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WO2025137394A1 - Utilisation de vésicules extracellulaires pour évaluer l'efficacité de traitement de thérapies conjuguées anticorps-médicament (adc) - Google Patents

Utilisation de vésicules extracellulaires pour évaluer l'efficacité de traitement de thérapies conjuguées anticorps-médicament (adc) Download PDF

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WO2025137394A1
WO2025137394A1 PCT/US2024/061184 US2024061184W WO2025137394A1 WO 2025137394 A1 WO2025137394 A1 WO 2025137394A1 US 2024061184 W US2024061184 W US 2024061184W WO 2025137394 A1 WO2025137394 A1 WO 2025137394A1
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tumor
evs
adc
resistance
time point
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Jihye HONG
Cesar M. CASTRO
Hyungsoon Im
Ursula A. WINTER
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General Hospital Corp
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G01N33/5008—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics
    • G01N33/5076—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics involving cell organelles, e.g. Golgi complex, endoplasmic reticulum
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G01N33/5008—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics
    • G01N33/5011—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics for testing antineoplastic activity
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
    • G01N33/57488—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites involving compounds identifable in body fluids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/705—Assays involving receptors, cell surface antigens or cell surface determinants
    • G01N2333/71—Assays involving receptors, cell surface antigens or cell surface determinants for growth factors; for growth regulators
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis

Definitions

  • the present disclosure provides methods of monitoring a tumor’s resistance to an antibody-drug-conjugate (ADC), e.g., administered to a subject, to treat the tumor, the methods including (a) obtaining a sample from the subj ect at a first time point; (b) isolating and quantifying extracellular vesicles (EVs) from the sample based on detection of EV biomarkers on the EVs in the sample; and (c) determining a presence or level of a tumor drug-resistance biomarker of the EVs isolated at the first time point, wherein a presence or level of the tumor drug-resistance biomarker indicates a resistance by the tumor to the ADC being administered to the patient.
  • ADC antibody-drug-conjugate
  • a presence of the tumor drug-resistance biomarker at the second time point, but not the first time point indicates that the tumor has developed a resistance to the ADC over time.
  • no presence of the ADC target antigen at the second time point, but a presence at the first time point indicates that the tumor lost the target antigen for the ADC and thus indicates a low efficacy for the ADC over time, because one of the main resistant mechanisms is the reduction or loss of ADC target antigens (e.g., HER2, MUC16, FRa) over time caused by the tumor.
  • ADC target antigens e.g., HER2, MUC16, FRa
  • the sample comprises a bodily fluid, such as blood or plasma, or the sample can include subject-derived organoids.
  • the EVs or tEVs are captured on a nanosensor chip for processing.
  • the EVs or tEVs are labeled, e.g., with a reporter group, such as a fluorescent reporter group, that are bound to antibodies that bind specifically to one or more tumor drug-resistance biomarkers or ADC target antigens.
  • Antibodies that bind specifically to tumor drug-resistance biomarkers or ADC target antigens tend not to bind, or to bind at a much lower level, to other proteins or other antigens that are not tumor drug-resistance biomarkers or ADV target antigens.
  • a change in one or more tumor drug-resistance biomarkers or ADC target antigens can be tracked over the course of a portion or all of the subject’s treatment.
  • the ADC is selected from the group consisting of Inotuzumab ozogamicin.
  • Gemtuzumab ozogamicin Trastuzumab ematansine, Bentuximab vedotin, Polatuzumab vendotin, Belantamab mafodotin, Trastuzumab deruxtean, Moxetumomab pasudotox, and Loncastuximab tesirine for hematological malignancies, and Ado-trastuzumab emtansine, Enfortumab vedotin, Fam-trastuzumab deruxtecan. Sacituzumab govitecan. Cetuximab sarotalocan, Disitamab vedotin and Tisotumab vedotin for solid tumors.
  • an additional anti -cancer drug e.g., a chemotherapeutic drug is added to the sample or is administered to the subject before a sample is obtained from the subject.
  • the ADC target antigens include any one or more of HER2.
  • MUC16 (4H11). and FOLR1, and/or the drug-resistance biomarkers include survivin and/or permeability glycoprotein 1 (PgP).
