Reducing statistical bias in gene sampling

Abstract

Provided herein are methods, systems, and storage media that may be useful, for example, in sequencing (e.g., by NGS) polymorphic alleles, such as detecting loss of heterozygosity (LOH) in human leukocyte antigen (HLA) genes or other polymorphic human genes. In some embodiments, the methods and systems include obtaining observed allele frequencies and observed binding propensities of the alleles to one or more bait molecules, and then applying an optimization model to determine adjusted allele frequencies that take these binding propensities into account, thereby adjusting for and / or minimizing any potential biases underlying different allele:bait binding propensities in determining the allele frequencies.

Description

[Technical Field]

[0001] Cross-reference of related applications This application claims the benefits of U.S. Provisional Application No. 62 / 982,677, filed on 27 February 2020, and U.S. Provisional Application No. 63 / 093,015, filed on 16 October 2020, each of which is incorporated herein by reference in whole.

[0002] This application relates to mitigating sampling bias in genomic testing using a hybrid capture process. [Background technology]

[0003] Genomic testing may be able to identify patients who are more likely to respond to specific treatments. Some genomic testing approaches use a hybrid capture step to enrich sequence reads at, for example, specific loci of interest (Frampton, GM et al. (2013) Nat. Biotechnol. 31:1023-1031). However, when hybrid capture is applied to polymorphic alleles, differences in allele binding to specific capture probes can introduce bias into the sampling. Accurate genomic profiling requires unbiased sequencing and allele frequency counting, regardless of specific polymorphisms.

[0004] One area where genomic testing is applied is determining personalized genomic information for cancer treatment. Highly polymorphic alleles can present challenges in obtaining accurate and comprehensive genomic information. For example, some tumors are characterized by loss of the HLA-I allele (called loss of heterozygosity or LOH). Whether a tumor has experienced LOH at a particular genetic location can be an important clinical fact, especially if that location is associated with an important biological function (e.g., the human immune system or other functions). For example, LOH at one or more HLA-I alleles may lead to fewer neoantigens being presented to the immune system, potentially resulting in immune evasion of the tumor. Furthermore, various gene loci may be modified by copy loss LOH (i.e., loss of one allele) or copy neutral LOH (i.e., one allele is lost, but the other is replicated, resulting in no net change in copy number).

[0005] Immunotherapy has revolutionized current treatments for patients with advanced cancer. Some therapies provide or stimulate an immune response against cancer (e.g., cell-based therapies), while others are thought to reactivate the patient's own T-cell-mediated immune response (e.g., immune checkpoint inhibitors or ICIs) [Reck, M., et al. N Engl J Med 375, 1823-1833 (2016), Hellmann, MD, et al. N Engl J Med 378, 2093-2104 (2018), Nghiem, PT, et al. N Engl J Med 374, 2542-2552 (2016), Robert, C., et al. N Engl J Med 372, 2521-2532 (2015), Le, DT, et al. N Engl J Med 372, 2509-2520 (2015)]. The adaptive immune system recognizes tumor cells via CD8+ T cells through the presentation of tumor-specific mutant peptides (neoantigens) displayed on major histocompatibility complex class I (MHC-I) proteins encoded by human leukocyte antigen class I (HLA-I). [Mok, TSK, et al. Lancet 393, 1819-1830 (2019), Schumacher, TN & Schreiber, RDScience 348, 69-74 (2015), Turajlic, S., et al. Lancet Oncol 18, 1009-1021 (2017)]. From this perspective, it might intuitively seem that tumors with increased tumor mutational burden (TMB) are more likely to be targeted by ICI-mediated immune stimulation because they have a greater number of potential neoantigens available for presentation [Hellmann, MD, et al. N Engl J Med 378, 2093-2104 (2018), Le, DT, et al. N Engl J Med 372, 2509-2520 (2015), Rizvi, NA, et al. Science 348, 124-128 (2015)], but this is not always the case. For example, in a clinical trial focused on non-small cell lung cancer (NSCLC), TMB was not a good predictor of patient survival.However, efforts to use HLA genotyping to predict the relative efficiency of neoantigen presentation and to use this information in conjunction with TMB to predict checkpoint responses show promise (Goodman AM, et al. Genome Med. 2020;12(1):45, Shim JH, et al. Ann Oncol. 2020;31(7):902-11). Responses to immunotherapies, such as ICI treatment, are known to vary among patients. Further methods and systems are needed to obtain unbiased polymorphic allele sequences and frequencies (e.g., predicting responsiveness to immunotherapy and rapidly stratifying patients for the potentially most effective treatment) to ensure that each patient receives the treatment most likely to be effective against their specific tumor. [Prior art documents] [Non-patent literature]

[0006] [Non-Patent Document 1] Frampton, GMet al. (2013) Nat.Biotechnol.31:1023-1031 [Non-Patent Document 2] Reck, M., et al.N Engl J Med 375,1823-1833(2016) [Non-Patent Document 3] Hellmann,MD,et al.N Engl J Med 378,2093-2104(2018) [Non-Patent Document 4] Nghiem,PT,et al.N Engl J Med 374,2542-2552(2016) [Non-Patent Document 5] Robert,C.,et al.N Engl J Med 372,2521-2532(2015) [Non-Patent Document 6] Le,DT,et al.N Engl J Med 372,2509-2520(2015) [Non-Patent Document 7] Mok, T. S. K., et al. Lancet 393, 1819 - 1830 (2019) [Non - Patent Document 8] Schumacher, T. N. & Schreiber, R. D. Science 348, 69 - 74 (2015) [Non - Patent Document 9] Turajlic, S., et al. Lancet Oncol 18, 1009 - 1021 (2017) [Non - Patent Document 10] Rizvi, N. A., et al. Science 348, 124 - 128 (2015) [Non - Patent Document 11] Goodman AM, et al. Genome Med. 2020;12(1):45 [Non - Patent Document 12] Shim JH, et al. Ann Oncol. 2020;31(7):902 - 11) [Summary of the Invention] [Means for Solving the Problems]

[0007] Therefore, in this specification, methods and systems for reducing sampling bias introduced by hybrid capture are provided. These methods and systems consider and reduce biases that may be introduced when obtaining genomic data related to polymorphic alleles, for example, resulting from hybrid capture of polynucleotides for sequencing.

[0008] For example, one of the highly polymorphic loci of the human genome that is important for personalized therapeutic approaches is the HLA-I locus. Stratifying potential immunotherapy patients using LOH at the HLA-I locus may allow for the identification of patients most likely to respond to immune reactivation therapies such as ICIs. As demonstrated herein, somatic loss of HLA-I has been shown to be a negative predictor of patient survival in ICI-treated NSCLC, which blunts the effects of high TMB. The landscape of somatic HLA-I LOH in more than 83,000 patient samples across 59 disease groups has also been determined, finding a 17% pan-cancer morbidity, as well as a significant enrichment in tumors with high TMB and inflammatory tumors represented by PD-L1 expression. Combining TMB and HLA-I LOH may allow for better selection of patients most likely to benefit from ICIs in inflammatory cancers, which is significant for the design of personalized cancer vaccines. Other loci known to be involved in LOH events are also described herein.

[0009] The method described herein includes identifying a plurality of chemical reactions such that each reaction corresponds to a bait molecule that binds to a different allele of a polymorphic gene, each reaction results in the capture of the corresponding allele fraction, the plurality of chemical reactions consist of a first subset of reactions and a second subset of reactions, the first and second subsets do not share a common reaction, and each of the first and second subsets contains at least one chemical reaction; identifying a plurality of equations that collectively relate the binding tendencies of each chemical reaction and the allele fractions of each captured allele; empirically identifying the relative binding tendencies of the first subset of the plurality of chemical reactions; and identifying the relative binding tendencies of the second subset by minimizing the total error.

[0010] In some embodiments, minimizing the total error is constrained by the requirement that the median of the relative coupling tendencies is equal to 1.

[0011] In some embodiments, one relative coupling tendency is set to equal 1.

[0012] In some embodiments, minimizing the total error includes performing a least-squares procedure.

[0013] In some embodiments, the method further includes performing a hybrid capture process to measure the raw allele frequencies in a patient's DNA sample, and scaling the measured raw allele frequencies using first and second subsets of relative binding tendencies to reduce sampling bias.

