Methods of detecting loss of heterozygosity and damaging mutations in immune-related genes in liquid biopsies

Abstract

Provided herein are methods of determining loss of heterozygosity and damaging mutations in human immune-related genes in liquid biopsies and applications thereof in cancer treatment.

Description

METHODS OF DETECTING LOSS OF HETEROZYGOSITY AND DAMAGING MUTATIONS IN IMMUNE-RELATED GENES IN LIQUID BIOPSIESSTATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0001] This invention was made in part with Government support under project no. ZIA BC 011871 by the National Institutes, National Cancer Institute. The Government has certain rights in this invention.SEQUENCE LISTING

[0002] The present application contains a Sequence Listing which has been submitted electronically in .XML format and is hereby incorporated herein by reference in its entirety. Said computer readable file, was created on 1 April 2025 and is named 060734-838860 SequenceListing.xml and is 7 kilobytes in size.FIELD OF THE INVENTION

[0003] The present invention relates to methods of determining loss of heterozygosity and damaging mutations in human immune-related genes in liquid biopsies and applications thereof in cancer treatment.BACKGROUND OF THE INVENTION

[0004] Liquid biopsy has developed into an important diagnostic and monitoring tool in cancer. Unlike traditional tumor biopsy, it is non-invasive, easy to administer and, in patients with widespread metastatic disease, allows for sampling of tumors from multiple different anatomic sites. Importantly, analysis of circulating tumor DNA (ctDNA) can be tailored to different clinical scenarios. Initial studies of liquid biopsy looked at the ability to detect ctDNA as a means of detecting cancer relapse following treatment (Stergiopoulou D et al, Nature Scientific Reports 2023). Its use has been expanded through the detection of somatic mutations in which targeted therapies are available. This includes hotspot mutations in EGFR T790M that are amenable to treatment with small molecule tyrosine kinase inhibitors (Hochmair JM et al, Target Oncol 2019). More recently, liquid biopsy has been developed to detect the presence of cancer prior to radiographic evidence of disease or presence of symptoms (Shema E, Nature ReviewsCancer 2023). To our knowledge, liquid biopsies have not been developed to predict response to immunotherapy.

[0005] Immunotherapy has revolutionized the treatment of cancer over the past decade. Many immunotherapy treatments harness the intrinsic ability of T cells to recognize and eliminate cancer cells. A large breadth of research has shed light on the ability of cancer cells to evade immunotherapy treatments by acquiring somatic mutations in genes important for antigen processing and presentation machinery (APM) and interferon-y response pathways (Norberg SM et al, Cancer Cell 2023). In a clinical study testing T cell receptor (TCR)-T cell therapy targeting an HLA-A*02:01 epitope of HPV16 E7, resistance to therapy was found in 2 tumors with damaging mutations in the HLA restriction element including an early stop codon introduced in the HLA-A alpha 1 helix of one tumor and an alanine to proline substitution in the HLA-A alpha 2 helix of another tumor (Nagarsheth et al, Nat. Med. 2021). Similarly, damaging mutations in genes encoding other non-redundant molecules important in APM and IFN response pathways have been found in tumors resistant to PD-1 inhibitor therapy including B2M, JAK1 and JAK2 (Zaretsky JM et al. NEJM 2016, Shin et al. Cancer Discovery 2017, Sade-Feldman et al. Nat Commun 2017).

[0006] A method to detect HLA loss of heterozygosity (LOH) has previously been developed as a means of identifying patients with tumors that may be resistant to immunotherapies requiring intact HLA class I molecules including TCR-T cell therapy (W0 2023137448). HLA LOH is another mechanism of immune evasion in certain cancers including cervical and head and neck (Montesion M et al, Cancer Discov 2021). Loss of the HLA restriction element has also been seen in tumors resistant to TCR-T cell therapy (Doran S et al. JCO 2019, Kim SP et al. Cancer Immunol Res 2022). The previous method is limited to LOH at HLA loci. Thus, there is a need in the art for methods to determine LOH and damaging mutation status in immune-related genes, which may or may not include HLA genes, via liquid biopsy.SUMMARY OF THE INVENTION

[0007] Provided herein is a method of detecting loss of heterozygosity (LOH) of an immune- related gene in a subject. The method may comprise providing biopsy immune-related gene sequence data, which may be obtained from cell-free DNA (cfDNA) of a liquid biopsy sample from the subject. The subject may have cancer. The cfDNA may comprise cancer cell DNA. Theimmune-related gene sequence data may comprise sequences of one or more immune-related genes, which may be obtained via targeted sequencing, whole genome sequence (WGS) data, or whole exome sequence (WES) data.

[0008] The method may comprise providing germline immune-related gene sequence data, which may be obtained from DNA of a germline cell sample of the subject. The germline cell sample may comprise peripheral blood monocytes (PBMC). The germline immune-related gene sequence data may comprise sequences of one or more immune-related genes, which may be obtained via targeted sequencing, WGS data, or WES data.

[0009] The method may comprise aligning nucleic acid sequences of both alleles of each immune-related gene from the germline cell DNA to a reference sequence for each immune- related gene to generate an alignment, and aligning cfDNA nucleic acid sequences to a reference genome so that reads at heterozygous bases can be counted. The method may comprise, from the alignment, identifying heterozygous bases of each immune-related gene. Each heterozygous base may be identified by nucleic acid sequence position and genome coordinate. The method may comprise, for each heterozygous base of each immune-related gene, counting allelic reads in the biopsy immune-related gene sequence data and counting allelic reads in the germline immune- related gene sequence data. A read count percentage of at least 25% may be indicative of a heterozygous base.

[0010] The method may comprise calculating a weight for each heterozygous base. The weight calculation may comprise, separately for allelic reads in the biopsy immune-related gene sequence data and the germline immune-related gene sequence data, for each heterozygous base of each immune-related gene, dividing the read count of each heterozygous base by the sum of read counts on all heterozygous bases. The weight calculation may also comprise calculating the average at each heterozygous base between the biopsy immune-related gene sequence data and the germline immune-related gene sequence data.

[0011] The method may comprise comparing the allelic read count from the biopsy immune- related gene sequence data to the allelic read count from the germline immune-related gene sequence data, which may be by performing a Student’s t-test with paired observation for each heterozygous base, in which each weight is multiplied by an observation comprising the read count at the heterozygous base in the biopsy immune-related gene sequence data and the read count at the same heterozygous base in the germline immune-related gene sequence data, togenerate a p-value for each immune-related gene. A significant p-value may be indicative of LOH for an immune-related gene in the liquid biopsy. The significant p-value may be 0.004. The one or more immune-related genes may be selected from B2M, TAPI, TAP2, TAPBP, CANX, CALR, PDIA3, NLRC5, JAK1, JAK2, STAT1, IFNGR1, IFNGR2, PSMB1, PSMB2, PSMB3, PSMB4, PSMB5, PSMB6, PSMB7, PSMB8, PSMB9, PSMB10, PSMB11, PSMA1, PSMA2, PSMA3, PSMA4, PSMA5, PSMA6, PSMB7, PSMA8, DFFA, DFFB, FAS, FADD, APAF1, BID, CASP2, CASP3, CASP6, CASP7, CASP8, CASP9, CASP10, CIITA, RFX5, RFXAP, RFXANK, and APLNR.

[0012] Provided herein is a method of detecting loss of heterozygosity (LOH) of an immune- related gene in a subject. The subject may have cancer. The method may comprise providing biopsy immune-related gene sequence data obtained from cell-free DNA (cfDNA) of a liquid biopsy sample from the subject. The cfDNA may comprise cancer cell DNA. The biopsy immune-related gene sequence data may comprise sequences of one or more immune-related genes. The immune-related gene sequence data may comprise sequences of one or more immune-related genes, which may be obtained via targeted sequencing, whole genome sequence (WGS) data, or whole exome sequence (WES) data.

[0013] The method may comprise providing germline immune-related gene sequence data obtained from DNA of a germline cell sample of the subject. The germline immune-related gene sequence data may comprise sequences of the one or more immune-related genes. The germline immune-related gene sequence data may comprise sequences of one or more immune-related genes, which may be obtained via targeted sequencing, WGS data, or WES data. The method may comprise aligning nucleic acid sequences of both alleles of each immune-related gene from the germline cell DNA to a reference sequence for each immune-related gene to generate an alignment, and aligning cfDNA nucleic acid sequences to a reference genome so that reads at heterozygous bases can be counted. The method may comprise, from the alignment, identifying heterozygous bases of each immune-related gene, wherein each heterozygous base is identified by nucleic acid sequence position and genome coordinate.

[0014] The method may comprise, for each heterozygous base of each immune-related gene, determining a total copy of the immune-related gene by creating a reference from the matched germline sample and normalizing the cfDNA by GC content correction, repetitiveness, and targetsize to make copy number calls on the immune-related gene, and estimating the copy number change by segmentation.

[0015] The method may comprise generating a p-value for the copy number change each immune-related gene. A significant p-value may be indicative of LOH for the immune-related gene in the liquid biopsy. The p-value may be <0.005. The one or more immune-related genes are selected from B2M, TAPI, TAP2, TAPBP, CANX, CALR, PDIA3, NLRC5, JAK1, JAK2, STAT1, IFNGR1, IFNGR2, PSMB1, PSMB2, PSMB3, PSMB4, PSMB5, PSMB6, PSMB7, PSMB8, PSMB9, PSMB10, PSMB11, PSMA1, PSMA2, PSMA3, PSMA4, PSMA5, PSMA6, PSMB7, PSMA8, DFFA, DFFB, FAS, FADD, APAF1, BID, CASP2, CASP3, CASP6, CASP7, CASP8, CASP9, CASP10, CIITA, RFX5, RFXAP, RFXANK, and APLNR.