  • the methods described herein include monitoring a patient’s response to cancer drug administration, the method including (a) isolating tEVs from a patient blood sample, and (b) determining whether the tEVs exhibit one or more biomarkers that indicate resistance to the cancer drug being administered to the patient.
  • the patient’s blood sample is taken before treatment, during treatment, and after treatment and the determination whether the tEVs exhibit one or more biomarkers indicating cancer drug resistance is done for each sample taken to investigate whether the isolated tEVs exhibit differential levels in the drug-resistance tumor cells.
  • the tEVs are isolated by capture on a nanosensor chip and are labeled with antibodies that bind specifically to target drug-resistance markers.
  • the tEVs can be isolated from in vitro models and from human samples and the samples compared and changes in the markers can be tracked over the course of the patient’s treatment.
  • the cancer drug being monitored during treatment can be an ADC, for example, Inotuzumab ozogamicin, Gemtuzumab ozogamicin, Trastuzumab ematansine, Bentuximab vedotin, Polatuzumab vendotin, Belantamab mafodotin.
  • trasstuzumab deruxtean Trastuzumab deruxtean, Moxetumomab pasudotox, and Loncastuximab tesirine for hematological malignancies and Ado-trastuzumab emtansine, Enfortumab vedotin, Famtrastuzumab deruxtecan, Sacituzumab govitecan, Cetuximab sarotalocan, Disitamab vedotin and Tisotumab vedotin for solid tumors.
  • tEVs are suitable targets for treatment monitoring. TEVs carry biomolecules that reflect the molecular status of their originating tumors, and biomarkers in tEVs show how cells change upon drug treatment. Compared to rare circulating tumor cells (CTC), tEVs are much more abundant in circulation and address the heterogeneity of tumors from which CTC analysis suffers.
  • CTC rare circulating tumor cells
  • tEVs Unlike soluble biomarkers (DNA, RNA, proteins), tEVs carry surface proteins representing their cellular origins. This allows us to differentially evaluate changes in the specific tumor drug-resistance biomarkers from tEVs and non- tEVs. Better understanding EV proteome changes to ADC response will revolutionize our understanding of resistance and capacity to detect it clinically. While focusing on ADCs, the success here will elevate EV assays for use as an omics-like tool and potential as a liquid biopsy for increased clinical success.
  • FIG. 1 is a schematic representation illustrating the experimental scheme of FLEX-based ADC -resistance monitoring platform and the characterization of FLEX substrate.
  • Extracellular vesicles are lipid-based microparticles, nanoparticle, or protein-rich aggregates present in a sample (e.g., a biological fluid) obtained from a subject.
  • EVs also include membrane vesicles secreted from cell surfaces (ectosomes), internal stores (exosomes), cancer cells (oncosomes), or released as a result of apoptosis and cell death.
  • ectosomes membrane vesicles secreted from cell surfaces
  • exosomes internal stores
  • cancer cells oncosomes
  • EVs can include additional components such as lipoproteins, proteins, nucleic acids, phospholipids, amphipathic lipids, gangliosides and other particles contained within the lipid membrane or encapsulated by the EVs.
  • EVs represent new opportunities as circulating cancer biomarkers. These cell- derived membrane-bound vesicles contain protein and nucleic acid cargo, providing a representative “snapshot” of the content of the secreting cells.
  • tEVs tumor-derived EVs
  • bodily fluids e.g., blood, urine
  • tumor-derived EV (tEV) analyses can be minimally invasive for repeated sampling and afford relatively unbiased readouts of the entire tumor, less affected by the scarcity of the samples or intratumoral heterogeneity 7 . This suggests that the methods described herein have particular utility' for longitudinal disease monitoring and early detection of relapse.
  • EVs can function as a novel biomarker for liquid biopsy in personalized medicine.
  • EVs are relatively new targets for analytical assays in clinics and possess unique physical and biological traits. They fall in size range much smaller than cells, but larger than proteins, and exist in a highly heterogeneous biological background. These properties impose technical difficulties, which often lead to variable findings.
  • identifying cell-specific (e.g., tumor origins) EVs and interrogating drug-resistance markers within the subpopulation require multiplexed analysis, ideally in a single EV resolution.
