Combination methods for profiling genetic mutations and chromatin accessibility

Abstract

The disclosure relates to targeted Chromatin Accessibility and Mutation Sequencing (tCAM-seq) for parallel analysis of genetic and epigenetic analysis of patients with pancreatic adenocarcinoma (PDAC) to 1) determine responsiveness to standard of care chemotherapy, and 2) identify actionable allelic variant mutations that can be used to guide treatment decisions. Prognostic methods and treatment strategies are also provided.

Description

COMBINATION METHODS FOR PROFILING GENETIC MUTATIONS ANDCHROMATIN ACCESSIBILITYCROSS-REFERENCE TO RELATED APPLICATIONSThis patent application claims priority to U.S. Provisional Patent Application No. 63 / 675,661, filed July 25, 2024, which is hereby incorporated by reference in its entirety.BACKGROUND

[0001] Analyses of genetic mutation and chromatin accessibility are often conducted independently, which may lead to inconsistent results and require two separate workflows. Thus, there is a need in the field for improved methods that allow processing from the same sample and provide high levels of accuracy for cancer prognostics and diagnostics.BRIEF DESCRIPTION OF THE FIGURES

[0002] FIG. 1A-1C shows the targeted Chromatin Accessibility and Mutation Sequencing (tCAM-seq) workflow. TN5 transposase-treated libraries were prepared from biological specimens to isolate accessible chromatin regions (ACRs). Next, the libraries are hybridized with biotinylated oligonucleotide probes targeting specific genomic regions of interest. Following hybridization, target-probe complexes are captured using Streptavidin Magnetic Beads, which selectively bind the biotinylated probes and the attached genomic fragments. Polymerase chain reaction (PCR) is then utilized to amplify the enriched genomic fragments to generate sufficient material for sequencing. The amplicons are then deep-sequenced to analyze variant allele frequencies (VAF) and accessible chromatin regions. The combination of VAF and ACR analysis may impact treatment decisions for a subject. For example, a subject with a poor prognostic score by ACR analysis may be predicted to have a poor response to chemotherapy and may receive an alternative therapy (e.g., alternative options for a ‘no-chemo’ decision).

[0003] FIG. 2 shows high concordance of the relative FPKM (Fragments Per Kilobase per Million mapped fragments) values (median R2= 0.77 (range 0.85 to 0.61)) by head-head comparison of tCAM-seq and Whole genome bulk-ATAC-seq on the same ATAC libraries generated from EpCAM+tumor cells obtained from patients with pancreatic cancer (n=24).

[0004] FIG. 3 shows increased sequencing depth by >1000x in the targeted TN5 ACR- sequencing as compared to traditional whole genome (bulk) ATAC-seq.

[0005] FIG. 4A-4B shows that the predictive ability of the tCAM-seq (FIG. 4A) remained uncompromised compared head-to-head with the whole genome (bulk) sequencing (FIG 4B), even after increasing the sequencing read depth in the tCAM-seq by >1000x.

[0006] FIG. 5 demonstrates the highly significant enrichment of HNF1A and HNF1B transcription factor binding motif within the regions that are open in chemotherapy responder patients and are silenced in non-responder patients.

[0007] FIG. 6 shows that tCAM-seq (e.g., Capture- Epigenetic Sequencing), when paired with immunohistochemistry of HNF1B, significantly stratifies patient survival (Log-rank test p value = 0.0184, HR= 0.2958, 95% CI =0.06615 to 1.323, with a median follow up time of 8.98 years).

[0008] FIG. 7A-7C demonstrates detection of the variant alleles from the selected regions using tCAM-seq (e.g., capture) but not from bulk epigenetic sequencing.

[0009] FIG. 8 shows a schematic of a current treatment scenario for a subject with pancreatic cancer compared to a proposed treatment scenario utilizing tCAM-seq methods described herein. In a current treatment scenario, a subject may undergo a genomic mutation panel test and receive standard of care chemotherapy. Some patients may respond, and other nonresponders may be given a targeted therapy. In the proposed scenario, a subject will undergo the standard genomic testing in addition to the integrated genetic and epigenetic approaches leveraged by tCAM-seq. With this approach, a subject receives concurrent testing of their 1) epigenetic signature which determines responsiveness to standard chemotherapy, and 2) tumor sequencing to identify actionable (e.g., druggable) mutations. A subject determined to be a non-responder to chemotherapy may undergo immediate treatment with a targeted therapy directed at the previously identified actionable mutation.SUMMARY

[0010] In some embodiments, the disclosure provides a method for detecting actionable allelic variant mutations and chromatin accessibility in one or more biological samples of a subject having or suspected of having pancreatic ductal adenocarcinoma (PDAC), the method comprising: a) enriching tumor cells comprising morphologically intact nuclei in the one or more biological samples from a treatment naive patient comprising morphologically intact nuclei to produce an enriched tumor sample,b) contacting the enriched tumor cells comprising morphologically intact nuclei with a transposase complex to produce a population of PDAC Accessible Chromatin Region (PDAC ACR) fragments representing PDAC ACRs in the one or more biological samples; c) contacting the PDAC ACR fragments with a set of oligonucleotide probes, wherein the PDAC ACR fragments hybridize with the oligonucleotide probes to generate a target-probe complex; d) contacting the target-probe complex with a second agent to capture the targetprobe complex; e) amplifying the target-probe complex to produce an amplified population of the target-probe complex comprising the PDAC ACR fragments; and f) determining tumor mutational load and chromatin accessibility in parallel from the target-probe complex in the same biological sample, comprising: i) quantifying variant allele frequency of one or more actionable allelic variant mutations comprising a single nucleotide variant (SNV), an insertion, a deletion, an indel, a copy number variation (CNV), or combinations thereof to determine the tumor mutational load in the one or more biological samples; and ii) quantifying the relative abundance of PDAC ACRs associated with a first phenotype to calculate a first phenotype score and quantifying the relative abundance of PDAC ACRs associated with a second phenotype to calculate a second phenotype score; wherein the first phenotype score is associated with good prognosis and responsiveness to chemotherapy, and the second phenotype score is associated with poor prognosis and nonresponsiveness to chemotherapy.

[0011] In some embodiments, the disclosure provides a method of treating a subject having, or suspected of having pancreatic ductal adenocarcinoma (PDAC), comprising detecting actionable allelic variant mutations and chromatin accessibility in one or more biological samples and treating with one or more targeted therapies and / or an epigenetic drug, the method comprising: a) enriching tumor cells comprising morphologically intact nuclei in a biological sample to produce an enriched tumor sample,b) contacting the enriched tumor cells comprising morphologically intact nuclei with a transposase complex to produce a population of PDAC Accessible Chromatin Region (PDAC ACR) fragments representing PDAC ACRs in the one or more biological samples; c) contacting the PDAC ACR fragments with a set of oligonucleotide probes, wherein the PDAC ACR fragments hybridize with the oligonucleotide probes to generate a target-probe complex; d) contacting the target-probe complex with a second agent to capture the targetprobe complex; e) amplifying the target-probe complex to produce an amplified population of the target-probe complex comprising the PDAC ACR fragments; and f) determining tumor mutational load and chromatin accessibility in parallel from the target-probe complex in the same biological sample, comprising: i) quantifying variant allele frequency of one or more actionable allelic variant mutations comprising a single nucleotide variant (SNV), an insertion, a deletion, an indel, a copy number variation (CNV), or combinations thereof, to determine the tumor mutational load in the one or more biological samples; and ii) quantifying the relative abundance of PDAC ACRs associated with a first phenotype to calculate a first phenotype score and quantifying the relative abundance of PDAC ACRs associated with a second phenotype to calculate a second phenotype score; wherein the first phenotype score is associated with good prognosis and responsiveness to chemotherapy, and the second phenotype score is associated with poor prognosis and nonresponsiveness to chemotherapy, wherein a subject with one or more actionable allelic variant mutations and a prognostic score above a threshold is treated with chemotherapy; and wherein a subject lacking one or more actionable allelic variant mutations and with a prognostic score above a threshold is treated with chemotherapy; and wherein a subject with one or more actionable allelic variant mutations and a prognostic score below a threshold is not treated with chemotherapy and is treated with an epigenetic drug and / or a targeted therapy; andwherein a subject lacking one or more actionable allelic variant mutations and with a prognostic score below a threshold is not treated with chemotherapy and is treated with an epigenetic drug.

[0012] In some aspects, the method further comprises: (i) tagging the chromatin accessible nucleic acid fragments to generate a plurality of tagged DNA fragments representing PDAC accessible chromatin regions (PDAC ACRs) of the intact nuclei presence in the tumor cells, and (ii) attaching a detectable label to the tagged DNA fragments to produce labeled chromatin accessible nucleic acid fragments.

[0013] In some aspects, the oligonucleotide probes comprise a first subset of oligonucleotide probes targeting a first set of PDAC ACRs in Table 1 A and a second set of oligonucleotide probes targeting a second set of PDAC ACRs on any chromosomal region in Table IB.

[0014] In some aspects, the first set of PDAC ACRs are indicative of a first phenotype and wherein the first phenotype is representative of good prognosis. In some aspects, the second set of PDAC ACRs are indicative of a second phenotype and wherein the second phenotype is representative of poor prognosis.

[0015] In some aspects, the method further comprises calculating a prognostic score based on a relative difference between the first phenotype score and the second phenotype score, normalized to a set of positive control oligonucleotide probes and a set of negative control oligonucleotide probes.

[0016] In some aspects, the method further comprises calculating the first phenotype score, wherein the first phenotype score is calculated by: (median FPKM of the first set of oligonucleotide probes I (median FPKM of the positive control oligonucleotide probes - median FPKM of the negative control oligonucleotide probes)).

[0017] In some aspects, the method further comprises calculating the second phenotype score, wherein the second phenotype is calculated by: (median FPKM of the second set of oligonucleotide probes I (median FPKM of the positive control oligonucleotide probes - median FPKM of the negative control oligonucleotide probes)).

[0018] In some aspects, the prognostic score is calculated by: (first phenotype score I second phenotype score), wherein a subject with a good prognosis has a prognostic score greater than 1 and a subject with a poor prognosis has a prognostic score less than 1. In some aspects, the method of any one of claims 1-10, wherein the prognostic score is at least between 0.1 to 5. In some aspects, the prognostic score is indicative of the subject’s responsiveness to one or more treatment modalities.

[0019] In some aspects, the transposase complex is Tn5.The method of any one of claims 1- 13 wherein the oligonucleotide probes are biotinylated. In some aspects, the second agent comprises biotin conjugated to a streptavidin, an avidin, or a neutravidin to form an affinity pair. In some aspects, affinity pair is further conjugated to a magnetic bead, an agarose bead, a magnetic resin bead, or a covalent bead. In some aspects, the second agent is a biotinylated- streptavidin magnetic bead.

[0020] In some aspects, the method further comprises sequencing the target-probe complexes.

[0021] In some aspects, the method further comprises detecting nuclear localization of specific transcription factors in the one or more biological samples. In some aspects, the transcription factor is selected from ZKSCAN1, EPAS1, RUNX2, ZNF410, MAFF, RREB1, NR3C2, SMAD1, RUNX1, ZNF32, ZSCAN4, HOXB1, POU3F1, ZBTB3, CEOCK, TCF15, GCM1, HINFP, CGBP, MYPOP, ZNF384, GMEB2, E2F5, AC012531.1, ZBTB7B, HOXC9, HNF4G, CREB1, ATF2, E2F2, SP3, ARID5A, ZFP161, OTP, PBX3, ZBTB33, ONECUT3, ONECUT3, DEX2, HNF4A, PRRX1, TCFE5, HOXB7, IRF6, GRHE1, FOXD2, ISL1, MEE, GATA2, GATA1, HMB0X1, NRF1, ZFHX3, ONECUT1, TET1, E2F3, DNMT1, CTCFL, CTCF, HNF1B, and HNF1A. In some aspects, the transcription factor is ZKSCAN1, HNF1B, or both. In some aspects, the detecting nuclear localization of transcription factors is by immunohistochemistry (IHC).

[0022] In some aspects, the method further comprises predicting a long duration of disease- free survival when the first phenotype is significantly higher than the second phenotype. In some aspects, the method further comprises predicting a short duration of disease-free survival when the second phenotype is significantly higher than the second phenotype. In some aspects, the first phenotype is non-recurrence of a cancer within one year of surgical resection and the second phenotype is recurrence of a cancer within one year of surgical resection. In some aspects, the first phenotype is a responder to one or more cancer treatment modalities and the second phenotype is a non-responder to one or more cancer treatment modalities. In some aspects, the first phenotype is having a median disease-free survival of between 50 to 1500 days and the second phenotype is having a median disease- free survival of between 1-350 days. In some aspects, the first phenotype is having a median progression- free survival of between 50 to 1500 days and the second phenotype is having a median progression- free survival of between 1 to 180 days.

[0023] In some aspects, the method further comprises quantifying variant allele frequency of one or more biomarkers selected from KRAS, TP53, SMAD4, CDKN2A, BRCA1, BRCA2, ATR, ATM, RAD51, PALB2, EZH2, or combinations thereof. In some aspects, the biomarkers comprise KRAS and / or TP53. In some aspects, a higher variant allele frequency as compared to a control is indicative of a higher tumor mutational load.

[0024] In some aspects, the one or more targeted therapies are selected from belzutifan, erlotinib hydrochloride, everolimus, olaparib, sunitinib malate, zenocutuzumab-zbco, Ado- trzstuzumab, afatinib, alectinib, axitinib, bosutinib, ceritinib, cobimetinib, crizotinib, dabrafenic, dasatinib, enasidenib, gefitinib, ibrutinib, idelalisib, imatinib, lapatinib, neratinib, nilotinib, niraparib, ponatinib, ribociclib, rucaparib, temsirolimus, tofacitinib, vemurafenib, cisplatin, carboplatin, oxaliplatin, talazoparib, immune checkpoint inhibitors, PARP inhibitors, PRMT inhibitors, KRAS inhibitors, and / or PRC2 inhibitors.

[0025] In some aspects, the PRC2 inhibitor is selected from MAK683, EED226, CPI- 1205, PF-06821497, or EPZ-6438 (Tazemetostat).

[0026] In some aspects, the epigenetic drug is selected from a DNA methyltransferase (DNMT) inhibitor, a histone deacetylase (HD AC) inhibitor, an enhancer of zeste homolog 2 (EZH2) inhibitor, a bromodomain and extra-terminal motif (BET) inhibitor, a histone acetyltransferase (HAT) inhibitor, a histone lysine methyltransferase (KMT) inhibitor, a protein arginine methyltransferase (PRMT) inhibitor, a proteolysis-targeting chimera (PROTAC) comprising a HD AC inhibitor, a DNMT inhibitor, a BET inhibitor, a EZH2 inhibitor, a HAT inhibitor, a KMT inhibitor, or combinations thereof.

[0027] In some aspects, the chemotherapy is selected from gemcitabine, nab-paclitaxel, fluorouracil (5-FU), irinotecan, oxaliplatin, leucovorin, capecitabine, cisplatin, or combinations thereof.

[0028] In some aspects, the subject is diagnosed with pancreatic ductal adenocarcinoma cancer (PDAC). In some aspects, the one or more biological samples are obtained before the subject has received a first dose of treatment with one or more treatment modalities. In some aspects, the one or more biological samples are obtained after the subject has received at least one dose of treatment with one or more treatment modalities. In some aspects, the one or more biological samples are selected from a tumor biopsy, a surgically resected tumor specimen, or a liquid biopsy. In some aspects, the subject is treatment naive to the one or more treatment modalities. In some aspects, the one or more biological samples is obtained from a tumor biopsy.