[0016] The sequence of each immune-related gene may be obtained by contacting DNA with a set of primers and a probe specific to the immune-related gene. The DNA may be sequenced at a depth of 100X to 300X, optionally at a depth of 200X. The one or more immune-related genes further comprise one or more HL A genes. The HLA genes may comprise one or more of HLA- A, HLA-B, HLA-C, HLA-DR, HLA-DP, HLA-DQ, HLA-DMA, and HLA-DMB.

[0017] Provided herein is a method of detecting a damaging mutation in an immune-related gene in a subject. The method may comprise providing biopsy immune-related gene sequence data obtained from cell-free DNA (cfDNA) of a liquid biopsy sample from the subject. The biopsy immune-related gene sequence data may comprise sequences of one or more immune-related genes. The one or more immune-related genes may be selected from non-HLA immune-related genes selected from B2M, TAPI, TAP2, TAPBP, CANX, CALR, PDIA3, NLRC5, JAK1, JAK2, STAT1, IFNGR1, IFNGR2, PSMB1, PSMB2, PSMB3, PSMB4, PSMB5, PSMB6, PSMB7, PSMB8, PSMB9, PSMB10, PSMB11, PSMA1, PSMA2, PSMA3, PSMA4, PSMA5, PSMA6, PSMB7, PSMA8, DFFA, DFFB, FAS, FADD, APAF1, BID, CASP2, CASP3, CASP6, CASP7, CASP8, CASP9, C ASP 10, CIITA, RFX5, RFXAP, RFXANK, and APLNR, and HLA immune-related genes selected from HLA-A, HLA-B, HLA-C, HLA-DR, HLA-DP, HLA-DQ, HLA-DMA, and HLA-DMB;

[0018] The method may comprise providing germline immune-related gene sequence data obtained from DNA of a germline cell sample of the subject. The germline immune-related gene sequence data may comprise sequences of the one or more immune-related genes. The method may comprise aligning sequences of each immune-related gene from the germline cell DNA to areference sequence for each immune-related gene to generate an alignment. The sequences of each immune-related gene from the germline cell DNA may be defined as wild-type. Each base of each sequence may be identified by sequence position and genome coordinate.

[0019] The method may comprise aligning the sequences of each immune-related gene from the cfDNA to a comparator sequence. For each non-HLA immune-related gene, the comparator sequence may be the sequence of the non-HLA immune-related gene from a reference genome. The reference genome may be hg38. For each HLA immune-related gene, the comparator sequence may be the sequence of the HLA immune-related gene from the germline cell DNA, from the reference genome, or from a reference sequence identified by Polysolver from a plurality of the HLA immune-related gene from a plurality of humans. The plurality of the HLA immune-related gene from a plurality of humans may be a database of HLA gene sequences, which may be the IMMUNOGENETICS INFORMATION SYSTEM®. The method may comprise identifying variant sequences between the germline cell DNA and cfDNA at each sequence position and genome coordinate in each immune-related gene using the comparator sequence and inputting the variant sequences between the germline cell DNA and cfDNA of each immune-related gene to one or more variant callers or comparing the variant sequences to a database comprising known damaging mutations. Each variant sequence may be identified as damaging or not damaging by each variant caller, database, or a combination thereof.

[0020] Also provided herein is a method of predicting a subject’s response to a cancer immunotherapy. The method may comprise detecting LOH for one or more immune-related genes, detecting a damaging mutation in one or more immune-related genes, or a combination thereof. The presence of LOH for the immune-related gene or a damaging mutation in the immune-related gene may be indicative that the subject will have a poor response to the cancer immunotherapy. The absence of LOH for the immune-related gene and the absence of a damaging mutation in the immune-related gene may be indicative that the subject will respond or is more likely to respond to the cancer immunotherapy.

[0021] Provided herein is a method of treating a cancer in a subject in need thereof. The method may comprise administering to the subject a cancer immunotherapy. The subject may have been predicted to respond or be more likely to respond to the cancer immunotherapy according to the method for detecting LOH in an immune-related gene or detecting a damaging mutation in an immune-related gene, or a combination thereof.

[0022] The cancer immunotherapy may be a T cell receptor (TCR)-based therapy. The TCR- based therapy may be selected from the group consisting of an immune checkpoint blockade, a T cell engager, a TCR-T cell therapy, tumor infiltrating lymphocytes, a cancer vaccine, and a cytokine therapy. The T cell engager may comprise immune mobilizing TCRs against cancer. The cancer immunotherapy may be administered as a single agent or in combination with a second cancer treatment.

[0023] Further provided herein are systems for carrying out the methods. The system may comprise a computing system including a processor in communication with a memory. The memory may include instructions, which, when executed, cause the processor to carry out the steps of one or more of the methods.BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

[0025] FIG. 1 shows sequencing results of cell free DNA and PBMC (germline control) from patient 4 treated in an E7 TCR-T cell trial (Nagarsheth et al, Nat Med 2021). Row 1 is sequencing results of HLA-A*02:01 allele from germline control (“PBMC.bam”). Row 2 is sequencing results of the same location for cell free DNA (“cfDNA.bam”). The red circle indicates the number of reads at amino acid position 51 (read count for G is 3 or 0%) for germline control. The dark blue circle indicates the number of reads at the same position (read count for G is 286 or 5%).

[0026] FIG. 2A shows treatment and sample collection timeline for patient 4 described above.

[0027] FIG. 2B is an illustration depicting identified damaging mutations to the target HLA complex for patients treated on the phase I portion of the E7 TCR-T cell trial (NCT02858310). A treatment-resistant tumor from patient 4 (top right) was found to have a nonsense mutation in HLA-A*02:01 at amino acid position 51.

[0028] FIG. 3 shows an exemplary method of detecting loss of non-HLA immune-related genes. The top picture provides the sequence of Beta-2-Microglobin (B2M). In order to determine allelic imbalance (i.e., loss), germline DNA sequence is evaluated to determine the presence of base pairs that may differ between the two alleles (i.e. paternal and maternal) in a patient’sgenome. The red box highlights common positions of single nucleotide polymorphisms (SNPs) within the intron of B2M, which may be a region where differences between the two alleles exist. In the bottom picture, heterozygous base pairs that are identified on germline DNA are used to determine the allele of origin (i.e., paternal or maternal) for cfDNA. The example demonstrates the presence of cfDNA from a gene originating from the paternal allele and absence of cfDNA from the maternal allele. Mathematical calculations can then be used to determine whether allelic imbalance exists. It is possible that, in this example, cfDNA originating from the maternal allele will be present in the plasma sample, but might not derive from the tumor and instead may derive from normal cells in the patient’s body. The statistical test detects an imbalance regardless of the contribution to cfDNA from normal cells.

[0029] FIG. 4 shows an exemplary method of detecting damaging mutations to immune-related genes. Germline DNA is used to determine the wild type (non-mutated) sequence. Next, sequenced circulating tumor DNA (ctDNA) is compared to germline DNA. In cases where variants are noted (bold / underlined region), it can be determined whether the variants are SNPs or somatic mutations by comparing them to germline DNA. Then, mutation annotation algorithms can be used to determine whether the mutation is damaging in nature.

[0030] FIG. 5 shows results of CNVkit analysis of cell free DNA from patient 4 on the E7 TCR- T cell trial. Column 1 indicates the 3 plasma samples collected at different timepoints (TS- cfDNAl at Day -10, TS-cfDNA2 at Day +10, TS-cfDNA3 at Day +40). Column 4 is the affected gene and column 6 is the copy number call where 1 is equivalent to “copy number 1” or LOH. The red highlighted text indicates 2 genes (IFNGR2 and CANX) that were found to demonstrate “copy number 1” in cfDNA. These 2 genes were also found to demonstrate “copy number 1” in a tumor biopsy collected at Day +40 (see FIG. 6).

[0031] FIG. 6 shows tumor sequencing results of biopsies collected from patients on the E7 TCR-T cell therapy trial. The red-highlighted column indicates results of tumor biopsy collected on Day +40 for patient 4. LOH (copy number 1) of CANX and IFNGR2 was seen on the tumor resistant to E7 TCR-T cell therapy.

[0032] FIG. 7 shows a potential usage of the method described herein in as an assay that may be classified as a clinical trial assay (CTA) that helps determine enrollment on clinical studies such as those for TCR-T cell therapies.

[0033] FIG. 8 is an exemplary computer system for effectuating the methods of determining loss of heterozygosity or detecting damaging mutations in immune-related genes in liquid biopsies as described herein.

[0034] FIG. 9A shows a schematic of the location of a damaging mutation in HLA-A*02:01 detected from a liquid biopsy from patent 4.

[0035] FIG. 9B shows the nature of the HLA-A*02:01 mutation in patient 4 on days -10 (as called by mutect-vardict-strelka), +10 (as called by mutect-mutect2-vardict-strelka), and +40 called by a method disclosed herein.

[0036] FIG. 9C shows the percent reads of the HLA-A*02:01 mutant allele in patient 4 at various time points.

[0037] FIG. 9D shows a summary of the presence of the damaging HLA-A*02:01 mutation in a liquid biopsy from patient 4 and its association with the lack of a clinical response of the patient’s tumor to E7 TCR-T cell therapy.

[0038] FIG. 10A shows a schematic of the location of a damaging mutation in HLA-A*02:01 detected from a liquid biopsy from patent 5.

[0039] FIG. 10B shows the emergence of the HLA-A*02:01 mutant allele in patient 5 over various time points.

[0040] FIG. 10C shows a summary of the emergence of the damaging HLA-A*02:01 mutation in a liquid biopsy from patient 5 and its association with resistance of the patient’s tumor to E7 TCR-T cell therapy by day +284.

