WO2020092038A1 - Cdkn2a screening germline expression - Google Patents

Abstract

Contemplated systems and methods are directed to confirmation of true somatic variants of CDKN2A in conjunction with RNA analysis to confirm expression of the CDKN2A variant. Such methods advantageously reduce the likelihood of false positive results. Contemplated systems and methods are also directed to identification of immunotherapy biomarkers, such as high PD-L1 expression, and high TMB in sarcoma patient samples that also harbor pathogenic CDKN2A variants. Thusly identified patients may exhibit a potential clinical benefit to immunotherapy.

Description

CDKN2A SCREENING GERMLINE EXPRESSION

[0001] Tliis application claims priority to our co-pending U.S. Provisional Patent Application with the serial numbers 62/753,858 which was filed October 31, 2018, and 62/840,941 which was filed on April 30, 2019. Each of these applications is incorporated by reference herein.

Field of the Invention

[0002] The field of the invention is omics analysis of tumor samples, especially as it relates to prediction of treatment response to palbociclib.

Background of the Invention

[0003] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.

[0004] All publications and patent applications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.

[0005] CDKN2A (cyclin-dependent kinase Inhibitor 2A) is expressed in many tissues and cell types, and its gene encodes two proteins, the INK4 family member pl 6 (or pl 6INK4a) and pl4arf. Notably, both proteins function as tumor suppressors by regulating the cell cycle: pl6 inhibits CDK4 and CDK6 (cyclin dependent kinases 4 and 6), which consequently will activate the retinoblastoma (Rb) family of proteins blocking cell cycle transition from the Gl to the S-phase. On the other hand, pl4ARF activates the p53 tumor suppressor. Clearly, CDKN2A has a central the regulatory function in cell division and cancer growth, and several test methods are known to test for CDKN2A function.

[0006] Where CDKN2A is defective, palbociclib (selective inhibitor of the cyclin-dependent kinases CDK4 and CDK6) can be administered to thereby substitute the inhibitory function of CDKN2A at least with regard to CDK4 and CDK6. While at least effecti ve to some degree in selected patients, administration of palbociclib will not always lead to a therapeutic effect.

[0007] Therefore, while various CDK4 and CDK6 inhibitors are known in the art, CDKN2A status will not always be a reliable predictor of therapeutic effect. Thus, there is still a need to provide improved CDKN2A tests that indicate likely effect of CDK4 and CDK6 inhibitors.

[0008] Furthermore, Gennline mutations in CDKN2A/P16^INK4A are known to predispose to hereditary melanoma, pancreatic cancer, and tobacco-related cancers, and also account for a subset of hereditary sarcoma. While targeted drug therapy has been shown to be at least somewhat effective in selected patients, it has remained unclear whether the use of immune therapy may provide an effective treatment avenue for such cancers.

[0009] Thus, there is still a need to provide systems and methods associated with CDKN2A tests that indicate likely treatment success of various cancers with immune therapy.

Summary of the Invention

[0010] The inventors have now disclosed a new method of treating a cancer in a patient that takes in account the expression level of CDKN2A variants. Preferably, the method comprises obtaining DNA omics data and RNA omics data from a tumor sample and a matched normal sample and using the DNA omics data and the RNA omics data from the tumor sample and the matched normal sample to identify one or more pathogenic CDKN2A variants, as well as to identify TMB and PD-L1 expression level. A patient is then subjected to immune therapy upon confirmation of a high TMB, a higher-than-normal PD-L1 expression, and/or presence of pathogenic CDKN2A variants In some embodiments, the DNA omics data are whole genome sequencing data or whole exome sequence data. The RN A omics data may be whole transcriptomic RNA sequencing data. A high TMB level is identified when somatic-specific non-synonymous exonic mutations are present in an amoimt of equal or greater than 200.

[0011] The method of treating cancer as disclosed herein may further comprise a step of determining expression of cancer related genes. The cancer related genes contemplated herein may comprise TP53, KMT2C, ATRX, RB1 , P1K3CA, and NF1.

