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WO2025081100A1 - Procédés, kits et systèmes pour déterminer le statut her2 des cancers et méthodes de traitement des cancers utilisant ces derniers - Google Patents

Procédés, kits et systèmes pour déterminer le statut her2 des cancers et méthodes de traitement des cancers utilisant ces derniers Download PDF

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WO2025081100A1
WO2025081100A1 PCT/US2024/051123 US2024051123W WO2025081100A1 WO 2025081100 A1 WO2025081100 A1 WO 2025081100A1 US 2024051123 W US2024051123 W US 2024051123W WO 2025081100 A1 WO2025081100 A1 WO 2025081100A1
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her2
cancer
loci
optionally
genomic loci
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Matthew FREEDMAN
Sylvan BACA
Geoff Otto
Travis CLARK
Anthony D’IPPOLITO
Matthew EATON
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Precede Biosciences Inc
Dana Farber Cancer Institute Inc
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Precede Biosciences Inc
Dana Farber Cancer Institute Inc
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    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/154—Methylation markers
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/156—Polymorphic or mutational markers
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • HER2 Human epidermal growth factor receptor 2
  • Receptor tyrosine- protein kinase erbB-2 Receptor tyrosine- protein kinase erbB-2
  • HER2-positive cancers tend to grow and spread more aggressively than HER2-negative cancers.
  • trastuzumab pertuzumab, margetuximab, trastuzumab emtansine, trastuzumab deruxtecan, lapatinib, neratinib, and tucatinib have been approved by the U.S. Food and Drug Administration (FDA) for the treatment of HER2-positive breast and/or gastric/gastroesophageal cancer (Meric-Bernstam et al., Clin Cancer Res (2019) 25(7):2033-2041 and Djaballah et al., American Society of Clinical Oncology Educational Book (2022) 42:219-232).
  • FDA U.S. Food and Drug Administration
  • IHC immunohistochemistry
  • ISH in situ hybridization
  • the cancer is HER2-positive. These cancers are usually treated with agents that target HER2.
  • Cancers that have an IHC result of 1+ or an IHC result of 2+ and no detectable ERRB2 amplification based on ISH testing are also commonly called HER2-low cancers. These cancers have recently been shown to respond to certain HER2-targeted agents, e.g., trastuzumab deruxtecan was recently approved by the FDA for the treatment of patients with HER2-low metastatic breast cancer (see Nicol ⁇ et al., Ther Adv Med Oncol (2023) 15:1-16).
  • the present disclosure is also based, at least in part, on the demonstration that genomic loci that are differentially modified based on different types of histone modifications (e.g., histone methylation marks such as H3K4me3 and histone acetylation marks such as H3K27ac) and/or DNA methylation can be combined into multimodal classifiers to determine HER2 status.
  • histone modifications e.g., histone methylation marks such as H3K4me3 and histone acetylation marks such as H3K27ac
  • DNA methylation e.g., DNA methylation
  • H3K4me3 histone methylation marks
  • H3K27ac histone acetylation marks
  • the present disclosure includes, among other things, technologies for the determination of HER2 status and for the detection, monitoring, and/or treatment of cancer (including, e.g., breast, gastric/gastroesophageal, colorectal and lung cancer) based on HER2 status.
  • cancer including, e.g., breast, gastric/gastroesophageal, colorectal and lung cancer
  • the present disclosure relates to the measurement of histone modifications in a sample obtained or derived from a subject to detect and/or treat cancer (including, e.g., breast, gastric/gastroesophageal, colorectal and lung cancer) based on HER2 status.
  • cancer including, e.g., breast, gastric/gastroesophageal, colorectal and lung cancer
  • the present disclosure includes, among other things, histone modification measurements in cell-free DNA (cfDNA) that are characteristic of cancer, and which in various embodiments are useful, e.g., for detecting, monitoring, selecting treatment for, and/or treating cancer (including, e.g., breast, gastric/gastroesophageal, colorectal and lung cancer) based on HER2 status.
  • cfDNA cell-free DNA
  • histone modification measurements in cfDNA can be used to detect or determine resistance of a cancer (e.g., breast, gastric/gastroesophageal, colorectal and lung cancer) to a therapy or transformation of a cancer from one subtype to another.
  • a cancer e.g., breast, gastric/gastroesophageal, colorectal and lung cancer
  • the present disclosure includes exemplary genomic loci that are differentially modified in HER2-positive vs. HER2-negative cancer, e.g., breast, gastric/gastroesophageal, colorectal and lung cancer.
  • genomic loci differentially modified in cfDNA are or include one or more enhancers.
  • genomic loci differentially modified in cfDNA are or include one or more promoters.
  • genomic loci differentially modified in cfDNA are from the HER2 amplicon. In various embodiments, genomic loci differentially modified in cfDNA are not from the HER2 amplicon. A person of ordinary skill is able to determine whether a genomic locus us from the HER2 amplicon.
  • the “HER2 amplicon” corresponds to the region around the ERBB2 transcript on chromosome 17 that frequently includes the genes STARD3, GRB7 and NR1D1. In some embodiments, the “HER2 amplicon” corresponds to chr17:37,776,630- 38,274,514 based on human genome build hg19.
  • a genomic locus is differentially modified if it is characterized by increased or decreased histone modification as compared to a reference (e.g., a sample from a HER2-negative or healthy subject). Increased or decreased histone modification
  • the present disclosure includes, among other things, DNA methylation measurements in cell-free DNA (cfDNA) that are characteristic of cancer, and which in various embodiments are useful, e.g., for detecting, monitoring, selecting treatment for, and/or treating cancer (including, e.g., breast, gastric/gastroesophageal, colorectal and lung cancer) based on HER2 status.
  • DNA methylation measurements in cfDNA can be used to detect or determine resistance of a cancer (e.g., breast, gastric/gastroesophageal, colorectal and lung cancer) to a therapy or transformation of a cancer from one subtype to another.
  • the present disclosure includes exemplary genomic loci that are differentially DNA methylated in HER2-positive vs. HER2-negative cancer, e.g., breast, gastric/gastroesophageal, colorectal and lung cancer.
  • a genomic locus is differentially modified if it is characterized by increased or decreased DNA methylation as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • genomic loci differentially modified in cfDNA are or include one or more enhancers.
  • genomic loci differentially modified in cfDNA are or include one or more promoters.
  • genomic loci differentially modified in cfDNA are from the HER2 amplicon. In various embodiments, genomic loci differentially modified in cfDNA are not from the HER2 amplicon.
  • the present disclosure further relates, in various embodiments, to the measurement of chromatin accessibility in cell-free DNA (cfDNA) to determine HER2 status.
  • cfDNA cell-free DNA
  • the present disclosure includes, among other things, chromatin accessibility measurements in cfDNA that are characteristic of HER2-positive cancers, which in various embodiments are useful, e.g., in detecting, monitoring, selecting treatment for, and/or treating a HER2-positive cancer.
  • transcription factor binding measurements in cfDNA can be used to detect or determine resistance of a cancer (e.g., breast, gastric/gastroesophageal, colorectal and lung cancer) to a therapy or transformation of a cancer from one subtype to another.
  • the present disclosure includes genomic loci that are differentially bound by transcription factors in HER2-positive vs. HER2-negative cancers.
  • genomic loci that are differentially bound by transcription factors in cfDNA are or include one or more enhancers.
  • genomic loci that are differentially bound by transcription factors in cfDNA are or include one or more promoters.
  • genomic loci that are differentially bound by transcription factors in cfDNA are from the HER2 amplicon. In various embodiments, genomic loci that are differentially bound by transcription factors in cfDNA are not from the HER2 amplicon.
  • histone acetylation e.g., H3K27ac
  • histone methylation e.g., H3K4me3
  • H3K4me3 corresponds and/or is correlated with transcription factor binding.
  • the present disclosure provides a method of determining the HER2 status of a cancer in a subject, the method comprising: quantifying, at one or more genomic loci in a biological sample, optionally in cell-free DNA (cfDNA) from a liquid biopsy sample, obtained or derived from the subject: (i) one or more histone modifications, (ii) chromatin accessibility, (iii) binding of one or more transcription factors, and/or (iv) DNA methylation, optionally wherein one or more of the quantified genomic loci is not from the HER2 amplicon.
  • cfDNA cell-free DNA
  • binding of one or more transcription factors is quantified using a transcription factor binding assay that detects binding of one or more of p300, mediator complex, cohesin complex, RNA pol II, FOXA1, ESR1, PR, MYC, EN1, FOXM1, KLF4, AP-2, RARa, or RUNX1.
  • the transcription factor binding assay is selected from ChIP-seq (Chromatin ImmunoPrecipitation sequencing), CUT&RUN (Cleavage Under Targets and Release Using Nuclease) sequencing, and CUT&Tag (Cleavage Under Targets and Tagmentation) sequencing.
  • a sample is a liquid biopsy sample comprising cfDNA
  • a method comprises: (a) quantifying H3K4me3 modifications at one or more genomic loci using an assay that comprises enriching for cfDNA comprising one or more H3K4me3 modifications and sequencing the cfDNA enriched for H3K4me3 modifications (e.g., using a cfChIP-seq assay); (b) quantifying H3K27ac modifications at one or more genomic loci using an assay that comprises enriching for cfDNA comprising one or more H3K27ac modifications and sequencing the cfDNA enriched for H3K27ac modifications (e.g., using a cfChIP-seq assay); and/or; (c) quantifying methylated DNA using an assay that comprises enriching for methylated cfDNA and sequencing the enriched cfDNA to determine a count of sequences with one or more methylated nucleotides (e.g., using a
  • cfDNA comprising H3K4me3 modifications is enriched using a method that comprises incubating the sample with an agent (e.g., an antibody) that binds H3K4me3 modifications;
  • the cfDNA comprising H3K27ac modifications is enriched using a method that comprises incubating the sample with an agent (e.g., an antibody) that binds H3K27ac modifications;
  • methylated cfDNA is enriched using a method that comprises incubating the sample with an agent (e.g., an antibody or a methyl binding domain) that binds methylated DNA.
  • quantifying H3K4me3 modifications, H3K27ac modifications, and/or DNA methylation comprises summing the number of sequence reads having at least one nucleotide overlap the one or more genomic loci.
  • sequence reads are adjusted on the basis of sequencing depth (e.g., quantile normalizing sequence reads to a common reference distribution) and/or ChIP quality prior to summing.
  • sequence counts are normalized to aggregate counts in a given sample across a set of regions (e.g., 10,000 regions) previously determined to have DNAse hypersensitivity in most cell types.
  • an estimate of local background signal is subtracted from the sequence reads at each genomic loci prior to summing.
  • sequence read density can be calculated by a method comprising: (a) summing background adjusted sequence counts at each of one or more genomic loci and dividing by the sum of the kilobases of the one or more genomic loci; or (b) for each genomic loci, dividing background adjusted fragment count by the number of kilobases of the genomic loci, and then summing for each loci.
  • one or more genomic loci include one or more genomic loci with an increased level of the one or more epigenetic biomarkers in (a) sample(s) obtained from a subject with a HER2-positive cancer as compared to a sample obtained from a subject with a HER2-negative cancer, and/or (b) sample(s) obtained from a subject with a HER2- positive cancer as compared to a sample obtained from a subject with a HER2-negative cancer.
  • a method comprises calculating a HER2-positive/HER2- negative ratio score.
  • a method comprises determining a HER2-positive/HER2- negative ratio score for two or more epigenetic markers.
  • the method comprises quantifying H3K4me3 modifications for at least 5, 10, 20, 30, 40, or 50 genomic loci in Table 1, optionally wherein one or more of the genomic loci are not from the HER2 amplicon.
  • the method comprises quantifying H3K27ac modifications for at least 5, 10, 20, 30, 40, or 50 genomic loci in Table 2, optionally wherein one or more of the genomic loci are not from the HER2 amplicon.
  • the method comprises quantifying DNA methylation for at least 5, 10, 20, 30, 40, or 50 genomic loci in Table 3, optionally wherein one or more of the genomic loci are not from the HER2 amplicon.
  • quantification of one or more epigenetic biomarkers at one or more genomic loci as compared to a reference indicates that a subject has a HER2-positive cancer, optionally a HER2-3+ cancer based on IHC testing.
  • quantification of one or more epigenetic biomarkers at one or more genomic loci as compared to a reference indicates that a subject has a HER2-negative cancer, optionally a HER2-0 cancer based on IHC testing.
  • a sample comprises a detectable amount of ctDNA (e.g., wherein estimated tumor fraction is >3% for the cfDNA, e.g., as determined by iChorCNA).
  • the area under the receiver operating characteristic (AUROC) for determining if a subject has a HER2-positive cancer vs. a HER2-negative cancer is greater than 0.5 (e.g., greater than 0.55, greater than 0.6, greater than 0.65, greater than 0.7, greater than 0.75, greater than 0.8, greater than 0.85, greater than 0.9, or greater than 0.95).
  • the HER2-positive cancer is a HER2-3+ cancer based on IHC testing and the HER2-negative cancer is a HER2-0 cancer based on IHC testing.
  • the subject has previously been determined to have cancer.
  • the validated classifier in step (e) was validated using liquid biopsy samples from the third cohort.
  • the classifier in step (d) was trained on two or more histone modification levels in the differential loci.
  • the two or more histone modification levels comprise H3K4me3 and H3K27ac modification levels.
  • the classifier in step (d) was trained on one or more histone modification levels and DNA methylation in the differential loci.
  • the one or more histone modification levels comprise H3K4me3 and/or H3K27ac modification levels.
  • the classifier in step (d) was trained using ridge regression, elastic- net regression, or lasso regression.
  • the one or more histone modification levels comprise H3K4me3 and H3K27ac modification levels.
  • the biological sample is a liquid biopsy sample, e.g., a plasma sample, serum sample, or urine sample.
  • the present disclosure provides a kit comprising reagents for quantifying one or more histone modifications, chromatin accessibility, binding of one or more transcription factors, and/or DNA methylation at one or more genomic loci, wherein the one or more genomic loci are selected from Tables 1-3, optionally wherein one or more of the genomic loci are not from the HER2 amplicon.
  • the kit comprises reagents for quantifying H3K4me3 for at least 5, 10, 20, 30, 40, or 50 genomic loci in Table 1, optionally wherein one or more of the genomic loci are not from the HER2 amplicon.
  • the kit comprises reagents for quantifying H3K27ac for at least 5, 10, 20, 30, 40, or 50 genomic loci in Table 2, optionally wherein one or more of the genomic loci are not from the HER2 amplicon. In some embodiments, the kit comprises reagents for quantifying DNA methylation for at least 5, 10, 20, 30, 40, or 50 genomic loci in Table 3, optionally wherein one or more of the genomic loci are not from the HER2 amplicon. [0065] In some embodiments, the kit comprises one or more antibodies for use in ChIP- seq, optionally wherein the one or more antibodies specifically bind H3K4me3- or H3K27ac- modified histones.
  • the sample preparation device comprises reagents for quantifying one or more histone modifications, chromatin accessibility, binding of one or more transcription factors, and/or DNA methylation at one or more genomic loci in cell-free DNA (cfDNA) from the biological sample, optionally the liquid biopsy sample.
  • the one or more genomic loci are selected from Tables 1-3.
  • the device comprises reagents for quantifying H3K4me3, e.g., for at least 5, 10, 20, 30, 40, or 50 genomic loci in Table 1.
  • the device comprises reagents for quantifying H3K27ac, e.g., for at least 5, 10, 20, 30, 40, or 50 genomic loci in Table 2.
  • a HER2 classifier has been trained using one or more genomic profiles of one or more histone modifications, chromatin accessibility, binding of one or more transcription factors, and/or DNA methylation for (i) one or more HER2-positive cell lines and one or more HER2-negative cell lines and/or (ii) one or more biological samples obtained from one or more cohorts of subjects who have previously been determined to have a HER2- positive cancer, optionally a HER2-3+, HER2-2+, or HER2-1+ cancer based on IHC testing or a HER2-low cancer based on IHC/ISH testing, and one or more biological samples obtained from one or more cohorts of subjects who have previously been determined to have a HER2-negative cancer, optionally a HER2-0 cancer based on IHC testing.
  • one or more of the genomic profiles used to train the HER2 classifier comprise one or more genomic profiles generated by in silico diluting sequence data from HER2-positive or HER2-negative cell lines with sequence data obtained from healthy donor plasma samples to achieve a simulated ctDNA percentage ranging from 0.5% to 50%.
  • one or more genomic profiles are for differential genomic loci found to have statistically significant different levels of one or more histone modifications, chromatin accessibility, binding of one or more transcription factors, and/or DNA methylation levels between one or more HER2-positive cell lines and one or more HER2-negative cell lines and/or between one or more biological samples obtained from one or more cohorts of subjects who have previously been determined to have a HER2-positive cancer, optionally a HER2-3+, HER2- 2+, or HER2-1+ cancer based on IHC testing or a HER2-low cancer based on IHC/ISH testing, and one or more biological samples obtained from one or more cohorts of subjects who have previously been determined to have a HER2-negative cancer, optionally a HER2-0 cancer based on IHC testing.
  • a HER2 classifier has been trained on two or more histone modification levels in the differential loci. In some embodiments, a HER2 classifier has been trained on one or more histone modification levels and DNA methylation levels in the differential loci. In some embodiments, a genomic profile of a subject used to classify comprises two or more histone modification levels.
  • a HER2 classifier has been tuned with plasma data. In some embodiments, a HER2 classifier has been trained with one or more genomic profiles of one or more histone modifications, chromatin accessibility, binding of one or more transcription factors, and/or DNA methylation for (i) one or more HER2-positive cell lines and one or more HER2-negative cell lines.
  • a HER2 classifier is a validated classifier.
  • a HER2 classifier has been validated by selecting a threshold such that the validated classifier predicts HER2-positive cancers, optionally a HER2-3+, HER2-2+, or HER2-1+ cancer based on IHC testing or a HER2-low cancer based on IHC/ISH testing, with an area under the receiver operating characteristic (AUROC) greater than 0.5 (e.g., greater than 0.55, greater than 0.6, greater than 0.65, greater than 0.7, greater than 0.75, greater than 0.8, greater than 0.85, greater than 0.9, or greater than 0.95).
  • AUROC receiver operating characteristic
  • a HER2 classifier has been validated on a cohort of an independent group of subjects with HER2-positive and HER2-negative cancers, wherein subjects falling within a group of predicted HER2-positive cancers display a validated epigenetic profile and subjects that do not fall within a group of HER2-positive cancers lack the validated epigenetic profile.
  • a HER2 classifier has been validated using liquid biopsy sample data.
  • a non-transitory computer readable storage medium may be encoded with a computer program, where the program may comprise instructions that when executed by one or more processors cause the one or more processors to perform operations to perform a method for determining HER2 status of a cancer in a subject (e.g., patient).
  • a computer system may include a memory and one or more processors coupled to the memory, wherein the one or more processors are configured to perform operations to perform a method for determining HER2 status of a cancer in a subject (e.g., patient).
  • a method of treating a subject having a cancer includes administering a HER2-targeted agent to a subject, wherein the subject has been determined to have a validated epigenetic profile indicative of a HER2-positive cancer based on analysis of a biological sample, optionally of cell-free DNA (cfDNA) from a liquid biopsy sample, obtained or derived from the subject.
  • cfDNA cell-free DNA
  • Fig.1 shows ROC curves for exemplary HER2 status classifiers that were generated in accordance with Example 2. As shown, different classifiers were generated using genomic loci from Tables 1-3 for different modifications, namely (i) H3K4me3 modifications, (ii) H3K27ac modifications, (iii) DNA methylation (DNAme) or (iv) all of the above (All).
  • Fig.7 shows an exemplary approach to developing an epigenomic, genome-wide HER2+ vs. HER2- breast cancer classifier, using information from both cell lines and plasma samples.
  • Epigenomic data from 26 breast cancer cell lines (8 HER2 IHC 3+ and 18 HER2 IHC 0) were used to train a lasso logistic regression model. Loci with robust regulatory signal in subjects with breast cancer were selected in a LOO schema and predictions of held-out samples yielded a classification AUC of 0.81 (HER2 IHC 3+/2+ISH+ vs.2+/1+/0).
  • Cell line models were
  • C As a control, fragments from the same patient datasets were sampled to match for sequencing depth, and real-world samples were found to perform better than simulated datasets (indicating that actual performance of classifiers at low ctDNA is actually better than that estimated by the in-silico method shown in (B)).
  • Fig.10 shows generation of HER2 classifiers for indications other than breast cancer (in particular, gastroesophageal adenocarcinoma (GEA) and ovarian cancer (OV)).
  • GAA gastroesophageal adenocarcinoma
  • OV ovarian cancer
  • Fig.11 shows that pan-cancer HER2 classification as determined by epigenomic lipid biopsy correlates with HER2 IHC.
  • HER2 IHC predictions 3+/2+ISH+ vs.2+/1+/0
  • a linear trend was observed between model probabilities and HER2 IHC status.
  • Fig.12 shows that plasma-based epigenomic classifiers of HER2-status have the potential to be applied to other indications (in addition to breast cancer, GEA, and OV) with
  • HER2+ and HER2- cell lines Loci with differential epigenetic modifications (DNAme, H3K27ac, and H3K4me) in HER2+ and HER2- cell lines were identified, and a HER2 status classifier was constructed using this data. The cell line classifier was then tuned using plasma sample data obtained from subjects with HER2+ or HER2- breast cancer.
  • “Final classifier” refers to a plasma sample-tuned classifier. Shown are exemplary weights and exemplary numbers of features that can be used in a classifier. As shown, a classifier generated using this protocol was capable of accurately classifying HER2 status in subjects having breast cancer. [0092] Fig.14 shows that a multi-analyte HER2 classifier can robustly stratify patients by HER2 status.
  • HER2+ probability for 3+, 1+/2+, and 0 (as determined by IHC) determined using a classifier built using methods described herein.
  • one HER2-0 sample showed a high probability of being HER2+ using a classifier provided herein.
  • FISH data showed highly amplified HER2, indicating that the IHC- determined status was incorrect.
  • the HER2 classifier result matched the FISH data and accurately classified the subject, demonstrating that, in some embodiments, methods provided herein can result in more accurate results than IHC methods, the current gold standard for determining HER2 status.
  • FIG. 16 shows results (probability of a sample being HER2+) for a collection of CRC samples.
  • Fig.16 provides a list of cancers in which ERBB2 amplicons have previously been detected.
  • Y-axis shows the number of amplicons previously detected. Boxed cancers indicate cancers for which anti-HER2 therapies have been approved.
  • Fig.16 shows the potential of HER2-targeted therapies to treat a number of cancers.
  • methods provided herein can be used to treat a cancer in which ERBB2 amplicons have previously been detected indicated (e.g., a cancer listed in Fig.16).
  • Fig.16 provides a list of cancers in which ERBB2 amplicons have previously been detected.
  • Y-axis shows the number of amplicons previously detected. Boxed cancers indicate cancers for which anti-HER2 therapies have been approved.
  • Fig.16 shows the potential of HER2-targeted therapies to treat a number of cancers.
  • HER2 Dimerization of the HER2 receptor results in the autophosphorylation of tyrosine residues within the cytoplasmic domain of the receptors and initiates a variety of signaling pathways leading to cell proliferation and tumorigenesis.