[0041] FIG. 11 shows a summary of the HLA-A*02:01 mutations as detected from samples of patient 5. In comparison, no HLA-A*02:01 mutations were detected from samples collected at various time points from patient 12.DETAILED DESCRIPTION

[0042] The inventors have developed methods for detecting loss of heterozygosity (LOH) or damaging mutations in immune-related genes via liquid biopsy, which can be used to determine whether a subject’s cancer exhibits LOH or has damaging mutations at one or more of the genes. This method provides a substantial advantage because it allows for LOH or mutation status to be determined before selecting a treatment for a patient who is a candidate for cancer immunotherapy. The method may be used to determine mechanisms of resistance to animmunotherapy treatment that a patient is receiving, and may provide information on why a patient’s cancer may no longer be responding to a specific immunotherapy

[0043] If LOH or damaging mutations in immune-related genes is detected in a patient’s cancer before treatment selection, this may improve outcomes by allowing for selection of a treatment that is more likely to help the patient rather than treating the patient with a therapy that is unlikely to work because of the LOH or damaging mutations. Furthermore, selecting a treatment utilizing this precision oncology approach will help the patient avoid toxic side-effects from any treatments that will not help them. An additional advantage is cost savings: the patient, their family, and insurance companies will save money by avoiding treatment with a therapy that will not work for the patient. Since some immunotherapies are quite expensive, in the range of $500,000 per patient, the cost savings could be substantial.

[0044] It would be advantageous to test for LOH or damaging mutations using a non-invasive method. Rather than surgically extracting solid tumor tissue for a biopsy to detect LOH or damaging mutation status, a non-invasive method such as liquid biopsy would allow for testing the biomarker status without the patient having to undergo surgery. This is helpful in several ways, including less pain for patients, lower cost to obtain a sample, and easier processing of the sample. Further, a liquid biopsy test may return more accurate results than a test using tumor tissue and allow for sampling across a patient’s tumors and tumor regions rather than taking a solid tumor tissue sample representative of only one region of one tumor. For example, if a solid tissue region of one tumor from a patient returns a negative test result, there is no guarantee that another region of the same tumor or another tumor does not harbor LOH or damaging mutations. With a liquid biopsy sample, information is obtained across tumors and tumor regions from a patient, allowing for detection of LOH or damaging mutations in any one of these tumors and / or regions.

[0045] The inventors developed methods described herein to detect LOH or damaging mutations via liquid biopsy. The inventors have discovered that detecting biallelic disruptions (that is, either LOH or damaging mutations) in non-HLA immune related genes, optionally in combination with HLA genes, may lead to immune resistance to cancer immunotherapies. The methods disclosed herein can detect both monoallelic and biallelic disruption and can distinguish between the two.1. Definitions

[0046] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the specification and the appended claims, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise.

[0047] For recitation of numeric ranges herein, each intervening number there between with the same degree of precision is explicitly contemplated. For example, for the range of 6-9, the numbers 7 and 8 are contemplated in addition to 6 and 9, and for the range 6.0-7.0, the numbers 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6,9, and 7.0 are explicitly contemplated.2. Detecting loss of heterozygosity and damaging mutations in immune-related genes

[0048] Provided herein is a method of detecting LOH or damaging mutations in immune-related genes from a subject. The subject may have a cancer. The method may comprise performing a liquid biopsy. a. Sample collection

[0049] The method may comprise obtaining or providing a sample from the subject. The sample used in the liquid biopsy may comprise cell-free DNA (cfDNA). The sample may be blood, urine sample, saliva, cerebrospinal fluid, stool, or any type of bodily fluid that contains enough DNA from tumors, cells from tumors, or cellular components from tumors. In one example, the sample is blood. The blood sample may comprise plasma, which may be extracted from the blood. In another example, the sample comprises circulating tumor cells (CTC), which may be extracted from the blood sample. The CTC may be extracted from a buffy coat of the blood. In a further example, the sample comprises extracellular vesicles, which may be extracted from a blood sample or a urine sample.

[0050] The liquid biopsy sample may be collected from a subject by any method that preserves the cfDNA, CTCs, EVs, or any component that is utilized for the LOH or damaging mutation detection. Blood may be collected in any way that is consistent with accepted medical standards that will minimize harm and pain to the subject. In one example, the blood sample is collected through a venous blood draw. Blood and plasma samples may be collected into tubes that maintain the integrity of the cfDNA, CTCs, EVs or other components utilized for the detection method. For example, Streck tubes can keep plasma at room temperature for 5-10 days and preserve the cfDNA. EDTA tubes have been utilized for collecting blood as well. Any tube thatpreserves the integrity of the sample for detection can be utilized. Similarly for sample storage, samples must be stored in such a way that preserves the integrity of the sample. This depends on the collection and storage tubes utilized for a given sample.

[0051] The method described herein may comprise providing or obtaining cfDNA DNA. Cell- free DNA may be extracted using any technique known in the art. In one example, cfDNA is extracted from plasma using the QIAMP CIRCULATING NUCLEIC ACID KIT. Other embodiments may use different kits and / or techniques for cfDNA extraction. In one example, at least 20 ng cfDNA is extracted. Cell-free DNA sequences may be obtained or provided from cfDNA extracted from CTCs; polymerase chain reaction (PCR) (e.g., digital PCR) on cfDNA; WES on CTC DNA; PCR on CTC DNA; whole genome sequencing (WGS) on cfDNA; WGS on CTCs; WES on EVs; PCR on EVs; WGS on EVs; or any other method that involves determining sequence and read count information from one or more immune-related genes from a genome of cancer cells from a given patient.

[0052] The method described herein optionally comprises providing or obtaining germline DNA from the subject. Germline DNA may be extracted from peripheral blood mononuclear cells (PBMC), which may be extracted from a blood sample. The germline DNA may also be extracted from a non-malignant tissue from the patient, which may be normal tissue adjacent to a tumor, or a saliva sample. The germline DNA may be sequenced, and the sequence may be used as a germline control for detecting LOH or damaging mutations. The germline DNA may be extracted using standard methods for DNA extraction, which are known in the art. In one example, at least 1 million PBMCs are obtained or provided for germline DNA extraction for use as a control. b. Sequencing

[0053] The method described herein may comprise obtaining or providing sequences from cell- free DNA (cfDNA or “biopsy sequence data”) and optionally germline DNA (germline sequence data) from the subject. The sequences may be of one or more immune-related genes. The immune-related genes may be one or more of human non-HLA immune-related genes B2M, TAPI, TAP2, TAPBP, CANX, CALR, PDIA3, NLRC5, JAK1, JAK2, STAT1, IFNGR1, IFNGR2, PSMB1, PSMB2, PSMB3, PSMB4, PSMB5, PSMB6, PSMB7, PSMB8, PSMB9, PSMB10, PSMB11, PSMA1, PSMA2, PSMA3, PSMA4, PSMA5, PSMA6, PSMB7, PSMA8, DFFA, DFFB, FAS, FADD, APAF1, BID, CASP2, CASP3, CASP6, CASP7, CASP8, CASP9,CASP10, CIITA, RFX5, RFXAP, RFXANK, and APLNR. The immune-related genes may further be one or more of HLA immune-related genes HLA-A, HLA-B, HLA-C, HLA-DR, HLA-DP, HLA-DQ, HLA-DMA, and HLA-DMB. In one example, the method does not include sequencing HLA genes. In another example, LOH is determined at one or more of B2M, TAPI, TAP2, TAPBP, CANX, CALR, PDIA3, NLRC5, JAK1, JAK2, STAT1, IFNGR1, IFNGR2, PSMB1, PSMB2, PSMB3, PSMB4, PSMB5, PSMB6, PSMB7, PSMB8, PSMB9, PSMB10, PSMB11, PSMA1, PSMA2, PSMA3, PSMA4, PSMA5, PSMA6, PSMB7, PSMA8, DFFA, DFFB, FAS, FADD, APAF1, BID, CASP2, CASP3, CASP6, CASP7, CASP8, CASP9, CASP10, CIITA, RFX5, RFXAP, RFXANK, and APLNR. In another example, damaging mutations are detected at one or more of non-HLA immune-related genes B2M, TAPI, TAP2, TAPBP, CANX, CALR, PDIA3, NLRC5, JAK1, JAK2, STAT1, IFNGR1, IFNGR2, PSMB1, PSMB2, PSMB3, PSMB4, PSMB5, PSMB6, PSMB7, PSMB8, PSMB9, PSMB10, PSMB11, PSMA1, PSMA2, PSMA3, PSMA4, PSMA5, PSMA6, PSMB7, PSMA8, DFFA, DFFB, FAS, FADD, APAF1, BID, CASP2, CASP3, CASP6, CASP7, CASP8, CASP9, CASP10, CIITA, RFX5, RFXAP, RFXANK, APLNR, and HLA immune-related genes HLA-A, HLA-B, HLA-C, HLA-DR, HLA-DP, HLA-DQ, HLA-DMA, and HLA-DMB. The sequences may be enriched for common SNPs in one or more of the immune-related genes, which may comprise one or more non-coding introns or UTRs. The use of SNP-enriched sequences may improve detection of LOH described herein.

[0054] In one example, the sequencing comprises whole exome sequencing (WES). The cfDNA may be from plasma and the germline DNA may be from PBMC. In another example, the sequencing comprises at least one of targeted sequencing and whole genome sequencing (WGS). The sequencing may comprise a PCR-based method, which may be digital PCR or another PCR- based method. The WES may be performed using any known method known in the art, which may be using an ILLUMINA sequencer.

[0055] The sequencing may comprise performing standard library preparation from the cfDNA and optionally the germline DNA. The sequencing may also comprise standard exome capture. In an alternative embodiment, custom capture kits may be used or created for more sensitively capturing a patient’s DNA from the sample before sequencing.