[0012] Furthermore, the inventors have disclosed a method of treating tumor in a patient, comprising: obtaining DNA omics data from a tumor sample and a non-tumor sample of a patient; obtaining RNA omics data from the tumor sample; using the DNA omics data from the tumor sample and the non-tumor sample of the patient to confirm somatic CDKN2A loss or mutation; using the RNA omics data to confirm expression of the CDKN2A loss or mutation; and treating the tumor in the patient upon confirmation of the expression or loss of the CDKN2A. The DNA omics data may be whole genome sequencing data or whole exome sequence data. In some embodiments, variant calling is used for confirming the somatic CDKN2A mutation. Variant calling is a method of identifying factual differences between sequence reads of test samples and a reference sequence. Variant calling is used to identify somatic variants with a high degree of confidence. Preferably, the inventors envision such variant calling being perfonned through joint probabilistic analysis of the DNA omics data from the tumor sample and the non-tumor sample of the patient. In some embodiments, the RNA omics data are RNA sequencing data. In some embodiments, the somatic CDKN2A mutation is expressed at a higher level.

[0013] The tumor contemplated to be treated herein may be pancreatic cancer, gall bladder cancer, or bile duct cancer. The tumor may be treated with a selective inhibitor of the cyclin- dependent kinases CDK4 and CDK6. The selective inhibitor of the cyclin-dependent kinases CDK4 and CDK6 may be palbociclib.

[0014] Moreover, the inventors have also disclosed a method of evaluating treatment options for pancreatic cancer, gall bladder cancer, or bile duct cancer with palbociclib, comprising the steps of: (a) obtaining DNA omics data from a tumor sample and a non-tumor sample of a patient; (b) obtaining RNA omics data from the tumor sample; using the DNA omics data from the tumor sample and the non-tumor sample of the patient to confirm somatic CDKN2A loss or mutation; (c) using the RN A omics data to confirm expression of the CDKN2A loss or mutation; and (d) treating the patient with palbociclib upon confirmation of the expression or loss of the CDKN2A. It is contemplated that the DN A omics data are whole genome sequencing data or whole exome sequence data, while the RNA omics data are whole transcriptomic sequencing data. In some embodiments, variant calling is used to confirm that the somatic CDKN2A mutation is perfonned through joint probabilistic analysis of the DNA omics data from the tumor sample and the non-tumor sample of the patient. Preferably, the RN A omics data are RN A sequencing data. Furthermore, the somatic CDKN2A mutation may result in a higher expression level. Brief Description of the drawings

[0015] Fig. 1 illustrates, in accordance to the embodiments herein, that tme somatic

CDKN2A variants had significantly higher TPM counts than germline variants (T-test p=0.0002).

[0016] Fig. 2 illustrates, in accordance to the embodiments herein, that RB was consistently expressed and RB status was not dependent on CDKN2A status.

Detailed Description

[0017] The inventors have now disclosed a new method of screening germline expression that makes it possible to correctly predict the cancer patient population that would benefit from immunotherapy. Previous studies had shown that tumor-only variant calling may lead to incorrect calls that can have implications for therapy effectiveness. To address such shortcomings, the inventors have proposed employing a correction with a matched normal sample. This enables distinguishing between gennline mutations and somatic mutations. The matched normal may be a healthy tissue from the same individual.

[0018] Furthermore, the inventors explored associati on of tumor mutati on burden (TMB), gene expression of PD-Ll, and expression of other immune checkpoint therapy-associated genes with somatic CDKN2A mutations in a database of sarcomas to identify potential clinical benefit of immunotherapy in patients with CDKN2A mutations.

[0019] To that end, the inventors performed retrospective analysis on whole exome sequencing (WES; ~150x coverage) and whole transcriptomic RNAseq (~200x10⁶ reads per tumor) data from a proprietary database was performed on 267 sarcoma patient samples to identify pathogenic CDKN2A alterations. PD-Ll gene expression was measured by RNAseq. WES was performed on tumor and matched normal tissue for each patient and used to measure TMB by counting all somatic-specific non-synonymous exonic mutations, with > 200 qualified as TMB high.