  • the present disclosure provides methods of isolating and enriching tumor EV particles, for use in monitoring and/or evaluating whether tumor cells in a subject have become resistant to ADCs over time.
  • ADC resistance can arise from altered protein recruitment and trafficking patterns. Analysis of changes in the extracellular vesicle proteome upon resistance identifies proteins that have altered distribution. We have found specific proteins in the EV proteome that play critical roles in ADC resistance to enable a better understanding of what drives resistance. Furthermore, EV analysis serves as a translatable, liquid biopsy tool to determine resistance in ovarian cancer patients.
  • ADCs such as Inotuzumab ozogamicin, Gemtuzumab ozogamicin, Trastuzumab ematansine, Bentuximab vedotin, Polatuzumab vendotin, Belantamab mafodotin.
  • EVs are nano-sized, membrane-enclosed vesicles actively shed by cells. EVs carry a set of biomolecules (e.g., transmembrane and intracellular proteins, RNAs) from their originating cells, which can serve as cellular surrogates (see, e.g., Im et al., Nat Biotechnol. 2014; 32(5) 490-495. doi: 10.1038/nbt.2886; Ramirez-Garrastacho et al., Br J Cancer. 2022; 126(3) 331-350. dor 10. 1038/s41416-021-01610-8; Shao et al., Nat Med. 2012; 18(12) 1835-1840.
  • biomolecules e.g., transmembrane and intracellular proteins, RNAs
  • EVs are secreted by tumor cells at higher rates than normal cells and can be identified in the blood of patients with cancer, e.g., ovarian cancer (Jo et al., Adv Sci (Weinh). 2023; e2301930. doi: 10.1002/advs.202301930; Yokoi et al., Sci Adv. 2023; 9(27) eade6958. doi: 10.1126/sciadv.ade6958; Zhang et al., Nat Biomed Eng.
  • tumor-derived EV tEV
  • analyses can be minimally invasive for repeated sampling and afford relatively unbiased readouts of the entire tumor, less affected by the scarcity of the samples or intratumoral heterogeneity. This suggests that tumor-derived EVs (tEVs) have particular utility for longitudinal disease monitoring and early detection of relapse (Lane et al.. Clin Transl Med. 2018; 7(1) 14. doi: 10.1186/s40169-018-0192-7).
  • tEVs tEVs carry protein markers of ADC resistance.
  • the tumor drugresistance biomarkers for ADCs include HER2, MUC16 (4H11), and FOLR1, which represent some of the ADC target antigens currently under investigation in clinical trials.
  • Other tumor drug-resistance biomarkers include PgP and survivin.
  • EVs are small vesicles ( ⁇ 200 nm) with limited numbers of epitopes and surface areas for labeling (i.e., weak detectable signals), which often requires sophisticated multi-step signal amplification strategies, such as DNA barcodes or enzymatic signal amplification (digital ELISA).
  • Flow cytometry often underestimates EV counts because many small vesicles could be missed due to their weak light scattering, or a swarm of vesicles could be counted as a single event. More importantly, many of these methods are suboptimal in detecting and quantifying very rare tumor-derived EVs in excessive background EVs and particles.
  • Table 1 Various single EV methods are summarized in Table 1 below.
  • the present methods include isolating particular EV populations (e.g., tumor- derived EVs (tEVs), e.g., ovarian cancer-derived tEVs) in a subject being treated for cancer and measuring the tEVs expression of tumor drug-resistance biomarkers over time and further administering ADC therapy informed by the relative levels of tumor drugresistance biomarker-positive (e.g., HER2, FOLR-1, MUC16 (4H11). PgP, and survivin) tEVs over time.
  • tEVs tumor- derived EVs
  • ovarian cancer-derived tEVs e.g., ovarian cancer-derived tEVs
  • a subject is an individual (e.g., a mammal such as a human) having or suspected of having cancer, e.g., a patient diagnosed with cancer.
  • the subject can be receiving ADC therapy, and/or another type of cancer treatment (e.g., radiation, surgery).
  • a sample is typically obtained from the subject, or the sample can be from a cell culture, e.g., containing cells from a subject.