[0056] In one example, the biopsy DNA and optionally the germline DNA are sequenced by using a targeted, gene-specific primer / probe set. Each pair of primers and associated probe maytarget an exonic region, an intronic region, or both, of each immune-related gene of interest. Each primer / probe set may be designed to sequence the most diverse region of an immune- related gene of interest, or region(s) where polymorphisms are more likely to be present, thereby maximizing the number of single nucleotide polymorphisms that can be analyzed. The DNA may be sequenced at a depth of 50X, 100X, 150X, 200X, 250X, or 300X, particularly 200X, or at a depth of at least 50X, 100X, 150X, 200X, 250X, or 300X.

[0057] Fastq files may be generated from the sequencing. In an alternative embodiment in which PCR-based methods are used for detection, the output files from PCR may be a different file format. Also in an alternative embodiment, sequencing could be performed on a different sequencer or type of sequencer and at a different sequencing depth, as well as using different library prep.

[0058] In one example, fastq files are aligned to a reference human genome sequence, which may be hg38, using an alignment tool, which may be BWA, although other alignment tools may also be used. In one example, nucleic acid regions of both alleles of each immune-related gene of interest may be aligned to the human reference genome. For both alleles of each immune-related gene of interest, biopsy DNA sequences and the germline DNA sequences may independently be aligned to the human reference genome. The output of alignment may comprise a bam file for each sample. For each subject, there may be a pair of bam files - one for the cfDNA (biopsy) and one for a matched germline sample. c. Identifying loss of heterozygosity at immune-related genes

[0059] The method of identifying LOH at immune-related genes may comprise determining a total copy number of each immune-related gene. Generally, the method may comprise examining heterozygous base pairs identified in germline DNA that are different between the 2 alleles (i.e. paternal and maternal). This is used to determine the allele of origin for a gene of interest identified in cfDNA. Figure 3 provides an example of the method. Mathematical calculations may then be used to determine whether there is a significant difference in allelic fractions of cfDNA from one allele (i.e., paternal or maternal) compared to the read fractions from that same allele in the germline control sample.

[0060] In one example, a bioinformatics software tool CNVkit is used (Talevich, E. et al., “CNVkit: Genome-wide copy number detection and visualization from targeted sequencing,” PLOS Computational Biology 12(4):el004873 (2014), the contents of which are incorporatedherein by reference). The copy number may be determined by assessing the coverage over the liquid biopsy and the matched germline sample to create a reference level that includes technical and biological variation. The read depths of the cfDNA are normalized by GC content correction, repetitiveness, and target size. The circular binary segmentation (CBS) algorithm may be used to infer copy number segments. The method may comprise calculating coverage across a target region of interest for each immune-related gene, and then estimating and segmenting a copy number change. The method may comprise use of only deduplicated reads based on a unique molecular identifier (umi) with retainment of all PCR duplicates. Parameter settings for implementation of CNVkit may be tuned and optimized. The CNVkit may provide a statistical p- value for each immune-related gene where a p-value <0.05 indicates statistical significance for loss of heterozygosity for the gene.

[0061] The method may include determining patient allele-specific genotype for each gene, which may be determined using germline sequencing data from the subject. The method may also utilize existing databases that contain population-level allele information for the gene. In one example, a software tool such as IGVTools count is used. In one example, a database of allelic sequences for non-HLA immune-related genes and a genotyping algorithm for determining allele-specific sequences for non-HLA immune-related genes is used to determine the heterozygous base positions. The method may comprise generating a table showing how many reads from each nucleotide are at each base location in each immune-related gene. The presence of at least 2 nucleotides at a given base location having a read count percentage of at least 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, or 35%, or a percentage in a range of two of the foregoing, may be indicative of a heterozygous location. In one example, the percentage is 25%. The method may further comprise generating a table, which may be by using R or another programming language, providing the read counts at the heterozygous bases for each immune-related gene of interest, for both germline and biopsy DNA samples. Then, a statistical test may be used to determine loss of each immune-related gene. The p-value cutoffs may be calculated for both alleles and if the p-value is smaller than the empirically determined cutoff, then loss of the allele will be called.

[0062] The method may comprise calculating weights before performing the statistical test to determine LOH status. To capture the biological and experimental system into a statistical model for calculating an indicator of LOH, weights may be utilized in the calculation.

[0063] In one example, reads are mapped to an immune-related gene from tumor DNA, liquid biopsy cfDNA, and germline DNA from two patients. The mapping may show that the phenomenon of unbalanced read stacking occurs in germline, liquid biopsy, and tumor samples, which may need to be addressed in some embodiments of the liquid biopsy test. This may be caused by exome capture and alignment. This phenomenon may be addressed in different ways, such as customized capture kits and alignment algorithms.

[0064] The weights may be calculated as follows. A weight may be calculated for each heterozygous base for a given immune-related gene and subject. For example, one subject’s heterozygous bases may be different than another subject’s heterozygous bases, and each subject and immune-related gene may have a different number of heterozygous bases.

[0065] To calculate the vector of weights for a given patient and immune-related gene, each heterozygous base’s read count may be divided by the sum of read counts on all the heterozygous bases, and this may be separately calculated for the biopsy DNA sequence data and the germline DNA sequence data. The average at each heterozygous base between the biopsy DNA and the germline DNA for a given immune-related gene and subject may be calculated as follows resulting in the weights, where the number of weights (that is, the length of the weight vector) is equal to the number of heterozygous bases for a given HLA gene for a given patient:

[0066] weight vector = [vector_of_plasma_read_counts_at_heterozygous_bases / sum(vector_of_plasma_read_counts_at _heterozygous_bases) + vector_of_normal_read_counts_at_heterozygous_bases / sum(vector_of_ normal_read_counts_at_heterozygous_bases)] / 2

[0067] In another example, the weights may be calculated by taking the average read count at each heterozygous base between the biopsy sample and normal sample for a given immune- related gene and subject and then summing those averages and dividing each average by the sum to obtain the weights, where, again, the length of the weight vector is equal to the number of heterozygous bases for a given immune-related gene and patient:

[0068] weight_vector = [(vector_of_plasma_read_counts_at_heterozygous_bases + vector_of_normal_read_counts_at_heterozygous_bases) / 2] / sum[(vector_of_plasma_read_counts _at_heterozygous_bases + vector_of_normal_read_counts_at_heterozygous_bases) / 2]

[0069] The weights may be calculated a different way as opposed to how they are calculated in the two examples above, or weights may not be used at all.

[0070] A statistical test involving the weights described above may be run, and may return a numerical value that indicates LOH positive or negative status. In one example, a Student’ s t-test with paired observations and with the weights multiplied by the observations may be performed. In another example, one or more other statistical tests may be used, which may or may not use weights. In one example where weights are used, the weights are calculated in different ways.

[0071] In one example, a Student’s t-test may be used to calculate a p-value that determines whether one group of observations is significantly different from another group of observations. The Student’s t-test may be appropriate because each observation is the read frequency at a heterozygous base. For a given subject and immune-related gene, the heterozygous base position is the same genomic location for both the biopsy sample and germline sample. The paired observations are therefore the read count at a given heterozygous base for the biopsy sample and the read count at the same heterozygous base for the normal sample from the same patient. The p-value determines if, across the heterozygous bases for that gene and that patient, there is a significant difference between read frequencies for a given allele. The read frequency at one heterozygous base is the read count at that base for that allele divided by the total read count at that heterozygous base from both alleles from that sample. If the read frequencies of that allele are significantly different in the biopsy DNA compared to the germline DNA, then an LOH of the immune-related gene is called and LOH status is positive. If the read frequencies are not significantly different, then LOH status is negative. This can be done separately for both alleles, to get a p-value for each allele. In other embodiments, the read counts, rather than the read frequencies, may be used to compare biopsy DNA sample to germline DNA sample for a given patient and immune-related gene. A method that does not involve comparing the biopsy DNA sample to germline DNA sample may be used. Such a method may only use information from the biopsy DNA sample.

[0072] In one example, the weights are multiplied by the frequency observations in a dot-product fashion and then the paired t-test is run, which may be in the R programming language, and may utilize built-in R functions or user-defined functions. In other embodiments, the same test may be in a different programming language, using different code, or may use a totally different test and also implement that in R or in a different programming language or with different code. The output of a test for each allele may be a p-value. The p-value cutoff may be 0.004. In other embodiments, the p-value cutoff may be 0.05, 0.01, 0.005, or 0.001. The p-value cutoff may bedetermined from testing the predictive method on samples and identifying the cutoff value that best distinguishes positive calls from negative calls to maximize the number of true positives and true negatives and minimizes the number of false positives and false negatives. The p-value cutoff may depend on the statistical test that is used. The p-value may be any value between 0 and 1 that best separates positive calls from negative calls.

[0073] If the p-value is lower than this for both alleles, then LOH status may be deemed positive, and if at least one of the allelic p-values is higher, then LOH status may be deemed negative. The test may also determine which allele, if any, was lost in the immune-related gene LOH event by observing the sign of the difference of quantities computed within the t-test. In other embodiments, which allele was lost may be determined by observing a sign of a parameter calculated as part of the test used in that alternative embodiment, or by some other type of indicator.

[0074] The method may comprise detecting LOH at one or more non-HLA immune-related genes, as well as one or more HLA genes as described in WO2023137448A1, the contents of which are incorporated herein by reference.