[0020] 267 sarcoma cases were analyzed from a clinical database of over 3,000 patients. Approximately 67% of sarcoma patients harbored at least one of 419 pathogenic variants identified in 202 genes. The putative cancer-related genes that most frequently contained pathogenic variants were TP 53 (N = 37), KMT2C (N = 21 ), ATRX (N 20), RBI (N = 11), PIK3CA (N = 10), NF1 (N = 10) and CDKN2A (N = 7). Comparing those with and without CDKN2A mutations, the inventor observed statistically significantly higher TMB and PD-L1 expression in those harboring pathogenic CDKN2A mutations (p < 0.05). Additionally, pathogenic variants in NF1 and COL4A2 (N = 4) were significantly associated with high TMB and increased checkpoint expression even when adjusting for multiple hypothesis correction (adj. p < 0.05).

[0021] Based on the above and other observations, the inventors noted that CDKN2A gene alterations are commonly observed in sarcomas, and particularly that certain immunotherapy biomarkers such as high PD-L1 expression and high TMB were present in sarcoma samples with pathogenic CDKN2A variants. Thus, the inventors contemplate that an association of pathogenic CDKN2A variants in patient samples with high PD-L1 expression and high TMB is indicative of potential clinical benefit to immunotherapy in this population.

[0022] In another aspect of this disclosure, the inventors found that clinical trial screening of CDKN2 A genomic alterations in patients with pancreatic cancer and hepatobiliary cancers requires greater precision than somatic sequencing alone.

[0023] The TAPUR (Targeted Agent and Profiling Utilization Registry) Study is a phase II multi-basket study that evaluates the anti-tumor activity of commercially available targeted agents in patients with advanced cancers with genomic alterations known to be drug targets. Results in two cohorts of PC (pancreatic cancer) and GBC (gall bladder or bile duct cancer) patients, each with CDKN2A loss or mutation, were reported at ASCO 2018. The conclusion was that monotherapy with palbociclib is not associated with clinical or therapeutic activity in these patients. However, the inventors contemplate that this may be a false conclusion because genomic targets may have been absent in these patients.

[0024] Thus, to correct predict which patients having PC or GBC would be receptive to an inhibitor of the cyclin-dependent kinases CDK4 and CDK6, such as palbociclib, the inventors contemplate that it is important to consider whether genomic targets are present in these patients. To investigate the presence/absence of suitable targets, the inventors analyzed a total of 158 GI patients (PC = 123, GB = 20, Bile Duct = 15) with deep whole exome sequencing (WES) of tumor and blood samples, and whole transcriptomic sequencing (RN A-Seq) (~200x10⁶ reads per tumor) from a commercial database. Variant calling was performed through joint probabilistic analysis of tumor and normal DNA reads, with germline status of variants being determined by heterozygous or homozygous alternate allele fraction in the germline sample. Gene expression levels were determined with BowTie alignments and RSEM quantification.

[0025] Notably, when only considering DNA sequencing results, 26 somatic variants and 12 germline variants were detected, with one sample overlapping with a germline and a somatic variant (r.A148T and p.A76Rfs*44). Counting all 11 discrete geimline variants as false positives, a total 37 of 158 samples would be positive for CDKN2A mutant status, a rate of 23% (l7%-31% Cl). However, if the 8 common germline variants are excluded, the call rate is 29/158 = 18% (l2%-25% Cl), and the false positive rate is 4/158 = 14% (4%-31% Cl).

[0026] Viewed from a different perspective, there were 26 somatic variants, 1 of which is not expressed, and there are 12 germline variants, with one sample overlapping with a germline and a somatic variant (r.A148T and p.A76Rfs*44). Counting all 11 germline variants as false positives, a total 37 of 158 samples would be positive for CDKN2A mutant status, a rate of 23% (l7%-31% Cl). 1 1 germline and one unexpressed DNA variant would be false positives, for a false positive rate of 32% (l8%-50% Cl). However, if the 8 common germline variants are excluded, the call rate is 29/158 = 18% (l2%-25% Cl). The false positive rate is 4/22 = 18% (12%-40% Cl). This data is shown in tables 1 and 2 below.