[0029] In some aspects, the one or more biological samples is obtained from a liquid biopsy. In some aspects, the one or more biological samples are blood. In some aspects, the one or more biological samples are plasma.

[0030] In some embodiments, the disclosure provides a kit for detecting actionable allelic variant mutations and chromatin accessibility in one or more biological samples of a subject with pancreatic ductal adenocarcinoma (PDAC), the kit comprising: a. one or more sets of oligonucleotide probes for hybridizing with PDAC ACRs in the one or more biological samples to generate a target-probe complex; b. a second agent to capture the target-probe complex; c. reagents for amplifying the target-probe complex to produce an amplified population of the target-probe complex; d. reagents for determining variant allele frequency of one or more actionable allelic variant mutations; and e. reagents and instructions for quantifying the relative abundance of the PDAC ACRs in a biological sample to calculate a prognostic score.

[0031] A kit for treating a subject having, or suspected of having pancreatic ductal adenocarcinoma (PDAC), comprising detecting one or more actionable allelic variant mutations and chromatin accessibility in one or more biological samples and treating with one or more targeted therapies and / or an epigenetic drug, the kit comprising: a. one or more sets of oligonucleotide probes for hybridizing with PDAC ACRs in the one or more biological samples to generate a target-probe complex; b. a second agent to capture the target-probe complex; c. reagents for amplifying the target-probe complex to produce an amplified population of the target-probe complex; d. reagents for determining variant allele frequency of the one or more actionable allelic variant mutations; and e. reagents and instructions for quantifying the relative abundance of the PDAC ACRs in a biological sample to calculate a prognostic score; and f. instructions for determining a treatment decision based on (d) and (e), wherein a subject with one or more actionable allelic variant mutations and a prognostic score above a threshold is treated with chemotherapy; and wherein a subject lacking one or more actionable allelic variant mutations and with a prognostic score above a threshold is treated with chemotherapy; andwherein a subject with one or more actionable allelic variant mutations and a prognostic score below a threshold is not treated with chemotherapy and is treated with an epigenetic drug and / or a targeted therapy; and wherein a subject lacking one or more actionable allelic variant mutations and with a prognostic score below a threshold is not treated with chemotherapy and is treated with an epigenetic drug.DETAILED DESCRIPTION

[0032] Analyses of genetic mutation and chromatin accessibility are two separate actionable prognostic paradigms for precision oncology. The targeted Chromatin Accessibility and Mutation Sequencing (tCAM-seq) described herein provides parallel analysis of genetic and epigenetic analysis of patients with pancreatic adenocarcinoma (PDAC) to 1) determine responsiveness to standard of care, and 2) identify actionable allelic variant mutations that can be used to guide treatment decisions. The method utilizes a set of custom designed biotinylated oligonucleotide DNA probes to capture the target DNA sequences, pooling them with streptavidin-labeled magnetic beads to enrich the target DNA fragments, and then sequencing them with ultra-deep high throughput Next Generation sequencing (NGS). In some embodiments, tCAM-seq can detect (a) an epigenetic landscape and (b) mutational status in parallel. In some embodiments, the ultra-deep NGS can be utilized on samples comprising circulating tumor DNA (ctDNA) and blood which can require deeper sequencing than tissue or tumor samples.

[0033] This ultra-deep NGS detects specific genetic mutations (variant alleles) on the target- captured DNA fragments by estimating the variant allele frequencies (VAFs) on the sequencing data. The higher the VAFs, the higher the number mutant form of the DNA fragments present in the DNA pool, and vice versa. Without wishing to be bound by any theory, it is believed that chromatin accessibility signature analysis can be performed using this same ultradeep capture-based sequencing platform.Definitions

[0034] This detailed description is intended only to acquaint others skilled in the art with the present invention, its principles, and its practical application so that others skilled in the art may adapt and apply the invention in its numerous forms, as they may be best suited to the requirements of a particular use. This description and its examples are intended for purposesof illustration only. This invention, therefore, is not limited to the embodiments described in this patent application and may be variously modified.

[0035] The term “biological sample" is to be understood as any in vivo, in vitro, or in situ sample of one or more cells or cell fragments. This can, for example, be a unicellular or multicellular organism, blood sample, biopsied tissue sample, tissue section, cytological sample, or any derivative of the foregoing (e.g., a subsample, portion, or purified cell population). In some embodiments, a biological sample is obtained from a mammal, including, but not limited to, a primate (including human), mouse, rat, cat, or dog.

[0036] The term “cancer” includes, but is not limited to, pancreatic ductal adenocarcinoma (PDAC), breast cancer, colorectal cancer, esophageal cancer, gallbladder cancer, gastric cancer, leukemia (<?.g., acute myeloid leukemia (AML) or chronic myeloid leukemia (CML)), liver cancer (e.g., hepatocellular carcinoma (HCC)), lung cancer (e.g., non-small cell lung cancer (NSCLC) or small cell lung cancer (SCLC)), lymphoma (e.g., non-Hodgkin lymphoma), ovarian cancer, pancreatic cancer, and prostate cancer, The term “cancer” also includes cancer metastasis of a primary tumor such as primary pancreatic cancer. Thus, if reference is made, for example, to pancreatic cancer, this also includes metastasis of the pancreatic cancer, for example metastasis to the lung, liver and / or lymph nodes.

[0037] The term “detectable label” refers to a moiety that can be attached directly or indirectly to an oligomer, such as an oligonucleotide, to thereby render the oligomer detectable by an instrument or method.

[0038] The term “transposase complex” refers to a complex that contains a transposase (which typically exists as a dimer of transposase polypeptides) that is bound to at least one adapter. The term “adapter” refers to a nucleic acid molecule that is capable of being attached to a polynucleotide of interest. An adapter can be single stranded or double stranded, and it can comprise DNA, RNA, and / or artificial nucleotides. The adapter can add one or more functionalities or properties to the polynucleotide of interest, such as providing a priming site for amplification or adding a barcode. By way of example, adapters can include a universal priming site for amplification. By way of further example, adapters can one or more barcode of various types or for various purposes, such as molecular barcodes, sample barcodes and / or target-specific barcodes. In practice, a transposase complex can be used to attach an adapter to the end of a DNA fragment generated by the enzymatic action of the transposase.

[0039] The terms “treat”, “treating” and “treatment” refer to a method of alleviating or abrogating a condition, disorder, or disease and / or the attendant symptoms thereof.

[0040] The term “accessible chromatin regions” or “ACR” or “open chromatin regions” refer to regions of DNA within a cell’s nucleus that are loosely packaged and available to proteins involved in transcription.

[0041] The term “open chromatin” or “euchromatin” refers to regions of DNA within the nucleus that are loosely packaged and readily accessible for transcription. The regions are typically associated with genes that are expressed or active within a cell.

[0042] The term “close chromatin” or “heterochromatin” refers to regions of DNA within the cell’s nucleus being tightly packaged, which hinders the access of proteins for transcription. The regions are typically associated with genes that are repressed are inactive within a cell.

[0043] The term “accessible chromatin region” (ACR) refers to a region of ‘open’ chromatin within the DNA of a cell, which is readily accessible for transcription. A set of ACRs can be associated with a specific phenotype. For example, a set of ACRs herein may be associated with subjects that have a ‘good’ prognosis and a separate set of ACRs herein may be associated with subjects that have a ‘poor’ prognosis.

[0044] The term “chromatin accessibility signature” refers to a defined set of accessible chromatin regions (ACRs) that are differentially represented, showing a defined pattern (signature) statistically associated (or correlated) with a specific cancer phenotype. For example, as published in Dhara et al., Nature Communications 2021, 1092 differentially represented ACRs are correlated with pancreatic cancer patients who responded or nonresponded to chemotherapy (also referred to herein as the ‘PDAC ACRs’). PDAC ACRs were categorized into the following groups: 1) ACRs representing good prognosis (See Table 1A, n=722), 2) ACRs representing poor prognosis (See Table IB, n=370), 3) positive control regions (336), 4) negative control regions (215). The relative abundance of oligonucleotide probes that bind to different sets of ACRs can be used to calculate a prognostic score. Provided herein is a set of oligonucleotide probes specific for ACRs associated with prognosis in pancreatic cancer.

[0045] The term “prognostic score (PS)” refers to the relative abundance of a first set of oligonucleotide probes that bind to a first set of ACRs compared to a second set of probes that bind to a second set of ACRs. The first set of oligonucleotide probes target ACRs associated with a first phenotype (good prognosis) and the second set of oligonucleotide probes target ACRs associated with a second phenotype (poor prognosis). The difference in relative abundance is normalized with a set of positive and negative control oligonucleotide probes. The first phenotype is calculated by (median FPKM of the first set of oligonucleotideprobes I (median FPKM of the positive control oligonucleotide probes - median FPKM of the negative control oligonucleotide probes)). The second phenotype is calculated by (median FPKM of the second set of oligonucleotide probes I (median FPKM of the positive control oligonucleotide probes - median FPKM of the negative control oligonucleotide probes)). The prognostic score is calculated by: (first phenotype I second phenotype) > 1 = “Responder to Chemotherapy or Good Prognosis” or (first phenotype I second phenotype) < 1 = “NonResponder to Chemotherapy or Poor Prognosis.” The prognostic score can be used to assess, determine, or predict a patient’s responsiveness to a treatment modality (e.g., chemotherapy, immunotherapy, or surgery). For example, a prognostic score of >1 indicates the patient will have a good prognosis while a prognostic of < 1 indicates the patient will have a poor prognosis.

[0046] The term “responder” refers to a patient who has good responsiveness to a treatment modality described herein. A responder may have a DFS of at least 50 days, at least 400 days, or longer.

[0047] The term “non-responder” refers to a patient who has poor responsiveness to a treatment modality described herein. A non-responder may have a DFS of less than 400 days, less than 100 days, less than 50 days, or shorter.

[0048] The term “actionable allelic variant mutation,” or “actionable mutation” refers to genetic alterations that can guide treatment decisions by indicating a patient’ s potential response to specific therapies. Actionable allelic variant mutations help predict whether a patient may respond to a particular drug. For example, a subject with an EGFR mutation may respond an EGFR inhibitor like gefitinib or erlotinib. Actionable allelic variant mutations may be identified through analysis of variant allele frequency (VAF).

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. Preferred methods and materials are described below, although methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the presently disclosed methods and compositions. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety.ATAC-library preparation

[0050] AT AC libraries may be prepared as described in detail in U.S. Application Nos. 17 / 268,195, 17 / 324,093, and PCT Application No.: PCT / US / 2024023298, each of which is hereby incorporated in its entirety. Briefly, intact nuclei are extracted from a biological sample (e.g., tumor biopsy or liquid biopsy). A Tn5 transposase complex is added to the intact nuclei. Following an incubation, transposed DNA fragments are extracted from the reaction solution and amplified to provide AT AC libraries.

[0051] The preparation of tumor specimens followed the procedure outlined below: first EpCAM+ PDAC malignant cells, or CD8+ T-lymphocytes are isolated from the tumor and peripheral blood respectively, and then ATAC-libraries are made.

[0052] In some embodiments, the method comprises providing a biological sample. The biological sample may be, for example, a blood sample, a tissue sample, or a cytological sample. In some embodiments, the biological sample comprises cancerous cells or cells suspected of being cancerous. In some such embodiments, the biological sample is unprocessed. In other such embodiments, the biological sample is processed to isolate a specific cell population. For example, a population of Epithelial Cell Adhesion Molecule positive (EpCAM+) cells (i.e., cells expressing the transmembrane protein EpCAM) may be isolated from a tissue sample such as tissue biopsied from a pancreatic tumor or, more specifically, a pancreatic ductal adenocarcinoma, or CD8+ T-lymphocyte cells from peripheral blood of a patient. Without being bound by theory, cancer cells typically express more EpCAM compared to healthy cells. Isolating cells expressing EpCAM may enrich and isolate tumor cells from the biological sample providing high quality materials for the targeted Chromatin Accessibility and Mutation Sequencing (tCAM-seq) analysis.

[0053] The method can comprise collecting a biological sample from a subject having been diagnosed with a cancer (e.g., pancreatic cancer) or is at risk of having a cancer. In some embodiments, the biological sample is a tumor biopsy or surgically resected tumor specimen. In some embodiments, the biological sample comprises shredded cells from the subject such as epithelial cells or the subject’s excreta (e.g., feces, saliva, sweat, earwax, mucus, urine). The biological sample can be collected by a physician, a surgeon, a healthcare provider, a laboratory technician or scientist. The biological sample may comprise pancreatic ductal adenocarcinoma cells. In some embodiments, the biological sample comprises treatment- naive malignant cells obtained from a subject. In some embodiments, the biological sample comprises malignant cells from a subject who has been treated with one or more cancer therapies. In some embodiments, the biological sample is enriched for tumor cells. Theenrichment can be achieved by contacting the biological sample with an agent (e.g., antibody- conjugated magnetic beads, EpCAM-conjugated magnetic beads) to isolate tumor cells from non-tumor cells in the biological sample to enrich the sample for tumor cells. In some embodiments, the agent is EpCAM-conjugated magnetic beads. In some embodiments, the agent is EpCAM-conjugated non-magnetic beads such as one used in flow-cytometry assays or immunohistochemistry assays.

[0054] In some embodiments, the biological sample can be obtained from a patient diagnosed with cancer. For example, in case of pancreatic cancer a patient may be referred to undergo endoscopic ultrasound and fine needle aspiration (EUS-FNA) for tissue diagnosis of a suspected pancreatic mass, which may result in the diagnosis of PDAC. This EUS-FNA or the laparoscopic surgery tissue acquisition process occurs prior to chemotherapy treatment and may provide treatment-naive malignant cells from all stages of PDAC.

[0055] In some embodiments, the method further comprises isolating nucleosomal DNA from the biological sample, such as an isolated cell population, or peripheral blood. In some such embodiments, nuclei are isolated and / or lysed in a manner that maintains nucleosome structure.

[0056] Nuclei are isolated or collected in such a manner as to ensure that nucleosomal structure is maintained. Thus, nuclei comprise regions of tightly packed or closed chromatin and regions of loosely packed or open chromatin.

[0057] In some embodiments, the method comprises fragmenting open chromatin regions of nuclei to obtain a population of fragments representing the open chromatin regions. In some embodiments, the method comprises tagging such fragments with, for example, an adapter. In some embodiments, the fragmenting and tagging occurs substantially simultaneously or in rapid succession. Certain transposases such as a hyperactive Tn5 transposase, loaded in vitro with adapters, can substantially simultaneously fragment and tag DNA with the adapters. Thus, in some embodiments, the method may comprise “tagmenting” the open chromatin regions using, for example, a hyperactive Tn5 transposase loaded with one or more adapters. In some embodiments, the morphologically intact nuclei are contacted with a transposase complex to produce a population of tagged nucleic acid fragments representing accessible chromatin regions (ACRs) of the morphologically intact nuclei.

[0058] In some embodiments, fragmenting and tagging ACRs comprises using enzymatic tagmentation such as sonication, microfluidic shearing, or chemical cleavage (e.g., hydrogen peroxide, potassium permanganate). In some embodiments, the method further comprisesdetermining the accessibility of at least one chromatin region. In some embodiments, the set of oligonucleotide probes represent chromatin regions that are differentially accessible between a first phenotype and a second phenotype (e.g., between treatment-resistant disease and treatment-sensitive disease; between a cancer likely to recur within one year following surgical resection and a cancer likely not to recur within one year following surgical resection). In some such embodiments, the set of oligonucleotide probes comprises (i) a first subset of oligonucleotide probes representative of accessible chromatin regions associated with the first phenotype and (ii) a second subset of oligonucleotide probes representative of accessible chromatin regions associated with the second phenotype. In some embodiments, the chromatin regions that are differentially accessible between a first phenotype and a second phenotype are selected from the PDAC ACRs set forth in Table 1A and / or IB.