[0075] Detecting the presence of LOH of one or more immune-related genes in the subject may be indicative of resistance or responsiveness to one or more cancer immunotherapies. If the subject exhibits LOH, then subject may be predicted to be a poor responder to, and thus may not be a candidate for, one or more cancer treatments, which may be one or more T cell receptor (TCR)-based immunotherapies. The TCR-based immunotherapy may comprise one or more of an immune checkpoint blockade (e.g., but not limited to, Pembrolizumab, avelumab, ipilimumab, nivolumab); a T cell engager, which may be an immune mobilizing monoclonal TCRs against cancer; a T cell receptor-T cell therapy (e.g., but not limited to, one targeting KK-LC-1, HPV-16 E7, GP100, KRASG12D, NY-ESO-1); tumor-infdtrating lymphocytes (TIL); a cancer vaccine (e.g., but not limited to, Sipuleucel-T); and cytokine therapy (e.g., but not limited to, interleukin- 2). If the subject does not exhibit LOH, then the subject may be predicted to more likely be responsive to one or more TCR-based immunotherapies. In one example, the amount of cfRNA from one or more of the immune-related genes may be measured. LOH of one copy of one of the immune-related genes (monoallelic disruption) in combination with a decrease in the amount of cfRNA for the one immune-related gene may be indicative of resistance to one or more cancerimmunotherapies. cfRNA may also be used to help confirm loss of one or more immune-related genes. d. Identifying damaging mutations in immune-related genes

[0076] The method of detecting damaging mutations in immune-related genes may comprise identifying damaging in one or more of the immune-related genes by performing an alignment to a comparator sequence using a variant caller, to a reference genome, and / or to a database of human sequences comprising alleles of the immune-related genes. The sequence of each non- HLA immune-related gene from the liquid biopsy may be aligned to a sequence of the non-HLA immune-related gene from a reference genome. In one example, the method may comprise use of the National Cancer Institute (NCI) Center for Cancer Research (CCR) Collaborative Bioinformatics Resource (CCBR) Pipeliner for exome variant calling (github.com / CCBR / XAVIER, the contents of which are incorporated herein by reference), which may comprise aligning umi de-duplicated reads using bwa-mem2 (Vasimuddin M. et al., “Efficient Architecture- Aware Acceleration of BWA-MEM for Multicore Systems,” IEEE Parallel and Distributed Processing Symposium (IPDPS) (2019) 10.1109 / IPDPS.2019.00041, the contents of which are incorporated herein by reference) to a reference human genome sequence, which may be hg38.

[0077] The sequence of each HLA immune-related gene from the liquid biopsy may be aligned to a sequence of the HLA immune-related gene from subject’s germline cell DNA, from a reference genome, or from a reference sequence identified by Poly solver from a plurality of the HLA immune-related gene from a plurality of humans. Polysolver may determine an allele of a germline HLA immune-related gene by performing a Bayesian calculation that uses base qualities of aligned reads, insert sizes, and ethnicity-dependent prior probabilities from known HLA alleles. A personalized reference genome determined from the solved HLA allele may be used for further somatic variant calling. Somatic variant sequences may be determined using the comparison between the personalized reference genome obtained from the germline cell DNA compared to the cfDNA. Additional fdtering may be performed including highest quality reads by mapping quality, base quality, PCR duplicate removal, and informative reads.

[0078] Polysolver may be used as described in Shukla, S., et al., “Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes,” Nat Biotechnol, 33, 1152-1158 (2015), the contents of which are incorporated herein by reference. IMGT may be as described atwww.ebi.ac.uk / ipd / imgt / hla / or Barker, J.B., et al., “The IPD-IMGT / HLA Database,” Nucleic Acids Research, 51, D1053-D1060 (2022), the contents of which are incorporated herein by reference.

[0079] The alignments may be stored as bam files. The alignments, which may be the aligned bam files, may be used as input to one or more somatic variant callers, which may include one or more of strelka, mutect, mutect2, vardict and varscan, in paired or unpaired mode. Variant sequences may also be identified as being damaging or not damaging by comparing a subject’s immune-related gene sequence to a database comprising known damaging mutations. The database may be ClinVar (www.ncbi.nlm.nih.gov / clinvar / , the contents of which are incorporated herein by reference) or COSMIC (cancer.sanger.ac.uk / cosmic, the contents of which are incorporated herein by reference). The union of all callers may be used to capture potential variants and damaging mutations. Variants may be annotated to identify deleterious non-synonymous changes using Variant Effect Predictor (VEP) (McLaren W, et al., “The Ensembl Variant Effect Predictor,” Genome Biology Jun 6; 17(1): 122. (2016); doi: 10.1186 / s 13059-016-0974-4, the contents of which are incorporated herein by reference), which relies on the Ensembl database and / or other annotation tools. In one example version 102 of VEP is used. Damaging mutations may be considered significant based on whether one variant caller determines the variant to be damaging or a combination of all potential variant callers, based on the level of sensitivity desired. An example of the method is provided in Figures 2, 4, and 9.

[0080] In one example, LOH at one or more HLA genes may be detected in combination with detecting damaging mutations in HLA genes as described herein. In another example, damaging mutations in HLA genes as described herein may be detected in combination with LOH of one or more HLA genes as described in WO2023137448A1. In a further example, LOH at one or more non-HLA immune-related genes is detected in combination with damaging mutations in immune-related genes, optionally in combination with LOH at one or more HLA genes or any combination of these which may portend resistance to TCR-based therapies.

[0081] Detecting the presence of damaging mutations in one or more immune-related genes in the subject may be indicative of resistance or responsiveness to one or more cancer immunotherapies. In one example, damaging mutations in immune-related genes, which may comprise one or more HLA genes, or LOH at one or more immune-related genes, whichoptionally comprise HLA genes, may be indicative of resistance or responsiveness to one or more cancer immunotherapies. If the subject exhibits: (a) damaging mutations in the one or more immune-related genes, or (b) damaging mutations in one or more HLA genes and LOH of one or more HLA genes, or (c) damaging mutations in the one or more immune-related genes or LOH in the one or more immune related genes (which may or may not comprise HLA genes), or (d) damaging mutation(s) or loss in both copies (e.g., biallelic disruption) of one or more HLA and / or non-HLA immune related gene(s), then the subject may be predicted to be a poor responder to, and thus may not be a candidate for, one or more cancer treatments, which may be one or more T cell receptor (TCR)-based immunotherapies. The TCR-based immunotherapy may comprise one or more of an immune checkpoint blockade (e.g., but not limited to, pembrolizumab, avelumab, ipilimumab, nivolumab); a T cell engager, which may be an immune mobilizing monoclonal TCRs against cancer; a T cell receptor-T cell therapy (e.g., but not limited to, one targeting KK-LC-1, HPV-16 E7, GP100, KRASG12D, NY-ESO-1); tumorinfiltrating lymphocytes (TIL); a cancer vaccine (e.g., but not limited to, Sipuleucel-T); and cytokine therapy (e.g., but not limited to, interleukin-2). If the subject does not exhibit damaging mutations at the one or more immune-related genes and LOH at the one or more immune-related genes, then the subject may be predicted to more likely be responsive to one or more TCR-based immunotherapies. In one example, the amount of cfRNA from one or more of the immune- related genes may be measured. A damaging mutation in one copy of one of the immune-related genes (monoallelic disruption) in combination with a decrease in the amount of cfRNA for the one immune-related gene may be indicative of resistance to one or more cancer immunotherapies. cfRNA may also be used to help confirm loss of one or more immune-related genes or whether a monoallelic disruption is likely to lead to resistance to one or more cancer immunotherapies.3. Cancer treatments

[0082] Provided herein is a method of treating cancer in a subject. The method may comprise administering a cancer treatment to the subject. One or more of loss of heterozygosity in one or more immune-related genes and damaging mutations in one or more immune-related genes may have been detected in one or more immune-related genes of the subject as described herein. In one example, if LOH or a damaging mutation has been detected in the subject, then a TCR-based immunotherapy may not be administered to the subject, and instead one or more of a CAR-T celltherapy, chemotherapy, radiation therapy, natural killer (NK) cell therapy, hormone therapy, and a targeted small molecule inhibitor may be administered to the subject. If LOH or a damaging mutation has not been detected in the subject, then a TCR-based immunotherapy may be administered to the subject. The TCR-based therapy may be one or more of immune checkpoint blockade, a T cell engager, a TCR-T cell therapy, tumor infdtrating lymphocytes, a cancer vaccine, and a cytokine therapy. Examples of immune checkpoint blockade therapy include Pembrolizumab (FDA-approved for multiple cancer types), Atezolizumab, Ipilimumab, Avelumab, and Nivolumab. Examples of T-cell engagers include KIMMTRAK, which is FDA- approved for treating uveal melanoma. Examples of TCR-T cell therapies include KK-LC-1- TCR-T cells, HPV-16 E7-TCR-T cells, gplOO-TCR-T cells, KRASG12D-TCR-T cells, and NY- ESO-l-TCR-T cells, all of which are being tested in clinical trials. Examples of TIL therapies include Lifileucel. Examples of cancer vaccines include Sipuleucel-T, which is FDA-approved for treating asymptomatic or minimally symptomatic metastatic castration-resistant prostate cancer (mCRPC). Cytokine therapies include Interleukin-2.

[0083] The methods disclosed herein may be used to determine whether a patient should receive additional treatment(s). For instance, some patients with localized or locally advanced cancer receive “definitive therapy” followed by “adjuvant therapy.” The second treatment (e.g., “adjuvant therapy”) is given to decrease the chance of the disease coming back or to improve the chance of cure. These second treatments may have side effects. The methods may be used to determine which patients may benefit most from receiving this second treatment. This second treatment could be an immunotherapy. A combination of HLA and non-HLA immune related genes with LOH and / or damaging mutations may be determined in combination with the HLA LOH method as described in WO2023137448A1 (the contents of which are incorporated herein by reference), to determine whether a patient should receive this second treatment.