Table 1: CDKN2A Somatic variants

Table 2: CDKN2A Germline variants

[0027] The results are further illustrated in Fig. 1 and Fig. 2. As shown in Fig. 1, RNAseq, true somatic CDKN2A variants had significantly higher TPM counts than gennline variants (T-test p=0.0002). As shown in Fig. 2, RNAseq, RB was consistently expressed and RB status was not dependent on CDKN2A status.

[0028] As can be seen from the above, the integration of somatic and germline variants, from matched tumor-normal patient samples with DNA omics data and RNA omics data is important to more accurately and comprehensively capture actionable information and precisely inform treatment selection for patients with cancer.

[0029] However, when also taking RNAseq data into account, the true somatic CDKN2A variants had significantly higher TPM counts than germline variants (T-test p = 0.0002). RB expression was not significantly different between the two groups. Therefore, it should be appreciated that the failure of palbociclib to show benefit in CDKN2A mutated PC and GBC patients in the 20 patient cohort of the TAPUR study could possibly be explained by patient selection rather than solely drug failure. Moreover, the failure of palbociclib to show benefit in CDKN2A mutated PC and GBC patients it is also unlikely related to RB loss. [0030] Thus, the inventors concluded that somatic only sequencing would have identified 37/158 patients as TAPUR eligible Population AF filtering at 0.5% would have removed 8 patients. Matched gennline: somatic sequencing further reduced the pool to 25/158 patients as true CDKN2A variants (15.8%). 4 patients (3%) would have been incorrectly considered TAPUR eligible. True somatic CDKN2A variants had significantly higher TPM counts than germline variants (T-test p=0.0002). RB was expressed in all cases at some level by

RNAseq, and this RB loss is an unlikely explanation for lack of clinical activity of palbociclib in this population.

[0031] It should be noted that throughout the present disclosure, the term“tumor” refers to, and is interchangeably used with one or more cancer cells, cancer tissues, malignant tumor cells, or malignant tumor tissue, that can be placed or found in one or more anatomical locations in a human body. It should be noted that the term“patient” as used herein includes both individuals that are diagnosed with a condition ( e.g cancer) as well as individuals undergoing examination and/or testing for the purpose of detecting or identifying a condition. Thus, a patient having a tumor refers to both individuals that are diagnosed with a cancer as well as individuals that are suspected to have a cancer. As used herein, the term“provide” or “providing” refers to and includes any acts of manufacturing, generating, placing, enabling to use, transferring, or making ready to use.

[0032] The disclosure herein contemplated administration of a drug to treat a tumor patient. The administration may be direct administration, for example, local and systemic

administration, e.g., including enteral, parenteral, pulmonary, and topical/transdermal administration, or it may be indirect administration. In other words, the term“administration” also refers to the phrase“cause to be administered.” The phrase“cause to be administered” refers to the actions taken by a medical professional (e.g., a physician), or a person controlling medical care of a subject, that control and/or permit the administration of the agent(s)/compound(s) at issue to the subject. Causing to be administered can involve diagnosis and/or determination of an appropriate therapeutic or prophylactic regimen, and/or prescribing particular agent(s)/compounds for a subject. Such prescribing can include, for example, drafting a prescription form, annotating a medical record, and the like.

[0033] The disclosure herein contemplates obtaining omics data. Any suitable methods and/or procedures to obtain omics data are contemplated. For example, the omics data can be obtained by obtaining tissues from an individual and processing the tissue to obtain DNA, RNA, protein, or any other biological substances from the tissue to further analyze relevant information. In another example, the omics data can be obtained directly from a database that stores omics information of an individual.

[0034] With respect to the analysis of tumor and matched normal tissue of a patient, numerous manners are deemed suitable for use herein so long as such methods will be able to generate a differential sequence object or other identification of location-specific difference between tumor and matched normal sequences. However, it is especially preferred that the differential sequence object is generated by incremental synchronous alignment of BAM files representing genomic sequence infonnation of the diseased and the matched normal sample. For example, particularly preferred methods include BAMBAM-based methods as described in US2012/0059670A 1 and US20120066001A1.