[0014] In some embodiments, the polymorphic genes include human leukocyte antigen genes. In some embodiments, the polymorphic genes include ST7 / RAY1, ARH1 / NOEY2, TSLC1, RB, PTEN, SMAD2, SMAD4, DCC, TP53, ATM, miR-15a, miR-16-1, NAT2, BRCA1, BRCA2, hOGG1, CDH1, IGF2, CDKN1C / P57, MEN1, PRKAR1A, H19, KRAS, BAP1, PTCH1, SMO, SUFU, NOTCH1, PPP6C, LATS1, CASP8, PTPN14, ARID1A, FBXW7, M6P / IGF2R, IFN-α, olfactory receptor genes, CBFA2T3, DUTT1, FHIT, APC, P1 6, FCMD, TSC2, miR-34, c-MPL, RUNX3, DIRAS3, NRAS, miR-9, FAM50B, PLAGL1, ER, FLT3, ZDBF2, GPR1, c-KIT, NAP1L5, GRB10, EGFR, PEG10, BRAF, MEST, JAK2, DAPK1, LIT1, WT1, NF-1, PR, c-CBL, DLK1, AKT1, SNURF, cytochrome P450 gene (CYP), ZNF587, SOCS1, TIMP2, RUNX1, AR, CEBPA, C19MC, EMP3, ZNF331, CDKN2A, PEG3, NNAT, GNAS, or GATA5.

[0015] In some embodiments, the method further includes determining whether the patient has experienced a loss of heterozygosity.

[0016] Furthermore, the system described herein includes one or more processors and a memory configured to store one or more computer program instructions, wherein when one or more computer program instructions are executed by one or more processors, the system identifies a plurality of chemical reactions such that each reaction corresponds to a bait molecule that binds to a different allele of a polymorphic gene, each reaction results in the capture of a corresponding allele fraction, the plurality of chemical reactions consist of a first subset of reactions and a second subset of reactions, the first and second subsets do not share a common reaction, and the first and second subsets each contain at least one chemical reaction, the system identifies a plurality of equations that collectively relate the binding tendencies of each chemical reaction and the allele fractions of each captured allele, receives the empirically identified relative binding tendencies of the first subset of the plurality of chemical reactions, and identifies the relative binding tendencies of the second subset by minimizing the total error.

[0017] In some embodiments of the system, minimizing the total error is constrained by the requirement that the median of the relative coupling tendencies is equal to 1.

[0018] In some embodiments of the system, one relative coupling tendency is set to equal 1.

[0019] In some embodiments of the system, minimizing the total error includes performing a least-squares procedure.

[0020] In some embodiments of the system, the method further includes receiving measured raw allele frequencies in a patient's DNA sample in one or more processors, wherein the measured raw allele frequencies are measured by performing a hybrid capture process, and scaling the measured raw allele frequencies using first and second subsets of relative binding tendencies in one or more processors, thereby reducing sampling bias.

[0021] In some embodiments of the system, the polymorphic genes include human leukocyte antigen genes. In some embodiments of the system, the polymorphic genes include ST7 / RAY1, ARH1 / NOEY2, TSLC1, RB, PTEN, SMAD2, SMAD4, DCC, TP53, ATM, miR-15a, miR-16-1, NAT2, BRCA1, BRCA2, hOGG1, CDH1, IGF2, CDKN1C / P57, MEN1, PRKAR1A, H19, KRAS, BAP1, PTCH1, SMO, SUFU, NOTCH1, PPP6C, LATS1, CASP8, PTPN14, ARID1A, FBXW7, M6P / IGF2R, IFN-α, olfactory receptor genes, CBFA2T3, DUTT1, FHIT, APC, These are P16, FCMD, TSC2, miR-34, c-MPL, RUNX3, DIRAS3, NRAS, miR-9, FAM50B, PLAGL1, ER, FLT3, ZDBF2, GPR1, c-KIT, NAP1L5, GRB10, EGFR, PEG10, BRAF, MEST, JAK2, DAPK1, LIT1, WT1, NF-1, PR, c-CBL, DLK1, AKT1, SNURF, cytochrome P450 gene (CYP), ZNF587, SOCS1, TIMP2, RUNX1, AR, CEBPA, C19MC, EMP3, ZNF331, CDKN2A, PEG3, NNAT, GNAS, or GATA5.

[0022] In some embodiments of the system, the method further includes determining, using one or more processors, whether the patient has experienced a loss of heterozygosity.

[0023] Certain aspects of the present disclosure relate to methods for determining allele frequencies. In some embodiments, the method includes: a) receiving observed allele frequencies for an allele of a gene in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the allele detected among a plurality of sequence reads corresponding to the gene, and the plurality of sequence reads are obtained by sequencing a gene or a portion thereof captured by hybridization with a bait molecule; b) receiving relative binding tendencies of the allele to a bait molecule in one or more processors, wherein the relative binding tendencies of the allele correspond to the tendency of nucleic acids encoding at least a portion of the allele to bind to a bait molecule in the presence of nucleic acids encoding portions of one or more other alleles of the gene; c) running an objective function in one or more processors to measure the difference between the relative binding tendencies of the allele and the observed allele frequencies; d) running an optimization model in one or more processors to minimize the objective function; and e) determining an adjusted allele frequency of the allele in one or more processors based on the optimization model and the observed allele frequencies.

[0024] In some embodiments, the optimization model is a least-squares optimization model. In some embodiments, the optimization model is subject to one or more constraints. In some embodiments, one or more constraints require that the median relative binding tendency for multiple alleles of a gene is equal to 1. In some embodiments, the observed allele frequency corresponds to the relative frequency of nucleic acids encoding at least some of the alleles detected among multiple sequence reads, compared to a reference value. In some embodiments, the reference value is the total number of sequence reads. In some embodiments, the reference value is the number of sequence reads corresponding to a reference gene.

[0025] In some embodiments according to any of the embodiments described herein, the gene is a human leukocyte antigen (HLA) gene encoding a major histocompatibility (MHC) class I molecule. In some embodiments, the gene is ST7 / RAY1, ARH1 / NOEY2, TSLC1, RB, PTEN, SMAD2, SMAD4, DCC, TP53, ATM, miR-15a, miR-16-1, NAT2, BRCA1, BRCA2, hOGG1, CDH1, IGF2, CDKN1C / P57, MEN1, PRKAR1A, H19, KRAS, BAP1, PTCH1, SMO, SUFU, NOTCH1, PPP6C, LATS1, CASP8, PTPN14, ARID1A, FBXW7, M6P / IGF2R, IFN-α, olfactory receptor gene, CBFA2T3, DUTT1, FHIT, APC, P16 These include FCMD, TSC2, miR-34, c-MPL, RUNX3, DIRAS3, NRAS, miR-9, FAM50B, PLAGL1, ER, FLT3, ZDBF2, GPR1, c-KIT, NAP1L5, GRB10, EGFR, PEG10, BRAF, MEST, JAK2, DAPK1, LIT1, WT1, NF-1, PR, c-CBL, DLK1, AKT1, SNURF, cytochrome P450 gene (CYP), ZNF587, SOCS1, TIMP2, RUNX1, AR, CEBPA, C19MC, EMP3, ZNF331, CDKN2A, PEG3, NNAT, GNAS, or GATA5. In some embodiments, the method further includes determining the adjusted allele frequency and then determining, at least in part, that the gene has undergone loss of heterozygosity (LOH). In some embodiments, multiple sequence reads were obtained by performing next-generation sequencing (NGS), whole-exome sequencing, or methylation sequencing on nucleic acids captured by hybridization with a bait molecule. In some embodiments, the method further comprises sequencing multiple polynucleotides by next-generation sequencing (NGS), whole-exome sequencing, or methylation sequencing to obtain multiple sequence reads before obtaining observed allele frequencies, wherein the multiple polynucleotides include nucleic acids encoding at least a portion of the alleles.In some embodiments, the method further comprises contacting a mixture of polynucleotides with a bait molecule under conditions suitable for hybridization, before sequencing the plurality of polynucleotides, wherein the mixture contains a plurality of polynucleotides that can hybridize with the bait molecule; and isolating the plurality of polynucleotides hybridized with the bait molecule, wherein the isolated plurality of polynucleotides are sequenced. In some embodiments, the method further comprises obtaining a sample from an organism before contacting the mixture of polynucleotides with a bait molecule, wherein the sample contains tumor cells and / or tumor nucleic acids; and extracting a mixture of polynucleotides from the sample, wherein the mixture of polynucleotides is from tumor cells and / or tumor nucleic acids. In some embodiments, the sample further comprises non-tumor cells. In some embodiments, the sample comprises a fluid, cells, or tissue. In some embodiments, the sample comprises blood or plasma. In some embodiments, the sample comprises a tumor biopsy or circulating tumor cells. In some embodiments, the sample from an organism is a nucleic acid sample. In some embodiments, the nucleic acid sample includes mRNA, genomic DNA, circulating tumor DNA, cell-free DNA, or cell-free RNA. In some embodiments, the method further includes obtaining the observed allele frequency for each of two or more alleles of a gene, wherein the observed allele frequency corresponds to the frequency of nucleic acids encoding at least a portion of each allele detected among a plurality of sequence reads corresponding to the gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule; obtaining the relative binding tendency of each of the two or more alleles to the bait molecule, wherein the second allele of the two or more alleles has a lower relative binding tendency to the bait molecule than the first allele of the two or more alleles; and identifying the second bait molecule, wherein the second allele of the two or more alleles has a higher relative binding tendency to the second bait molecule than the first bait molecule.In some embodiments, the second bait molecule includes a sequence complementary to at least a portion of the second allele of two or more alleles.