[0084] The methods disclosed herein may be used to determine whether a patient’s cancer is developing resistance to an immunotherapy they are currently receiving. Detection of a combination of HLA and non-HLA immune related genes with LOH and / or damaging mutations may be determined in combination with the HLA LOH method as described in WO2023137448A1 (the contents of which are incorporated herein by reference), to determine whether the patient should continue the current cancer therapy or switch to another treatment.4. System

[0085] FIG. 8 is a schematic block diagram of an example device 200 that may be used with one or more embodiments described herein, e.g., as a component of the system for determining heterozygosity loss of-immune-related genes in liquid biopsies. Device 200 may alternatively be used for identifying damaging mutations in immune-related genes, either alone or in combination with LOH in immune-related genes.

[0086] Device 200 comprises one or more network interfaces 210 (e.g., wired, wireless, PLC, etc.), at least one processor 220, and a memory 240 interconnected by a system bus 250, as well as a power supply 260 (e.g., battery, plug-in, etc.).

[0087] Network interface(s) 210 include the mechanical, electrical, and signaling circuitry for communicating data over the communication links coupled to a communication network.Network interfaces 210 are configured to transmit and / or receive data using a variety of different communication protocols. As illustrated, the box representing network interfaces 210 is shown for simplicity, and it is appreciated that such interfaces may represent different types of network connections such as wireless and wired (physical) connections. Network interfaces 210 are shown separately from power supply 260, however it is appreciated that the interfaces that support PLC protocols may communicate through power supply 260 and / or may be an integral component coupled to power supply 260.

[0088] Memory 240 includes a plurality of storage locations that are addressable by processor 220 and network interfaces 210 for storing software programs and data structures associated with the embodiments described herein. In some embodiments, device 200 may have limited memory or no memory (e.g., no memory for storage other than for programs / processes operating on the device and associated caches). Memory 240 can include instructions executable by the processor 220 that, when executed by the processor 220, cause the processor 220 to implement aspects of the system 100 and associated methods outlined herein.

[0089] Processor 220 comprises hardware elements or logic adapted to execute the software programs (e.g., instructions) and manipulate data structures 245. An operating system 242, portions of which are typically resident in memory 240 and executed by the processor, functionally organizes device 200 by, inter alia, invoking operations in support of software processes and / or services executing on the device. These software processes and / or services may include heterozygosity loss determination or damaging mutation determinationprocesses / services 290, which can include aspects of the methods and / or implementations of various modules described herein. Note that while the heterozygosity loss determination processes / services or damaging mutation identification processes / services 290 is illustrated in centralized memory 240, alternative embodiments provide for the process to be operated within the network interfaces 210, such as a component of a MAC layer, and / or as part of a distributed computing network environment.

[0090] It will be apparent to those skilled in the art that other processor and memory types, including various computer-readable media, may be used to store and execute program instructions pertaining to the techniques described herein. Also, while the description illustrates various processes, it is expressly contemplated that various processes may be embodied as modules or engines configured to operate in accordance with the techniques herein (e.g., according to the functionality of a similar process). In this context, the term module and engine may be interchangeable. In general, the term module or engine refers to model or an organization of interrelated software components / functions. Further, while the heterozygosity loss determination processes / services 290 is shown as a standalone process, those skilled in the art will appreciate that this process may be executed as a routine or module within other processes.

[0091] The present invention has multiple aspects, illustrated by the following non-limiting examples.Example 1 Detecting Damaging Mutations in Immune-Related Genes

[0092] This example demonstrates a method of detecting damaging mutations in immune-related genes via liquid biopsy. To determine whether damaging somatic mutations in genes important for antigen processing and presentation machinery (APM) could be detected by liquid biopsy, we collected plasma and tumor from a patient who received treatment with E7 TCR-T cell therapy (Nagarsheth et al, Nat Med 2021). Cell free DNA and PBMC DNA (germline control) was isolated, and a custom probe kit designed by Agilent was used to amplify specific genes of interest including HLA class I alleles, B2M, TAPI, TAP2, TAPBP, CANX, CALR, PDIA3, NLRC5, JAK1, JAK2, STAT1, IFNGR1 and IFNGR2. Targeted genomic sequencing was performed on PBMC DNA (germline control), and cell free DNA isolated from plasma. Analysisof the cell free DNA revealed a damaging mutation in HLA-A*02:01 locus at amino acid position 51 where the coding sequence changed from TAC (tyrosine) to TAG (stop codon) resulting in premature termination of the HLA molecule. The damaging mutation was found in 5% of the circulating cell free DNA molecules encoding the HLA-A*02:01 allele which was much higher than control PBMC DNA (Figure 1). This same damaging mutation was found on a tumor biopsy collected approximately 1 month after collection of this plasma specimen (Figure 2A-B). This data indicates the potential of detecting damaging mutations in genes important for APM and IFN response by liquid biopsy, providing a potential new predictive assay for immunotherapies including TCR-T cell therapy.Example 2 Detecting Loss of Heterozygosity for Immune-Related Genes

[0093] This example demonstrates a method of detecting LOH in immune-related genes via liquid biopsy. To determine whether LOH in genes important for APM and intcrfcron-y response pathways could be detected by liquid biopsy, we collected plasma and tumor from a patient (patient 4) who received treatment with E7 TCR-T cell therapy (Nagarsheth et al, Nat Med 2021). Cell free DNA and PBMC DNA (germline control) was isolated, and a custom probe kit was used to amplify exonic and intronic regions of specific genes of interest including B2M, TAPI, TAP2, TAPBP, CANX, CALR, PDIA3, NLRC5, JAK1, JAK2, STAT1, IFNGR1 and IFNGR2. Targeted genomic sequencing was performed on PBMC DNA (germline control), and cell free DNA isolated from plasma. CNVkit on amplicon mode was used to determine copy number variants. Analysis revealed LOH of IFNGR2 (copy number 1) on Day +10 and LOH of CANX (copy number 1) on Day +40 of E7 TCR-T cell therapy (Figure 5). A tumor collected at Day +40 revealed the same copy loss in CANX and IFNGR2 as found by liquid biopsy (Figure 6). In addition, liquid biopsy revealed loss of other genes not found by traditional biopsy including JAK2 at Day +40. This data indicates the potential of detecting LOH in genes important for APM and IFN-y response by liquid biopsy, providing a potential new assay for immunotherapies including TCR-T cell therapy.Example 3Detecting Damaging Mutations in HLA Genes in Cancer Patients

[0094] Damaging mutations in HLA genes were analyzed in liquid biopsy samples from 3 patients with HPV-associated cancers who were treated with E7 TCR-T cell therapy (patients 4, 5 and 12) (Nagarsheth et al, Nat Med 2021). In patients 4 and 5, the analysis identified HLA- A*02:01 damaging mutations (FIGS. 9-10) in liquid biopsies that were also found in time- matched tumor specimens. Interestingly, the HLA damaging mutation found in a resistant tumor biopsied post-treatment from patient 4 was found in the plasma prior to receiving E7 TCR-T cell therapy (day -10). This patient to did not have a clinical response to therapy.

[0095] In patient 5, the HLA damaging mutation found in a resistant tumor nearly 1 year following an exceptional clinical response to E7 TCR-T cell therapy was not found in plasma prior to treatment (day -12) or early post-treatment (day +150) but began to emerge later at the time of progression on day +284. FIG. 1 1 shows the results of damaging mutation analysis in patients 5 and 12 at various time points. No HLA-A*02:01 damaging mutations were found in tumor or plasma from patient 12.

[0096] These findings of this example suggest the ability of liquid biopsy to predict response, and monitor for the development of resistance, to TCR-based therapeutics. This provides important proof-of-concept of this approach in patients with cancer.Example 4Use of Polysolver Improves HLA Somatic Mutation Calling

[0097] This example demonstrates that using Polysolver to perform alignments in identifying damaging mutations for HLA immune-related genes may improve mapping quality and accuracy of mutation calls. Three different alignment methods were used for a tumor sample and a liquid biopsy from a subject with the germline HLA-A*02:01 :01 :01 allele. Xavier is a pipeline used at the National Institutes of Health and relies on hg38 as the alignment tool. MHC Hammer and Polysolver (which relies on IMGT) were also tested. The results are shown in the table below.Table 1

[0098] The results show that using Polysolver to identify damaging mutations in HLA genes provided the highest alignment score (70) and more accurately and precisely identified the HLA allele in which the mutation occurred (HLA-A*02:01 :01 :01). In contrast, using Xavier on a liquid biopsy only made a mutation call in HLA- A, but did not identify the specific allele, and while MHC-Hammer made the somatic mutation call in HLA-A, it identified the allele as HLA- A*02:01 :216 (i.e., more fully identifying the 4thdigit as compared to only HLA-A*02:01 :01 :01).

[0099] Accordingly, the mapping quality for a Polysolver alignment approach led to improved mutation calling as compared to the hg38 reference genome and thus may provide improved accuracy and precision of HLA somatic mutation calls.