[0035] Likewise, with respect to RNA sequence information, it is contemplated that all manners of RNA sequencing are deemed suitable for use herein. However, especially preferred methods include those that are based on isolation and/or reverse transcription of polyadenylated RNA. Moreover, suitable data formats for RNA will include various raw formats, FASTA, SAM, and BAM formats. Moreover, it should also be noted that where the RNA sequence information is in BAM fonnat, omic analysis may be performed using a BAMBAM in which germline DNA, somatic DNA, and RNA can be concurrently processed. In addition, it should also be appreciated that panomic analysis as presented herein may also include protein quantification and activity determination of selected proteins. Such proteomic analysis can be performed from freshly resected tissue, from frozen or otherwise preserved tissue, and even from FFPE tissue samples. Most preferably, proteomics analysis is quantitative (i.e., provides quantitative information of the expressed polypeptide) and qualitative (i.e., provides numeric or qualitative specified activity of the polypeptide).

Example suitable techniques for conducting such quantitative proteomic analysis on tissue samples are describe in U.S. Pat. Nos. 7,473,532; 8,455,215; and 9,163,275, and are available via OncoPlex Diagnostics (see URL www.oncoplexdx.com).

[0036] Where the omics data is obtained from the tissue of an individual, any suitable methods of obtaining a tumor sample (tumor cells or tumor tissue) or normal (or healthy) tissue from the patient are contemplated. Most typically, a tumor sample or normal tissue sample can be obtained from the patient via a biopsy (including liquid biopsy, or obtained via tissue excision during a surgery or an independent biopsy procedure, etc.), which can be fresh or processed (e.g., frozen, etc.) until further process for obtaining omics data from the tissue. For example, tissues or cells may be fresh or frozen. In other example, the tissues or cells may be in a fomi of cell/tissue extracts. In some embodiments, the tissues or cells may be obtained from a single or multiple different tissues or anatomical regions. For example, a metastatic breast cancer tissue can be obtained from the patient’s breast as well as other organs (e.g., liver, brain, lymph node, blood, lug, etc.) for metastasized breast cancer tissues In another example, a normal tissue or matched normal tissue (e.g., patient’s non-cancerous breast tissue) of the patient can be obtained from any part of the body or organs, preferably from liver, blood, or any other tissues near the tumor (in a close anatomical distance, etc.).

[0037] In some embodiments, tumor samples can be obtained from the patient in multiple time points in order to determine any changes in the tumor samples over a relevant time period. For example, tumor samples (or suspected tumor samples) may be obtained before and after the samples are determined or diagnosed as cancerous. In another example, tumor samples (or suspected tumor samples) may be obtained before, during, and/or after (e.g., upon completion, etc.) a one time or a series of a cancer treatment (e.g., radiotherapy, chemotherapy, immunotherapy, etc.). In still another example, the tumor samples (or suspected tumor samples) may be obtained during the progress of the tumor upon identifying a new metastasized tissues or cells.

[0038] From the obtained tumor samples (cells or tissue) or healthy samples (cells or tissue), DNA (e.g., genomic DNA, extrachromosomal DNA, etc.), RNA (e.g., mRNA, miRNA, siRNA, shRNA, etc.), and/or proteins (e.g., membrane protein, cytosolic protein, nucleic protein, etc.) can be isolated and further analyzed to obtain omics data. Alternatively and/or additionally, a step of obtaining omics data may include receiving omics data from a database that stores omics infonnation of one or more patients and/or healthy individuals. For example, omics data of the patient’s tumor may be obtained from isolated DNA, RNA, and/or proteins from the patient’s tumor tissue, and the obtained omics data may be stored in a database (e.g. , cloud database, a server, etc .) with other omics data set of other patients having the same type of tumor or different types of tumor. Omics data obtained from the healthy individual or the matched normal tissue (or normal tissue) of the patient can be also stored in the database such that the relevant data set can be retrieved from the database upon analysis. Likewise, where protein data are obtained, these data may also include protein activity, especially where the protein has enzymatic activity (e.g., polymerase, kinase, hydrolase, lyase, ligase, oxidoreductase, etc.).