  • a sample can include, but is not limited to, cells, lysed cells, cellular extracts, nuclear extracts, extracellular fluid, media in which cells (e.g., cancer cells from the subject) are cultured, blood, plasma, serum, gastrointestinal secretions, homogenates of tissues or tumors, ascites, synovial fluid, feces, saliva, sputum, cyst fluid, amniotic fluid, cerebrospinal fluid, peritoneal fluid, lung lavage fluid, semen, lymphatic fluid, tears, and prostatic fluid.
  • the sample is obtained from a subject at multiple time points, e.g., at least two time points.
  • a single sample can be compared to a reference, e.g., a similar sample from an individual known to be healthy and/or cancer-free or to have cancer with a good response to the ADC.
  • An EV tumor biomarker profile can indicate the origin of a cancer or the type of cancer cells found in a sample from a subject.
  • MUC1. HER2. EGFR. and EpCAM are four biomarkers that can be used to identify breast cancer cells in a subject. Many EVs secreted by these breast cancer cells also contain these four tumor markers. Therefore, monitoring EVs that comprise one or more of MUC1, HER2, EGFR, and EpCAM, e.g., tEVs, in a subject can give information regarding a subject s breast cancer, including development of drug resistance.
  • a sample from a subject e.g., a sample comprising a population of EVs (optionally EVs obtained from a biofluid such as blood, serum, or plasma) can then be applied to the substrate, wherein the antibodies or antigen binding portions thereof that bind to the selected EV tumor biomarkers capture and enrich the EV population for tEVs having the specified EV tumor biomarkers.
  • a sample comprising a population of EVs e.g., EVs obtained from a biofluid such as blood, serum, or plasma
  • the antibodies or antigen binding portions thereof that bind to the selected EV tumor biomarkers capture and enrich the EV population for tEVs having the specified EV tumor biomarkers.
  • enriching EVs e.g., tEVs
  • probing the tEVs for levels of tumor drug-resistance biomarker(s) can be carried out at multiple time points (e.g., over time or longitudinally).
  • enriching EVs e.g., tEVs
  • probing the tEVs for levels of drug-resistance biomarkers can be carried out at one, two, three, four, five, or more time points.
  • the level (as determined by antibody detection) of tumor drug-resistance biomarker(s) at a first time point can be used to determine the relative level of the tumor drug-resistance biomarker(s) at a second time point by comparing the level of tumor drug-resistance biomarker(s) signal at the second time point to the tumor drug-resistance biomarker(s) signal at the first time point and noting an increase or decrease of the level of the tumor drug-resistance biomarker(s) signal.
  • the level of the tumor drug-resistance biomarker(s) signal at a third (or fourth, or fifth, etc.) time point can be compared to the level of drug-resistance biomarker(s) signal at the first time point, or the level of the drug-resistance biomarker(s) signal at the third (or fourth, or fifth, etc.) time point can be compared to the level of drug-resistance biomarker(s) signal at any previous time point to analyze whether there are any trends in drug-resistance biomarker(s) signal over time.
  • PgP, and/or survivin-positive tEVs indicates cancer cells in the subject are in the process of becoming, or have become, resistant to ADCs, for example the ADC used to treat the subject’s cancer.
  • a relative decrease or no significant change in levels of drug-resistance biomarker(s)-positive indicates cancer cells in the subject have not become resistant to the ADC (e.g., used to treat the subject’s cancer).
  • the relative levels of tumor drug-resistance biomarker(s) and biomarker(s)- positive tEVs thus can be used to determine whether the subject receives additional ADC treatments comprising the same drug used to treat the subject’s cancer between the previously evaluated time points or receives a different treatment using a different chemotherapeutic agent (or other treatment modality, such as immunotherapy, radiotherapy, or surgical resection).
  • a relative increase in tumor drug-resistance biomarker(s) or biomarker(s)-positive (e.g., HER2, FOLR-1, MUC16 (4H11). PgP, and/or survivin) tEVs over a previous level of drug-resistance biomarker(s)-positive tEVs indicates whether the subject should continue to receive treatment with the same ADC.
  • a relative increase in drug-resistance biomarker(s)-positive tEVs over the previous level of drug-resistance biomarker(s)-positive tEVs indicates the subject should not receive further treatment with the same ADC.
  • a relative increase in drugresistance biomarker(s)-positive EVs over the previous level of drug-resistance biomarker(s)-positive tEVs indicates the subject should not receive treatment with any further ADC.