[0026] Furthermore, in some other embodiments, a method for selecting a bait molecule is provided herein, which includes obtaining observed allele frequencies for two or more alleles of a gene, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of each allele detected among a plurality of sequence reads corresponding to the gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a first bait molecule; obtaining the relative binding tendency of two or more alleles of a gene to a first bait molecule, wherein the second allele of the two or more alleles has a lower relative binding tendency to the first bait molecule than the first allele of the two or more alleles; and identifying or selecting a sequence of the second bait molecule, wherein the second allele of the two or more alleles has a higher relative binding tendency to the second bait molecule than the first bait molecule. In some embodiments, the second bait molecule includes a sequence complementary to at least a portion of the second allele of the two or more alleles of the gene. In some embodiments, the second bait molecule includes a sequence that is at least partially based on the sequences of the second and third alleles of two or more alleles of a gene, wherein the second and third alleles have a lower relative binding tendency to the first bait molecule than the first allele.

[0027] In some other embodiments, non-temporary computer-readable storage media are provided herein. In some embodiments, the non-temporary computer-readable storage media includes one or more programs executed by one or more processors of a device, and the one or more programs include instructions that, when executed by one or more processors, cause the device to perform a method according to any of the embodiments described herein.

[0028] Furthermore, in some other embodiments, methods for detecting loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes are provided herein. In some embodiments, the method is a) receiving observed allele frequencies for an HLA allele in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA allele detected among a plurality of sequence reads corresponding to the HLA gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule; and b) receiving the relative binding tendency of an HLA allele to a bait molecule in one or more processors, wherein the relative binding tendency of an HLA allele corresponds to a portion of one or more other HLA alleles The method includes: receiving, in the presence of encoding nucleic acids, corresponding to the tendency of nucleic acids encoding at least a portion of an HLA allele to bind to a bait molecule; a) running an objective function by one or more processors to measure the difference between the relative binding tendency of the HLA alleles and the observed allele frequency; d) running an optimization model by one or more processors to minimize the objective function; e) determining a tuned allele frequency of the HLA allele based on the optimization model and the observed allele frequency; and f) determining that LOH has occurred if the tuned allele frequency of the HLA allele is less than a predetermined threshold by one or more processors. In some embodiments, the HLA gene is a human HLA-A, HLA-B, or HLA-C gene. In some embodiments, multiple sequence reads are obtained by sequencing nucleic acids obtained from a sample containing tumor cells and / or tumor nucleic acids. In some embodiments, the sample further includes non-tumor cells.

[0029] Furthermore, in some other embodiments, methods for identifying individuals with cancer are provided herein, where loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes indicates that individuals with certain types of disease tend to respond to certain treatments. In some embodiments, the method comprises detecting LOH of HLA genes in a sample from an individual, where LOH of HLA genes is detected according to a method according to any embodiment described herein. In some embodiments, LOH of HLA genes in a sample indicates that the individual is not likely to benefit from treatments including ICIs. In some embodiments, the detection of the absence of LOH of HLA genes in a sample indicates that the individual is likely to benefit from treatments including ICIs. In some embodiments, the method further comprises detecting tumor mutagenesis (TMB) in a sample obtained from an individual. In some embodiments, the method further comprises obtaining knowledge of high tumor mutagenesis (TMB) in a sample obtained from an individual. In some embodiments, LOH of HLA genes and high TMB indicate that the individual is likely to benefit from treatments including ICIs. In some embodiments, LOH of HLA genes without low TMB or high TMB indicates that individuals are less likely to benefit from treatments including ICIs.

[0030] Furthermore, in some other embodiments, methods for selecting a therapy for an individual with cancer are provided herein. In some embodiments, the method comprises detecting loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from an individual, wherein LOH of the HLA genes is detected according to a method according to any embodiment described herein. In some embodiments, LOH of the HLA genes in a sample indicates that the individual is unlikely to benefit from a therapy including an ICI. In some embodiments, detecting the absence of LOH of the HLA genes in a sample indicates that the individual is likely to benefit from a therapy including an ICI. In some embodiments, the method further comprises detecting tumor mutagenesis (TMB) in a sample obtained from an individual. In some embodiments, the method further comprises obtaining knowledge of high tumor mutagenesis (TMB) in a sample obtained from an individual. In some embodiments, LOH of the HLA genes and high TMB indicate that the individual is likely to benefit from a therapy including an ICI. In some embodiments, LOH of the HLA genes and low TMB, or LOH of the HLA genes without high TMB, indicates that the individual is unlikely to benefit from a therapy including an ICI.

[0031] Furthermore, in some other embodiments, methods for identifying one or more treatment options for an individual with cancer are provided herein. In some embodiments, the method includes (a) acquiring knowledge of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from an individual, wherein the LOH of HLA genes is detected and acquired according to a method according to any embodiment described herein, and (b) generating a report that includes one or more treatment options identified for the individual, at least in part on such knowledge. In some embodiments, the LOH of HLA genes in a sample indicates that the individual is unlikely to benefit from treatments including ICIs. In some embodiments, the one or more treatment options do not include treatments including ICIs. In some embodiments, the method includes (a) acquiring knowledge of the absence of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from an individual, wherein the absence of LOH of HLA genes is detected and acquired according to a method according to any embodiment described herein, and (b) generating a report that includes one or more treatment options identified for the individual, at least in part on such knowledge. In some embodiments, the absence of LOH in an HLA gene in a sample indicates that the individual is likely to benefit from treatment including an ICI. In some embodiments, the method further includes detecting tumor mutational burden (TMB) in a sample obtained from an individual. In some embodiments, LOH and high TMB in an HLA gene indicate that the individual is likely to benefit from treatment including an ICI. In some embodiments, LOH and low TMB in an HLA gene, or LOH in an HLA gene without high TMB, indicates that the individual is not likely to benefit from treatment including an ICI. In some embodiments, the method further includes gaining knowledge of high TMB in a sample from an individual, where one or more treatment options include treatment including an ICI.

[0032] Furthermore, in some other embodiments, methods for selecting treatment for individuals with cancer are provided herein. In some embodiments, the method comprises acquiring knowledge of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from an individual with cancer, wherein LOH of the HLA genes is detected according to a method according to any embodiment described herein. In some embodiments, in response to acquiring such knowledge, (i) the individual is classified as a candidate not to receive treatment with an immune checkpoint inhibitor (ICI), (ii) the individual is identified as not likely to respond to treatment including an immune checkpoint inhibitor (ICI), and / or (iii) the individual is classified as a candidate to receive treatment other than an immune checkpoint inhibitor (ICI). In some embodiments, the method comprises acquiring knowledge of the absence of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from an individual with cancer, wherein the absence of LOH of the HLA genes is detected according to a method according to any embodiment described herein. In some embodiments, in response to acquiring such knowledge, (i) the individual is classified as a candidate for treatment with an immune checkpoint inhibitor (ICI), and / or (ii) the individual is identified as likely to respond to treatment including an immune checkpoint inhibitor (ICI). In some embodiments, the method includes acquiring knowledge of the LOH of the human leukocyte antigen (HLA) gene in a sample obtained from the individual, and acquiring knowledge of high tumor mutagenesis (TMB) in a sample obtained from the individual. In some embodiments, in response to acquiring such knowledge, (i) the individual is classified as a candidate for treatment with an immune checkpoint inhibitor (ICI), and / or (ii) the individual is identified as likely to respond to treatment including an immune checkpoint inhibitor (ICI).

[0033] Furthermore, in some other embodiments, methods for predicting the survival time of individuals with cancer treated with immune checkpoint inhibitors (ICIs) are provided herein. In some embodiments, the method comprises acquiring knowledge of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from the individual, wherein LOH of the HLA genes is detected according to a method according to any embodiment described herein. In some embodiments, in response to acquiring such knowledge, the individual is predicted to have a shorter survival time after ICI treatment compared to the survival time of individuals treated with ICIs in which the cancer does not exhibit LOH of the HLA genes. In some embodiments, the method comprises acquiring knowledge of the absence of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from the individual, wherein the absence of LOH of the HLA genes is detected according to a method according to any embodiment described herein. In some embodiments, in response to acquiring such knowledge, the individual is predicted to have a longer survival time after ICI treatment compared to the survival time of individuals treated with ICIs in which the cancer does not exhibit LOH of the HLA genes. In some embodiments, the method comprises acquiring knowledge of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from an individual and acquiring knowledge of high tumor mutagenesis (TMB) in a sample obtained from an individual, wherein LOH of the HLA gene is detected according to the method of any embodiment described herein. In some embodiments, in response to acquiring such knowledge, it is predicted that the individual will have a longer survival period after treatment with an ICI compared to the survival period of an individual treated with an ICI in which the cancer has LOH of the HLA gene without high TMB.