  • Amplification or overexpression of HER2 occurs in approximately 15-30% of breast cancers and 10-30% of gastric/gastroesophageal cancers. These cancers, known as HER2-positive breast cancers, tend to grow and spread more aggressively than HER2-negative breast and gastric/gastroesophageal cancers.
  • HER2 overexpression has also been seen in other cancers like ovary, endometrium, bladder, lung, colon, and head and neck.
  • cancers do not respond to treatment with drugs that target HER2 and may instead be treated with one or more of surgery and/or radiation, endocrine therapy (if positive for a hormone receptor such as estrogen receptor), chemotherapy and immunotherapy. If the IHC result is 1+, the cancer is considered HER2-negative. These cancers do not usually respond to treatment with drugs that target HER2 but have recently been shown to respond to certain HER2-targeted agents (e.g., see Nicol ⁇ et al., Ther Adv Med Oncol (2023) 15:1-16). If the IHC result is 2+, the HER2 status of the tumor is not clear and is called “equivocal.” This means that the HER2 status needs to be tested with ISH to clarify the result.
  • the cancer is HER2-positive. These cancers are usually treated with agents that target HER2.
  • Cancers that have an IHC result of 1+ or an IHC result of 2+ and no detectable ERRB2 amplification based on ISH testing are also commonly called HER2-low cancers. These cancers have recently been shown to respond to certain HER2-targeted agents, e.g., trastuzumab deruxtecan was recently approved by the FDA for the treatment of patients with HER2-low metastatic breast cancer (see Nicol ⁇ et al., Ther Adv Med Oncol (2023) 15:1-16).
  • HER2-targeted agents [0103] The introduction of HER2-targeted agents has dramatically influenced the outcome of patients with HER2-positive breast and gastric/gastroesophageal cancers and more recently patients with HER2-positive colorectal and lung cancers. Many HER2-targeted agents are in development and being tested in clinical trials for these and other HER2-positive cancers. These include CAR-T, CAR-NK, and CAR-M therapies as well as cancer vaccines (e.g., see Vila et al., Cancers (Basel) (2023) 15(7):1987, the entire content of which is incorporated herein by reference). Antibodies
  • trastuzumab is a HER2-targeted antibody that can be used to treat both early- stage and advanced HER2-positive breast cancer.
  • This therapeutic agent is often administered with chemotherapy, but it might also be used alone (especially if chemotherapy alone has already been tried). When started before (neoadjuvant) or after (adjuvant) surgery to treat early breast cancer, this therapeutic agent is usually given for 6 months to a year. For advanced breast cancer, treatment is often given for as long as the therapeutic agent is helpful. This therapeutic agent is administered intravenously.
  • trastuzumab and hyaluronidase injection is administered subcutaneously.
  • An antibody-drug conjugate includes an antibody or antibody fragment linked to a chemotherapeutic agent.
  • the HER2-targeted antibody acts like a homing signal by attaching to HER2 protein on cancer cells, bringing the chemotherapeutic agent directly to the cancer cells.
  • Trastuzumab emtansine is an ADC that includes the HER2-targeted antibody trastuzumab conjugated to the chemotherapeutic agent emtansine, which is similar to paclitaxel.
  • Tucatinib is administered as a pill, typically twice a day. Tucatinib is used to treat advanced breast cancer, after at least one other HER2-targeted agent has been tried. It is usually administered with trastuzumab and the chemotherapeutic agent capecitabine. Other HER2-targeted agents and other cancers [0115] While the sections above focus on FDA approved HER2-targeted agents, many other HER2-targeted agents are being developed and/or assessed in clinical trials.
  • HER2-targeted agents are approved to treat other types of HER2-positive cancers, e.g., breast cancer, doctors can prescribe them off-label for colorectal cancer.
  • the most common HER2-targeted drug regimens include trastuzumab plus either tucatinib, lapatinib, or pertuzumab.
  • trastuzumab is FDA approved for the treatment of metastatic HER2-positive gastric/gastroesophageal cancer and typically administered once every 3 weeks along with chemotherapy.
  • a sample analyzed using methods, kits and systems provided herein can be a sample obtained from a human subject.
  • a human subject is a subject diagnosed or seeking diagnosis as having, diagnosed as, or seeking diagnosis as at risk of having, and/or diagnosed as or seeking diagnosis as at immediate risk of having, a HER2-positive cancer, e.g., HER2-positive breast cancer, gastric/gastroesophageal cancer, colorectal cancer, lung cancer, etc.
  • a human subject is a subject identified as needing HER2 status screening.
  • a human subject is a subject identified as needing HER2 status screening by a medical practitioner.
  • a human subject is identified as in need of HER2 status screening based on an initial cancer diagnosis, e.g., a breast cancer, gastric/gastroesophageal cancer, colorectal cancer, lung cancer, etc. diagnosis.
  • a human subject is a subject not yet diagnosed as having, not at risk of having, not at immediate risk of having, not diagnosed as having, and/or not seeking diagnosis for a cancer. Genetic factors may also contribute to HER2-positive cancer risk, as evidenced by individuals with a family history of HER2-positive cancer.
  • HER2-targeted agents are used with a lung cancer patient who has been determined to be HER2-positive based on the methods described herein.
  • the HER2 mutations are L755S or D769Y mutations and the subject is administered a kinase inhibitor of the present disclosure, e.g., neratinib (see Gaibar et al., J Oncol (2020) 2020:6375956).
  • a sample from a subject e.g., a human can be obtained from a liquid biopsy.
  • a sample and/or reference is obtained from serum, plasma, or urine.
  • the sample is serum.
  • a sample comprises circulating tumor DNA (ctDNA).
  • a sample is derived from about 1 mL of blood obtained from the subject.
  • a sample is derived from about 0.5-5 mL of blood obtained from the subject, e.g., about 0.5 to about 2 mL, about 0.5 to 1.75 mL, about 0.5 to 1.5 mL, about 0.75 to 1.25 mL, about 0.9 to 1.1 mL, about 1 mL, about 2 mL, about 3 mL, about 4 mL, or about 5 mL of blood.
  • a sample is a sample of cell-free DNA (cfDNA).
  • cfDNA is typically found in human biofluids (e.g., plasma, serum, or urine) in short, double-stranded
  • cfDNA Circulating tumor DNA
  • ctDNA Circulating tumor DNA
  • ctDNA Circulating tumor DNA
  • ctDNA can be present in human biofluids bound to leukocytes and erythrocytes or not bound to leukocytes and erythrocytes.
  • Various tests for detection of tumor-derived ctDNA are based on detection of genetic or epigenetic modifications that are characteristic of cancer (e.g., of a relevant cancer).
  • the percentage of ctDNA in the liquid biopsy sample is assessed using ichorCNA which estimates the percentage of ctDNA in a sample probabilistically (see Adalsteinsson et al., Nat Commun (2017) 8(1):1324 the entire contents of which are incorporated herein by reference).
  • ichorCNA estimates the percentage of ctDNA in a sample probabilistically
  • a method comprises isolating DNA (e.g., cfDNA) from a liquid biopsy sample.
  • DNA e.g., cfDNA
  • Various methods of isolating nucleic acids from a sample e.g., of isolating cfDNA from blood or plasma
  • Nucleic acids can be isolated using, without limitation, standard DNA purification techniques, by direct gene capture (e.g., by clarification of a sample to remove assay-inhibiting agents and capturing a target nucleic acid, if present, from the clarified sample with a capture agent to produce a capture complex and isolating the capture complex to recover the target nucleic acid).
  • Reagents and protocols for obtaining and analyzing cfDNA and ctDNA, such as circulating in blood or other tissue are commercially available as described in the Examples and
  • samples can be collected from individuals repeatedly over a period of time (e.g., once daily, weekly, monthly, annually, biannually, etc.). In various embodiments, such samples can be used to verify results from earlier detections and/or to identify an alteration in biological pattern because of, for example, disease progression, resistance to therapy, treatment, remission, and the like. For example, subject samples can be taken and monitored every month, every two months, or combinations of one, two, or three- month intervals according to the present disclosure. In various embodiments, samples can be collected for monitoring over time beginning at or at certain clinically determined stages, such as at resistance to a therapy, before radiographic progression, after radiographic progression, and/or at tissue biopsy.
  • Samples include materials prepared by processes including, without limitation, steps such as concentration, dilution, adjustment of pH, removal of high abundance polypeptides (e.g., albumin, gamma globulin, and transferrin, etc.), addition of preservatives, addition of calibrants, addition of protease inhibitors, addition of denaturants, desalting, concentration and/or extraction of sample nucleic acids, and/or amplification of sample nucleic acids (e.g., by PCR or other nucleic acid amplification techniques).
  • steps such as concentration, dilution, adjustment of pH, removal of high abundance polypeptides (e.g., albumin, gamma globulin, and transferrin, etc.), addition of preservatives, addition of calibrants, addition of protease inhibitors, addition of denaturants, desalting, concentration and/or extraction of sample nucleic acids, and/or amplification of sample nucleic acids (e.g., by PCR or other nucleic
  • Samples also include materials prepared by techniques that isolate, e.g., nucleosomes or transcription factors and/or nucleic acids associated with nucleosomes or transcription factors.
  • Removal from a sample of proteins that are not desirable for a relevant purpose or context can be achieved using high affinity reagents, high molecular weight filters, ultracentrifugation and/or electrodialysis.
  • High affinity reagents include antibodies or other reagents (e.g., aptamers) that selectively bind to high abundance proteins.
  • Sample preparation can also include ion exchange chromatography,
  • Molecular weight filters include membranes that separate molecules based on size and molecular weight. Such filters may further employ reverse osmosis, nanofiltration, ultrafiltration and microfiltration. Ultracentrifugation is the centrifugation of a sample at about 15,000-60,000 rpm while monitoring with an optical system the sedimentation (or lack thereof) of particles.
  • Electrodialysis is a procedure which uses an electromembrane or semipermeable membrane in a process in which ions are transported through semi-permeable membranes from one solution to another under the influence of a potential gradient. Since the membranes used in electrodialysis may have the ability to selectively transport ions having positive or negative charge, reject ions of the opposite charge, or to allow species to migrate through a semipermeable membrane based on size and charge, it renders electrodialysis useful for concentration, removal, or separation of electrolytes.
  • Separation and purification in the present disclosure may include any procedure known in the art, such as capillary electrophoresis (e.g., in capillary or on-chip) or chromatography (e.g., in capillary, column or on a chip).
  • Electrophoresis is a method that can be used to separate ionic molecules under the influence of an electric field. Electrophoresis can be conducted in a gel, capillary, or in a microchannel on a chip. Examples of gels used for electrophoresis include starch, acrylamide, polyethylene oxides, agarose, or combinations thereof.
  • a gel can be modified by its cross-linking, addition of detergents, or denaturants, immobilization of enzymes or antibodies (affinity electrophoresis) or substrates (zymography) and incorporation of a pH gradient.
  • capillaries used for electrophoresis include capillaries that interface with an electrospray.
  • Capillary electrophoresis (CE) is preferred for separating complex hydrophilic molecules and highly charged solutes. CE technology can also be implemented on microfluidic chips.
  • CE can be further segmented into separation techniques such as capillary zone electrophoresis (CZE), capillary isoelectric focusing (CIEF), capillary isotachophoresis (CITP) and capillary electrochromatography (CEC).
  • CZE capillary zone electrophoresis
  • CIEF capillary isoelectric focusing
  • CITP capillary isotachophoresis
  • CEC capillary electrochromatography
  • An embodiment to couple CE techniques to electrospray ionization involves the use of volatile solutions, for example, aqueous mixtures containing a volatile acid and/or base and an organic such as an alcohol or acetonitrile.
  • CEC is a hybrid technique between traditional high performance liquid chromatography (HPLC) and CE.
  • Separation and purification techniques used in the present disclosure can include any chromatography procedures known in the art. Chromatography can be based on the differential adsorption and elution of certain analytes or partitioning of analytes between mobile and stationary phases. Different examples of chromatography include, but not limited to, liquid chromatography (LC), gas chromatography (GC), high performance liquid chromatography (HPLC), etc.
  • LC liquid chromatography
  • GC gas chromatography
  • HPLC high performance liquid chromatography
  • whole blood is collected from a subject, and a plasma layer is separated by centrifugation. cfDNA may be then extracted from the plasma using methods known in the art.
  • Histone methylation is understood to increase or decrease expression of associated coding sequences, depending on which histone residue is methylated.
  • Histone methylation is an essential modification that can cause monomethylation (me1), dimethylation (me2), and trimethylation (me3) of several amino acids, thus directly affecting heterochromatin formation, gene imprinting, X chromosome inactivation, and gene transcriptional regulation.
  • Histone methyltransferases promote monomethylation, dimethylation, or trimethylation of histones while histone demethylases promote demethylation of histones.
  • lysine Lys or K
  • arginine Arg or R
  • His or H histidine
  • Histone methylation only occurs at specific lysine and arginine sites of histone H3 and H4.
  • histone H3 lysine 4, 9, 26, 27, 36, 56, and 79 and arginine 2, 8, and 17 can be methylated.
  • histone H4 has fewer methylation sites, in which only lysine 5, 12, and 20 and
  • Histone methylation is often associated with transcriptional activation or inhibition of downstream genes.
  • the methylation of histone H3K4, R8, R17, K26, K36, K79, H4R3, and K12 can activate gene transcription.
  • the methylation of histone H3K9, K27, K56, H4K5, and K20 can inhibit gene transcription.
  • H3K4 methylation generally activates gene expression
  • H3K27 methylation generally represses gene expression.
  • HATs histone acetyltransferases
  • HDACs histone acetyltransferases
  • H3K9ac and H3K27ac levels can be associated with promoter and enhancer activities.
  • H3K27ac enhances not only the kinetics of transcriptional activation, but also accelerates the transition of RNA polymerase II from the initiation state to the elongation state.
  • Differential modification of a genomic locus e.g., differential histone methylation and/or differential histone acetylation
  • histones can be depleted at regulatory loci, including within enhancers, insulators and transcribed gene bodies. Active regulatory elements of the genome are generally accessible.
  • Differential accessibility of a genomic locus can refer to, or be determined by or detected as, a comparative difference or change in modification status of one or more genomic loci between a first sample, condition, disease, or state and a second or reference sample, condition, disease, or state.
  • a reference is typically produced by measurement using a methodology identical, similar, or comparable to that by which a compared non-reference measurement was taken.
  • a reference can correspond to a subject having breast cancer and/or a breast cancer subtype, e.g., HER2-positive or HER2-negative breast cancer.
  • a reference can correspond to a subject having gastric/gastroesophageal cancer and/or a gastric/gastroesophageal cancer subtype, e.g., HER2- positive or HER2-negative gastric/gastroesophageal cancer.
  • a reference can correspond to a subject having colorectal cancer and/or a colorectal cancer subtype, e.g., HER2-positive or HER2-negative colorectal cancer.
  • a reference is a predetermined threshold.
  • the predetermined threshold has previously been shown to be capable of distinguishing HER2-positive and HER2-negative cancers (e.g., distinguish with an AUROC of
  • a reference is a measurement from a liquid biopsy sample. In some embodiments, a reference is a measurement from liquid biopsy samples obtained from a cohort of subjects. In some embodiments, a reference is a normalized sample. In some embodiments, a reference is a measurement obtained from liquid biopsy samples obtained from a cohort of subjects who have previously been determined to have a HER2-positive or HER2-negative cancer, including, e.g., a HER2-positive or HER2-negative breast cancer.
  • a reference is a non-contemporaneous sample from the same source, e.g., a prior sample from the same source, e.g., from the same subject.
  • a reference for the modification status of one or more genomic loci can be the modification status of the one or more genomic loci (e.g., one or more differentially modified genomic loci) in a sample (e.g., a sample from a subject), or a plurality of samples, known to represent a particular state (e.g., a HER2-positive cancer or HER2-negative cancer).
  • a reference for the accessibility status of one or more genomic loci can be the accessibility status of the one or more genomic loci (e.g., one or more differentially accessible genomic loci) in a sample (e.g., a sample from a subject), or a plurality of samples, known to represent a particular state (e.g., a HER2-positive cancer or HER2-negative cancer).
  • differential modification or differential accessibility can refer to a differential (e.g., between a sample and a reference) with an absolute log2(fold-change) that is greater than or equal to 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0 or more, or any range in between, inclusive, e.g., as measured according to an assay provided herein.
  • the log2(fold-change) values are based on ratios of HER2-positive to HER2-negative reads, i.e., positive log2(fold-change) values indicate that sequencing reads in a particular genomic locus are associated with a HER2-positive status while a negative log2(fold-change) value indicates that sequencing reads in a particular genomic locus are associated with a HER2-negative status.
  • Enhancers are genomic loci that can be differentially modified or differentially accessible in and/or between conditions, diseases, and other states. Enhancers are cis-acting DNA regulatory regions that are thought to bind trans-acting proteins that contribute to expression patterns of associated genes. Chromatin ImmunoPrecipitation sequencing (ChIP-seq) of histone modifications (e.g., acetylation) have identified millions of enhancers in mammalian
  • TFs transcription factors
  • master transcription factors associate with active enhancers with important impacts on gene expression and cell function.
  • Certain such transcription factors preferentially associate with enhancers that regulate genes required for establishing cell identity and function, including enhancer domains known as “super-enhancers”.
  • master TFs can participate in inter-connected auto-regulatory circuitries or “cliques” that are self-reinforcing, show marked cell selectivity, and function to maintain cell state and/or cell survival.
  • ChIP-chip, ChIP-exo, ChIP Re-ChIP, and ChIPmentation are other alternative techniques that could be used.
  • ChIP can involve various steps including one or more of fixation, sonication, immunoprecipitation, and analysis of the immunoprecipitated DNA.
  • ChIP has become a very widely used tissue-based technique for determining the in vivo location of binding sites of various transcription factors and histones. Because the proteins are captured at the sites of their binding with DNA, ChIP helps to detect DNA-protein interactions that take place in living cells.
  • ChIP can be coupled to many commonly used molecular biology techniques such as PCR and real-time PCR, PCR with single-stranded conformational polymorphism, Southern blot analysis, Western blot analysis, cloning, and microarray.
  • PCR and real-time PCR PCR with single-stranded conformational polymorphism
  • Southern blot analysis Southern blot analysis
  • Western blot analysis Western blot analysis
  • cloning and microarray.
  • microarray microarray
  • Formaldehyde is one of the most used cross-linking agents.
  • One advantage of using formaldehyde can be the ease of reversibility of the cross-links and its ability to form bonds that span approximately 2 angstroms. This means that formaldehyde can bind molecules in close association with each other.
  • formaldehyde can be added to the medium in the cell culture flask or plate. It enters the cells through the cell membrane and cross-links the proteins to the chromatin. Formaldehyde fixation of tumor tissues has also been done.
  • Harvested chromatin can be sonicated in one or more sonication cycles. DNA can be typically broken into to 100–500 bp fragments to pinpoint the location of the DNA sequence of interest.
  • An alternative to sonication can be nuclease digestion of the chromatin, e.g., in N- ChIP methods. Purification of chromatin can be achieved using a cesium chloride (CsCl) gradient centrifugation.
  • CsCl cesium chloride
  • Chromatin can be enriched for a particular histone modification using an agent that binds the histone modification (e.g., immunoprecipitating using one or more antibodies that bind a target epitope).
  • an antibody used in ChIP can selectively bind a particular transcription factor or one or more particular histone modifications, such as one or more particular histone acetylation modifications or histone methylation modifications.
  • an antibody used to bind a target epitope can be a “pan” antibody (e.g., a pan- acetylation antibody, a pan-methylation antibody, an antibody that binds a group of histone modifications associated with increased transcription activation, and/or an antibody that binds a group of histone modifications associated with increased transcription repression).
  • a pan- acetylation antibody e.g., a pan-acetylation antibody, a pan-methylation antibody, an antibody that binds a group of histone modifications associated with increased transcription activation, and/or an antibody that binds a group of histone modifications associated with increased transcription repression.
  • ChIP-seq combines chromatin immunoprecipitation (ChIP) with massively parallel DNA sequencing to identify the binding sites of DNA-associated proteins. ChIP-seq can be used to map DNA-binding proteins, e.g., transcription factor binding sites and histone modifications in a genome-wide manner.
  • Cell-free Chromatin ImmunoPrecipitation sequencing involves applying ChIP-seq to samples that include cell-free DNA, e.g., liquid biopsy samples including cfDNA such as plasma samples including cfDNA (e.g., see Sadeh et al., Nat Biotechnol (2021) 39: 586–598 and Jang et al., Life Sci Alliance (2023) 6(12):e202302003 the entire contents of each of which are incorporated herein by reference).
  • cfChIP-seq uses antibodies or antibody fragments that bind specific histone modifications (e.g., H3K4me3 and/or H3K27ac) and/or transcription factors that are coupled (covalently or non-covalently) to beads, e.g., magnetic beads such as Dynabeads® magnetic beads and incubated with a volume, e.g., about 1 mL of thawed plasma obtained from a subject.
  • specific histone modifications e.g., H3K4me3 and/or H3K27ac
  • transcription factors that are coupled (covalently or non-covalently) to beads, e.g., magnetic beads such as Dynabeads® magnetic beads and incubated with a volume, e.g., about 1 mL of thawed plasma obtained from a subject.
  • exemplary antibodies that bind H3K4me3 include PA5-27029 (available from Thermo Fisher Scientific in Waltham, MA) and C15410003 (available from Diagenode in Denville, NJ) and exemplary antibodies that bind H3K27ac include ab21623 or ab4729 (both available from Abcam in Cambridge, UK) and C15210016 (available from Diagenode in Denville, NJ).
  • the antibodies or antibody fragments can be covalently coupled to beads, e.g., epoxy beads.
  • the antibodies or antibody fragments can be non-covalently coupled to beads, e.g., Protein A or Protein G beads such as Dynabeads® Protein A or Dynabeads® Protein G beads.
  • a cfDNA library is then typically prepared from the captured cfDNA. Library preparation can be done on-bead or after releasing the captured cfDNA by digestion of bound histones, e.g., using proteinase K. The cfDNA library is then sequenced to generate reads of captured cfDNA sequences, e.g., by next-generation
  • a cfChIP-seq bioinformatic pipeline can include, e.g., alignment of sequence reads to a reference genome with BWA or Bowtie2. Aligned reads can be used to call and quantify peaks as compared to a reference. In some embodiments, histone modifications at a given genomic loci can be quantified using sequencing data.
  • histone modifications can be quantified by counting the number of sequence reads that fall within a genomic loci (e.g., have at least one nucleotide overlapping with a genomic loci). In some embodiments, non-uniquely mapped and/or redundant sequence reads are discarded prior to quantifying histone modifications. In some embodiments, when quantifying histone modifications, sequence reads that fall within high noise regions of the genome are ignored. [0155] In some embodiments, sequence reads are adjusted on the basis of sequencing depth prior to counting. Adjusting on the basis of sequencing depth can include, e.g., quantile normalizing sequence reads to a common reference distribution. In some embodiments, sequence reads are adjusted on the basis of ChIP quality prior to counting.
  • sequence reads are normalized relative to aggregate counts across a set of regions (e.g., 1,000, 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, 10,000 or more regions) previously determined to have DNAse hypersensitivity in most cell types.
  • an estimate of local background signal is subtracted from the count of sequence reads at each genomic loci.
  • CUT&Tag involves antibody-based binding of a target protein, e.g., transcription factor or histone modification of interest, where antibody incubation is directly followed by the shearing of the chromatin and library preparation (see Kaya-Okur et al., Nat Comm (2019) 10:1930).