  • the subject only receives further ADC treatments with the same drug if there is a decrease or no change in the tumor drug-resistance biomarker(s)- positive tEVs over the previous (or any previous) level of drug-resistance biomarker(s)- positive tEVs. In some embodiments, the subject receives further ADC treatments with the same drug only if there is not an increase in drug-resistance biomarker(s)-positive tEVs over the previous (or any previous) level of drug-resistance biomarker(s)-positive tEVs.
  • administering is dependent on the relative level of drug-resistance biomarker(s)-positive tEVs over the previous (or any previous) level of drug-resistance biomarker(s)-positive tEVs.
  • cancer As used herein, the terms “cancer.” “tumor” or “tumor tissue” has the meaning as understood by one skilled in the art.
  • a cancer, tumor, or tumor tissue can include tumor cells that are neoplastic cells with abnormal growth properties. Tumors, tumor tissue, and tumor cells can be benign or malignant. Cancer can include primary malignant cells or tumors (e.g., those whose cells have not migrated to sites in the subject’s body other than the site of the original malignancy or tumor) and secondary malignant cells or tumors (e.g., those arising from metastasis, the migration of malignant cells or tumor cells to secondary sites that are different from the site of the original tumor).
  • primary malignant cells or tumors e.g., those whose cells have not migrated to sites in the subject’s body other than the site of the original malignancy or tumor
  • secondary malignant cells or tumors e.g., those arising from metastasis, the migration of malignant cells or tumor cells to secondary sites that are different from
  • cancer examples include, but are not limited to, carcinoma, lymphoma, blastoma, sarcoma, and leukemia or lymphoid malignancies. Additional examples of such cancers are noted below and include: squamous cell cancer (e.g., epithelial squamous cell cancer), lung cancer including small-cell lung cancer, non-small cell lung cancer, adenocarcinoma of the lung and squamous carcinoma of the lung, cancer of the peritoneum, hepatocellular cancer, gastric or stomach cancer including gastrointestinal cancer, pancreatic cancer, glioblastoma, cervical cancer, ovarian cancer, liver cancer, cholangiocarcinoma, bladder cancer, hepatoma, breast cancer, colon cancer, rectal cancer, colorectal cancer, endometrial cancer or uterine carcinoma, salivary gland carcinoma, kidney or renal cancer, prostate cancer, vulvar cancer, thyroid cancer, hepatic carcinoma, anal carcinoma, penile carcinoma, as well as head and neck cancer.
  • One of the benefits of the longitudinal monitoring of drug-resistance of the currently described methods is that drug-resistance in a subject can be detected prior to an observable increase in size of the subject’s cancer (e.g., tumor). With an early detection of drug-resistance, the longitudinal monitoring of drug-resistance of the currently described methods can terminate the toxic treatment early to reduce side effects or minimize unnecessary' treatment.
  • the efficacy of the longitudinal monitoring of drug-resistance of the currently described methods for predicting drug-resistance can be at least 95%.
  • Other benefits of the longitudinal monitoring of drug-resistance of the currently described methods include predicting drug treatment efficacy, minimizing the detection of residual diseases, and facilitating early detection of disease recurrence. Nanoplasmonic Technology for Single EV Analyses
  • nPLEX nano-plasmonic exosome
  • FLEX fluorescence-amplified extracellular vesicle sensing
  • the signal amplification occurs in multiple colors, enabling multiplexed, multichannel imaging and detection of single EVs.
  • the results indicate that conventional fluorescence imaging using a plain substrate detected only 10-15% of total EVs due to weak fluorescence signals of small EVs; these weak signals get amplified by using nanoplasmonic chips.
  • the assay is simple and compatible with conventional immunostaining and imaging but does not require any additional chemical reactions to achieve enough sensitivity for single EV detection.
  • the plasmon enhancements allow us to use near-infrared fluorophores (e.g.. Cy7) that are barely used for EV imaging due to weak signals. This enabled us to develop multichannel single-EV imaging in a broader spectrum range.
  • ADC resistance models were created, and through whole proteomic and enrichment analysis, specific EV-cargo biomarkers p associated with olaparib response, e.g., resistance, were identified. The expression of these biomarkers at a single EV or tEV level was investigated using nPLEX.