[0034] In some other embodiments, a method for monitoring individuals with cancer is provided herein, comprising acquiring knowledge of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from the individual, wherein LOH of the HLA gene is detected according to a method according to any embodiment described herein, and in response to acquiring such knowledge, the individual is predicted to have an increased risk of recurrence compared to an individual whose cancer does not exhibit LOH of the HLA gene.

[0035] In some other embodiments, a method for screening individuals with cancer is provided herein, comprising acquiring knowledge of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from the individual, wherein LOH of the HLA gene is detected according to a method according to any of the embodiments described herein, and in response to acquiring such knowledge, the individual is predicted to have an increased risk of recurrence compared to an individual that does not show LOH of the HLA gene.

[0036] Furthermore, in some other embodiments, a method for evaluating individuals with cancer is provided herein, comprising acquiring knowledge of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from the individual, wherein LOH of the HLA gene is detected according to a method according to any of the embodiments described herein, and LOH of the HLA gene is identified as indicating that the cancer has an increased risk of recurrence in the individual compared to an individual that does not exhibit LOH of the HLA gene.

[0037] In some embodiments according to any of the embodiments described herein, the LOH of an HLA gene is obtained by: a) receiving the allele frequency observed for an HLA allele in one or more processors, wherein the observed allele frequency corresponds to the frequency of nucleic acids encoding at least a portion of the HLA allele detected among a plurality of sequence reads corresponding to the HLA gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule; and b) receiving the relative binding tendency of an HLA allele to a bait molecule in one or more processors, wherein the relative binding tendency of an HLA allele corresponds to one or more other HLA This is determined by: receiving a nucleic acid that codes for at least a portion of an HLA allele in the presence of a nucleic acid that codes for a portion of an A allele, corresponding to the tendency of the nucleic acid that codes for at least a portion of an HLA allele to bind to a bait molecule; c) running an objective function by one or more processors to measure the difference between the relative binding tendency of the HLA allele and the observed allele frequency; d) running an optimization model configured to minimize the objective function by one or more processors; e) determining a tuned allele frequency of the HLA allele based on the optimization model and the observed allele frequency by one or more processors; and f) determining that a LOH has occurred if the tuned allele frequency of the HLA allele is smaller than a predetermined threshold.

[0038] Furthermore, in some other embodiments, methods for treating cancer or slowing the progression of cancer are provided herein. In some embodiments, the method is to (1) detect loss of heterozygosity (LOH) of a human leukocyte antigen (HLA) gene in a sample obtained from an individual, wherein the LOH of the HLA gene is obtained by a) receiving observed allele frequencies for an HLA allele in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA allele detected among a plurality of sequence reads corresponding to the HLA gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding a gene or a portion thereof captured by hybridization with a bait molecule, and b) receiving the relative binding tendency of the HLA allele to a bait molecule in one or more processors, wherein the relative binding tendency of the HLA allele is obtained by sequencing nucleic acids encoding a portion of one or more other HLA alleles (2) Detecting LOH of an HLA gene, which is detected by receiving, a) running an objective function by one or more processors to measure the difference between the relative binding tendency of the HLA allele and the observed allele frequency, and a) running an optimization model by one or more processors to minimize the objective function, a) determining a tuned allele frequency of the HLA allele based on the optimization model and the observed allele frequency, and a) determining that LOH has occurred if the tuned allele frequency of the HLA allele is less than a predetermined threshold by one or more processors, and (2) administering to the individual an effective dose of a non-immune checkpoint inhibitor (ICI) treatment, at least in part, based on the detection of LOH of an HLA gene.In some embodiments, the method involves (1) detecting the absence of loss of heterozygosity (LOH) of a human leukocyte antigen (HLA) gene in a sample obtained from an individual, wherein the absence of LOH of the HLA gene is obtained by a) receiving the observed allele frequency for an HLA allele in one or more processors, wherein the observed allele frequency corresponds to the frequency of nucleic acids encoding at least a portion of the HLA allele detected among a plurality of sequence reads corresponding to the HLA gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule, and b) receiving the relative binding tendency of the HLA allele to the bait molecule in one or more processors, wherein the relative binding tendency of the HLA allele is obtained in the presence of nucleic acids encoding a portion of one or more other HLA alleles. (2) Detecting LOH of an HLA gene, which is detected by (1) receiving, (2) administering an effective amount of an immune checkpoint inhibitor (ICI) to an individual, at least partially based on the detection of the absence of LOH of an HLA gene, which corresponds to the tendency of nucleic acids encoding at least part to bind to a bait molecule; (3) running an objective function by one or more processors to measure the difference between the relative binding tendency of the HLA allele and the observed allele frequency; (4) running an optimization model by one or more processors to minimize the objective function; (5) determining a tuned allele frequency of the HLA allele based on the optimization model and the observed allele frequency; and (6) determining that LOH did not occur if the tuned allele frequency of the HLA allele is greater than a predetermined threshold.In some embodiments, the method involves (1) detecting loss of heterozygosity (LOH) of a human leukocyte antigen (HLA) gene in a sample obtained from an individual, wherein the LOH of the HLA gene is obtained by a) receiving the allele frequency observed for an HLA allele in one or more processors, wherein the observed allele frequency corresponds to the frequency of nucleic acids encoding at least a portion of the HLA allele detected among a plurality of sequence reads corresponding to the HLA gene, and the plurality of sequence reads are obtained by sequencing the HLA gene or nucleic acids encoding a portion thereof captured by hybridization with a bait molecule, and b) receiving the relative binding tendency of the HLA allele to the bait molecule in one or more processors, wherein the relative binding tendency of the HLA allele indicates that, in the presence of nucleic acids encoding a portion of one or more other HLA alleles, the nucleic acids encoding at least a portion of the HLA allele bind to the bait molecule. (3) detecting LOH of HLA genes by receiving, corresponding to the tendency, a) running an objective function by one or more processors to measure the difference between the relative binding tendency of HLA alleles and the observed allele frequency, d) running an optimization model by one or more processors to minimize the objective function, e) determining the adjusted allele frequency of the HLA allele based on the optimization model and the observed allele frequency by one or more processors, f) determining that LOH has occurred if the adjusted allele frequency of the HLA allele is less than a predetermined threshold by one or more processors, and g) acquiring or detecting knowledge of high tumor mutational burden (TMB) in a sample obtained from an individual, and (3) administering to the individual an effective dose of a treatment including an immune checkpoint inhibitor (ICI) at least in part based on the detection of LOH of HLA genes and high TMB.

[0039] In some other embodiments, immune checkpoint inhibitors (ICIs) for use in treating or slowing the progression of cancer in an individual are provided herein, wherein loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample obtained from an individual is obtained by a) receiving observed allele frequencies for HLA alleles in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA alleles detected among a plurality of sequence reads corresponding to the HLA gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule, and b) receiving the relative binding tendency of the HLA alleles to the bait molecule in one or more processors. The detection is performed by: (a) receiving the relative binding tendency of an HLA allele, which corresponds to the tendency of a nucleic acid encoding at least a portion of an HLA allele to bind to a bait molecule in the presence of nucleic acids encoding a portion of one or more other HLA alleles; (b) running an objective function with one or more processors to measure the difference between the relative binding tendency of the HLA allele and the observed allele frequency; (d) running an optimization model with one or more processors to minimize the objective function; (e) determining the adjusted allele frequency of the HLA allele based on the optimization model and the observed allele frequency with one or more processors; and (f) determining that LOH did not occur if the adjusted allele frequency of the HLA allele is greater than a predetermined threshold with one or more processors.

[0040] In some other embodiments, immune checkpoint inhibitors (ICIs) for use in the manufacture of drugs to treat or slow the progression of cancer in an individual are provided herein, wherein loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample obtained from an individual is obtained by a) receiving observed allele frequencies for HLA alleles in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA alleles detected among a plurality of sequence reads corresponding to the HLA gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule, and b) receiving the relative binding tendency of the HLA alleles to the bait molecule in one or more processors. The detection is performed by: (a) receiving the relative binding tendency of an HLA allele, which corresponds to the tendency of at least one nucleic acid encoding a portion of an HLA allele to bind to a bait molecule in the presence of nucleic acids encoding a portion of one or more other HLA alleles; (b) running an objective function with one or more processors to measure the difference between the relative binding tendency of the HLA allele and the observed allele frequency; (d) running an optimization model with one or more processors to minimize the objective function; (e) determining the adjusted allele frequency of the HLA allele based on the optimization model and the observed allele frequency with one or more processors; and (f) determining that LOH did not occur if the adjusted allele frequency of the HLA allele is greater than a predetermined threshold with one or more processors.

