity; a measurement electrode adapted for positioning at a second location of the brain of the subject associated with or infiltrated by the tumor, wherein the measurement electrode is configured to record an electrical signal from the second location; and memory operably coupled to the processor wherein the memory includes instructions stored thereon, which when executed by the processor, cause the processor to: receive the electrical signal from the second location of the brain of the subject via the measurement electrode; detect one or more electrical biomarkers of tumor features from the electrical signal; adjust one or more programmed neuromodulation parameters based on the one or more detected electrical biomarkers; and modulate the neuronal activity of the first location via the adjustable neuromodulating device in a manner effective to treat the tumor. In some embodiments, the adjustable neuromodulating device includes a stimulation electrode, wherein the one or more adjusted neuromodulation parameters includes the amplitude and/or frequency of the electrical stimulation applied by the stimulation electrode. In some embodiments, the adjustable neuromodulating device includes an implantable drug delivery device configured to deliver one or more doses of a pharmaceutical drug, wherein the one or more adjusted neuromodulation parameters includes the size of the pharmaceutical drug dose delivered by the implantable drug delivery device. In some embodiments, such a system may further include: a LIFU emitting device. In some embodiments, LIFU emitted by the LIFU emitting device is adjusted based on the one or more detected electrical biomarkers.

In addition, the kits may further include, in certain embodiments, instructions for practicing the subject methods. These instructions may be present in the subject kits in a variety of forms, one or more of which may be present in the kit. For example, instructions may be present as printed information on a suitable medium or substrate, e.g., a piece or pieces of paper on which the information is printed, in the packaging of the kit, in a package insert, and the like. Another form of these instructions is a computer readable medium, e.g., diskette, compact disk (CD), flash drive, and the like, on which the information has been recorded. Yet another form of these instructions that may be present is a website address which may be used via the internet to access the information at a removed site.

UTILITY

The methods and systems of the present disclosure, e.g., as described above, find use in a variety of applications wherein it is desirable to build a deeper understanding of the malignant glioma inputs, connectivity, and circuit dynamics that govern glioma progression in order to develop new therapeutic treatments and improve patient outcomes. In some embodiments, the methods and systems described herein find use wherein it is desirable to identify and characterize the role of tumor heterogeneities in, e.g., tumor proliferation and glioma progression in order to enable patient specific tumor characterization, disease monitoring, and therapeutic treatment. In some embodiments, the methods and systems described herein find use wherein it is desirable to identify the subpopulations of glioma cells that play an outsized role in promoting the tumor driven functional remodeling of brain circuitry that governs glioma progression in order for, e.g., more targeted, precision medicine-based treatments addressing malignant gliomas to be developed and applied.

In certain aspects, the methods and systems of the present disclosure find use wherein it is desirable to improve glioma monitoring in patients such that treatment plans may be better informed, and patient outcomes may be improved. In some embodiments, the methods and systems described herein find use in the longitudinal ambulatory monitoring of disease states and treatment effects. In some embodiments, the methods and systems described herein find use in developing personalized treatments for patients with a variety of presently lethal brain cancers. In some embodiments, the methods and systems described herein find use in treating a subject suffering from brain cancer such as, c.g., malignant gliomas. In some embodiments, the methods and systems described herein find use in targeting and inhibiting activity-dependent malignant glioma growth.

In certain aspects, the methods and systems of the present disclosure may advance the development of clinically useful devices to both monitor and therapeutically treat brain tumor growth in patients. In some embodiments, the subject methods and systems may de-risk industry investment in biomedical devices to treat patients having brain cancer with electrical stimulation using both non-invasive and invasive techniques. In some embodiments, the subject methods and systems may clarify potential endpoints for future clinical trials, increase understanding of both cancer related and non-cancer related brain diseases, and improve the ability to provide personalized therapies to patients afflicted with a variety of brain diseases.

EXEMPLARY NON-LIMITING ASPECTS OF THE DISCLOSURE

Aspects, including embodiments, of the present subject matter described above may be beneficial alone or in combination, with one or more other aspects or embodiments. Without limiting the foregoing description, certain non-limiting aspects of the disclosure are provided below. As will be apparent to those of ordinary skill in the ait upon reading this disclosure, each of the individually numbered aspects may be used or combined with any one of the preceding or following individually numbered aspects. This is intended to provide support for all such combinations of aspects and is not limited to combinations of aspects explicitly provided below. It will be apparent to one of ordinary skill in the art that various changes and modifications can be made without departing from the spirit or scope of the invention.

1. A method of identifying electrical biomarkers of tumor features in the brain of a subject, the method comprising: non-invasively mapping the brain of the subject to identify a first volume of the brain infiltrated by a first tumor; obtaining data associated with or indicative of one or more features of interest for the first tumor; positioning a first measurement electrode configured to measure electrical brain activity at a location associated with or inside of the first volume of the brain based on the non-invasive mapping; recording the electrical brain activity measured by the first measurement electrode; and identifying an electrical biomarker of a tumor feature of interest from the obtained tumor feature data and the recorded electrical brain activity.

2. The method according to Aspect 1, wherein the first tumor is a glioma.

3. The method according to any one of the preceding Aspects, wherein the non-invasive mapping comprises structural and/or functional imaging.

4. The method according to Aspect 3, wherein the non-invasive mapping comprises structural imaging using magnetic resonance imaging (MRI).

5. The method according to Aspect 3 or 4, wherein the non-invasive mapping comprises functional imaging using magnetoencephalography (MEG).

6. The method according to any one of the preceding Aspects, wherein the non-invasive mapping comprises mapping the connectivity of the brain.

7. The method according to Aspect 6, wherein the non-invasive mapping comprises determining the functional connectivity of the first volume of the brain with a plurality of other volumes of the brain.

8. The method according to Aspect 7, wherein the non-invasive mapping comprises determining the functional connectivity of one or more additional volumes of the brain infiltrated by the first tumor with a plurality of other volumes of the brain.

9. The method according to Aspect 7 or 8, wherein the functional connectivity of each volume of the brain is determined relative to a different volume of the brain contralaterally equivalent to the volume.

10. The method according to Aspect 9, wherein the determined functional connectivity is used to identify high functional connectivity (HFC) and/or low functional connectivity (LFC) volumes. 11 . The method according to any one of Aspects 7-10, wherein the functional connectivity is determined using imaginary coherence (IC) metrics and MEG.

12. The method according to any one of Aspects 6-11, wherein the non-invasive mapping comprises identifying one or more neural circuits associated with a first section of the first tumor.

13. The method according to Aspect 12, wherein the first tumor section corresponds to the first volume of the brain.

14. The method according to Aspect 12, wherein the first tumor section is separate from the first volume of the brain.

15. The method according to any one of Aspects 12-14, wherein the one or more neural circuits arc identified using domain specific knowledge of the region of the brain in which the first tumor section is located.

16. The method according to Aspect 15, wherein the first tumor section is located within the lateral prefrontal cortex (LPFC) of the brain and the one or more identified neural circuits are related to speech production.

17. The method according to any one of Aspects 12-16, wherein the one or more neural circuits are identified by measuring neuronal activity in the brain of the subject while the subject receives a stimuli or performs a task associated with the neural circuit.

18. The method according to Aspect 17, wherein the neuronal activity is measured using MEG or electroencephalogram (EEG).

19. The method according to any one of the preceding Aspects, wherein the method further comprises determining that the obtained tumor feature data is associated with or indicative of a tumor feature.

20. The method according to any one of the preceding Aspects, wherein the tumor feature data is associated with or indicative of two or more features of interest.

21. The method according to Aspect 20, wherein the tumor feature data comprises separate data for two or more of the features of interest.

22. The method according to Aspect 20 or 21, wherein the tumor feature data comprises data simultaneously indicative of or associated with two or more of the features of interest.

23. The method according to Aspect 22, wherein one tumor feature is indicative of or associated with another tumor feature. 24. The method according to any one of the preceding Aspects, wherein the tumor feature data comprises one or more of a subtype or classification of the first tumor, a characteristic of a microenvironment of the first tumor, information regarding a stimuli received by the subject, information regarding an action performed by the subject, a neuronal input of the first tumor, and control data.

25. The method according to Aspect 24, wherein the subtype or classification comprises a determination of malignancy.

26. The method according to Aspect 24 or 25, wherein the subtype or classification relates to a prognosis of the first tumor.

27. The method according to any one of Aspects 24-26, wherein the subtype or classification comprises a tumor grade.

28. The method according to any one of Aspects 24-27, wherein the subtype or classification relates to a type of cell a population of the first tumor cells originated from or resemble.

29. The method according to Aspect 28, wherein the type of cell is an astrocyte and/or an oligodendrocyte.

30. The method according to any one of Aspects 24-29, wherein the first tumor microenvironment is located within the first region.

31. The method according to any one of Aspects 24-29, wherein the first tumor microenvironment is not located within the first region.

32. The method according to Aspect 30 or 31, wherein the characteristic of the microenvironment comprises the identification of one or more cell populations located within the first tumor microenvironment.

33. The method according to any one of Aspects 30-32, wherein the characteristic of the microenvironment is related to the expression or regulation of specific genes by one or more cell populations of the microenvironment.

34. The method according to any one of Aspects 30-33, wherein the characteristic of the microenvironment comprises the identification of one or more proteins located within the first tumor microenvironment.

35. The method according to Aspect 34, wherein the one or more proteins comprise one or more synaptogenic proteins. 36. The method according to any one of Aspects 24-35, wherein the information regarding the stimuli comprises a time the stimuli was delivered in relation to the recorded electrical brain activity and/or a quantitative metric of stimuli magnitude or intensity.

37. The method according to Aspect 36, wherein the stimuli received by the subject comprises a visual stimuli, an auditory stimuli, and/or electrical neurostimulation of a volume of the brain.

38. The method according to Aspect 37, wherein the stimuli depends on the region of the brain in which the first volume is located.

39. The method according to Aspect 37 or 38, wherein the stimuli is a visual and/or auditory stimuli associated with a specific word or phrase and the quantitative metric relates to the commonality of the word or phrase.

40. The method according to any one of Aspects 24-39, wherein the information regarding the action comprises a time the action was performed in relation to the recorded electrical brain activity and/or some quantitative metric of action magnitude or intensity or action completion.

41. The method according to Aspect 40, wherein the action depends on the region of the brain in which the first volume is located.

42. The method according to Aspect 40 or 41, wherein the action performed by the subject comprises attempting to perform a language task or physical task and the quantitative metric relates to the successful completion of the task.

43. The method according to any one of Aspects 24-42, wherein the neuronal input is determined based on the region of the brain in which the first volume is located, the identification of one or more cell populations located within the first volume, and/or the results of one or more xenograft experiments.

44. The method according to Aspect 43, wherein the neuronal input is determined to promote tumor growth or progression.

45. The method according to any one of Aspects 24-44, wherein the control data comprises data generated by a control volume of a brain, wherein the control volume is located in an equivalent region of the brain as a section of the first tumor.

46. The method according to Aspect 45, wherein the first tumor section corresponds to the first volume of the brain. 47. The method according to Aspect 45 or 46, wherein the control volume is a volume of the subject’s brain contralatcrally equivalent to the first tumor section.

48. The method according to Aspect 45 or 46, wherein the control volume is a volume of a control subject’s brain.

49. The method according to any one of the preceding Aspects, wherein obtaining the first tumor feature data comprises one or more of performing a biopsy of the first tumor, performing a laboratory experiment, analyzing images of the first tumor, recording electrical brain activity within a volume of the subject’s brain not infiltrated by or associated with the first tumor, recording electrical brain activity from one or more additional subjects, recording an action performed by the subject, recording a stimuli received by the subject, and procuring data generated from previously performed experiments and/or studies.

50. The method according to Aspect 49, wherein the biopsy is performed on a portion of the first volume or on tissue adjacent to the first volume.

51. The method according to Aspect 50, wherein the biopsy is a resection of the first tumor.

52. The method according to Aspect 51, wherein the tumor feature data comprises the volume of the first tumor in the brain of the subject before and/or after tumor resection.

53. The method according to any one of Aspects 50-52, wherein the first measurement electrode is positioned based on tumor feature data obtained from the biopsy.

54. The method according to any one of Aspects 50-53, wherein the electrical brain activity measured by the first measurement electrode is recorded before and/or after the tumor biopsy.

55. The method according to any one of Aspects 50-54, wherein the laboratory experiment is performed on cells or tissue obtained from the biopsy.

56. The method according to Aspect 55, wherein the laboratory experiment comprises performing one or more of tran scrip tomic profiling or sequencing, genetic profiling or sequencing, microscopy, mass spectrometry, flow cytometry, immunohistochemistry and/or immunofluorescence analysis, neuronal organoid model experiments, and xenograft model experiments.

57. The method according to Aspect 55 or 56, wherein a characteristic of a microenvironment of the first tumor is obtained by performing the laboratory experiment. 58. The method according to Aspect 57, wherein the characteristic of the microenvironment is determined to be associated with or indicative of a tumor feature of interest by performing a neuronal organoid model experiment or a xenograft model experiment.

59. The method according to Aspect 58, wherein the neuronal organoid model experiment and/or xenograft model experiment is performed using cells or tissue obtained from the biopsy.

60. The method according to Aspect 59, wherein the neuronal organoid model experiment or xenograft model experiment are performed using cells or tissue not obtained from the subject comprising or being characterized by the characteristic of the microenvironment.

61. The method according to Aspect 59 or 60, wherein the tumor feature comprises a neuronal input of the first tumor.

62. The method according to any one of Aspects 49-61, wherein the first tumor is biopsied and/or the tumor images are analyzed in order to determine a subtype or classification of the first tumor.

63. The method according to any one of Aspects 49-62, wherein the non-tumor associated electrical brain activity and/or the additional subject electrical brain activity is recorded in order to function as a control.

64. The method according to any one of Aspects 49-62, wherein the recorded non-tumor associated electrical brain activity and/or the recorded additional subject electrical brain activity is recorded from the control volume of the brain according to any one of Aspects 45- 48.

65. The method according to Aspect 63 or 64, wherein the subject receives a stimuli or performs an action associated with the region of the brain in which the first volume is located while the electrical brain activity measured by the first measurement electrode is recorded.

66. The method according to Aspect 65, wherein the non-tumor associated electrical brain activity and/or the additional subject electrical brain activity is recorded while the subject and/or the additional subject receives the stimuli or performs the action.

67. The method according to Aspect 65 or 66, wherein the received stimuli or performed action is selected based on the non-invasive mapping. 68. The method according to any one of Aspects 49-67, wherein the procured previously generated data is used as a control.

69. The method according to any one of Aspects 49-68, wherein the procured previously generated data is used to determine that the obtained tumor feature data is associated with or indicative of a tumor feature of interest.

70. The method according to any one of the preceding Aspects, wherein the first measurement electrode is positioned based on the obtained tumor feature data.

71. The method according to any one of the preceding Aspects, wherein the first measurement electrode is positioned at a location inside of the first volume of the brain.

72. The method according to any one of Aspects 1-70, wherein the first measurement electrode is positioned at a location functionally connected with or immediately adjacent to the first volume of the brain.

73. The method according to Aspect 71 or 72, wherein the measurements of the first measurement electrode are recorded while the subject receives a stimuli or performs an action.

74. The method according to any one of Aspects 71-73, wherein one or more additional measurement electrodes configured to measure electrical brain activity are positioned at a location inside of, functionally connected with, and/or immediately adjacent to the first volume of the brain.

75. The method according to Aspect 74, wherein the one or more additional measurement electrodes are positioned based on the non-invasive mapping and/or the obtained tumor feature data.

76. The method according to Aspect 74 or 75, wherein the method further comprises recording the electrical brain activity measured by the one or more additional measurement electrodes.

77. The method according to any one of Aspects 74-76, wherein the measurements of the first measurement electrode are recorded based on measurements generated by the one or more additional measurement electrodes.

78. The method according to Aspect 77, wherein the method further comprises: monitoring the measurements generated by the one or more additional measurement electrodes for a specific pattern or event of electrical brain activity; and recording the measurements of the first measurement electrode when the specific pattern or event of electrical brain activity is detected.

79. The method according to Aspect 76, wherein the measurements of the one or more of additional measurement electrodes are recorded based on measurements generated by the first measurement electrode.

80. The method according to any one of the preceding Aspects, wherein the identified electrical biomarker occurs during a resting state.

81. The method according to any one of the preceding Aspects, wherein the identified electrical biomarker occurs during the performance of a specific task or reception of a specific stimuli.

82. The method according to any one of the preceding Aspects, wherein the identified electrical biomarker can be detected using a single measurement electrode.

83. The method according to any one of the preceding Aspects, wherein the identified electrical biomarker can be detected using two or more measurement electrodes.

84. The method according to any one of the Aspects 1-82, wherein the identified electrical biomarker is identified based on a relationship between the measurements generated by two or more measurement electrodes positioned at two or more locations of the brain.

85. The method according to any one of the preceding Aspects, wherein the identified electrical biomarker relates to hyperexcitability.

86. The method according to Aspect 85, wherein the electrical biomarker comprises the power of the electrical brain activity at one or more specific frequency bands.

87. The method according to Aspect 86, wherein the one or more specific frequency bands comprise the frequency band ranging from 70 hertz (Hz) to 110 Hz.

88. The method according to Aspect 86 or 87, wherein the electrical biomarker comprises a signature of relative power across a plurality of frequency bands.

89. The method according to any one of the preceding Aspects, wherein the identified electrical biomarker relates to the recruitment of the first volume of the brain into a specific neural circuit. 90. The method according to Aspect 89, wherein the tumor feature data comprises control data and at least one of a subtype or classification of the first tumor and a characteristic of the microenvironment of the first volume of the brain.

91. The method according to Aspect 90, wherein the control data comprises information regarding the involvement of a control volume of a brain in the specific neural circuit.

92. The method according to Aspect 91, wherein the control volume comprises a subtype or classification of tumor different from the first tumor or a microenvironment characteristic different from the first volume of the brain.

93. The method according to any one of Aspects 89-92, wherein the specific neural circuit relates to the performance of a specific task or reception of a specific stimuli by the subject.

94. The method according to any one of the preceding Aspects, wherein the identified electrical biomarker relates to a dynamic of a specific neural circuit or neural network involving the first volume of the brain.

95. The method according to Aspect 94, wherein the dynamic is related to the effect of the first tumor on cognition.

96. The method according to Aspect 95, wherein the dynamic is the decodability of the electrical brain activity of the specific neural circuit or neural network.

97. The method according to Aspect 94, wherein the dynamic is hyperexcitability associated with the specific neural circuit or neural network.

98. The method according to any one of the preceding Aspects, wherein the identified electrical biomarker relates to a subtype or classification of tumor and/or a characteristic of a tumor microenvironment.

99. The method according to any one of the preceding Aspects, wherein the tumor feature data is obtained at two or more different timepoints.

100. The method according to Aspect 99, wherein the two or more different timepoints are at least a week apart.

101. The method according to Aspect 100, wherein the two or more different timepoints are at least a month apart.

102. The method according to any one of Aspects 99-101, wherein electrical brain activity measured by the first electrode is recorded for each timepoint. 103. The method according to Aspect 102, wherein electrical brain activity is recorded from each measurement electrode positioned at a location associated with or inside of the first volume of the brain for each timepoint.

104. The method according to Aspect 102 or 103, wherein one or more distinct recordings are generated for each timepoint.

105. The method according to Aspects 104, wherein the method further comprises: continuously monitoring the electrical brain activity measurements from each measurement electrode positioned at a location associated with or inside of the first volume of the brain during the time period from the first timepoint to the final timepoint for a specific pattern or event of electrical brain activity; and recording the measurements from one or more of the measurement electrodes when the specific pattern or event of electrical brain activity is detected.

106. The method according to Aspect 102 or 103, wherein the electrical brain activity measurements are continuously recorded during the time period from the first timepoint to the final timepoint.

107. The method according to any one of Aspects 102-106, wherein the tumor feature data is associated with or indicative of tumor growth, tumor progression, and/or treatment response.

108. The method according to Aspect 107, wherein the tumor feature data comprises the total volume of the first tumor and/or the distribution of the first tumor within the brain at the two or more different timepoints.

109. The method according to Aspect 108, wherein an electrical biomarker of tumor growth is identified using the tumor feature data and the recorded electrical brain activity measurements.

110. The method according to any one of Aspects 107-109, wherein the tumor feature data comprises a subtype or classification of the first tumor and/or data associated with or indicative of the connectivity of the first tumor at the two or more different timepoints.

111. The method according to Aspect 110, wherein an electrical biomarker of tumor progression is identified using the tumor feature data and the recorded electrical brain activity measurements. 112. The method according to any one of Aspects 107-1 11 , wherein the method further comprises treating the first tumor in the subject.

113. The method according to Aspect 112, wherein the treatment comprises surgery, radiotherapy, and/or chemotherapy.

114. The method according to Aspect 113, wherein the treatment comprises tumor resection followed by chemoradiation.

115. The method according to any one of Aspects 112-114, wherein the treatment occurs during a time period in between the first timepoint and the final timepoint.

116. The method according to Aspect 115, wherein the treatment begins after the first timepoint.

117. The method according to Aspect 115 or 116, wherein an electrical biomarker of treatment response is identified using the tumor feature data and the recorded electrical brain activity measurements.

118. The method according to any one of the preceding Aspects, wherein the method further comprises preprocessing the recorded electrical brain activity measurements.

119. The method according to Aspect 118, wherein the preprocessing comprises reducing or filtering the recorded electrical brain activity measurements.

120. The method according to Aspect 119, wherein the preprocessing comprises filtering the recorded electrical brain activity measurements using a high-pass, band pass, and/or notch filter.

121. The method according to any one of Aspects 118-120, wherein the preprocessing comprises extracting signal features from the recorded electrical brain activity measurements.

122. The method according to any one of Aspects 118-121, wherein the preprocessing comprises transforming the recorded electrical brain activity measurements.

123. The method according to any one of the preceding Aspects, wherein the electrical biomarker is identified using a statistical model and/or a machine learning model.

124. The method according to Aspect 123, wherein the electrical biomarker is identified using a mixed effects regression model.

125. The method according to Aspect 123 or 124, wherein the electrical biomarker is identified using recursive partitioning. 126. The method according to any one of Aspects 1 3-125, wherein the electrical biomarkcr is identified using principal component analysis (PCA).

127. The method according to any one of the preceding Aspects, wherein the method further comprises determining if the identified electrical biomarker meets a predetermined threshold of statistical significance.

128. A method of identifying electrical biomarkers of tumor features in the brain of a subject, the method comprising: obtaining tumor feature data for one or more tumor features of interest and one or more recordings of electrical brain activity measurements for each of a plurality of additional subjects according to the method of any one of Aspects 1-127; processing the obtained tumor feature data and electrical brain activity recordings such that the processed tumor feature data and electrical brain activity recordings are readily comparable across subjects; and identifying an electrical biomarker of a tumor feature from the obtained tumor feature data and the recorded electrical brain activity.

129. The method according to Aspect 128, wherein the electrical biomarker is identified using a statistical model and/or a machine learning model.

130. The method according to Aspect 129, wherein the electrical biomarker is identified using a machine learning model and the method further comprises: training a machine learning model to identify an electrical biomarker of a tumor feature from a first subset of the obtained tumor feature data and electrical brain activity recordings; and applying the trained machine learning model to a second subset of the electrical brain activity recordings to detect the electrical biomarker of the tumor feature in the second subset of recordings.

131. The method according to Aspects 130, wherein the obtained tumor feature data and electrical brain activity recordings are saved to a database.

132. The method according to Aspect 131, wherein the trained machine learning model is continuously updated based on the tumor feature data and electrical brain activity recordings saved to the database. 133. The method according to any one of the preceding Aspects, wherein the method further comprises monitoring the tumor in a subject using the identified electrical biomarkcr.

134. The method according to Aspect 133, wherein the monitored subject is the subject having the first tumor or a different subject having a second tumor capable of being monitored using the identified electrical biomarker.

135. The method according to Aspect 133 or 134, wherein the tumor feature data is associated with or indicative of tumor growth, tumor progression, and/or treatment response.

136. The method according to Aspect 135, wherein the method further comprises identifying exacerbating factors of tumor growth or progression based on the electrical biomarker monitoring.