  • OVCA429, and Jurkat cell lines were purchased from American Type Culture Collection (ATCC).
  • ATCC American Type Culture Collection
  • TIOSE6 cell line was obtained by transfecting NOSE cells with hTERT.
  • SKOV3-MUC16 cell line was obtained from Dr. Yeku Oladepo at Mass General Hospital.
  • SKOV3, SKOV3-MUC16, OV90, TIOSE6, OVCA429. and Jurkat cell lines were cultured using RPMI 1640 medium (Gibco) containing 10% (v/v) FBS (Gibco) and 1% (v/v) PS (Gibco).
  • the OVCAR3 cell line was cultured using RPMI 1640 medium with 20% (v/v) FBS, 1% (v/v) PS, and 1.6 ml of human recombinant insulin (Gibco). All cell lines were tested using My co Strip® (Invivogen) and free from mycoplasma contamination.
  • EV isolation cells were plated on a 150 mm cell culture dish and cell culture media was replaced with 50 ml of EV-depleted medium, supplemented with 2% of EV- depleted FBS (Gibco), and cultured for 48 hours.
  • 10 ml of sepharose column was prepared a day before the isolation using CL-4B sepharose resin (Cytiva) for size exclusion chromatography (SEC).
  • the EV-containing medium was passed through a 40 pm cell strainer (Thermo Fisher) and filtered through a 0.8 pm membrane filter unit (Millipore Sigma).
  • EV Capture on FLEX chip To establish the ovarian cancer marker-specific capture setting of EVs, EVs from SKOV3, SKOV3-MUC16. OVCA429, OVCAR3. OV90, Jurkat and TIOSE cell lines were detected for CD24, EpCAM, HE4, TNC, and VCAN expression level by immunofluorescence imaging on the TPFE-printed glass substrate. 20 pl of EV solution was treated on a glass substrate, washed with PBS-T thrice, and blocked with 10% BSA/PBS. Then, the primary antibody was treated with various antibody concentrations, and the secondary antibody was treated.
  • the capture marker antibody concentration was titrated by finding the highest signal-to-noise ratio between the captured EV count of marker-positive EVs and marker-negative EVs on the FLEX chip. Titrated capture antibody concentration was used as ovarian cancer marker capture set in further ovarian cancer EV analysis.
  • the FLEX chip For capturing EVs on the FLEX substrate, the FLEX chip w as cut into 5 mm x 5 mm size, washed with 100% isopropanol and DI water, and incubated in 1 mM Sodium citrate solution in DI water to induce the physisorption of capture antibodies on the gold surface of FLEX substrate. Then, chips were blown with nitrogen gas and incubated with capture antibody (CD24. EpCAM. HE4. TNC, VCAN) for 1 hour. Chips were washed thrice with PBS-T, blocked with 10% BSA/PBS, and fluorescently labeled EVs were treated on the FLEX surface.
  • capture antibody CD24. EpCAM. HE4. TNC, VCAN
  • TFP Fluorescent TFP-stained EVs w ere passed through the Zeba columns (7K molecular weight cutoff; Thermofisher Scientific) twice, following the manufacturer’s protocol. Then, fluorescently labeled EVs were specifically- captured on FLEX chips using an ovarian cancer marker capture set.
  • EVs were stained with AF488. AF555, and AF647 and treated to the plain gold substrate and FLEX substrate. These substrates were treated with sodium citrate solution, treated with EVs, and fixed with 4% PFA/PBS. Then, EV counts from each substrate were compared after imaging.
  • OVCAR3 EVs were attached to an aldehyde/sulfate bead or treated to FLEX chips at various concentrations (from 10 4 EVs to 10 8 EVs in 20 pl). Then, the CD63 expression was detected using the flow Cytometry method or FLEX imaging method. Relative CD63 expression level was calculated by normalization with the highest expression level among several concentrations.
  • Images were analyzed using ImageJ (Fiji) and custom Python code.
  • the ComDet plugin was used in ImageJ to detect EV locations and convert them to intensity profiles with x and y locations.
  • averaged fluorescence intensities were calculated from a 3 x 3 fixed pixel, and colocalization between fluorescence channels was calculated using the code.