[0041] In some other embodiments, immune checkpoint inhibitors (ICIs) for use in the manufacture of drugs to treat or slow the progression of cancer in an individual are provided herein, wherein in a sample obtained from an individual, loss of heterozygosity (LOH) and high tumor mutagenesis (TMB) of human leukocyte antigen (HLA) genes are obtained by: a) receiving observed allele frequencies for HLA alleles in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA alleles detected among a plurality of sequence reads corresponding to the HLA gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule; and b) receiving the relative binding tendency of the HLA alleles to the bait molecule in one or more processors, H (f) if the relative binding tendency of an LA allele corresponds to the tendency of a nucleic acid encoding at least a portion of an HLA allele to bind to a bait molecule in the presence of nucleic acids encoding a portion of one or more other HLA alleles, (g) receiving, (a) running an objective function with one or more processors to measure the difference between the relative binding tendency of the HLA allele and the observed allele frequency, (d) running an optimization model with one or more processors to minimize the objective function, (e) determining the adjusted allele frequency of the HLA allele based on the optimization model and the observed allele frequency with one or more processors, (f) determining that LOH has occurred if the adjusted allele frequency of the HLA allele is greater than a predetermined threshold with one or more processors, and (g) detecting a high tumor mutational burden (TMB) in a sample obtained from an individual.

[0042] Furthermore, in some other embodiments, one or more processors are used to receive observed allele frequencies for an HLA allele, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA allele detected among multiple sequence reads corresponding to the HLA gene, and the multiple sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule; and one or more processors are used to receive the relative binding tendency of an HLA allele to a bait molecule, wherein the relative binding tendency of an HLA allele indicates that, in the presence of nucleic acids encoding a portion of one or more other HLA alleles, the nucleic acids encoding at least a portion of the HLA allele bind to the bait molecule. Provided herein is a non-temporary computer-readable storage medium comprising one or more programs executable by one or more computer processors for performing a method that includes receiving, using one or more processors to execute an objective function to measure the difference between the relative binding tendency of HLA alleles and the observed allele frequency, using one or more processors to execute an optimization model to minimize the objective function, using one or more processors to determine a tuned allele frequency of an HLA allele based on the optimization model and the observed allele frequency, and using one or more processors to determine that a LOH has occurred if the tuned allele frequency of an HLA allele is less than a predetermined threshold.In some other embodiments, a system is provided herein comprising one or more processors and a memory configured to store one or more computer program instructions, wherein one or more computer program instructions, when executed by one or more processors, determine the allele frequency observed for an HLA allele, wherein the observed allele frequency corresponds to the frequency of nucleic acids encoding at least a portion of the HLA allele detected among a plurality of sequence reads corresponding to an HLA gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding a gene or a portion thereof captured by hybridization with a bait molecule, and the bait The system is configured to determine the relative binding tendency of an HLA allele to a bait molecule, such that the relative binding tendency of an HLA allele corresponds to the tendency of at least one nucleic acid encoding a portion of an HLA allele to bind to a bait molecule in the presence of nucleic acids encoding portions of one or more other HLA alleles; to perform an objective function to measure the difference between the relative binding tendency of the HLA allele and the observed allele frequency; to perform an optimization model to minimize the objective function; to determine a tuned allele frequency of the HLA allele based on the optimization model and the observed allele frequency; and to determine that LOH has occurred if the tuned allele frequency of the HLA allele is less than a predetermined threshold. In some embodiments, the HLA gene is a human HLA-A, HLA-B, or HLA-C gene. In some embodiments, multiple sequence reads are obtained by sequencing nucleic acids obtained from a sample containing tumor cells and / or tumor nucleic acids. In some embodiments, the sample further includes non-tumor cells. In some embodiments, the sample is from a tumor biopsy or tumor specimen. In some embodiments, the sample includes cell-free DNA (cfDNA) of the tumor. In some embodiments, the sample includes a fluid, cells, or tissue. In some embodiments, the sample includes blood or plasma. In some embodiments, the sample includes a tumor biopsy or circulating tumor cells. In some embodiments, the sample is a nucleic acid sample. In some embodiments, the nucleic acid sample includes mRNA, genomic DNA, circulating tumor DNA, cell-free DNA, or cell-free RNA.In some embodiments, the method further comprises using one or more processors to acquire or detect knowledge of tumor mutational burden (TMB) from multiple sequence reads, the multiple sequence reads being obtained by sequencing at least a portion of the nucleic acids of the genome. In some embodiments, the TMB is determined based on the number of non-driver somatic coding mutations per megabase of the sequenced genome.

[0043] In some other embodiments, methods for detecting loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes are provided herein, comprising: (1) providing a plurality of nucleic acids obtained from an individual-derived sample, wherein the plurality of nucleic acids include a nucleic acid encoding an HLA gene; (2) optionally ligating one or more adapters to one or more nucleic acids from the plurality of nucleic acids; (3) amplifying nucleic acids from the plurality of nucleic acids; (4) capturing a plurality of nucleic acids corresponding to an HLA gene, wherein the plurality of nucleic acids corresponding to an HLA gene are captured from the nucleic acids amplified by hybridization with a bait molecule; (5) sequencing the captured nucleic acids by a sequencer to obtain a plurality of sequence reads corresponding to an HLA gene; fitting one or more values ​​associated with one or more of the plurality of sequence reads to a model by one or more processors; and (6) detecting LOH of the HLA gene and relative binding tendencies for HLA alleles of the HLA gene based on the model. In some embodiments, LOH of an HLA gene and the relative binding tendency of an HLA allele of an HLA gene are detected by: a) obtaining the observed allele frequency for an HLA allele, such that the observed allele frequency corresponds to the frequency of nucleic acids encoding at least a portion of the HLA allele detected among multiple sequence reads corresponding to the HLA gene; b) obtaining the relative binding tendency of an HLA allele to a bait molecule, such that the relative binding tendency of an HLA allele corresponds to the tendency of nucleic acids encoding at least a portion of an HLA allele to bind to a bait molecule in the presence of nucleic acids encoding the portion of one or more other HLA alleles; c) applying an objective function to measure the difference between the relative binding tendency of an HLA allele and the observed allele frequency; d) applying an optimization model to minimize the objective function; e) determining the adjusted allele frequency of an HLA allele based on the optimization model and the observed allele frequency; and f) determining that LOH has occurred if the adjusted allele frequency of an HLA allele is smaller than a predetermined threshold.In some embodiments, the method further includes administering an effective dose of a non-immune checkpoint inhibitor (ICI) therapy to the individual, at least partially based on the detection of LOH of the HLA gene. In some embodiments, the method further includes recommending a non-immune checkpoint inhibitor (ICI) therapy, at least partially based on the detection of LOH of the HLA gene. In some embodiments, the method further includes detecting or acquiring knowledge of a high tumor mutagenesis (TMB) in a sample (or a second sample obtained from the individual). In some embodiments, the method further includes administering an effective dose of an immune checkpoint inhibitor (ICI) to the individual, at least partially based on the detection of LOH of the HLA gene and high TMB. In some embodiments, the method further includes recommending a therapy including an immune checkpoint inhibitor (ICI) to the individual, at least partially based on the detection of LOH of the HLA gene and high TMB. In some embodiments, the HLA gene is a human HLA-A, HLA-B, or HLA-C gene. In some embodiments, the method further includes extracting a plurality of nucleic acids from the sample prior to (1). In some embodiments, the sample comprises tumor cells and / or tumor nucleic acids. In some embodiments, the sample further comprises non-tumor cells. In some embodiments, the sample is from a tumor biopsy or tumor specimen. In some embodiments, the sample comprises tumor cell-free DNA (cfDNA). In some embodiments, the sample comprises fluid, cells, or tissue. In some embodiments, the sample comprises blood or plasma. In some embodiments, the sample comprises a tumor biopsy or circulating tumor cells. In some embodiments, the sample is a nucleic acid sample. In some embodiments, the nucleic acid sample comprises mRNA, genomic DNA, circulating tumor DNA, cell-free DNA, or cell-free RNA. In some embodiments, the TMB is determined based on the number of non-driver somatic coding mutations per megabase of the sequenced genome.

[0044] In some embodiments according to any of the embodiments described herein, the ICI comprises a PD-1 inhibitor, a PD-L1 inhibitor, or a CTLA-4 inhibitor. In some embodiments, the method further includes detecting tumor mutational burden (TMB) in a sample obtained from an individual. In some embodiments, the LOH of the HLA gene and high TMB in the sample identify the individual as one that may benefit from treatment comprising an ICI. In some embodiments, the individual is administered an effective dose of an immune checkpoint inhibitor (ICI) based at least partially on the LOH of the HLA gene and high TMB in the sample. In some embodiments, high TMB refers to a TMB of 10 mutations / Mb or greater or 13 mutations / Mb or greater. In some embodiments, the HLA gene is the HLA-I gene.