137. The method according to Aspect 135 or 136, wherein the method further comprises altering the monitored subject’s treatment plan based on the electrical biomarker monitoring.

138. The method according to any one of Aspects 133-137, wherein the monitoring is ambulatory monitoring.

139. The method according to any one of Aspects 133-138, wherein the method further comprises developing a therapeutic neuromodulatory treatment effective in treating the tumor in the monitored subject using the identified electrical biomarker.

140. The method according to Aspect 139, wherein the identified electrical biomarker is associated with or indicative of neuronal activity that promotes tumor growth or tumor progression.

141. The method according to Aspect 140, wherein the identified electrical biomarker is associated with or indicative of hyperexcitability.

142. The method according to Aspect 141, wherein the therapeutic neuromodulatory treatment is adjusted to reduce the tumor promoting neuronal activity using the electrical biomarker.

143. The method according to Aspect 142, wherein the identified electrical biomarker is associated with or indicative of the present severity of the tumor.

144. The method according to Aspect 143, wherein the therapeutic neuromodulatory treatment is adjusted based on changes in the severity of the tumor determined by monitoring the electrical biomarker over a period of time.

145. The method according to Aspect 144, wherein the period of time is at least a month. 146. The method according to any one of Aspects 139-145, wherein the therapeutic ncuromodulatory treatment comprises delivering electrical stimulation to a location associated with or infiltrated by the tumor.

147. The method according to Aspect 146, wherein the electrical stimulation is applied to depolarizing tumor cells, neurons located within a tumor infiltrated region of the monitored subject’s brain, or neurons projecting into a tumor infiltrated region of the monitored subject’s brain.

148. The method according to Aspect 146 or 147, wherein the applied electrical stimulation is sufficient to prevent or inhibit tumor cells from repolarizing.

149. The method according to Aspect 148, wherein the location the electrical stimulation is applied is determined using non-invasive mapping and/or by performing an organoid or xenograft experiment.

150. The method according to Aspect 148, wherein one or more characteristics of the electrical stimulation are determined by performing an organoid or xenograft experiment.

151. The method according to Aspect 150, wherein the one or more characteristics of the electrical stimulation comprise the frequency of the electrical stimulation.

152. The method according to any one of Aspects 149-151, wherein the organoid or xenograft experiment is performed using cells derived from the monitored subject or another individual having a tumor with one or more of the same tumor features as the tumor of the monitored subject.

153. The method according to any one of Aspects 139-152, wherein the therapeutic ncuromodulatory treatment comprises delivering a pharmaceutical drug to a section of the tumor.

154. The method according to Aspect 153, wherein the section of the tumor comprises a HFC volume of the tumor.

155. The method according to Aspect 148, wherein the location the pharmaceutical drug is applied and/or the active pharmaceutical ingredient of the pharmaceutical ding is determined using non-invasive mapping and/or by performing an organoid or xenograft experiment.

156. The method according to Aspect 155, wherein the organoid or xenograft experiment is performed using cells derived from the monitored subject or another individual having a tumor with one or more of the same tumor features as the tumor of the monitored subject. 157. The method according to any one of Aspects 139- 156, wherein the therapeutic ncuromodulatory treatment comprises delivering low-intensity focused ultrasound (LIFU) to a tumor infiltrated region of the monitored subject’s brain.

158. The method according to any one of Aspects 146-157, wherein the method further comprises administrating the developed therapeutic neuromodulatory treatment to a tumor in the brain of a subject using the identified electrical biomarker, wherein the treated subject is different from the monitored subject and the subject having the first tumor.

159. The method according to Aspect 158, wherein the method further comprises determining that the treated tumor has one or more specific tumor features associated with the therapeutic neuromodulatory treatment.

160. The method according to Aspect 159, wherein the one or more therapeutic neuromodulatory treatment specific tumor features are determined by performing non- invasive mapping and/or a biopsy.

161. The method according to Aspect 159 or 160, wherein the one or more therapeutic neuromodulatory treatment specific tumor features are determined by detecting an electrical biomarker of the tumor features.

162. The method according to Aspect 158, wherein the identified electrical biomarker is associated with or indicative of neuronal activity that promotes tumor growth or tumor progression.

163. The method according to Aspect 162, wherein the amplitude of a delivered therapeutic electrical stimulation is adjusted based on the presence of the identified electrical biomarker.

164. The method according to Aspect 163, wherein the amplitude of the delivered electrical stimulation is adjusted based on a quantitative metric of the magnitude or intensity of the identified electrical biomarker.

165. A system configured to perform the method according to any one of Aspects 1-164.

166. A method for treating a brain tumor in a subject, the method comprising: positioning a stimulation electrode at a first location of the brain of the subject associated with or infiltrated by the tumor; and applying electrical stimulation to the first location via the stimulation electrode in a manner effective to treat the tumor in the subject. 167. The method according to Aspect 166, wherein the stimulation electrode is in contact with and applies electrical stimulation to depolarizing tumor cells, neurons located within a tumor infiltrated region of the subject’s brain, and/or neurons projecting into a tumor infiltrated region of the subject’s brain.

168. The method according to Aspect 166 or 167, wherein the applied electrical stimulation is sufficient to prevent or inhibit tumor cells from repolarizing.

169. The method according to Aspect 168, wherein the location the stimulation electrode is positioned is determined using non-invasive mapping and/or by performing an organoid or xenograft experiment.

170. The method according to Aspect 169, wherein the non-invasive mapping comprises determining the functional connectivity of one or more sections of the tumor with the rest of the brain.

171. The method according to Aspect 170, wherein the determined functional connectivity is used to identify high functional connectivity (HFC) and/or low functional connectivity (LFC) sections of the tumor.

172. The method according to Aspect 168, wherein one or more characteristics of the electrical stimulation are determined by performing an organoid or xenograft experiment.

173. The method according to Aspect 172, wherein the one or more characteristics of the electrical stimulation comprise the frequency of the electrical stimulation.

174. The method according to any one of Aspects 169-173, wherein the organoid or xenograft experiment is performed using cells derived from the subject.

175. The method according to any one of Aspects 166-174, wherein the method further comprises obtaining data associated with or indicative of one or more features of interest for the tumor.

176. The method according to Aspect 175, wherein the stimulation electrode is positioned and/or the electrical stimulation is applied based on the obtained tumor feature data.

177. The method according to Aspect 176, wherein the tumor feature data is obtained by performing a biopsy.

178. The method according to any one of Aspects 166-177, wherein the method further comprises: positioning a measurement electrode configured to measure electrical brain activity at a second location of the brain of the subject associated with or infiltrated by the tumor; detecting one or more electrical biomarkers identified according to any one of Aspects 1-164 via the measurement electrode; and applying the electrical stimulation based on the one or more detected electrical biomarkers.

179. The method according to Aspect 178, wherein one or more of the detected electrical biomarkers are associated with or indicative of neuronal activity that promotes tumor growth or tumor progression.

180. The method according to Aspect 179, wherein the amplitude of the applied electrical stimulation is adjusted based on the one or more detected electrical biomarkers.

181. The method according to Aspect 180, wherein the amplitude of the applied electrical stimulation is adjusted based on a quantitative metric of the magnitude or intensity of the one or more detected electrical biomarkers.

182. The method according to any one of Aspects 178-181, wherein the electrical stimulation is applied at least two times, wherein the electrical stimulation is spatially and/or temporally different based on the one or more detected electrical biomarkers.

183. The method according any one of Aspects 178-182, wherein the method further comprises positioning one or more additional measurement electrodes and one or more additional stimulation electrodes.

184. The method according to Aspect 183, wherein the additional measurement and stimulation electrodes are positioned based on an assessment of brain locations the tumor cells are likely to invade.

185. The method according to Aspect 184, wherein the additional measurement electrodes are used to monitor tumor growth and the additional stimulation electrodes are used to slow tumor growth.

186. The method of any one of Aspects 166-185, wherein the method further comprises assessing effectiveness of the treatment in the subject.

187. The method according to Aspect 186, wherein an assessment of treatment efficacy is generated using the measurements of each measurement electrode.

188. A system for treating a brain tumor in a subject, the system comprising: a stimulation electrode adapted for positioning at a first location of the brain of the subject associated with or infiltrated by the tumor; and a processor programmed to instruct the stimulation electrode to apply an electrical stimulation to the first location in a manner effective to treat the tumor in the subject.

189. The system according to Aspect 188, wherein the system further comprises: a measurement electrode adapted for positioning at a second location of the brain of the subject associated with or infiltrated by the tumor, wherein the measurement electrode is configured to record an electrical signal from the second location, wherein the processor is programmed to: receive the electrical signal from the second location of the brain of the subject via the measurement electrode; detect one or more electrical biomarkers identified according to any one of Aspects 1-164 from the electrical signal; modulate one or more programmed electrical stimulation parameters based on the one or more detected electrical biomarkers; and apply the modulated electrical stimulation to the first location via the stimulation electrode in a manner effective to treat the tumor.

190. A closed-loop method for treating a brain tumor in a subject, the method comprising: positioning an adjustable neuromodulating device at a first location of the brain of the subject associated with or infiltrated by the tumor, wherein the adjustable neuromodulating device is configured to modulate neuronal activity at the first location; positioning a measurement electrode configured to measure electrical brain activity at a second location of the brain of the subject associated with or infiltrated by the tumor; detecting one or more electrical biomarkers identified according to any one of Aspects 1-164 via the measurement electrode; determining one or more parameters of the adjustable neuromodulating device based on the one or more detected electrical biomarkers; and modulating neuronal activity at the first location via the adjustable neuromodulating device in a manner effective to treat the tumor in the subject. 191. The closed-loop method of Aspect 190, wherein the adjustable neuromodulating device comprises a stimulation electrode configured to apply electrical stimulation to the first location.

192. The closed-loop method of Aspect 191, wherein the stimulation electrode is in contact with and applies electrical stimulation to depolarizing tumor cells, neurons located within a tumor infiltrated region of the subject’s brain, and/or neurons projecting into a tumor infiltrated region of the subject’s brain.

193. The closed- loop method of Aspect 192, wherein the applied electrical stimulation is sufficient to prevent or inhibit tumor cells from repolarizing.

194. The closed-loop method of any one of Aspects 190-193, wherein the method further comprises obtaining data associated with or indicative of one or more features of interest for the tumor.

195. The closed- loop method of Aspect 194, wherein the tumor feature data is obtained by performing a biopsy.

196. The closed-loop method of Aspect 194 or 195, wherein the stimulation electrode is positioned and/or the electrical stimulation is applied based on the tumor feature data.

197. The closed-loop method of any one of Aspects 194-196, wherein the adjustable neuromodulating device comprises an implantable drug delivery device configured to deliver one or more doses of a pharmaceutical drug.

198. The closed-loop method of Aspect 197, wherein the active pharmaceutical ingredient of the pharmaceutical drug is determined using the tumor feature data.

199. The closed-loop method of Aspect 197 or 198, wherein the size of the delivered drug dose is determined based on the or more detected electrical biomarkers.

200. The closed- loop method of any one of Aspects 190-199, wherein low-intensity focused ultrasound (LIFU) is delivered to a tumor infiltrated region of the brain based on the one or more detected electrical biomarkers.

201. The closed- loop method of any one of Aspects 190-200, wherein the method further comprises assessing effectiveness of the treatment in the subject.

202. The closed-loop method of Aspect 201, wherein an assessment of treatment efficacy is generated based on the one or more detected electrical biomarkers.

203. A closed-loop system for treating a brain tumor in a subject, the system comprising: an adjustable neuromodulating device adapted for positioning at a first location of the brain of the subject associated with or infiltrated by the tumor, wherein the adjustable neuromodulating device is configured to modulate neuronal activity; and a measurement electrode adapted for positioning at a second location of the brain of the subject associated with or infiltrated by the tumor, wherein the measurement electrode is configured to record an electrical signal from the second location, wherein the processor is programmed to: receive the electrical signal from the second location of the brain of the subject via the measurement electrode; detect one or more electrical biomarkers identified according to any one of Aspects 1-164 from the electrical signal; adjust one or more programmed neuromodulation parameters based on the one or more detected electrical biomarkers; and modulate the neuronal activity of the first location via the adjustable neuromodulating device in a manner effective to treat the tumor.

204. The closed-loop system of Aspect 203, wherein the adjustable neuromodulating device comprises a stimulation electrode, wherein the one or more adjusted neuromodulation parameters comprises the amplitude and/or frequency of the electrical stimulation applied by the stimulation electrode.

205. The closed-loop system of Aspect 203 or 204, wherein the adjustable neuromodulating device comprises an implantable drug delivery device configured to deliver one or more doses of a pharmaceutical drug, wherein the one or more adjusted neuromodulation parameters comprises the size of the pharmaceutical drug dose delivered by the implantable drug delivery device.

206. The closed-loop system of any one of Aspects 203-205, wherein the system further comprises a low-intensity focused ultrasound (LIFU) emitting device.

207. The closed- loop system of Aspect 206, wherein LIFU emitted by the LIFU emitting device is adjusted based on the one or more detected electrical biomarkers. EX MPI.ES

As demonstrated in the above disclosure, the present invention has a wide variety of applications. The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how to make and use the present invention and are not intended to limit the scope of what the inventors regard as their invention nor are they intended to represent that the experiments below are all or the only experiments performed. Those of skill in the art will readily recognize a variety of noncritical parameters that could be changed or modified to yield essentially similar results. Efforts have been made to ensure accuracy with respect to numbers used (e.g. amounts, percentages, etc.) but some experimental errors and deviations should be accounted for.

Example 1: Characterizing the tumor microenvironment and its effect on patient outcomes

Overview

Gliomas synaptically integrate into neural circuits [1,2]. Previous research has demonstrated bidirectional interactions between neurons and glioma cells, with neuronal activity driving glioma growth [1-4] and gliomas increasing neuronal excitability [2,5-8]. It was sought to determine how glioma-induced neuronal changes influence neural circuits underlying cognition and whether these interactions influence patient survival. Using intracranial brain recordings during lexical retrieval language tasks in awake humans together with site-specific tumor tissue biopsies and cell biology experiments, it was found that gliomas remodel functional neural circuitry such that task-relevant neural responses activate tumor-infiltrated cortex well beyond the cortical regions that are normally recruited in the healthy brain. Site-directed biopsies from regions within the tumor that exhibit high functional connectivity between the tumor and the rest of the brain are enriched for a glioblastoma subpopulation that exhibits a distinct synaptogenic and neuronotrophic phenotype. Tumor cells from functionally connected regions secrete the synaptogenic factor thrombospondin- 1, which contributes to the differential neuron-glioma interactions observed in functionally connected tumor regions compared with tumor regions with less functional connectivity. Pharmacological inhibition of thrombospondin- 1 using the FDA-approved drug gabapentin decreases glioblastoma proliferation. The degree of functional connectivity between glioblastoma and the normal brain negatively affects both patient survival and performance in language tasks. Data from the experiments found below demonstrate that high-grade gliomas functionally remodel neural circuits in the human brain, which both promotes tumor progression and impairs cognition.

Introduction

Malignant brain tumours such as glioblastomas exist within the context of complex neural circuitry. Neuronal activity promotes glioma growth through both paracrine signaling (neuroligin-3 and brain-derived neurotrophic factor (BDNF)) and AMPAR (a-amino-3- hydroxy-5-methyl-4-isoxazole propionic acid receptor)-mediated excitatory electrochemical synapses [1-4],

Likewise, glioblastomas influence neurons, inducing neuronal hyperexcitability through the secretion of non-synaptic glutamate and synaptogenic factors [5,6] and reducing inhibitory interneurons [7]. Beyond preclinical models, it was demonstrated in awake, resting patients that glioblastoma- infiltrated cortex exhibits increased neuronal excitability [2] . The mechanisms by which glioblastomas maintain the ability to engage with neuronal circuitry and alter cortical function remain incompletely understood [9]. It was theorized that deciphering the processes by which gliomas remodel neural circuits will uncover therapeutic vulnerabilities for these lethal brain cancers. To address these gaps in knowledge, intraoperative electrophysiology was performed while patients engaged in language tasks: local field potentials were analyzed in glioblastoma- infiltrated cortex during speech initiation, the decodability of neural responses was determined and biological drivers of synaptic enrichment in glioblastoma cells was revealed (FIG. 5).

FIGS. 5A to 5E experimental workflow used to elucidate how various glioma characteristics affect neural circuits, cognition, and clinical outcomes. (A) Schematic of study workflow. In human participants with dominant hemisphere gliomas, subcortical high density electrocorticography were applied during audiovisual speech initiation to assess tumour intrinsic neuronal circuit dynamics. Focusing on glioblastoma, long-range functional connectivity was then assessed using magnetoencephalography (MEG) imaginary coherence. (B) Extra-operative language assessments were performed for correlation with biological assays. (C) Long-range measure of tumour intrinsic functional connectivity identified regions of high and low connectivity for site specific biopsies which were used for in vivo and in vitro cell biology experiments. (D) Multiple layered approach including clinical variables, cognition assessments, human and animal model network dynamics, and cell biology experiments serves as a platform for glioma influence on neural circuit dynamics.

Results

Glioblastomas remodel neural circuits

Glioblastomas and other high-grade gliomas interact with neural elements, resulting in cellular- and network-level changes [10-13], While neurons within glioblastoma-infiltrated brain are hyperexcitable at rest, the extent of task- specific neuronal hyperexcitability and the ability to extract neural features from glioma-infiltrated cortex remain unclear. To examine cognitive task-specific neuronal activity from glioblastoma-infiltrated cortex, a cohort of adult patients with cortically projecting tumours in the lateral prefrontal cortex (LPFC) was selected (FIG. 6A). Electrocorticography (ECoG) electrodes were placed over tumour- infiltrated and normal- appearing cortex. ECoG signals filtered between 70-110 Hz were used for analysis of high-gamma band range power (HGp), which is strongly related to local neuronal population spikes [14,15] and is increased by cortical hyperexcitability [16]. Spectral data demonstrated clear separation of frequencies across tumour and non-tumour electrodes (FIG. 1A and FIG. 7A).

ECoG was recorded from the dominant hemisphere LPFC during auditory and visual picture naming as an illustrative example of a well-defined cognitive neuronal circuit [17]. While patients were awake and speaking, HGp was recorded for single-electrode (FIG. 7B) and group-level analysis. HGp data from control and non-tumour conditions demonstrate the expected neural time course of speech motor planning within the LPFC (FIG. 6B and FIGS. 7C-7D), consistent with previously established models of speech initiation demonstrated in non-human primates and humans [18,19]. Next, the same analysis was performed focused only on electrode arrays recording from tumour-infiltrated cortex. Countering the theory that glioblastoma-synaptic integration may result in physiologically disorganized neural responses, task-relevant neural activity was found within the entire region of the tumour- infiltrated cortex, including cortical regions that are not typically implicated in speech production (FTG. IB) — a notable finding that indicates tumour-induced functional remodeling of language circuitry. Similarly, it was found that, across WHO grade 2-4 glioma subtypes, task-specific neuronal responses for speech initiation are maintained within the LPFC (FIGS. 6C-6D). These findings demonstrate that neuronal activity within tumour- affected cortex is physiologically organized, including neuronal activity elicited by speech tasks in regions that are outside of regions that are typically involved in speech production.

In light of this finding of preserved task-evoked neural responses from tumour- infiltrated cortex, it was next examined whether the magnitude of neural responses may differ in tumour-affected cortical language areas. Therefore, tumour-infiltrated and normalappearing cortex were pair-matched (FIGS. 7D-7E), demonstrating increased HGp during speech production in glioblastoma- infiltrated cortex, consistent with hyperexcitability (FIGS. 1C-1D).

Neural computations for speech vary by condition. Vocalization of infrequently used (low frequency) words, for example, requires a more intricate coordination of articulatory elements than that of commonly used (high frequency) words [20,21], Therefore, the decodability of neuronal signals from normal-appearing and glioblastoma-infiltrated cortex was determined using a logistic regression classifier to distinguish between low-frequency and high-frequency word trial conditions (FIG. IE). Identical training and leave-one- participant-out cross-validation paradigms were implemented for both conditions. Normalappearing cortex produced above-chance decoding between low- and high-frequency word trials. By contrast, glioblastoma- infiltrated cortex did not decode word trials above chance. These data further demonstrate that glioblastoma infiltration into the human cortex maintains task-specific neuronal responses, including neuronal hyperexcitability, yet tumour-affected cortex loses the ability to decode complex word conditions.

FIGS. 1A to IE high-grade gliomas remodel long-range functional neural circuits. (A) In participants with dominant hemisphere glioblastomas, subdural ECoG was applied over the posterior lateral frontal cortex during an audiovisual speech initiation task to assess circuit dynamics. Spectral data show the expected pattern of HGp increasing above 50 Hz in addition to clear separation of frequencies across tumour and non-tumour electrodes. (B) The posterior lateral frontal cortex (outlined area) time series of HGp within tumour-infiltrated cortex between -600 ms and speech onset (0 ms). (C) High-gamma (HG) recordings from averaged electrodes within each patient, while averaging the effect across the sampled region of cortex for an individual showing greater HG power within electrodes overlying tumour- infiltrated cortex (n = 14 patients, Fi,2i = 25.562, P = 0.00005). Data are median (centre dot), first to third quartiles (bars) and the minimum and maximum points (whiskers). (D) Electrodes were compared between non-tumour and tumour-infiltrated regions; the false- discovery rate (FDR)-corrected HGp demonstrates task-relevant hyperexcitability

(P - 0.016). Data are mean ± s.e.m. (E) Event-related spectral perturbations (ERSPs) during a naming task for low-frequency words (low freq., left column) and high-frequency words (high freq., middle column) in normal-appearing non-tumour regions (top row) and glioma- infiltrated (bottom row) cortex. Signals from high-frequency word trials were able to be decoded above chance in normal-appearing cortex (mean accuracy = 0.56, P = 0.000089) but not in glioma-infiltrated cortex (mean classifier accuracy = 0.49, P = 0.72) using a regularized logistic regression classifier with leave-one-participant-out cross-validation (right column). Data are mean ± 95% confidence interval. AU, arbitrary units. For (B)-(D), statistical analysis was performed using two-sided linear mixed-effects models (B)-(D), and corrections for multiple comparisons were performed using FDR adjustment (B) and (D).

For (E) P values were determined using two-tailed Student’s /-tests with Bonferroni multiplecomparison correction for the number of timepoints (left, ERSP) and one-sided Z-tests (right, classification accuracy).

FIGS. 6A to 6D electrode locations and spectral data across cortically infiltrating diffuse glioma. (A) Electrodes overlying normal appearing and glioma-infiltrated regions a cohort of 14 adult patients with cortically projecting glioma in the lateral prefrontal cortex. Electrodes over non-tumour regions are shown in white and those over tumour-infiltrative regions in black. (B) Positive control conditions included speech initiation responses within non-infiltrated cortex of the left lateral prefrontal cortex (LPFC) for non-cortically projecting glioblastoma. (Left) Axial FLAIR MRI demonstrates tumour location within insular cortex. Hemisphere of language dominance on the left was performed according to study protocol. (Middle) Black outline illustrates LPFC with ECoG recordings obtained from electrode A24, denoted by the red dot. White star represents frontal lobe motor cortex. (Right) Identical to non-tumour comparisons for cortically projecting gliomas, speech responses demonstrate elevate high gamma power (HGp) prior to speech onset consistent with speech motor planning. Dark line and shaded region represent mean and 95% confidence interval, respectively. (C) Selectivity of maintained tumour intrinsic task- specific cortical responses is identified across diffuse glioma subtypes (adapted from Aabedi et al. 2021). Spectral data show clear separation of frequencies across tumour (glioma-infiltrated) and non-tumour (normal-appearing) electrodes. Group level analysis of participants (n = 12) demonstrates speech initiation responses across WHO 2-4 diffuse glioma. (D) Glioma subtype- specific speech initiation spectral responses for electrodes above normal-appearing and glioma- infiltrated cortex showing conserved phenotype with task-specific hyperexcitability observed only in participants with glioblastoma. Subtypes: grade 2 and 3 oligodendroglioma (n = 4), grade 2 and 3 astrocytoma (n = 4), and glioblastoma (n = 4). P value determined by two- tailed Student’ s t-test and corrections for multiple comparisons were made using the FDR method (A).