  • Tn5 is a Mg 2+ -dependent enzyme so Mg 2+ can be added to activate the reaction, which results in the chromatin being cut close to the protein binding site and simultaneous addition of the NGS adapter DNA sequences. Chromatin cleavage and library preparation can be achieved in one single step.
  • CUT&RUN is an epigenomic profiling strategy in which antibody-targeted controlled cleavage by micrococcal nuclease releases specific protein-DNA complexes into the supernatant for paired-end DNA sequencing (see Skene and Henikoff, Elife (2017) 6:1-35, Skene et al., Nat Protoc (2016) 13:1006-1019). As only targeted fragments enter into solution, and the vast majority of DNA is left behind, CUT&RUN has low background levels.
  • a sample is incubated with an antibody or antibody fragment that binds the target protein, e.g., transcription factor or histone modification of interest.
  • Suitable DNA sequencing technologies include, e.g., next generation sequencing (NGS) approaches. Additional steps that are required to prepare DNA for sequencing via an appropriate sequencing approach can be incorporated into methods described herein.
  • NGS next generation sequencing
  • a method described herein comprises attaching (e.g., ligating) DNA adapters to cfDNA.
  • DNA adapters can be attached prior to, during, or after enrichment for a histone modification.
  • a method comprises amplifying cfDNA after attaching DNA adapters.
  • Techniques for Detecting and Quantifying Chromatin Accessibility [0159] Various techniques of molecular biology are well known in the art and/or disclosed in the present application for detecting and quantifying chromatin accessibility.
  • the methods, kits and systems of the present disclosure involve the detection and quantification of chromatin accessibility in samples, e.g., in liquid biopsy samples including cfDNA such as plasma samples including cfDNA. ATAC-seq (Assay of Transpose Accessible
  • a typical MNase- seq assay can include a first step in which nuclei are isolated from either native or crosslinked chromatin and digested using MNase with titration.
  • In vivo formaldehyde crosslinking step that is designed to capture the interaction between proteins and DNA. This crosslinking allows bound proteins to shield their associated DNA from digestion by MNase.
  • samples are digested with MNase, which can be specifically activated by addition of Ca2+ to the buffer. Digestion can be halted by chelating the reaction, at which point the samples are RNase treated, crosslinks are reversed, and proteins are digested away from the chromatin.
  • DNA can then be isolated via a phenol-chloroform extraction. Uncut DNA is purified and mononucleosome bands are isolated and excised through gel electrophoresis. Isolated DNA can be amplified by adding adapters to generate a library, and sequenced. MNase-seq primarily
  • FAIRE-seq is a method in which nucleosome-depleted regions of DNA (NDRs) are isolated from chromatin.
  • a typical FAIRE-seq assay can include a first step in which cells are fixed using formaldehyde so that histones are crosslinked to interacting DNA. Crosslinked chromatin can then be sheared by sonication that generates protein-free DNA and protein- crosslinked DNA fragments.
  • Protein-free DNA can be isolated using a phenol–chloroform extraction: DNA crosslinked with protein stays in organic phase, while protein-free DNA stays in aqueous phase. Highly crosslinked DNA remains in the organic phase and the non-crosslinked DNA is pulled to the aqueous phase. Non-crosslinked DNA from the aqueous phase can then be amplified and sequenced. Reads enriched in the sequencing pool tend to have lower nucleosome and transcription factor binding and are therefore inferred to come from accessible regions.
  • NOMe-seq is a method to identify nucleosome-depleted regions of DNA (NDRs) with M.CviPI methyltransferase that methylates cytosine in GpC dinucleotides not protected by nucleosomes or other proteins. Unlike C m pG, GpC m in the human genome does not occur naturally in most cell types. GpC m levels at open chromatin regions can be compared to background signals and used to detect and quantify NDRs.
  • a typical NOMe-seq protocol can include a step in which samples are treated with M.CviPI and S-adenosylhomocysteine (SAM) to methylate accessible GpC sites.
  • SAM S-adenosylhomocysteine
  • M.CviPI treated DNA can be sheared using a sonicator, so that DNA fragments can be sequenced.
  • DNA is treated with bisulfite, which converts unmethylated cytosine to uracil using sodium bisulfite, while methylated cytosine is unaffected.
  • a library is generated using adapters and sequenced. Accessible chromatin is expected to have high levels of GpC m but low levels of C m pG. Therefore, NOMe-seq identifies NDRs using the two separate methylation analyses that serve as independent (but opposite) measures, providing matched chromatin designations for each regulatory element.
  • ATAC-seq uses hyperactive Tn5 transposase that preferentially cuts accessible chromatin regions and simultaneously inserts adapters to the fragmented region (Buenrostro et al., Nat Methods (2013) 10(12):1213-1218 the entirety of which is incorporated herein by reference).
  • a typical ATAC-seq assay can include a first step in which samples are incubated with Tn5 transposase. DNA can then be isolated and purified. DNA fragmented and tagged by
  • Bisulfite sequencing (BS-Seq), Whole Genome Bisulfite Sequencing (WGBS), Methylated DNA ImmunoPrecipitation sequencing (MeDIP-seq), or Methyl-CpG-Binding Domain sequencing (MBD-seq) are exemplary techniques of molecular biology useful in detecting and quantifying chromatin accessibility in samples.
  • Reduced representation bisulfite sequencing (RRBS) is another alternative method that could be used (see Meissner et al., Nucleic Acids Res (2005) 33(18):5868-5877).
  • Illumina Infinium arrays could also be used to detect and quantify DNA methylation.
  • DNA methylation typically refers to the methylation of the 5’ position of cytosine (mC) by DNA methyltransferases (DNMT). It is a major epigenetic modification in humans and many other species. In mammals, most DNA methylations occur within the context of CpG dinucleotides. DNA methylation is thought to be a repressive chromatin modification. Aberrant methylation can lead to many diseases including cancers (Robertson, Nat Rev Genet (2005) 6:597–610 and Bergman and Cedar, Nat Struct Mol Biol (2013) 20:274–281).
  • BS-Seq Bisulfite sequencing
  • WGBS Whole-Genome Bisulfite Sequencing
  • genomic DNA is treated with sodium bisulfite and then sequenced, providing single-base resolution of methylated cytosines in the genome.
  • unmethylated cytosines are deaminated to uracils which, upon sequencing, are converted to thymidines.
  • methylated cytosines resist deamination and are read as cytosines. The location of the methylated cytosines can then be determined by comparing treated and untreated sequences.
  • methylated DNA can be sequenced using a method that comprises enriching for cfDNA that comprises methylated DNA. Enrichment can be accomplished e.g., using an agent that selectively binds methylated DNA (e.g., an antibody as in MeDIP-seq or a
  • an agent that binds methylated DNA is attached (e.g., via a covalent or noncovalent bond) to a physical support (e.g., a bead, a magnetic bead, an agarose bead, or a magnetic epoxy bead), wherein the attaching can be prior to, during, or after incubation with a sample.
  • a physical support e.g., a bead, a magnetic bead, an agarose bead, or a magnetic epoxy bead
  • MeDIP-seq antibody or antibody-fragment that binds 5-methylcytidine (5mC) is used to enrich methylated DNA fragments, then these fragments are sequenced and analyzed. If using 5mC-specific antibodies or antibody fragments, methylated DNA is isolated from genomic DNA via immunoprecipitation. Anti-5mC antibodies are incubated with fragmented genomic DNA and precipitated, followed by DNA purification and sequencing.
  • Methyl-CpG-Binding Domain sequencing (MBD-seq) is similar to MeDIP-seq except that it uses methyl binding domain (MBD) proteins instead of antibodies or antibody fragments to bind methylated DNA.
  • DNA methylation at a given genomic loci can be quantified by sequencing methylated DNA.
  • DNA methylation at a genomic loci can be quantified by counting the number of sequence reads that overlap with the genomic loci (e.g., comprise at least one nucleotide that overlaps with the genomic loci).
  • Suitable DNA sequencing technologies include, e.g., next generation sequencing (NGS) approaches. Additional steps that are required to prepare DNA for sequencing via an appropriate sequencing approach can be incorporated into methods described herein.
  • NGS next generation sequencing
  • a method described herein comprises attaching (e.g., ligating) DNA adapters to cfDNA.
  • DNA adapters can be attached prior to, during, or after enrichment for a histone modification. Classifiers
  • the present disclosure provides methods for obtaining a classifier, e.g., a validated classifier that can be used to determine HER2 status.
  • a subject is determined to have a validated epigenetic profile indicative of a HER2-positive cancer based on analysis of a biological sample, optionally of cell-free DNA (cfDNA) from a liquid biopsy sample, obtained or derived from the subject, wherein the presence of the validated epigenetic profile has been determined using a validated classifier.
  • cfDNA cell-free DNA
  • a classifier e.g., a validated classifier that can be used to determine HER2 status and that the present disclosure is not limited to classifiers obtained in accordance with this method.
  • Exemplary Genomic Loci The present disclosure includes the identification of exemplary genomic loci that are differentially modified and/or differentially accessible in HER2-positive (e.g., HER2-3+ cancer based on IHC testing) vs. HER2-negative cancer (e.g., HER2-0 cancer based on IHC testing).
  • the present disclosure encompasses methods that use any of the genomic loci in Table 1-3 and also subregions thereof, i.e., references herein to methods that involve detecting and/or quantifying one or more histone modifications, chromatin accessibility, binding of one or more transcription factors, and/or DNA methylation at one or more genomic loci of Table 1-3 encompasses methods that detect these marks anywhere within these genomic loci including within any subregions. For example, where Table 2 references chr1:1097716-1101022 as a genomic locus for detecting and/or quantifying
  • a subregion may span at least 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500 or at least 3000 contiguous base pairs that are located between the lower and upper coordinates of a genomic locus recited in Tables 1-3.
  • a subregion may span less than 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500 or at least 3000 contiguous base pairs that are located between the lower and upper coordinates of a genomic locus recited in Tables 1-3.
  • a subregion may have the same central coordinate as a genomic locus recited in Tables 1-3.
  • a subregion may have a different central coordinate as a genomic locus recited in Tables 1-3.
  • a classifier is generated using a set of differentially modified and/or differentially accessible genomic loci that are correlated with HER2-positive status and a set of differentially modified and/or differentially accessible loci that are correlated with HER2-negative status. Sequence reads that fall into each selected genomic locus are analyzed and counted, e.g., as described herein including the Examples.
  • counts from genomic loci that are correlated with HER2-positive status are aggregated and counts from genomic loci that are correlated with HER2-negative status are aggregated.
  • a ratio of the aggregated HER2-positive and HER2-negative counts is used to determine HER2 status.
  • Other ways of using the genomic loci and related sequencing data to generate and apply a classifier to determine HER2 status are described herein and known in the art, e.g., without limitation, methods that use a learning statistical classifier system or a combination of learning statistical classifier systems.
  • exemplary genomic loci from Table 1, 2 or 3 are used in a monomodal classifier, e.g., a classifier that uses a single histone modification (e.g., H3K4me3 or H3K27ac) or DNA methylation at one or more genomic loci for purposes of determining HER2 status.
  • exemplary genomic loci from Table 1, 2 and/or 3 are used in a monomodal classifier, e.g., a classifier that uses a single histone modification (e.g., H3K4me3 or H3K27ac) or DNA methylation at one or more genomic loci for purposes of determining HER2 status.
  • exemplary genomic loci from Table 1, 2 and/or 3 are used in
  • a multimodal classifier e.g., a classifier that uses more than one histone modification (e.g., H3K4me3 and H3K27ac) or one or more histone modifications (e.g., H3K4me3 and/or H3K27ac) and DNA methylation at one or more genomic loci for purposes of determining HER2 status.
  • histone modification e.g., H3K4me3 and H3K27ac
  • H3K4me3 and/or H3K27ac histone modifications
  • DNA methylation e.g., DNA methylation at one or more genomic loci for purposes of determining HER2 status.
  • Differential H3K4me3 modification [0185] Genomic loci demonstrating differential H3K4 methylation (in particular H3K4 trimethylation, H3K4me3) in HER2-positive vs.
  • HER2-negative cancer are provided in Table 1 which shows the chromosomal coordinates of each genomic locus and its observed log2(fold- change) (HER2-positive/HER2-negative).
  • the genomic loci are sorted based on their chromosomal coordinates which are based on human genome build hg19.
  • a person of skill in the art will recognize that the methods disclosed herein do not require that every genomic locus listed in Table 1 be assessed for H3K4me3 modification. Instead, a subset of loci may be assessed for H3K4me3 modification.
  • the present disclosure particularly includes, among other things, subsets of the genomic loci of Table 1, which have an absolute log2(fold-change) of 6.0 or higher, 5.5 or higher, 5.0 or higher, 4.5 or higher, 4.0 or higher, 3.5 or higher, 3.0 or higher, 2.5 or higher, 2.0 or higher, 1.9 or higher, 1.8 or higher, 1.7 or higher, 1.6 or higher, 1.5 or higher, 1.4 or higher, 1.3 or higher, 1.2 or higher, 1.1 or higher, 1.0 or higher, 0.9 or higher, 0.8 or higher, 0.7 or higher, 0.6 or higher, or 0.5 or higher.
  • the present disclosure also includes subsets of the genomic loci of Table 1, which have an absolute log2(fold-change) of 6.0 or higher, 5.5 to less than 6.0, 5.0 to less than 5.5, 4.5 to less than 5.0,
  • a sample or subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000, 1500, 2000, 2500, or 3000 loci identified in Table 1 (or any subset thereof) are differentially H3K4me3 modified as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a reference e.g., a sample from a HER2-negative or healthy subject.
  • a subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2- positive) if at least a number of loci identified in a Table 1 (or any subset thereof) having a lower bound selected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, or 300 and an upper bound selected from 10, 15, 20, 25, 50, 75, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000, 1500, 2000, 2500, or 3000 is found to be differentially H3K4me3 modified as compared to a reference (e.g., a sample from a HER2- negative or healthy subject).
  • a reference e.g., a sample from a HER2- negative or healthy subject.
  • a sample or subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2- positive) if at least 1, 2, 3, 4, 5, 10, 20, 30, 40, or 50 loci identified in Table 1 (e.g., about 1 to about 1,000, about 5 to about 3,000, about 10 to about 1000, about 25 to about 200, about 5, about 10, about 20, or about 50 loci) are differentially H3K4me3 modified as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a reference e.g., a sample from a HER2-negative or healthy subject.
  • a sample or subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 1%, 2%, 3%, 4%, 5%, 10%, 20%, 30%, 40%, 50%, 75%, or 100% of loci identified in Table 1 are differentially H3K4me3 modified as compared to a reference (e.g., a sample from a HER2- negative or healthy subject).
  • a reference e.g., a sample from a HER2- negative or healthy subject.
  • a sample or subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least a percent of loci identified in Table 1 having a lower bound selected from 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 1%, 2%, 3%, 4%, 5%, or 10%, and an upper bound selected from 1%, 2%,
  • a sample or subject from which the sample is derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least one (e.g., at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10) of the top 3, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000, 1500, 2000, 2500, or 3000 loci identified in Table 1 are differentially H3K4me3 modified as compared to a reference (e.g., a sample from a HER2- negative or healthy subject) (wherein, e.g., the “top” 10 loci refers to the loci with 10 highest absolute log2(fold-change) in Table 1).
  • a reference e.g., a sample from a HER2- negative or healthy subject
  • a subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least five of the top 25 loci identified in Table 1 are differentially H3K4me3 modified as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least five of the top 50 loci identified in Table 1 are differentially H3K4me3 modified as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a sample or subject from which the sample is derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least one of the top 50 loci (e.g., at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10, at least 15, at least 20, or at least 25, at least 30, at least 35, at least 40, at least 45, or 50) identified in Table 1 and at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000, 1500, 2000, 2500, or 3000 loci identified in Table 1 (or any subset thereof) in total are differentially H3K4me3 modified as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a reference e.g., a sample from a HER2-negative or healthy
  • a sample or subject from which the sample is derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least five of the top 25 loci identified in Table 1 and at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000, 1500, 2000, 2500, or 3000 loci identified in Table 1 (or any subset thereof) in total are differentially H3K4me3 modified as compared to a reference (e.g., a sample from a HER2- negative or healthy subject).
  • a reference e.g., a sample from a HER2- negative or healthy subject.
  • a sample or subject from which the sample is derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least five of the top 50 loci identified in Table 1 and at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000, 1500, 2000, 2500, or 3000 loci
  • HER2 status e.g., HER2-positive
  • an increase or decrease in a value measuring methylation can be, or is expressed as, a log2(fold-change), e.g., a log2(fold-change) of at least 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 75%, 100%, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, 10-fold, 15-fold, 20-fold, or greater, or any range in between, inclusive, such as an increase or decrease of 0.1-fold to 10- fold, 0.2-fold to 5-fold, 0.2-fold to 4.0-fold, 0.4-4.0-fold, 0.4-fold to 4.0-fold, 0.6-fold to 4.0- fold, 0.8-fold to 4.0-fold, 1.0-fold to 4.0-fold.1.2-fold to 4.0-fold.1.4-fold to 4.0-fold, 1.6-fold
  • a sample or subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 k4 loci identified in Table 5 (or any subset thereof) are differentially H3K4me3 modified as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a reference e.g., a sample from a HER2-negative or healthy subject.
  • a sample or subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or 17 k4 loci identified in Table 7 (or any subset thereof) are differentially H3K4me3 modified as compared to a reference (e.g., a sample from a HER2- negative or healthy subject).
  • a reference e.g., a sample from a HER2- negative or healthy subject.
  • Differential H3K27ac modification Genomic loci demonstrating differential H3K27ac modification in HER2-positive vs.
  • HER2-negative cancer are provided in Table 2, which shows the chromosomal coordinates of each genomic locus and its observed log2(fold-change) (HER2-positive/HER2-negative).
  • the genomic loci are sorted based on their chromosomal coordinates which are based on human genome build hg19. [0195]
  • a person of skill in the art will recognize that the methods disclosed herein do not require that every genomic locus listed in Table 2 be assessed for H3K27ac modification. Instead, a subset of loci may be assessed for H3K27ac modification.
  • Subsets of the genomic loci of Table 2 can be selected (e.g., for use in determining HER2 status) based on various performance criteria, e.g., to select genomic loci that demonstrate differential modification with a particular level of statistical significance and/or a particular threshold of differential between relevant states (e.g., a measured log2(fold-change)). Subsets of the genomic loci may also be selected based on an algorithm, e.g., during the process of obtaining a classifier.
  • subsets of loci of Table 2 are together, individually, and/or in randomly selected subsets, at least as informative (e.g., as statistically significant and/or reliable) for uses disclosed herein, e.g., for determining HER2 status. See also the Examples of the present disclosure for experiments showing that informative classifiers can be generated using many different combinations of the loci.
  • the present disclosure particularly includes, among other things, subsets of the genomic loci of Table 2, which have an
  • the present disclosure also includes subsets of the genomic loci of Table 2, which have an absolute log2(fold-change) of 6.0 or higher, 5.5 to less than 6.0, 5.0 to less than 5.5, 4.5 to less than 5.0, 4.0 to less than 4.5, 3.8 to less than 4.0, 3.6 to less than 3.8, 3.4 to less than 3.6, 3.2 to less than 3.4, 3.0 to less than 3.2, 2.8 to less than 3.0, 2.6 to less than 2.8, 2.4 to less than 2.6, 2.2 to less than 2.4, 2.0 to less than 2.2, 1.8 to less than 2.0, 1.6 to less than 1.8, 1.4 to less than 1.6, 1.2 to less than 1.4, 1.0 to less than 1.2, 0.8 to less than 1.0, or 0.6 to less than 0.8.
  • a sample or subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000, 1500, 2000, 2500, or 3000 loci identified in Table 2 (or any subset thereof) are H3K27ac modified as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a reference e.g., a sample from a HER2-negative or healthy subject.
  • a subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least a number of loci identified in a Table 2 (or any subset thereof) having a lower bound selected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, or 300 and an upper bound selected from 10, 15, 20, 25, 50, 75, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000, 1500, 2000, 2500, or 3000 is found to be H3K27ac modified as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a reference e.g., a sample from a HER2-negative or healthy subject.
  • a sample or subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least 1, 2, 3, 4, 5, 10, 20, 30, 40, or 50 loci identified in Table 2 (e.g., about 1 to about 1,000, about 5 to about 3,000, about 10 to about 1000, about 25 to about 200, about 5, about 10, about 20, or about 50 loci) are H3K27ac modified as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a reference e.g., a sample from a HER2-negative or healthy subject.
  • a sample or subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2- positive) if at least 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 1%, 2%, 3%, 4%, 5%, 10%, 20%, 30%, 40%, 50%, 75%, or 100% of loci identified in Table 2 are H3K27ac modified as compared to a
  • a sample or subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least a percent of loci identified in Table 2 having a lower bound selected from 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 1%, 2%, 3%, 4%, 5%, or 10%, and an upper bound selected from 1%, 2%, 3%, 4%, 5%, 10%, 20%, 30%, 40%, 50%, 75%, or 100% is found to be H3K27ac modified as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a reference e.g., a sample from a HER2-negative or healthy subject.
  • a sample or subject from which the sample is derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least one (e.g., at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10) of the top 3, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000, 1500, 2000, 2500, or 3000 loci identified in Table 2 are H3K27ac modified as compared to a reference (e.g., a sample from a HER2-negative or healthy subject) (wherein, e.g., the “top” 10 loci refers to the loci with 10 highest absolute log2(fold-change) in Table 2).
  • a reference e.g., a sample from a HER2-negative or healthy subject
  • a subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least one of the top 50 loci identified in Table 2 is H3K27ac modified as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least five of the top 10 loci identified in Table 2 are H3K27ac modified as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least five of the top 25 loci identified in Table 2 are H3K27ac modified as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a reference e.g., a sample from a HER2-negative or healthy subject.
  • a sample or subject from which the sample is derived is determined to have a particular HER2 status (e.g., HER2- positive) if at least one of the top 50 loci (e.g., at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10, at least 15, at least 20, or at least 25, at least 30, at least 35, at least 40, at least 45, or 50) identified in Table 2 and at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000, 1500, 2000, 2500, or 3000 loci identified in Table 2 (or any subset thereof) in total are H3K27ac modified as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a reference e.g., a sample from a HER2-negative or healthy subject
  • a sample or subject from which the sample is derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least five of the top 25 loci identified in Table 2 and at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000, 1500, 2000, 2500, or 3000 loci identified in Table 2 (or any subset thereof) in total are H3K27ac modified as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a sample or subject from which the sample is derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least
  • differentially H3K27ac modified refers to an acetylation status characterized by an increase or decrease in a value measuring acetylation (e.g., of read counts and/or normalized read counts for a given genomic locus), and/or a mean, median and/or mode thereof, and/or a log thereof (e.g., log base 2 (log2)), of at least 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 75%, 100%, 2-fold, 3-fold, 4- fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, 10-fold, 15-fold, 20-fold, 25-fold, 30-fold, 35-fold, 40- fold, 45-fold, 50-fold, or greater, or any range in between, inclusive, such as 1% to 50%, 50% to 2-fold, 25% to 50-fold, 25% to 30-fold, 25% to
  • an increase or decrease in a value measuring acetylation can be, or is expressed as, a log2(fold-change), e.g., a log2(fold-change) of at least 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 75%, 100%, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, 10-fold, 15-fold, 20-fold, or greater, or any range in between, inclusive, such as an increase or decrease of 0.1-fold to 10- fold, 0.2-fold to 5-fold, 0.2-fold to 4.0-fold, 0.4-4.0-fold, 0.4-fold to 4.0-fold, 0.6-fold to 4.0- fold, 0.8-fold to 4.0-fold, 1.0-fold to 4.0-fold.1.2-fold to 4.0-fold.1.4-fold to 4.0-fold, 1.6
  • a sample or subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, or 29 k27
  • a sample or subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, or 43 k27 loci identified in Table 6 (or any subset thereof) are differentially H3K27ac modified as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a reference e.g., a sample from a HER2-negative or healthy subject.