[0045] It should be understood that one, some, or all of the characteristics of the various embodiments described herein can be combined to form other embodiments of the present invention. These and other aspects of the present invention will be apparent to those skilled in the art. These and other embodiments of the present invention are further described by the following detailed description. In certain embodiments, for example, the following items are provided: (Item 1) A method for detecting loss of heterozygosity (LOH) in human leukocyte antigen (HLA) genes, To provide a plurality of nucleic acids obtained from an individual-derived sample, wherein the plurality of nucleic acids include a nucleic acid encoding an HLA gene. Selectively ligating one or more adapters to one or more nucleic acids from the plurality of nucleic acids, Amplifying nucleic acids from the aforementioned plurality of nucleic acids, The capture of multiple nucleic acids corresponding to the HLA gene, wherein the multiple nucleic acids corresponding to the HLA gene are captured from the amplified nucleic acid by hybridization with a bait molecule. The captured nucleic acid is sequenced using a sequencer to obtain multiple sequence reads corresponding to the HLA gene, One or more processors fit one or more values ​​associated with one or more of the plurality of array reads to the model, A method comprising detecting the LOH of the HLA gene and the relative binding tendency of the HLA allele of the HLA gene based on the aforementioned model. (Item 2) The relative binding tendency of the LOH of the HLA gene and the HLA allele of the HLA gene is a) Obtaining the observed allele frequency for an HLA allele, wherein the observed allele frequency corresponds to the frequency of nucleic acids encoding at least a portion of the HLA allele detected among the plurality of sequence reads corresponding to the HLA gene. b) Obtaining the relative binding tendency of the HLA allele to the bait molecule, wherein the relative binding tendency of the HLA allele corresponds to the tendency of at least a portion of the nucleic acid encoding the HLA allele to bind to the bait molecule in the presence of nucleic acids encoding a portion of one or more other HLA alleles. c) Applying an objective function to measure the difference between the relative binding tendency of the HLA allele and the observed allele frequency, d) Applying an optimization model to minimize the objective function, e) Determining the adjusted allele frequency of the HLA allele based on the optimization model and the observed allele frequency, f) The method according to item 1, wherein it is determined that LOH has occurred if the adjusted allele frequency of the HLA allele is less than a predetermined threshold, and is detected by the method described above. (Item 3) The method according to item 1 or 2, further comprising administering to the individual an effective amount of a treatment other than an immune checkpoint inhibitor (ICI), at least in part, based on the detection of LOH of the HLA gene. (Item 4) The method according to item 1 or 2, further comprising recommending a non-immune checkpoint inhibitor (ICI) treatment based at least partially on the detection of LOH of the HLA gene. (Item 5) The method according to item 1 or 2, further comprising detecting a high tumor mutational burden (TMB) in the sample or acquiring knowledge thereof. (Item 6) The method according to item 5, further comprising administering an effective amount of an immune checkpoint inhibitor (ICI) to the individual, at least in part, based on the detection of LOH and high TMB of the HLA gene. (Item 7) The method according to item 5, further comprising recommending treatment including an immune checkpoint inhibitor (ICI) to the individual, at least in part, based on the detection of LOH and high TMB of the HLA gene. (Item 8) The method according to any one of items 1 to 7, wherein the HLA gene is a human HLA-A, HLA-B, or HLA-C gene. (Item 9) The method according to any one of items 1 to 8, further comprising extracting the plurality of nucleic acids from the sample before (1). (Item 10) The method according to any one of items 1 to 9, wherein the sample comprises tumor cells and / or tumor nucleic acids. (Item 11) The method according to item 10, wherein the sample further comprises non-tumor cells. (Item 12) The method according to item 10, wherein the sample is from a tumor biopsy or tumor specimen. (Item 13) The method according to item 10, wherein the sample contains tumor cell-free DNA (cfDNA). (Item 14) The method according to item 10, wherein the sample comprises a fluid, cells, or tissue. (Item 15) The method according to item 14, wherein the sample includes blood or plasma. (Item 16) The method according to item 10, wherein the sample includes a tumor biopsy or circulating tumor cells. (Item 17) The method according to item 16, wherein the sample from the individual is a nucleic acid sample. (Item 18) The method according to item 17, wherein the nucleic acid sample comprises mRNA, genomic DNA, circulating tumor DNA, cell-free DNA, or cell-free RNA. (Item 19) The method according to any one of items 5 to 18, wherein the TMB is determined based on the number of non-driver somatic coding mutations per megabase of sequenced genome. (Item 20) Each reaction corresponds to a bait molecule that binds to a different allele of the polymorphic gene, and each reaction results in the capture of the corresponding allele fraction. Identifying the multiple chemical reactions such that the multiple chemical reactions consist of a first subset of reactions and a second subset of reactions, the first and second subsets do not share a common reaction, and each of the first and second subsets contains at least one chemical reaction, Identifying multiple equations that collectively relate the bonding tendencies of each chemical reaction and the allele fractions of each captured allele, To empirically identify the relative bonding tendencies of the first subset of the aforementioned multiple chemical reactions, A method comprising identifying the relative coupling tendency of the second subset by minimizing the total error. (Item 21) The method for item 20, wherein minimizing the total error is constrained by the median of the relative coupling trends being equal to 1. (Item 22) The method described in item 20, wherein one relative bonding tendency is set to equal 1. (Item 23) The method of item 20, wherein minimizing the total error includes performing a least-squares procedure. (Item 24) Perform a hybrid capture process to measure the raw allele frequency in the patient's DNA sample, The method according to item 20, further comprising scaling the measured raw allele frequencies using the first and second subsets of relative binding tendencies, thereby reducing sampling bias. (Item 25) The method according to item 20, wherein the polymorphic gene includes a human leukocyte antigen gene. (Item 26) The aforementioned polymorphic genes are ST7 / RAY1, ARH1 / NOEY2, TSLC1, RB, PTEN, SMAD2, SMAD4, DCC, TP53, ATM, miR-15a, miR-16-1, NAT2, BRCA1, BRCA2, hOGG1, CDH1, IGF2, CDKN1C / P57, MEN1, PRKAR1A, H19, KRAS, BAP1, PTCH1, SMO, SUFU, NOTCH1, PPP6C, LATS1, CASP8, PTPN14, ARID1A, FBXW7, M6P / IGF2R, IFN-α, olfactory receptor genes, CBFA2T3, DUTT1, FHIT, APC, P16, FCMD, TSC 2. The method described in item 20, which is miR-34, c-MPL, RUNX3, DIRAS3, NRAS, miR-9, FAM50B, PLAGL1, ER, FLT3, ZDBF2, GPR1, c-KIT, NAP1L5, GRB10, EGFR, PEG10, BRAF, MEST, JAK2, DAPK1, LIT1, WT1, NF-1, PR, c-CBL, DLK1, AKT1, SNURF, cytochrome P450 gene (CYP), ZNF587, SOCS1, TIMP2, RUNX1, AR, CEBPA, C19MC, EMP3, ZNF331, CDKN2A, PEG3, NNAT, GNAS, or GATA5. (Item 27) The method according to item 24, further comprising determining whether the patient has experienced a loss of heterozygosity. (Item 28) It is a system, One or more processors, A memory configured to store one or more computer program instructions, wherein when the one or more computer program instructions are executed by the one or more processors, Each reaction corresponds to a bait molecule that binds to a different allele of the polymorphic gene, and each reaction results in the capture of the corresponding allele fraction. Identifying the multiple chemical reactions such that the multiple chemical reactions consist of a first subset of reactions and a second subset of reactions, the first and second subsets do not share a common reaction, and each of the first and second subsets contains at least one chemical reaction, Identifying multiple equations that collectively relate the bonding tendencies of each chemical reaction and the allele fractions of each captured allele, Receiving the empirically identified relative bonding tendencies of the first subset of the aforementioned multiple chemical reactions, A system configured to identify the relative coupling tendency of the second subset by minimizing the total error. (Item 29) The system described in item 28, wherein minimizing the aforementioned total error is constrained by the median of the relative coupling tendencies being equal to 1. (Item 30) The system described in item 28, in which one relative bonding tendency is set to equal 1. (Item 31) The system according to item 28, wherein minimizing the total error includes performing a least-squares procedure. (Item 32) When one or more computer program instructions are executed by one or more processors, The one or more processors receive the measured raw allele frequencies in a patient's DNA sample, wherein the measured raw allele frequencies are received as measured by performing a hybrid capture process. The system according to item 28, further configured in one or more processors to scale the measured raw allele frequencies using the first and second subsets of relative coupling tendencies, thereby reducing sampling bias. (Item 33) The system described in item 28, wherein the aforementioned polymorphic gene includes a human leukocyte antigen gene. (Item 34) The aforementioned polymorphic genes are ST7 / RAY1, ARH1 / NOEY2, TSLC1, RB, PTEN, SMAD2, SMAD4, DCC, TP53, ATM, miR-15a, miR-16-1, NAT2, BRCA1, BRCA2, hOGG1, CDH1, IGF2, CDKN1C / P57, MEN1, PRKAR1A, H19, KRAS, BAP1, PTCH1, SMO, SUFU, NOTCH1, PPP6C, LATS1, CASP8, PTPN14, ARID1A, FBXW7, M6P / IGF2R, IFN-α, olfactory receptor genes, CBFA2T3, DUTT1, FHIT, APC, P16, FCMD, TSC2 The system