FIGS. 7 A to 7E speech initiation neural activity in the lateral prefrontal cortex (LPFC). (A) Spectral data of time vs frequency from 100-150 Hz for all tumour and nontumour electrodes show the expected pattern of HgP increasing above 50 Hz. (B) High gamma power (HGp) recording from single electrodes overlying tumour-infiltrated regions of brain. Dark line and shaded region represent mean and 95% confidence interval, respectively. (C) Reconstructed time series of HGp from non-tumour electrodes demonstrating expected spatial and temporal pattern of neural activity within lateral prefrontal cortex. (D) (E) Electrodes matched to anatomical areas across tumour and non- tumour demonstrate hyperexcitability with glioma-infiltrated cortex. Plots are centred on median, with dark bars indicating the first and third quartiles, and whiskers the minimum and maximum points (n = 101 per group, P = 0.0163). P value determined by two-sided linear mixed-effects model with corrections for multiple comparisons made using the FDR method (b) and two-tailed Student’s t-test (e). *P < 0.05.

Synaptogenic tumour cells promote connectivity

Having demonstrated that gliomas remodel neuronal circuits, it was next examined whether specific molecularly defined glioma cellular subpopulations influence functional integration of the tumour into neural circuitry. Glioblastoma cells are heterogeneous [22- 24] and previous findings indicate that oligodendrocyte-precursor-cell-like subpopulations are enriched for synaptic gene expression [2], whereas astrocyte-like subpopulations secrete synaptogenic factors [8,25], Thus, functionally connected regions may vary within tumours and differences in functional connectivity between tumour regions may be due at least in part to varying subpopulations of glioma cells. With the goal of sampling functionally connected regions within gliomas, neuronal oscillations were measured within glioma-infiltrated brain using magnetoencephalography (MEG) and primary patient glioblastoma tissues with varying functional connectivity were sampled during surgical tumour resection [26-28]. The connectivity of an individual voxel was derived by the mean imaginary coherence between the index voxel and the rest of the brain [29-31]. Intratumoural functional connectivity correlated with neuronal activity within tumour-infiltrated cortex and high functional connectivity (HFC) voxels were identified both within tumour regions that were contrastenhancing or T2/FLAIR hyperintense on magnetic resonance imaging (MRI; FIG. 8).

To investigate the differences between functionally connected, HFC and non- functionally connected low functional connectivity (EFC) tumour regions, bulk and singlecell RNA sequencing (RNA-seq) analyses were performed. Bulk RNA-seq transcriptomic analysis revealed upregulation in HFC tumour regions of genes that are involved in the assembly of neural circuits, including axon pathfinding genes (NTNG1, also known as netrin Gl), synapse-associated genes (for example, SYNPO, also known as synaptopodin) and synaptogenic factors including a sevenfold upregulation of thrombospondin- 1 (THBSI, encoding TSP-1). THB I . which encodes a known synaptogenic factor that is secreted in the healthy brain by astrocytes [32], was particularly interesting in the context of the observed remodeling of functional language circuitry described above (FIGS. 9A-9C).

To further assess cellular subpopulation contribution to THBSI expression, single-cell sequencing analysis of biopsy samples from HFC and EFC tumour regions was performed. Malignant tumour cells were inferred on the basis of the expression programs and detection of tumour- specific genetic alterations, including copy-number variants (FIGS. 10A-10E). It was found that 2.44% of all tumour cells expressed THBSI, and that HFC tumour cells expressed higher levels of THBSI compared with EFC tumour cells (FIG. 2A and FIG. 10G). Within LFC-region samples, THBSI expression primarily derives from a non-tumour astrocyte population (FIGS. 10E-10G). This suggests that, within low-connectivity intratumoural regions, astrocytes chiefly express THBSI, whereas, within HFC regions, highgrade glioma cells express THBSI in addition to astrocytes and myeloid cells, which may promote the observed neural circuit remodeling (FIGS. 10H-10J). Notably, myeloid cells, which include bonc-marrow-dcrivcd macrophages, microglia, dendritic cells and neutrophils, chiefly comprise the glioblastoma tumour immune microenvironment (FIGS. 10D-10E), and the microglial cell surface molecules CD36 and CD47 can function as TSP-1 receptors [33,34], Although the role of TSP- 1 in the tumour immune microenvironment is not yet clear, myeloid cell expression of TSP- 1 suggests that multiple cell types in the tumour microenvironment of HFC regions may contribute to altered synaptic connectivity. Elevated expression of THBS1 within HFC regions was confirmed by protein-level analysis using HFC and LFC patient-derived glioblastoma biopsy tissues. Concordant with transcriptomic profiles, immunohistochemistry analysis demonstrated increased TSP-1 expression within HFC tissues (FIG. 11 A). Immunofluorescence and confocal microscopy analysis confirmed that malignant tumour cells express TSP-1 in HFC tissue (FIG. 2B). The fact that a subpopulation of malignant tumour cells in HFC regions produce TSP-1 suggests a differential potential of tumour cells in the HFC regions to promote synaptogenesis and thereby connectivity, consistent with the cancer biology principal that cellular subpopulations assume distinct roles within the heterogenous cancer ecosystem, which may be defined at least in part by functional connectivity measures.

Hypothesizing that this subpopulation of HFC glioma cells may promote synaptogenesis and consequent remodeling of connectivity as observed in glioma-associated language networks above, whether HFC-associated glioma cells promote structural synapse formation, similar to normal astrocytes [35-37] and certain astrocyte-like glioblastoma cells [8,25] was next examined. Primary patient glioblastoma biopsies from HFC and LFC regions was first analysed using immunohistochemistry and confocal microscopy. Increased presynaptic neuronal puncta (synapsin-1; FIG. 2C) together with increased postsynaptic puncta density and cluster size on neurons (PSD95⁺neurofilament⁺), and synapsin-PSD95 puncta colocalization (FIG. 2D and FIG. 1 IB) was found within HFC regions compared with LFC regions. Together, these data indicate increased synapse stability and/or synapse formation in high-connectivity regions of glioblastoma, supporting a role for TSP- 1 in glioma-associated neural-circuit remodeling.

Primary patient-derived glioma cultures from HFC and LFC tumour regions were generated to perform further mechanistic experiments. High-grade glioma cells from HFC and LFC tumour regions were co-cultured with mouse hippocampal neurons to test the effects of TSP-l^high- and TSP-l^low -expressing primary patient-derived glioma cells on synaptic connectivity of neurons (FIGS. 11C-11D). The size of postsynaptic puncta (marked by the postsynaptic marker homer- 1) and the number of colocalized pre- and postsynaptic puncta were then quantified in HFC and LFC co-cultures with neurons. This demonstrated an increased number of colocalization points and postsynaptic homer- 1⁺ punctum size in glioma and neuronal processes in HFC-neuron co-cultures compared with LFC-neuron co-cultures (FIG. 2E), additionally indicating a role for HFC glioma cells in synaptogenesis.

To further investigate the functional distinctions between malignant subpopulations isolated from HFC and LFC regions, neuron-glioma interactions was tested in a neuronal organoid model. HFC and LFC glioma cells were co-cultured with GFP-labelled human neuron organoids generated from an induced pluripotent stem (iPS) cell line integrated with a doxycycline-inducible human NGN2 transgene to drive neuronal differentiation [38]. Quantification of postsynaptic homer- 1 in induced-neuron organoids revealed a relative increase in postsynaptic puncta density when co-cultured with HFC glioma cells compared with LFC glioma cells (FIG. HE). Live-cell imaging of neuronal organoids co-cultured with HFC and LFC glioma cells revealed that HFC glioma cultures exhibit prominent neuronal tropism and integrate extensively in the organoids, whereas LFC glioma cells displayed minimal integration with neuron organoids (FIG. 2F). Notably, exogenous administration of TSP-1 to induced-neuron-LFC co-culture reversed this phenotype and promoted robust LFC glioma integration into the neuronal organoid (FIG. 2F), further implicating TSP-1 in neuron-glioma interactions. The electrophysical properties of TSP-l^hlgh-expressing cells in co-culture with neurons were analyzed using multi-electrode array (MEA) electrophysiology. After co-culture for 48 h, the total number of network bursts (a measure of neuronal activity) from cortical neuron co-culture with TSP-l^lllgll-expressing HFC cells was increased relative to cortical neurons alone or under LFC co-culture conditions. Neurons in co-culture with HFC glioma cells also demonstrated increased network synchrony as measured by the area under normalized cross-correlation (the area under interelectrode cross-correlation normalized to the autocorrelations; FIG. 2G and FIG. 1 IF).

Gliomas exhibit intratumoural heterogeneity with subpopulations of cancer cells assuming particular roles [23,24], The human data presented above demonstrate localizational heterogeneity of functional integration in glioblastoma with normal brain circuity and suggest that, within intratumoural regions of HFC, a tumour subpopulation with synaptogenic properties exists. Next, the structural synapses in TSP-l^hlgh-expressing HFC glioma cell-infiltrated mouse brain was examined. RFP-labelled HFC or LFC glioma cells were stereotactically xenografted into the CAI region of the mouse hippocampus [2] (FIG. 3A). After a period of engraftment and growth, immuno-electron microscopy analysis identified neuron-to-neuron and neuron-to-glioma synapses [2] (FIG. 3B). The total number of synapses (neuron-to-neuron and neuron-to-glioma combined) was significantly higher in HFC glioma xenografts than in LFC glioma xenografts (FIG. 3B and FIG. 11G), further demonstrating a greater synaptogenic potential of glioma cells isolated from HFC patient tumour regions.

FIGS. 2A to 2G illustrate that tumour-infiltrated circuits exhibit areas of synaptic remodeling characterized by glioma cells expressing synaptogenic factors. (A) Single-cell RNA-seq feature plot analysis of THBS1 in HFC (n = 6,666 cells, 3 participants) tissues; within HFC samples, THBS1 is primarily in glioblastoma cells (circled). (B) TSP-1 immunofluorescence analysis of nestin-positive tumour cells in HFC and LFC tissues, n = 13 (HFC) and n = 11 (LFC) sections, 3 per group. P = 0.000073. Scale bar, 50 pm. The box plot shows the median (centre line), interquartile range (box limits) and minimum and maximum values (whiskers). (C) The synapsin-1 puncta count in HFC and LFC glioblastoma tissue samples, n = 25 regions, 4 per group. P = 0.000014. Red, synapsin-1 (presynaptic puncta); white, neurofilament heavy and medium (neurons). Scale bar, 10 pm. Inset: magnified view of synapsin-1 puncta on neurons. Scale bar, 3 pm. (D) PSD95 puncta count, n = 1 (HFC) and n = 9 (LFC) sections, 3 per group. P = 0.04. Red, PSD95 (postsynaptic puncta); white, neurofilament heavy and medium chains (NFH/M) (neurons). Scale bar, 10 pm. Inset: magnified view of PSD95 puncta on neurons. Scale bar, 3 pm. (E) Representative confocal images showing synaptic punctum colocalization (yellow arrows). Red, synapsin-1; green, homer- 1 (postsynaptic puncta); white, MAP2 (neurons); blue, 4',6-diamidino-2-phenylindole dihydrochloride (DAPI). Scale bar, 10 pm. Quantification of the number of colocalized pre- and postsynaptic puncta (n = 13 (HFC) and n = 10 (LFC) regions, 2 per group; P = 0.005) and homer- 1 puncta size in neuron-glioma co-culture (P = 0.000024). (F) TSP-1 rescue of induced neuron (iN) organoids in co-culture with HFC and LFC cells for 6 h. Scale bar, 300 pm. Quantification of glioblastoma (GBM) cell integration measured on the basis of the fluorescence intensity of RFP-positivc glioblastoma cells in the organoids. Significant differences between HFC and EFC groups (asterisks) and LFC and LFC + TSP-1 (hash) are indicated, n = 2 (HFC and LFC groups) and n = 1 (LFC + TSP-1 group). Scale bar, 300 pm. (G) Representative MEA raster plots showing individual spikes (tick mark), bursts (cluster of spikes in blue) and synchronized network bursts (pink) after 48 h co-culture of cortical neurons (CN) with HFC and LFC cells (outlined in red and blue, respectively). Quantification of network burst frequency (Hz) (n = 2 (CN only), n = 3 (CN + HFC) and n = 4 (CN + LFC); P = 0.05) and network synchrony (area under normalized crosscorrelation; n = 2 (CN only), n = 3 (CN + HFC) and n = 4 (CN + LFC); P = 0.0129 (CN versus CN + HFC); P = 0.0308 (CN + HFC versus CN + LFC)). Data are mean ± s.e.m. (B)- (G). P values were determined using two-tailed Student’s /-tests (B)-(F) and one-way analysis of variance (ANOVA) with Tukey’s post hoc test (G). *P < 0.05, **P < 0.01,

< 0.0001; NS, not significant.

FIGS. 3A to 3H illustrate that high-grade gliomas exhibit bidirectional interactions with HFC brain regions. (A) Representative micrograph showing RFP-labelled glioblastoma xenografted into the mouse hippocampus. Scale bar, 500 pm. (B) Immuno-electron microscopy analysis of HFC or LFC cell xenografts. The asterisk denotes immuno-gold particle labelling of RFP. Postsynaptic density in RFP⁺ tumour cells (pseudocoloured red), synaptic cleft and clustered synaptic vesicles in apposing presynaptic neuron (pseudocoloured yellow) identify both neuron-glioma synapses in HFC-PDX (left) and neuron-neuron synapses in LFC-PDX (right). Quantification of the total number (neuronneuron combined with neuron-glioma) of synapses per field of view in HFC/LFC xenografts, n - 4 mice per group. P - 0.0019. Scale bar, 1,000 nm. Data are median (centre line), with first and third quartiles (box limits) and the minimum and maximum points (whiskers). (C) Representative immunohistochemistry images in glioblastoma tissues demonstrate increased Ki-67 protein expression in HFC samples, n = 13 (HFC) and n = 14 (LFC) regions, 4 per group. P = 0.04. Scale bar, 50 pm. (D) Glioblastoma cells from HFC tissues show a marked increase in the proliferative index when co-cultured with mouse hippocampal neurons, n = 27 (HFC), n = 14 (HFC + neurons), n = 32 (LFC) and n = 14 regions (LFC + neurons), 3 per group. (E) SEM images of HFC and LFC cells cultured in the presence or absence of neuronal conditioned medium (NCM) shows tumour microtubes (TMTs) that connect neighbouring cells through cytoplasmic extensions. Quantification of TMTs per cell, n = 10 (HFC), n = 8 (HFC + NCM), n = 17 (LFC) and n = 13 (LFC + NCM) regions, 2 per group. P = 0.0455. Scale bars, 20 pm (full fields) and 10 pm (magnified view). (F) Quantification of the mean microtube length per spheroid, n = 11 (HFC), n - 16 (HFC + NCM), n = 5 (LFC) and n = 6 (LFC + NCM) spheroids, 1 per group. P - 0.000011. (G) Representative SEM images showing TMTs and quantification of TMTs per cell from HFC shCtrl and HFC shTSP-1 conditions, n = 5 regions, 2 per group. P = 0.0012. Scale bar, 20 pm. (H) Kaplan-Meier survival curves of mice bearing HFC or LFC xenografts, n = 4 (HFC) and n = 5 (LFC). P = 0.03. Data are mean ± s.e.m. (B)- (H). P values were determined using two-tailed Student’s /-tests (B)-(G) and two-tailed logrank analysis (H).*P < 0.05, **P < 0.01, ****P < 0.0001; NS, not significant.

FIGS. 8A to 8C gamma power and tumour-intrinsic connectivity imaging correlations. (A) Linear regression statistics illustrating that gamma power (a measure of neuronal activity) correlates with number of intratumoural high functional connectivity voxels in glioblastoma (n = 18 patients; P - 0.00002). Shaded area represents the 95% confidence interval predicted by the linear regression model. (B) Sampling of functionally connected intratumoural regions using MEG was performed exclusively in participants with dominant hemisphere glioblastoma at the point of initial diagnosis. Site-directed tissue biopsies from HFC and LFC regions were taken as determined by MRI. Table illustrates sitespecific sampling of each annotated specimen as it relates to contrast enhancing (CE) region and FLAIR tumour. Site- specific samples were acquired without regard for whether they originated from enhancing or FLAIR regions. (C) While samples were not acquired based on whether they originated from contrast enhancing or FLAIR regions, the stereotactic coordinates of each sample were acquired. While 57.78% of HFC samples originated from contrast enhancing regions, this did not reach statistical significance. P = 0.1923 two-sided chi square, P = 0.235 two-sided Fisher’s exact test. P value determined by two-sided linear regression analysis (A). NS, not significant.

FIGS. 9A to 9C neurogenic gene expression in glioblastoma. (A) Bulk RNA transcriptomic profile of HFC tissues showed a neurogenic signature including elevated (7- fold) expression of thrombospondin- 1 TSP-1) (n = 3-4 per group). (B) Unsupervised principal component (PCA) analysis of bulk RNA sequencing data obtained from glioblastoma primary patient HFC (n = 3) and LFC (n = 4) samples. (C) Volcano plots of IDH-WT glioblastoma samples revealed 144 differentially expressed genes between HFC and LFC tumour regions. The blue dots represent all differentially expressed genes, where differential expression is defined by the parameters: adjusted p-value < 0.05 and absolute log2fold change > 1. P value calculated using two-sided Wald test and adjusted for multiple comparisons with the Benjamini-Hochberg method.

FIGS. 10A to 10J TSP-1 expression in single-cell primary patient-derived glioblastoma. (A) — (C), Tumour cell validation using copy number variant assessment on three matched pairs of HFC and LFC samples from FC1 (SF#1), FC2 (SF#2), and FC3 (SF#3) glioblastoma patients. Trisomy 7 and monosomy 10 co-occur in most cells in FC 1. Trisomy 7 is an early event, while monosomy 10 is a late event in FC2. FC3 patient sample contains no copy number variation but has high level amplification of NTRK2 gene. (D) Single-cell RNA transcriptomic profile UMAP confirms distinct cell populations including non-tumour astrocytes and neurons. (E) Gene enrichment profile used to identify each of the UMAP cell populations. (F) (G) Feature plot for TSP-1 in combined (HFC + LFC) and LFC (n = 7,065 cells, 3 participants) population; within LFC samples, TSP-1 expression is primarily from non-tumour astrocytes (suggesting that within low connectivity intratumoural regions, normal astrocytes secrete TSP-1 to generate connectivity mirroring normal physiology). (H) Dot plots showing TSP-1 expression (grey to red scale) and percentage (number of cells expressing the gene) of TSP-1 -positive cells in tumour cells and non-tumour astrocyte populations in HFC and LFC samples (n = 3 per group). Out of the total HFC tumour cells (n = 5325, 3 patients), 157 cells are TSP-1 positive accounting for a percentage of 2.95, while only 1.59% (51 cells out of a total of 3212 LFC tumour cells [n = 3 patients]) express TSP-1. However, in the non-tumour astrocyte population, the number of TSP-1 -positive cells are higher in LFC (n = 34 out of a total of 41 astrocytes, accounting for 82.9%) compared to HFC (n = 15 out of a total of 20 astrocytes, accounting for 75%) samples. (I) Violin plots illustrating significantly increased TSP-1 expression within HFC region glioblastoma cells relative to LFC regions (P = 1.4* 10^-7). (J) Compared to HFC, slight trend of increased TSP-1 expression within non-tumour astrocytes of LFC population. However, this trend did not reach statistical significance, likely due to the small number of non-tumour astrocytes captured (P = 0.45). P values determined by two- tailed Student’s t-test.

FIGS. 11A to 11G TSP-1 expression, synaptic puncta colocalization in primary patient-derived glioblastoma tissues, neuron organoid-glioblastoma co-culture model and structural synapse formation in patient-derived xenograft models. (A) TSP-1 (immunohistochemistry) expression in arbitrary units (A.U.) in HFC and LFC tissues (n = 16 regions; 3 per group; P = 0.04). Scale bar, 50 pm. (B) Representative confocal images of primary patient-derived HFC and LFC tissues showing regions of synaptic puncta colocalization (white arrows). Orange, synapsin-1 (presynaptic puncta); red, PSD95 (postsynaptic puncta); green, neurofilament (neurons). Scale bar, 15 pm. Quantification of the number of colocalized pre- and postsynaptic puncta (HFC: n = 7; LFC: n = 10 regions, 2 per group; P = 0.05). Quantification of postsynaptic PSD95 puncta size (HFC: n = 6; LFC: n = 9, 2 per group; P = 0.0441). Scale bar, 15 pm. (C) Primary patient-derived cultures and mouse hippocampal neuron controls. Neurofilament (heavy and medium chains) and nestin antibodies used as specific markers to label mouse hippocampal neurons and glioblastoma cells, respectively in glioma-neuron co-culture. Left panel: mouse hippocampal neurons alone in culture for 14 days only express neurofilament (green) and not nestin (orange). Right panel: Nestin (orange) expression in GBM cells co-cultured with neurofilament (green) labelled mouse hippocampal neurons for 14 days. Scale bar, 100 pm. (D) Cell cultures tested for mycoplasma using a commercially available kit (PCR Mycoplasma Test Kit FC, PromoCell, Heidelberg, Germany) shows absence of a positive band at -270 bp. Tested primary patient-derived lines shows internal control DNA at -479 bp indicated a successfully performed PCR. (E) Neuron organoids (GFP labelled) were generated from an iPSC cell line integrated with doxycycline inducible human NGN2 transgene and co-cultured with RFP labelled HFC and LFC cells (pseudo-coloured white) for two weeks. Quantification of postsynaptic Homer- 1 puncta density (calculated by dividing the number of puncta measured with the area of the image field) in 2-week induced neuron (iN) organoid sections (n = 8 per group; P = 0.0009). Scale bar, 10 pm. (F) Multi-electrode array of glioma-neuron co-culture and control conditions. Magnified view of multi-electrode array (MEA), showing RFP- labelled glioblastoma cells in co-culture with neurons (top row). Scale bar, 100 pm. Representative raster plot showing individual spikes/extracellular action potentials (tick mark), bursts (cluster of spikes in blue) and synchronized network bursts (pink) of mouse cortical neuron (DIV 18) only condition (bottom row). The cumulative trace above the raster plots depicts the population spike time histogram indicating the synchronized activity between the different electrodes (network burst). (G) Structural synapses in primary patient- derived glioblastoma xenografts. Quantification of neuron-to-neuron (P - 0.1381) and neuron-glioma synapses (P = 0.0005) per high power field (hpf) in HFC and LFC xenografts (top row). Specificity negative controls for immuno-gold labelling (bottom row). (Left) HFC xenograft with secondary antibody only (no primary antibody) control and (right) non-glioma bearing negative control tissue demonstrating few randomly distributed immunogold particles across the tissue specimen. Scale bar, 1000 nm. Data presented as mean ± s.e.m. P values determined by two-tailed Student’s t-test. *P < 0.05; **P < 0.01; ***P < 0.001; NS, not significant.

HFC promotes tumour progression

Neurons promote glioma cell proliferation [1-4] and it was hypothesized that HFC cells may represent a cellular subpopulation within glioblastomas that are differentially regulated by neuronal factors. It was found that primary patient biopsies from HFC and LFC regions demonstrated increased Ki-67 proliferative marker staining within HFC regions (FIG. 3C). To test whether HFC cells differentially proliferate in response to neuronal factors compared with LFC primary patient cultures, patient-derived HFC and LFC cells cultured alone or in co-culture with mouse hippocampal neurons were treated with 5-ethynyl-2'- deoxyuridine (EdU) overnight. HFC glioma cells exhibit a fivefold increase in proliferation when cultured with neurons. By contrast, the LFC glioma in vitro cell proliferation index (determined as the fraction of DAPI cells co-expressing EdU) is similar with and without hippocampal neurons in vitro (FIG. 3D and FIG. 12). These results indicate that the ability of HFC cells to proliferate is contingent on the presence of neuronally secreted factors and that, in the absence of neuronal signals, they tend to acquire a dormant tumour phenotype.