  • HER2-negative cancer are provided in Table 3, which shows the chromosomal coordinates of each genomic locus and its observed log2(fold-change) (HER2-positive/HER2-negative).
  • the genomic loci are sorted based on their chromosomal coordinates which are based on human genome build hg19.
  • a person of skill in the art will recognize that the methods disclosed herein do not require that every genomic locus listed in Table 3 be assessed for DNA methylation. Instead, a subset of loci may be assessed for DNA methylation.
  • Subsets of the genomic loci of Table 3 can be selected (e.g., for use in determining HER2 status) based on various performance criteria, e.g., to select genomic loci that demonstrate differential modification with a particular level of statistical significance and/or a particular threshold of differential between relevant states (e.g., a measured log2(fold-change)). Subsets of the genomic loci may also be selected based on an algorithm, e.g., during the process of obtaining a classifier. Those of skill in the art will appreciate that such subsets of loci of Table 3, and loci included in such subsets, are together, individually, and/or in randomly selected subsets, at least as informative (e.g., as statistically
  • the present disclosure particularly includes, among other things, subsets of the genomic loci of Table 3, which have an absolute log2(fold-change) of 6.0 or higher, 5.5 or higher, 5.0 or higher, 4.5 or higher, 4.0 or higher, 3.5 or higher, 3.0 or higher, 2.5 or higher, 2.0 or higher, 1.9 or higher, 1.8 or higher, 1.7 or higher, 1.6 or higher, 1.5 or higher, 1.4 or higher, 1.3 or higher, 1.2 or higher, 1.1 or higher, 1.0 or higher, 0.9 or higher, 0.8 or higher, 0.7 or higher, 0.6 or higher, or 0.5 or higher.
  • the present disclosure also includes subsets of the genomic loci of Table 3, which have an absolute log2(fold-change) of 6.0 or higher, 5.5 to less than 6.0, 5.0 to less than 5.5, 4.5 to less than 5.0, 4.0 to less than 4.5, 3.8 to less than 4.0, 3.6 to less than 3.8, 3.4 to less than 3.6, 3.2 to less than 3.4, 3.0 to less than 3.2, 2.8 to less than 3.0, 2.6 to less than 2.8, 2.4 to less than 2.6, 2.2 to less than 2.4, 2.0 to less than 2.2, 1.8 to less than 2.0, 1.6 to less than 1.8, 1.4 to less than 1.6, 1.2 to less than 1.4, 1.0 to less than 1.2, 0.8 to less than 1.0, or 0.6 to less than 0.8.
  • a subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2- positive) if at least a number of loci identified in a Table 3 (or any subset thereof) having a lower bound selected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, or 300 and an upper bound selected from 10, 15, 20, 25, 50, 75, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000, 1500, 2000, 2500, or 3000 is found to be differentially DNA methylated as compared to a reference (e.g., a sample from a HER2- negative or healthy subject).
  • a reference e.g., a sample from a HER2- negative or healthy subject.
  • a sample or subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2- positive) if at least 1, 2, 3, 4, 5, 10, 20, 30, 40, or 50 loci identified in Table 3 (e.g., about 1 to about 1,000, about 5 to about 3,000, about 10 to about 1000, about 25 to about 200, about 5, about 10, about 20, or about 50 loci) are differentially DNA methylated as compared to a
  • a sample or subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least a percent of loci identified in Table 3 having a lower bound selected from 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 1%, 2%, 3%, 4%, 5%, or 10%, and an upper bound selected from 1%, 2%, 3%, 4%, 5%, 10%, 20%, 30%, 40%, 50%, 75%, or 100% is found to be differentially DNA methylated as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a reference e.g., a sample from a HER2-negative or healthy subject.
  • a sample or subject from which the sample is derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least one (e.g., at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10) of the top 3, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000, 1500, 2000, 2500, or 3000 loci identified in Table 3 are differentially DNA methylated as compared to a reference (e.g., a sample from a HER2-negative or healthy subject) (wherein, e.g., the “top” 10 loci refers to the loci with 10 highest absolute log2(fold-change) in Table 3).
  • a reference e.g., a sample from a HER2-negative or healthy subject
  • a subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least one of the top 10 loci identified in Table 3 is differentially DNA methylated as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least one of the top 25 loci identified in Table 3 is differentially DNA methylated as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least one of the top 50 loci identified in Table 3 is differentially DNA methylated as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2- positive) if at least five of the top 10 loci identified in Table 3 are differentially DNA methylated
  • a subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least five of the top 25 loci identified in Table 3 are differentially DNA methylated as compared to a reference (e.g., a sample from a HER2- negative or healthy subject).
  • a subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least five of the top 50 loci identified in Table 3 are differentially DNA methylated as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a reference e.g., a sample from a HER2-negative or healthy subject.
  • a sample or subject from which the sample is derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least one of the top 10 loci (e.g., at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or 10) identified in Table 3 and at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000, 1500, 2000, 2500, or 3000 loci identified in Table 3 (or any subset thereof) in total are differentially DNA methylated as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a reference e.g., a sample from a HER2-negative or healthy subject.
  • a sample or subject from which the sample is derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least one of the top 25 loci identified in Table 3 (e.g., at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10, at least 15, at least 20, or 25) and at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000, 1500, 2000, 2500, or 3000 loci identified in Table 3 (or any subset thereof) in total are differentially DNA methylated as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a reference e.g., a sample from a HER2-negative or healthy subject.
  • a sample or subject from which the sample is derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least one of the top 50 loci (e.g., at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10, at least 15, at least 20, or at least 25, at least 30, at least 35, at least 40, at least 45, or 50) identified in Table 3 and at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000, 1500, 2000, 2500, or 3000 loci identified in Table 3 (or any subset thereof) in total are differentially DNA methylated as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a sample or subject from which the sample is derived is determined to have a particular HER2 status (
  • a sample or subject from which the sample is derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least five of the top 50 loci identified in Table 3 and at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000, 1500, 2000, 2500, or 3000 loci identified in Table 3 (or any subset thereof) in total are differentially DNA methylated as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a reference e.g., a sample from a HER2-negative or healthy subject.
  • differentially DNA methylated refers to a methylation status characterized by an increase or decrease in a value measuring methylation (e.g., of read counts and/or normalized read counts for a given genomic locus), and/or a mean, median and/or mode thereof, and/or a log thereof (e.g., log base 2 (log2)), of at least 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 75%, 100%, 2-fold, 3-fold, 4- fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, 10-fold, 15-fold, 20-fold, 25-fold, 30-fold, 35-fold, 40- fold, 45-fold, 50-fold, or greater, or any range in between, inclusive, such as 1% to 50%, 50% to 2-fold, 25% to 50-fold, 25% to 30-fold, 25% to 20-fold, 25% to
  • an increase or decrease in a value measuring methylation can be, or is expressed as, a log2(fold-change), e.g., a log2(fold-change) of at least 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 75%, 100%, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, 10-fold, 15-fold, 20-fold, or greater, or any range in between, inclusive, such as an increase of 0.1-fold to 10-fold, 0.2-fold to 5-fold, 0.2-fold to 4.0-fold, 0.4-4.0-fold, 0.4-fold to 4.0-fold, 0.6-fold to 4.0-fold, 0.8-fold to 4.0-fold, 1.0-fold to 4.0-fold.1.2-fold to 4.0-fold.1.4-fold to 4.0-fold, 1.6-fold to 0.1-fold to
  • a sample or subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 mbd loci identified in Table 5 (or any subset thereof) are differentially methylated as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a reference e.g., a sample from a HER2-negative or healthy subject.
  • a sample or subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 mbd loci identified in Table 6 (or any subset thereof) are differentially methylated as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a reference e.g., a sample from a HER2-negative or healthy subject.
  • a sample or subject from which the sample is obtained or derived is determined to have a particular HER2 status (e.g., HER2-positive) if at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 mbd loci identified in Table 7 (or any subset thereof) are differentially methylated as compared to a reference (e.g., a sample from a HER2-negative or healthy subject).
  • a reference e.g., a sample from a HER2-negative or healthy subject.
  • Differential chromatin accessibility or transcription factor binding Genomic loci provided in Tables 1-3 can also demonstrate differential chromatin accessibility or transcription factor binding in HER2-positive cancer (e.g., HER2-3+ cancer based on IHC testing) vs.
  • HER2-negative cancer e.g., HER2-0 cancer based on IHC testing.
  • histone methylation e.g., H3K4me3
  • histone acetylation e.g., H3K27ac
  • DNA methylation corresponds and/or is correlated with chromatin accessibility.
  • chromatin accessibility corresponds and/or is correlated with H3K27ac modifications.
  • HER2 status may be determined by detecting and quantifying chromatin accessibility at one or more genomic loci in Table 2 in accordance with the section above discussing exemplary genomic loci with differential H3K27ac modifications.
  • chromatin accessibility corresponds and/or is correlated with DNA methylation.
  • HER2 status may be determined by detecting and quantifying chromatin accessibility at one or more genomic loci in Table 3 in accordance with the section above discussing exemplary genomic loci with differential DNA methylation.
  • histone methylation e.g., H3K4me3 corresponds and/or is correlated with transcription factor binding.
  • binding of p300, mediator complex, cohesin complex or RNA pol II corresponds and/or is correlated with H3K27ac modifications.
  • HER2 status may be determined by detecting and quantifying binding of p300, mediator complex, cohesin complex or RNA pol II at one or more genomic loci in Table 2 in accordance with the section above discussing exemplary genomic loci with differential H3K27ac modifications.
  • HER2 status may be determined by detecting and quantifying binding of FOXA1, ESR1, PR, MYC, EN1, FOXM1, KLF4, AP-2, RARa, RUNX1 at one or more genomic loci in Tables 1-3 in accordance with the sections above discussing exemplary genomic loci with differential histone methylation (e.g., H3K4me3), histone acetylation (e.g., H3K27ac) or DNA methylation.
  • Methods, kits and systems of the present disclosure include analysis of differentially modified and/or differentially accessible genomic loci to determine the HER2 status of a cancer.
  • a subject can be referred to as “asymptomatic” if the subject does not report, and/or demonstrate by non-invasively observable indicia (e.g., without one, several, or all of device-based probing, tissue sample analysis, bodily fluid analysis, surgery, or cancer screening), sufficient characteristics of cancer to support a medically reasonable suspicion that the subject is likely suffering from cancer, e.g., breast cancer, gastric/gastroesophageal cancer, colorectal cancer, or lung cancer.
  • Detection of early-stage cancer can be achieved using methods, kits and systems of the present disclosure, with attendant medical benefits including potential for early treatment and attendant improvement in therapeutic outcomes.
  • methods, kits, and systems of the present disclosure can be applied to a human subject that has increased susceptibility for cancer.
  • methods, kits and systems of the present disclosure can be applied to a symptomatic human subject.
  • a subject can be referred to as “symptomatic” if the subject report, and/or demonstrates by non-invasively observable indicia
  • a sample from a subject where the subject has a cancer that is known or suspected of being HER2-positive (or HER2-negative), can be assayed according to one or more embodiments of the present disclosure to determine if the cancer is in fact HER2-positive (or HER2-negative).
  • methods, kits and systems of the present disclosure can be used to determine that a subject has a HER2-positive cancer, optionally a HER2-positive cancer that correlates with HER2-3+, HER2-2+, or HER2-1+ scoring based on IHC testing or a HER2- low cancer based on IHC/ISH testing.
  • methods, kits and systems of the present disclosure can be used to determine that a subject has a HER2-negative cancer, optionally a HER2-negative cancer that correlates with HER2-0 scoring based on IHC testing.
  • methods, kits and systems of the present disclosure can be used to validate or confirm a prior determination that a subject has a HER2-positive cancer, optionally a HER2-3+, HER2-2+, or HER2-1+ cancer based on IHC testing or a HER2-low cancer based on IHC/ISH testing.
  • HER2 status is not limited to HER2-positive and
  • HER2-positive cancer detection in accordance with the present disclosure is carried out annually, and/or in which a subject is asymptomatic at time of detecting
  • methods, kits and systems of the present disclosure are especially likely to detect early-stage HER2-positive cancer.
  • detecting in accordance with methods, kits and systems of the present disclosure reduces cancer mortality, e.g., by early cancer diagnosis.
  • HER2 status determination in accordance with the present disclosure is performed once for a given subject or multiple times for a given subject.
  • HER2 status determination in accordance with the present disclosure is performed on a regular basis, e.g., every six months, annually, every two years, every three years, every four years, every five years, or every ten years.
  • methods, kits and systems disclosed herein provide a determination of HER2 status. In other instances, methods, kits and systems disclosed herein will be indicative of HER2 status but not definitive for HER2 status.
  • treatment of cancer includes administration of a therapeutic regimen including one or more treatments provided herein as available, appropriate, and/or preferred for a particular HER2 status.
  • methods, kits and systems can be used to determine whether a particular subject and/or cancer is likely to be and/or is characterized as responsive to HER2 targeted therapy. In some such embodiments, methods, kits and systems can be followed by treatment of the subject with a HER2 targeted therapy.
  • Responsiveness can refer to improvement in prognosis (e.g., increased time to cancer recurrence or increased life expectancy, e.g., overall survival, recurrence-free survival, metastasis-free survival, or disease-free survival). Responsiveness can refer to achievement of a treatment benefit, including e.g., improvement in one or more symptoms of cancer, e.g., breast cancer, gastric/gastroesophageal cancer, colorectal cancer, or lung cancer.
  • prognosis e.g., increased time to cancer recurrence or increased life expectancy, e.g., overall survival, recurrence-free survival, metastasis-free survival, or disease-free survival.
  • Responsiveness can refer to achievement of a treatment benefit, including e.g., improvement in one or more symptoms of cancer, e.g., breast cancer, gastric/gastroesophageal cancer, colorectal cancer, or lung cancer.
  • Responsiveness can be measured quantitatively (e.g., as in the case of tumor size; as in the case of measurement of histone modification, chromatin accessibility, transcription factor binding, or DNA methylation at one or more genomic loci; or as in the calculation of clinical benefit (CBR)), or qualitatively (e.g., by measures such as “pathological complete response” (pCR), “clinical complete remission” (cCR), “clinical partial remission” (cPR), “clinical stable disease” (cSD), “clinical progressive disease” (cPD), or other qualitative criteria).
  • CBR clinical benefit
  • Resistance can refer to the inability or unlikelihood of a therapy to achieve a desired therapeutic effect (e.g., a reduction in tumor size, improvement in prognosis, or other treatment benefit such as, e.g., improvement in one or more symptoms of cancer) in a subject and/or cancer. Resistance includes both acquired and natural resistance. In certain embodiments, resistance includes the extent to which one or more desired therapeutic benefits results from
  • kits and systems can be used to detect the clinical efficacy of a course of therapy for cancer, e.g., breast cancer, gastric/gastroesophageal cancer, colorectal cancer, or lung cancer.
  • methods and/or compositions of the present disclosure could be used to determine the presence, absence, or HER2 status of a cancer in a subject over the course of treatment.
  • Methods and/or compositions of the present disclosure could be used in conjunction with, or confirmed by, other means of determining the presence, absence, or HER2 status of a cancer including, for example measurements of tumor size or character by techniques such as CT, PET, mammogram, ultrasound, palpation, histology, caliper measurement after biopsy or surgical resection, or by various qualitative, quantitative, or semi quantitative scoring systems including without limitation based on IHC or ISH testing, residual cancer burden (Symmans et al., J Clin Oncol (2007) 25:4414-4422, incorporated by reference herein in its entirety) or Miller-Payne score (Ogston et al., Breast (2003) 12:320-327, incorporated by reference herein in its entirety) in a qualitative fashion like “pathological complete response” (pCR), “clinical complete remission” (cCR), “clinical partial remission” (cPR), “clinical stable disease” (cSD), or “clinical progressive disease” (cPD).
  • pathological complete response pCR
  • monitoring progression entails obtaining and characterizing samples from a subject at at least a first and a second time point.
  • a subject has already been diagnosed with a cancer (e.g., a HER2-positive or HER2-negative cancer).
  • a cancer has gone into remission for a subject (e.g., the subject has minimal residual disease).
  • methods, kits, and systems described herein can be useful, e.g., for detecting reoccurrence of cancer, and can be faster, less expensive, and/or less invasive than, e.g., approaches that rely on tissue biopsies and/or imaging techniques.
  • methods, kits and systems for HER2 status determination provided herein can inform treatment and/or payment (e.g., reimbursement for or reduction of cost of medical care, such as detecting or treatment) decisions and/or actions, e.g., by individuals, healthcare facilities, healthcare practitioners, health insurance providers, governmental bodies, or other parties interested in healthcare cost.
  • treatment and/or payment e.g., reimbursement for or reduction of cost of medical care, such as detecting or treatment
  • decisions and/or actions e.g., by individuals, healthcare facilities, healthcare practitioners, health insurance providers, governmental bodies, or other parties interested in healthcare cost.
  • methods, kits and systems for HER2 status determination can inform decision making relating to whether health insurance providers reimburse a healthcare cost payer or recipient (or not), e.g., for (1) HER2 status determination itself (e.g., reimbursement for detecting otherwise unavailable, available only for periodic/regular detecting, or available only for temporally- and/or incidentally- motivated detecting); and/or for (2) treatment, including initiating, maintaining, and/or altering therapy, e.g., based on the determined HER2 status.
  • HER2 status determination e.g., reimbursement for detecting otherwise unavailable, available only for periodic/regular detecting, or available only for temporally- and/or incidentally- motivated detecting
  • treatment including initiating, maintaining, and/or altering therapy, e.g., based on the determined HER2 status.
  • methods, kits and systems for HER2 status determination provided herein are used as the basis for, to contribute to, or support a determination as to whether a reimbursement or cost reduction will be provided to a healthcare cost payer or recipient.
  • a party seeking reimbursement or cost reduction can provide results of HER2 status determination conducted in accordance with the present disclosure together with a request for such reimbursement or reduction of a healthcare cost.
  • a party making a determination as to whether or not to provide a reimbursement or reduction of a healthcare cost will reach a determination based in whole or in part upon receipt and/or review of results of HER2 status determination conducted in accordance with the present disclosure.
  • HER2 status determination using methods, kits and systems disclosed herein can be used in classifying subjects, samples, and/or tumors (e.g., breast, gastric/gastroesophageal, colorectal, or lung cancer subjects, samples, and/or tumors).
  • tumors e.g., breast, gastric/gastroesophageal, colorectal, or lung cancer subjects, samples, and/or tumors.
  • methods, kits and systems disclosed herein can be used to generate a set of subjects, samples, and/or tumors identified according to the present methods, kits and systems each classified as corresponding to a particular HER2 status, and optionally using two or more of such classified subjects, samples, and/or tumors to identify biomarkers that distinguish the classes (i.e., distinguish the subjects, samples, and/or tumors according to their class, e.g., according to their HER2 status).
  • Sequence reads can be aligned to human genome build hg19, e.g., using the Burrows-Wheeler Aligner (BWA). Non-uniquely mapping and redundant reads are optionally discarded.
  • BWA Burrows-Wheeler Aligner
  • Non-uniquely mapping and redundant reads are optionally discarded.
  • MACS v2.1.1.20140616 can be used for sequence (e.g., ChIP-seq) peak calling with a q-value (FDR) threshold of 0.01.
  • Sequence (e.g., ChIP-seq) data quality can optionally be evaluated by any of one or more of a variety of measures, including total peak number, FRiP (fraction of reads in peak) score, number of high- confidence peaks (e.g., enriched > ten-fold over background), and percent of peak overlap with “blacklist” DHS peaks derived from the ENCODE project (Amemiya et al., Sci Rep (2019) 9(1):9354). If the sequence (e.g., ChIP-seq) data quality is below a particular threshold, the data may be discarded and the assay repeated.
  • measures including total peak number, FRiP (fraction of reads in peak) score, number of high- confidence peaks (e.g., enriched > ten-fold over background), and percent of peak overlap with “blacklist” DHS peaks derived from the ENCODE project (Amemiya et al., Sci Rep (2019) 9(1):9354). If the sequence
  • Sequence e.g., ChIP-seq
  • selected genomic loci that are differentially modified as provided herein for the relevant histone modification e.g., as provided in Tables 1-2 and/or 5-7
  • the number of reads overlapping the selected genomic loci for the relevant histone modification can be summed, e.g., in some embodiments all the genomic loci that are differentially modified with an absolute log2(fold-change) ⁇ 4.0 are selected.
  • the average number of reads in the local background of each ChIP-seq peak is subtracted to improve signal to noise.
  • a method comprises determining a HER2-positive/ HER2-negative ratio score, e.g., by a method that comprises (a) calculating a HER2-positive sequence read density, calculating a HER2-negative sequence read density, and dividing the HER2-positive sequence read density by the HER2- negative sequence read density.
  • a HER2-positive sequence read density can be determined by a method that comprises calculating sequence read density using one or more genomic loci with an increased level of one or more epigenetic biomarkers in sample(s) obtained from one or more subjects with a HER2 -positive cancer as compared to one or more sample(s) obtained from subjects with a HER2-negative cancer.
  • a HER2 - negative sequence read density can be determined by a method that comprises calculating sequence read density using one or more genomic loci with an increased level of one or more epigenetic biomarkers in sample(s) obtained from one or more subjects with a HER2-negative cancer as compared to one or more sample(s) obtained from subjects with a HER2-positive cancer.
  • a HER2-positive/ HER2-negative ratio score is determined for H3K4me3 modifications.
  • a HER2-positive/ HER2-negative ratio score is determined for H3K27ac modifications.
  • a HER2-positive/ HER2- negative ratio score is determined for methylated DNA. In some embodiments, a HER2-positive/ HER2-negative ratio score is determined for H3K4me3 modifications and H3K27ac modifications, H3K4me3 and methylated DNA, or H3K27ac and methylated DNA. In some embodiments, a HER2-positve/ HER2-negative ratio score is determined for each of H3K4me3 modifications, H3K27ac modifications, and methylated DNA. In some embodiments, two or more HER2-positive/ER-negative ratio scores for different epigenetic biomarkers can be combined. In some embodiments, each ratio score can be combined using fitted values that have been determined using a logistic regression. [0242] The data can then be log2-transformed and quantile normalized to match the distribution of the data used to train a classifier. Normalized data can be used as input into a logistic regression.
  • kits and systems for HER2 status determination of the present disclosure are at least for in vitro use. Accordingly, all aspects and embodiments of the present disclosure can be performed and/or used at least in vitro.