[0039] As used herein, omics data includes but is not limited to information related to genomics, proteomics, and transcriptomics, as well as specific gene expression or transcript analysis, and other characteristics and biological functions of a cell . With respect to genomics data, suitable genomics data includes DNA sequence analysis information that can be obtained by whole genome sequencing and/or exome sequencing (typically at a coverage depth of at least 10x, more typically at least 20x) of both tumor and matched normal sample. Alternatively, DNA data may also be provided from an already established sequence record (e.g., SAM, BAM, FASTA, FASTQ, or VCF file) from a prior sequence determination. Therefore, data sets may include unprocessed or processed data sets, and exemplary data sets include those having BAM fonnat, SAM format, FASTQ fomiat, or FASTA format.

However, it is especially preferred that the data sets are provided in BAM format or as BAMBAM diff objects (e.g., US2012/0059670A1 and US2012/0066001 Al). Omics data can be derived from whole genome sequencing, exome sequencing, transcriptome sequencing (e.g., RNA-seq), or from gene specific analyses (e.g., PCR, qPCR, hybridization, LCR, etc.). Likewise, computational analysis of the sequence data may be performed in numerous manners. In most preferred methods, however, analysis is performed in silico by location- guided synchronous alignment of tumor and normal samples as, for example, disclosed in US 2012/0059670A1 and US 2012/0066001A1 using BAM files and BAM servers. Such analysis advantageously reduces false positive neoepitopes and significantly reduces demands on memory and computational resources.

[0040] Where it is desired to obtain the tumor-specific omics data, numerous manners are deemed suitable for use herein so long as such methods will be able to generate a differential sequence object or other identification of location-specific difference between tumor and matched normal sequences. Exemplary methods include sequence comparison against an external reference sequence (e.g., hgl8, or hgl9), sequence comparison against an internal reference sequence (e.g., matched normal), and sequence processing against known common mutational patterns (e.g., SNVs). Therefore, contemplated methods and programs to detect mutations between tumor and matched normal, tumor and liquid biopsy, and matched normal and liquid biopsy include iCallSV (URL: github .conVrhshah/iCallSV),VarScan (URL:

varscan.sourceforge.net), MuTect (URL: github.com/broadinstitute/mutect), Strelka (URL: github.com/Illumina/strelka), Somatic Sniper (URL: gmt.genome.wustl.edu/somatic-sniper/), and BAMBAM (US 2012/0059670).

[0041] However, in especially preferred aspects of the inventive subject matter, the sequence analysis is performed by incremental synchronous alignment of the first sequence data (tumor sample) with the second sequence data (matched normal), for example, using an algorithm as for example, described in Cancer Res 2013 Oct 1; 73(l9):6036-45, US 2012/0059670 and US 2012/0066001 to so generate the patient and tumor specific mutation data. As will be readily appreciated, the sequence analysis may also be performed in such methods comparing omics data from the tumor sample and matched normal omics data to so arrive at an analysis that can not only inform a user of mutations that are genuine to the tumor within a patient, but also of mutations that have newly arisen during treatment (e.g., via comparison of matched normal and matched nonnal/tumor, or via comparison of tumor). In addition, using such algorithms (and especially BAMBAM), allele frequencies and/or clonal populations for specific mutations can be readily determined, which may advantageously provide an indication of treatment success with respect to a specific tumor cell fraction or population. Thus, exemplary subtypes of genomics data may include, but not limited to genome amplification (as represented genomic copy number aberrations), somatic mutations (e.g., point mutation (e.g, nonsense mutation, missense mutation, etc.), deletion, insertion, etc.), genomic rearrangements (e.g, intrachromosomal rearrangement, extrachromosomal rearrangement, translocation, etc.), appearance and copy numbers of extrachromosomal genomes (e.g, double minute chromosome, etc.). In addition, genomic data may also include mutation burden that is measured by the number of mutations carried by the cells or appeared in the cells in the tissue in a predetermined period of time or within a relevant time period.