Given the neuronal tropism exhibited by HFC glioma cells together with the concept that neural network integration requires invasion of brain parenchyma to reach and colocalize with neuronal elements, the effects of neuronal conditioned medium on invasion of HFC and LFC glioma cells was tested using a spheroid invasion assay. LFC glioma cells demonstrated no differences in spheroid volume in the presence or absence of neuronal conditioned medium; however, HFC glioma cells exhibited an increased spheroid invasion area in response to neuronal conditioned medium. In addition to increased invasion area, HFC glioma cells extended long processes representing tumour microtubes in response to neuronal conditioned medium (FIG. 13A). Tumour microtubes connect glioma cells in a gap-junction- coupled network [1,39-41] through which neuronal-activity-induced currents are amplified [2]. Scanning electron microscopy (SEM) was performed on TSP-l^hlgh-expressing HFC and LFC cells in the presence or absence of neuronal conditioned medium, demonstrating robust cytoplasmic extensions connecting HFC cells (FIG. 3E). The change in mean spheroid volume was also quantified. It was found that neuronal conditioned medium increased both invasion and microtube length in HFC but not LFC cultures (FIG. 3F and FIG. 13A). Concordantly, the invasive marker MET was increased within HFC samples compared with LFC samples (FIGS. 9B-9C). Primary patient-derived HFC cells were then transduced with a short hairpin RNA (shRNA) control or shRNA against THBS1 to knockdown TSP-1. Cell viability was confirmed using a live/dead assay with robust knockdown of the target protein (FIGS. 14A-14B). Knockdown of THBS1 in HFC cells decreased the number of tumour microtubes relative to the control conditions (FIG. 3G), consistent with the known role for TSP-1 in tumour microtube formation [39].

Glioblastoma cell invasion bears negative prognostic value. Therefore, survival studies of mice that were orthotopically xenografted with patient-derived HFC or LFC glioma cells were performed. Mice bearing HFC tumours exhibited greater tumour burden and shorter survival compared with LFC-tumour- enografted mice (FIG. 3H and FIG. 13D). Taken together, these results suggest that functionally connected intratumoural regions are enriched for a tumour cell population that is differentially responsive to neuronal signals and exhibits a proliferative, invasive and integrative phenotype in the neuronal microenvironment that negatively influences survival in a preclinical model.

FIGS. 12A to 12B proliferation of primary patient-derived glioblastoma cell monoculture and neuron co-culture conditions. Primary patient glioblastoma cells from HFC regions illustrate marked increase in proliferation when co-cultured with mouse hippocampal neurons. (A) Quantification of proliferation indices of HFC (n = 4) and LFC (n = 4) glioma cells alone in culture (in the absence of neurons) from individual patient lines, determined by quantifying the fraction of EdU labelled cells/DAPI labelled cells; P = 0.00005). (B) Representative confocal images illustrating proliferating HFC and LFC glioma cells (EdU⁺, green) in the absence or presence of mouse hippocampal neurons (72h co-culturc). Scale bar, 100 pm. Data presented as mean ± s.e.m. P value determined by two-tailed Student’s t-test.

0.0001.

FIGS. 13A to 13D activity-dependent invasion of TSP-1 positive HFC cells. (A) 3D spheroid invasion assay showing representative micrographs imaged 24 h after addition of invasion matrix. Analysis includes quantification of mean spheroid invasion area normalized for each sample to the initial (0 h) spheroid area (HFC: n = 11 ; HFC + NCM: n = 16; LFC: n = 5; LFC + NCM: n = 6 spheroids, 1 per group; P = 0.02). Scale bar, 200 pm. Data presented as mean ± s.e.m. P values determined by two-tailed Student’s t-test. (B) Representative confocal images of primary patient-derived HFC and LFC tissues showing MET-positive glioma cells (white arrows). Red, MET; orange, Nestin (HFC/LFC-GBM cells); blue, DAPI. Scale bar, 30 pm. Quantification of MET-positive glioma cells per high- power field (HFC: n = 8; LFC: n = 11, 3 per group; P = 0.0005). (C) Representative immunohistochemistry images of MET staining in HFC and LFC tissues demonstrate increased tissue level protein expression (HFC: n = 26; LFC: n = 24 regions, 4 per group; P = 0.0329). Scale bar, 50 pm. Data presented as mean ± s.e.m. P values determined by two-tailed Student’ s t-test. (D) Representative confocal images showing the diffuse infiltrative pattern of HFC cells in the hippocampus in comparison to the LFC cells. Quantification of tumour burden of HFC and LFC hippocampal xenografts using rank order analysis (HFC: n = 13; LFC: n = 11 regions; P = 0.002). Scale bar, 100 pm. Data presented as mean ± s.e.m. P value determined by two-tailed Mann- Whitney test. *P < 0.05; **P < 0.01; ***P < 0.001; NS, not significant.

FIGS. 14A to 14B cell viability and TSP-1 knockdown validation. (A) Cell viability determined by live/dead cell assay. Representative images illustrating no significant cell death 2 weeks post-transduction of HFC cells with control (shCont) or TSP-1 shRNAs. Live (green) and dead (red) cells were imaged from four random fields per well and were visualized under a fluorescence microscope. The percentage of live cells were calculated as the number of live cells (in green) divided by the total number of cells (green + red) per image field (n = 4 regions per group; P = 0.0720). Scale bar-, 100 pm. (B) ELISA experiments performed on cell culture supernatants demonstrating strong reduction of TSP-1 expression after knockdown of TSP-1 in two different primary patient-derived HFC cell lines compared to the control scramble condition (n = 2 per group). Data presented as mean ± s.c.m. P values determined by two-tailed Student’s t-test. N.S., not significant.

Glioma connectivity shortens patient survival

We next investigated the effects of tumour-intrinsic functional connectivity on patient survival and cognition. First, the hypothesis that gliomas exhibiting increased functional connectivity may be more aggressive was tested, given the robust influence of neuronal activity on tumour progression [2-4] . A human survival analysis of patients with newly diagnosed glioblastoma was performed. After controlling for known correlates of survival (age, tumour volume, completion of chemotherapy and radiation, and extent of tumour resection) [42], neural oscillations and functional connectivity were measured within tumour- infiltrated brain using MEG. A Kaplan-Meier survival analysis illustrates an overall survival of 71 weeks for patients with functional connectivity compared with an overall survival of 123 weeks for participants without HFC voxels, illustrating a striking inverse relationship between survival and functional connectivity of the tumour (mean follow-up time, 50.5 months) (FIG. 15A). To identify clinically relevant survival risk groups, recursive partitioning survival analysis using the partDSA algorithm was used [42-44]. Within this analysis important prognostic variables, such as MGMT promoter methylation status, was controlled for. Overall survival risk was based on the interactive effects of all known prognostic variables (for example, age at diagnosis, sex, tumour location, chemotherapy, radiotherapy, the presence of functional connectivity within the tumour, pre- and postoperative tumour volume, and the extent of resection). The first division was based on known risk factors such as age and extent of tumour resection. Within this hierarchical model of partitioning, the degree of connectivity was identified as the next most important variable, which divided risk groups 2 and 3. Risk group 1 (black) had the worst outcomes and is the combination of patients older than 72 years or any age with less than 97% extent of tumour resection (subtotal resection). Risk group 3 (grey) had the best survival, and these are patients are younger than 62 years with over 97% extent of tumour resection and absence of functional connectivity in the tumour. Intermediate risk group 2 (red) revealed an interesting interaction between age and HFC. This group had two subsets: patients with over 97% resection of tumour and age younger than 72 years with intratumoural connectivity; and those between 62 and 72 years without functional integration (FIG. 4A-4B). These results demonstrate the notable prognostic value of connectivity on survival. Whether TSP-1, a secreted synaptogenic protein [32,35], can be identified in the patient serum and whether circulating TSP- 1 is correlated with functional connectivity was next examined. Circulating TSP-1 levels in the patient serum exhibited a notable positive correlation with intratumoural functional connectivity (FIG. 4C).

We hypothesized that, beyond survival, intratumoural functional connectivity may also influence cognition. Therefore, visual picture and auditory naming testing was performed in the cohort of patients with dominant hemisphere glioblastoma, given their correlation with aphasia in clinical populations [45,46], Linear regression of the number of HFC voxels within tumours with language task performance demonstrated an inverse relationship between language cognitive performance and tumour functional connectivity (FIG. 15B-15C). Together, these findings suggest that functional integration of glioblastoma into neural circuits negatively influences cognition and survival.

FIGS. 4A to 4H demonstrate how intratumoural connectivity in patients with highgrade glioma is correlated with survival and TSP-1. (A)(B) Schematic (A) and partDSA model (B) of overall survival in patients, incorporating the effects of glioblastoma intrinsic functional connectivity, therapeutic and clinical factors by recursive partitioning results into three risk groups. Risk group 1 (black) patients have the shortest survival, including a combination of (1) patients older than 72 and (2) patients younger than 72 with an extent of tumour resection (EOR) of less than 97%. Risk group 3 (grey) patients have the best survival, including patients who are younger than 62 with an extent of tumour resection of greater than 97% and no intratumoural connectivity. Intermediate risk group 2 (red) comprises a combination of patients with greater than 97% extent of resection and (1) an age of younger than 72 with tumour intrinsic connectivity or (2) patients between 62 and 72 year’s without connectivity. (C) Linear regression statistics illustrate that serum TSP-1 is correlated with the extent of intratumoural functional connectivity, n = 56. P = 0.01. (D) Representative MEA raster plots showing neuronal spikes (black tick marks), bursts (cluster of spikes in blue) and synchronized network bursts (pink) of neuron HFC co-cultures (outlined in red) and 24-48 h exposure of neuron-HFC co-culture to (50 pM) GBP (outlined in orange). Quantification of the weighted mean firing rate (Hz) and network synchrony (area under normalized cross- correlation) from HFC and HFC + GBP glioma-neuron co-culture (weighted mean firing rate: n = 4 well, 2 per group; P = 0.04; area under normalized cross-correlation: n = 3 (HFC) and n = 4 (HFC + GBP); P = 0.007). (E) Representative confocal images from neuron-HFC glioma co-culture showing a decrease in HFC cell proliferation after TPIBS1 knockdown using shRNA (n = 10 (HFC shCtrl) and n = 9 (HFC shTSP-1); P = 0.0068). Red, HNA (human nuclei); white, Ki-67. Scale bar, 30 pm. (F) Representative confocal images from neuron-HFC glioma co-culture showing a decrease in HFC cell proliferation after gabapentin (32 pM) treatment for TSP-1 inhibition, n = 16 (HFC) and n = 15 (HFC + GBP), 2 per group. P = 0.0007. Red, HNA (human nuclei); white, Ki-67. Scale bar, 30 pm. (G) Schematic for gabapentin treatment of HFC xenografted mice, i.p., intraperitoneal. (H) Representative confocal images, and quantification demonstrating a decrease in the proliferation index (Ki- 67⁺HNA⁺/HNA⁺) after gabapentin treatment in mice bearing HFC xenografts, n = 9 mice per group. P = 0.046. Red, HNA (human nuclei); white, Ki-67. Scale bar, 70 pm. Data are mean ± s.e.m. (D)-(F) and (H). P values were determined using two-sided linear regression analysis (C), and two-tailed (D)-(F) and one-tailed (H) Student’s /-tests. *P < 0.05, **P < 0.01, ***P< 0.001; NS, not significant.

FIGS. 15A to 15C patient survival and language task performance. (A) Kaplan-Meier human survival analysis illustrates 71-week overall survival for patients with HFC voxels as determined by contrast-enhanced Tl-weighted images as compared to 123-weeks for participants without HFC voxels within their glioblastoma (mean follow-up months 50.5, range 4.9-155.9 months). (B) Picture and auditory naming language task performance across the study population. (C) Linear regression statistics illustrating a negative correlation between the number of intratumoural high functional connectivity voxels and baseline auditory and picture naming scores (n = 31, P - 0.0181). P value determined by two-tailed linear regression analysis.

TSP-1 as a therapeutic target

Given the premise that TSP-1 serves as a regulator of neuronal activity-driven glioma growth, it was sought to target TSP-1 therapeutically using gabapentin (GBP), which blocks the thrombospondin receptor a25-l [47], In neuron-glioma co-cultures, individual spikes, bursts (cluster of spikes) and synchronized network bursts were reduced after 24-48 h exposure to GBP (FIG. 4D). Primary patient-derived HFC cells were transduced with shRNA-control or shRNA against THBS1 or treated with GBP. Pharmacological TSP-1 inhibition using GBP did not influence the proliferation of HFC cells grown alone in culture, verifying that there were no tumour cell-intrinsic effects of GBP (FIG. 16). By contrast, genetic or pharmacological targeting of TSP-1 resulted in a marked decrease in proliferation of HFC glioma cells co-cultured with neurons (FIGS. 4E-4F). GBP administration to mice bearing HFC patient-derived xenografts (PDX) resulted in a marked decrease in glioma proliferation (Ki-67⁺HNA⁺/HNA⁺) in gabapentin-treated mice bearing HFC xenografts relative to vehicle-treated controls (FIGS. 4G-4H).

FIG. 16 anti-proliferative effects of TSP-1 inhibition in glioblastoma is limited to activity -dependent mechanisms. Representative confocal images from HFC glioma monoculture showing no significant change in proliferation (as measured by the number of human nuclear antigen (HNA)-positive cells co-labelled with Ki67 divided by the total number of HNA-positive tumour cells counted across all areas quantified) upon pharmacological TSP-1 inhibition using (32 pM) gabapentin (HFC: n = 7; LFC: n = 9 regions, 2 per group; P = 0.50). Red, HNA (human nuclei); white, Ki67. Scale bar, 30 pm. Data presented as mean ± s.e.m. P values determined by two-tailed Student’s t-test. NS, not significant.

Summary

Integration of high-grade glioma into neural networks is manifested by bidirectional interactions whereby neuronal activity increases glioma growth and gliomas increase neuronal excitability. To understand whether glioma-neuronal interactions influence neural circuit dynamics, short-range electrocorticography analysis of tumour-infiltrated cortex was used in humans to demonstrate language-task-specific activation as well as functional remodeling of language circuits. It was further demonstrated that distinct intratumoural regions maintain functional connectivity through a subpopulation of TSP- 1 -expressing malignant cells (HFC glioma cells). This molecularly distinct glioma subpopulation is differentially responsive to neuronal signals, exhibiting a synaptogenic, proliferative, invasive and integrative profile. Previous research has suggested that neuronal activity promotes glioma proliferation through paracrine and synaptic signaling [1-4], and it has now been shown that patients with glioblastoma exhibiting functional connectivity between the tumour and the rest of the brain experience a shorter overall survival compared with patients without HFC. Pharmacological inhibition of TSP- 1 decreases glioblastoma cell proliferation and network synchrony within the tumour microenvironment, highlighting a therapeutic strategy for treating gliomas.

The neuronal microenvironment has was demonstrated to be a crucial regulator of glioma growth. Both paracrine signaling and connectivity remodeling may contribute to network-level changes in patients, affecting both cognition and survival. In patients, the role of neural network dynamics on survival and cognition was investigated paving the way for experiments exploring how glioma-network interactions influence cognition. The experimental results were unexpected given previous studies using a heterogenous population of patients with both IDH wild type (WT) and mutant WHO grade III and IV gliomas have suggested that functional connectivity improves overall survival [49-51 ] . It was hypothesized that such previous research may have been confounded by functional connectivity methods that are heavily influenced by the presence of tumour vascularity, limited spatial resolution and a heterogenous patient cohort. Nonetheless, the evidence in this study that glioblastomas remodel functional circuits and that functional connectivity negatively influences survival allows new methods of monitoring disease proliferation to be explored. It is possible that glioma originating in functionally connected cortical regions are more strongly connected and may therefore exhibit greater network distribution, thereby encouraging distinct glioblastoma subpopulations with the ability to migrate [52], The results of this study contribute to a better understanding of the cross-talk between neurons and gliomas as well as how functional integration affects clinical outcomes, opening the door to a range of pharmacological and neuromodulation therapeutic strategies focused on improving cognitive outcomes and survival.

Materials and Methods

Patients and samples

Each participant in the study was recruited from a prospective registry of adults aged 18-85 with newly diagnosed frontal, temporal and parietal IDH-WT high-grade gliomas with detailed language assessments and baseline MEG recordings. Inclusionary criteria included the following: native English speaking, aged 18-85 years, and no previous history of psychiatric illness, neurological illness, or drug or alcohol abuse. All human clcctrocorticography data were obtained during lexical retrieval language tasks from 14 adult awake patients undergoing intraoperative brain mapping for surgical resection. Tumours from eight patients were used for RNA-seq experiments. Site-directed tumour biopsies from 19 patients were used for immunofluorescence/immunohistochemistry analysis and 24 patients were used for immunocytochemistry and cell-based functional assays. Tumours from eight patients were used for mouse xenograft experiments.

The study began by examining short-range circuit dynamics in a subset of 14 patients with dominant hemisphere glioblastoma infiltrating speech production areas of the inferior frontal lobe using ECoG in the intraoperative setting (FIG. 5A). Molecular studies were then focused on patients with surgically treated IDH-WT glioblastoma, and extraoperative language assessments and imaginary coherence as a long-range measure of functional connectivity using MEG were performed (FIGS. 5A-5B). This enabled functional connectivity data to be imported into the operating room in which site-specific tissue biopsies of human glioma from regions with differing measures of functional connectivity were performed for in vivo and in vitro cell biology experiments including primary patient cultures (n = 19 patients) and multimodal tissue profiling, including microscopy, sequencing, proteomics and patient-derived tumour xenografting (FIG. 5C). This layered approach — combining clinical variables, cognition assessments, human and animal models of network dynamics, in addition to cell biology — served as a platform to study the clinical implications of glioma-neuron interactions (FIG. 5D).

Human ECoG and data analyses

The hemisphere of language dominance was determined using baseline magnetic source imaging. In brief, the participants sat in a 275-channel whole-head CTF Omega 2000 system (CTF Systems) sampling at 1,200 Hz while they performed an auditory-verb generation task. The resulting time series were then reconstructed in source space with an adaptive spatial filter after registration with high-resolution MRI. Finally, changes in betaband activity during verb generation were compared across hemispheres to generate an overall laterality index. All of the participants were left-dominant and underwent electrophysiological recording of the left hemisphere. An intraoperative testing paradigm that was previously established was implemented [9]. Noise in the operating room was minimized through rigorous enforcement of the following: (1) all personnel were requested to cease verbal communication; (2) telephones and alarms were muted; and (3) surgical suction and all other non-essential machinery were temporarily shut down. A 15 inch laptop computer (60 Hz refresh rate) running a custom MATLAB script integrated with PsychToolbox 3 (http ://psychtoolbox. org/) was placed 30 cm away from each participant. The script initiated a picture-naming task that consisted of a single block of 48 unique stimuli, each depicting a common object or animal through coloured line drawings. Each stimulus was presented at the point of central fixation and occupied 75% of the display. After presentation of each stimulus, the participants were required to vocalize a single word that best described the item.

Intraoperative photographs with and without subdural electrodes present were used to localize each electrode contact combined with stereotactic techniques [9,53]. Images were registered using landmarks from gyral anatomy and vascular arrangement to preoperative Tl- and T2- weighted MRI scans. Tumour boundaries were localized on MRI scans and electrodes within 10 mm of necrotic tumour core tissue were identified as ‘tumour’ contacts. Electrodes overlying the hypointense core of the tumour extending from the contrast enhancing rim to the edge of FLAIR were considered to be tumour electrodes, and electrodes completely outside of any T1 post gadolinium or FLAIR signal were considered to be nontumour or normal appearing by a trained co-author blinded to the electrophysiologic data [2]. Glioma-infiltrated regions were defined on the basis of two criteria previously established in the literature [9], including mass-like region of T2- weighted FLAIR sequences signal. Imaging was confirmed by gross inspection of the cortex confirming dilation and/or an abnormal vascular pattern. Previous research has shown that regions of non-enhancing disease consist of infiltrating tumour cells intermixed with neurons and normal glial cells [2,54]. These labels were reviewed and compared to labels derived during intraoperative stereotactic neuronavigation to reach a consensus (Brainlab).

Each participant received a training session 2 days before participation to ensure familiarity with the task. ECoG signals were acquired during a period after stopping the administration of anaesthetics (minimum drug wash out period of 20 min) and the patient was judged to be alert and awake after an extensive post-emergence wakefulness assessment to ensure adequate arousal [55]. Intraoperative tasks consisted of naming pictorial representations of common objects and animals (picture naming) and naming common objects and animals through auditory descriptions (auditory naming) [56], Post-operative videos were rcanalyscd to ensure that all data were collected and correct responses only were included for analysis. Audio was sampled at 44.1 kHz from a dual-channel microphone placed 5 cm from the participant and electrophysiological signals were amplified (g.tec). Recordings were acquired at 4,800 Hz and downsampled to 1,200 Hz during the initial stages of processing. During offline analyses, audio and electrophysiological recordings were manually aligned, resampled and segmented into epochs (speech-locked). These epochs set time = 0 ms as speech onset and included ±2,000 ms for a total of 4,000 ms of signal per trial. Trials were discarded if (1) an incorrect response was given (including fillers and interjections) or (2) there was a greater than 2 s delay between stimulus presentation and response so as to maintain consistent trial dynamics and ensure that the neural signal indeed reflected the experimental manipulations. Channels with excessive noise artifacts were visually identified and removed if their kurtosis exceeded 5.0. After the rejection of artifactual channels, data were referenced to a common average, high-pass filtered at 0.1 Hz to remove slow-drift artifacts, and bandpass filtered between 70-110 Hz using a 300-Order FIR filter to focus the analyses on the high-gamma band range, which is strongly related to local mean population spiking rates. To extract the ERSPs, electrophysiological signals were first downsampled to 600 Hz, then high-pass filtered at 0.1 Hz to remove DC-offset and low- frequency drift, notch-filtered at 60 Hz and its harmonics to remove line noise, and bandpass- filtered between 70 and 170 Hz (that is, the high-gamma range) using a Hamming windowed sine FIR filter. These signals were finally smoothed using a 100 ms Gaussian kernel, downsampled to 100 Hz and --scored across each trial. Electrodes were subsequently rereferenced to the common average for each participant to facilitate group comparisons, and regions of interest were defined according to the Automated Anatomical Labelling Atlas (https ://www .gin.cnrs.fr/en/tools/aal/). The location of grid implantation was solely directed by clinical indications. The accuracy of the final registration for each participant was independently confirmed using gyral and sulcal anatomy to triangulate the location of each electrode registered to the template surface and was then compared to intraoperative photographs of the actual cortex with the overlying grid(s) [57]. The HGp was then calculated using the square of the Hilbert transform on the filtered data. The HGp was then averaged across the resting-state time series, yielding a single measure of neural responsivity for each electrode contact. The HGp was then averaged across patients during the task response period, yielding a single measure of neuronal responsivity for each channel. The HGp levels were then compared between tumour and normal appealing channels. Linear mixed-effects modeling was used to perform statistical comparisons with repeated measures using the nlme package in R (v.3.1-161; https: //cran.r-project.org/web/packages/ nlme/citation. html). The signal’s origin (that is, normal-appearing/glioma-infiltrated cortex) was modelled as a fixed effect and the participants were modelled as random effects. For continuous variables without repeated measures, /-tests were used. A threshold of P < 0.05 was used to denote statistical significance and corrections for multiple comparisons were made using the Bonferroni method.

To decode between low-frequency words (for example, rooster) and high-frequency words (for example, car), signals from normal-appearing and glioma- infiltrated electrodes were extracted from the anterior temporal lobe after participant-level registration to a common MNI atlas. Responses were time-locked to speech onset and the signal envelope was extracted using a Hilbert transform after applying a bandpass filter in the high-gamma range (70-170 Hz). Subsequently, an 12-regularized logistic regression classifier was trained (cost of 1) to distinguish neural responses during vocalization of low-frequency words (for example, rooster) from high-frequency words (for example, car). Model performance was determined by taking the accuracy on a held-out participant and averaging it across all folds (that is, leave-one-participant-out cross-validation) and statistical significance was determined by testing this accuracy against a binomial distribution. This process was conducted separately for normal-appearing and glioma- infiltrated cortex using an identical preprocessing, training and testing paradigm.