  • methods of the present disclosure can be implemented on and/or in conjunction with a computer program and computer system. In some embodiments, methods of the present disclosure can be implemented on and/or in conjunction with a non-transitory computer readable storage medium encoded with the computer program, wherein the program comprises instructions that when executed by one or more processors cause the one or more processors to perform operations to perform the method.
  • a computer system can also store and manipulate data generated by methods of the present disclosure that comprise a plurality of genomic locus modification status and/or accessibility status changes/profiles, which data can be used by a computer system in implementing methods disclosed herein.
  • a computer system receives modification status and/or accessibility status data; (ii) stores the data; and (iii) compares the data in any number of ways described herein (e.g., analysis relative to appropriate references), e.g., to determine HER2 status.
  • a computer system compares the genomic data in any number of ways described herein (e.g., analysis relative to appropriate references), e.g., to determine HER2 status.
  • a computer system compares the genomic data in any number of ways described herein (e.g., analysis relative to appropriate references), e.g., to determine HER2 status.
  • a computer system compares the genomic data in any number of ways described herein (e.g., analysis relative to appropriate references), e.g., to determine HER2
  • the software components can comprise both software components that are standard in the art and components that are special to the present disclosure (e.g., dCHIP software described in Lin et al., Bioinformatics (2004) 20:1233-1240, incorporated herein by reference in its entirety; radial basis machine learning algorithms (RBM) known in the art).
  • Methods of the present disclosure can also be programmed or modeled in mathematical software packages that allow symbolic entry of equations and high-level specification of processing, including specific algorithms to be used, thereby freeing a user of the need to procedurally program individual equations and algorithms.
  • a computer system comprises a database for storage of genomic locus modification status and/or accessibility status data. Such stored profiles can be accessed and used to perform comparisons of interest at a later point in time.
  • exemplary program structures and computer systems described herein other, alternative program structures and computer systems will be readily apparent to the skilled artisan.
  • an algorithm can be a single learning statistical classifier system.
  • learning statistical classifier systems include a machine learning algorithmic technique capable of adapting to complex datasets (e.g., a panel of genomic loci of interest) and making decisions based upon such datasets.
  • a single learning statistical classifier system such as a classification tree (e.g., random forest) is used.
  • learning statistical classifier systems include, but are not limited to, those described in the Examples and also those using inductive learning (e.g., decision/classification trees such as random forests, classification and regression trees (C&RT), boosted trees, etc.), Probably Approximately Correct (PAC) learning, connectionist learning (e.g., neural networks (NN), artificial neural networks (ANN), neuro fuzzy networks (NFN), network structures, perceptrons such as multi-layer perceptrons, multi-layer feed-forward networks, applications of neural networks, Bayesian learning in belief networks, etc.), reinforcement learning (e.g., passive learning in a known environment such as naive learning, adaptive dynamic learning, and temporal difference learning, passive learning in an unknown environment, active learning in an unknown environment, learning action-value functions, applications of reinforcement learning, etc.), and genetic algorithms and evolutionary programming.
  • inductive learning e.g., decision/classification trees such as random forests, classification and regression trees (C&RT), boosted trees, etc.
  • PAC Probably Approximately Correct
  • connectionist learning e.
  • methods of the present disclosure can include sending classification results to a medical practitioner, e.g., an oncologist.
  • a medical practitioner e.g., an oncologist.
  • the area under the receiver operating characteristic (AUROC) for determining if a subject has a particular HER2 cancer status e.g., a HER2- positive cancer vs.
  • a HER2-negative cancer is greater than 0.5 (e.g., greater than 0.55, greater than 0.6, greater than 0.65, greater than 0.7, greater than 0.75, greater than 0.8, greater than 0.85, greater than 0.9, or greater than 0.95).
  • a therapeutic agent or regimen is administered to a subject based on the HER2 status of a cancer (e.g., breast cancer, gastric/gastroesophageal cancer, colorectal cancer, lung cancer, etc.).
  • a cancer e.g., breast cancer, gastric/gastroesophageal cancer, colorectal cancer, lung cancer, etc.
  • the therapeutic agent or regimen provided herein will be available, appropriate, and/or preferred for the determined HER2 status.
  • Those of skill in the art will be aware of recommended and/or governmentally approved formulations and/or dosages for various therapeutic agents provided herein.
  • compositions for delivery of one or more therapeutic agents to a subject include pharmaceutical compositions for delivery of one or more therapeutic agents to a subject.
  • a pharmaceutical composition may be in any form known in the art, including formulations for administration according to any route known in the art. A suitable means of administration can be selected based on the age and condition of a subject.
  • Pharmaceutical composition forms of the present disclosure can include, e.g., liquid, semi-solid and solid dosage forms.
  • Pharmaceutical composition forms of the present disclosure can include, e.g., liquid solutions (e.g., injectable and infusible solutions), dispersions or suspensions, tablets, pills, powders, and liposomes.
  • compositions can be formulated for administration by a parenteral mode (e.g., intravenous, subcutaneous, intraperitoneal, or intramuscular injection) or a non-parenteral mode.
  • parenteral administration refers to modes of administration other than enteral and topical administration, usually by injection or infusion.
  • the compositions provided herein are present in unit dosage form, which unit dosage form can be suitable for self-administration.
  • a unit dosage form may be provided within a container, e.g., a pill, vial, cartridge, prefilled syringe, or disposable pen.
  • a pharmaceutical composition of the present disclosure can be in an injectable or infusible form.
  • the present disclosure includes sterile formulations for injection or infusion, which can be formulated in accordance with conventional pharmaceutical practices.
  • Sterile solutions can be prepared by incorporating a composition described herein in the required amount in an appropriate solvent with one or a combination of ingredients enumerated above, as required, followed by filter sterilization.
  • Solutions can be formulated, e.g., using distilled water, physiological saline, or an isotonic solution containing glucose and other supplements such as D- sorbitol, D-mannose, D-mannitol, or sodium chloride as an aqueous solution for injection, optionally in combination with a suitable solubilizing agent, for example, an alcohol such as ethanol and/or a polyalcohol such as propylene glycol or polyethylene glycol, and/or a nonionic surfactant such as polysorbate 80TM or HCO-50, and the like.
  • a suitable solubilizing agent for example, an alcohol such as ethanol and/or a polyalcohol such as propylene glycol or polyethylene glycol, and/or a nonionic surfactant such as polysorbate 80TM or HCO-50, and the like.
  • sterile powders for the preparation of sterile injectable solutions methods for preparation include vacuum drying and freeze-drying that yield a powder of a composition described herein plus any
  • a pharmaceutical composition can be formulated, for example, as a buffered solution at a suitable concentration and suitable for storage, e.g., at 2-8°C (e.g., 4°C).
  • a pharmaceutical composition of the present disclosure can be formulated as a solution, microemulsion, dispersion, liposome, or other ordered structure suitable for stable storage at high concentration.
  • dispersions are prepared by incorporating a composition described herein into a sterile vehicle that contains a basic dispersion medium and the required other ingredients from those enumerated above.
  • a pharmaceutical composition can be formulated to include a pharmaceutically acceptable carrier or excipient.
  • pharmaceutically acceptable carriers include, without limitation, any and all solvents, dispersion media, coatings, antibacterial and antifungal agents, isotonic and absorption delaying agents, and the like that are physiologically compatible.
  • compositions can be formulated with a carrier that will protect the therapeutic agent against rapid release, such as a controlled release formulation, including implants and microencapsulated delivery systems.
  • a carrier that will protect the therapeutic agent against rapid release
  • Biodegradable, biocompatible polymers can be used, such as ethylene vinyl acetate, polyanhydrides, polyglycolic acid, collagen, polyorthoesters, and polylactic acid.
  • Many methods for the preparation of such formulations are known in the art. See, e.g., J. R. Robinson (1978) “Sustained and Controlled Release Drug Delivery Systems,” Marcel Dekker, Inc., New York.
  • Route of administration can be parenteral, for example, administration by injection.
  • Administration by injection can be by intravenous injection, intramuscular injection, intraperitoneal injection, subcutaneous injection. Administration can be systemic or local.
  • a composition described herein can be therapeutically delivered to a subject by way of local administration.
  • local administration or “local delivery,” can refer to delivery that does not rely upon transport of the composition or therapeutic agent to its intended target tissue or site via the vascular system.
  • the composition may be
  • a pharmaceutical composition can be administered parenterally in the form of an injectable formulation comprising a sterile solution or suspension in water or another pharmaceutically acceptable liquid.
  • a pharmaceutical composition can be formulated by suitably combining the therapeutic molecule with pharmaceutically acceptable vehicles or media, such as sterile water and physiological saline, vegetable oil, emulsifier, suspension agent, surfactant, stabilizer, flavoring excipient, diluent, vehicle, preservative, binder, followed by mixing in a unit dose form required for generally accepted pharmaceutical practices.
  • pharmaceutically acceptable vehicles or media such as sterile water and physiological saline, vegetable oil, emulsifier, suspension agent, surfactant, stabilizer, flavoring excipient, diluent, vehicle, preservative, binder.
  • examples of oily liquid include sesame oil and soybean oil, and it may be combined with benzyl benzoate or benzyl alcohol as a solubilizing agent.
  • subcutaneous administration can be accomplished by means of a device, such as a syringe, a prefilled syringe, an auto-injector (e.g., disposable or reusable), a pen injector, a patch injector, a wearable injector, an ambulatory syringe infusion pump with subcutaneous infusion sets, or other device for combining with a therapeutic agent for subcutaneous injection.
  • a device such as a syringe, a prefilled syringe, an auto-injector (e.g., disposable or reusable), a pen injector, a patch injector, a wearable injector, an ambulatory syringe infusion pump with subcutaneous infusion sets, or other device for combining with a therapeutic agent for subcutaneous injection.
  • An injection system of the present disclosure may employ a delivery pen as described in U.S. Pat. No.5,308,341.
  • Pen devices most commonly used for self-delivery of insulin to patients with diabetes, are well known in the art. Such devices can include at least one injection needle, are typically pre-filled with one or more therapeutic unit doses of a solution that includes the therapeutic agent and are useful for rapidly delivering solution to a subject with as little pain as possible.
  • One medication delivery pen includes a vial holder into which a vial of a therapeutic or other medication may be received.
  • the pen may be an entirely mechanical device or it may be combined with electronic circuitry to accurately set and/or indicate the dosage of medication that is injected into the user. See, e.g., U.S. Pat. No.6,192,891.
  • U.S. Pat. No.6,192,891 See, e.g., U.S. Pat. No.6,192,891.
  • the needle of the pen device is disposable and the kits include one or more disposable replacement needles.
  • Pen devices suitable for delivery of any one of the presently featured compositions are also described in, e.g., U.S. Pat. Nos.6,277,099; 6,200,296; and 6,146,361, the disclosures of each of which are incorporated herein by reference in their entirety.
  • a microneedle-based pen device is described in, e.g., U.S. Pat. No.7,556,615, the disclosure of which is incorporated herein by reference in its entirety. See also the Precision Pen Injector (PPI) device, MOLLY TM , manufactured by Scandinavian Health Ltd.
  • PPI Precision Pen Injector
  • administration of a therapeutic agent as described herein is achieved by administering to a subject a nucleic acid encoding a therapeutic agent described herein.
  • Nucleic acids encoding a therapeutic agent described herein can be incorporated into a gene construct to be used as a part of a gene therapy protocol to deliver nucleic acids that can be used to express and produce therapeutic agent within cells.
  • Expression constructs of such components may be administered in any therapeutically effective carrier, e.g., any formulation or composition capable of effectively delivering the component gene to cells in vivo.
  • Approaches include insertion of the subject gene in viral vectors including recombinant retroviruses, adenovirus, adeno-associated virus, lentivirus, and herpes simplex virus-1 (HSV-1), or recombinant bacterial or eukaryotic plasmids.
  • Viral vectors can transfect cells directly; plasmid DNA can be delivered with the help of, for example, cationic liposomes (lipofectin) or derivatized, polylysine conjugates, gramicidin S, artificial viral envelopes or other such intracellular carriers, as well as direct injection of the gene construct or CaPO 4 precipitation.
  • a composition can be formulated for storage at a temperature below 0°C (e.g., -20°C or -80°C).
  • the composition can be formulated for storage for up to 2 years (e.g., one month, two months, three months, four months, five months, six months, seven months, eight months, nine months, 10 months, 11 months, 1 year, or 2 years) at 2-8°C (e.g., 4°C).
  • compositions described herein are stable in storage for at least 1 year at 2-8°C (e.g., 4°C).
  • a pharmaceutical composition can include a therapeutically effective amount of a therapeutic agent described herein. Such effective amounts can be readily determined by one of ordinary skill in the art. A therapeutically effective amount can be an amount at which any toxic
  • a dose can also be chosen to reduce or avoid production of antibodies or other host immune responses against a therapeutic agent.
  • Those of skill in the art will appreciate that data obtained from cell culture assays and animal studies can be used in formulating a range of dosage for use in humans.
  • the amount of active ingredient included in a pharmaceutical composition is such that a suitable dose within the designated range can be administered to subjects.
  • the dose and method of administration can vary depending on weight, age, condition, and other characteristics of a patient, and can be suitably selected as needed by those skilled in the art.
  • compositions including certain therapeutic agents can be administered as a fixed dose, or in a milligram per kilogram (mg/kg) dose.
  • an exemplary single dose of certain pharmaceutical compositions described herein can include certain therapeutic agents as described herein in an amount equal to, e.g., 0.001 to 1000 mg/kg, 1-1000 mg/kg, 1-100 mg/kg, 0.5-50 mg/kg, 0.1-100 mg/kg, 0.5-25 mg/kg, 1-20 mg/kg, and 1-10 mg/kg body weight.
  • Exemplary dosages of a composition described herein include, without limitation, 0.1 mg/kg, 0.5 mg/kg, 1 mg/kg, 2 mg/kg, 4 mg/kg, 8 mg/kg, or 20 mg/kg. The present disclosure is not limited to such ranges or dosages.
  • the present disclosure further includes methods of preparing pharmaceutical compositions of the present disclosure and kits including pharmaceutical compositions of the present disclosure.
  • therapeutic agents of the present disclosure can be administered to a subject in a course of treatment that further includes administration of one or more additional therapeutic agents or therapies that are not therapeutic agents (e.g., surgery or radiation).
  • Combination therapies of the present disclosure can include simultaneous exposure of a subject to therapeutic agents of two or more therapeutic regimens.
  • a therapeutic agent as described herein can be administered together with (e.g., at the same time and/or in the same composition as) an additional agent or therapy.
  • a therapeutic agent of the present disclosure can be administered separately from an additional therapeutic agent or therapy (e.g., at a different time and/or in a different composition than the additional therapeutic agent or therapy).
  • an additional therapeutic agent or therapy administered in combination with a therapeutic agent as described herein can be administered at the same time as therapeutic agent, on the same day as therapeutic agent, or in the same week as therapeutic agent.
  • an additional therapeutic agent or therapy administered in combination with a therapeutic agent as described herein can be administered such that administration of the therapeutic agent and the additional therapeutic agent or therapy are separated by one or more hours before or after, one or more days before or after, one or more weeks before or after, or one or more months before or after administration of the therapeutic agent.
  • the administration frequency and/or dosage of one or more additional therapeutic agents can be the same as, similar to, or different from the administration frequency of a therapeutic agent.
  • the two or more regimens can be administered simultaneously; in some embodiments, such regimens can be administered sequentially (e.g., all “doses” of a first regimen are administered prior to administration of any doses of a second regimen); in some embodiments, such therapeutic agents are administered in overlapping dosing regimens.
  • administration of a therapeutic agent can be to a subject having previously received, scheduled to receive, or in the course of a treatment regimen including an additional cancer therapy.
  • therapeutic agent combination therapies can demonstrate synergy and/or greater-than-additive effects between a therapeutic agent and one or more additional therapeutic agents with which it is administered in combination.
  • a therapeutic agent can be administered in any effective amount as determined independently or as determined by the joint action of therapeutic agent and any of one or more additional therapeutic agents or therapies administered.
  • Administration of the therapeutic agent may, in some embodiments, reduce the therapeutically effective dosage, required dosage, or administered dosage of the additional therapeutic agent or therapy relative to a reference regimen for administration of additional therapeutic agent or therapy or therapy absent the therapeutic agent.
  • a composition described herein can replace or augment other previously or
  • kits for detecting modification and/or accessibility of one or more genomic loci The present disclosure includes kits for quantifying one or more histone modifications, chromatin accessibility, binding of one or more transcription factors, and/or DNA methylation at one or more genomic loci. Kits of the present disclosure can include, e.g., reagents such as buffers and/or antibodies useful in the detection and quantification of histone modifications.
  • a kit of the present disclosure can include at least one antibody that selective binds a histone modification selected from H3K9ac, H3K14ac, H3K18ac, H3K23ac, H3K27ac, H3K4me1, H3K4me2, or H3K4me3, or pan acetylation.
  • a kit of the present disclosure can include at least one antibody that selective binds H3K4me3 modifications.
  • a kit of the present disclosure can include at least one antibody that selective binds H3K27ac modifications.
  • kits of the present disclosure can include instructional materials disclosing or describing the use of the kit in a method of determining HER2 status and/or treatment disclosed herein.
  • a kit of the present disclosure can include one or more therapeutic agents useful in the treatment of cancer, e.g., as disclosed herein, optionally in combination with instruction materials for treatment of cancer, e.g., breast cancer, gastric/gastroesophageal cancer, colorectal cancer, lung cancer, etc. based on HER2 status.
  • the kit comprises reagents for isolation of cell-free DNA (cfDNA) from a liquid biopsy sample. In some embodiments, the kit comprises reagents for library preparation for sequencing. In some embodiments, the kit comprises reagents for sequencing. In some embodiments, the kit comprises instructions for determining if a subject has a HER2-positive cancer.
  • the present disclosure includes systems for detecting modification and/or accessibility of one or more genomic loci. In some embodiments, the present disclosure provides systems for quantifying one or more histone modifications, chromatin accessibility, binding of one or more transcription factors, and/or DNA methylation at one or more genomic loci.
  • Systems of the present disclosure can include a sequencer configured to generate a sequencing dataset from a sample; and a non-transitory computer readable storage medium and/or a computer system.
  • the non-transitory computer readable storage medium is encoded with a computer program, wherein the program comprises instructions that when executed by one or more processors cause the one or more processors to perform operations to perform a method of the present disclosure.
  • the computer system comprises a memory and one or more processors coupled to the memory, wherein the one or more processors are configured to perform a method of the present disclosure.
  • the sequencer is configured to generate a Whole Genome Sequencing (WGS) dataset from the sample.
  • the system also includes a sample preparation device configured to prepare the sample for sequencing from a biological
  • the sample preparation device may include reagents for quantifying one or more histone modifications, chromatin accessibility, binding of one or more transcription factors, and/or DNA methylation at one or more genomic loci in cell-free DNA (cfDNA) from the biological sample, optionally the liquid biopsy sample.
  • reagents such as buffers and/or antibodies useful in the detection and quantification of histone modifications.
  • a system of the present disclosure can include at least one antibody that selective binds a histone modification selected from H3K9ac, H3K14ac, H3K18ac, H3K23ac, H3K27ac, H3K4me1, H3K4me2, or H3K4me3, or pan acetylation.
  • a system of the present disclosure can include at least one antibody that selective binds H3K4me3 modifications.
  • a system of the present disclosure can include at least one antibody that selective binds H3K27ac modifications.
  • a system of the present disclosure can include instructional materials disclosing or describing the use of the system in a method of determining HER2 status and/or treatment disclosed herein.
  • a system of the present disclosure comprises reagents for quantifying one or more histone modifications, chromatin accessibility, binding of one or more transcription factors, and/or DNA methylation at one or more genomic loci, wherein the one or more genomic loci are selected from Tabled 1-3, optionally wherein one or more of the genomic loci are not from the HER2 amplicon.
  • the system comprises reagents for quantifying H3K4me3 for at least 5, 10, 20, 30, 40, or 50 genomic loci in Table 1, optionally wherein one or more of the genomic loci are not from the HER2 amplicon.
  • the system comprises reagents for quantifying H3K27ac for at least 5, 10, 20, 30, 40, or 50 genomic loci in Table 2, optionally wherein one or more of the genomic loci are not from the HER2 amplicon.
  • the system comprises one or more antibodies for use in ChIP-seq, optionally wherein the one or more antibodies specifically bind H3K4me3- or H3K27ac-modified histones.
  • the system comprises reagents for quantifying DNA methylation for at least 5, 10, 20, 30, 40, or 50 genomic loci in Table 3, optionally wherein one or more of the genomic loci are not from the HER2 amplicon.
  • the system comprises one or more methyl-binding domains for use in MBD-seq.
  • the system comprises reagents for isolation of cell-free DNA (cfDNA) from a liquid biopsy sample.
  • the sequencer comprises reagents for library preparation for sequencing.
  • the sequencer comprises reagents for sequencing.
  • the system comprises instructions for determining if a subject has a HER2-positive cancer.
  • the cloud computing environment 1600 may include one or more resource providers 1602a, 1602b, 1602c (collectively, 1602). Each resource provider 1602 may include computing resources.
  • computing resources may include any hardware and/or software used to process data.
  • computing resources may include hardware and/or software capable of executing algorithms, computer programs, and/or computer applications.
  • illustrative computing resources may include application servers and/or databases with storage and retrieval capabilities.
  • Each resource provider 1602 may be connected to any other resource provider 1602 in the cloud computing environment 1600.
  • the resource providers 1602 may be connected over a computer network 1608.
  • Each resource provider 1602 may be connected to one or more computing device 1604a, 1604b, 1604c (collectively, 1604), over the computer network 1608.
  • the cloud computing environment 1600 may include a resource manager 1606.
  • the resource manager 1606 may be connected to the resource providers 1602 and the computing devices 1604 over the computer network 1608.
  • the resource manager 1606 may facilitate the provision of computing resources by one or more resource providers 1602 to one or more computing devices 1604.
  • the resource manager 1606 may receive a request for a computing resource from a particular computing device 1604.
  • the resource manager 1606 may identify one or more resource providers 1602 capable of providing the computing resource requested by the computing device 1604.
  • the resource manager 1606 may select a resource provider 1602 to provide the computing resource.
  • the resource manager 1606 may facilitate a connection between the resource provider 1602 and a particular computing device 1604.
  • the resource manager 1606 may establish a connection between a particular resource provider 1602 and a particular computing device 1604. In some implementations, the resource manager 1606 may redirect a particular computing device 1604 to a particular resource provider 1602 with the requested computing resource.
  • Fig. 17 shows an example of a computing device 1700 and a mobile computing device 1750 that can be used in the methods and systems described in this disclosure.
  • the computing device 1700 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers.
  • the mobile computing device 1750 is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart-phones, and other similar computing devices.
  • the components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to be limiting.
  • the computing device 1700 includes a processor 1702, a memory 1704, a storage device 1706, a high-speed interface 1708 connecting to the memory 1704 and multiple high-speed expansion ports 1710, and a low-speed interface 1712 connecting to a low-speed expansion port 1714 and the storage device 1706.
  • Each of the processor 1702, the memory 1704, the storage device 1706, the high-speed interface 1708, the high-speed expansion ports 1710, and the low- speed interface 1712 are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate.
  • the processor 1702 can process instructions for execution within the computing device 1700, including instructions stored in the memory 1704 or on the storage device 1706 to display graphical information for a GUI on an external input/output device, such as a display 1716 coupled to the high-speed interface 1708.
  • multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory.
  • multiple computing devices may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system). Also, multiple computing devices may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system).
  • a processor any number of processors (e.g., one or more processors) of any number of computing devices (e.g., one or more computing devices).
  • the memory 1704 stores information within the computing device 1700.
  • the memory 1704 is a volatile memory unit or units.
  • the memory 1704 is a non-volatile memory unit or units.
  • the memory 1704 may also be another form of computer-readable medium, such as a magnetic or optical disk.
  • the storage device 1706 is capable of providing mass storage for the computing device 1700.
  • the storage device 1706 may be or contain a computer- readable medium, such as a hard disk device, an optical disk device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations.
  • Instructions can be stored in an information carrier.
  • the instructions when executed by one or more processing devices (for example, processor 1702), perform one or more methods, such as those described above.
  • the instructions can also be stored by one or more storage devices such as computer- or machine-readable mediums (for example, the memory 1704, the storage device 1706, or memory on the processor 1702).
  • the high-speed interface 1708 manages bandwidth-intensive operations for the computing device 1700, while the low-speed interface 1712 manages lower bandwidth-intensive operations. Such allocation of functions is an example only.
  • the high- speed interface 1708 is coupled to the memory 1704, the display 1716 (e.g., through a graphics processor or accelerator), and to the high-speed expansion ports 1710, which may accept various expansion cards (not shown).
  • the low-speed interface 1712 is coupled to the storage device 1706 and the low-speed expansion port 1714.
  • the low-speed expansion port 1714 which may include various communication ports (e.g., USB, Bluetooth®, Ethernet, wireless Ethernet) may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter.
  • the computing device 1700 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a standard server 1720, or multiple times in a group of such servers. In addition, it may be implemented in a personal computer such
  • the mobile computing device 1750 includes a processor 1752, a memory 1764, an input/output device such as a display 1754, a communication interface 1766, and a transceiver 1768, among other components.
  • the mobile computing device 1750 may also be provided with a storage device, such as a micro-drive or other device, to provide additional storage.
  • a storage device such as a micro-drive or other device
  • the processor 1752 can execute instructions within the mobile computing device 1750, including instructions stored in the memory 1764.
  • the processor 1752 may be implemented as a chipset of chips that include separate and multiple analog and digital processors.
  • the processor 1752 may provide, for example, for coordination of the other components of the mobile computing device 1750, such as control of user interfaces, applications run by the mobile computing device 1750, and wireless communication by the mobile computing device 1750.
  • the processor 1752 may communicate with a user through a control interface 1758 and a display interface 1756 coupled to the display 1754.
  • the display 1754 may be, for example, a TFT (Thin-Film-Transistor Liquid Crystal Display) display or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology.
  • the display interface 1756 may comprise appropriate circuitry for driving the display 1754 to present graphical and other information to a user.
  • the control interface 1758 may receive commands from a user and convert them for submission to the processor 1752.
  • an external interface 1762 may provide communication with the processor 1752, so as to enable near area communication of the mobile computing device 1750 with other devices.
  • the external interface 1762 may provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces may also be used.
  • the memory 1764 stores information within the mobile computing device 1750.
  • the memory 1764 can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units.
  • An expansion memory 1774 may also be provided and connected to the mobile computing device 1750 through an expansion interface 1772, which may include, for example, a SIMM (Single In Line Memory Module) card interface.
  • SIMM Single In Line Memory Module
  • the expansion memory 1774 may provide extra storage space for the mobile computing device 1750, or may also store applications or other information for the mobile computing device 1750.
  • the expansion memory 1774 may include instructions to carry out or supplement the processes described above, and may include secure information also.
  • the expansion memory 1774 may be provided as a security module for the mobile computing device 1750, and may be programmed with instructions that permit secure use of the mobile computing device 1750.
  • secure applications may be provided via the SIMM cards, along with additional information, such as placing identifying information on the SIMM card in a non-hackable manner.
  • the memory may include, for example, flash memory and/or NVRAM memory (non-volatile random access memory), as discussed below.
  • instructions are stored in an information carrier and, when executed by one or more processing devices (for example, processor 1752), perform one or more methods, such as those described above.
  • the instructions can also be stored by one or more storage devices, such as one or more computer- or machine-readable mediums (for example, the memory 1764, the expansion memory 1774, or memory on the processor 1752).
  • the instructions can be received in a propagated signal, for example, over the transceiver 1768 or the external interface 1762.
  • the mobile computing device 1750 may communicate wirelessly through the communication interface 1766, which may include digital signal processing circuitry where necessary.
  • the communication interface 1766 may provide for communications under various modes or protocols, such as GSM voice calls (Global System for Mobile communications), SMS (Short Message Service), EMS (Enhanced Messaging Service), or MMS messaging (Multimedia Messaging Service), CDMA (code division multiple access), TDMA (time division multiple access), PDC (Personal Digital Cellular), WCDMA (Wideband Code Division Multiple Access), CDMA2000, or GPRS (General Packet Radio Service), among others.
  • GSM voice calls Global System for Mobile communications
  • SMS Short Message Service
  • EMS Enhanced Messaging Service
  • MMS Multimedia Messaging Service
  • CDMA code division multiple access
  • TDMA time division multiple access
  • PDC Personal Digital Cellular
  • WCDMA Wideband Code Division Multiple Access
  • CDMA2000 Code Division Multiple Access
  • GPRS General Packet Radio Service
  • Such sound may include sound from voice telephone calls, may include recorded sound (e.g., voice messages, music files, etc.) and may also include sound generated by applications operating on the mobile computing device 1750.
  • the mobile computing device 1750 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a cellular telephone 1780. It may also be implemented as part of a smart-phone 1782, personal digital assistant, or other similar mobile device. [0300] Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof.
  • ASICs application specific integrated circuits
  • These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
  • programmable processor which may be special or general purpose
  • These computer programs include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language.
  • machine-readable medium and computer- readable medium refer to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that
  • feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
  • feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
  • Systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components.
  • the components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.
  • the computing system can include clients and servers.
  • a client and server are generally remote from each other and typically interact through a communication network.
  • the relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
  • Certain embodiments described herein make use of computer algorithms in the form of software instructions executed by a computer processor, for example in a classifier.
  • the software instructions include a machine learning (ML) module, for example as a classifier.
  • ML machine learning
  • a machine learning module refers to a computer implemented process (e.g., a software function) that implements one or more specific machine learning techniques, e.g., artificial neural networks (ANNs), e.g., convolutional neural networks (CNNs), random forest, decision trees, support vector machines, and the like, in order to determine, for a given input, one or more output values.
  • ANNs artificial neural networks
  • CNNs convolutional neural networks
  • the input comprises image
  • 12366276v1 Attorney Docket No.2014191-0028 data and/or alphanumeric data which can include 2D and/or 3D datasets, numbers, words, phrases, or lengthier strings, for example.
  • the one or more output values comprise image data (e.g.2D and/or 3D datasets) and/or values representing numeric values, words, phrases, or other alphanumeric strings.
  • machine learning modules implementing machine learning techniques are trained, for example, using datasets that include categories of data described herein. Such training may be used to determine various parameters of machine learning algorithms implemented by a machine learning module, such as weights associated with layers in neural networks.
  • a machine learning module is trained, e.g., to accomplish a specific task such as identifying certain response strings, values of determined parameters are fixed and the (e.g., unchanging, static) machine learning module is used to process new data (e.g., different from the training data) and accomplish its trained task without further updates to its parameters (e.g., the machine learning module does not receive feedback and/or updates).
  • available input data includes training data and validation data, e.g., where the validation data is separate and non-overlapping with the training data. For example, in certain embodiments, training data is used during the training process to optimize a model, whereas validation data is used to check the accuracy of the model while operating on previously unseen data.
  • training data is divided into batches (e.g., portions) that is sequentially used (e.g., in random order) as sets of inputs to train a model.
  • a model is trained multiple times (e.g., epochs) on the entire set of training data.
  • machine learning modules may receive feedback, e.g., based on user review of accuracy, and such feedback may be used as additional training data, to dynamically update the machine learning module.
  • two or more machine learning modules may be combined and implemented as a single module and/or a single software application. In certain embodiments, two or more machine learning modules may also be implemented separately, e.g., as separate software applications.
  • a machine learning module may be software and/or hardware.
  • a machine learning module may be implemented entirely as software, or certain functions of a ANN module may be carried out via specialized hardware (e.g., via an application specific integrated circuit (ASIC) and/or field programmable gate arrays (FPGAs)).
  • ASIC application specific integrated circuit
  • FPGAs field programmable gate arrays
  • machine learning modules implementing machine learning techniques may be composed of individual nodes (e.g. units, neurons). A node may receive a set
  • a node may have at least one parameter to apply and/or a set of instructions to perform (e.g., mathematical functions to execute) over the set of inputs.
  • node instructions may include a step to provide various relative importance to the set of inputs using various parameters, such as weights. The weights may be applied by performing scalar multiplication (e.g., or other mathematical function) between a set of inputs values and the parameters, resulting in a set of weighted inputs.
  • a node may have a transfer function to combine the set of weighted inputs into one output value.
  • a transfer function may be implemented by a summation of all the weighted inputs and the addition of an offset (e.g., bias) value.
  • a node may have an activation function to introduce non-linearity into the output value.
  • Non-limiting examples of the activation function include Rectified Linear Activation (ReLu), logistic (e.g., sigmoid), hyperbolic tangent (tanh), and softmax.
  • a node may have a capability of remembering previous states (e.g., recurrent nodes).
  • the machine learning module comprises a deep learning architecture composed of nodes organized into layers.
  • a layer is a set of nodes that receives data input (e.g., weighted or non-weighted input), transforms it (e.g., by carrying out instructions, e.g., applying a set of functions e.g., linear and/or non-linear functions), and passes transformed values as output (e.g., to the next layer).
  • the set of nodes in a particular layer may share the same parameters and instructions without interacting with each other.
  • a machine learning module may be composed of at least one layer (e.g., ordered).
  • Examples of types of layers include convolutional layers (e.g., layers with a kernel, a matrix of parameters that is slid across an input to be multiplied with multiple input values to reduce them to a single output value); fully connected (FC) layers (e.g.
  • convolutional layers e.g., layers with a kernel, a matrix of parameters that is slid across an input to be multiplied with multiple input values to reduce them to a single output value
  • FC layers e.g.
  • recurrent layers long/short term memory (LSTM) layers, gated recurrent unit (GRU) layers (e.g., nodes with the various abilities to memorize and apply their previous inputs and/or outputs); batch normalization (BN) layers (e.g., layers that normalize a set of outputs from another layer, allowing for more independent learning of individual layers); activation layers (e.g., layers with nodes that only contain an activation function); and/or (un)pooling layers [e.g., layers
  • the performance of a machine learning module may be characterized by its ability to produce an output data with specific accuracy.
  • a training process is performed to find optimal parameters, such as weights, for each node in each layer of the machine learning module.
  • the training process of a machine learning module may involve using output data to calculate an objective function (e.g., cost function, loss function, error function) that needs to be optimized (e.g., minimized, maximized).
  • an objective function e.g., cost function, loss function, error function
  • a machine learning objective function may be a combination of a loss function and regularization parameter.
  • the loss function is related to how well the output is able to predict the input.
  • the loss function may take various forms, like mean squared error, mean absolute error, binary cross-entropy, categorical cross-entropy, for example.
  • the regularization term may be needed to prevent overfitting and improve generalization of the training process. Examples of regularization techniques include L1 Regularization or Lasso Regression, L2 Regularization or Ridge Regression, and Dropout (e.g., dropping layer outputs at random during training process).
  • objective function optimization of a machine learning module may involve finding at least one (e.g., all) of the present global optima (e.g., as opposed to local optima).
  • the algorithm for objective function optimization follows principles of mathematical optimization for a multi-variable function and relies on achieving specific accuracy of the process.
  • objective function optimization algorithms include gradient descent, nonlinear conjugate gradient, random search, Levenberg-Marquardt algorithm, limited-memory Broyden-Fietcher-Goldfarb-Shanno algorithm, pattern search, basin hopping method, Krylov method, Adam method, genetic algorithm, particle swarm optimization, surrogate optimization, and simulated annealing.
  • Methods disclosed herein may utilize one or more machine-learned models as a classifier.
  • a machine-learned model may be or include an artificial neural network.
  • a machine- learned model may employ, for example, an attention-based model (e.g., a transformer model, such as, for example, a vision transformer), a transformer model (e.g., a vision transformer), a regression-based model (e.g., a logistic regression model), a regularization-based model (e.g., an elastic net model or a ridge regression model), an instance-based model (e.g., a support vector
  • an attention-based model e.g., a transformer model, such as, for example, a vision transformer
  • a transformer model e.g., a vision transformer
  • a regression-based model e.g., a logistic regression model
  • regularization-based model e.g., an elastic net model or a ridge regression model
  • an instance-based model e.g., a support vector
  • a Bayesian-based model e.g., a naive-based model or a Gaussian naive-based model
  • a clustering-based model e.g., an expectation maximization model
  • an ensemble-based model e.g., an adaptive boosting model, a random forest model, a bootstrap-aggregation model, or a gradient boosting machine model
  • a neural-network-based model e.g., a convolutional neural network, a recurrent neural network, autoencoder, a back propagation network, or a stochastic gradient descent network.
  • a machine-learned model used as a classifier is or is derived from a decision tree methodology, a neural boosted methodology, a bootstrap forest methodology, a boosted tree methodology, a k nearest neighbors methodology, a generalized regression forward selection methodology, a generalized regression pruned forward selection methodology, a fit stepwise methodology, a generalized regression lasso methodology, a generalized regression elastic net methodology, a generalized regression ridge methodology, a nominal logistic methodology, a support vector machines methodology, a discriminant methodology, a na ⁇ ve Bayes methodology, or a combination thereof.
  • a machine-learned model is or is derived from a decision tree methodology, a neural boosted methodology, a bootstrap forest methodology, a boosted tree methodology, a generalized regression lasso methodology, a generalized regression elastic net methodology, a generalized regression ridge methodology, a nominal logistic methodology, a support vector machines methodology, a discriminant methodology, or a combination thereof.
  • a machine-learned model is or is derived from a decision tree methodology, a neural boosted methodology, a bootstrap forest methodology, a boosted tree methodology, a support vector machines methodology, or a combination thereof.
  • Accessibility status can be determined by various assays known in the art, including without limitation ChIP-seq as one example. Where two samples are separately analyzed by the same assay or comparable assays for detection of accessible DNA sequences, differences in chromatin accessibility status of genomic loci can be detected. Accessibility status can be compared to a standard or reference. A sample that has an accessibility status that differs in accessibility status from a standard or reference can be referred to as differentially modified. Suitable assays for determining chromatin accessibility are known in the art.
  • Exemplary assays include ATAC-seq (Assay of Transpose Accessible Chromatin sequencing), NOMe-seq (Nucleosome Occupancy and Methylome sequencing), FAIRE-seq (Formaldehyde-Assisted Isolation of Regulatory Elements sequencing), MNase-seq (Micrococcal Nuclease digestion with sequencing), and/or a DNase hypersensitivity assay.
  • Administration typically refers to the administration of a disease appropriate (e.g., HER2-positive cancer appropriate) treatment.
  • the disease appropriate treatment may comprise administering a composition to a subject, for example to achieve delivery of an agent that is, is included in, or is otherwise delivered by, the composition.
  • the disease appropriate treatment may comprise administering an appropriate surgical procedure or radiological procedure, optionally in combination with administration of a composition.
  • agent may refer to any chemical or physical entity, including without limitation any of one or more of an atom, e.g., a radioactive atom, molecule, compound, conjugate, polypeptide, polynucleotide, polysaccharide, lipid, cell, or combination or complex thereof.
  • antibody includes, without limitation, human antibodies, non-human antibodies, synthetic and/or engineered antibodies, fragments thereof, and agents including the same.
  • Antibodies can be naturally occurring immunoglobulins (e.g., generated by an organism reacting to an antigen). Synthetic, non-naturally occurring, or engineered antibodies can be produced by recombinant engineering, chemical synthesis, or other artificial systems or methodologies known to those of skill in the art.
  • each heavy chain includes a heavy chain variable domain (VH) and a heavy chain constant domain (CH).
  • VH heavy chain variable domain
  • CH heavy chain constant domain
  • the heavy chain constant domain includes three CH domains: CH1, CH2 and CH3.
  • a short region known as the “switch”, connects the heavy chain variable and constant regions.
  • the “hinge” connects CH2 and CH3 domains to the rest of the immunoglobulin.
  • Each light chain includes a light chain variable domain (VL) and a light chain constant domain (CL), separated from one another by another “switch.”
  • Each variable domain contains three hypervariable loops known as “complement determining regions” (CDR1, CDR2, and CDR3) and four somewhat invariant “framework” regions (FR1, FR2, FR3, and FR4).
  • CDR1, CDR2, and CDR3 Complement determining regions
  • FR1, FR2, FR3, and FR4 four somewhat invariant “framework” regions
  • the three CDRs and four FRs are arranged from amino-terminus to carboxy-terminus in the following order: FR1, CDR1, FR2, CDR2, FR3, CDR3, and FR4.
  • the variable regions of a heavy and/or a light chain are typically understood to provide a binding moiety that can interact with an antigen.
  • Constant domains can mediate binding of an antibody to various immune system cells (e.g., effector cells and/or cells that mediate cytotoxicity), receptors, and elements of the complement system.
  • Heavy and light chains are linked to one another by a single disulfide bond, and two other disulfide bonds connect the heavy chain hinge regions to one another, so that the dimers are connected to one another and the tetramer is formed.
  • the FR regions form the beta sheets that provide the structural framework for the domains, and the CDR loop regions from both the heavy and light chains are brought together in three- dimensional space so that they create a single hypervariable antigen binding site located at the tip of the Y structure.
  • an antibody is a polyclonal, monoclonal, monospecific, or multispecific antibody (e.g., a bispecific antibody).
  • an antibody includes at least one light chain monomer or dimer, at least one heavy chain monomer or dimer, at least one heavy chain-light chain dimer, or a tetramer that includes two heavy chain monomers and two light chain monomers.
  • antibody can include (unless otherwise stated or clear from context) any art-known constructs or formats utilizing antibody structural and/or functional features including without limitation intrabodies, domain antibodies, antibody mimetics, Zybodies®, Fab fragments, Fab’ fragments, F(ab’)2 fragments, Fd’ fragments, Fd fragments, isolated CDRs or sets thereof, single chain antibodies, single-chain Fvs (scFvs), disulfide-linked Fvs (sdFv), polypeptide-Fc fusions, single domain antibodies (e.g., shark single domain antibodies such as IgNAR or fragments thereof), cameloid antibodies, camelized antibodies, masked antibodies (e.g., Probodies®), affybodies, anti-idiotypic (anti-Id) antibodies (including, e.g., anti-anti-Id antibodies), Small Modular ImmunoPharmaceuticals (SMIPs), single chain or Tandem diabodies (TandAb®), VHHs
  • SMIPs single
  • an antibody includes one or more structural elements recognized by those skilled in the art as a complementarity determining region (CDR) or variable domain.
  • an antibody can be a covalently modified (“conjugated”) antibody (e.g., an antibody that includes a polypeptide including one or more canonical immunoglobulin sequence elements sufficient to confer specific binding to a particular antigen, where the polypeptide is covalently linked with one or more of a therapeutic agent, a detectable moiety, another polypeptide, a glycan, or a polyethylene glycol molecule).
  • conjugated antibody e.g., an antibody that includes a polypeptide including one or more canonical immunoglobulin sequence elements sufficient to confer specific binding to a particular antigen, where the polypeptide is covalently linked with one or more of a therapeutic agent, a detectable moiety, another polypeptide, a glycan, or a polyethylene glycol molecule.
  • antibody sequence elements are humanized, primatized, chimeric, etc.,
  • An antibody including a heavy chain constant domain can be, without limitation, an antibody of any known class, including but not limited to, IgA, secretory IgA, IgG, IgE and IgM, based on heavy chain constant domain amino acid sequence (e.g., alpha ( ⁇ ), delta ( ⁇ ), epsilon ( ⁇ ), gamma ( ⁇ ) and mu ( ⁇ )).
  • IgG subclasses are also well known to those in the art and
  • Isotype refers to the Ab class or subclass (e.g., IgM or IgG1) that is encoded by the heavy chain constant region genes.
  • a “light chain” can be of a distinct type, e.g., kappa ( ⁇ ) or lambda ( ⁇ ), based on the amino acid sequence of the light chain constant domain.
  • an antibody has constant region sequences that are characteristic of mouse, rabbit, primate, or human immunoglobulins. Naturally produced immunoglobulins are glycosylated, typically on the CH2 domain.
  • affinity and/or other binding attributes of Fc regions for Fc receptors can be modulated through glycosylation or other modification.
  • an antibody may lack a covalent modification (e.g., attachment of a glycan) that it would have if produced naturally.
  • antibodies produced and/or utilized in accordance with the present invention include glycosylated Fc domains, including Fc domains with modified or engineered glycosylation.
  • an antibody can be specific for a particular histone modification (e.g., an antibody can bind one histone modification, e.g., H3K27ac with a higher affinity than other histone modifications, under conditions that are commonly used in ChIP-seq experiments).
  • an antibody is specific for an H3K9ac, H3K14ac, H3K18ac, H3K23ac, H3K27ac, H3K4me1, H3K4me2, or H3K4me3 modification.
  • an antibody is specific for an H3K27ac modification.
  • an antibody is specific for an H3K4me3 modification.
  • an antibody is a “pan” antibody.
  • the term pan antibody refers to an antibody that can bind a group of histone modifications having one or more features that are similar.
  • a pan antibody is a pan-methylation antibody (e.g., an antibody that can bind a histone, e.g., H3 that comprises at least one methylated lysine, wherein the at least one methylated lysine can be at any one of a plurality of amino acid positions, e.g., in some embodiments, a pan-methylation antibody can bind an H3 protein comprising a methylated lysine at any position).
  • a pan antibody is a pan-acetylation antibody (e.g., an antibody that can bind a histone, e.g., H3 that comprises at least one acetylated lysine, wherein the at least one acetylated lysine can be at any one of a plurality of amino acid positions, e.g., a pan-acetylation antibody can bind an H3 protein comprising an acetylated lysine at any position).
  • a pan antibody can bind one or more histone modifications that are associated with transcription activation.
  • an “antibody fragment” refers to a portion of an antibody or antibody agent as described herein, and typically refers to a portion that includes an antigen-binding portion or variable region thereof.
  • An antibody fragment can be produced by any means. For example, in some embodiments, an antibody fragment can be enzymatically or chemically produced by fragmentation of an intact antibody or antibody agent. Alternatively, in some embodiments, an antibody fragment can be recombinantly produced, i.e., by expression of an engineered nucleic acid sequence.
  • an antibody fragment can be wholly or partially synthetically produced.
  • an antibody fragment (particularly an antigen-binding antibody fragment) can have a length of at least about 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190 amino acids or more, in some embodiments at least about 200 amino acids.
  • Two events or entities are “associated” with one another, as that term is used herein, if the presence, level and/or form of one is correlated with that of the other.
  • a particular entity e.g., an epigenetic profile comprising one or more histone modifications at a set of genomic loci, etc.
  • a particular disease, disorder, or condition if its presence, level and/or form correlates with incidence of and/or susceptibility to the disease, disorder, or condition (e.g., across a relevant population).