[0042] Moreover, it should be noted that some data sets are preferably reflective of a tumor and a matched normal sample of the same patient to so obtain patient and tumor specific information. In such embodiments, genetic germ line alterations not giving rise to the tumor (e.g., silent mutation, SNP, etc.) can be excluded. Of course, it should be recognized that the tumor sample may be from an initial tumor, from the tumor upon start of treatment, from a recurrent tumor or metastatic site, etc. In most cases, the matched normal sample of the patient may be blood, or non-diseased tissue from the same tissue type as the tumor.

[0043] In addition, omics data of cancer and/or normal cells comprises transcriptome data set that includes sequence information and expression level (including expression profiling, copy number, or splice variant analysis) of RNA(s) (preferably cellular mRNAs) that is obtained from the patient, from the cancer tissue (diseased tissue) and/or matched normal tissue of the patient or a healthy individual. There are numerous methods of transcriptomic analysis known in the art, and all of the known methods are deemed suitable for use herein (e.g., RNAseq, RNA hybridization arrays, qPCR, etc.). Consequently, preferred materials include mRNA and primary transcripts (hnRNA), and RNA sequence information may be obtained from reverse transcribed polyA⁺-RNA, which is in turn obtained from a tumor sample and a matched normal (healthy) sample of the same patient. Likewise, it should be noted that while polyA -RNA is typically preferred as a representation of the transcriptome, other forms of RNA (lin-RNA, non-polyadenylated RNA, siRNA, miRNA, etc.) are also deemed suitable for use herein. Preferred methods include quantitative RNA (linRNA or mRNA) analysis and/or quantitative proteomics analysis, especially including RNAseq. In other aspects, RNA quantification and sequencing is performed using RNA-seq, qPCR and/or rtPCR based methods, although various alternative methods (e.g., solid phase hybridization-based methods) are also deemed suitable. Viewed from another perspective, transcriptomic analysis may be suitable (alone or in combination with genomic analysis) to identify and quantify genes having a cancer- and patient-specific mutation.

[0044] Preferably, the transcriptomics data set includes allele-specific sequence information and copy number information. In such embodiment, the transcriptomics data set includes all read information of at least a portion of a gene, preferably at least lOx, at least 20x, or at least 3 Ox. Allele-specific copy numbers, more specifically, majority and minority copy numbers, are calculated using a dynamic windowing approach that expands and contracts the window's genomic width according to the coverage in the germline data, as described in detail in US 9824181, which is incorporated by reference herein. As used herein, the majority allele is the allele that has majority copy numbers (>50% of total copy numbers (read support) or most copy numbers) and the minority allele is the allele that has minority copy numbers (<50% of total copy numbers (read support) or least copy numbers).

[0045] It should be appreciated that one or more desired nucleic acids or genes may be selected for a particular disease (e.g., cancer, etc.), disease stage, specific mutation, or even on the basis of personal mutational profiles or presence of expressed neoepitopes.

Alternatively, where discovery or scanning for new mutations or changes in expression of a particular gene is desired, RNAseq is preferred to so cover at least part of a patient transcriptome. Moreover, it should be appreciated that analysis can be performed static or over a time course with repeated sampling to obtain a dynamic picture without the need for biopsy of the tumor or a metastasis.

[0046] Further, omics data of cancer and/or normal cells comprises proteomics data set that includes protein expression levels (quantification of protein molecules), post-translational modification, protein-protein interaction, protein-nucleotide interaction, protein-lipid interaction, and so on. Thus, it should also be appreciated that proteomic analysis as presented herein may also include activity determination of selected proteins. Such proteomic analysis can be performed from freshly resected tissue, from frozen or otherwise preserved tissue, and even from FFPE tissue samples. Most preferably, proteomics analysis is quantitative (i.e., provides quantitative information of the expressed polypeptide) and qualitative (i.e., provides numeric or qualitative specified activity of the polypeptide). Any suitable types of analysis are contemplated. However, particularly preferred proteomics methods include antibody-based methods and mass spectroscopic methods. Moreover, it should be noted that the proteomics analysis may not only provide qualitative or quantitative information about the protein per se, but may also include protein activity data where the protein has catalytic or other functional activity. One exemplary technique for conducting proteomic assays is described in US 7473532, incorporated by reference herein. Further suitable methods of identification and even quantification of protein expression include various mass spectroscopic analyses (e.g., selective reaction monitoring (SRM), multiple reaction monitoring (MRM), and consecutive reaction monitoring (CRM)).