MEG recordings and data analysis

MEG recordings were performed according to an established protocol [30,31]. In brief, the study participants had continuous resting state MEG recorded with a 275-channel whole-head CTF Omega 2000 system (CTF Systems) using a sampling rate of 1,200 Hz. During resting-state recordings, the participants were awake with their eyes closed. Surface landmarks were co-registered to structural magnetic resonance images to generate the head shape. Within the alpha frequency band, an artifact-free 1 min epoch was selected for further analysis if the patient’s head movement did not exceed 0.5 cm. This artifact-free, 1 min epoch was then analyzed using the NUTMEG software suite (v.4; UCSF Biomagnetic Imaging Laboratory) to reconstruct whole-brain oscillatory activity from MEG sensors so as to construct functional connectivity (imaginary coherence (IC)) metrics [54,58,59]. Spatially normalized structural magnetic resonance images were used to overlay a volume-of-interest projection (grid size = 8 mm; approximately 3,000 voxels per participant) such that each voxel contained the entire time series of activity for that location derived by all the MEG sensor recordings. The time series within each voxel was then bandpass-filtered for the alpha band (8-12 Hz) and reconstructed in source space using a minimum-variance adaptive spatial filtering technique [54,60]. The alpha frequency band was selected because it was the most consistently identified peak in the power spectra from this sampling window in the patient series. Functional connectivity estimates were calculated using IC, a technique known to reduce overestimation biases in MEG data generated from common references, cross-talk and volume conduction [26,28].

Resting-state MEG was also used to measure intratumoural gamma activity. A spatial beamformer was applied to extract neural signals at the voxel level from manually defined regions of interest corresponding to FLAIR signal abnormality (that is, within the infiltrative margin of the tumour) [61]. These source-space signals were then downsampled to 300 Hz, notch filtered at 60 Hz to remove line noise and rereferenced to the common average. Spectral activity from 1 to 50 Hz was estimated at each voxel using Thomson’s multitaper method (pmtm in MATLAB R2021b) with 29 Slepian tapers. Next, gamma power from 30 to 50 Hz was computed at each voxel after subtracting the aperiodic component from each spectrum by fitting a Lorentzian function in semi-log space [62], A point estimate of intratumoural gamma activity was subsequently computed by averaging the activity across all voxels for each participant and regressed against the corresponding number of manually counted intratumoural HFC nodes.

Functional connectivity map

The functional connectivity of an individual voxel was derived by the mean IC between the index voxel and the rest of the brain, referenced to its contralesional pair [30]. It is possible that there are regions within gliomas with varying amounts of functional connectivity. Moreover, there are individual patients with more or less functional connectivity. These differences were addressed in the experimental model. Intratumoural differences in functional connectivity were addressed by the following: in comparison to contralcsional voxels, a two-tailed /-test was used to test the null hypothesis that the Z- transformed connectivity IC between the index voxel and non-tumour voxel is equal to the mean of the Z-transformed connectivity between all contralateral voxels and the same set of voxels. The resultant functional connectivity values were separated into tertiles: upper tertile (HFC) and lower tertile (LFC). Functional connectivity maps were created by projecting connectivity data onto each individual patient’s preoperative structural magnetic resonance images and imported into the operating room neuronavigation console. Stereotactic site- directed biopsies from HFC (upper tertile) and LFC (lower tertile) intratumoural regions were taken and x, y, z coordinates determined using Brainlab neuro-navigation. Thus, only the extremes of intratumoural connectivity (high and low connectivity, HFC and LFC, respectively) were analysed for these experiments. Rather than raw values, each functional connectivity measure represents a Z-transformed value and it therefore remains likely that the HFC distinction for one patient does not perfectly coincide with the HFC distinction in another patient’ s tumour (intertumoural heterogeneity).

Measurement of tumour volume and, calculation of volumetric extent of resection

Pre-operative and post-operative tumour volumes were quantified using BrainLab Smartbrush (v.2.6; Brainlab). Pre-operative MRI scans were obtained within 24 h before resection, and post-operative scans were all obtained within 72 h after resection. Total contrast-enhancing tumour volumes were measured at both pre-operative and post-operative timepoints. The total contrast-enhancing tumour volume was measured on Tl-weighted postcontrast images, and the non- enhancing tumour volume was measured on T2 or FLAIR sequences. Manual segmentation was performed with region-of-interest analysis ‘painting’ inclusion regions based on fluid-attenuated inversion-recovery (FLAIR) sequences from pre- and post-operative MRI scans to quantify tumour volume. The extent of resection was calculated as follows: (pre-operative tumour volume - post-operative tumour volume)/pre- operative tumour volume x 100%. Manual segmentations were performed for which the tumour volumetric measurements were verified for accuracy after an initial training period. Volumetric measurements were performed blinded to patients’ clinical outcomes. All of the patients in the cohort had available preoperative and postoperative MRI scans for analysis. To ensure that post-operative FLAIR signal was not surgically induced oedema or ischaemia, FLAIR pre- and post-operative MRTs were carefully compared alongside DWI sequences before including each region in the volume segmentation [42] . HFC voxels with T1 post gadolinium contrast enhancing tumour were considered to be HFC-positive for survival analysis.

Language assessments

One to two days before tumour resection, patients underwent baseline language evaluation, which consisted of naming pictorial representations of common objects and animals (picture naming) and naming common objects and animals through auditory descriptions (auditory naming). Visual picture naming and auditory stimulus naming testing were used given their known significance and clinical correlation with outcomes in clinical patient population [63,64]. The correct answers for these tasks (delivered on a laptop with a 15 inch monitor (60 Hz refresh rate) positioned two feet away from the seated patient in a quiet clinical setting) were matched on word frequency (that is, commonality within the English language) using SUBTLEXwr scores provided by the Elixcon project and content category. Task stimuli were randomized and presented using PsychToolbox. The task order was randomly selected by the psychometrist for each participant. Slides were manually advanced by the psychometrist either immediately after the participant provided a response or after 6 s if no response was given. The tasks were scored on a scale from 0 to 4 by a trained clinical research coordinator who was initially blinded to all clinical data (including imaging studies). No participants had uncorrcctablc visual or hearing loss. Details of the administration and scoring of auditory and picture naming language tasks can be found in previous studies [27,55,65].

Isolation and culture of primary patient-derived glioblastoma cells

Tumour tissues with high (HFC) and low (LFC) functional connectivity sampled during surgery based on preoperative MEG were processed for quality control by a certified neuropathologist and were subsequently used to generate primary patient-derived cultures. Patient-matched samples were acquired from site-directed HFC and LFC intratumoural regions from the same patient. Intratumoural HFC and LFC tissues were dissociated both mechanically and enzymatically and then passed through a 40 pm filter to remove debris. The filtered cell suspension was then treated with ACK lysis buffer (Invitrogen) to remove red blood cells and subsequently cultured as free-floating neurospheres in a defined, serum- free medium designated tumour sphere culture medium, consisting of Dulbccco’s modified Eagle’s medium (DMEM-F12; Invitrogen), B27 (Invitrogen), N2 (Invitrogen), human-EGF (20 ng ml^-1; Peprotech), human-FGF (20 ng ml^-1; Peprotech). Normocin (InvivoGen) was also added to the cell culture medium in combination with penicillin-streptomycin (Invitrogen) to prevent mycoplasma, bacterial and fungal contaminations. Cell cultures were routinely tested for mycoplasma (PCR Mycoplasma Test Kit PC, PromoCell) and no positive results were obtained (FIG. 11D).

Bulk RNA-seq and analysis

RNA was isolated from HFC (n = 3) and LFC (n = 4) tumour samples using the RNeasy Plus Universal Mini Kit (QIAGEN) and RNA quality was confirmed using the Advanced Analytical Fragment Analyzer. RNA-seq libraries were generated using the TruSeq Stranded RNA Library Prep Kit v2 (RS-122- 2001, Illumina) and 100 bp paired-end reads were sequenced on the Illumina HiSeq 2500 system to at least 26 million reads per sample at the Functional Genomics Core Facility at UCSF. Quality control of FASTQ files was performed using FASTQC (h ttp: Z/www .bioinformatics. babraham .ac.uk/projects/ fastqc/). Reads were trimmed with Trimmomatic (v.0.32) [66] to remove leading and trailing bases with quality scores of less than 20 as well as any bases that did not have an average quality score of 20 within a sliding window of 4 bases. Any reads shorter than 72 bases after trimming were removed. Reads were subsequently mapped to the human reference genome GRCh38 (htt ps :// ww w.ncbi.nlm.nih.gov/ assembly /GCF_000001405.39/) [67] using HISAT2 [68] (v.2.1.0) with the default parameters. For differential expression analysis, exon-level count data was extracted from the mapped HISAT2 output using featureCounts [69]. Differential expression analysis was performed using DESeq2 [70] using the apeglm parameter [71] to accurately calculate log-transformed fold changes and setting a false- discovery rate of 0.05. Differentially expressed genes were identified as those with log- transformed fold changes of greater than 1 and an adjusted P value of less than 0.05. Unsupervised gene expression principal component analysis and volcano plots of IDH-WT glioblastoma (FIGS. 9B-9C) revealed 144 differentially expressed genes between HFC and LFC tumour regions, including 40 genes involved in nervous system development. Single-cell sequencing

Single-cell suspension generation: Fresh tumour samples were acquired from the operating room and transported to the laboratory space in PBS and on ice. Tumour tissue was minced with #10 scalpels (Integra LifeSciences) and then digested in papain (Worthington Biochemical, LK003178) for 45 min at 37 °C. Digested tumour tissue was then incubated in red blood cell lysis buffer (eBioscience, 00-4300-54) for 10 min at room temperature. Finally, the samples were sequentially filtered through 70 pm and 40 pm filters to generate a single-cell suspension.

Single-cell sequencing and analysis: Single-cell suspensions of three patient- matched HFC and LFC tumour tissues were generated as described above and processed for single-cell RNA-seq using the Chromium Next GEM Single Cell 3' GEM, Library & Gel Bead Kit v3.1 on the lOx Chromium controller (lOx Genomics) using the manufacturer’s recommended default protocol and settings, at a target cell recovery of 5,000 cells per sample. Although single-cell sequencing does not capture all cell types within the central nervous system microenvironment, the sequencing pipeline used in this study has been demonstrated to identify neurons and was therefore chosen for use in physiologically annotated fresh glioblastoma samples, compared with single-nucleus RNA-seq, which is commonly applied for frozen archived tissues [72,73]. One hundred base pair paired-end reads were sequenced on the Illumina NovaSeq 6000 system at the Center for Advanced Technology at the University of California San Francisco, and the resulting FASTQ files were processed using the CellRanger analysis suite (v.3.0.2; https ://github. com/lOXGenomics/cellranger) for alignment to the hg38 reference genome, identification of empty droplets, and determination of the count threshold for further analysis. A cell quality filter of greater than 500 features but fewer than 10,000 features per cell, and less than 20% of read counts attributed to mitochondrial genes, was used. Single-cell UMI count data were preprocessed in Seurat (v.3.0.1) [74,75] using the setransform workflow [76], with scaling based on the regression of UMI count and the percentage of reads attributed to mitochondrial genes per cell. Dimensionality reduction was performed using principal component analysis and then principal component loadings were corrected for batch effects using Harmony [77]. Uniform manifold approximation and projection was performed on the reduced data with a minimum distance metric of 0.4 and Louvain clustering was performed using a resolution of 0.2. Marker selection was performed in Seurat using a minimum difference in the fraction of detection of 0.5 and a minimum log-transformed fold change of 0.5. The single-cell transcriptome was assessed from 6,666 HFC-region cells and 7,065 LFC-region cells.

Immunohistochemistry and immunofluorescence analysis

After rehydration, 5.0 pm paraffin-embedded sections were processed for antigen retrieval followed by blocking and primary antibody incubation overnight at 4 °C. The following primary antibodies were used: rabbit anti-synapsin 1 (1: 1,000, EMD Millipore), mouse anti-PSD95 (1:100, UC Davis), mouse anti-nestin (1:500, Abeam), mouse anti- neurofilament (M+H; 1:1,000, Novus Biologicals), mouse anti-TSP-1 (1:20, Invitrogen), rabbit anti-TSP-1 (1 :50, Abeam), rabbit anti-MET (1 : 100, Abeam) and rabbit anti-Ki-67 (1:100, Abeam). Species-specific secondary antibodies were used: Alexa 488 goat antichicken IgG, Alexa 488 goat anti-rabbit IgG, Alexa 568 goat anti-rabbit IgG, Alexa 568 goat anti-mouse IgG, Alexa 647 goat anti-rabbit IgG, all used at 1:500 (Invitrogen). After DAPI nuclear counter staining (Vector Laboratories, 1:1,000), coverslips were mounted with Fluoromount-G mounting medium (SouthernBiotech) for immunofluorescence analysis. The number of synapsin-1 and PSD95 puncta was quantified using spots (with automatic intensity maximum spot detection thresholds and a spot diameter of 1.0 pm) detection function of Imaris. The ratio of pre- and postsynaptic puncta was calculated by dividing the total number of synapsin-1 or PSD95 puncta on neurofilament-positive neurons to the total number of cells stained with DAPI in 135 pm x 135 pm field areas for quantification. Alternatively, the sections were incubated in DAB horseradish peroxidase (Vector Laboratories) for chemical colorimetric detection after incubation in ImmPress anti-rabbit IgG (Novus Biologicals) and counterstained with Harris haematoxylin for immunohistochemistry analysis.

Glioma-mouse hippocampal neuron co-culture

Glioma cells were plated on poly-D-lysine and laminin-coated coverslips (Neuvitro) at a density of 10,000 cells per well in 24-well plates. Approximately 24 h later, 40,000 embryonic mouse hippocampal neurons (Gibco) were seeded on top of the glioma cells and maintained with serum-free Neurobasal medium supplemented with B27, gentamicin and GlutaMAX (Gibco). After 2 weeks of co-culture, cells were fixed with 4% paraformaldehyde (PFA) for 30 min at 4 °C and incubated in blocking solution (5% normal donkey and goat serum, 0.25% Triton X-100 in PBS) at room temperature for 1 h. Next, they were treated with primary antibodies diluted in the blocking solutions overnight at 4 °C. The following antibodies were used: rabbit anti-homer-1 (1:250, Pierce), mouse anti-synapsin-1 (1:200) and chicken anti-MAP2 (1:500, Abeam). The coverslips were then rinsed three times in PBS and incubated in secondary antibody solution (Alexa 488 goat anti-chicken IgG; Alexa 568 goat anti-mouse IgG, and Alexa 647 goat anti-rabbit IgG, all used at 1:500 (Invitrogen) in antibody diluent solution for 1 h at room temperature. The coverslips were rinsed three times in PBS and then mounted with VECTA antifade mounting medium with DAPI (Vector Laboratories).

Confocal imaging and quantification of synapsin-1 and homer- 1 staining and colocalization analysis: Images were captured at 1,024 x 1,024 resolution using a xlO objective on the Nikon C2 confocal microscope. The confocal microscope settings for the homer- 1 Alexa488 and synapsin-1 Alexa647 channels were held constant across all of the samples that were used for the experiment. Collected images were then imported into Imaris software (Imaris v.9.2.1, Bitplane) and the threshold value for each channel was manually adjusted and the colocalized voxels of the synapsin-1 puncta with the homer- 1 marker was detected by creating a colocalization channel using the built-in the colocalization module of the Imaris software. Furthermore, the colocalization events were quantified by running the built-in spot detection algorithm of Imaris in conjunction with the colocalization channel. Next, dendrites labelled by MAP2 was visualized in TRITC channel and reconstructed using the Filament tool of Imaris software; the number of colocalized puncta representing synapses were counted and presented as the number of synapsin-1- and homer- 1 -positive puncta per 10 pm of dendrite length. Areas of homer- 1 immunolabelled synaptic puncta were reconstructed using Imaris software Surface tool on maximal-intensity projections. Surfaces were built using a surface area detail level of 0.1 pm, thresholding by absolute intensity and taking all voxel >1.0 into account. The area sizes of individual anti-homerl-immunostained puncta were analysed and the mean values were calculated.

Induced neuron organoid and glioma co-culture: Induced neuron organoids were generated from a WTC11 iPS cell clone integrated by human NGN2 transgene induction as described previously [38,78]. In brief, iNeuron organoids were generated by the transgenic human iPS cell WTC11 line by NGN2 induction through addition of 2 pg ml^-1 doxycycline in the 1 : 1 mixture of Neurobasal and BrainPhys neural medium containing 1 % B-27 supplement, 0.5% GlutaMAX, 0.2 pM compound E, 10 ng ml^-1 BDNF and 10 ng ml^-1 NT-3 for 10 days to induce neuronal differentiation. Next, neuron maturation was triggered by feeding the organoids with approximately 8-month-old organoid conditioned medium derived from astrocytes. Astrocytes were differentiated from the human iPS cell WTC11 line and cultured in a medium consisting of DMEM/F12 containing GlutaMAX, sodium bicarbonate, sodium pyruvate, N-2 supplement, B-27 supplement (Gibco), 2 pg ml^-1 heparin, 10 ng ml^-1 EGF and 10 ng ml^-1 FGF2. Neuron organoids were characterized as postmitotic and stained for MAP2 and pill-tubulin to validate neuronal induction efficiency. After 14 days of neuronal differentiation, HFC/LFC glioma cells labelled with RFP were added to the neuron organoid culture at a ratio of 1:3. Before iNeuron induction, the transgenic human iPS cell line WTC11 was transduced with GFP lentivirus. A Zeiss Cell Observer spinningdisc confocal microscope (Carl Zeiss) fitted with a temperature- and carbon-dioxide- controlled chamber was used to record live interactions of glioma cells with neuron organoids. Organoids were imaged every 10 min for a 6 h period, starting at the time of coculture initiation, using a lOx objective with 0.4 NA. To assess the effect of exogenous TSP- 1 on the functional integration between glioma cells and neurons, human recombinant TSP-1 (R&D Systems) was applied at a dose of 5 pg ml^-1 to the LFC-neuron organoid co-culture. Live-cell image analyses were performed using Image!. In brief, a region of interest was drawn around each GFP-positive neuron organoid and the fluorescence intensity (integrated density) of the RFP-positive glioblastoma cells was measured in the outlined regions of interest for each of the indicated timepoints. At the end of two weeks, organoids from HFC and LFC co-cultures were embedded in OCT and sectioned at 10 pm thickness for homer- 1 immunofluorescence staining. Determination of homer- 1 expression was performed by analysing homer- 1 puncta density of neuron-organoid-HFC and LFC co-cultures.

MEA recordings

Preparation of MEA plates: 24- well CytoView multi-electrode plates were prepared (Axion Biosystems) before the addition of cells by coating with poly-D-lysine (Thermo Fisher Scientific), laminin (Fisher Scientific) and fibronectin (Coming). In brief, 1 day before the establishment of cultures, a solution of 0.1 mg ml^-1 of poly-D-lysine was added to the MEA plates at a volume of 100 pl per well and incubated at room temperature for 2 h. After 2 h, poly-D-lysine was aspirated and the plates were washed three times with sterile water, and allowed to air dry in a biosafety cabinet and stored at 4 °C. The next day, the plates were coated with 100 pl of 5 pg ml^-1 laminin and 1 pg ml^-1 fibronectin and incubated for 2 h at 37 °C before cell seeding.

Preparation of cortical cultures: Primary cortical cultures were established from El 8 CD1 mice (Charles River Laboratories). Timed-pregnant CD1 dams were killed by CO2 euthanasia in accordance with UCSF Institutional Animal Care and Use Committee (IACUC). Dissection of complete cortex from El 8 embryos was performed in ice-cold HBSS (Gibco) under a dissecting microscope (Zeiss). Dissected cortices were minced to 1 mm² pieces and enzymatically digested in 5 ml of 0.25% trypsin reconstituted from 2.5% trypsin (Corning) in calcium- and magnesium-free Hank’s Balanced Salt Solution (Worthington Biochemical Corporation) for 30 min at 37 °C. Then, 0.5 ml of 10 mg ml^-1 of DNase (Sigma- Aldrich) was added in the last 5 min of dissociation. Mechanical dissociation was then carried about by trituration using fire-polished glass Pasteur pipettes until tissue was homogeneously suspended with no visible sections/aggregates and subsequently filtered through a 40 pm cell strainer (Thermo Fisher Scientific). Cells were collected by centrifugation at 500g for 5 min and the resulting cell pellet was resuspended in fresh complete BrainPhys culture medium (lx BrainPhys culture medium (StemCell Technologies) supplemented with B27 (Invitrogen), N2 (Invitrogen) and penicillinstreptomycin antibiotics (Invitrogen). Then, 10 pl of cell suspension was mixed 1:1 with Trypan Blue, and the viable cell concentration was quantified using a haemocytometer. Further dilution was performed to bring viable cell concentration to 100,000 cells per 10 pl. Droplets of 10 pl were then added directly over the electrode field of each pretreated MEA well and stored in the cell culture incubator for 1 h to allow cell adhesion. The wells were then carefully flooded with 500 pl complete BrainPhys medium and the cultures were maintained and allowed to mature in a tissue culture incubator with semi-weekly half-volume medium changes.

Recordings of spontaneous neuronal activity and analysis: Spontaneous extracellular neuronal recordings were carried out using the Maestro Edge system with an integrated heating system and temperature controller (Axion Biosystems) in combination with the Axion 24- well CytoView MEA plates (each well housing a 4 x 4 16-channel electrode array that are 350 m away from each other) and Axion Integrated Studio (AxIS) Navigator (v.3.5.2; Axion Biosystems). In brief, to record spontaneous neuronal activity, the Neural Real-Time module was used. The neuronal firing events/action potentials (herein referred to as the spike) was defined by applying an adaptive threshold crossing method, that sets the threshold for spike detection for each channel/electrode to 5 s.d. of the noise level [79]; activity exceeding this threshold was counted as a spike. Unless otherwise stated, all analysis considers only active channels, defined as channels exhibiting >5 spikes per min. Raw data files were obtained by sampling the channels simultaneously with a gain of l,000x and a sampling frequency of 12.5 kHz per channel using a band-pass filter (200-3,000 Hz). To detect single-electrode bursting activity, an interspike interval threshold was used, setting the minimum number of spikes at 5 and the maximum interspike interval at 100 ms. The network bursting activity (simultaneous bursts at multiple MEA electrodes) was analysed by Neural Metric Tool (v.1.2.3; Axion Biosystems). For this purpose, the Adaptive algorithm was selected using the following settings: minimum number of spikes = 50 and minimum electrodes = 35%. Quantification of network synchrony was computed through AxIS software by calculating the area under the normalized cross-correlogram (AUNCC) as described previously [80-83]. AUNCC represents the area under interelectrode crosscorrelation normalized to the autocorrelations, with higher values indicating greater synchronicity of the network. For additional neural data analysis, including mean firing rate of each electrode (the ratio of the total number of spikes per second and the total duration of recording (1,800 s)) and weighted mean firing rate (defined as the spike rate per well multiplied by the number of active electrodes in the associated well), raw data files were processed offline using the Statistics Compiler function in AxIS. Statistics Compiler output files were processed in Microsoft Excel (Microsoft) and with custom Python scripts to organize and extract individual parameter data for each well of each MEA plate and for data normalization. Raster plots illustrating spike histogram and network bursts were generated using Neural Metric Tool (Axion Biosystems).

Glioma-neuron co-culture and gabapentin treatment: Spontaneous neuronal activity from cortical cultures grown on MEA plates was recorded in 30 min sessions on days in vitro 1 (DIV1), DIV7 and DIV15. Bright- field images were captured at each of the above timepoints to assess the neuronal cell density and electrode coverage. Primary cortical neurons showed a constant maturation trend from DTV7 to DIV15, and the co-culture experiments were initiated when neurons showed a synchronous activity pattern network at DIV15. Baseline data were therefore recorded on DIV 15 immediately before addition of glioma cells in the presence or absence of gabapentin. For glioma cell co-culture, a singlecell suspension from cultured neurospheres of primary patient-derived HFC and LFC were prepared and diluted to a viable cell concentration of 20,000 cells per 5 pl. Droplets of 5 pl were then plated on top of differentiating neurons in the MEA plate. After plating, glioma cells were allowed to adhere for approximately 1 h after which HFC cultures were exposed for next 24-48 h to a working concentration of 50 pM gabapentin [84,85] diluted in complete BrainPhys medium or an equivalent amount of vehicle (sterile water) as a control. Each condition was run on two wells (experimental replicates). Neurons from two different embryos were used as biological triplicates (n = 2). Presented data from MEA recordings reflects well-wide averages from active electrodes, with the number of wells per condition represented by n values.