Claims

CLAIMSWhat is claimed is:

1. A method of detecting loss of heterozygosity (LOH) of an immune-related gene in a subject, comprising:(a) providing biopsy immune-related gene sequence data obtained from cell- free DNA (cfDNA) of a liquid biopsy sample from the subject, wherein the biopsy immune-related gene sequence data comprises sequences of one or more immune-related genes;(b) providing germline immune-related gene sequence data obtained from DNA of a germline cell sample of the subject, wherein the germline immune-related gene sequence data comprises sequences of the one or more immune-related genes;(c) aligning nucleic acid sequences of both alleles of each immune-related gene from the germline cell DNA to a reference sequence for each immune-related gene to generate an alignment, and aligning cfDNA nucleic acid sequences to a reference genome so that reads at heterozygous bases can be counted;(d) from the alignment, identifying heterozygous bases of each immune- related gene, wherein each heterozygous base is identified by nucleic acid sequence position and genome coordinate;(e) for each heterozygous base of each immune-related gene, counting allelic reads in the biopsy immune-related gene sequence data and counting allelic reads in the germline immune-related gene sequence data, wherein a read count percentage of at least 25% is indicative of a heterozygous base;(f) calculating a weight for each heterozygous base by:(i) separately for allelic reads in the biopsy immune-related gene sequence data and the germline immune-related gene sequence data, for each heterozygous base of each immune-related gene,dividing the read count of each heterozygous base by the sum of read counts on all heterozygous bases; and(ii) calculating the average at each heterozygous base between the biopsy immune-related gene sequence data and the germline immune-related gene sequence data;(g) comparing the allelic read count from the biopsy immune-related gene sequence data to the allelic read count from the germline immune-related gene sequence data, by performing a Student’s t-test with paired observation for each heterozygous base, in which each weight is multiplied by an observation comprising the read count at the heterozygous base in the biopsy immune-related gene sequence data and the read count at the same heterozygous base in the germline DNA sequence data, to generate a p-value for each immune-related gene; wherein a significant p-value, which optionally is 0.004, is indicative of LOH for the immune-related gene in the liquid biopsy, and wherein the one or more immune-related genes are selected from B2M, TAPI, TAP2, TAPBP, CANX, CALR, PDIA3, NLRC5, JAK1, JAK2, STAT1, IFNGR1, IFNGR2, PSMB1, PSMB2, PSMB3, PSMB4, PSMB5, PSMB6, PSMB7, PSMB8, PSMB9, PSMB10, PSMB11, PSMA1, PSMA2, PSMA3, PSMA4, PSMA5, PSMA6, PSMB7, PSMA8, DFFA, DFFB, FAS, FADD, APAF1, BID, CASP2, CASP3, CASP6, CASP7, CASP8, CASP9, CASP10, CIITA, RFX5, RFXAP, RFXANK, and APLNR.

2. A method of detecting loss of heterozygosity (LOH) of an immune-related gene in a subject, comprising:(a) providing biopsy immune-related gene sequence data obtained from cell- free DNA (cfDNA) of a liquid biopsy sample from the subject, wherein the biopsy immune-related gene sequence data comprises sequences of one or more immune-related genes;(b) providing germline immune-related gene sequence data obtained from DNA of a germline cell sample of the subject, wherein the germlineimmune-related gene sequence data comprises sequences of the one or more immune-related genes;(c) aligning nucleic acid sequences of both alleles of each immune-related gene from the germline cell DNA to a reference sequence for each immune-related gene to generate an alignment, and aligning cfDNA nucleic acid sequences to a reference genome so that reads at heterozygous bases can be counted;(d) from the alignment, identifying heterozygous bases of each immune- related gene, wherein each heterozygous base is identified by nucleic acid sequence position and genome coordinate;(e) for each heterozygous base of each immune-related gene, determine a total copy of the immune-related gene by creating a reference from the matched germline sample and normalizing the cfDNA by GC content correction, repetitiveness, and target size to make copy number calls on the immune- related gene, wherein copy number calls are made by calculating coverage across a target region of interest of the immune-related gene, and estimating the copy number change by segmentation;(f) generating a p-value for the copy number change each immune-related gene; wherein a significant p-value, which optionally is <0.005, is indicative of LOH for the immune-related gene in the liquid biopsy, and wherein the one or more immune-related genes are selected from B2M, TAPI, TAP2, TAPBP, CANX, CALR, PDIA3, NLRC5, JAK1, JAK2, STAT1, IFNGR1, IFNGR2, PSMB1, PSMB2, PSMB3, PSMB4, PSMB5, PSMB6, PSMB7, PSMB8, PSMB9, PSMB10, PSMB11, PSMA1, PSMA2, PSMA3, PSMA4, PSMA5, PSMA6, PSMB7, PSMA8, DFFA, DFFB, FAS, FADD, APAF1, BID, CASP2, CASP3, CASP6, CASP7, CASP8, CASP9, CASP10, CIITA, RFX5, RFXAP, RFXANK, and APLNR.

3. The method of claim 1 or 2, wherein the germline cell sample comprises peripheral blood monocytes.

4. The method of claim 1 or 2, wherein the subject has a cancer, and wherein the cfDNA comprises cancer cell DNA.

5. The method of claim 1 or 2, wherein the sequence of each immune-related gene is obtained by contacting DNA with a set of primers and a probe specific to the immune-related gene.

6. The method of claim 1 or 2, wherein the DNA is sequenced at a depth of 100X to 300X.

7. The method of claim 6, wherein the DNA is sequence at a depth of 200X.

8. The method of claim 1 or 2, wherein the one or more immune-related genes further comprise one or more HL A genes.

9. The method of claim 8, wherein the one or more HLA genes comprise one or more of HLA- A, HLA-B, HLA-C, HLA-DR, HLA-DP, HLA-DQ, HLA-DMA, and HLA-DMB.

10. A method of predicting a subject’s response to a cancer immunotherapy, comprising detecting LOH for the immune-related gene according to the method of claim 1 or 2, wherein the presence of LOH for the immune-related gene is indicative that the subject will have a poor response to the cancer immunotherapy, and wherein the absence of LOH for the immune- related gene is indicative that the subject will respond or is more likely to respond to the cancer immunotherapy.

11. The method of claim 10 wherein the cancer immunotherapy is a T cell receptor (TCR)-based therapy.

12. The method of claim 11, wherein the TCR-based therapy is selected from the group consisting of an immune checkpoint blockade, a T cell engager, a TCR-T cell therapy, tumor infdtrating lymphocytes, a cancer vaccine, and a cytokine therapy.

13. The method of claim 12, wherein the T cell engager comprises immune mobilizing TCRs against cancer.

14. The method of any one of claims 10-12, wherein the cancer immunotherapy is administered as a single agent or in combination with a second cancer treatment.

15. A method of treating a cancer in a subject in need thereof, comprising administering to the subject a cancer immunotherapy, wherein the subject has been predicted to respond or be more likely to respond to the cancer immunotherapy according to the method of claim 10.

16. The method of claim 15, wherein the cancer immunotherapy comprises a TCR- based therapy.

17. The method of claim 16, wherein the TCR-based therapy is selected from the group consisting of an immune checkpoint blockade, a T cell engager, a TCR-T cell therapy, tumor infiltrating lymphocytes, a cancer vaccine, and a cytokine therapy.

18. A system for detecting loss of heterozygosity (LOH) of an immune-related gene in a subject, comprising: a computing system including a processor in communication with a memory, the memory including instructions, which, when executed, cause the processor to:(a) provide biopsy immune-related gene sequence data obtained from cell-free DNA (cfDNA) of a liquid biopsy sample from the subject, wherein the biopsy immune-related gene sequence data comprises sequences of one or more immune-related genes;(b) provide germline immune-related gene sequence data obtained from DNA of a germline cell sample of the subject, wherein the germline immune- related gene sequence data comprises sequences of the one or more immune-related genes;(c) align nucleic acid sequences of both alleles of each immune-related gene from the germline cell DNA to a reference sequence for each immune- related gene to generate an alignment, and aligning cfDNA nucleic acid sequences to a reference genome so that reads at heterozygous bases can be counted;(d) from the alignment, identify heterozygous bases of each immune-related gene, wherein each heterozygous base is identified by nucleic acid sequence position and genome coordinate;(e) for each heterozygous base of each immune-related gene, count allelic reads in the biopsy immune-related gene sequence data and counting allelic reads in the germline immune-related gene sequence data, wherein a read count percentage of at least 25% is indicative of a heterozygous base;(f) calculate a weight for each heterozygous base by:(i) separately for allelic reads in the biopsy immune-related gene sequence data and the germline immune-related gene sequence data, for each heterozygous base of each immune-related gene, dividing the read count of each heterozygous base by the sum of read counts on all heterozygous bases; and(ii) calculating the average at each heterozygous base between the biopsy immune-related gene sequence data and the germline immune-related gene sequence data;(g) compare the allelic read count from the biopsy immune-related gene sequence data to the allelic read count from the germline immune-related gene sequence data, by performing a Student’s t-test with paired observation for each heterozygous base, in which each weight is multiplied by an observation comprising the read count at the heterozygous base in the biopsy immune-related gene sequence data and the read count at the same heterozygous base in the germline DNA sequence data, to generate a p-value for each immune-related gene; wherein a significant p-value, which optionally is 0.004, is indicative of LOH for the immune-related gene in the liquid biopsy, and wherein the one or more immune-related genes are selected from B2M, TAPI, TAP2, TAPBP, CANX, CALR, PDIA3, NLRC5, JAK1, JAK2, STAT1, IFNGR1, IFNGR2, PSMB1, PSMB2, PSMB3, PSMB4, PSMB5, PSMB6, PSMB7, PSMB8, PSMB9, PSMB10, PSMB11, PSMA1, PSMA2, PSMA3, PSMA4, PSMA5, PSMA6, PSMB7, PSMA8, DFFA, DFFB, FAS, FADD, APAF1, BID, CASP2, CASP3, CASP6, CASP7, CASP8, CASP9, CASP10, CIITA, RFX5, RFXAP, RFXANK, and APLNR.