[0059] In some embodiments, at least some of the differentially accessible chromatin regions include a promoter, an enhancer, and / or other regulatory elements. In some embodiments, the biological sample comprises malignant or diseased tissue. In other embodiments, the biological sample comprises normal tissue.

[0060] In some embodiments, the fragmenting and tagging step comprises contacting morphologically intact nuclei with a transposase complex. In some such embodiments, a transposase complex comprises a transposase enzyme (which is usually in the form of a dimer of transposase polypeptides) and a pair of adapters. In some embodiments, isolated nuclei are lysed when contacted with a transposase complex and, thus, the method may comprise lysis of intact nuclei.

[0061] In some embodiments, the transposase is prokaryotic, eukaryotic, or from a virus. In some embodiments, the transposase is a hyperactive transposase. In some embodiments, the transposase is an RNase transpose, such as a Tn transposase. In some such embodiments, the transposase is a Tn5 transposase or derived from a Tn5 transposase. In certain preferred embodiments, the transposase is a hyperactive Tn5 transposase (e.g., a Tn5 transposase having an L372P mutation). In some embodiments, the transposase is a MuA transposase or derived from a MuA transposase. In some embodiments, the transposase is a Vibhar transposase (e.g., from Vibrio harveyi) or derived from a Vibhar transposase. In the above examples, a transposase derived from a parent transposase can comprise a peptide fragment with at least about 50%, about 55%, about 60%, about 65%, about 70%, about 75%, about 80%, about 85%, about 90%, about 91%, about 92%, about 93%, about 94%, about 95%, about 96%, about 97%, about 98%, or about 99% amino acid sequence homology and / oridentity to a corresponding peptide fragment of the parent transposase. The peptide fragment can be at least about 10, about 15, about 20, about 25, about 30, about 35, about 40, about 45, about 50, about 60, about 70, about 80, about 90, about 100, about 150, about 200, about 250, about 300, about 400, or about 500 amino acids in length. For example, a transposase derived from Tn5 can comprise a peptide fragment that is 50 amino acids in length and about 80% homologous to a corresponding fragment in a parent Tn5 transposase.

[0062] In an exemplary method described herein, the transposase complex comprises a transposase loaded with two adapter molecules that each contain a recognition sequence at one end. The transposase catalyzes substantially simultaneous fragmenting of the sample and tagging of the fragments with sequences that are adjacent to the transposon recognition sequence (z.<?., “tagmentation”). In some cases, the transposase enzyme can insert the nucleic acid sequence into the polynucleotide in a substantially sequence-independent manner. In some embodiments, a preliminary step includes loading a transposase with one or more oligonucleotide adapters. Typically, the adapters comprise oligonucleotides that have been annealed together so that at least the transposase recognition sequence is double stranded.

[0063] Morphologically intact nuclei or intact nucleosomes of the enriched tumor cells (e.g., pancreatic ductal adenocarcinoma cells) can be extracted from the biological sample. The extracted intact nuclei can be used to generate an ATAC-library using enzymatic-based or non-enzymatic-based fragmentation of the genomic DNA into fragments. In some embodiments, fragmenting and tagging the accessible chromatin regions (ACRs) comprise using enzymatic tagementation such as using Tn transposase-based or restriction enzymebased tagmentation. In some embodiments, fragmenting and tagging ACRs comprising using non-enzymatic tagmentation such as sonication, microfluidic shearing, or chemical cleavage (e.g., hydrogen peroxide, potassium permanganate).

[0064] The method may further comprise attaching a detectable label to the tagged DNA fragments to produce labeled fragments; and / or contacting the detectable labeled fragments to the set of targeting oligonucleotide probes. In some embodiments, the detectable label comprises a fluorochrome, a chromophore, an enzyme, or a chemiluminescence compound, such as acridinione. The method may further comprise quantifying the amount of amplified targeted accessible chromatin region fragments to obtain the first phenotype and the second phenotype. In some embodiments, the amplified targeted accessible chromatin region fragments are quantified by tCAM-seq, polymerase chain reaction (e.g., RT-PCR, qPCR), fluorometry, and / or gel electrophoresis.

[0065] In some embodiments, tumor cells are isolated from non-tumor cells in the sample by contacting the one or more biological samples with EpCAM-conjugated magnetic beads to produce an enriched tumor cell sample, the enriched tumor cells comprising morphologically intact nuclei or intact nucleosomes.

[0066] In some embodiments, circulating tumor nucleosomes are isolated from the biological sample using conventional methods and kits.

[0067] In some embodiments, the enriched tumor cells comprising morphologically intact nuclei or intact nucleosomes, or the isolated circulating tumor nucleosomes are contacted with a transposase complex to produce a population of tagged DNA fragments representing accessible chromatin regions (ACRs) in the one or more biological samples.

[0068] In some embodiments, chromatin accessible nucleic acid fragments are tagged to generate a plurality of tagged DNA fragments representing accessible chromatin regions (ACRs) of the intact nuclei presence in the tumor cells or circulating tumor nucleosomes, and a detectable label is attached to the tagged DNA fragments to produce labeled chromatin accessible nucleic acid fragments.PDAC Accessible Chromatin Regions (PDAC ACRs)

[0069] As previously described, more than one thousand (1092) open chromatin peaks are differentially accessible (absolute Iog2 fold change > 1 and FDR-adjusted P < 0.001) between PDAC patients who recurred within a year of surgery and patients who did not recur (maximum follow-up of 660 days) by ATAC-seq (Dhara et al. Pancreatic cancer prognosis is predicted by an ATAC-array technology for assessing chromatin accessibility’. Nat Commun 12, 3044 (2021), and WO2024211731A1). The 1092 open chromatin peaks are referred to herein as the ‘PDAC ACRs.’

[0070] In some embodiments, the tagged nucleic acid fragments are contacted with a plurality of biotinylated oligonucleotide probes targeting specific genomic regions of interest. In some embodiments, the specific genomic regions of interest comprise the 1092 PDAC ACRs set forth in Table 1A and Table IB.

[0071] In some embodiments, the biotinylated oligonucleotide probes comprise nucleic acid sequences that bind to a first set of ACRs in Table 1A that are associated with ‘good’ prognosis. In some embodiments, the first set of ACRs are associated with a first phenotype. In some embodiments, the biotinylated oligonucleotide probes comprise nucleic acid sequences that bind to a second set of ACRs in Table IB that are associated with ‘poor’prognosis. In some embodiments, the first set of ACRs are associated with a second phenotype.

[0072] In some embodiments, the PDAC ACRs bound to the biotinylated oligonucleotide probes comprise a target-probe complex. In some embodiments, the target-probe complex is captured using Streptavidin magnetic beads which selectively bind the biotinylated probes and the attached genomic fragments.

[0073] In some embodiments, the first phenotype is determined by quantifying the relative amount of ACRs within Table 1A. In some embodiments, the first phenotype is determined by quantifying relative FPKM of the tagged fragments to the first set of oligonucleotide probes. In some embodiments, the second phenotype is determined by quantifying the relative amount of ACRs within Table IB. In some embodiments, the second phenotype is determined by quantifying relative FPKM of the tagged fragments to the second set of oligonucleotide probes.

[0074] In some embodiments, PDAC-ACRs representing ‘good’ prognosis in Table 1A are analyzed by tCAM-seq. In some embodiments, PDAC-ACRs representing ‘poor’ prognosis in Table IB are analyzed by by tCAM-seq. In some embodiments, the first set of ACRs in Table 1A are associated with ‘good’ prognosis. In some embodiments, the second set of ACRs in Table IB are associated with ‘poor’ prognosis.Table 1A. PDAC ACRs - Good PrognosisTable IB. PDAC ACRs - Poor PrognosisAmplification of the PDAC ACRs

[0075] Following ATAC-library preparation and hybridization to the biotinylated oligonucleotide probes targeting specific genomic regions of interest, the enriched genomic fragments are amplified to generate sufficient material for sequencing.

[0076] In some embodiments, the enriched genomic fragments are amplified by PCR prior to analysis by tCAM-seq. In some embodiments, the amplifying step comprises an amplification reaction that results in a relatively uniform amplification of substantially all template sequences in a sample (e.g., at least 85%, 90%, or 95% of the template sequences). In some embodiments, the amplifying step comprises polymerase chain reaction (PCR). In some embodiments, the amplifying step comprises PCR using primers specific for adapter sequences appended to the fragments during the fragmenting and tagging step. In some embodiments, the amplifying step comprises PCR using primers specific for a gene within the corresponding accessible chromatin region. In some embodiments, the primers are PDAC ACR-specific oligonucleotide primers. In some embodiments, the ACRs are amplified prior to analysis to ensure enough genetic material for capture by the oligonucleotide probes and normalize the amount of material across samples.

[0077] The PCR methods described herein are designed to reduce the effect of size- and GC- bias from the library construction process. Further, the amplification step is intended to generate libraries that are minimally amplified as most PCR bias comes from later PCR cycles that occur during limited reagent concentrations. Determination of the optimal number of amplification cycles reduces artifacts associated with saturation PCR of complex libraries. These methods are particularly useful for by tCAM-seq because of the highly diverse fragment sizes. If a library is enriched for long fragments, library fragmentation can be optimized using more or fewer cells per reaction. The described PCR method has the additional benefit of serving as an estimate for library complexity. For example, if >6 additional cycles are needed (>11 total cycles) library complexity becomes a concern. Library complexity can be improved by optimizing the input cell number or by making libraries of technical replicates.Detection of Actionable Allelic Variant Mutations

[0078] Described herein are custom panels for integrative detection of genetic mutations and targeted chromatin accessibility profiles in a single sample. The first aspect is to assess the alteration of chromatin accessibility patterns in response to the same drug by assessing the relative abundance of ACRs associated with prognosis. The second aspect of the custom panel assesses the specific set of actionable allelic variant mutations (or variant allele frequency, “VAF”) in tumor cells associated with a cancer and determine responsiveness to a targeted therapy.

[0079] In addition, the alteration of allelic variant mutations in response to a therapy can be used to assess tumor mutational load. For instance, if a cancer drug is working on a patient, then the tumor burden will reduce and that will be reflected by the reduction in the variant mutations of the selected canonical driver mutations on the panel.

[0080] The dual application platform enables one cancer specimen to yield two separate pieces of information - it quantitatively detects the presence of tumor cells in a clinical diagnostic solid or liquid biopsy specimen (or in a cell suspension) as well as the relative epigenetic profile of the same sample. It is especially useful in monitoring the systemic tumor burden and epigenetic alterations of a patient while a cancer therapy is ongoing. Conventionally, mutation profiles of tumor driver genes, e.g., KRAS and T53, of pancreatic tumors can be obtained by ultra-deep sequencing method separately to estimate tumor enrichment following EpCAM sorting. The present disclosure provides a platform for performing both mutation analysis and epigenetic profiling simultaneously on the same specimen.

[0081] In some embodiments, the method comprises using a deep-sequencing platform, such as Illumina’ s amplicon sequencing platform, and selecting customized genomic target coordinates for detecting mutation alleles of genes of interest and chromatin accessibility regions. For the mutation analysis, a panel of canonical driver genes of pancreatic cancer are analyzed for variant allele frequency. In some embodiments, the panel comprises four canonical driver genes of pancreatic cancer - KRAS, TP53, CDKN2A, SMAD4. Over 95% of pancreatic cancers cases (of all stages) possess gain-of-function mutations in KRAS oncogene, and over 75% pancreatic tumors possess loss-of-function mutations in the tumor suppressor gene P53. KRAS and T53 are the two driver genes relevant for a wide range of malignant diseases, and they are not mutually exclusive - i.e., both the genes can be impaired in a same tumor. Analyzing variant allele frequencies (VAFs) of these two genes can cover 100% of pancreatic cancer cases (Dhara et al Nature Communications 2021). The frequency of alteration of the remaining two tumor suppressor genes CDKN2A and SMAD4 are about 25% each, mostly altered by structural alterations (insertion / deletion).

[0082] More than half of all cancer types globally possess oncogenic mutations in KRAS, and the mutant allele repertoires of KRAS gene is well characterized. Altogether, six mutant alleles have been identified in the KRAS oncogene, four of them are on codon 12 (G12D, G12V, G12C, G12R), and two are on codon 61 (Q61R, and Q61H). All these mutations result in a gain-of-function of the KRAS oncogene, i.e., uncontrolled aberrant overactivity of thefunctional protein of KRAS. The most frequent variant alleles for the tumor suppressor gene P53 are G266V, R175H, R248Q, R248W, R273H, R282G, R282Q, R282W, and Y220C that have been predominant variant alleles in pancreatic cancers. A representative list of cancer driver genes or coding sequences and their chromosomal coordinates that can be analyzed by the by tCAM-seq method are listed in Table 2.Table 2. Actionable Allelic Variant Mutations

[0083] In various embodiments, the variant allele is single nucleotide variant (SNVs), an insertion, a deletion, an indel, a copy number variation (CNV), or combinations thereof. In some embodiments, the method comprises quantifying variant allele frequency of one or more biomarkers selected from KRAS, TP53, SMAD4, CDKN2A, BRCA1, BRCA2, ATR, ATM, RAD51, PALB2, EZH2, or combinations thereof. In some embodiments, the biomarkers comprise KRAS and / or TP53. In some embodiments, the biomarkers comprise KRAS. In some embodiments, the biomarkers comprise TP53.

[0084] In addition to these four canonical driver mutations, about 5-8% of pancreatic tumors possess loss-of-function mutations BRCA1 and BRCA2 genes. Mutations in BRCA genes lead to the deficiency of DNA repair mechanisms - specifically Homologous Repair Deficiency (HRD). Mutations in BRCA and related gens such as ATM, ATR, RAD51 lead to this HRD phenotype, which is clinically actionable. HRD tumors are more susceptible to platinum-based therapies (Cisplatin or Oxaliplatin) and PARP inhibitor therapies (Olaparib, Rucaparib, and Talazoparib) via synthetic lethal mechanisms. The present disclosure provides methods for detecting mutations within selected desired chromosomal coordinates of these specific genes.ACR-Targeting Oligonucleotide Probes

[0085] ACR-targeting oligonucleotide probes were designed which are complementary to the accessible chromatin region (ACR) (between the start and the end loci) to detect the specificregion. One or more sets of oligonucleotide probes may target a specific open chromatin region.

[0086] In some embodiments, the set of targeting oligonucleotide probes required for effectively determining the prognostic score is about 15 to 20,000. In some embodiments, the PDAC ATAC-array comprises between 15-1,000 oligonucleotide probes. In some embodiments, the PDAC ATAC-array comprises between 1,000 to 5,000 oligonucleotide probes. In some embodiments, the PDAC ATAC-array comprises between 5,000 to 10,000 oligonucleotide probes. In some embodiments, the PDAC ATAC-array comprises between 10,000 to 20,000 oligonucleotide probes.