Mice and housing conditions

All in vivo experiments were conducted in accordance with the protocols approved by the UCSF Institutional Animal Care and Use Committee (IACUC) and performed in accordance with institutional guidelines. Animals were maintained under pathogen-free conditions, in temperature- and humidity-controlled housing, with free access to food and water, under a 12 h-12 h light-dark cycle. For brain tumour xenograft experiments, the IACUC does not set a limit on maximal tumour volume but rather on indications of morbidity. These limits were not exceeded in any of the experiments as mice were euthanized if they exhibited signs of neurological morbidity or lost 15% or more of their body weight.

Orthotopic xenografting for neuronal circuit integration and mouse survival experiments For all xenograft studies, NSG mice (NOD-SCID-IL2R gamma chain-deficient, The Jackson Laboratory) were used. Male and female mice were used equally. For immunoelectron microscopy experiments, a single-cell suspension from cultured neurospheres of HFC and LFC (n = 2 each) labelled with red fluorescent protein (RFP) were prepared in sterile DMEM immediately before the xenograft procedure. Mice (n = 8; 2 biological replicates per patient line) at postnatal day 28-30 were anaesthetized with 1-4% isoflurane and placed into a stereotactic apparatus. The cranium was exposed through a midiinc incision under aseptic conditions. Approximately 50,000 cells in 2 pl sterile PBS were stereotactically implanted into the CAI region of the hippocampus through a 31 -gauge burr hole, using a digital pump at infusion rate of 0.4 pl min^-1 and 31 -gauge Hamilton syringe. Stereotactic coordinates used were as follows: 1.5 mm lateral to midline, 1.8 mm posterior to bregma, -1.4 mm deep to cranial surface. At the completion of infusion, the syringe needle was allowed to remain in place for a minimum of 2 min, then manually withdrawn at a rate of 0.875 mm min^-1 to minimize backflow of the injected cell suspension. For survival studies, morbidity criteria used were either: reduction of weight by 15% initial weight, or clinical signs such as hunched posture, lethargy or persistent decumbency. Kaplan-Meier survival analysis using log-rank testing was performed to determine statistical significance.

Quantification of tumour cell burden

Cell quantification was performed by a blinded investigator at 10-20x magnification using the Zeiss LSM800 scanning confocal microscope and Zen 2011 imaging software (Carl Zeiss). The area for quantification was selected for a l-in-6 series of 40 pm coronal sections (240 pm apart from one another). Immunohistochemistry was performed on brain sections from HFC and LFC xenografts to stain for human nuclear antigen (HNA)-positive tumour cells. The tumour burden was evaluated using blinded rank-order analysis as previously reported [86]. For each mouse, the section with the maximal amount of tumour burden was selected as defined by the number of HNA-positive cells. From this section, a tiled xlO image of the centre of the section (3 x 3 tiles stitched together) was created, therefore generating a single image for each mouse that represents the maximal amount of tumour burden. Next, the images generated from each mouse were compared and ranked in order from the image with the least to maximum number of cells. Subsequently, scores were assigned and ranged from 1 to 24 with 1 and 24 representing images with the lowest and highest number of HNA-positive cells, respectively. After scoring, the experimenter was unblinded and the scores from each image were assigned to each of the respective HFC or LFC experimental conditions. Statistical differences between HFC and LFC scores were evaluated using two-tailed unpaired Mann- Whitney tests. Sample preparation and image acquisition for electron microscopy

Twelve weeks after xenografting, mice were euthanized by transcardial perfusion with Karnovsky’s fixative: 2% glutaraldehyde (EMS, 16000) and 4% PFA (EMS, 15700) in 0.1 M sodium cacodylate (EMS, 12300), pH 7.4. Transmission electron microscopy (TEM) was performed in the tumour mass within the CAI region of the hippocampus for all xenograft analysis. The samples were then post-fixed in 1% osmium tetroxide (EMS, 19100) for 1 h at 4 °C, washed three times with ultrafiltered water, then en bloc stained overnight at 4 °C. The samples were dehydrated in graded ethanol (50%, 75% and 95%) for 15 min each at 4 °C; the samples were then allowed to equilibrate to room temperature and were rinsed in 100% ethanol twice, followed by acetonitrile for 15 min. The samples were infiltrated with EMbed-812 resin (EMS, 14120) mixed 1:1 with acetonitrile for 2 h followed by 2:1 EMbed- 812:acetonitrile overnight. The samples were then placed into EMbed-812 for 2 h, then placed into TAAB capsules filled with fresh resin, which were then placed into a 65 °C oven overnight. Sections were taken between 40 nm and 60 nm on a Leica Ultracut S (Leica) and mounted on 100-mesh Ni grids (EMS FCFIOO-Ni). For immunohistochemistry, microetching was done with 10% periodic acid and eluting of osmium with 10% sodium metaperiodate for 15 min at room temperature on parafilm. Grids were rinsed with water three times, followed by 0.5 M glycine quench, and then incubated in blocking solution (0.5% BSA, 0.5% ovalbumin in PBST) at room temperature for 20 min. Primary goat anti-RFP (1: 300, ABIN6254205) was diluted in the same blocking solution and incubated overnight at 4 °C. The next day, grids were rinsed in PBS three times, and incubated in secondary antibodies (1:10 10 nm gold-conjugated IgG, TED Pella, 15796) for 1 h at room temperature and rinsed with PBST followed by water. For each staining set, samples that did not contain any RFP- expressing cells were stained simultaneously to control for any non-specific binding. Grids were contrast stained for 30 s in 3.5% uranyl acetate in 50% acetone followed by staining in 0.2% lead citrate for 90 s. The samples were imaged using a JEOL JEM-1400 TEM at 120 kV and images were collected using a Gatan Orius digital camera.

Electron microscopy data analysis

Sections from the xenografted hippocampi of mice were imaged as above using TEM imaging. Here 101 sections of HFC xenografts across 4 mice and 104 sections of LFC xenografts across 3 mice were analyzed. Electron microscopy images were taken at x6,000 with a field of view of 15.75 pm². Glioma cells were counted and analyzed after unequivocal identification of a cluster of immunogold particle labelling with 15 or more particles. The total number of synapses, including neuron-to-neuron and neuron-to-glioma synapses (identified by: (1) the presence of synaptic vesicle clusters; (2) visually apparent synaptic cleft; and (3) identification of clear postsynaptic density in the glioma cell) were counted.

EdU assay

The EdU -incorporation assay was performed using an EdU assay kit (Invitrogen) according to the manufacturer’s instructions. Patient-derived HFC/LFC glioma cells were seeded on poly-D-lysine- and laminin-coated coverslips at 10,000 cells per well of a 24-well plate. After 24 h of seeding, embryonic mouse hippocampal neurons were added to the glioma cells at 40,000 cells per well for the glioma-neuron co-culture group. After 72 h, glioma cells alone or in coculture with neurons were treated with 20 pM EdU overnight at 37 °C. Subsequently, the cells were fixed with 4% PFA and stained using the Click-iT EdU kit protocol. The proliferation index was then determined by quantifying the fraction of EdU- labelled cells/D API-labelled cells using confocal microscopy at xlO magnification.

3D spheroid invasion assay

Glioma cell invasion was evaluated by performing an invasion assay using the Cultrex 3D Spheroid Cell Invasion Assay Kit (Trevigen) according to the manufacturer’s protocol. In brief, 3,000 cells were resuspended in 50 pl of spheroid formation matrix solution (prepared in culture medium) in a round-bottom 96-well plate. Spheroids were allowed to form for 72 h and images were taken at xlO magnification before addition of invasion matrix (0 h). Working on ice, 50 pl of invasion matrix was then added to each well and the plate was incubated at 37 °C. After 1 h of gel formation, for the glioma cell + mouse conditioned medium (mCM) group, 100 pl of mouse hippocampal neuron supernatant was added to the wells to assess the effect of neuronal secreted factors on the invasiveness of the glioma cells. After 24 h of incubation at 37 °C, invasions were observed under a microscope and images were taken at xlO. Microtube lengths as well as the area of each spheroid measured at 0 h (pre-invasion) and 24 h (post-invasion) were analyzed using ImageJ and the difference was used to calculate the total area of cell invasion. SEM analysis

SEM was performed as described previously [39] to investigate the tumour microtubes in greater detail. Primary patient-derived HFC/LFC glioma cells were seeded on poly-D-lysine- and laminin-coated coverslips (Neuvitro) at 10,000 cells per well of a 24-well plate. For the glioma cell + mCM and glioma cell + mCM + GBP groups, mouse neuronal conditioned medium was added to the wells in the absence or presence of gabapentin (32 pM), respectively, to assess the effect of neuronal secreted factors and gabapentin modulation on tumour microtube formation of glioma cells [47,87,88]. After culture for

1 week, the samples were fixed with 4% PFA for 30 min at 4 °C, washed with PBS and ultrafiltered water. After serial stepwise ethanol dehydration, coverslips were mounted on SEM stubs (TED Pella) using conductive adhesive tape (12 mm OD PELCO Tabs, TED Pella). The samples were then sputter-coated with a 2 nm layer of gold palladium. Tumour microtubes were observed under field emission SEM (Sigma 500; Carl Zeiss Microscopy) and micrographs were recorded at an accelerating voltage of 1.0 kV.

THBS1 shRNA knockdown

Primary patient-derived HFC glioma cells were seeded in 6-well plates at a density of

2 x 10⁵ cells per well. After overnight incubation, cells were infected with lentiviral particles expressing a shRNA targeting THBS1 or a control scramble construct expressing green fluorescent protein (GFP) according to the manufacturer’ s protocol using polybrene transfection reagent and subsequently kept in a 5% CO2 incubator at 37 °C for 24 h. After

24 h, the medium was replaced with fresh complete culture medium. Cells were checked 72 h after transduction for GFP expression to evaluate the efficiency of the transduction. The lentiviral shRNA constructs targeting THBS1 (5'-AGACATCTTCCAAGCATATAA-3'; SEQ ID NO:1) and control scrambled shRNA (5'-CCTAAGGTTAAGTCGCCCTCG-3'; SEQ ID NO:2) were designed and constructed by VectorBuilder.

Cell viability assay

The cell viability of primary patient-derived HFC cells after THBS1 knockdown was evaluated using the Live/Dead Viability/Cytotoxicity kit (Molecular Probes). One-week after transduction, HFC cells expressing scramble shRNA or shRNA targeting THBS1 were seeded on poly-D-lysine- and laminin-coated coverslips (Neuvitro) at 10,000 cells per well of a 24-well plate. After 1 week of culture, a 500 pl cell-staining solution at a final concentration of 2 pM calccin AM and 4 pM ethidium bromide (EthD-1) in DPBS was added to each well, and the plates were incubated for 45 min at room temperature in dark. As an indicator of cell viability, the calcein-AM is metabolically converted by intracellular esterase activity resulting in the green, fluorescent product, calcein. EthD-1 is excluded from live cells but is readily taken up by dead cells and stains the DNA emitting red fluorescence. Live and dead cells were imaged from four random fields per well and were visualized under a fluorescence microscope. The percentage of live cells was calculated as the number of live cells (in green) divided by the number of total cells (green + red) per image field.

In vitro cell proliferation assessment after THBS1 shRNA knockdown

After 1 week of transduction, primary patient-derived high connectivity glioma cells expressing scramble shRNA or shRNA against THBS1 were seeded on poly-D-lysine- and laminin-coated coverslips (Neuvitro) at 10,000 cells per well of a 24-well plate. Approximately 24 h later, 40,000 embryonic mouse hippocampal neurons (Gibco) were seeded on top of the glioma cells and maintained with serum-free Neurobasal medium supplemented with B27, gentamicin and GlutaMAX (Gibco). After 1 week of co-culture, cells were fixed with 4% PFA for 30 min at 4 °C and incubated in blocking solution (5% normal donkey and goat serum, 0.25% Triton X-100 in PBS) at room temperature for 1 h. Next, cells were treated with primary antibodies diluted in the blocking solutions overnight at 4 °C. The following antibodies were used: rabbit anti-Ki-67 (1:500, Abeam) and human nuclear antigen (HNA; mouse anti-human nuclei, 235-1; 1:100, Millipore). The coverslips were then rinsed three times in PBS and incubated in secondary antibody solution (Alexa 488 goat anti-rabbit IgG, and Alexa 647 goat anti-mouse IgG) all used at 1:500 (Invitrogen) in antibody diluent solution for 1 h at room temperature. The coverslips were rinsed three times in PBS and then mounted with VECTA antifade mounting medium with DAPI (Vector Laboratories). To calculate the proliferation index, the total number of HNA-positive cells co-labelled with Ki-67 was divided by the total number of human nuclei-labelled cells per image field visualized using confocal microscopy at x40 magnification. In vitro cell proliferation assessment after pharmacological inhibition of TSP- ! by gabapentin

The effect of TSP- 1 inhibition by gabapentin on glioma cell proliferation was evaluated by Ki-67 immunofluorescence staining, as described above. In brief, primary patient-derived high connectivity glioma cells were seeded on poly-D-lysine- and laminin- coated coverslips (Neuvitro) at 10,000 cells per well of a 24-well plate. Approximately 24 h later, 40,000 embryonic mouse hippocampal neurons (Gibco) were seeded on top of the glioma cells and maintained with serum-free Neurobasal medium supplemented with B27, gentamicin and GlutaMAX (Gibco). The next day, cultures were treated either with vehicle (sterile water) or 32 pM gabapentin followed by daily half-medium switches of fresh 32 pM gabapentin until 1 week of coculture [88]. Subsequently, cells were fixed and immunostained for Ki-67 and HNA labelling for proliferation assessment as described above.

Orthotopic xenografting for proliferation assessment and pharmacological inhibition of'TSP- 1 by gabapentin

A single-cell suspension from cultured neurospheres of primary patient-derived HFC and LFC were prepared in sterile HBSS immediately before the xenograft procedure by dissociation with TrypLE (Thermo Fisher Scientific). Mice (4-6 biological replicates per patient line) at postnatal day 28-30 were anaesthetized with 1-4% isoflurane and placed into a stereotactic apparatus. The cranium was exposed through midline incision under aseptic conditions. Approximately 150,000 cells in 3 pl sterile HBSS were stereotactically implanted into the premotor cortex (M2) through a 26-gauge burr hole, using a digital pump at infusion rate of 1.0 pl min^-1. Stereotactic coordinates used were as follows: 1.0 mm lateral to midline, 1.0 mm anterior to bregma, -1.0 mm deep to cortical surface. Four weeks after xenograft, HFC/LFC-bearing mice were treated with systemic administration of gabapentin (200 mg kg^-1 ; Sigma- Aldrich; formulated in saline) through intraperitoneal injection for 28 consecutive days. Controls were treated with an identical volume of the relevant vehicle. At 8 weeks after xenograft, mice were euthanized and coronal brain sections at 40 pm were obtained for immunohistochemistry. Confocal imaging and quantification of tumour burden and cell proliferation

Cell quantification was performed by a blinded investigator at x 10-20 magnification using the Zeiss LSM800 scanning confocal microscope and Zen imaging software (Carl Zeiss). The area for quantification was selected for a l-in-6 series of 40 pm coronal sections (240 pm apart from one another). Within 3 sections of maximal tumour burden, all HNA- positive (mouse anti-human nuclei, 235-1; 1:100, Millipore) cells were quantified to determine the tumour burden within the areas quantified. HNA-positive tumour cells were then assessed for double-labelling with Ki-67. To calculate the proliferation index (the percentage of proliferating tumour cells for each animal), the total number of HNA-positive cells co-labelled with Ki-67 across all areas quantified was divided by the total number of human nuclei-positive cells counted across all areas quantified. Differences in proliferation indices were calculated using unpaired, one-tailed Student’s /-tests.

ELISA

Peripheral blood samples from newly diagnosed patients with glioblastoma (n = 56) were collected and allowed to clot for 30 min at room temperature before centrifugation for 15 min at 1,000g. The serum was stored at -80 °C until analysis. The TSP-1 level was determined using the Quantikine immunosorbent assay kits according to the manufacturer’s instructions (R&D Systems). To confirm the functional protein-level knockdown of THBS1, the TSP-1 level was also measured in cell culture supernatants collected from scramble and 777B.S7-shRNA transduced HFC cells (collected one-week after infection) using the Picokine ELISA kit (Boster Biological Technology).

Statistics and reproducibility

Statistical tests were conducted using Prism (GraphPad) software unless otherwise indicated. The significance test of different groups was determined using Student’s /-tests and one-way ANOVA with Tukey’s post hoc tests. P < 0.05 was considered to be statistically significant. Two-tailed unpaired Mann- Whitney tests were used to analyze the tumour cell burden of HFC and LFC hippocampal xenografts using the same significance denotation as above. Two-tailed log-rank analyses were used to analyze the statistical significance of Kaplan-Meier survival curves for human patients. All of the micrographs shown are representative of three independently conducted experiments, with similar results obtained. Statistical analyses for RNA-seq data are described above in the respective sections. The tumour volumes of patients with glioblastoma were calculated by manual segmentation with region-of-interest analysis ‘painting’ inclusion regions on the basis of FLAIR sequences. Volumetric measurements were made blinded to patients’ clinical outcomes. Manual segmentations were performed by co-authors A.E., A. A. and A.L. with tumour volumetries verified for accuracy after an initial training period. Student’s /-tests and/² tests were used to compare continuous and categorical variables between patient cohorts, respectively. Patient overall survival (OS) was defined as the time from the date of first surgery or original biopsy (if it occurred before surgery) until death or the last contact date. Alive patients were censored at the time of loss to follow-up or last follow-up date. Median follow-up was estimated using the reverse Kaplan-Meier method.

To identify clinically relevant survival risk groups in a multivariate setting, recursive partitioning analyses was used for survival data using the partDSA algorithm (v.0.9.14) [42- 441. Survival trees use recursive partitioning to divide patients into different risk groups. The Brier score was the chosen loss function for splitting and pruning. Such methods are nonparametric and, therefore, do not require the proportional hazards assumption. All known prognostic variables were included in the trees, including age at diagnosis, sex, MGMT promoter methylation status, tumour location, chemotherapy, radiotherapy, the presence of functional connectivity within the tumour, pre- and post-operative tumour volume and the extent of resection. MGMT methylation status was included in both univariate Cox proportional-hazard modelling in addition to multivariate recursive partitioning survival analysis. The tree that minimized the fivefold cross-validated error as well as the most parsimonious tree within one standard error of the overall minimum error were selected for review. Leaves of the resulting trees defined the final risk groups from which the corresponding Kaplan-Meier curves were generated. Median OS times and hazard ratios were generated and compared between risk groups using the Kaplan-Meier method and the Cox proportional hazards model, respectively. The proportional hazards assumption was verified. Example 2: Tumor circuit mapping and precision-medicine therapies

Introduction

Malignant gliomas, the most common primary brain cancer in children and adults, are a major cause of neurological morbidity and mortality with no effective therapies. The results of the above performed experiments of the disclosure provide the perspective that brain cancers are electrically active tissues, a marked departure from the traditional view of brain tumors as simply space-occupying masses. This new understanding demands a fundamentally different approach. As demonstrated above, tumor proliferation and invasion are regulated by interactions with neurons through both activity regulated paracrine signaling and through direct synaptic signaling. Further, cancer cells establish a glioma-to-glioma gap junction-coupled cell network that synaptically integrates into the neural circuits it invades [2,1,89]. The above experiments of the disclosure illustrate that this malignant circuitry evolves over time, as more inputs from healthy brain are recruited into the malignant network, and as the tumor instructs functional remodeling of brain circuitry at the expense of patient neurological function. Accordingly, the findings of the experiments enable the development of new treatments which have previously been encumbered by limited understanding of the tumorspecific and evolving neural inputs governing glioma progression. Thus, the critical need to develop tools to monitor and control malignant glioma circuity in patients with brain cancer may now be more effectively addressed.

The above findings of the disclosure, when paired with previous studies, strongly support the concept that neuronal activity governs malignant glioma progression [1- 4,48,52,89,90] (FIG. 17). One important mechanism by which synaptic integration of glioma cancer cells into the functional neural circuits of patients may promote tumor proliferation is through glioma membrane depolarization [2,89]. It has been hypothesized that depolarizing currents promote glioma proliferation and growth, through voltage sensitive mechanisms that remain to be fully elucidated [2,89]. It was therefore hypothesized that malignant currents may represent the cumulative effects of glioma cell membrane depolarizations due to short- range and long-range neuronal circuit projections, as well as through tumor intrinsic mechanisms of depolarizing currents [90,91 ].

The experiments of the present disclosure suggest that malignant glioma network establishment and evolution is patient-specific, so that each tumor circuit must be mapped. Glioma dependence on the activity of neuron-glioma circuits therefore creates a therapeutic opportunity, and concordantly, the findings of the above experiments performed highlight the potential for brain cancer therapy using neuromodulatory approaches. No devices yet exist for glioma circuit mapping, monitoring, or neuromodulatory stimulation. The FDA approved Optune Gio system creates low-intensity electrical fields in the scalp called tumor treating fields demonstrating a modest survival benefit. However, electrical currents to the scalp have not been demonstrated to influence neuronal circuits governing glioma growth and, further, existing systems are unable to monitor tumor progression in patients. This technology gap contributes to the paucity of therapies. However, the findings of the experiments of the present disclosure enable the development of targeted precision-medicine therapies to monitor glioma progression and treat malignant glioma growth and progression using neuromodulation strategies.

In pursuing neuromodulation strategies addressing gliomas, subdural and depth electrodes will be implanted to map (sense) malignant glioma circuit neuronal inputs and define glioma circuit dynamics in patients. Concurrently, low-intensity focused ultrasound (LIFU) will be applied to modulate the activity of malignant neuronal circuits in the shortterm setting to determine the influence of modulated activity on glioblastoma progression. Commercially available implanted electrodes will subsequently be used to chronically map malignant glioma neuronal inputs throughout chemoradiation to identify electrical biomarkers of treatment response and the therapeutic efficacy of neuromodulatory approaches to reduce glioma growth through the delivery of depolarization blocking stimulation. Finally, new custom devices will be developed which monitor the electrical activity of glioblastoma growth (sense) and deliver therapeutic stimulation (stim) for brain cancer applications. It is strongly suggested by the above performed experiments that malignant glioma demonstrates a unique electrical biomarker of tumor growth and, further, appropriate neuromodulation of malignant glioma circuits will reduce tumor growth and improve patient outcomes. It is expected that neuromodulation can achieve a depolarization block in malignant gliomas and that new responsive devices adapted for patient tumor mapping and monitoring of malignant glioma circuits can be developed.

FIG. 17 neuron-glioma interactions in the central nervous system. Neuronal activity promotes glioma progression through activity regulated secretion of paracrine growth factors and by the electrochemical communication mediated by synapses between neurons and glioma cells, as well as through potassium-evoked glioma currents. Such electrochemical signals are amplified in a glioma-to-glioma gap junction-coupled network that serves to amplify and synchronize depolarizing currents in the tumor cell network. Membrane depolarization is sufficient to promote glioma cell proliferation through voltage-dependent mechanisms. ‘Hub’ cells autonomously generate currents that spread through the gap junction-connected tumor network to drive a tumor-intrinsic rhythm of periodic depolarization and consequent calcium transients important for tumor growth. AMPA receptor-mediated synaptic signaling between neurons and glioma cells promotes both tumor cell proliferation and invasion. In turn, glioma cell secretion of factors such as glutamate and synaptogenic proteins promotes neuronal hyperexcitability and functional remodeling of neural circuits, thereby increasing neuronal activity in the tumor microenvironment. Glioma- induced increases in excitatory neuronal activity enhance activity-regulated influences on glioma progression.

Mapping malignant glioma circuitry

An approach to therapeutically modulate glioma circuit dynamics will be engineered using deep knowledge of malignant circuit inputs and evolution of circuit connectivity and dynamics over the disease course. Cutting edge intact brain circuit interrogation tools will be leveraged to map and visualize glioma circuitry in the well-characterized patient-derived xenograft models demonstrated above and in patients with pediatric and adult glioma types, including DIPG, pediatric glioblastoma, and adult glioblastoma (FIG. 18). Glioma connectivity will be mapped using rabies-based monosynaptic tracing to induce robust eGFP expression in neuronal inputs to infected glioma cells, a technique that has been used successfully to map neuronal inputs to normal glial precursor cells [92] .