  • FIGs. 2A-2E are a series of graphs that show the results of nanoparticle tracking analysis (NTA) of cell line-derived EVs for their size and concentration measurements.
  • NTA nanoparticle tracking analysis
  • the numbers and size distributions of EVs were analyzed by nanoparticle tracking analysis (Nanosight® LM10, Malvern).
  • Each sample was prepared by 1000-fold dilution with PBS and manually loaded in the chamber with a 1 mL syringe.
  • the particles were detected using a 405 nm laser module, and their Brownian motions at room temperature were captured by a CCD camera with a camera level 10 in the NTA softw are.
  • Each measurement w as recorded for 20 seconds with a 30-frame rate per second, and each sample was measured in quadruplicate.
  • the particle analysis was conducted with a detection threshold of 2.
  • FIG. 3 is a series of graphs showing the results of flow cytometry analysis to investigate target antigen expression levels in cells vs EVs. The results discussed below showed the specific expressions of those markers in cells, as expected in the above table, and similar profiles in their EVs. These results validated the use of EVs to interrogate expression levels of target proteins in their originating cancer cells.
  • ovarian cancer biomarkers EpCAM. CD24, HE4, TNC. and VCAN
  • TISOE6 ovarian benign cell line, and Jurkat, B-cells
  • FIGs. 4A-4E the results of testing of ovarian cancer biomarkers for ovarian cancer EV capture (glass imaging) using EVs from the SkOV3 and OV90 ovarian cancer cell line and the TIOSE6 benign cell line.
  • the most effective dilutions in each experiment are highlighted with gray shading.
  • Antibody titration was used for the glass substrate-EV detection method (imaged EVs on glass for titration before using a FLEX chip).
  • ovarian cancer markers were detected in several ovarian cancer cell- derived EVs ( Figure 4A), showing the capture markers used for ovarian cancer-EVs were well expressed in ovarian cancer cell line EVs.
  • Ovarian cancer markers were well- expressed in each marker positive cell-line derived EVs as their expression level increased according to increasing antibody concentration, while TIOSE6 benign cell lines EVs did not express markers, therefore showing minimal expression level in all antibody dilutions.
  • Relative CD63 expression level was calculated by normalization with the highest expression level among several concentrations, and CD63 expression level was detectable with only 10 3 EVs on FLEX method, while more than 10 7 EVs were required for bead-fl o ⁇ cytometry to detect CD63 expression on EVs, showing that FLEX method is -1000 fold higher sensitivity' than the “gold standard'’ method.

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Abstract

La présente divulgation concerne des procédés de détection et d'analyse de la résistance tumorale, par exemple, dans le temps, à des conjugués anticorps-médicament (ADC) utilisés pour traiter le cancer.
PCT/US2024/061184 2023-12-19 2024-12-19 Utilisation de vésicules extracellulaires pour évaluer l'efficacité de traitement de thérapies conjuguées anticorps-médicament (adc) Pending WO2025137394A1 (fr)

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US20110177099A1 (en) * 2008-03-14 2011-07-21 Mark Lackner Genetic variations associated with drug resistance
US20180104187A1 (en) * 2016-10-19 2018-04-19 Northwestern University Extracellular vesicle-based diagnostics and engineered exosomes for targeted therapeutics against cancer
WO2024151873A1 (fr) * 2023-01-12 2024-07-18 The General Hospital Corporation Analyse moléculaire de vésicules extracellulaires (ve) pour la prédiction et la surveillance de la résistance aux médicaments dans le cancer

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US20230123746A1 (en) * 2020-04-14 2023-04-20 The General Hospital Corporation Methods and Systems of Enhancing Electromagnetic Radiation Signals from Extracellular Vesicles

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US20110177099A1 (en) * 2008-03-14 2011-07-21 Mark Lackner Genetic variations associated with drug resistance
US20180104187A1 (en) * 2016-10-19 2018-04-19 Northwestern University Extracellular vesicle-based diagnostics and engineered exosomes for targeted therapeutics against cancer
WO2024151873A1 (fr) * 2023-01-12 2024-07-18 The General Hospital Corporation Analyse moléculaire de vésicules extracellulaires (ve) pour la prédiction et la surveillance de la résistance aux médicaments dans le cancer

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