[0087] In some embodiments, the PDAC-ACRs required for effectively determining the prognostic score is about 1092. In some embodiments, the PDAC-ACRs used to determine the prognostic score is about 10 to 2000. In some embodiments, the probes can detect between 10 to 100 PDAC ACRs. In some embodiments, the probes can detect between 100 to 200 PDAC ACRs. In some embodiments, the probes can detect between 200 to 300 PDAC ACRs. In some embodiments, the probes can detect between 300 to 400 PDAC ACRs. In some embodiments, the probes can detect between 400 to 500 PDAC ACRs. In some embodiments, the probes can detect between 500 to 600 PDAC ACRs. In some embodiments, the probes can detect between 600 to 700 PDAC ACRs. In some embodiments, the probes can detect between 700 to 800 PDAC ACRs. In some embodiments, the probes can detect between 800 to 900 PDAC ACRs. In some embodiments, the probes can detect between 400 to 500 PDAC ACRs. In some embodiments, the probes can detect between 500 to 1000 PDAC ACRs. In some embodiments, the probes can detect between 1000 to 1500 PDAC ACRs. In some embodiments, the probes can detect between 1500 to 2000 PDAC ACRs. In some embodiments, the oligonucleotide probes can detect any of the PDAC ACRs in Table 1A or Table IB.Biotinylated magnetic bead capturing

[0088] The tagged DNA fragments (e.g., tagged DNA fragments from nucleosomes isolated from enriched tumor cells or circulating tumor nucleosomes) are amplified and captured to further enrich the sample for tumor DNA fragments. In various embodiments, the second agent comprises biotin conjugated to a streptavidin, an avidin, or a neutravidin to form an affinity pair. In various embodiments, the affinity pair is further conjugated to a magneticbead, an agarose bead, a magnetic resin bead, or a covalent bead. In some embodiments, the second agent is a biotinylated- streptavidin magnetic bead.Nuclear Localization of Transcription Factors

[0089] As previously described in Dhara et al. (Pancreatic cancer prognosis is predicted by a PDAC ATAC-array technology for assessing chromatin accessibility. Nat Commun 12, 3044 (2021)), an in silica search identified that HNFlb predicted differential accessibility in PDAC patients, which was further confirmed by immunohistochemistry (IHC). IHC analysis demonstrated significant segregation of disease-free survival (DFS) between patients with strong nuclear localization of HNFlb versus patients with weak / no nuclear localization of HNFlb.

[0090] In some embodiments, the method further comprises detecting nuclear localization of HNFlb. As mentioned herein, data obtained from the Targeted Mutation-ACR-Sequencing approach disclosed herein can be supplemented with or confirmed by transcription factor expression and / or nuclear localization data (e.g., obtained by immunohistochemistry) for particular transcription factors (TFs), such as HNFlb. As a further example, any patient having PS <1 with HNFlb negative can be predicted as poor prognosis; any patient having >1 with HNFlb positive can be predicted as good prognosis; and any patient either PS <1 with HNFlb positive or PS >1 with HNFlb negative can be predicted as intermediate prognosis.

[0091] In some embodiments, the difference in DFS based on Targeted Mutation-ACR- Sequencing prognosis score is increased when combined with HNFlb nuclear localization. In some embodiments, the method further comprises detecting nuclear localization of a transcription factor assessing nuclear localization of one or more biomarkers capable of modulating gene expression through complementary binding to one or more specific regions on the amplicons of the tagged DNA fragments. In some embodiments, the transcription factors is selected from ZKSCAN1, EPAS1, RUNX2, ZNF410, MAFF, RREB1, NR3C2, SMAD1, RUNX1, ZNF32, ZSCAN4, HOXB1, POU3F1, ZBTB3, CLOCK, TCF15, GCM1, HINFP, CGBP, MYPOP, ZNF384, GMEB2, E2F5, AC012531.1, ZBTB7B, HOXC9, HNF4G, CREB1, ATF2, E2F2, SP3, ARID5A, ZFP161, OTP, PBX3, ZBTB33, ONECUT3, ONECUT3, DLX2, HNF4A, PRRX1, TCFL5, HOXB7, IRF6, GRHL1, FOXD2, ISL1, MLL, GATA2, GATA1, HMB0X1, NRF1, ZFHX3, ONECUT1, TET1, E2F3, DNMT1, CTCFL,CTCF, HNF1B, and HNF1A. In some embodiments, the transcription factor is ZKSCAN1, HNF1B, or both.Calculation of Prognostic Score

[0092] Described herein are methods for determining the prognostic score of a subject using by tCAM-seq. The prognostic score can be used to assess, determine or predict the subject’s responsiveness to one or more treatment modalities, disease-free survival score, and / or likelihood of cancer recurrence for pancreatic cancer and / or other cancers.

[0093] TN5 transposase-treated libraries were prepared from biological specimens to isolate accessible chromatin regions (ACRs). Next, the libraries are hybridized with biotinylated oligonucleotide probes targeting specific genomic regions of interest. Following hybridization, target-probe complexes are captured using Streptavidin Magnetic Beads, which selectively bind the biotinylated probes and the attached genomic fragments. Polymerase chain reaction (PCR) was then utilized to amplify the enriched genomic fragments to generate sufficient material for sequencing. The amplicons were then deep- sequenced to analyze variant allele frequencies (VAF) and accessible chromatin regions. Additional details for this methodology are provided in Example 1.

[0094] The sequencing data was subjected to an analysis pipeline where the probes in every sample are mapped to the PDAC ACR differential regions (‘good’ and ‘poor’) and control regions (‘positive’ and ‘negative’). PDAC ACRs were categorized into the following groups: “Good”= ACRs representing good prognosis (722), “Poor”= ACRs representing poor prognosis (370), “Positive”= positive control regions (336), “Negative”= negative control regions (215).

[0095] To calculate the prognostic score, Fragments Per Kilobase of transcript per Million mapped reads (FPKM) values were normalized across each of the pre-determined PDAC ACRs representing ‘good,’ and ‘poor’ prognosis (See Table 1A and Table IB) in addition to positive control ACRs.

[0096] In some embodiments, the FPKM of a first set of oligonucleotide probes targeting ACRs representing good prognosis is calculated. In some embodiments, the FPKM of a second set of oligonucleotide probes targeting ACRs representing poor prognosis is calculated. In some embodiments, the FPKM of a third set of oligonucleotide probes targeting ACRs representing positive control regions is calculated. In some embodiments, theFPKM of a fourth set of oligonucleotide probes targeting ACRs representing negative control regions is calculated.

[0097] In some embodiments, the first set of oligonucleotide probes are associated with a “good” prognosis. In some embodiments, the second set of oligonucleotide probes are associated with a “poor” prognosis. In some embodiments, the third set of oligonucleotide probes are associated with positive control regions. In some embodiments, the fourth set of oligonucleotide probes are associated with negative control regions.

[0098] In some embodiments, a prognostic score is calculated for each individual subject. In some embodiments, the prognostic score is calculated by analyzing relative expression of oligonucleotide probes bound to ACRs representing a first phenotype compared to oligonucleotide probes representing a second phenotype. In some embodiments, the first phenotype score is calculated. In some embodiments, the first phenotype represents good prognosis. In some embodiments, the first phenotype score is calculated by: (median FPKM of the first set of oligonucleotide probes I (median FPKM of the positive control oligonucleotide probes - median FPKM of the negative control oligonucleotide probes)).

[0099] In some embodiments, the second phenotype score is calculated. In some embodiments, the second phenotype represents poor prognosis. In some embodiments, the second phenotype score is calculated by: (median FPKM of the second set of oligonucleotide probes I (median FPKM of the positive control oligonucleotide probes - median FPKM of the negative control oligonucleotide probes)).

[0100] In some embodiments, the prognostic score (PS) is calculated by analyzing the relative expression of the first phenotype (e.g., first phenotype score) compared to the second phenotype (e.g., second phenotype score). In some embodiments, the prognostic score is calculated by the following formula: a) (first phenotype I second phenotype) > 1 = “Responder to Chemotherapy or Good Prognosis” b) (first phenotype I second phenotype) < 1 = “Non-Responder to Chemotherapy or Poor Prognosis” c) (first phenotype I second phenotype) = 1 = Inconclusive

[0101] Using the formula described above, the individualized PS for each patient can be calculated and the PS of good and poor prognosis groups can be analyzed.

[0102] As an exemplary embodiment, a patient with PDAC (PT 12) is screened and is determined to have ‘good’ prognosis. PT12 has a first phenotype score of 1.71 and a secondphenotype score of 1.59. To calculate prognostic score, the following calculation is conducted: 1.71 1 1.59 = 1.08. The subject has a PS > 1 which predicts the subject to be a responder to chemotherapy and have a good prognosis. Broadly, the PT12 sample exhibits increased expression of ACRs associated with the ‘good prognosis’ accessible chromatin regions in Table 1A, as compared to expression of ACRs associated with the ‘poor prognosis’ accessible chromatin regions in Table IB.

[0103] Each probe is mapped to the ‘good’ or ‘poor’ prognostic PDAC ACR region, and the median normalized FPKM of all the probes for the AT AC differential regions were analyzed to generate a prognostic score.

[0104] In some embodiments, a prognostic score of between 1 to 2, between 1 to 1.5, or between 1 to 1.3 provides the prediction for a good prognosis. In some embodiments, a prognostic score of about 1.0, 1.2, 1.5, 1.8, or above provides the prediction for a good prognosis. In some embodiments, a prognostic score of above 1.0 provides the prediction for a good prognosis.

[0105] In some embodiments, a prognostic score of between 0.1 to 1, between 0.2 to 0.6, or between 0.4 to 0.8 provides the prediction for a poor prognosis. In some embodiments, a prognostic score of about 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1 or less provides the prediction for a poor prognosis. In some embodiments, a prognostic score of less than 1.0 provides the prediction for a poor prognosis.Diagnosis, prognosis, and treatment of cancer

[0106] In one aspect, the present disclosure provides a diagnostic or prognostic method. In certain embodiments, the diagnostic or prognostic method may distinguish between treatment-resistant and treatment-sensitive cancers. In certain embodiments, the diagnostic or prognostic method may distinguish between rapidly recurrent and non-recurrent tumors. In some such embodiments, the tumors are pancreatic tumors, such as pancreatic ductal adenocarcinoma.

[0107] In certain embodiments, the diagnostic or prognostic method comprises determining a epigenetic landscape from a biological sample obtained from a patient, wherein the epigenetic landscape comprises at least two, alternatively at least five, at least ten, at least twenty, at least thirty, at least forty, at least fifty, at least one hundred, at least two hundred, at least three hundred, at least four hundred, at least five hundred, at least six hundred, at least seven hundred, at least eight hundred, at least nine hundred, or at least one thousandchromatin regions selected from the list of chromatin regions in Table 1A-B; and providing a diagnosis or prognosis based on the determination. In some embodiments, the diagnostic or prognostic method comprises determining a epigenetic landscape comprising no more than 100 open chromatin regions in Table 1A-B.

[0108] In one aspect, the present disclosure provides a method for monitoring tumor load and chromatin accessibility in one or more biological samples of a subject, the method comprising: obtaining nucleosomes from the one or more biological samples by (i) contacting the one or more biological samples with a first agent comprising EpCAM-conjugated magnetic beads to isolate tumor cells from non-tumor cells in the sample to produce an enriched tumor cell sample, wherein the one or more biological samples comprise tumor cells, wherein the enriched tumor cells comprise morphologically intact nuclei contacting the enriched tumor cells comprising morphologically intact nuclei with a transposase complex to produce a population of PDAC ACR fragments representing accessible chromatin regions (ACRs) in the one or more biological samples; hybridizing a set of ACR-targeting oligonucleotide probes to the PDAC ACRs to generate a target-probe complex, wherein the ACR-targeting oligonucleotide probes are capable of binding to a specific region on the ACRs; contacting the PDAC ACR fragments with a second agent to capture the target-probe complex, amplifying the target-probe complex to produce an amplified population of the target-probe complex, and sequencing tumor mutational load and chromatin accessibility in parallel from the same biological sample.

[0109] In some embodiments, the sequencing tumor mutational load and chromatin accessibility in parallel from the same biological sample, comprising: a) quantifying variant allele frequency of one or more actionable allelic variant mutations in Table 2 to determine the tumor mutational load in the one or more biological samples; and b) calculating a prognostic score based on the relative abundance of PDAC ACRs associated with a first phenotype and a second phenotype; wherein the first phenotype is associated with good prognosis and the subject is a responder to chemotherapy, and the second phenotype is associated with poor prognosis and the subject is a non-responder to chemotherapy.

[0110] In certain embodiments, the method comprises performing surgical resection to remove a pancreatic ductal adenocarcinoma from a patient, wherein prior to said resection a biological sample from the patient has been tested to determine nuclear localization of one or more transcription factors.

[0111] In one aspect, the present disclosure provides a method for treating a disease or condition such as cancer, particularly pancreatic cancer. In certain embodiments, the method comprises administering a drug to the patient, wherein prior to administering the drug, a biological sample from the patent has been tested to determine an epigenetic landscape of the biological sample.

[0112] In certain embodiments, the patient is identified as likely being a non-responder to a treatment modality. In some such embodiments, the treatment modality is surgical resection with or without adjuvant chemotherapy. In certain embodiments, the patient is identified as having a tumor likely to recur within one year following surgical resection and adjuvant chemotherapy. In some such embodiments, the tumor is a pancreatic ductal adenocarcinoma.

[0113] In certain embodiments, the epigenetic landscape comprises a plurality of pre-selected differentially accessible chromatin regions and the plurality of pre-selected differentially accessible chromatin regions comprise at least two, alternatively at least five, at least ten, at least twenty, at least thirty, at least forty, at least fifty, at least one hundred, at least two hundred, at least three hundred, at least four hundred, at least five hundred, at least six hundred, at least seven hundred, at least eight hundred, at least nine hundred, or at least one thousand chromatin regions selected from the list of chromatin regions in Table 1A-B, which provides a signature of >1000 loci that were differentially accessible between recurrent (disease-free survival (DFS) < 1 year) and non-recurrent patients (DFS > 1 year). In typical embodiments, the epigenetic landscape comprises no more than 100 pre-selected differentially accessible chromatin regions in Table 1A-B.

[0114] In certain embodiments, the method comprises administering an epigenetic drug to the patient, wherein prior to said administration a biological sample from the patient has been tested to determine nuclear localization of one or more transcription factors.

[0115] In one aspect, the present disclosure provides a method for treating cancer in a patient in need thereof. In certain embodiments, the method comprises (a) assessing if the patient is likely to be a responder or a non-responder to a first treatment modality by determining or having determined an epigenetic landscape of a biological sample obtained from the cancer patient; and (b) treating the cancer patient with: (i) the first treatment modality if the patient is determined to be a likely responder to the first treatment modality; or (ii) a second treatment modality if the patient is determined to be a likely non-responder to the first treatment modality. In some embodiments, the first treatment modality is a chemotherapeutic drug. Insome embodiments, the second treatment modality is an epigenetic drug and / or a targeted therapy.

[0116] In some embodiments, the method can detect (a) an epigenetic signature and (b) a mutational status in parallel of a subject in need thereof. In some embodiments, the method can identify actionable allelic variant mutations, wherein a subject is identified with a mutation that is responsive to a specific treatment (e.g., a subject with a BRCA2 mutation may receive a PARP inhibitor). In some embodiments, the method provides an epigenetic signature which indicates responsiveness to standard chemotherapeutic drugs. In some embodiments, a subject with a poor prognostic score and an actionable allelic variant mutation (e.g., BRCA2) may forgo standard chemotherapeutic drugs and receive treatment with a specific drug that targets that mutation, e.g., BRCA2.Prognosis of Cancer

[0117] Described herein are methods for determining a prognosis of a cancer. The method can comprise determining and / or providing a prognostic score indicative of a subject’s responsiveness to one or more treatment modalities or indicative of a duration of disease-free survival. In some embodiments, the method further comprises determining or providing a first phenotype, a second phenotype, a good prognosis report, a poor prognosis report, a disease-free survival score, a report of a likelihood of cancer recurrence or non-recurrence, and / or a prediction of treatment modality responsiveness.