described in item 28, which is miR-34, c-MPL, RUNX3, DIRAS3, NRAS, miR-9, FAM50B, PLAGL1, ER, FLT3, ZDBF2, GPR1, c-KIT, NAP1L5, GRB10, EGFR, PEG10, BRAF, MEST, JAK2, DAPK1, LIT1, WT1, NF-1, PR, c-CBL, DLK1, AKT1, SNURF, cytochrome P450 gene (CYP), ZNF587, SOCS1, TIMP2, RUNX1, AR, CEBPA, C19MC, EMP3, ZNF331, CDKN2A, PEG3, NNAT, GNAS, or GATA5. (Item 35) The system according to item 32, further comprising determining, using one or more processors, whether the patient has experienced a loss of heterozygosity. (Item 36) A method for determining allele frequency, a) Receiving allele frequencies observed for an allele of a gene in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the allele detected among a plurality of sequence reads corresponding to the gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule. b) Receiving the relative binding tendency of the allele to the bait molecule in one or more processors, wherein the relative binding tendency of the allele corresponds to the tendency of at least a portion of the nucleic acid encoding the allele to bind to the bait molecule in the presence of nucleic acids encoding portions of one or more other alleles of the gene. c) Using one or more processors, execute the objective function to measure the difference between the relative binding tendency of the alleles and the observed allele frequency, d) Executing the optimization model using one or more processors to minimize the objective function, e) A method comprising determining the adjusted allele frequency of an allele based on the optimization model and the observed allele frequency using one or more processors. (Item 37) The method according to item 36, wherein the optimization model is a least-squares optimization model. (Item 38) The method according to item 36 or 37, wherein the optimization model is subject to one or more constraints. (Item 39) The method according to item 38, wherein one or more of the constraints require that the median relative binding tendencies for multiple alleles of the gene are equal to 1. (Item 40) The method according to any one of items 36 to 39, wherein the observed allele frequency corresponds to the relative frequency of nucleic acids encoding at least a portion of the allele detected among the plurality of sequence reads, compared to a reference value. (Item 41) The method according to item 40, wherein the aforementioned reference value is the total number of sequence reads. (Item 42) The method according to item 40, wherein the reference value is the number of sequence reads corresponding to the reference gene. (Item 43) The method according to any one of items 36 to 42, wherein the gene is a human leukocyte antigen (HLA) gene encoding a major histocompatibility (MHC) class I molecule. (Item 44) The aforementioned genes are ST7 / RAY1, ARH1 / NOEY2, TSLC1, RB, PTEN, SMAD2, SMAD4, DCC, TP53, ATM, miR-15a, miR-16-1, NAT2, BRCA1, BRCA2, hOGG1, CDH1, IGF2, CDKN1C / P57, MEN1, PRKAR1A, H19, KRAS, BAP1, PTCH1, SMO, SUFU, NOTCH1, PPP6C, LATS1, CASP8, PTPN14, ARID1A, FBXW7, M6P / IGF2R, IFN-α, olfactory receptor genes, CBFA2T3, DUTT1, FHIT, APC, P16, FCMD, TSC2, miR- The method described in any one of items 36-42, wherein the method is c-MPL, RUNX3, DIRAS3, NRAS, miR-9, FAM50B, PLAGL1, ER, FLT3, ZDBF2, GPR1, c-KIT, NAP1L5, GRB10, EGFR, PEG10, BRAF, MEST, JAK2, DAPK1, LIT1, WT1, NF-1, PR, c-CBL, DLK1, AKT1, SNURF, cytochrome P450 gene (CYP), ZNF587, SOCS1, TIMP2, RUNX1, AR, CEBPA, C19MC, EMP3, ZNF331, CDKN2A, PEG3, NNAT, GNAS, or GATA5. (Item 45) The method according to any one of items 36 to 44, further comprising determining the adjusted allele frequency and then determining, at least in part, that the gene has undergone loss of heterozygosity (LOH). (Item 46) The method according to any one of items 36 to 45, wherein the plurality of sequence reads are obtained by performing next-generation sequencing (NGS), whole exome sequencing, or methylation sequencing on nucleic acids captured by hybridization with the bait molecule. (Item 47) The method according to any one of items 36 to 46, further comprising sequencing a plurality of polynucleotides by next-generation sequencing (NGS), whole exome sequencing, or methylation sequencing to obtain the plurality of sequence reads before receiving the observed allele frequencies, wherein the plurality of polynucleotides comprises nucleic acids encoding at least a portion of the alleles. (Item 48) Before sequencing the aforementioned plurality of polynucleotides, Contacting a mixture of polynucleotides with the bait molecule under conditions suitable for hybridization, wherein the mixture contains a plurality of polynucleotides capable of hybridizing with the bait molecule. The method of item 47, further comprising isolating a plurality of polynucleotides hybridized with the bait molecule, wherein the isolated plurality of polynucleotides hybridized with the bait molecule are sequenced. (Item 49) Before bringing the polynucleotide mixture into contact with the bait molecule, Obtaining a sample from an individual, wherein the sample contains tumor cells and / or tumor nucleic acids, The method according to item 48, further comprising extracting the mixture of polynucleotides from the sample, wherein the mixture of polynucleotides is from the tumor cells and / or tumor nucleic acids. (Item 50) The method according to item 49, wherein the sample further comprises non-tumor cells. (Item 51) The method according to item 49, wherein the sample is from a tumor biopsy or tumor specimen. (Item 52) The method described in item 49, wherein the sample contains tumor cell-free DNA (cfDNA). (Item 53) (1) Receiving allele frequencies observed for each of two or more alleles of a gene in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of each allele detected among a plurality of sequence reads corresponding to the gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule. (2) One or more processors receive the relative binding tendency of each of two or more alleles to the bait molecule, wherein the second allele of the two or more alleles has a lower relative binding tendency to the bait molecule than the first allele of the two or more alleles. (3) The method according to any one of items 36 to 52, further comprising identifying a second bait molecule by one or more processors such that the second allele of the two or more alleles has a higher relative binding tendency to the second bait molecule than to the first bait molecule. (Item 54) The method according to item 53, wherein the second bait molecule contains a sequence complementary to at least a portion of the second allele of the two or more alleles. (Item 55) A non-temporary computer-readable storage medium comprising one or more programs executed by one or more processors of a device, wherein, when the one or more programs are executed by the one or more processors, the one or more programs include instructions causing the device to perform the method described in any one of items 36-46, 53, and 54. (Item 56) A method for detecting loss of heterozygosity (LOH) in human leukocyte antigen (HLA) genes, g) Receiving allele frequencies observed for an HLA allele in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA allele detected among a plurality of sequence reads corresponding to the HLA gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule. h) Receiving the relative binding tendency of the HLA allele to the bait molecule in one or more processors, wherein the relative binding tendency of the HLA allele corresponds to the tendency of at least a portion of the nucleic acid encoding the HLA allele to bind to the bait molecule in the presence of nucleic acids encoding a portion of one or more other HLA alleles. i) Using one or more processors, execute the objective function to measure the difference between the relative binding tendency of the HLA alleles and the observed allele frequency, j) Executing the optimization model using one or more processors to minimize the objective function, k) Using one or more processors, determine the adjusted allele frequency of the HLA allele based on the optimization model and the observed allele frequency, l) A method comprising determining, by one or more processors, that a Low-Level Hysteresis (LOH) has occurred if the adjusted allele frequency of the HLA allele is less than a predetermined threshold. (Item 57) The method according to item 56, wherein the HLA gene is a human HLA-A, HLA-B, or HLA-C gene. (Item 58) The method according to item 56 or 57, wherein the plurality of sequence reads are obtained by sequencing nucleic acids obtained from a sample containing tumor cells and / or tumor nucleic acids. (Item 59) The method according to item 58, wherein the sample further comprises non-tumor cells. (Item 60) The method according to item 58, wherein the sample is from a tumor biopsy or tumor specimen. (Item 61) The method described in item 58, wherein the sample contains tumor cell-free DNA (cfDNA). (Item 62) The method according to item 58, wherein the sample comprises a fluid, cells, or tissue. (Item 63) The method according to item 62, wherein the sample includes blood or plasma. (Item 64) The method according to item 58, wherein the sample includes a tumor biopsy or circulating tumor cells. (Item 65) The method described in item 58, wherein the sample is a nucleic acid sample. (Item 66) The method according to item 65, wherein the nucleic acid sample comprises mRNA, genomic DNA, circulating tumor DNA, cell-free DNA, or cell-free RNA. (Item 67) A method for identifying an individual with cancer who may benefit from a treatment comprising an immune checkpoint inhibitor (ICI), comprising detecting loss of heterozygosity (LOH) of a human leukocyte antigen (HLA) gene in a sample from the individual, wherein the LOH of the HLA gene is detected according to the method described in any one of items 36 to 66. (Item 68) A method for selecting a therapy for an individual having cancer, comprising detecting loss of heterozygosity (LOH) of a human leukocyte antigen (HLA) gene in a sample from the individual, wherein the LOH of the HLA gene is detected according to the method described in any one of items 36 to 66. (Item 69) A method for identifying one or more treatment options for an individual with cancer, (a) To acquire knowledge of loss of heterozygosity (LOH) of the human leukocyte antigen (HLA) gene in a