  • two or more entities are physically “associated” with one another if they interact, directly or indirectly, so that they are and/or remain in physical proximity with one another.
  • two or more entities that are physically associated with one another are covalently linked to one another; in some embodiments, two or more entities that are physically associated with one another are not covalently linked to one another but are non- covalently associated, for example by means of hydrogen bonds, van der Waals interaction, hydrophobic interactions, magnetism, or a combination thereof.
  • Between or “From” As used herein, the term “between” refers to content that falls between indicated upper and lower, or first and second, boundaries, inclusive of the boundaries. Similarly, the term “from”, when used in the context of a range of values, indicates that the range includes content that falls between indicated upper and lower, or first and second, boundaries, inclusive of the boundaries.
  • biological sample typically refers to a sample obtained or derived from a biological source (e.g., a tissue or organism or cell) of interest, as described herein.
  • a biological source is or includes an organism, such as a human subject.
  • a biological sample is or includes a biological tissue or fluid.
  • a biological sample can be or include cells, tissue, or bodily fluid.
  • Bodily fluids refer to fluids that are excreted or secreted from the body as well as fluids that are normally not (e.g., blood, serum, plasma, Cowper’s fluid or pre- ejaculate fluid, chyle, chyme, stool, interstitial fluid, intracellular fluid, lymph, menses, saliva, sebum, semen, serum, sweat, synovial fluid, tears, urine, vitreous humor, vomit).
  • a biological sample can be or include blood, blood components, cell-free DNA (cfDNA), circulating-tumor DNA (ctDNA), ascites, biopsy samples, surgical specimens, cell- containing body fluids, sputum, saliva, feces, urine, cerebrospinal fluid, peritoneal fluid, pleural fluid, lymph, gynecological fluids, secretions, excretions, skin swabs, vaginal swabs, oral swabs, nasal swabs, washings or lavages such as a ductal lavages or bronchoalveolar lavages, aspirates, scrapings, or bone marrow.
  • cfDNA cell-free DNA
  • ctDNA circulating-tumor DNA
  • a biological sample is a liquid biopsy sample obtained from a bodily fluid.
  • a biological sample is or includes DNA obtained from a single subject or from a plurality of subjects.
  • a biological sample can be a “primary sample” obtained directly from a biological source or can be a “processed sample”, i.e., a sample that was derived from a primary sample, e.g., via dilution, purification, mixing with one or more reagents, or any other processing step(s) as described herein.
  • Blood component refers to any component of whole blood, including red blood cells, white blood cells, plasma, platelets, endothelial cells, mesothelial cells, epithelial cells, cell-free DNA (cfDNA), and circulating- tumor DNA (ctDNA). Blood components also include the components of plasma, including proteins, metabolites, lipids, nucleic acids, and carbohydrates, and any other cells that can be present in blood, e.g., due to pregnancy, organ transplant, infection, injury, or disease.
  • cancer As used herein, the terms “cancer,” “malignancy,” “tumor,” and “carcinoma,” are used interchangeably to refer to a disease, disorder, or condition in which cells exhibit or exhibited relatively abnormal, uncontrolled, and/or autonomous growth, so that they display or displayed an abnormally elevated proliferation rate and/or aberrant growth phenotype.
  • a cancer can include one or more tumors.
  • a cancer can be or include cells that are precancerous (e.g., benign), malignant, pre-metastatic, metastatic, and/or non-metastatic.
  • a cancer can be or include a solid tumor.
  • a cancer be associated with HER2-positive status, e.g., a HER2-positive breast cancer, gastric/gastroesophageal cancer, colorectal cancer, lung cancer, etc.
  • Combination therapy refers to administration to a subject of two or more therapeutic agents or therapeutic regimens such that the two or more therapeutic agents or therapeutic regimens together treat a disease, condition, or disorder of the subject.
  • the two or more therapeutic agents or therapeutic regimens can be administered simultaneously, sequentially, or in overlapping dosing regimens.
  • combination therapy includes but does not require that the two therapeutic agents or therapeutic regimens be administered together in a single composition, nor at the same time.
  • the term “corresponding to” may be used to designate the position/identity of a structural element in a compound or composition through comparison with an appropriate reference compound or composition.
  • a monomeric residue in a polymer may be identified as “corresponding to” a residue in an appropriate reference polymer.
  • residues in a provided polypeptide or polynucleotide sequence are often designated (e.g., numbered or labeled) according to the scheme of a related reference sequence (even if, e.g., such designation does not reflect literal numbering of the provided sequence).
  • a reference sequence includes a particular amino acid motif at positions 100-110
  • a second related sequence includes the same motif at positions 110-120
  • the motif positions of the second related sequence can be said to “correspond to” positions 100-110 of the reference sequence.
  • corresponding positions can be readily identified, e.g., by alignment of sequences, and that such alignment is commonly accomplished by any of a variety of known tools, strategies, and/or algorithms, including without limitation software programs such as, for example, BLAST, CS-BLAST, CUDASW++, DIAMOND, FASTA, GGSEARCH/GLSEARCH, Genoogle, HMMER, HHpred/HHsearch, IDF, Infernal, KLAST, USEARCH, parasail, PSI-BLAST, PSI-Search, ScalaBLAST, Sequilab, SAM, SSEARCH,
  • software programs such as, for example, BLAST, CS-BLAST, CUDASW++, DIAMOND, FASTA, GGSEARCH/GLSEARCH, Genoogle, HMMER, HHpred/HHsearch, IDF, Infernal, KLAST, USEARCH, parasail, PSI-BLAST, PSI-Search, ScalaBLAST, Sequilab, S
  • a nucleic acid sequence can correspond to a sequence that is identical or substantially identical (e.g., at least 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical) to the complement of the nucleic acid sequence, e.g., over a length of at least 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500 or more nucleic acid residues.
  • diagnosis includes the act, process, and/or outcome of determining whether, and/or the qualitative of quantitative probability that, a subject has or will develop the condition, disease, or related state.
  • diagnosing can include a determination relating to prognosis and/or likely response to one or more general or particular therapeutic agents or regimens.
  • Differentially accessible describes a genomic locus for which chromatin accessibility status differs between a first condition or sample and a second condition or sample (e.g., a standard or reference).
  • a differentially accessible genomic locus can include a greater or smaller measured accessibility under a selected condition of interest, such as HER2-positive state, as compared to a reference state, such as HER2-negative state.
  • Differentially modified describes a genomic locus for which histone modification status and/or DNA methylation status differs between a first condition or sample and a second condition or sample (e.g., a standard or reference).
  • a differentially modified genomic locus can include a greater or smaller number or frequency of histone modification and/or DNA methylations under a selected condition of interest, such as HER2-positive state, as compared to a reference state, such as HER2-negative state.
  • Epigenetic modification refers to heritable alterations to the genome that are not due to changes in DNA sequence.
  • Epigenetic modifications include chemical modifications such as, e.g., DNA methylation and histone modification.
  • epigenetic modifications can cause a change in chromatin structure, DNA accessibility, and/or transcription factor binding.
  • epigenetic modifications can be detected or measured directly (e.g., by using an agent that binds an epigenetic modification (e.g., an antibody that binds H3K4me3 or H3K27ac)).
  • epigenetic modifications can be measured indirectly, e.g., by measuring or detecting one or more attributes, changes in which are indicative of changes in epigenetic modifications.
  • chromatin accessibility and/or transcription factor binding can be used as a measure of epigenetic modifications at a given locus.
  • the term “epigenetic marker” refers to an indicator of epigenetic state, and includes, e.g., epigenetic modifications and assays that measure transcription factor biding or chromatin accessibility.
  • the term “epigenetic biomarker” refers to an epigenetic marker that can be used in the detection of a disease or condition.
  • Identity refers to the overall relatedness between polymeric molecules, e.g., between nucleic acid molecules (e.g., DNA molecules) and/or between polypeptide molecules.
  • % sequence identity refers to a relationship between two or more sequences, as determined by comparing the sequences.
  • identity also means the degree of sequence relatedness between protein and nucleic acid sequences as determined by the match between strings of such sequences.
  • Identity (often referred to as “similarity”) can be readily calculated by known methods, including those described in: Computational Molecular Biology (Lesk, A. M. ed.) Oxford University Press, NY (1988); Biocomputing: Informatics and Genome Projects (Smith, D. W. ed.) Academic Press, NY (1994); Computer Analysis of Sequence Data, Part I (Griffin, A.
  • BLAST basic local alignment search tool
  • GCG Genetics Computer Group
  • BLASTP BLASTN
  • BLASTX Altschul et al., J Mol Biol (1990) 215:403-410
  • DNASTAR DNASTAR, Inc., Madison, Wisconsin
  • FASTA program incorporating the Smith-Waterman algorithm (Pearson, Comput Methods Genome Res [Proc Int Symp] (1994), Meeting Date 1992, 111-120. Eds. Suhai, Sandor. Plenum, New York, NY (the contents of each of which is separately incorporated herein by reference in its entirety).
  • Methylation status can be determined by various assays known in the art, including without limitation Bisulfite sequencing (BS-Seq), Whole Genome Bisulfite Sequencing (WGBS), Methylated DNA ImmunoPrecipitation sequencing (MeDIP-seq), or Methyl-CpG-Binding Domain sequencing (MBD-seq).
  • BS-Seq Bisulfite sequencing
  • WGBS Whole Genome Bisulfite Sequencing
  • Methylated DNA ImmunoPrecipitation sequencing Methylated DNA ImmunoPrecipitation sequencing
  • MBD-seq Methyl-CpG-Binding Domain sequencing
  • methylation status of genomic loci can be detected. Methylation status can be compared to a standard or reference. A sample that has a methylation status that differs from a standard or reference can be referred to as differentially modified.
  • Modification status or “histone modification status” of a genomic locus refers to the frequency with which DNA sequences corresponding to the genomic locus are identified in an assay for detection of DNA sequences associated with histones bearing one or more histone modifications (e.g., one or more particular histone modifications) and/or the density (e.g., the measured density) of histone modifications (e.g., one or more particular histone modifications) corresponding to the genomic locus. Modification status can be determined by various assays known in the art, including without limitation ChIP-seq as one example.
  • CUT&RUN Cleavage Under Targets and Release Using Nuclease
  • CUT&Tag Cleavage Under Targets and Tagmentation
  • Modification status can be compared to a standard or reference.
  • a sample that has a modification status that differs in modification status or histone modification status from a standard or reference can be referred to as differentially modified.
  • a regulatory sequence is a nucleic acid sequence that controls expression of a coding sequence, e.g., a promoter sequence or an enhancer sequence.
  • a regulatory sequence can control or impact one or more aspects of gene expression (e.g., cell- type-specific expression, inducible expression, etc.).
  • Subject refers to an organism, typically a mammal (e.g., a human).
  • a subject is suffering from a disease, disorder or condition (e.g., HER2-positive cancer, e.g., HER2-positive breast cancer, gastric/gastroesophageal cancer, colorectal cancer, lung cancer, etc.).
  • a subject is susceptible to a disease, disorder, or condition.
  • a subject displays one or more symptoms or characteristics of a disease, disorder or condition.
  • a subject is not suffering from a disease, disorder or condition.
  • a subject does not display any symptom or characteristic of a disease, disorder, or condition.
  • a subject has one or more features characteristic of susceptibility to or risk of a disease, disorder, or condition.
  • a subject is a subject that has been tested for a disease, disorder, or condition, and/or to whom therapy has been administered.
  • a human subject can be interchangeably referred to as a “patient” or “individual”.
  • Therapeutic agent refers to any agent that elicits a desired pharmacological effect when administered to a subject.
  • an agent is considered to be a therapeutic agent if it demonstrates a statistically significant effect across an appropriate population.
  • the appropriate population can be a population of model organisms or a human population.
  • an appropriate population can be defined by various criteria, such as a certain age group, gender, genetic background, preexisting clinical conditions, etc.
  • a therapeutic agent is a substance that can be used for treatment of a disease, disorder, or condition (e.g., HER2-positive cancer, e.g., HER2-positive breast cancer, gastric/gastroesophageal cancer, colorectal cancer, lung cancer, etc.).
  • a therapeutic agent is an agent that has been or is required to be approved by a government agency before it can be marketed for administration to humans. In some embodiments, a therapeutic agent is an agent for which a medical prescription is required for administration to humans. [0344] Therapeutically effective amount: As used herein, “therapeutically effective amount” refers to an amount that produces the desired effect for which it is administered. In some embodiments, the term refers to an amount that is sufficient, when administered to a population suffering from or susceptible to a disease, disorder, and/or condition (e.g., HER2- positive cancer, e.g., HER2-positive breast cancer, gastric/gastroesophageal cancer, colorectal
  • a therapeutically effective amount is one that reduces the incidence and/or severity of, and/or delays onset of, one or more symptoms of the disease, disorder, and/or condition.
  • a therapeutically effective amount does not in fact require successful treatment be achieved in a particular individual. Rather, a therapeutically effective amount may be that amount that provides a particular desired pharmacological response in a significant number of subjects when administered to patients in need of such treatment.
  • treatment refers to administration of a therapy that partially or completely alleviates, ameliorates, relieves, inhibits, delays onset of, reduces severity of, and/or reduces incidence of one or more symptoms, features, and/or causes of a particular disease, disorder, or condition, or is administered for the purpose of achieving any such result.
  • such treatment can be of a subject who does not exhibit signs of the relevant disease, disorder, or condition and/or of a subject who exhibits only early signs of the disease, disorder, or condition (e.g., HER2-positive cancer, e.g., HER2-positive breast cancer, gastric/gastroesophageal cancer, colorectal cancer, lung cancer, etc.).
  • HER2-positive cancer e.g., HER2-positive breast cancer, gastric/gastroesophageal cancer, colorectal cancer, lung cancer, etc.
  • treatment can be of a subject who exhibits one or more established signs of the relevant disease, disorder and/or condition.
  • treatment can be of a subject who has been diagnosed as suffering from the relevant disease, disorder, and/or condition.
  • treatment can be of a subject known to have one or more susceptibility factors that are statistically correlated with increased risk of development of the relevant disease, disorder, or condition.
  • a “prophylactic treatment” includes a treatment administered to a subject who does not display signs or symptoms of a condition to be treated or displays only early signs or symptoms of the condition to be treated such that treatment
  • a prophylactic treatment functions as a preventative treatment against a condition.
  • a “therapeutic treatment” includes a treatment administered to a subject who displays symptoms or signs of a condition and is administered to the subject for the purpose of reducing the severity or progression of the condition.
  • a method of determining the HER2 status of a cancer in a subject comprising: quantifying, at one or more genomic loci in a biological sample, optionally in cell-free DNA (cfDNA) from a liquid biopsy sample, obtained or derived from the subject: one or more epigenetic biomarkers, wherein the one or more epigenetic biomarkers comprise: (i) one or more histone modifications, (ii) chromatin accessibility, (iii) binding of one or more transcription factors, and/or (iv) DNA methylation, optionally wherein one or more of the quantified genomic loci is not from the HER2 amplicon.
  • the method of embodiment 1, wherein the one or more histone modifications are quantified using a histone modification assay that measures one or more of H3K9ac, H3K14ac, H3K18ac, H3K23ac, H3K27ac, H3K4me1, H3K4me2, H3K4me3, and pan-acetylation.
  • a histone modification assay that measures one or more of H3K9ac, H3K14ac, H3K18ac, H3K23ac, H3K27ac, H3K4me1, H3K4me2, H3K4me3, and pan-acetylation.
  • the histone modification assay is selected from ChIP-seq (Chromatin ImmunoPrecipitation sequencing), CUT&RUN (Cleavage Under Targets and Release Using Nuclease) sequencing, and CUT&Tag (Cleavage Under Targets and Tagmentation) sequencing. 6. The method of any one of embodiments 1-5, wherein chromatin accessibility is quantified using a chromatin accessibility assay selected from ATAC-seq (Assay of Transpose
  • any one of embodiments 1-6 wherein the binding of one or more transcription factors is quantified using a transcription factor binding assay that detects binding of one or more of p300, mediator complex, cohesin complex, RNA pol II, FOXA1, ESR1, PR, MYC, EN1, FOXM1, KLF4, AP-2, RARa, or RUNX1.
  • the transcription factor binding assay is selected from ChIP-seq (Chromatin ImmunoPrecipitation sequencing), CUT&RUN (Cleavage Under Targets and Release Using Nuclease) sequencing, and CUT&Tag (Cleavage Under Targets and Tagmentation) sequencing.
  • the method of embodiment 11, comprising quantifying H3K4me3 and H3K27ac modifications. 13.
  • the method of embodiment 10, comprising quantifying one or more histone modifications and DNA methylation. 14.
  • the method of embodiment 13, comprising quantifying H3K4me3 and/or H3K27ac modifications and DNA methylation.
  • the method of embodiment 14, comprising quantifying H3K4me3 modifications, H3K27ac modifications and DNA methylation.
  • the method comprises: (a) quantifying H3K4me3 modifications at one or more genomic loci using an assay that comprises enriching for cfDNA comprising one or more H3K4me3 modifications and sequencing the cfDNA enriched for H3K4me3 modifications (e.g., using a cfChIP-seq assay); (b) quantifying H3K27ac modifications at one or more genomic loci using an assay that comprises enriching for cfDNA comprising one or more H3K27ac modifications and sequencing the cfDNA enriched for H3K27ac modifications (e.g., using a cfChIP-seq assay); and/or; (c) quantifying methylated DNA using an assay that comprises enriching for methylated cfDNA and sequencing the enriched cfDNA to determine a count of sequences with one or more methylated nucleotides
  • 12366276v1 Attorney Docket No.2014191-0028 more agents (a) in sequence, or (b) in parallel (e.g., wherein the sample is divided into fractions and each fraction is incubated with a different agent). 22. The method of any one of embodiments 18-21, wherein the sequencing is performed using a next generation sequencing method. 23. The method of any one of embodiments 18-22, wherein the method comprises attaching (e.g., ligating) adapters to cfDNA obtained from the subject (e.g., attaching after cfDNA has been enriched for cfDNA comprising one or more H3K4me3 modifications, cfDNA comprising one or more H3K27ac modifications, and/or methylated cfDNA). 24.
  • attaching e.g., ligating
  • the reference is a predetermined threshold, a measurement from a liquid biopsy sample, a measurement from liquid biopsy samples obtained from a cohort of subjects, and/or a normalized value, optionally wherein: the predetermined threshold and the normalized value were previously shown to distinguish HER2-positive and HER2-negative cancers (e.g., distinguish with an AUROC of greater than 0.5); the reference is a measurement from a liquid biopsy sample obtained from a cohort of subjects who have previously been determined to have a HER2-positive or a HER2-negative cancer. 37.
  • sequence read density is calculated by: (a) summing background adjusted sequence counts at each of the one or more genomic loci and dividing by the sum of the kilobases of the one or more genomic loci; or (b) for each genomic loci, dividing background adjusted fragment count by the number of kilobases of the genomic loci, and then summing for each loci. 38.
  • the one or more genomic loci include one or more genomic loci with an increased level of the one or more epigenetic biomarkers in (a) sample(s) obtained from a subject with a HER2-positive cancer as compared to a sample obtained from a subject with a HER2-negative cancer, and/or (b) sample(s) obtained from a subject with a HER2-positive cancer as compared to a sample obtained from a subject with a HER2-negative cancer.
  • the method of embodiment 38 comprising calculating a HER2-positive/HER2-negative ratio score, by a method comprising:
  • the method of embodiment 39 comprising: (a) determining a HER2-positive/HER2-negative ratio score for H3K4me3 modifications; (b) determining a HER2-positive/HER2-negative ratio score for H3K27ac modifications; and/or (c) determining a HER2-positive/HER2-negative ratio score for methylated DNA. 41. The method of embodiment 40, comprising performing each of (a)-(c), and combining each of the ratio scores. 42. The method of embodiment 41, where the ratio scores are combined using fitted values determined using a logistic regression. 43.
  • the method comprises: (a) quantifying H3K4me3 modifications for at least 5, 10, 20, 30, 40, or 50 genomic loci in Table 1, optionally wherein one or more of the genomic loci are not from the HER2 amplicon;
  • any one of embodiments 1-48 wherein the sample comprises a detectable amount of ctDNA (e.g., wherein estimated tumor fraction is >3% for the cfDNA, e.g., as determined by iChorCNA).
  • 50. The method of any one of embodiments 1-49, wherein the cancer is breast cancer, gastric/gastroesophageal cancer, colorectal cancer, or lung cancer.
  • 51. The method of embodiment 50, wherein the cancer is breast cancer.
  • 52 The method of any one of embodiments 1-51, wherein the subject has previously been determined to have the cancer, the subject has an increased susceptibility to cancer, and/or wherein the method comprises determining whether the subject has a cancer. 53.
  • a method of treating a subject having a cancer comprising: administering a HER2-targeted agent to the subject if the subject has been determined to have a validated epigenetic profile indicative of a HER2-positive cancer based on analysis of a biological sample, optionally of cell-free DNA (cfDNA) from a liquid biopsy sample, obtained or derived from the subject, and, if the subject has not been determined to have a validated epigenetic profile indicative of a HER2-positive cancer, not administering a HER2-targeted agent, wherein the presence of the validated epigenetic profile has been determined using a validated classifier, wherein the validated classifier has been obtained by: (a) determining a genomic profile of one or more histone modifications, chromatin accessibility, binding of one or more transcription factors, and/or DNA methylation in (i) one or more HER2-positive cell lines or (ii) biological samples obtained from a first cohort of subjects who have previously been determined to have a HER2-positive cancer, optionally
  • step (c) The method of embodiment 60, wherein the differential loci in step (c) were identified by comparing the genomic profile of one or more histone modifications and/or DNA methylation in (i) one or more HER2-positive cell lines and (ii) one or more HER2-negative cell lines.
  • the classifier in step (d) was trained on histone modification, chromatin accessibility, binding of transcription factor, and/or DNA methylation levels in the differential loci that were obtained by in silico mixing sequence data from one or more HER2-positive cell lines and sequence data obtained from liquid biopsy samples of healthy subjects.
  • any one of embodiments 60-63 wherein the classifier in step (d) was trained on one or more histone modification levels and DNA methylation in the differential loci, and/or wherein the classifier in step (d) was trained using ridge regression, lastic net regression, or lasso regression.
  • the one or more histone modification levels comprise H3K4me3 and/or H3K27ac modification levels.
  • any one of embodiments 60-67 wherein the genomic profile of one or more histone modifications, chromatin accessibility, binding of one or more transcription factors, and/or DNA methylation has been determined in one or more HER2-positive cell lines; and wherein, in step (d), the samples are diluted in silico by mixing different proportions of sequencing fragments from healthy donor plasma samples and cell lines to achieve a simulated ctDNA percentages ranging from 0.5% to 50% 69.
  • the method of any one of embodiments 60-68 wherein the genomic profile of one or more histone modifications, chromatin accessibility, binding of one or more transcription factors, and/or DNA methylation has been determined in one or more HER2-positive cell lines; and wherein the method further comprises tuning the classifier with plasma data. 70.