[0118] In some embodiments, prognosis of a cancer comprises determining a first phenotype score and a second phenotype score. The first phenotype score represents good prognosis. The second phenotype represents poor prognosis. In some embodiments, prognosis of the cancer comprises using the first and second phenotype scores to determine the likelihood of recurrence or non-recurrence of a cancer. For example, a high first phenotype score may indicate reduced likelihood of recurrence of the cancer within a year or less. A high second phenotype score may indicate increased likelihood of recurrence of the cancer within a year or less.

[0119] The differential of the first phenotype score and the second phenotype score can be normalized with a positive and negative control to determine a prognostic score.

[0120] In some embodiments, a prognostic score is calculated for each individual subject. In some embodiments, the prognostic score is calculated by analyzing relative expression of oligonucleotide probes bound to ACRs representing a first phenotype compared tooligonucleotide probes representing a second phenotype. In some embodiments, the first phenotype score is calculated. In some embodiments, the first phenotype represents good prognosis. In some embodiments, the first phenotype is calculated by: (median FPKM of the first set of oligonucleotide probes I (median FPKM of the positive control oligonucleotide probes - median FPKM of the negative control oligonucleotide probes)).

[0121] In some embodiments, the second phenotype score is calculated. In some embodiments, the second phenotype represents poor prognosis. In some embodiments, the second phenotype is calculated by: (median FPKM of the second set of oligonucleotide probes I (median FPKM of the positive control oligonucleotide probes - median FPKM of the negative control oligonucleotide probes)).

[0122] In some embodiments, the prognostic score (PS) is calculated by analyzing the relative expression of the first phenotype compared to the second phenotype. In some embodiments, the prognostic score is calculated by the following formula: a) (first phenotype score I second phenotype score) > 1 = “Responder to Chemotherapy or Good Prognosis” b) (first phenotype score I second phenotype score) < 1 = “Non- Responder to Chemotherapy or Poor Prognosis” c) (first phenotype score I second phenotype score) = 1 = Inconclusive

[0123] For example, a prognostic score of less than 1 indicates recurrence of the cancer or poor responsiveness (e.g., non-responder) to one or more cancer treatment modalities. Conversely, a prognostic score of higher than 1 indicates non-recurrence of the cancer or good responsiveness (e.g., responder) to one or more cancer treatment modalities.

[0124] In some embodiments, the one or more treatment modalities are selected from resecting cancerous tissue, neo-adjuvant chemotherapy, adjuvant chemotherapy, immunotherapy, an epigenetic drug, or combinations thereof. In some embodiments, the epigenetic drug is selected from DNMT inhibitor, an HDAC inhibitor, an EZH2 inhibitor, or combinations thereof. In some embodiments, the DNMT inhibitor is azacytidine or decitabine. In some embodiments, the HDAC inhibitor is vorinostat or romidepsin. In some embodiments, the neo-adjuvant chemotherapy and / or adjuvant chemotherapy is selected from gemcitabine, nab-paclitaxel, fluorouracil (5-FU), irinotecan, oxaliplatin, leucovorin, capecitabine, cisplatin, or combinations thereof.

[0125] In some embodiments, the prognostic score is used to predict the disease-free survival (DFS) of the subject. For example, a subject having a prognostic score of higher than 0.6 may1have a median DFS of at least of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more years, or at least 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, or more days.

[0126] In some embodiments, the prognostic score is used to predict the progression-free survival (PFS) of the subject. For example, a subject having a prognostic score of higher than 0.6 may have a median PFS of at least of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more years, or at least 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, or more days.

[0127] As an illustrating example, the prognostic score can be determined by assessing the differential values of good prognosis and poor prognosis by obtaining the first and second phenotypes of accessible chromatin regions as shown in Table 3. In some embodiments, the prognostic score can be determined by assessing the differential values of good prognosis and poor prognosis and obtaining the phenotype of less than 12, 8, 4, or 3 accessible chromatin regions as shown in Table 3. The accessible chromatic regions in Table 3 can be used to determine the prognostic score.

[0128] In some embodiments, the prognostic score is determined or provided by a physician, a surgeon, a healthcare provider, a laboratory technician or scientist, or a data analyst.Treatment of Cancer

[0129] Described herein are methods for determining treatment modalities for a cancer such as pancreatic or other cancers. The method comprises determining and / or receiving a prognostic score indicative of a subject’s responsiveness to one or more treatment modalities or indicative of a duration of disease-free survival and treating the subject with the one or more treatment modalities based on the prognostic score. In some embodiments, the prognostic score is determined using tCAMseq. In some embodiments, the method further comprises determining or receiving a first phenotype, a second phenotype, a good prognosis report, a poor prognosis report, a disease-free survival score, a report of a likelihood of cancer recurrence or non-recurrence, and / or or prediction of treatment modality responsiveness.

[0130] A subject having a prognostic score higher than 1, a good prognosis report, indication of non-recurrence, or a high disease- free survival score may be prescribed or administered with one or more treatment modalities. Conversely, a subject having a prognostic score lower than 1, a poor prognosis report, indication or recurrence, or a low disease-free survival score may be advised to have no further treatment modalities.

[0131] In some embodiments, the one or more treatment modalities are selected from resecting cancerous tissue, neo-adjuvant chemotherapy, adjuvant chemotherapy, immunotherapy, an epigenetic drug, a targeted therapy, or combinations thereof.

[0132] In some embodiments, the epigenetic drug is selected from DNMT inhibitor, an HD AC inhibitor, an EZH2 inhibitor, or combinations thereof. In some embodiments, the DNMT inhibitor is azacytidine or decitabine. In some embodiments, the HD AC inhibitor is vorinostat or romidepsin.

[0133] In some embodiments, the targeted therapy is selected from from belzutifan, erlotinib hydrochloride, everolimus, olaparib, sunitinib malate, zenocutuzumab-zbco, Ado- trzstuzumab, afatinib, alectinib, axitinib, bosutinib, ceritinib, cobimetinib, crizotinib, dabrafenic, dasatinib, enasidenib, gefitinib, ibrutinib, idelalisib, imatinib, lapatinib, neratinib, nilotinib, niraparib, ponatinib, ribociclib, rucaparib, temsirolimus, tofacitinib, vemurafenib, cisplatin, carboplatin, oxaliplatin, talazoparib, immune checkpoint inhibitors, PARP inhibitors, PRMT inhibitors, KRAS inhibitors, and / or PRC2 inhibitors or combinations thereof.

[0134] In some embodiments, the neo-adjuvant chemotherapy and / or adjuvant chemotherapy is selected from gemcitabine, nab-paclitaxel, fluorouracil (5-FU), irinotecan, oxaliplatin, leucovorin, capecitabine, cisplatin, or combinations thereof.

[0135] In some embodiments, the immunotherapy is selected from PD-1 inhibitors, PD-L1 inhibitors, CTLA-4 inhibitors, LAG-3 inhibitors, cytokines, chemokines, monoclonal antibodies, CAR-T cell therapies, Adoptive T cell therapies, cell therapies, and / or other immune modulators, or combinations thereof.

[0136] In one aspect, the present disclosure provides a method for treating a subject having, or suspected of having pancreatic ductal adenocarcinoma (PDAC), comprising monitoring tumor mutational load and chromatin accessibility in one or more biological samples and treating with one or more targeted therapies and / or an epigenetic drug, the method comprising: obtaining nucleosomes from the one or more biological samples by (i) contacting the one or more biological samples with a first agent comprising EpCAM-conjugated magnetic beads to isolate tumor cells from non-tumor cells in the sample to produce an enriched tumor cell sample, wherein the one or more biological samples comprise tumor cells, wherein the enriched tumor cells comprise morphologically intact nuclei or intact nucleosomes contacting the enriched tumor cells comprising morphologically intact nuclei with a transposase complex to produce a population of PDAC ACR fragments representingaccessible chromatin regions (ACRs) in the one or more biological samples; hybridizing a set of ACR-targeting oligonucleotide probes to the PDAC ACRs to generate a target-probe complex, wherein the ACR-targeting oligonucleotide probes are capable of binding to a specific region on the ACRs; contacting the PDAC ACR fragments with a second agent to capture the target-probe complex, amplifying the target-probe complex to produce an amplified population of the target-probe complex, and sequencing tumor mutational load and chromatin accessibility in parallel from the same biological sample.

[0137] In some embodiments, determining the tumor mutational load and chromatin accessibility in parallel from the same biological sample, comprises: a) quantifying variant allele frequency of one or more actionable allelic variant mutations in Table 2 to determine the tumor mutational load in the one or more biological samples; and b) quantifying the abundance of PDAC ACRs associated with a first phenotype and a second phenotype to determine chromatin accessibility in the sample; wherein the first phenotype is associated with good prognosis and responsiveness to chemotherapy, and the second phenotype is associated with poor prognosis and non-responsiveness to chemotherapy. In some embodiments, quantifying the abundance of PDAC ACRs associated with a first phenotype and a second phenotype is used to calculate a first phenotype score and a second phenotype score. In some embodiments, the first phenotype score and the second phenotype score are used to calculate the prognostic score.

[0138] In some embodiments, a subject with one or more actionable allelic variant mutations and a prognostic score above a threshold is treated with chemotherapy. In some embodiments, a subject lacking one or more actionable allelic variant mutations and with a prognostic score above a threshold is treated with chemotherapy. In some embodiments, a subject with one or more actionable allelic variant mutations and a prognostic score below a threshold is not treated with chemotherapy and is treated with an epigenetic drug and / or a targeted therapy. In some embodiments, a subject lacking one or more actionable allelic variant mutations and with a prognostic score below a threshold is not treated with chemotherapy and is treated with an epigenetic drug.

[0139] In some embodiments, the threshold is 1, wherein a subject with a prognostic score below 1 is associated with poor prognosis and a subject with a prognostic score above 1 is associated with good prognosis.

[0140] In some embodiments, a subject with one or more actionable allelic variant mutations and an abundance of good prognostic ACRs is treated with chemotherapy. In someembodiments, a subject lacking one or more actionable allelic variant mutations and an abundance of good prognostic ACRs is treated with chemotherapy. In some embodiments, a subject with one or more actionable allelic variant mutations and an abundance of poor prognostic ACRs is not treated with chemotherapy and is treated with an epigenetic drug and / or a targeted therapy. In some embodiments, a subject lacking one or more actionable allelic variant mutations and an abundance of poor prognostic ACRs is not treated with chemotherapy and is treated with an epigenetic drug.

[0141] In some embodiments, an abundance of good prognostic ACRs is a chromatin accessibility signature wherein the relative amount of good prognostic ACRs is greater than the relative amount of poor prognostic ACRs. In some embodiments, an abundance of poor prognostic ACRs is a chromatin accessibility signature wherein the relative amount of poor prognostic ACRs is greater than the relative amount of good prognostic ACRs.

[0142] In some embodiments, the one or more treatment modalities may be determined based upon the identification of an actionable allelic variant mutations. For example, a subject may be identified with a mutation that is responsive to a specific treatment (e.g., a subject with a BRCA2 mutation may receive a PARP inhibitor). In some embodiments, a subject with a poor prognostic score and an actionable allelic variant mutations (e.g., BRCA2) may forgo standard chemotherapeutic drugs and receive treatment with a specific drug that targets BRCA2. In some embodiments, a subject identified with a mismatch-repair-deficient tumor (MMRd) may receive treatment with an immune checkpoint inhibitor. In some embodiments, a subject identified with a BRCA mutation may receive treatment with cisplatin, carboplatin and / or oxaliplatin. In some embodiments, a subject identified with a BRCA mutation may receive a PARP inhibitor (e.g., Olaparib, rucaparib, and / or talazoparib). In some embodiments, the treatment modalities are prescribed or administered by a physician, a surgeon, a healthcare provider, a laboratory technician or scientist, or a data analyst. Also described herein is an epigenetic drug for use in a method of treating pancreatic ductal adenocarcinoma (PDAC) in a subject, wherein the subject is identified as a candidate for epigenetic drug therapy by a method of the invention. In some embodiments, the method comprises identifying a subject as a candidate for epigenetic drug therapy based on the tumor mutational load and chromatic accessibility in the one or more biological samples. In some embodiments, the method comprises identifying a subject as a candidate for epigenetic drug therapy based on the prognostic score. In some embodiments, the method comprisesidentifying a subject as a candidate for epigenetic drug therapy based on the first and / or second phenotype.Also described herein is a method of predicting whether a subject is likely to respond to a therapeutic intervention based on the tumor load and chromatin accessibility in biological samples obtained from the subject. In some embodiments, a subject who is predicted to response to a therapeutic intervention is identified as a candidate for that therapeutic intervention. Tumor load and chromatin accessibility may be determined by any suitable method described herein. In some embodiments, the method comprises: (a) enriching tumor cells comprising morphologically intact nuclei in one or more biological samples obtained from a subject having or suspected of having PDAC to produce an enriched tumor sample; (b) contacting the enriched tumor cells comprising morphologically intact nuclei with a transposase complex to produce a population of PDAC Accessible Chromatin Region (PDAC ACR) fragments representing PDAC ACRs in the one or more biological samples; (c) contacting the PDAC ACR fragments with a set of oligonucleotide probes, wherein the PDAC ACR fragments hybridize with the oligonucleotide probes to generate a target-probe complex; (d) contacting the target-probe complex with a second agent to capture the targetprobe complex; (e) amplifying the target-probe complex to produce an amplified population of the target-probe complex comprising the PDAC ACR fragments; and (f) determining tumor mutational load and chromatin accessibility in parallel from the target-probe complex in the same biological sample, comprising: (i) quantifying variant allele frequency of one or more actionable allelic variant mutations in Table 2 to determine the tumor mutational load in the one or more biological samples; and (ii) calculating a prognostic score based on the relative abundance of PDAC ACRs associated with a first phenotype and a second phenotype; wherein the first phenotype is associated with good prognosis and responsiveness to chemotherapy, and the second phenotype is associated with poor prognosis and nonresponsiveness to chemotherapy. In some embodiments, a prognostic score above a threshold is indicative that the subject will respond to chemotherapy. In some embodiments, a tumor mutational load above a threshold and a prognostic score below a threshold is indicative that the subject will respond to treatment with an epigenetic drug and / or a targeted therapy. In some embodiments, a tumor mutational load and prognostic score below a threshold is indicative that the subject will respond to treatment with an epigenetic drug.Subjects and biological samples

[0143] In some embodiments, biological samples (e.g., solid tumor biopsy, liquid biopsy) are collected from patients who have been diagnosed with cancer, e.g., pancreatic cancer. The subject can be treatment naive to a treatment modality. In some embodiments, biological samples (e.g., solid tumor biopsy, liquid biopsy) are collected from patients who are suspected of having cancer, e.g., pancreatic cancer.

[0144] The biological sample can be tumor cells isolated from solid tumor biopsy and enriched using EpCAM-conjugated magnetic beads.

[0145] The biological sample can be circulating tumor nucleosomes in the subject’s blood such as plasma, or other bodily fluids such as urine, saliva, sperm, tear, or sweat.Kits

[0146] In some embodiments, the disclosure provides a kit for monitoring tumor mutational load and chromatin accessibility in one or more biological samples of a subject with pancreatic ductal adenocarcinoma (PDAC), the kit comprising: a) one or more sets of oligonucleotide probes for hybridizing with PDAC ACRs in the one or more biological samples to generate a target-probe complex; b) a second agent to capture the target-probe complex; c) reagents for amplifying the target-probe complex to produce an amplified population of the target-probe complex; d) reagents for sequencing tumor mutational load in the biological sample; and e) instructions for quantifying the relative abundance of the PDAC ACRs in a biological sample to calculate a prognostic score.