To attain a dynamic view of neuronal-glioma network activity, high sensitivity green and red genetically encoded calcium indicators expressed in glioma cells and in neurons together with whole cortex wide field calcium imaging and fiber photometry will be utilized to monitor the circuit dynamics of the tumor network and the infiltrated normal brain, with and without optogenetic control of neuronal activity in regions of connectivity with the tumor using .established optogenetic neuron-glioma paradigms [3,89], As gliomas exhibit mechanisms of adaptive synaptic plasticity and also influence neuronal firing patterns through multiple mechanisms promoting neuronal hypcrcxcitability and functional neural circuit remodeling, as demonstrated by the above experiments, glioma circuit maps and patterns of activity evolve with tumor progression. Therefore neuron-glioma circuit maps will be evaluated using monosynaptic tracing and patterns of activity in awake, behaving mice with whole cortex widefield calcium imaging [94] (for cortical tumors) and fiber photometry [95] (for deep tumors) over the course of tumor growth.

To establish neuronal and malignant glioma circuit hyperexcitability and synchrony in patients, building on preclinical animal models, high gamma power (HgP) will be measured through local field potentials using implanted commercially available FDA approved high density subdural and depth electrodes into glioma- infiltrated cortex in patients within newly diagnosed cortically projecting malignant gliomas for short-term mapping. To establish malignant circuit hyperexcitability as a hallmark of malignant glioma progression, subdural electrodes will be implanted overlying glioma-infiltrated cortex at the time of tumor biopsy confirming progression to establish an electrophysiological model of malignant glioma progression at the point of tumor recurrence. Neuromodulation through low-intensity focused ultrasound (LIFU) has the potential to offer noninvasive neuromodulation therapy in deep brain regions and with high spatial specificity. Therefore, the effect of established LIFU protocols on malignant circuit hyperexcitability will be determined as measured by HgP. Computational analyses of neuron and glioma network activity will be analyzed using available tools [94] .

FIG. 18 implantation of FDA approved high density subdural electrodes overlying glioma infiltrated cortex at initial diagnosis and following biopsy proven malignant glioma recurrence will establish neuronal and malignant circuit hyperexcitability as a hallmark of tumor progression.

Establishing circuit modulatory therapy for high-grade gliomas

Circuit modulatory therapies will be established for high-grade gliomas in preclinical models and human high-grade glioma patients using commercially available FDA approved devices. Optogenetic techniques suggests that glioma growth is robustly modulated by membrane depolarization [2,89]. Furthermore, neuronal activity drives glioma growth both during resting and activity periods. Accordingly, it is hypothesized that subdural electrode and depth electrode stimulation in preclinical disease models and patients can be used to inhibit membrane depolarization of input neurons or the tumor cells themselves and thereby inhibit glioma growth (FIG. 19). To test this hypothesis, optogenetic depolarization of patient-derived glioma cells will be induced across low and high frequency bands to determine the frequency of stimulation that achieves failure in glioma membrane repolarization (depolarization block). Using commercially available FDA approved electrodes, a long-term neural biomarker of malignant glioma progression will be established from initial diagnosis through first recurrence for chronic implantation, informed by the acute phase spectral frequency analysis informed through completion of the above experiments of Example 2. Malignant circuit modulation therapy will then be established in patients through the short-term delivery of stimulation frequencies confirmed in the aforementioned preclinical models to establish a depolarization block to patients with cortically projecting diffuse gliomas. To attain long-term stimulation to accomplish a depolarizing block of neuronal-glioma network activity, acute phase stimulation models will be utilized for chronic implantation.

FIG. 19 chronic implantation of FDA approved subdural high density subdural electrodes overlying glioma infiltrated cortex and cortical depth electrodes following initial resection through first recurrence will establish neuronal and malignant circuit hyperexcitability as a hallmark of tumor progression throughout the disease trajectory and permit delivery of depolarization currents to inhibit malignant glioma proliferation.

Developing a novel implanted device system

A novel implanted device system will be developed which senses glioma neurophysiology for longitudinal ambulatory monitoring of disease and provides closed loop feedback neuromodulation to slow progression for patients with malignant glioma.

As demonstrated by the above disclosure (Example 1), neuronal inputs have been demonstrated to play a central role in driving malignant glioma pathophysiology. While existing devices designed for movement disorders and epilepsy have revolutionized the clinical care of patients, no such treatments exist for patients with brain cancer. Given the aforementioned findings, it is hypothesized that glioma circuit activity can be longitudinally monitored (sense) and that depolarization blocking neuronal stimulation (stim) will inhibit tumor growth and progression (FIG. 20). A prototype device has been designed, which will be refined during the completion of the above proposed experiments (Example 1). Successful completion of the proposed experiments will produce a novel prototype sense-stim device custom-designed to sense and adaptively slow malignant circuit activity in patients with brain cancer.

This disclosure is expected to advance the development of clinically useful devices to both monitor and therapeutically treat activity-dependent malignant glioma growth for patients. This work will de-risk industry investment in biomedical devices to treat patients with brain cancer with electrical stimulation using both noninvasive and invasive techniques. It will also motivate and clarify potential endpoints for future clinical trials. These results will have an important positive impact because this increased understanding and technological development will improve the ability to treat patients with presently lethal brain cancers.

FIGS. 20A to 20C a novel responsive device that can longitudinally monitor (sense) and then deliver depolarization blocking currents to therapeutically inhibit malignant glioma proliferation, refined throughout the completion of the above experiments.

Example 3: Electrophysiologic and spatially resolved genomic signatures of glioma- infiltrated cortex

Overview

Neocortical circuits selectively organize neuronal signals encoding features of cognitive processing, and route them to specialized short and long-range circuits. Since glioma-infiltrated cortex is demonstrably excitable and can participate in cognitive processing, the underlying cortical laminar structure and functionality may still be preserved despite tumor infiltration. Moreover, as glioma cells remodel existing neural circuits and microenvironmental factors drive cellular invasion and proliferation neuron-glioma interactions may be layer specific. As the electrophysiologic, structural, and genomic landscape of glioma-infiltrated cortex remains poorly understood, it was sought to investigate these regions using a multimodal approach. The power spectra of normal-appearing and glioma-infiltrated cortex were recorded using subdural high-density electrode arrays at testing and validation sites across the US and Europe. Immunohistochemistry of formalin-fixed paraffin-embedded (FFPE) samples of infiltrated cortex enabled protein-level neuronal and glioma identification to assess laminar preservation and spatial patterns of invasion. Spatial transcriptomics profiling of FFPE tissues, single-cell and single nuclei RNA-sequencing, was used to identify cell populations, location-matched genomic alterations, and cell-cell communication within and across samples. Increased delta range (1-4 Hz) power and decreased power in the beta range (12-20 Hz) was identified as a robust feature of glioma- infiltrated cortex which was maintained across glioma subtypes and preserved in a validation cohort encompassing magnetoencephalography (n= 140 patients) and subdural electrocorticography (n= 12 patients).

Tissue proteomics and spatial genomics analyses was performed in 8 test set and 32 validation set cortical samples, which revealed tumor burden greatest within infragranular cortical lamina regardless of glioma subtype. Genomic analyses confirmed preservation of cortical laminar structure as well as layer specific differences in glioma-related expression programs such as hypoxia, inflammation, and synaptogenesis compared with control conditions. Cell-cell communication analyses demonstrated greater layer specific interactions in glioma-infiltrated cortex across layers. These findings demonstrate that cortical laminar structure may be preserved in glioma-infiltrated cortex, supporting glioma specific spectral frequency alterations. Additionally, these findings demonstrate that cortical remodeling following glioma infiltration alters spatiotemporal activity and cell-cell interactions.

This is the first known study to investigate glioma-infiltrated cortex using a multimodal approach of state-of-the-art techniques. These findings may serve as an atlas for probing the specific interactions and roles of cells in glioma-infiltrated cortex across subtypes.

FIG. 21 depicts a schematic of the study workflow. In human subjects with WHO 2-4 gliomas, magnetoencephalography and electrocorticography was applied during resting state to create spectral density plots for each participant. An elastic net logistic regression classifier was trained to define the unique spectral features of glioma infiltrated cortex. Cortical laminar structure of glioma infiltrated cortex was then analyzed using molecular features. A multiple layered approach was used to spatially resolve neuronal expression programs by analyzing defined molecular drivers of neuronal circuits, including cell-cell communications, using, e.g., spatial resolved transcriptomics.

Results

Delta-beta shifts were found to be an electrophysiologic hallmark of infiltrated cortex (FIG. 22). Resting-state electrophysiologic recordings were non-invasively acquired from 140 patients with low- and high-grade gliomas via magnetoencephalography. Using a spatial beamforming technique, a source-space reconstruction was performed, allowing direct measurement of electrophysiologic activity within gliomas. For each patient, an elastic net logistic regression classifier with a stratified 10-fold cross-validation paradigm was fit to distinguish between power spectra arising from the glioma and homologous brain regions in the contralateral hemisphere. Model significance was determined non-parametrically by retraining each model 1,000 times with randomly permuted class labels and testing the true phi coefficient against this null distribution. Out of 140 patients, 127 (90.7%) had statistically significant models (p < 0.05), indicating that the neural activity within gliomas is highly discriminable from homologous regions. After pooling the coefficients from each model, it was found that increased power in the delta range (1-4 Hz) and decreased power in the beta range (12-20 Hz) was a signature of glioma activity (p < 0.05). This signature was preserved across molecular subtypes including lp/19q-codeleted IDH mutant oligodendrogliomas, IDH mutant astrocytomas, and IDH wild type glioblastomas.

Glioma burden was found to be differentially present in cortical layers 5-6 (FIG. 23). Glioma infiltrated cortical samples were acquired and immunohistochemistry analysis was performed for the presence of neurons (NeuN) and glioma cells (IDH-1 or SOX2). Cortical laminar structure was found to be preserved despite glioma infiltration. Histological analyses revealed tumor burden decreases from infragranular to supragranular layers regardless of glioma subtype.

Spatial transcriptomics was found to identify conserved cortical structure (FIGS. 24A to 24B). Spatial transcriptomics was performed from cortical glioma samples. Genomic analyses identified layer specific neuronal populations confirming the presence of distinct excitatory and inhibitory neuronal populations. All glioma samples were homogenized into a single common cortex accounting for slight differences in neuronal populations across cortical regions. Expression programs for neuron and malignant cell expression programs defined greater malignancy within lower cortical laminae L5-6 and the reduction of GABA- A expression within these corresponding lamina.

Layer specific gene changes and contributions to, e.g., excitatory inputs were investigated (FIGS. 25A to 25B). Relative to controls, glioma infiltrated cortex demonstrated a reduction in GABA-A receptor expression with a corresponding increase in expression programs associated with excitatory inputs within upper cortical laminae.

Cell-cell interactions analysis was found to support reduced communication within layer 5 (FIGS. 26 A to 26B). The predominant inhibitory neuronal population within glioma infiltrated cortex was found to be parvalbumin and SST positive neurons, which demonstrate a relative loss specifically within Layer 5-6, resulting in an increased number of proteinprotein interactions across laminae — with a corresponding decrease in the number of cell-cell interactions — within L5-6 relative to control condition.

Example 4; Pathology specific, pharmacologically reversible loss of neuronal function in human gliomas

As evident from the above Examples, it has been demonstrated that neuronal activity is preserved within glioma-infiltrated cortex and maintains the ability to engage in task specific computations, albeit with information loss. These findings support a potential therapeutic target to rescue cognitive impairments seen in nearly all afflicted patients if neuronal activity can be recovered.

An in vivo human behavioral and electrophysiological model was developed to assess neuronal population tuning responses within primary sensory cortex including the decodability of stimuli and task-related changes in population spiking in a cohort of 14 human patients undergoing awake resection. High-density electrode arrays were placed to record human local field potentials from gliomas projecting to the cortical surface. Next, a static sensory detection threshold task was designed. A tachometer was used to stimulate two different face and hand sites while macroelectrode cortical contacts were used to record over both tumor-infiltrated (50 electrodes) and normal appearing cortex (52 electrodes) for a total of 11,200 trials. In tumor- infiltrated cortex, the ability to decode the site of stimulation using the oscillatory power in both theta (4-8 Hz) and gamma (32-70 Hz) bands was reduced compared to normal appearing cortex. However, tumor-infiltrated cortex maintained selectivity between hand and face stimulation.

The degree of loss of selectivity correlated with WHO grade and pathology. WHO grade 4 tumors and oligodendrogliomas demonstrated a greater loss of selectivity compared to astrocytomas. Targeted next-generation tumor sequencing uncovered novel molecular targets of glioma-neuronal integration. GABAergic agonist was postulated to restore neuronal selectivity in aging and neurodegenerative disease. Therefore, GABA agonist dose was computed next (estimated by time off Propofol and beginning of behavioral testing). Higher concentrations of GABA agonist correlated with improved theta band selectivity and decodability. These results suggest that loss of neuronal computations in glioma-infiltrated cortex is caused by loss of neuronal selectivity and is both tumor selective and pharmacologically reversible.

FIG. 27 demonstrates the sensory static detection model of neuronal population tuning. The model includes a sensory discrimination task where participants must judge between hand and face stimulation until an intensity threshold below which they cannot discriminate is reached. Sensory static detection thresholds were found to be elevated within glioma-infiltrated cortex (FIG. 28). Stimulation intensity was lowered until patients could not discriminate between hand and face stimulation. A threshold was determined for L/R hand and L/R face.

A reduced ability to discriminate between hand and face stimulation was found within local circuits of tumor- infiltrated cortex (FIG. 29). In addition, functional specificity responses were found (FIG. 30). For example, WHO 2/3 isocitrate dehydrogenase mutant (IDHm) gliomas retained the ability to discriminate between hand and face stimulation (as compared to, e.g., WHO grade 4 tumors and oligodendrogliomas). Further, It was found that IDHm oligodendrogliomas lose theta discrimination, while astrocytomas lose gamma discrimination (FIG. 31). These results may suggest that oligodendrogliomas may be neuronally distinct over astrocytomas, WHO grade 2 tumors may be neuronally distinct over WHO grade 4 tumors, and IDH mutant tumors may be neuronally distinct over IDH wildtypes tumors (suggesting, e.g., that IDH may drive cortical remodeling and/or glioma progression) using methods of detecting electrical brain activity (e.g., recording signals from cortical electrodes).

FIG. 32 provides results from the above-described experiments demonstrating that pharmacological GABAA agonists (such as, e.g., Propofol) restore neuronal specificity within hemispheric glioma infiltrated cortex.

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In at least some of the previously described embodiments, one or more elements used in an embodiment can interchangeably be used in another embodiment unless such a replacement is not technically feasible. It will be appreciated by those skilled in the ail that various other omissions, additions and modifications may be made to the methods and structures described above without departing from the scope of the claimed subject matter. All such modifications and changes are intended to fall within the scope of the subject matter, as defined by the appended claims.

It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to embodiments containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “ a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to “at least one of A, B, or C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “ a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B.”

In addition, where features or aspects of the disclosure are described in terms of Markush groups, those skilled in the art will recognize that the disclosure is also thereby described in terms of any individual member or subgroup of members of the Markush group. As will be understood by one skilled in the art, for any and all purposes, such as in terms of providing a written description, all ranges disclosed herein also encompass any and all possible sub-ranges and combinations of sub-ranges thereof. Any listed range can be easily recognized as sufficiently describing and enabling the same range being broken down into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non-limiting example, each range discussed herein can be readily broken down into a lower third, middle third and upper third, etc. As will also be understood by one skilled in the art all language such as “up to,” “at least,” “greater than,” “less than,” and the like include the number recited and refer to ranges which can be subsequently broken down into sub-ranges as discussed above. Finally, as will be understood by one skilled in the art, a range includes each individual member. Thus, for example, a group having 1-3 articles refers to groups having 1, 2, or 3 articles. Similarly, a group having 1-5 articles refers to groups having 1, 2, 3, 4, or 5 articles, and so forth.

Although the foregoing invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, it is readily apparent to those of ordinary skill in the art in light of the teachings of this invention that certain changes and modifications may be made thereto without departing from the spirit or scope of the appended claims.

Accordingly, the preceding merely illustrates the principles of the invention. It will be appreciated that those skilled in the art will be able to devise various arrangements which, although not explicitly described or shown herein, embody the principles of the invention and are included within its spirit and scope. Furthermore, all examples and conditional language recited herein are principally intended to aid the reader in understanding the principles of the invention and the concepts contributed by the inventors to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the invention as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents and equivalents developed in the future, i.c., any elements developed that perform the same function, regardless of structure. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims.

The scope of the present invention, therefore, is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of present invention is embodied by the appended claims. In the claims, 35 U.S.C. § 112(f) or 35 U.S.C. § 112(6) is expressly defined as being invoked for a limitation in the claim only when the exact phrase "means for" or the exact phrase "step for" is recited at the beginning of such limitation in the claim; if such exact phrase is not used in a limitation in the claim, then 35 U.S.C. § 112 (f) or 35 U.S.C. §112(6) is not invoked.

Claims

WHAT IS CLAIMED IS:

2. The method according to Claim 1, wherein the first tumor is a glioma.

3. The method according to any one of the preceding claims, wherein the non-invasive mapping comprises structural and/or functional imaging.

4. The method according to Claim 3, wherein the non-invasive mapping comprises structural imaging using magnetic resonance imaging (MRI).

5. The method according to Claim 3 or 4, wherein the non-invasive mapping comprises functional imaging using magnetoencephalography (MEG).

6. The method according to any one of the preceding claims, wherein the non-invasive mapping comprises mapping the connectivity of the brain.

7. The method according to Claim 6, wherein the non-invasive mapping comprises determining the functional connectivity of the first volume of the brain with a plurality of other volumes of the brain.

8. The method according to Claim 7, wherein the non-invasive mapping comprises determining the functional connectivity of one or more additional volumes of the brain infiltrated by the first tumor with a plurality of other volumes of the brain.

9. The method according to Claim 7 or 8, wherein the functional connectivity of each volume of the brain is determined relative to a different volume of the brain contralaterally equivalent to the volume.

10. The method according to Claim 9, wherein the determined functional connectivity is used to identify high functional connectivity (HFC) and/or low functional connectivity (LFC) volumes.

11. The method according to any one of Claims 7-10, wherein the functional connectivity is determined using imaginary coherence (IC) metrics and MEG.

12. The method according to any one of Claims 6-11, wherein the non-invasive mapping comprises identifying one or more neural circuits associated with a first section of the first tumor.

13. The method according to Claim 12, wherein the first tumor section corresponds to the first volume of the brain.

14. The method according to Claim 12, wherein the first tumor section is separate from the first volume of the brain.

15. The method according to any one of Claims 12-14, wherein the one or more neural circuits arc identified using domain specific knowledge of the region of the brain in which the first tumor section is located.

16. The method according to Claim 15, wherein the first tumor section is located within the lateral prefrontal cortex (LPFC) of the brain and the one or more identified neural circuits are related to speech production.

17. The method according to any one of Claims 12-16, wherein the one or more neural circuits are identified by measuring neuronal activity in the brain of the subject while the subject receives a stimuli or performs a task associated with the neural circuit.

18. The method according to Claim 17, wherein the neuronal activity is measured using MEG or electroencephalogram (EEG).

19. The method according to any one of the preceding claims, wherein the method further comprises determining that the obtained tumor feature data is associated with or indicative of a tumor feature.

20. The method according to any one of the preceding claims, wherein the tumor feature data is associated with or indicative of two or more features of interest.

21. The method according to Claim 20, wherein the tumor feature data comprises separate data for two or more of the features of interest.

22. The method according to Claim 20 or 21, wherein the tumor feature data comprises data simultaneously indicative of or associated with two or more of the features of interest.

23. The method according to Claim 22, wherein one tumor feature is indicative of or associated with another tumor feature.

24. The method according to any one of the preceding claims, wherein the tumor feature data comprises one or more of a subtype or classification of the first tumor, a characteristic of a microenvironment of the first tumor, information regarding a stimuli received by the subject, information regarding an action performed by the subject, a neuronal input of the first tumor, and control data.

25. The method according to Claim 24, wherein the subtype or classification comprises a determination of malignancy.

26. The method according to Claim 24 or 25, wherein the subtype or classification relates to a prognosis of the first tumor.

27. The method according to any one of Claims 24-26, wherein the subtype or classification comprises a tumor grade.

28. The method according to any one of Claims 24-27, wherein the subtype or classification relates to a type of cell a population of the first tumor cells originated from or resemble.

29. The method according to Claim 28, wherein the type of cell is an astrocyte and/or an oligodendrocyte.

30. The method according to any one of Claims 24-29, wherein the first tumor microenvironment is located within the first region.

31. The method according to any one of Claims 24-29, wherein the first tumor microenvironment is not located within the first region.

32. The method according to Claim 30 or 31, wherein the characteristic of the microenvironment comprises the identification of one or more cell populations located within the first tumor microenvironment.

33. The method according to any one of Claims 30-32, wherein the characteristic of the microenvironment is related to the expression or regulation of specific genes by one or more cell populations of the microenvironment.

34. The method according to any one of Claims 30-33, wherein the characteristic of the microenvironment comprises the identification of one or more proteins located within the first tumor microenvironment.

35. The method according to Claim 34, wherein the one or more proteins comprise one or more synaptogenic proteins.

36. The method according to any one of Claims 24-35, wherein the information regarding the stimuli comprises a time the stimuli was delivered in relation to the recorded electrical brain activity and/or a quantitative metric of stimuli magnitude or intensity.

37. The method according to Claim 36, wherein the stimuli received by the subject comprises a visual stimuli, an auditory stimuli, and/or electrical neurostimulation of a volume of the brain.

38. The method according to Claim 37, wherein the stimuli depends on the region of the brain in which the first volume is located.

39. The method according to Claim 37 or 38, wherein the stimuli is a visual and/or auditory stimuli associated with a specific word or phrase and the quantitative metric relates to the commonality of the word or phrase.

40. The method according to any one of Claims 24-39, wherein the information regarding the action comprises a time the action was performed in relation to the recorded electrical brain activity and/or some quantitative metric of action magnitude or intensity or action completion.

41. The method according to Claim 40, wherein the action depends on the region of the brain in which the first volume is located.

42. The method according to Claim 40 or 41, wherein the action performed by the subject comprises attempting to perform a language task or physical task and the quantitative metric relates to the successful completion of the task.

43. The method according to any one of Claims 24-42, wherein the neuronal input is determined based on the region of the brain in which the first volume is located, the identification of one or more cell populations located within the first volume, and/or the results of one or more xenograft experiments.

44. The method according to Claim 43, wherein the neuronal input is determined to promote tumor growth or progression.

45. The method according to any one of Claims 24-44, wherein the control data comprises data generated by a control volume of a brain, wherein the control volume is located in an equivalent region of the brain as a section of the first tumor.

46. The method according to Claim 45, wherein the first tumor section corresponds to the first volume of the brain.

47. The method according to Claim 45 or 46, wherein the control volume is a volume of the subject’s brain contralaterally equivalent to the first tumor section.

48. The method according to Claim 45 or 46, wherein the control volume is a volume of a control subject’s brain.

49. The method according to any one of the preceding claims, wherein obtaining the first tumor feature data comprises one or more of performing a biopsy of the first tumor, performing a laboratory experiment, analyzing images of the first tumor, recording electrical brain activity within a volume of the subject’s brain not infiltrated by or associated with the first tumor, recording electrical brain activity from one or more additional subjects, recording an action performed by the subject, recording a stimuli received by the subject, and procuring data generated from previously performed experiments and/or studies.

50. The method according to Claim 49, wherein the biopsy is performed on a portion of the first volume or on tissue adjacent to the first volume.

51. The method according to Claim 50, wherein the biopsy is a resection of the first tumor.

52. The method according to Claim 51, wherein the tumor feature data comprises the volume of the first tumor in the brain of the subject before and/or after tumor resection.

53. The method according to any one of Claims 50-52, wherein the first measurement electrode is positioned based on tumor feature data obtained from the biopsy.

54. The method according to any one of Claims 50-53, wherein the electrical brain activity measured by the first measurement electrode is recorded before and/or after the tumor biopsy.