[0047] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms“comprises” and“comprising” should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refers to at least one of something selected from the group consisting of A, B, C... , and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc. Moreover, as used in the description herein and throughout the claims that follow, the meaning of“a,”“an,” and“the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of“in” includes“in” and “on” unless the context clearly dictates otherwise.

Claims

CLAIMS What is claimed is:

1. A method of treating a cancer in a patient in need thereof, comprising:

obtaining DNA omics data and RNA omics data from a tumor sample and a matched normal sample;

using the DNA omics data and the RNA omics data from the tumor sample and the matched normal sample to identify one or more pathogenic CDKN2A variants; using the DNA omics data and the RNA omics data from the tumor sample and the matched normal sample to identify TMB and PD-L1 expression level; and subjecting the patient to immune therapy upon confirmation of a high TMB, a higher- than-normal PD-L1 expression, and/or presence of

pathogenic CDKN2A variants.

2. The method of claim 1, wherein the DNA omics data are whole genome sequencing data or whole exome sequence data.

3. The method of claim 1, wherein the RNA omics data are whole transcriptomic RNAseq data.

4. The method of claim 1, wherein high TMB is present when somatic-specific non- synonymous exonic mutations are present in an amount of equal or greater than 200.

5. The method of claim 1 , further comprising a step of determining expression of cancer related genes.

6. The method of claim 5, wherein the cancer related genes comprise TP53, KMT2C, ATRX, RBI, PIK3CA, and NF1.

7. A method of treating tumor in a patient, comprising:

obtaining DNA omics data from a tumor sample and a non-tumor sample of a patient; obtaining RNA omics data from the tumor sample;

using the DNA omics data from the tumor sample and the non-tumor sample of the patient to confirm somatic CDKN2A loss or mutation;

using the RNA omics data to confirm expression of the CDKN2A loss or mutation; and treating the tumor in the patient upon confirmation of the expression or loss of the CDKN2A.

8. The method of claim 7, wherein the DNA omics data are whole genome sequencing data or whole exome sequence data.

9. The method of claim 7, wherein somatic CDKN2A mutation is confirmed by variant calling.

10. The method of claim 9, wherein the variant calling comprises joint probabilistic analysis of the DNA omics data from the tumor sample and the non-tumor sample of the patient.

11. The method of claim 7, wherein the RNA omics data are RNA sequencing data.

12. The method of claim 7, wherein the somatic CDKN2A mutation is expressed at a higher level.

13. The method of claim 7, wherein the tumor is pancreatic cancer, gall bladder cancer, or bile duct cancer.

14. The method of claim 7, wherein the tumor is treated with a selective inhibitor of the cyclin-dependent kinases CDK4 and CDK6.

15. The method of claim 14, wherein the selective inhibitor of the cyclin-dependent kinases CDK4 and CDK6 is palbociclib.

16. A method of evaluating treatment options for pancreatic cancer, gall bladder cancer, or bile duct cancer with palbociclib, comprising:

obtaining DNA omics data from a tumor sample and a non-tumor sample of a patient; obtaining RNA omics data from the tumor sample;

using the DNA omics data from the tumor sample and the non-tumor sample of the patient to confirm somatic CDKN2A loss or mutation;

using the RNA omics data to confirm expression of the CDKN2A loss or mutation; and

treating the patient with palbociclib upon confirmation of the expression or loss of the CDKN2A.

17. The method of claim 16, wherein the DNA omics data are whole genome sequencing data or whole exome sequence data.

18. The method of claim 16, wherein the RNA omics data are whole transcriptomic sequencing data.

19. The method of claim 16, wherein somatic CDKN2A mutation is confirmed by variant calling.

20. The method of claim 19, wherein the variant calling comprises joint probabilistic analysis of the DNA omics data from the tumor sample and the non-tumor sample of the patient.

21. The method of claim 16, wherein the RNA omics data are RNA sequencing data.

22. The method of claim 16, wherein the somatic CDKN2A mutation is expressed at a higher level.