  • the classifier is tuned with plasma data using a transfer learning process comprising: (i) using the classifier to compute predictions on plasma samples in the form of probits (e.g., using a formula of log2(probability HER2+ / 1 – probability HER2+)); (ii) using the probits as offset terms in a new model (e.g., a lasso logistic regression model) using all the same features, but instead training on plasma data (e.g., using a leave-one-
  • a transfer learning process comprising: (i) using the classifier to compute predictions on plasma samples in the form of probits (e.g., using a formula of log2(probability HER2+ / 1 – probability HER2+)); (ii) using the probits as offset terms in a new model (e.g., a lasso logistic regression model) using all the same features, but instead training on plasma data (e.g., using a leave-one-
  • a new model e.g.
  • a kit comprising reagents for quantifying one or more histone modifications, chromatin accessibility, binding of one or more transcription factors, and/or DNA methylation at one or more genomic loci, wherein the one or more genomic loci are selected from Tables 1-3 and 4-7, optionally wherein one or more of the genomic loci are not from the HER2 amplicon. 74.
  • kits for quantifying comprising reagents for quantifying: (a) H3K4me3 modifications for at least 5, 10, 20, 30, 40, or 50 genomic loci in Table 1, optionally wherein one or more of the genomic loci are not from the HER2 amplicon; (b) H3K27ac modifications for at least 5, 10, 20, 30, 40, or 50 genomic loci in Table 2, optionally wherein one or more of the genomic loci are not from the HER2 amplicon; (c) DNA methylation for at least 5, 10, 20, 30, 40, or 50 genomic loci in Table 3, optionally wherein one or more of the genomic loci are not from the HER2 amplicon; (d) H3K4me3 modifications for one or more of the k4 analyte loci listed in Table 5, optionally wherein one or more of the genomic loci are not from the HER2 amplicon; (e) H3K27ac modifications for one or more of the k27 analyte loci listed in Table 5, optionally wherein one or more of the genomic
  • kit of any one of embodiments 72-74 wherein the kit comprises one or more antibodies for use in ChIP-seq, optionally wherein the one or more antibodies specifically bind H3K4me3- or H3K27ac-modified histones.
  • kit of embodiment 72-75 wherein the kit comprises one or more methyl-binding domains for use in MBD-seq or wherein the kit comprises one or more antibodies that bidn methylated DNA for use in MeDIP-seq.
  • kit of any one of embodiments 72-76 wherein the kit comprises reagents for isolation of cell-free DNA (cfDNA) from a liquid biopsy sample. 78.
  • a computer system comprising a memory and one or more processors coupled to the memory, wherein the one or more processors are configured to perform operations to perform the method of any one of embodiments 1-71.
  • a system for determining the HER2 status of a cancer in a subject comprising a sequencer configured to generate a sequencing dataset from a sample; and a non-transitory
  • the sample preparation device comprises reagents for quantifying one or more histone modifications, chromatin accessibility, binding of one or more transcription factors, and/or DNA methylation at one or more genomic loci in cell-free DNA (cfDNA) from the biological sample, optionally the liquid biopsy sample.
  • the one or more genomic loci are selected from Tables 1-3 or 5-7, optionally wherein one or more of the genomic loci are not from the HER2 amplicon.
  • the reagents comprise one or more methyl- binding domains for use in MBD-seq. 91.
  • the device comprises reagents for library preparation for sequencing.
  • the sequencer comprises reagents for sequencing. 94.
  • a method of determining the HER2 status of a cancer in a subject comprising: receiving (e.g., by a processor of a computing device) one or more genomic profiles of one or more histone modifications, chromatin accessibility, binding of one or more transcription factors, and/or DNA methylation for the subject; and determining whether the subject has an epigenetic profile indicative of a HER2-positive cancer by classifying (e.g., by the processor) the genomic profile using a HER2 classifier.
  • genomic profiles used to train the HER2 classifier are for differential genomic loci found to have statistically significant differences in levels of one or more histone modifications, chromatin accessibility, binding of one or more transcription factors, and/or DNA methylation in (a) one or more HER2-positive cell lines as compared to one or more HER2-negative cell lines and/or (b) one or more biological samples obtained from one or more cohorts of subjects who have previously been determined to have a HER2-positive cancer, optionally a HER2-3+, HER2-2+, or HER2-1+ cancer based on IHC testing or a HER2-low cancer based on IHC/ISH testing, as compared to one or more biological samples obtained from one or more cohorts of subjects who have previously been determined to have a HER2-negative cancer, optionally a HER2-0 cancer based on IHC testing.
  • HER2 classifier has been tuned using a transfer learning process comprising: (i) using a HER2 classifier trained using genomic profiles of one or more histone modifications, chromatin accessibility, binding of one or more transcription factors, and/or DNA methylation in one or more HER2-positive cell lines and one or more HER2-negative cell lines to compute predictions on plasma samples in the form of probits (e.g., using a formula of log2(probability HER2+ / 1 – probability HER2+)); and
  • cancer specific genomic loci are regions that are enriched for H3K4me3 and/or H3K27ac modifications in HER2-positive subjects as compared to HER2-negative subjects and that correlate with ctDNA at the HER2 locus.
  • HER2 classifier is a validated classifier.
  • HER2 classifier has been validated on an independent group of subjects with HER2-positive and HER2-negative cancers, wherein subjects falling within a group of predicted HER2-positive cancers display a validated epigenetic profile and subjects that do not fall within a group of HER2-positive cancers lack the validated epigenetic profile.
  • 114. The method of any one of embodiments 111-113, wherein the HER2 classifier has been validated using liquid biopsy sample data.
  • 115. A non-transitory computer readable storage medium encoded with a computer program, wherein the program comprises instructions that when executed by one or more processors cause the one or more processors to perform operations to perform the method of any one of embodiments 94-114.
  • a computer system comprising a memory and one or more processors coupled to the memory, wherein the one or more processors are configured to perform operations to perform the method of any one of embodiments 94-114.
  • a method of treating a subject having a cancer comprising: administering a HER2-targeted agent to the subject, wherein the subject has been determined to have a validated epigenetic profile indicative of a HER2-positive cancer based on analysis of a biological sample, optionally of cell-free DNA (cfDNA) from a liquid biopsy sample, obtained or derived from the subject, wherein the presence of the validated epigenetic profile has been determined using a classifier (e.g., a validated classifier) according to a method of any one of embodiments 94-114.
  • a classifier e.g., a validated classifier
  • EXAMPLES [0346] demonstrate the identification and use of differentially modified and/or differentially accessible genomic loci in HER2-positive and HER2-negative cell lines and/or from cfDNA in plasma samples obtained from subjects with HER2-positive and HER2-negative breast cancers.
  • the present Examples show that differentially modified and/or differentially accessible genomic loci of the present disclosure can be used to determine HER2 status from cfDNA in plasma samples obtained from subjects with HER2-positive and HER2- negative cancers.
  • Example 1 Materials and Methods [0347] This Example describes the materials and methods that were used to generate sequencing data that was then used in Examples 2 and 3 to create HER2 status classifiers.
  • HER2-positive cell lines were used: ZR7530, BT474, UACC893, HCC202, HCC1419, HCC1954, HCC2218, and SKBR3.
  • HER2-negative cell lines were used: ZR751, BT483, BT549, DU4475, HS578T, T47D, BT20, CAMA1, MCF7, HCC38, HCC70, HCC1143, HCC1187, HCC1428, HCC1500, HCC1599, HCC1806, and HCC2157.
  • Plasma samples were used: ZR7530, BT474, UACC893, HCC202, HCC1419, HCC1954, HCC2218, and SKBR3.
  • HER2-negative cell lines were used: ZR751, BT483, BT549, DU4475, HS578T, T47D, BT20, CAMA1, MCF7, HCC38, HCC70, HCC1143, HCC1187, HCC1428, HCC
  • Plasma samples were prepared from whole blood collected in EDTA blood collection tubes or Streck cell-free DNA BCT with 4-6 hours of collection and plasma was stored at -80 ⁇ C until use.
  • Whole blood was obtained from breast cancer patients under a protocol approved by an IRB.
  • Breast cancer patients had previously been determined to be HER2-positive (HER2-3+ based on IHC testing) or HER2-negative (HER2-0 based on IHC testing). Informed content was obtained in each case and samples were de-identified.
  • Chromatin immunoprecipitation for histone marks (H3K4me3 and H3K27ac) in cell lines was performed using methods similar to those previously described in Schones et al., Cell (2008) 132(5):887-898, which is incorporated by reference herein in its entirety. Briefly, the cells were lysed and the chromatin was MNase digested to generate approximately 80% mononucleosomes. Nucleosomes were then incubated with antibodies that bind H3K4me3 modifications or H3K27ac modifications that were previously conjugated to magnetic epoxy beads (Invitrogen) with constant mild shaking overnight. The beads were then washed and rinsed.
  • Chromatin immunoprecipitation for histone marks (H3K4me3 and H3K27ac) in plasma samples was performed using methods similar to those previously described in Sadeh et al., Nat Biotechnol (2021) 39: 586–598 and Jang et al., Life Sci Alliance (2023) 6(12):e202302003. Briefly, about 1 mL frozen plasma was thawed and then prepared for ChIP.
  • Example 2 HER2 status classifiers based on ratios of aggregate signals across different subsets of genomic loci that are correlated with HER2-positive or HER2-negative status [0354]
  • genomic loci likely to differentiate HER2- positive and HER2-negative samples based on H3K4me3 modification, H3K27ac modification or DNA methylation were first identified.
  • union peak maps were created by merging peak coordinates for all of the cell lines, removing regions likely to be artifactual (the ENCODE “blacklist” regions, see Amemiya et al., Sci Rep (2019) 9(1):9354) and discarding all peaks less than 50 bp in length.
  • Fig.1 shows ROC curves for exemplary HER2 status classifiers that were generated in accordance with this method. As shown, different classifiers were generated using genomic loci from Tables 1-3 for different modifications, namely (i) H3K4me3 modifications, (ii) H3K27ac modifications, (iii) DNA methylation (DNAme) or (iv) all of the above (All).
  • Fig.2 shows a representative, non-limiting graph that demonstrates the accuracy of HER2 status (based on AUCROC) determination using the classifiers that were generated in accordance with this Example.
  • Example 3 HER2 status classifiers based on complex modeling of signals across different subsets of individual genomic loci that are differentiated based on HER2-positive and HER2-negative status
  • Fig.3 shows ROC curves for exemplary HER2 status classifiers that were generated in accordance with this method.
  • genomic loci from Tables 1-3 for different modifications, namely (i) H3K4me3 modifications, (ii) H3K27ac modifications, (iii) DNA methylation (DNAme) or (iv) all of the above (All) and (b) using different subsets of genomic loci in Tables 1-3 for a particular modification, namely (i) all genomic loci with an absolute log2(fold-change) ⁇ 0.5, (ii) all genomic loci with an absolute log2(fold-change) ⁇ 1, (iii) all genomic loci with an absolute log2(fold-change) ⁇ 2, (iv) all genomic loci with an absolute log2(fold-change) ⁇ 3, and (v) all genomic loci with an absolute log2(fold-change) ⁇ 4.
  • Fig.4 shows representative, non-limiting graphs that demonstrates the accuracy of HER2 status (based on AUCROC) determination using the classifiers that were generated in accordance with this Example.
  • Example 4 Classification of HER2 Status in Plasma Samples [0361]
  • the present example provides classifiers for determining HER2 status in plasma samples obtained from patients with cancer, including plasma samples obtained from subjects with breast cancer, gastroesophageal cancer (GEA), and ovarian cancer (OV).
  • GAA gastroesophageal cancer
  • OV ovarian cancer
  • the present example provides data demonstrating the ability of approaches described
  • Fig.5 provides a summary of the epigenomic platform used in the present example.
  • the platform can be used to offer dynamic resolution into target and pathway biology using small volumes of a biological sample (e.g., ⁇ 1 mL of plasma) comprising cfDNA (cell free DNA).
  • Binding agents e.g., antibodies
  • that bind modifications associated with active enhancers e.g., H3K27ac
  • active promoters e.g., H3K4me3
  • DNA methylation can be used to enrich for associated DNA fragments from a small volume of biological sample (e.g., ⁇ 1 mL of plasma) and sequenced to provide genome-wide epigenomic maps that capture the underlying transcriptional state of the tumor cells (Baca et al., Nature Medicine (2023) 29:2737-2741).
  • RNA-seq a methyl-binding domain
  • ERBB2 a methyl-binding domain
  • HER2 regulatory signal was computed using H3K27ac and H3K4me3 plasma signal at the HER2 locus.
  • regions enriched for H3K27ac and H3K4me3, whose signal correlated with ctDNA at the HER2 locus were first selected to focus on breast cancer specific regions (in a leave-one-out schema). These regions were then subset based on whether they overlap the HER2 TSS (+/- 2kb) or regions defined as HER2-specific enhancers as defined in the literature (e.g., as described in Hait et al., Nucleic Acids Research (2022) 50.10:e55-e55, the contents of which are incorporated by reference herein in their entirety). Signal at these regions were summed across H3K4me3 and H3K27ac analytes for each sample and converted to a z-score to yield the final metric used to correlate to RNA-seq.
  • HER2 Amplicon refers to whether the identified features are in a region proximal to HER2.
  • a HER2 classifier provided herein uses one or more of the loci listed in Table 5.
  • a HER2 classifier provided herein uses one or more of the loci listed in Table 6.
  • a GEA model was trained via transfer learning, using the same method, described above, that was used to generate the breast cancer model.
  • the breast cancer cell line classifier described above was tuned with GEA plasma data.
  • the resulting GEA model comprised many of the same features as the tuned breast cancer model, plus a handful of additional loci that were added during the transfer learning process.
  • the loci and weights used in the resulting GEA model are provided in Table 7.
  • a HER2 classifier provided herein uses one or more of the loci listed in Table 7.
  • 179 plasma samples were obtained from 172 patients with advanced breast (BC), gastro-esophageal (GEA) and ovarian (OV) cancers who had associated HER2 status scored from tissue-based IHC/ISH according to ASCO/CAP guidelines, (T1). Samples were taken at baseline or at progression and those with detectable cell free DNA, as assessed by iChorCNA, were profiled for genome-wide epigenomic signals across histone modifications associated with active enhancers and promoters and DNA methylation.
  • Table 4 Plasma samples characterized using classifiers provided in Example 4 Breast cancer Ovarian cancer Gastroesophageal cancer ble by ichorCNA (90 of 179; 50%).
  • Fig.8(B) shows an exemplary protocol for characterizing performance of classifiers in samples with low amounts of ctDNA (specifically, samples with ctDNA below LoD of ichorCNA).
  • Fig.6(A) shows that breast cancer ERBB2 expression, as determined by RNA- seq, correlates with ERBB2 regulatory signal measured in matched plasma samples, demonstrating the potential of plasma-based classifiers described herein for determining breast cancer HER2 status.
  • Fig.6(B) shows ctDNA-corrected gene-regulatory signal at the ERBB2 locus in plasma obtained from subjects with breast cancer.
  • loci with robust regulatory signal were selected using a leave one out (LOO) schema and further filtered based on their specificity to the ERBB2 gene (as determined based on a review of prior publications). Performance was assessed via AUC in a leave-one-out cross-validation schema.
  • LEO leave one out
  • Fig.6(C) shows that, in plasma samples with detectable ctDNA, numerous loci, including promoters and enhancers, show statistically significant differences in epigenetic modifications (in particular, differences in H3K27ac, H3K4me3 and DNA methylation modifications) between HER2 IHC 3+/2+ISH+ and 2+/1+/0 subjects, demonstrating the potential of using regions outside the HER2 amplicon for HER2 status classification.
  • Fig.7 provides further details regarding the construction of the breast cancer HER2 classifier characterized in the present Example.
  • Loci with differential epigenetic modifications (DNAme, H3K27ac, and H3K4me) in HER2+ and HER2- cell lines were identified, and a HER2 status classifier was constructed using this data.
  • the cell line classifier was then tuned using plasma sample data obtained from subjects with HER2+ or HER2- breast
  • a classifier generated using this protocol was capable of accurately classifying HER2 status in subjects having breast cancer.
  • a breast cancer classifier provided herein can provide accurate classification of HER2 status even at relative low concentrations of ctDNA (e.g., ctDNA ⁇ 3%).
  • Fig.8(A) shows that, across all subjects having breast cancer, including those with ctDNA estimates below the LoD of ichorCNA, a LOO AUC of 0.86 was observed (HER2 IHC 3+/2+ISH+ vs.2+/1+/0).
  • Fig.8(C) shows that real-world samples were found to perform better than simulated datasets, indicating that actual performance of classifiers at low ctDNA% is actually better than that estimated using the in-silico method provide in the present example.
  • Fig. 8(D) shows that a plasma tuned HER2 classifier was able to predict IHC status of samples simulated to have a ctDNA% of 1-3% with an LOO AUC of 0.83. Because, as demonstrated in Fig.8(C), the simulated procedure yields a conservative estimate of classifier results, the provided AUC of 0.83 is expected to be the lower-bound for actual performance within the indicated ctDNA range.
  • HER2 status at progression was also evaluated in a subset of pts with benchmarked HER2 IHC to assess dynamic changes in receptor status by LBx.
  • Fig.9(A) shows that HER2 classification via epigenomic liquid biopsy tracks HER2 status changes over time (e.g., during a treatment regimen).
  • the plasma-based breast cancer classifier accurately detected the change in HER2 status, demonstrating that classifiers provided herein can track changes in HER2 status over time.
  • Fig.9(B) shows that methods provided herein can be used to track status changes across treatment regimens (e.g., during treatment regimens.
  • HER2 IHC Shown are changes in HER2 IHC, probability of HER2+ IHC as measured by an epigenomic plasma classifier described herein, and HER2 mRNA expression in matched tissue in one patient from which data for more than one timepoint is available. For this patient, concordant changes across plasma, IHC, and RNA-seq were observed. Notably, after beginning an anti- HER2 therapy (Trastuzumab deruxtecan) a decrease in the HER2 model score determined using epigenomic liquid biopsy was observed.
  • an anti- HER2 therapy Trastuzumab deruxtecan
  • Methods that comprising tracking HER2 status over time can be useful, e.g., for predicting a patient’s continued responsiveness to a HER2-targeted therapy, and/or to determine an appropriate treatment regimen for a subject having cancer (e.g., breast, GEA, or OV cancer).
  • cancer e.g., breast, GEA, or OV cancer
  • Fig.14 shows an example in which a multi-analyte HER2 classifier can provide more accurate results than an IHC protocol.
  • a multi-analyte HER2 classifier robustly stratifies patients by HER2 status.
  • one sample with an IHC score of 0 showed a high probability of being HER2+ using a multi-analyte HER2 classifier.
  • FISH data for the same sample showed highly amplified HER2, indicating that the IHC determined status was incorrect.
  • HER2 Status Classifiers in Further Cancers [0381] Fig.10 shows generation of HER2 classifiers for indications other than breast cancer (in particular, gastroesophageal adenocarcinoma (GEA) and ovarian cancer (OV)).
  • GAA gastroesophageal adenocarcinoma
  • OV ovarian cancer
  • the HER2 breast cancer cell line classifier described in the present Example (above) was tuned with GEA plasma samples using a LOO schema, to generate a GEA HER2 status classifier.
  • Fig.10(A) shows that the resulting GEA classifier distinguished 3+/2+ISH+ vs.2+/1+/0 with an AUC of 0.96 in samples with detectable ctDNA (ichorCNA).
  • a similar procedure was performed to generate an OV HER2 classifier, which showed a classification AUC of 1.
  • This data demonstrates that methods provided herein can be used to determine HER2 status and/or be readily adapted to determine HER2 in a variety of cancers (e.g., cancers previously shown to have upregulated HER2 and/or ERBB2 amplicons, e.g., as described herein).
  • Fig.11 shows that pan-cancer HER2 classification as determined by epigenomic lipid biopsy (LBx) correlates with HER2 IHC.
  • aggregated HER2 IHC predictions (3+/2+ISH+ vs.2+/1+/0), across indications, show a linear trend between model probabilities and HER2 IHC status.
  • the classifier used to determine HER2 status was trained on plasma samples obtained from 3+/2+ISH+ subjects, a statistically significant separation in model probabilities was observed between IHC 3+ samples and those with other IHC scores.
  • Fig.15 shows the potential of HER2 classifiers described herein to determine HER2 status of colorectal cancer (CRC).
  • Fig.15(A) shows detection of promoter signal at the
  • HER2 classification of breast cancer patients by epigenomic liquid biopsy was concordant with standard tissue-based IHC for 64/72 (89%) of breast cancer samples (AUC 0.9, T1). Accurate classification of 11/14 (79%, GEA) and 4/4 (100%, OV) patients was also achieved using an indication-refined HER2 classifier.
  • HER2 classifier predictions were also concordant with IHC-based HER2 status for all longitudinally collected samples, including one patient whose status switched from HER2+ to HER2- over time.
  • Tables 1-3 identify exemplary genomic loci that are differentially modified and/or differentially accessible in HER2-positive (e.g., HER2-3+ cancer based on IHC testing) vs. HER2-negative cancer (e.g., HER2-0 cancer based on IHC testing).
  • Table 1 is based on differential H3K4me3 modifications.
  • Table 2 is based on differential H3K27ac modifications.
  • Table 3 is based on differential DNA methylation.
  • Table 5 provides loci and weights for an exemplary HER2 classifier, developed using breast cancer cell lines.
  • Table 6 provides loci and weights for an exemplary HER2 classifier, tuned using breast cancer plasma samples.
  • Table 7 provides loci and weights for an exemplary HER2 classifier, developed using GEA plasma
  • Table 1 Exemplary genomic loci that are differentially H3K4me3 modified in HER2-positive (e.g., HER2-3+ cancer based on IHC testing) vs. HER2- negative cancer (e.g., HER2-0 cancer based on IHC testing).
  • Table 3 Exemplary genomic loci that are differentially DNA methylated in HER2-positive (e.g. , HER2-3+ cancer based on IHC testing) vs. HER2- negative cancer (e.g., HER2-0 cancer based on IHC testing).
  • compositions or methods are described as having, including, or comprising specific elements, it is to be understood that compositions or methods that consist essentially of, consist of, or do not comprise the recited elements are likewise hereby disclosed. All references cited herein are hereby incorporated by reference.

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Abstract

La présente invention comprend, entre autres, des procédés, des kits et des systèmes pour déterminer le statut HER2 d'un cancer, par exemple un cancer du sein, gastrique/gastro-œsophagien, colorectal ou pulmonaire. Dans divers modes de réalisation, la présente invention concerne l'utilisation d'une ou de plusieurs modifications d'histone, l'accessibilité de la chromatine, la liaison d'un ou de plusieurs facteurs de transcription, et/ou la méthylation de l'ADN qui sont caractéristiques de l'état HER2 du cancer. Dans certains modes de réalisation, des modifications différentielles et/ou une accessibilité différentielle sont détectées et quantifiées au niveau d'un ou de plusieurs loci génomiques d'un échantillon biologique, par exemple, dans de l'ADN acellulaire (ADNcf) à partir d'un échantillon de biopsie liquide obtenu ou dérivé d'un sujet atteint d'un cancer. Dans divers modes de réalisation, un état HER2 déterminé est utile, par exemple, dans la sélection du traitement et/ou le traitement d'un cancer, par exemple, un cancer du sein, gastrique/gastro-oesophagien, colorectal ou pulmonaire.
PCT/US2024/051123 2023-10-13 2024-10-11 Procédés, kits et systèmes pour déterminer le statut her2 des cancers et méthodes de traitement des cancers utilisant ces derniers Pending WO2025081100A1 (fr)

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