19. A system for detecting loss of heterozygosity (LOH) of an immune-related gene in a subject, comprising: a computing system including a processor in communication with a memory, the memory including instructions, which, when executed, cause the processor to:(a) provide biopsy immune-related gene sequence data obtained from cell-free DNA (cfDNA) of a liquid biopsy sample from the subject, wherein the biopsy immune-related gene sequence data comprises sequences of one or more immune-related genes;(b) provide germline immune-related gene sequence data obtained from DNA of a germline cell sample of the subject, wherein the germline immune- related gene sequence data comprises sequences of the one or more immune-related genes;(c) align nucleic acid sequences of both alleles of each immune-related gene from the germline cell DNA to a reference sequence for each immune- related gene to generate an alignment, and aligning cfDNA nucleic acid sequences to a reference genome so that reads at heterozygous bases can be counted;(d) from the alignment, identify heterozygous bases of each immune-related gene, wherein each heterozygous base is identified by nucleic acid sequence position and genome coordinate;(e) for each heterozygous base of each immune-related gene, determine a total copy of the immune-related gene by creating a reference from the matched germline sample and normalizing the cfDNA by GC content correction, repetitiveness, and target size to make copy number calls on the immune- related gene, wherein copy number calls are made by calculating coverage across a target region of interest of the immune-related gene, and estimating the copy number change by segmentation;(f) generate a p-value for the copy number change each immune-related gene; wherein a significant p-value, which optionally is <0.005, is indicative of LOH for the immune-related gene in the liquid biopsy, and wherein the one or more immune- related genes are selected from B2M, TAPI, TAP2, TAPBP, CANX, CALR, PDIA3,NLRC5, JAK1, JAK2, STAT1 , IFNGR1, IFNGR2, PSMB1 , PSMB2, PSMB3, PSMB4, PSMB5, PSMB6, PSMB7, PSMB8, PSMB9, PSMB10, PSMB11, PSMA1, PSMA2, PSMA3, PSMA4, PSMA5, PSMA6, PSMB7, PSMA8, DFFA, DFFB, FAS, FADD, APAF1, BID, CASP2, CASP3, CASP6, CASP7, CASP8, CASP9, CASP1O, CIITA, RFX5, RFXAP, RFXANK, and APLNR.

20. The system of claim 18 or 19, wherein the germline cell sample comprises peripheral blood monocytes.

21. The system of claim 18 or 19, wherein the subject has a cancer, and wherein the cfDNA comprises cancer cell DNA.

22. A system for predicting a subject’s response to a cancer immunotherapy, comprising: a computing system including a processor in communication with a memory, the memory including instructions, which, when executed, cause the processor to: detect LOH for the immune-related gene according to claim 1 or 2, wherein the presence of LOH for the immune-related gene is indicative that the subject will have a poor response to the cancer treatment, and wherein the absence of LOH for the immune- related gene detected by the processor is indicative that the subject will respond or is more likely to respond to the cancer treatment.

23. The system of claim 22, wherein the cancer immunotherapy is a TCR-based immunotherapy.

24. A system of treating a cancer in a subject in need thereof, comprising: a computing system including a processor in communication with a memory, the memory including instructions, which, when executed, cause the processor to: instruct that the subject be administered a cancer immunotherapy, wherein the subject has been predicted by the processor to respond to the cancer treatment according to the system of claim 22.

25. The system of claim 24, wherein the cancer immunotherapy is a TCR-based therapy.

26. The system of claim 25, wherein the TCR-based therapy is selected from the group consisting of an immune checkpoint blockade, a T cell engager, a TCR-T cell therapy, tumor infdtrating lymphocytes, a cancer vaccine, and a cytokine therapy.

27. The system of claim 24 or 25, wherein the cancer immunotherapy is administered as a single agent or in combination with a second cancer treatment.

28. A method of detecting a damaging mutation in an immune-related gene in a subject, comprising:(a) providing biopsy immune-related gene sequence data obtained from cell- free DNA (cfDNA) of a liquid biopsy sample from the subject, wherein the biopsy immune-related gene sequence data comprises sequences of one or more immune-related genes, wherein the one or more immune- related genes are selected from non-HLA immune-related genes selected from B2M, TAPI, TAP2, TAPBP, CANX, CALR, PDIA3, NLRC5, JAK1, JAK2, STAT1, IFNGR1, IFNGR2, PSMB1, PSMB2, PSMB3, PSMB4, PSMB5, PSMB6, PSMB7, PSMB8, PSMB9, PSMB10, PSMB11, PSMA1, PSMA2, PSMA3, PSMA4, PSMA5, PSMA6, PSMB7, PSMA8, DFFA, DFFB, FAS, FADD, APAF1, BID, CASP2, CASP3, CASP6, CASP7, CASP8, CASP9, C ASP 10, CIITA, RFX5, RFXAP, RFXANK, and APLNR, and HLA immune-related genes selected from HLA-A, HLA-B, HLA-C, HLA-DR, HLA-DP, HLA-DQ, HLA-DMA, and HLA-DMB;(b) providing germline immune-related gene sequence data obtained from DNA of a germline cell sample of the subject, wherein the germline immune-related gene sequence data comprises sequences of the one or more immune-related genes;(c) aligning sequences of each immune-related gene from the germline cell DNA to a reference sequence for each immune-related gene to generate an alignment, wherein the sequences of each immune-related gene from the germline cell DNA is defined as wild-type and wherein each base of each sequence is identified by sequence position and genome coordinate;(d) aligning the sequences of each immune-related gene from the cfDNA to a comparator sequence, wherein for each non-HLA immune-related gene the comparator sequence is a sequence of the non-HLA immune-related gene from a reference genome, and for each HLA immune-related gene, the comparator sequence is a sequence of the HLA immune-related gene from the germline cell DNA, from the reference genome, or from a reference sequence identified by Poly solver from a plurality of the HLA immune-related gene from a plurality of humans;(e) identifying variant sequences between the germline cell DNA and cfDNA at each sequence position and genome coordinate in each immune-related gene using the comparator sequence and inputting the variant sequences between the germline cell DNA and cfDNA of each immune-related gene to one or more variant callers or comparing the variant sequences to a database comprising known damaging mutations, wherein each variant sequence is identified as damaging or not damaging by each variant caller, database, or a combination thereof.

29. The method of claim 28, wherein the germline cell sample comprises peripheral blood monocytes.

30. The method of claim 28, wherein the subject has a cancer, and wherein the cfDNA comprises cancer cell DNA.

31. A system for detecting a damaging mutation in an immune-related gene in a subject, comprising: a computing system including a processor in communication with a memory, the memory including instructions, which, when executed, cause the processor to:(a) provide biopsy immune-related gene sequence data obtained from cell-free DNA (cfDNA) of a liquid biopsy sample from the subject, wherein the biopsy immune-related gene sequence data comprises sequences of one or more immune-related genes, wherein the one or more immune-related genes are selected from non-HLA immune-related genes selected from B2M, TAPI, TAP2, TAPBP, CANX, CALR, PDIA3, NLRC5, JAK1,JAK2, STAT1, IFNGR1, IFNGR2, PSMB1, PSMB2, PSMB3, PSMB4, PSMB5, PSMB6, PSMB7, PSMB8, PSMB9, PSMB10, PSMB11, PSMA1, PSMA2, PSMA3, PSMA4, PSMA5, PSMA6, PSMB7, PSMA8, DFFA, DFFB, FAS, FADD, APAF1, BID, CASP2, CASP3, CASP6, CASP7, CASP8, CASP9, C ASP 10, CIITA, RFX5, RFXAP, RFXANK, and APLNR, and HLA immune-related genes selected from HLA-A, HLA-B, HLA-C, HLA-DR, HLA-DP, HLA-DQ, HLA-DMA, and HLA- DMB;(b) provide germline immune-related gene sequence data obtained from DNA of a germline cell sample of the subject, wherein the germline immune- related gene sequence data comprises sequences of the one or more immune-related genes;(c) align sequences of each immune-related gene from the germline cell DNA to a reference sequence for each immune-related gene to generate an alignment, wherein the sequences of each immune-related gene from the germline cell DNA is defined as wild-type and wherein each base of each sequence is identified by sequence position and genome coordinate;(d) align the sequences of each immune-related gene from the cfDNA to a comparator sequence, wherein for each non-HLA immune-related gene the comparator sequence is the sequence of the non-HLA immune-related gene from a reference genome, and for each HLA immune-related gene, the comparator sequence is the sequence of the HLA immune-related gene from the germline cell DNA, from the reference genome, or from a reference sequence identified by Poly solver from a plurality of the HLA immune-related gene from a plurality of humans;(e) identify variant sequences between the germline cell DNA and cfDNA at each sequence position and genome coordinate in each immune-related gene using the comparator sequence and inputting the variant sequences between the germline cell DNA and cfDNA of each immune-related gene to one or more variant callers or comparing the variant sequences to a database comprising known damaging mutations, wherein each variantsequence is identified as damaging or not damaging by each variant caller, database, or a combination thereof.

32. A method of predicting a subject’s response to a cancer immunotherapy, comprising detecting LOH for an immune-related gene according to the method of claim 1 or 2 and detecting a damaging mutation in an immune-related gene according to the method of claim 28, wherein the presence of LOH for the immune-related gene or a damaging mutation in the immune-related gene is indicative that the subject will have a poor response to the cancer treatment, and wherein the absence of LOH and a damaging mutation for the immune-related gene is indicative that the subject will respond or is more likely to respond to the cancer treatment.

33. The method of claim 32, wherein the cancer immunotherapy is a T cell receptor (TCR)-based therapy.

34. The method of claim 33, wherein the TCR-based therapy is selected from the group consisting of an immune checkpoint blockade, a T cell engager, a TCR-T cell therapy, tumor infiltrating lymphocytes, a cancer vaccine, and a cytokine therapy.

35. The method of claim 34, wherein the T cell engager comprises immune mobilizing TCRs against cancer.

36. The method of any one of claims 32-34, wherein the cancer immunotherapy is administered as a single agent or in combination with a second cancer treatment.

37. A method of treating a cancer in a subject in need thereof, comprising administering to the subject a cancer immunotherapy, wherein the subject has been predicted to respond or be more likely to respond to the cancer immunotherapy according to the method of claim 32.

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