[0147] A kit for treating a subject having, or suspected of having pancreatic ductal adenocarcinoma (PDAC), comprising monitoring tumor mutational load and chromatin accessibility in one or more biological samples and treating with one or more targeted therapies and / or an epigenetic drug, the kit comprising: a) one or more sets of oligonucleotide probes for hybridizing with PDAC ACRs in the one or more biological samples to generate a target-probe complex; b) a second agent to capture the target-probe complex; c) reagents for amplifying the target-probe complex to produce an amplified population of the target-probe complex; d) reagents for sequencing tumor mutational load in the biological sample and determining actionable allelic variant mutations; ande) instructions for quantifying the relative abundance of the PDAC ACRs in a biological sample to calculate a prognostic score; and f) instructions for determining a treatment decisions based on (i) and (j), wherein wherein a subject with one or more actionable allelic variant mutations and a prognostic score above a threshold is treated with chemotherapy; and wherein a subject lacking one or more actionable allelic variant mutations and with a prognostic score above a threshold is treated with chemotherapy; and wherein a subject with one or more actionable allelic variant mutations and a prognostic score below a threshold is not treated with chemotherapy and is treated with an epigenetic drug and / or a targeted therapy; and wherein a subject lacking one or more actionable allelic variant mutations and with a prognostic score below a threshold is not treated with chemotherapy and is treated with an epigenetic drug.

[0148] In some embodiments, the targeting oligonucleotide probes are provided in a panel. In some embodiments, the targeting oligonucleotide probes comprise a combination of oligonucleotide probes targeting the ACRs in Table 1A-B.

[0149] In some embodiments, the biological sample comprises pancreatic ductal adenocarcinoma cells having morphologically intact nuclei or intact nucleosome (histone- DNA) structure.

[0150] In some embodiments, the kit further comprises reagents and instructions for obtaining the biological sample by contacting the morphologically intact nuclei or intact nucleosome (histone-DNA) structure to a transposase complex to produce a population of tagged DNA fragments representing the targeted accessible chromatin region fragments.

[0151] In some embodiments, the kit further comprises reagents and instructions for enriching the biological sample obtained from the subject for tumor cells.

[0152] In some embodiments, the reagents comprise antibody-conjugated magnetic beads, EpC AM-conjugated magnetic beads, or a combination thereof to isolate tumor cells from non-tumor cells in the biological sample. The enrichment can be achieved by contacting the biological sample with an agent (e.g., antibody-conjugated magnetic beads, EpCAM- conjugated magnetic beads) to isolate tumor cells from non-tumor cells in the biological sample to enrich the sample for tumor cells. In some embodiments, the agent is EpCAM- conjugated magnetic beads. In some embodiments, the agent is EpCAM-conjugated nonmagnetic beads such as one used in flow-cytometry assays or immunohistochemistry assays.

[0153] In some embodiments, the kit further comprising reagents and instructions for: (i) attaching a detectable label to the tagged DNA fragments to produce labeled fragments; and (ii) contacting the detectable labeled fragments to the set of oligonucleotide probes.

[0154] In some embodiments, the kit further comprising instructions for determining a prognostic score indicative of the subject’s responsiveness to one or more treatment modalities based on a differential score of the first phenotype and the second phenotype.

[0155] In some embodiments, the instruction comprises normalizing the differential value with at least one of a positive control value and a negative control value to obtain the prognostic score.

[0156] In some embodiments, the prognostic score is at least 0.1. In some embodiments, the prognostic score is less than 5.

[0157] In some embodiments, the kit further comprises reagents and instructions for detecting nuclear localization of HNFlb or another transcription factor.

[0158] In some embodiments, the transcription factors are selected from ZKSCAN1, EPAS1, RUNX2, ZNF410, MAFF, RREB1, NR3C2, SMAD1, RUNX1, ZNF32, ZSCAN4, HOXB1, POU3F1, ZBTB3, CEOCK, TCF15, GCM1, HINFP, CGBP, MYPOP, ZNF384, GMEB2, E2F5, AC012531.1, ZBTB7B, HOXC9, HNF4G, CREB1, ATF2, E2F2, SP3, ARID5A, ZFP161, OTP, PBX3, ZBTB33, ONECUT3, ONECUT3, DEX2, HNF4A, PRRX1, TCFE5, HOXB7, IRF6, GRHE1, FOXD2, ISL1, MEE, GATA2, GATA1, HMB0X1, NRF1, ZFHX3, ONECUT1, TET1, E2F3, DNMT1, CTCFL, CTCF, HNF1B, and HNF1A.

[0159] In some embodiments, the transcription factors are selected from at least 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 of transcription factors ZKSCAN1, EPAS1, RUNX2, ZNF410, MAFF, RREB1, NR3C2, SMAD1, RUNX1, ZNF32, ZSCAN4, HOXB1, POU3F1, ZBTB3, CLOCK, TCF15, GCM1, HINFP, CGBP, MYPOP, ZNF384, GMEB2, E2F5, AC012531.1, ZBTB7B, HOXC9, HNF4G, CREB1, ATF2, E2F2, SP3, ARID5A, ZFP161, OTP, PBX3, ZBTB33, ONECUT3, ONECUT3, DLX2, HNF4A, PRRX1, TCFL5, HOXB7, IRF6, GRHL1, FOXD2, ISL1, MLL, GATA2, GATA1, HMB0X1, NRF1, ZFHX3, ONECUT1, TET1, E2F3, DNMT1, CTCFL, CTCF, HNF1B, and HNF1A.

[0160] In some embodiments, the transcription factors are selected ZKSCAN1A, HNF1B, or a combination thereof.

[0161] In some embodiments, the kit further comprises reagents and instructions for predicting a long duration of disease-free survival when the first phenotype score is significantly higher than the second phenotype score and / or predicting a short duration ofdisease-free survival when the first phenotype score is significantly lower than the second phenotype score.

[0162] In some embodiments, the kit further comprises reagents and instructions for determining a first phenotype score and second phenotype score of the subject’s responsiveness to one or more treatment modalities.

[0163] In some embodiments, the second phenotype is recurrence of a cancer within 6 months, or 1, 2, 3, 4, or 5 years of surgical resection and / or the first phenotype is nonrecurrence of a cancer within 6 months, or 1, 2, 3, 4, or 5 years of surgical resection. In some embodiments, the second phenotype is recurrence of a cancer within one year of surgical resection and the first phenotype is non-recurrence of a cancer within one year of surgical resection.

[0164] In some embodiments, the second phenotype is non-responder to a cancer therapy. In some embodiments, the first phenotype is responder to a cancer therapy. In some embodiments, the cancer therapy is selected from chemotherapy, immunotherapy, radiation, or combinations thereof.

[0165] In some embodiments, the first phenotype is having a median disease-free survival (DFS) of at least of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more years.

[0166] In some embodiments, the first phenotype is having a median disease-free survival (DFS) of at least 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, or more days.

[0167] In some embodiments, the first phenotype is having a median disease-free survival (DFS) of between 50 to 1500 days, between 100 to 1000 days, between 300 to 800 days, between 200 to 500 days, or between 400 to 600 days. In some embodiments, the second phenotype is having a median disease-free survival of at least 350 days.

[0168] In some embodiments, the second phenotype is having a median disease-free survival of less than 5, 4, 3, 2, 1 or less years. In some embodiments, the second phenotype is having a median disease-free survival of less than 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 or less months. In some embodiments, the second phenotype is having a median disease-free survival of less than 350, 300, 250, 200, 150, 100, 50, 10, or less days. In some embodiments, the second phenotype is having a median disease-free survival of less than 50 days.

[0169] In some embodiments, the second phenotype is having a median disease-free survival of between 1 to 350 days, between 50-300 days, between 10-100 days, or between 40 to 250 days.

[0170] In some embodiments, the biological sample used to generate the ATAC-library, which can be used as ATAC- Array template DNA, comprises treatment-naive malignant cells obtained from a subject. In some embodiments, the subject is a treatment naive patient who has not received the one or more treatment modalities.

[0171] In some embodiments, the subject is under treatment or has been treated with the one or more treatment modalities.

[0172] In some embodiments, the one or more treatment modalities are selected from resecting cancerous tissue, neo-adjuvant chemotherapy, adjuvant chemotherapy, immunotherapy, an epigenetic drug, or combinations thereof. In some embodiments, the epigenetic drug is selected from DNMT inhibitor, an HDAC inhibitor, an EZH2 inhibitor, or combinations thereof. In some embodiments, the DNMT inhibitor is azacytidine or decitabine. In some embodiments, the HDAC inhibitor is vorinostat or romidepsin.

[0173] In some embodiments, the neo-adjuvant chemotherapy and / or adjuvant chemotherapy is selected from gemcitabine, nab-paclitaxel, fluorouracil (5-FU), irinotecan, oxaliplatin, leucovorin, capecitabine, cisplatin, or combinations thereof.

[0174] In some embodiments, the biological sample is selected from a tumor biopsy or surgically resected tumor specimen.

[0175] In some embodiments, obtaining the targeted accessible chromatin region fragments does not include sequencing the tagged fragments or amplicons thereof. In some embodiments, the amplified targeted accessible chromatin fragments comprise a mean size of about 50 bp to about 1100 bp, about 100 bp to about 350 bp, about 150bp to about 800 bp, about 80 bp to about 150 bp, or about 150 bp to about 400 bp. In some embodiments, the amplified targeted accessible chromatin fragments comprise a mean size of 80 bp, about 100 bp, about 120 bp, about 150 bp, about 180 bp, about 200 bp, about 250 bp, about 300 bp, about 400 bp, about 500 bp, about 600 bp, about 700 bp, about 800 bp, about 900 bp, about 1000 bp, about 1100 bp, or longer. In some embodiments, the amplified targeted accessible chromatin fragments comprise a mean size of about 120 bp.Embodiments

[0176] The present disclosure provides methods, compositions, and systems for simultaneously investigating variant allele frequency (VAF) of selected genes, and targeted chromatin accessibility profiles using the same biological samples. In some embodiments, the biological samples are collected from patients who have been diagnosed with cancer. In some embodiments, the cancer is pancreatic cancer. The present disclosure provides methods that integrate analyses of genetic mutation and chromatin accessibility together on one platform using a capture -based ultradeep Epigenetic-sequencing method. Genetic mutations can be detected by capture-based ultradeep sequencing, we hypothesized that the quantitative estimation of chromatin accessibility profiles on targeted genomic cA-regulatory regions can also be performed by this same ultradeep capture-based sequencing method without compromising the library heterogeneity. The advantage of this integrative analysis is, both the mutation profiles of the selected biomarkers e.g., genes (as the desired target genomic coordinates embedded on the detection panel), and the differential accessibility profiles of the desired target cA-regulatory elements (as the desired target genomic coordinates embedded on the same detection panel) can be simultaneously estimated from the same biological sample. This is especially advantageous when the sample materials are limited, for example, in case of clinical diagnosis of solid or liquid biopsy specimens.

[0177] In one aspect, the present disclosure is directed to a method for monitoring tumor load and chromatin accessibility in one or more biological samples of a subject, the method comprising:(a) obtaining nucleosomes from the one or more biological samples by(i) contacting the one or more biological samples with a first agent comprising EpCAM-conjugated magnetic beads to isolate tumor cells from non-tumor cells in the sample to produce an enriched tumor cell sample, wherein the one or more biological samples comprise tumor cells, wherein the enriched tumor cells comprise morphologically intact nuclei or intact nucleosomes; or(ii) isolating nucleosomes from the one or more biological samples, wherein the one or more biological samples comprise circulating tumor nucleosomes;(b) contacting the enriched tumor cells comprising morphologically intact nuclei or intact nucleosomes in step (a)(i) or the isolated circulating tumor nucleosomes in (a)(ii) with a transposase complex to produce a population of tagged DNA fragments representing accessible chromatin regions (ACRs) in the one or more biological samples;(c) amplifying the tagged DNA fragments to generate a plurality of amplicons of the tagged DNA fragments; contacting the amplicons of the tagged DNA fragments with a second agent to capture the amplicons;(d) hybridizing a set of ACR-targeting oligonucleotide probes to the amplicons to generate a plurality of target-captured DNA fragments to allow sequencing of the targeted DNA fragments, wherein the ACR-targeting oligonucleotide probes are capable of binding to a specific region on the ACRs;(e) hybridizing a set of biomarker-targeting oligonucleotide probes to the amplicons to generate a plurality of target-captured DNA fragments to allow sequencing of the targeted DNA fragments, wherein the biomarker-targeting oligonucleotide probes are capable of binding to a specific region of a polynucleotide encoding a protein of any biomarker in Table 2; and(f) quantifying variant allele frequency of one or more biomarkers in Table 2 to determine the tumor load in the one or more biological samples.

[0178] In various embodiments, the second agent comprises biotin conjugated to a streptavidin, an avidin, or a neutravidin to form an affinity pair. In various embodiments, the affinity pair is further conjugated to a magnetic bead, an agarose bead, a magnetic resin bead, or a covalent bead. In some embodiments, the second agent is a biotinylated- streptavidin magnetic bead.

[0179] In accordance with any of the embodiments, the method further comprising:(i) tagging the chromatin accessible nucleic acid fragments to generate a plurality of tagged DNA fragments representing accessible chromatin regions (ACRs) of the intact nuclei presence in the tumor cells or circulating tumor nucleosomes, and(ii) attaching a detectable label to the tagged DNA fragments to produce labeled chromatin accessible nucleic acid fragments.

[0180] In accordance with any of the embodiments, the ACR-targeting oligonucleotide probes comprise a first subset of ACR-targeting oligonucleotide probes targeting an ACR on any chromosomal region in Table 1A and a second subset of ACR-targeting probes targeting and ACR on any chromosomal region in Table IB.

[0181] In accordance with any of the embodiments, the first subset of ACR-targeting oligonucleotide probes target an ACR comprising a polynucleotide encoding any one of SPATA4; MAP2K2, PPAP2B, CNBP, FIG4, TTC19, PAPL, RN7SL300P, or apolynucleotide in the 5’ or 3’ proximity thereof. In some embodiments, the ACR is indicative of a first phenotype.

[0182] In accordance with any of the embodiments, the second subset of ACR-targeting oligonucleotide probes target an ACR comprising a polynucleotide encoding any one of ARHGEF10, CLDN23, and C12orf36, or a polynucleotide in the 5’ or 3’ proximity thereof. In some embodiments, the ACR is indicative of a second phenotype.

[0183] In accordance with any of the embodiments, the method further comprises sequencing the target-captured DNA fragments. In some embodiments, the method further comprises quantifying the sequence read depths of the target-captured DNA fragments captured by the first subset of ACR-targeting oligonucleotide probe to determine a first phenotype score. In some embodiments, the method further comprises quantifying the sequence read depths of the target-captured DNA fragments captured by the second subset of ACR-targeting oligonucleotide probe to determine a second phenotype score.

[0184] In accordance with any of the embodiments, the method further comprises determining a prognostic score based on the first and second phenotype score of the one or more biological samples.

[0185] In accordance with any of the embodiments, the method further comprises the step of comparing the first phenotype score to the second phenotype score to obtain a differential value.

[0186] In accordance with any of the embodiments, the method further comprises normalizing the differential value with at a positive control value to obtain the prognostic score.

[0187] In accordance with any of the embodiments, the method further comprises normalizing the differential value with a negative control value to obtain the prognostic score.

[0188] In accordance with any of the embodiments, the prognostic score is at least between 0.1 to 5.

[0189] In accordance with any of the embodiments, the prognostic score is indicative of the subject’s responsiveness to one or more treatment modalities.