55. The method according to any one of Claims 50-54, wherein the laboratory experiment is performed on cells or tissue obtained from the biopsy.

56. The method according to Claim 55, wherein the laboratory experiment comprises performing one or more of transcriptomic profiling or sequencing, genetic profiling or sequencing, microscopy, mass spectrometry, flow cytometry, immunohistochemistry and/or immunofluorescence analysis, neuronal organoid model experiments, and xenograft model experiments.

57. The method according to Claim 55 or 56, wherein a characteristic of a microenvironment of the first tumor is obtained by performing the laboratory experiment.

58. The method according to Claim 57, wherein the characteristic of the microenvironment is determined to be associated with or indicative of a tumor feature of interest by performing a neuronal organoid model experiment or a xenograft model experiment.

59. The method according to Claim 58, wherein the neuronal organoid model experiment and/or xenograft model experiment is performed using cells or tissue obtained from the biopsy.

60. The method according to Claim 59, wherein the neuronal organoid model experiment or xenograft model experiment arc performed using cells or tissue not obtained from the subject comprising or being characterized by the characteristic of the microenvironment.

61. The method according to Claim 59 or 60, wherein the tumor feature comprises a neuronal input of the first tumor.

62. The method according to any one of Claims 49-61, wherein the first tumor is biopsied and/or the tumor images are analyzed in order to determine a subtype or classification of the first tumor.

63. The method according to any one of Claims 49-62, wherein the non-tumor associated electrical brain activity and/or the additional subject electrical brain activity is recorded in order to function as a control.

64. The method according to any one of Claims 49-62, wherein the recorded non-tumor associated electrical brain activity and/or the recorded additional subject electrical brain activity is recorded from the control volume of the brain according to any one of Claims 45-

65. The method according to Claim 63 or 64, wherein the subject receives a stimuli or performs an action associated with the region of the brain in which the first volume is located while the electrical brain activity measured by the first measurement electrode is recorded.

66. The method according to Claim 65, wherein the non-tumor associated electrical brain activity and/or the additional subject electrical brain activity is recorded while the subject and/or the additional subject receives the stimuli or performs the action.

67. The method according to Claim 65 or 66, wherein the received stimuli or performed action is selected based on the non-invasive mapping.

68. The method according to any one of Claims 49-67, wherein the procured previously generated data is used as a control.

69. The method according to any one of Claims 49-68, wherein the procured previously generated data is used to determine that the obtained tumor feature data is associated with or indicative of a tumor feature of interest.

70. The method according to any one of the preceding claims, wherein the first measurement electrode is positioned based on the obtained tumor feature data.

71. The method according to any one of the preceding claims, wherein the first measurement electrode is positioned at a location inside of the first volume of the brain.

72. The method according to any one of Claims 1-70, wherein the first measurement electrode is positioned at a location functionally connected with or immediately adjacent to the first volume of the brain.

73. The method according to Claim 71 or 72, wherein the measurements of the first measurement electrode arc recorded while the subject receives a stimuli or performs an action.

74. The method according to any one of Claims 71-73, wherein one or more additional measurement electrodes configured to measure electrical brain activity are positioned at a location inside of, functionally connected with, and/or immediately adjacent to the first volume of the brain.

75. The method according to Claim 74, wherein the one or more additional measurement electrodes are positioned based on the non-invasive mapping and/or the obtained tumor feature data.

76. The method according to Claim 74 or 75, wherein the method further comprises recording the electrical brain activity measured by the one or more additional measurement electrodes.

77. The method according to any one of Claims 74-76, wherein the measurements of the first measurement electrode are recorded based on measurements generated by the one or more additional measurement electrodes.

78. The method according to Claim 77, wherein the method further comprises: monitoring the measurements generated by the one or more additional measurement electrodes for a specific pattern or event of electrical brain activity; and recording the measurements of the first measurement electrode when the specific pattern or event of electrical brain activity is detected.

79. The method according to Claim 76, wherein the measurements of the one or more of additional measurement electrodes are recorded based on measurements generated by the first measurement electrode.

80. The method according to any one of the preceding claims, wherein the identified electrical biomarkcr occurs during a resting state.

81. The method according to any one of the preceding claims, wherein the identified electrical biomarker occurs during the performance of a specific task or reception of a specific stimuli.

82. The method according to any one of the preceding claims, wherein the identified electrical biomarker can be detected using a single measurement electrode.

83. The method according to any one of the preceding claims, wherein the identified electrical biomarker can be detected using two or more measurement electrodes.

84. The method according to any one of the Claims 1-83, wherein the identified electrical biomarker is identified based on a relationship between the measurements generated by two or more measurement electrodes positioned at two or more locations of the brain.

85. The method according to any one of the preceding claims, wherein the identified electrical biomarker relates to hyperexcitability.

86. The method according to Claim 85, wherein the electrical biomarker comprises the power of the electrical brain activity at one or more specific frequency bands.

87. The method according to Claim 86, wherein the one or more specific frequency bands comprise the frequency band ranging from 70 hertz (Hz) to 110 Hz.

88. The method according to Claim 86 or 87, wherein the electrical biomarker comprises a signature of relative power across a plurality of frequency bands.

89. The method according to any one of the preceding claims, wherein the identified electrical biomarkcr relates to the recruitment of the first volume of the brain into a specific neural circuit.

90. The method according to Claim 89, wherein the tumor feature data comprises control data and at least one of a subtype or classification of the first tumor and a characteristic of the microenvironment of the first volume of the brain.

91. The method according to Claim 90, wherein the control data comprises information regarding the involvement of a control volume of a brain in the specific neural circuit.

92. The method according to Claim 91, wherein the control volume comprises a subtype or classification of tumor different from the first tumor or a microenvironment characteristic different from the first volume of the brain.

93. The method according to any one of Claims 89-92, wherein the specific neural circuit relates to the performance of a specific task or reception of a specific stimuli by the subject.

94. The method according to any one of the preceding claims, wherein the identified electrical biomarker relates to a dynamic of a specific neural circuit or neural network involving the first volume of the brain.

95. The method according to Claim 94, wherein the dynamic is related to the effect of the first tumor on cognition.

96. The method according to Claim 95, wherein the dynamic is the decodability of the electrical brain activity of the specific neural circuit or neural network.

97. The method according to Claim 94, wherein the dynamic is hyperexcitability associated with the specific neural circuit or neural network.

98. The method according to any one of the preceding claims, wherein the identified electrical biomarkcr relates to a subtype or classification of tumor and/or a characteristic of a tumor microenvironment.

99. The method according to any one of the preceding claims, wherein the tumor feature data is obtained at two or more different timepoints.

100. The method according to Claim 99, wherein the two or more different timepoints are at least a week apart.

101. The method according to Claim 100, wherein the two or more different timepoints are at least a month apart.

102. The method according to any one of Claims 99-101, wherein electrical brain activity measured by the first electrode is recorded for each timepoint.

103. The method according to Claim 102, wherein electrical brain activity is recorded from each measurement electrode positioned at a location associated with or inside of the first volume of the brain for each timepoint.

104. The method according to Claim 102 or 103, wherein one or more distinct recordings are generated for each timepoint.

105. The method according to Claims 104, wherein the method further comprises: continuously monitoring the electrical brain activity measurements from each measurement electrode positioned at a location associated with or inside of the first volume of the brain during the time period from the first timepoint to the final timepoint for a specific pattern or event of electrical brain activity; and recording the measurements from one or more of the measurement electrodes when the specific pattern or event of electrical brain activity is detected.

106. The method according to Claim 102 or 103, wherein the electrical brain activity measurements arc continuously recorded during the time period from the first timepoint to the final timepoint.

107. The method according to any one of Claims 102-106, wherein the tumor feature data is associated with or indicative of tumor growth, tumor progression, and/or treatment response.

108. The method according to Claim 107, wherein the tumor feature data comprises the total volume of the first tumor and/or the distribution of the first tumor within the brain at the two or more different timepoints.

109. The method according to Claim 108, wherein an electrical biomarker of tumor growth is identified using the tumor feature data and the recorded electrical brain activity measurements.

110. The method according to any one of Claims 107-109, wherein the tumor feature data comprises a subtype or classification of the first tumor and/or data associated with or indicative of the connectivity of the first tumor at the two or more different timepoints.

111. The method according to Claim 110, wherein an electrical biomarker of tumor progression is identified using the tumor feature data and the recorded electrical brain activity measurements.

112. The method according to any one of Claims 107-111, wherein the method further comprises treating the first tumor in the subject.

113. The method according to Claim 112, wherein the treatment comprises surgery, radiotherapy, and/or chemotherapy.

114. The method according to Claim 113, wherein the treatment comprises tumor resection followed by chemoradiation.

115. The method according to any one of Claims 112-114, wherein the treatment occurs during a time period in between the first timepoint and the final timepoint.

116. The method according to Claim 115, wherein the treatment begins after the first timepoint.

117. The method according to Claim 115 or 116, wherein an electrical biomarker of treatment response is identified using the tumor feature data and the recorded electrical brain activity measurements.

118. The method according to any one of the preceding claims, wherein the method further comprises preprocessing the recorded electrical brain activity measurements.

119. The method according to Claim 118, wherein the preprocessing comprises reducing or filtering the recorded electrical brain activity measurements.

120. The method according to Claim 119, wherein the preprocessing comprises filtering the recorded electrical brain activity measurements using a high-pass, band pass, and/or notch filter.

121. The method according to any one of Claims 118-120, wherein the preprocessing comprises extracting signal features from the recorded electrical brain activity measurements.

122. The method according to any one of Claims 118-121, wherein the preprocessing comprises transforming the recorded electrical brain activity measurements.

123. The method according to any one of the preceding claims, wherein the electrical biomarker is identified using a statistical model and/or a machine learning model.

124. The method according to Claim 123, wherein the electrical biomarkcr is identified using a mixed effects regression model.

125. The method according to Claim 123 or 124, wherein the electrical biomarker is identified using recursive partitioning.

126. The method according to any one of Claims 123-125, wherein the electrical biomarker is identified using principal component analysis (PCA).

127. The method according to any one of the preceding claims, wherein the method further comprises determining if the identified electrical biomarker meets a predetermined threshold of statistical significance.

128. A method of identifying electrical biomarkers of tumor features in the brain of a subject, the method comprising: obtaining tumor feature data for one or more tumor features of interest and one or more recordings of electrical brain activity measurements for each of a plurality of additional subjects according to the method of any one of Claims 1-127; processing the obtained tumor feature data and electrical brain activity recordings such that the processed tumor feature data and electrical brain activity recordings are readily comparable across subjects; and identifying an electrical biomarker of a tumor feature from the obtained tumor feature data and the recorded electrical brain activity.

129. The method according to Claim 128, wherein the electrical biomarker is identified using a statistical model and/or a machine learning model.

130. The method according to Claim 129, wherein the electrical biomarker is identified using a machine learning model and the method further comprises: training a machine learning model to identify an electrical biomarker of a tumor feature from a first subset of the obtained tumor feature data and electrical brain activity recordings; and applying the trained machine learning model to a second subset of the electrical brain activity recordings to detect the electrical biomarker of the tumor feature in the second subset of recordings.

131. The method according to Claims 130, wherein the obtained tumor feature data and electrical brain activity recordings are saved to a database.

132. The method according to Claim 131, wherein the trained machine learning model is continuously updated based on the tumor feature data and electrical brain activity recordings saved to the database.

133. The method according to any one of the preceding claims, wherein the method further comprises monitoring the tumor in a subject using the identified electrical biomarker.

134. The method according to Claim 133, wherein the monitored subject is the subject having the first tumor or a different subject having a second tumor capable of being monitored using the identified electrical biomarker.

135. The method according to Claim 133 or 134, wherein the tumor feature data is associated with or indicative of tumor growth, tumor progression, and/or treatment response.

136. The method according to Claim 135, wherein the method further comprises identifying exacerbating factors of tumor growth or progression based on the electrical biomarker monitoring.

137. The method according to Claim 135 or 136, wherein the method further comprises altering the monitored subject’s treatment plan based on the electrical biomarkcr monitoring.

138. The method according to any one of Claims 133- 137, wherein the monitoring is ambulatory monitoring.

139. The method according to any one of Claims 133-138, wherein the method further comprises developing a therapeutic neuromodulatory treatment effective in treating the tumor in the monitored subject using the identified electrical biomarker.

140. The method according to Claim 139, wherein the identified electrical biomarker is associated with or indicative of neuronal activity that promotes tumor growth or tumor progression.

141. The method according to Claim 140, wherein the identified electrical biomarker is associated with or indicative of hyperexcitability.

142. The method according to Claim 141, wherein the therapeutic neuromodulatory treatment is adjusted to reduce the tumor promoting neuronal activity using the electrical biomarker.

143. The method according to Claim 142, wherein the identified electrical biomarker is associated with or indicative of the present severity of the tumor.

144. The method according to Claim 143, wherein the therapeutic neuromodulatory treatment is adjusted based on changes in the severity of the tumor determined by monitoring the electrical biomarker over a period of time.

145. The method according to Claim 144, wherein the period of time is at least a month.

146. The method according to any one of Claims 139-145, wherein the therapeutic neuromodulatory treatment comprises delivering electrical stimulation to a location associated with or infiltrated by the tumor.

147. The method according to Claim 146, wherein the electrical stimulation is applied to depolarizing tumor cells, neurons located within a tumor infiltrated region of the monitored subject’s brain, or neurons projecting into a tumor infiltrated region of the monitored subject’s brain.

148. The method according to Claim 146 or 147, wherein the applied electrical stimulation is sufficient to prevent or inhibit tumor cells from repolarizing.

149. The method according to Claim 148, wherein the location the electrical stimulation is applied is determined using non-invasive mapping and/or by performing an organoid or xenograft experiment.

150. The method according to Claim 148, wherein one or more characteristics of the electrical stimulation are determined by performing an organoid or xenograft experiment.

151. The method according to Claim 150, wherein the one or more characteristics of the electrical stimulation comprise the frequency of the electrical stimulation.

152. The method according to any one of Claims 149-151, wherein the organoid or xenograft experiment is performed using cells derived from the monitored subject or another individual having a tumor with one or more of the same tumor features as the tumor of the monitored subject.

153. The method according to any one of Claims 139-152, wherein the therapeutic neuromodulatory treatment comprises delivering a neuromodulatory drug to a section of the tumor.

154. The method according to Claim 153, wherein the section of the tumor comprises a HFC volume of the tumor.

1 5. The method according to Claim 148, wherein the location the neuromodulatory drug is applied and/or the active pharmaceutical ingredient of the neuromodulatory drug is determined using non-invasive mapping and/or by performing an organoid or xenograft experiment.

156. The method according to Claim 155, wherein the organoid or xenograft experiment is performed using cells derived from the monitored subject or another individual having a tumor with one or more of the same tumor features as the tumor of the monitored subject.

157. The method according to any one of Claims 139-156, wherein the therapeutic neuromodulatory treatment comprises delivering low-intensity focused ultrasound (LIFU) to a tumor infiltrated region of the monitored subject’s brain.

158. The method according to any one of Claims 146-157, wherein the method further comprises administrating the developed therapeutic neuromodulatory treatment to a tumor in the brain of a subject using the identified electrical biomarker, wherein the treated subject is different from the monitored subject and the subject having the first tumor.

159. The method according to Claim 158, wherein the method further comprises determining that the treated tumor has one or more specific tumor features associated with the therapeutic neuromodulatory treatment.

160. The method according to Claim 159, wherein the one or more therapeutic neuromodulatory treatment specific tumor features are determined by performing non- invasive mapping and/or a biopsy.

161. The method according to Claim 159 or 160, wherein the one or more therapeutic neuromodulatory treatment specific tumor features are determined by detecting an electrical biomarker of the tumor features.

162. The method according to Claim 158, wherein the identified electrical biomarker is associated with or indicative of neuronal activity that promotes tumor growth or tumor progression.

163. The method according to Claim 162, wherein the amplitude of a delivered therapeutic electrical stimulation is adjusted based on the presence of the identified electrical biomarker.

164. The method according to Claim 163, wherein the amplitude of the delivered electrical stimulation is adjusted based on a quantitative metric of the magnitude or intensity of the identified electrical biomarker.

165. A system configured to perform the method according to any one of Claims 1-164.

166. A method for treating a brain tumor in a subject, the method comprising: positioning a stimulation electrode at a first location of the brain of the subject associated with or infiltrated by the tumor; and applying electrical stimulation to the first location via the stimulation electrode in a manner effective to treat the tumor in the subject.

167. The method according to Claim 166, wherein the stimulation electrode is in contact with and applies electrical stimulation to depolarizing tumor cells, neurons located within a tumor infiltrated region of the subject’s brain, and/or neurons projecting into a tumor infiltrated region of the subject’s brain.

168. The method according to Claim 166 or 167, wherein the applied electrical stimulation is sufficient to prevent or inhibit tumor cells from repolarizing.

169. The method according to Claim 168, wherein the location the stimulation electrode is positioned is determined using non-invasive mapping and/or by performing an organoid or xenograft experiment.

170. The method according to Claim 169, wherein the non-invasive mapping comprises determining the functional connectivity of one or more sections of the tumor with the rest of the brain.

171. The method according to Claim 170, wherein the determined functional connectivity is used to identify high functional connectivity (HFC) and/or low functional connectivity (LFC) sections of the tumor.

172. The method according to Claim 168, wherein one or more characteristics of the electrical stimulation are determined by performing an organoid or xenograft experiment.

173. The method according to Claim 172, wherein the one or more characteristics of the electrical stimulation comprise the frequency of the electrical stimulation.

174. The method according to any one of Claims 169-173, wherein the organoid or xenograft experiment is performed using cells derived from the subject.

175. The method according to any one of Claims 166-174, wherein the method further comprises obtaining data associated with or indicative of one or more features of interest for the tumor.

176. The method according to Claim 175, wherein the stimulation electrode is positioned and/or the electrical stimulation is applied based on the obtained tumor feature data.

177. The method according to Claim 176, wherein the tumor feature data is obtained by performing a biopsy.

178. The method according to any one of Claims 166-177, wherein the method further comprises: positioning a measurement electrode configured to measure electrical brain activity at a second location of the brain of the subject associated with or infiltrated by the tumor; detecting one or more electrical biomarkers identified according to any one of Claims 1-164 via the measurement electrode; and applying the electrical stimulation based on the one or more detected electrical biomarkers.

179. The method according to Claim 178, wherein one or more of the detected electrical biomarkers are associated with or indicative of neuronal activity that promotes tumor growth or tumor progression.

180. The method according to Claim 179, wherein the amplitude of the applied electrical stimulation is adjusted based on the one or more detected electrical biomarkers.

181. The method according to Claim 180, wherein the amplitude of the applied electrical stimulation is adjusted based on a quantitative metric of the magnitude or intensity of the one or more detected electrical biomarkers.

182. The method according to any one of Claims 178-181, wherein the electrical stimulation is applied at least two times, wherein the electrical stimulation is spatially and/or temporally different based on the one or more detected electrical biomarkers.

183. The method according any one of Claims 178-182, wherein the method further comprises positioning one or more additional measurement electrodes and one or more additional stimulation electrodes.

184. The method according to Claim 183, wherein the additional measurement and stimulation electrodes are positioned based on an assessment of brain locations the tumor cells are likely to invade.

185. The method according to Claim 184, wherein the additional measurement electrodes are used to monitor tumor growth and the additional stimulation electrodes are used to slow tumor growth.

186. The method of any one of Claims 166-185, wherein the method further comprises assessing effectiveness of the treatment in the subject.

187. The method according to Claim 186, wherein an assessment of treatment efficacy is generated using the measurements of each measurement electrode.

189. The system according to Claim 188, wherein the system further comprises: a measurement electrode adapted for positioning at a second location of the brain of the subject associated with or infiltrated by the tumor, wherein the measurement electrode is configured to record an electrical signal from the second location, wherein the processor is programmed to: receive the electrical signal from the second location of the brain of the subject via the measurement electrode; detect one or more electrical biomarkers identified according to any one of Claims 1-164 from the electrical signal; modulate one or more programmed electrical stimulation parameters based on the one or more detected electrical biomarkers; and apply the modulated electrical stimulation to the first location via the stimulation electrode in a manner effective to treat the tumor.

190. A closed-loop method for treating a brain tumor in a subject, the method comprising: positioning an adjustable neuromodulating device at a first location of the brain of the subject associated with or infiltrated by the tumor, wherein the adjustable neuromodulating device is configured to modulate neuronal activity at the first location; positioning a measurement electrode configured to measure electrical brain activity at a second location of the brain of the subject associated with or infiltrated by the tumor; detecting one or more electrical biomarkers identified according to any one of Claims 1-164 via the measurement electrode; determining one or more parameters of the adjustable neuromodulating device based on the one or more detected electrical biomarkers; and modulating neuronal activity at the first location via the adjustable neuromodulating device in a manner effective to treat the tumor in the subject.

191. The closed-loop method of Claim 190, wherein the adjustable neuromodulating device comprises a stimulation electrode configured to apply electrical stimulation to the first location.

192. The closed-loop method of Claim 191, wherein the stimulation electrode is in contact with and applies electrical stimulation to depolarizing tumor cells, neurons located within a tumor infiltrated region of the subject’s brain, and/or neurons projecting into a tumor infiltrated region of the subject’s brain.

193. The closed- loop method of Claim 192, wherein the applied electrical stimulation is sufficient to prevent or inhibit tumor cells from repolarizing.

194. The closed- loop method of any one of Claims 190-193, wherein the method further comprises obtaining data associated with or indicative of one or more features of interest for the tumor.

195. The closed-loop method of Claim 194, wherein the tumor feature data is obtained by performing a biopsy.

196. The closed-loop method of Claim 194 or 195, wherein the stimulation electrode is positioned and/or the electrical stimulation is applied based on the tumor feature data.

197. The closed-loop method of any one of Claims 194- 196, wherein the adjustable ncuromodulating device comprises an implantable drug delivery device configured to deliver one or more doses of a neuromodulatory drug.

198. The closed-loop method of Claim 197, wherein the active pharmaceutical ingredient of the neuromodulatory drug is determined using the tumor feature data.

199. The closed-loop method of Claim 197 or 198, wherein the size of the delivered drug dose is determined based on the or more detected electrical biomarkers.

200. The closed- loop method of any one of Claims 190-199, wherein low-intensity focused ultrasound (LIFU) is delivered to a tumor infiltrated region of the brain based on the one or more detected electrical biomarkers.

201. The closed-loop method of any one of Claims 190-200, wherein the method further comprises assessing effectiveness of the treatment in the subject.

202. The closed-loop method of Claim 201, wherein an assessment of treatment efficacy is generated based on the one or more detected electrical biomarkers.

203. A closed-loop system for treating a brain tumor in a subject, the system comprising: an adjustable neuromodulating device adapted for positioning at a first location of the brain of the subject associated with or infiltrated by the tumor, wherein the adjustable neuromodulating device is configured to modulate neuronal activity; and a measurement electrode adapted for positioning at a second location of the brain of the subject associated with or infiltrated by the tumor, wherein the measurement electrode is configured to record an electrical signal from the second location, wherein the processor is programmed to: receive the electrical signal from the second location of the brain of the subject via the measurement electrode; detect one or more electrical biomarkers identified according to any one of Claims 1-164 from the electrical signal; adjust one or more programmed neuromodulation parameters based on the one or more detected electrical biomarkers; and modulate the neuronal activity of the first location via the adjustable neuromodulating device in a manner effective to treat the tumor.

204. The closed-loop system of Claim 203, wherein the adjustable neuromodulating device comprises a stimulation electrode, wherein the one or more adjusted neuromodulation parameters comprises the amplitude and/or frequency of the electrical stimulation applied by the stimulation electrode.

205. The closed- loop system of Claim 203 or 204, wherein the adjustable neuromodulating device comprises an implantable drug delivery device configured to deliver one or more doses of a neuromodulatory drug, wherein the one or more adjusted neuromodulation parameters comprises the size of the neuromodulatory drug dose delivered by the implantable drug delivery device.

206. The closed-loop system of any one of Claims 203-205, wherein the system further comprises a low-intensity focused ultrasound (LIFU) emitting device.

207. The closed- loop system of Claim 206, wherein LIFU emitted by the LIFU emitting device is adjusted based on the one or more detected electrical biomarkers.