[0190] In accordance with any of the embodiments, the method further comprises detecting enrichment of specific transcription factor binding motifs correlating nuclear localization of the transcription factor assessing nuclear localization of one or more biomarkers capable of modulating gene expression through complementary binding to one or more specific regions on the amplicons of the tagged DNA fragments. In some embodiments, the transcriptionfactors is selected from ZKSCAN1, EPAS1, RUNX2, ZNF410, MAFF, RREB1, NR3C2, SMAD1, RUNX1, ZNF32, ZSCAN4, H0XB1, POU3F1, ZBTB3, CEOCK, TCF15, GCM1, HINFP, CGBP, MYPOP, ZNF384, GMEB2, E2F5, AC012531.1, ZBTB7B, H0XC9, HNF4G, CREB1, ATF2, E2F2, SP3, ARID5A, ZFP161, OTP, PBX3, ZBTB33, ONECUT3, ONECUT3, DEX2, HNF4A, PRRX1, TCFL5, H0XB7, IRF6, GRHE1, FOXD2, ISL1, MEE, GATA2, GATA1, HMB0X1, NRF1, ZFHX3, ONECUT1, TET1, E2F3, DNMT1, CTCFL, CTCF, HNF1B, and HNF1A. In some embodiments, the transcription factor is ZKSCAN1, HNF1B, or both.

[0191] In accordance with any of the embodiments, the method further comprises predicting a short duration of disease- free survival when the first epigenetic value is significantly higher than the second epigenetic value.

[0192] In accordance with any of the embodiments, the method further comprises predicting a long duration of disease-free survival when the second epigenetic value is significantly higher than the first epigenetic value.

[0193] In accordance with any of the embodiments, the first phenotype is non-recurrence of a cancer and the second phenotype is -recurrence of a cancer within one year of surgical resection.

[0194] In accordance with any of the embodiments, the first phenotype is non-responder and the second phenotype is responder to one or more cancer treatment modalities.

[0195] In accordance with any of the embodiments, the first phenotype is having a median disease-free survival of between 50 to 1500 days and the second phenotype is having a median disease-free survival of between 1 to 350 days.

[0196] In accordance with any of the embodiments, the first phenotype is having a median progression- free survival of between 50 to 1500 days and the second phenotype is having a median progression- free survival of between 1 to 180 days.

[0197] In accordance with any of the embodiments, the variant allele is single nucleotide variant (SNVs), an insertion, a deletion, an indel, a copy number variation (CNV), or combinations thereof.

[0198] In accordance with any of the embodiments, the method comprises quantifying variant allele frequency of one or more biomarkers selected from KRAS, TP53, SMAD4, CDKN2A, BRCA1, BRCA2, ATR, ATM, RAD51, PALB2, EZH2, or combinations thereof. In some embodiments, the biomarkers comprise KRAS and TP53. In some embodiments, the biomarkers comprise KRAS. In some embodiments, the biomarkers comprise TP53.

[0199] In accordance with any of the embodiments, a higher variant allele frequency as compared to a control is indicative of a higher tumor load. In some embodiments, the higher tumor load is indicative of poor responsiveness to the one or more treatment modalities.

[0200] In accordance with any of the embodiments, the one or more treatment modalities are selected from resecting cancerous tissue, neo-adjuvant chemotherapy, adjuvant chemotherapy, immunotherapy, and an epigenetic drug. In some embodiments, the epigenetic drug is selected from a DNA methyltransferase (DNMT) inhibitor, a histone deacetylase (HD AC) inhibitor, an enhancer of zeste homolog 2 (EZH2) inhibitor, a bromodomain and extra-terminal motif (BET) inhibitor, a histone acetyltransferase (HAT) inhibitor, a histone lysine methyltransferase (KMT) inhibitor, a protein arginine methyltransferase (PRMT) inhibitor, a proteolysis-targeting chimera (PROTAC) comprising a HD AC inhibitor, a DNMT inhibitor, a BET inhibitor, a EZH2 inhibitor, a HAT inhibitor, a KMT inhibitor, or combinations thereof.

[0201] In accordance with any of the embodiments, the neo-adjuvant chemotherapy and / or adjuvant chemotherapy is selected from gemcitabine, nab-paclitaxel, fluorouracil (5-FU), irinotecan, oxaliplatin, leucovorin, capecitabine, cisplatin, or combinations thereof.

[0202] In accordance with any of the embodiments, the subject is diagnosed with pancreatic ductal adenocarcinoma cancer.

[0203] In accordance with any of the embodiments, the one or more biological samples are obtained before the subject has received a first dose of treatment with one or more treatment modalities.

[0204] In accordance with any of the embodiments, the one or more biological samples are obtained after the subject has received at least one dose of treatment with one or more treatment modalities.

[0205] In accordance with any of the embodiments, the one or more biological samples are selected from a tumor biopsy, a surgically resected tumor specimen, or a liquid biopsy.

[0206] In accordance with any of the embodiments, the subject is treatment naive to the one or more treatment modalities.

[0207] In accordance with any of the embodiments, the one or more biological samples in (a)(i) is obtained from a tumor biopsy.

[0208] In accordance with any of the embodiments, the one or more biological samples in (a)(ii) is obtained from a liquid biopsy. In some embodiments, the one or more biological samples are blood. In some embodiments, the one or more biological samples are plasma.EXAMPLESExample 1 - Integrative detection of oncogene mutation and chromatin accessibility profiles in biological samples

[0209] Analyses of genetic mutation and chromatin accessibility are two separate actionable prognostic paradigms for precision oncology. Here the integration of these two paradigms is demonstrated together on a single platform with a targeted Chromatin Accessibility and Mutation Sequencing (tCAM-seq) methodology (FIG. 1A-1C).

[0210] Sequencing read depth can be increased to a desired level using this method. Because of the high depth of sequencing reads, the abundance proportion of variant and reference alleles can be quantitatively estimated on each locus covered by sequencing. The higher the depth, the higher the confidence of the variant calling. The abundance proportion of the variant allele gives an accurate estimation of “rarity” of the clone contributing the specific mutation (variant allele) in the DNA pool.

[0211] The relative quantitation of TN5-transposase enzyme accessible regions was analyzed to assess whether it would retain accuracy upon increasing the read depth up to a limit where the variant calling (mutations) can also be performed with confidence. To test this, TN5 transposase-treated and PCR-amplified ACR (Accessible Chromatin Regions) -libraries were prepared from biological specimens as described previously (Dhara et al. Pancreatic cancer prognosis is predicted by an ATAC-array technology for assessing chromatin accessibility. Nat Commun 12, 3044 (2021), and WO2024211731A).Methods

[0212] Briefly, freshly collected pancreatic tumor specimens were collected and dissociated with an enzyme cocktail followed by magnetic separation of EpCAM-i- PDAC malignant cells. The custom-made panel was designed by using Illumina’s Design Studio software for the desired targets as described in Tables 1A and IB, and then the specific biotinylated probes for this custom panel were manufactured by Illumina.

[0213] The process of making capture- ACR libraries following the protocol as previously described (Dhara et al. 2021). Patient tissue samples were collected into tubes containing 3 ml DMEM, and 30ul Poloxamerl88 solution, ensuring preservation during transport at 4°C. Upon receipt, samples are promptly processed, with initial dissociation performed using the Miltenyi GentleMACS Dissociator and enzymatic cocktails (Enzymes H, R, and A) to yieldsingle-cell suspensions. Subsequent steps remove red blood cell contamination with ACK lysing buffer Next, malignant epithelial cells were isolated from the tumor microenvironment by using EpCAM-based magnetic bead selection, enriching for EpCAM -positive cells from the dissociated tumor suspension, followed by immunomagnetic selection. The viable EpCAM-positive cells were counted and aliquoted, with optimal cell lumbers of about 70,000 cells per reaction to ensure robust library preparation. However, it was found that a minimum cell number of 7000 EpCAM-positive cells yielded a satisfactory ACR-library.

[0214] An important quality control measure follows, requiring at least a fivefold (5x) increase in DNA concentration after PCR amplification of the transposed DNA to confirm efficient Tn5 integration into accessible chromatin. Library amplification was performed using NEBNext High-Fidelity PCR Master Mix and custom barcoded primers from Integrated DNA Technologies (IDT), optimized for low-input, high-complexity libraries. The thermal cycling protocol includes an initial 5-minute extension at 72°C to complete extension from Tn5 insertions, followed by 12 PCR cycles tailored to maximize yield while conserving the library heterogeneity.

[0215] Post-cleanup, libraries were either quantified individually or pooled for enrichment. For genomic DNA applications, pooling was done by volume when using higher input amounts (50-1000 ng) or by mass if lower input amounts are used or when balancing multiple samples from different preparations. Pools for enrichment are typically 12-plex, although configurations from 1-plex up to 12-plex may be used. Quantification of preenriched libraries, commonly performed using a fluorometric assay such as the Qubit dsDNA Broad Range assay, ensures accurate pooling and helps maintain balanced representation of libraries in downstream sequencing.

[0216] The enrichment phase begins with hybridization of the pooled pre-enriched libraries to biotinylated oligonucleotide probes targeting specific genomic regions of interest (Table 2). The panel was designed by selecting the set of canonical driver mutations of pancreatic cancer, (e.g., KRAS, TP53, CDKN2A, SMAD4), and other relevant genes (e.g., BRCA1, EZH2 RAD51). Along with these genic regions, 1092 accessible chromatin regions (ACRs) were analyzed from Table 1A and Table IB. The differential pattern of the 1092 ACRs has been shown to predict the chemotherapy response in patients with pancreatic cancer. The hybridization was performed at 62°C for most panels with reactions set up with Enrichment Hyb Buffer 2 (EHB2), NHB2 hybridization blockers, and the probe panel in precise proportions to achieve a 100 pl reaction volume.

[0217] Following hybridization, target-probe complexes were captured using Streptavidin Magnetic Beads 3 (SMB3), which selectively bind the biotinylated probes and the attached genomic fragments. Multiple wash steps with preheated Enhanced Enrichment Wash (EEW) buffer ensure stringent removal of nonspecific DNA, which is critical for achieving high on- target rates and reducing background noise in sequencing data. The enriched DNA fragments were then eluted from the beads using an alkaline elution buffer (EE1 combined with HP3), neutralized with ET2, and prepared for a final amplification step.

[0218] A second limited-cycle PCR, using the Illumina primer cocktails, amplifies the enriched genomic fragments to generate sufficient material for sequencing, typically using 12 PCR cycles. A final purification via the Qiagen MinElute PCR Purification Kit ensures removal of excess primers and reaction components, yielding highly pure enriched libraries. Libraries were then quantified and analyzed using an Agilent Bioanalyzer or TapeStation to confirm the correct fragment size distribution — typically centered around -350 bp for genomic DNA — ensuring they meet quality specifications for sequencing.

[0219] For sequencing, libraries were diluted to specific starting molarities appropriate for the selected Illumina platform. For example, genomic DNA libraries might be diluted to 2 nM for instruments such as the NextSeq or NovaSeq, with final loading concentrations ranging from -150-200 pM depending on the platform’s flow cell type and desired coverage depth. The protocol supports paired-end sequencing, typically recommending 2 x 101 cycles for standard applications, although longer reads (e.g., 2 x 126 or 2 x 151) can be used for increased coverage or overlapping reads. The custom panel captured the desired target coordinates from the TN5-amplified ACR-libraries and sequencing was then performed using ultra high throughput sequencers (e.g., Illumina NovaSeq 6000 / NovaSeqX platform).

[0220] Bioinformatic analysis of the capture TN5-ACR-libraries was compared with traditional ATAC-seq libraries. Alignment and DESEQ2 normalization was then performed. Capture targeted TN5 ACR-sequencing and traditional whole genome (bulk) ATAC-seq data were processed using a custom pipeline. FASTQ files were quality trimmed and aligned to hgl9 (GRCh38_v30) using Bowtie2 with the following parameters: '—local —very-sensitive- local -no-mixed -no-discordant -phred33'. Read groups were added and duplicates marked using PICARD. Aligned BAM files were overlapped with target regions and quantified using **Rsamtools**. DESeq2 size factors were estimated using predefined positive and negative control regions and applied to normalize read counts across samples. Normalized counts were used for prognostic score calculations.Results

[0221] High concordance was observed for the relative FPKM (Fragments Per Kilobase per Million mapped fragments) values by direct comparison of tCAM-seq and whole genome bulk-ATAC-seq on the same ATAC libraries generated from the same subjects with pancreatic cancer, demonstrating the feasibility of this approach (FIG. 2).

[0222] In addition, the predictive ability of the tCAM-seq (FIG. 4A) remained uncompromised compared head-to-head with the whole genome (bulk) sequencing (FIG 4B), even after increasing the sequencing read depth in the tCAM-seq by >1000x. Therefore, the variant alleles can be detected using tCAM-seq (e.g., capture) but not from bulk epigenetic sequencing (FIG. 7A-7B).

[0223] Enrichment of HNFlb transcription factor binding motif was detected within the genomic regions that were open in chemotherapy responder patients and silenced in nonresponder patients (FIG. 5). In addition, IHC analysis demonstrated significant segregation of disease-free survival (DFS) between patients with strong nuclear localization of HNFlb versus patients with weak / no nuclear localization of HNFlb (data not shown). Further, the combination of tCAM-seq (e.g., Capture-Epigenetic Sequencing) and detection of HNFlb significantly stratifies patient survival (FIG. 6).Example 2 - Variant allele frequency analysis

[0224] Targeted Chromatin Accessibility and Mutation Sequencing (tCAM-seq) workflow: The disclosure provides a tCAM-seq bioinformatic analysis workflow with specific adjustments to accommodate ultra-deep sequence data. The raw FASTQ files are first trimmed using Trim Galore. Read QC is performed by FASTQC. The samples were then aligned to known targets excised from the hgl9 genome using bowtie2 (v2.2.6, Parameters: - X2000 -local -mm -no-mixed —no- discordant). Duplicate read removal is then performed using Mark Duplicates. In order to account for Tn5 shift, all positive strand reads were shifted by +4 bps and all negative strand reads were shifted by -5bps using Deep tools alignment Sieve. Reads per peak were then quantified by htseq-count. Sequence depth is assessed by quantifying the number of unique reads per peak.Mutation analyses (Variant Allele Frequency calculations)

[0225] Variants were called using GATK Mutect2 (tumor-only mode) following Broad’s somatic short variant calling best practices. Tumor BAM files were scanned within ATAC capture regions and annotated with a germline resource and a normal panel (Mutect2-WGS- panel-b3). Filtered variant calls were processed through **FilterMutectCalls** and further annotated with **dbSNP** and **SnpEff**. Variants were imported into R and filtered with the following criteria: reference depth > 1, alternate depth > 1, and variant allele frequency > 1%. The tCAM-seq approach showed increased sequencing depth compared to traditional whole genome (bulk) ATAC-seq (FIG. 3).

[0226] In conclusion, the dual application platform enabled detection of actionable mutations as well as prediction of drug response of an individual patient, simultaneously on the same platform using a single tumor specimen. By this method, it is possible to detect the actionable mutations as well as actionable differential chromatin accessibility of the selected genomic regions. AS an illustrative example, a current treatment scenario for a subject with pancreatic cancer compared to a proposed treatment scenario utilizing tCAM-seq methods is demonstrated in FIG. 8. In a current treatment scenario, a subject may undergo a genomic mutation panel test and receive standard of care chemotherapy. Some patients may respond and other non-responders may be given a targeted therapy. In the proposed scenario, a subject will undergo the standard genomic testing in addition to the integrated genetic and epigenetic approaches leveraged by tCAM-seq. With this approach, a subject receives concurrent testing of their 1) epigenetic signature which determines responsiveness to standard chemotherapy, and 2) tumor sequencing to identify actionable (e.g., druggable) mutations. A subject determined to be a non-responder to chemotherapy may undergo immediate treatment with a targeted therapy directed at the previously identified actionable mutation.EQUIVALENTS AND INCORPORATION BY REFERENCE

[0227] All references cited herein are incorporated by reference to the same extent as if each individual publication, database entry (e.g., Genbank sequences or GenelD entries), patent application, or patent, was specifically and individually indicated incorporated by reference in its entirety, for all purposes. This statement of incorporation by reference is intended by Applicants, pursuant to 37 C.F.R. § 1.57(b)(1), to relate to each and every individual publication, database entry (e.g., Genbank sequences or GenelD entries), patent application, or patent, each of which is clearly identified in compliance with 37 C.F.R. § 1.57(b)(2), even if such citation is not immediately adjacent to a dedicated statement of incorporation byreference. The inclusion of dedicated statements of incorporation by reference, if any, within the specification does not in any way weaken this general statement of incorporation by reference. Citation of the references herein is not intended as an admission that the reference is pertinent prior art, nor does it constitute any admission as to the contents or date of these publications or documents.

[0228] While the invention has been particularly shown and described with reference to a preferred embodiment and various alternate embodiments, it is understood by persons skilled in the relevant art that various changes in form and details can be made therein without departing from the spirit and scope of the invention.

Claims

WHAT IS CLAIMED IS:

1. A method for detecting actionable allelic variant mutations and chromatin accessibility in one or more biological samples of a subject having or suspected of having pancreatic ductal adenocarcinoma (PDAC), the method comprising: a) enriching tumor cells comprising morphologically intact nuclei in the one or more biological samples from a treatment naive patient comprising morphologically intact nuclei to produce an enriched tumor sample, b) contacting the enriched tumor cells comprising morphologically intact nuclei with a transposase complex to produce a population of PDAC Accessible Chromatin Region (PDAC ACR) fragments representing PDAC ACRs in the one or more biological samples; c) contacting the PDAC ACR fragments with a set of oligonucleotide probes, wherein the PDAC ACR fragments hybridize with the oligonucleotide probes to generate a target-probe complex; d) contacting the target-probe complex with a second agent to capture the target-probe complex; e) amplifying the target-probe complex to produce an amplified population of the targetprobe complex comprising the PDAC ACR fragments; and f) determining tumor mutational load and chromatin accessibility in parallel from the target-probe complex in the same biological sample, comprising: i) quantifying variant allele frequency of one or more actionable allelic variant mutations comprising a single nucleotide variant (SNV), an insertion, a deletion, an indel, a copy number variation (CNV), or combinations thereof to determine the tumor mutational load in the one or more biological samples; and ii) quantifying the relative abundance of PDAC ACRs associated with a first phenotype to calculate a first phenotype score and quantifying the relative abundance of PDAC ACRs associated with a second phenotype to calculate a second phenotype score; wherein the first phenotype score is associated with good prognosis and responsiveness to chemotherapy, and the secondphenotype score is associated with poor prognosis and non-responsiveness to chemotherapy.

2. A method of treating a subject having, or suspected of having pancreatic ductal adenocarcinoma (PDAC), comprising detecting actionable allelic variant mutations and chromatin accessibility in one or more biological samples and treating with one or more targeted therapies and / or an epigenetic drug, the method comprising: a) enriching tumor cells comprising morphologically intact nuclei in a biological sample to produce an enriched tumor sample, b) contacting the enriched tumor cells comprising morphologically intact nuclei with a transposase complex to produce a population of PDAC Accessible Chromatin Region (PDAC ACR) fragments representing PDAC ACRs in the one or more biological samples; c) contacting the PDAC ACR fragments with a set of oligonucleotide probes, wherein the PDAC ACR fragments hybridize with the oligonucleotide probes to generate a target-probe complex; d) contacting the target-probe complex with a second agent to capture the target-probe complex; e) amplifying the target-probe complex to produce an amplified population of the targetprobe complex comprising the PDAC ACR fragments; and f) determining tumor mutational load and chromatin accessibility in parallel from the target-probe complex in the same biological sample, comprising: i) quantifying variant allele frequency of one or more actionable allelic variant mutations comprising a single nucleotide variant (SNV), an insertion, a deletion, an indel, a copy number variation (CNV), or combinations thereof, to determine the tumor mutational load in the one or more biological samples; and ii) quantifying the relative abundance of PDAC ACRs associated with a first phenotype to calculate a first phenotype score and quantifying the relative abundance of PDAC ACRs associated with a second phenotype to calculate a second phenotype score; wherein the first phenotype score is associated withgood prognosis and responsiveness to chemotherapy, and the second phenotype score is associated with poor prognosis and non-responsiveness to chemotherapy, wherein a subject with one or more actionable allelic variant mutations and a prognostic score above a threshold is treated with chemotherapy; and wherein a subject lacking one or more actionable allelic variant mutations and with a prognostic score above a threshold is treated with chemotherapy; and wherein a subject with one or more actionable allelic variant mutations and a prognostic score below a threshold is not treated with chemotherapy and is treated with an epigenetic drug and / or a targeted therapy; and wherein a subject lacking one or more actionable allelic variant mutations and with a prognostic score below a threshold is not treated with chemotherapy and is treated with an epigenetic drug.

3. The method of any one of claims 1-2 further comprising:(i) tagging the chromatin accessible nucleic acid fragments to generate a plurality of tagged DNA fragments representing PDAC accessible chromatin regions (PDAC ACRs) of the intact nuclei presence in the tumor cells, and(ii) attaching a detectable label to the tagged DNA fragments to produce labeled chromatin accessible nucleic acid fragments.

4. The method of any one of claims 1-3, wherein the oligonucleotide probes comprise a first subset of oligonucleotide probes targeting a first set of PDAC ACRs in Table A and a second set of oligonucleotide probes targeting a second set of PDAC ACRs on any chromosomal region in Table IB.

5. The method of claim 4, wherein the first set of PDAC ACRs are indicative of a first phenotype and wherein the first phenotype is representative of good prognosis.

6. The method of claim 4, wherein the second set of PDAC ACRs are indicative of a second phenotype and wherein the second phenotype is representative of poor prognosis.

7. The method of any one of claims 1-6, wherein the method further comprises calculating a prognostic score based on a relative difference between the first phenotype score and the second phenotype score, normalized to a set of positive control oligonucleotide probes and a set of negative control oligonucleotide probes.

8. The method of claim 7, further comprising calculating the first phenotype score, wherein the first phenotype score is calculated by: (median FPKM of the first set of oligonucleotide probes / (median FPKM of the positive control oligonucleotide probes - median FPKM of the negative control oligonucleotide probes)).

9. The method of claim 7, further comprising calculating the second phenotype score, wherein the second phenotype is calculated by: (median FPKM of the second set of oligonucleotide probes / (median FPKM of the positive control oligonucleotide probes - median FPKM of the negative control oligonucleotide probes)).

10. The method of any one of claims 1-9, wherein the prognostic score is calculated by: (first phenotype score / second phenotype score), wherein a subject with a good prognosis has a prognostic score greater than 1 and a subject with a poor prognosis has a prognostic score less than 1.

11. The method of any one of claims 1-10, wherein the prognostic score is at least between 0.1 to 5.

12. The method of any one of claims 1-11, wherein the prognostic score is indicative of the subject’s responsiveness to one or more treatment modalities.

13. The method of any one of claims 1-12, wherein the transposase complex is Tn5.

14. The method of any one of claims 1-13 wherein the oligonucleotide probes are biotinylated.

15. The method of claim 14, wherein the second agent comprises biotin conjugated to a streptavidin, an avidin, or a neutravidin to form an affinity pair.

16. The method of claim 15, wherein the affinity pair is further conjugated to a magnetic bead, an agarose bead, a magnetic resin bead, or a covalent bead.

17. The method of any one of claims 1-16, wherein the second agent is a biotinylated- streptavidin magnetic bead.

18. The method of any one of claims 1-17, further comprising sequencing the target-probe complexes.

19. The method of any one of claims 1-18, further comprising detecting nuclear localization of specific transcription factors in the one or more biological samples.

20. The method of claim 19, wherein the transcription factor is selected from ZKSCAN1, EPAS1, RUNX2, ZNF410, MAFF, RREB1, NR3C2, SMAD1, RUNX1, ZNF32, ZSCAN4, HOXB1, POU3F1, ZBTB3, CLOCK, TCF15, GCM1, HINFP, CGBP, MYPOP, ZNF384, GMEB2, E2F5, AC012531.1, ZBTB7B, HOXC9, HNF4G, CREB1, ATF2, E2F2, SP3, ARID5A, ZFP161, OTP, PBX3, ZBTB33, ONECUT3, ONECUT3, DLX2, HNF4A, PRRX1, TCFL5, HOXB7, IRF6, GRHL1, FOXD2, ISL1, MLL, GATA2, GATA1, HMB0X1, NRF1, ZFHX3, ONECUT1, TET1, E2F3, DNMT1, CTCFL, CTCF, HNF1B, and HNF1A.

21. The method of claim 20, wherein the transcription factor is ZKSCAN1, HNF1B, or both.

22. The method of any one of claims 19-21, wherein the detecting nuclear localization of transcription factors is by immunohistochemistry (IHC).

23. The method of any one of claims 1-22, further comprising predicting a long duration of disease-free survival when the first phenotype is significantly higher than the second phenotype.

24. The method of any one of claims 1-23, further comprising predicting a short duration of disease-free survival when the second phenotype is significantly higher than the second phenotype.

25. The method of any one of claims 1-24, wherein the first phenotype is non-recurrence of a cancer within one year of surgical resection and the second phenotype is recurrence of a cancer within one year of surgical resection.

26. The method of any one of claims 1-25, wherein the first phenotype is a responder to one or more cancer treatment modalities and the second phenotype is a non-responder to one or more cancer treatment modalities.

27. The method of any one of claims 1-26, wherein the first phenotype is having a median disease-free survival of between 50 to 1500 days and the second phenotype is having a median disease-free survival of between 1-350 days.

28. The method of any one of claims 1-27, wherein the first phenotype is having a median progression-free survival of between 50 to 1500 days and the second phenotype is having a median progression-free survival of between 1 to 180 days.

29. The method of any one of claims 1-28, further comprising quantifying variant allele frequency of one or more biomarkers selected from KRAS, TP53, SMAD4, CDKN2A, BRCA1, BRCA2, ATR, ATM, RAD51, PALB2, EZH2, or combinations thereof.

30. The method of claim 29, wherein the biomarkers comprise KRAS and / or TP53.

31. The method of any one of claims 1-30, wherein a higher variant allele frequency as compared to a control is indicative of a higher tumor mutational load.

32. The method of any one of claims 1-31, wherein the one or more targeted therapies are selected from belzutifan, erlotinib hydrochloride, everolimus, olaparib, sunitinib malate, zenocutuzumab-zbco, Ado-trzstuzumab, afatinib, alectinib, axitinib, bosutinib, ceritinib, cobimetinib, crizotinib, dabrafenic, dasatinib, enasidenib, gefitinib, ibrutinib, idelalisib, imatinib, lapatinib, neratinib, nilotinib, niraparib, ponatinib, ribociclib, rucaparib, temsirolimus, tofacitinib, vemurafenib, cisplatin, carboplatin, oxaliplatin, talazoparib, immune checkpoint inhibitors, PARP inhibitors, PRMT inhibitors, KRAS inhibitors, and / or PRC2 inhibitors.

33. The method of claim 32, wherein the PRC2 inhibitor is selected from MAK683, EED226, CPI-1205, PF-06821497, or EPZ-6438 (Tazemetostat).

34. The method of any one of claims 1-33, wherein the epigenetic drug is selected from a DNA methyltransferase (DNMT) inhibitor, a histone deacetylase (HD AC) inhibitor, an enhancer of zeste homolog 2 (EZH2) inhibitor, a bromodomain and extra-terminal motif (BET) inhibitor, a histone acetyltransferase (HAT) inhibitor, a histone lysine methyltransferase (KMT) inhibitor, a protein arginine methyltransferase (PRMT) inhibitor, a proteolysis-targeting chimera (PROTAC) comprising a HD AC inhibitor, a DNMT inhibitor, a BET inhibitor, a EZH2 inhibitor, a HAT inhibitor, a KMT inhibitor, or combinations thereof.

35. The method of any one of claims 1-34, wherein the chemotherapy is selected from gemcitabine, nab-paclitaxel, fluorouracil (5-FU), irinotecan, oxaliplatin, leucovorin, capecitabine, cisplatin, or combinations thereof.

36. The method of any one of claims 1-35, wherein the subject is diagnosed with pancreatic ductal adenocarcinoma cancer (PDAC).

37. The method of any one of claims 1-36, wherein the one or more biological samples are obtained before the subject has received a first dose of treatment with one or more treatment modalities.

38. The method of any one of claims 1-37, wherein the one or more biological samples are obtained after the subject has received at least one dose of treatment with one or more treatment modalities.

39. The method of any one of claims 1-38, wherein the one or more biological samples are selected from a tumor biopsy, a surgically resected tumor specimen, or a liquid biopsy.

40. The method of any one of claims 1-39, wherein the subject is treatment naive to the one or more treatment modalities.

41. The method of any one of claims 1-40, wherein the one or more biological samples is obtained from a tumor biopsy.

42. The method of any one of claims 1-41, wherein the one or more biological samples is obtained from a liquid biopsy.

43. The method of claim 42, wherein the one or more biological samples are blood.

44. The method of claim 42, wherein the one or more biological samples are plasma.

45. A kit for detecting actionable allelic variant mutations and chromatin accessibility in one or more biological samples of a subject with pancreatic ductal adenocarcinoma (PDAC), the kit comprising: a) one or more sets of oligonucleotide probes for hybridizing with PDAC ACRs in the one or more biological samples to generate a target-probe complex; b) a second agent to capture the target-probe complex;c) reagents for amplifying the target-probe complex to produce an amplified population of the target-probe complex; d) reagents for determining variant allele frequency of one or more actionable allelic variant mutations; and e) reagents and instructions for quantifying the relative abundance of the PDAC ACRs in a biological sample to calculate a prognostic score.

46. A kit for treating a subject having, or suspected of having pancreatic ductal adenocarcinoma (PDAC), comprising detecting one or more actionable allelic variant mutations and chromatin accessibility in one or more biological samples and treating with one or more targeted therapies and / or an epigenetic drug, the kit comprising: a) one or more sets of oligonucleotide probes for hybridizing with PDAC ACRs in the one or more biological samples to generate a target-probe complex; b) a second agent to capture the target-probe complex; c) reagents for amplifying the target-probe complex to produce an amplified population of the target-probe complex; d) reagents for determining variant allele frequency of the one or more actionable allelic variant mutations; and e) reagents and instructions for quantifying the relative abundance of the PDAC ACRs in a biological sample to calculate a prognostic score; and f) instructions for determining a treatment decision based on (d) and (e), wherein a subject with one or more actionable allelic variant mutations and a prognostic score above a threshold is treated with chemotherapy; and wherein a subject lacking one or more actionable allelic variant mutations and with a prognostic score above a threshold is treated with chemotherapy; and wherein a subject with one or more actionable allelic variant mutations and a prognostic score below a threshold is not treated with chemotherapy and is treated with an epigenetic drug and / or a targeted therapy; and wherein a subject lacking one or more actionable allelic variant mutations and with a prognostic score below a threshold is not treated with chemotherapy and is treated with an epigenetic drug.

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