sample from the individual, wherein the LOH of the HLA gene is detected according to the method described in any one of items 36 to 66, (b) A method comprising generating a report that includes one or more treatment options identified for the individual, based at least in part on the knowledge described above. (Item 70) The method according to any one of items 67-69, wherein the LOH of the HLA gene in the sample indicates that the individual is unlikely to benefit from treatment including ICI. (Item 71) The method according to item 70, wherein one or more of the aforementioned treatment options do not include treatments containing an ICI. (Item 72) A method for selecting a treatment for an individual with cancer, comprising acquiring knowledge of loss of heterozygosity (LOH) of a human leukocyte antigen (HLA) gene in a sample from the individual with cancer, wherein the LOH of the HLA gene is detected in accordance with the method described in any one of items 36 to 66, and in response to the acquisition of such knowledge, (i) the individual is classified as a candidate not to receive treatment with an immune checkpoint inhibitor (ICI), (ii) the individual is identified as not likely to respond to a treatment including an immune checkpoint inhibitor (ICI), and / or (iii) the individual is classified as a candidate to receive a treatment other than an immune checkpoint inhibitor (ICI). (Item 73) A method for predicting the survival period of an individual having cancer treated with an immune checkpoint inhibitor (ICI), comprising acquiring knowledge of loss of heterozygosity (LOH) of a human leukocyte antigen (HLA) gene in a sample from the individual, wherein the LOH of the HLA gene is detected according to the method described in any one of items 36 to 66, and in response to the acquisition of such knowledge, the individual is predicted to have a shorter survival period after treatment with the ICI compared to the survival period of an individual treated with the ICI in which the cancer does not exhibit LOH of the HLA gene. (Item 74) A method for monitoring an individual having cancer, comprising acquiring knowledge of loss of heterozygosity (LOH) of a human leukocyte antigen (HLA) gene in a sample from the individual, wherein the LOH of the HLA gene is detected according to the method described in any one of items 36 to 66, and in response to the acquisition of such knowledge, the individual is predicted to have an increased risk of recurrence compared to an individual whose cancer does not exhibit LOH of the HLA gene. (Item 75) A method for evaluating an individual having cancer, comprising acquiring knowledge of loss of heterozygosity (LOH) of a human leukocyte antigen (HLA) gene in a sample from the individual, wherein the LOH of the HLA gene is detected according to the method described in any one of items 36 to 66, and the LOH of the HLA gene is identified as indicating that the cancer in the individual has an increased risk of recurrence compared to an individual not showing LOH of the HLA gene. (Item 76) A method for screening individuals for cancer, comprising acquiring knowledge of loss of heterozygosity (LOH) of a human leukocyte antigen (HLA) gene in a sample from the individual, wherein the LOH of the HLA gene is detected according to the method described in any one of items 36 to 66, and in response to the acquisition of such knowledge, the individual is predicted to have an increased risk of recurrence compared to an individual in which the cancer does not exhibit LOH of the HLA gene. (Item 77) The LOH of the aforementioned HLA gene, Receiving allele frequencies observed for an HLA allele in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA allele detected among a plurality of sequence reads corresponding to the HLA gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule. Receiving the relative binding tendency of the HLA allele to the bait molecule in one or more processors, wherein the relative binding tendency of the HLA allele corresponds to the tendency of at least a portion of the nucleic acid encoding the HLA allele to bind to the bait molecule in the presence of nucleic acids encoding portions of one or more other HLA alleles. The objective function used by one or more processors to measure the difference between the relative binding tendency of the HLA alleles and the observed allele frequency, The process involves determining an optimization model configured to minimize the objective function using one or more processors, The one or more processors determine the adjusted allele frequency of the HLA allele based on the optimization model and the observed allele frequency. The method according to any one of items 67 to 76, wherein one or more processors determine that a low-onset heart attack (LOH) has occurred if the adjusted allele frequency of the HLA allele is less than a predetermined threshold. (Item 78) A method for treating cancer or a method for slowing the progression of cancer, (1) Detecting loss of heterozygosity (LOH) of the human leukocyte antigen (HLA) gene in a sample obtained from an individual, wherein the LOH of the HLA gene is a) Receiving allele frequencies observed for an HLA allele in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA allele detected among a plurality of sequence reads corresponding to the HLA gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule. b) Receiving the relative binding tendency of the HLA allele to the bait molecule in one or more processors, wherein the relative binding tendency of the HLA allele corresponds to the tendency of at least a portion of the nucleic acid encoding the HLA allele to bind to the bait molecule in the presence of nucleic acids encoding portions of one or more other HLA alleles. c) Using one or more processors, execute the objective function to measure the difference between the relative binding tendency of the HLA alleles and the observed allele frequency. d) Executing the optimization model using one or more processors to minimize the objective function. e) Using one or more processors, determine the adjusted allele frequency of the HLA allele based on the optimization model and the observed allele frequency, and f) The detection of LOH by determining that LOH has occurred when the adjusted allele frequency of an HLA allele is less than a predetermined threshold by one or more processors, (2) A method comprising administering to the individual an effective amount of a treatment other than an immune checkpoint inhibitor (ICI), at least in part, based on the detection of LOH of the HLA gene. (Item 79) A method for treating cancer or a method for slowing the progression of cancer, (1) Detecting the absence of loss of heterozygosity (LOH) in the human leukocyte antigen (HLA) gene in a sample obtained from an individual, wherein the absence of LOH in the HLA gene is a) Receiving allele frequencies observed for an HLA allele in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA allele detected among a plurality of sequence reads corresponding to the HLA gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule. b) Receiving the relative binding tendency of the HLA allele to the bait molecule in one or more processors, wherein the relative binding tendency of the HLA allele corresponds to the tendency of at least a portion of the nucleic acid encoding the HLA allele to bind to the bait molecule in the presence of nucleic acids encoding portions of one or more other HLA alleles. c) Using one or more processors, execute the objective function to measure the difference between the relative binding tendency of the HLA alleles and the observed allele frequency. d) Executing the optimization model using one or more processors to minimize the objective function. e) Using one or more processors, determine the adjusted allele frequency of the HLA allele based on the optimization model and the observed allele frequency, and f) If the adjusted allele frequency of the HLA allele is greater than a predetermined threshold, one or more processors determine that LOH did not occur, thereby detecting that LOH did not occur. (2) A method comprising administering an effective amount of an immune checkpoint inhibitor (ICI) to the individual, at least partially based on the detection of the absence of LOH in the HLA gene. (Item 80) The method according to any one of items 67 to 79, wherein the ICI comprises a PD-1 inhibitor, a PD-L1 inhibitor, or a CTLA-4 inhibitor. (Item 81) The method according to any one of items 67 to 80, further comprising detecting tumor mutational burden (TMB) in a sample obtained from the individual. (Item 82) The method according to any one of items 67 to 80, further comprising obtaining knowledge of tumor mutational burden (TMB) in a sample obtained from the said individual. (Item 83) The method according to any one of items 67 to 82, wherein the treatment or the one or more treatment options further comprises a second therapeutic agent. (Item 84) The method according to any one of items 67-69 and 72-83, wherein the LOH and high TMB of the HLA gene in the sample indicates that the individual is likely to benefit from treatment including an immune checkpoint inhibitor (ICI). (Item 85) The method described in item 84, wherein one or more of the aforementioned treatment options include treatments containing an ICI. (Item 86) The method according to any one of items 81 to 85, wherein LOH and high TMB of the HLA gene are detected in the same sample obtained from the individual. (Item 87) The method according to any one of items 81 to 85, wherein the LOH and high TMB of the HLA gene are detected in different samples obtained from the individual. (Item 88) A method for selecting a treatment for an individual having cancer, comprising: (a) acquiring knowledge of loss of heterozygosity (LOH) of a human leukocyte antigen (HLA) gene in a sample from an individual having cancer, wherein the LOH of the HLA gene is detected in accordance with the method described in any one of items 36 to 66; and (b) acquiring knowledge of high tumor mutagenesis (TMB) in a sample from the individual having cancer, wherein in response to the acquisition of the knowledge in (a) and (b), (i) the individual is classified as a candidate for treatment with an immune checkpoint inhibitor (ICI); (ii) the individual is identified as likely to respond to a treatment comprising an immune checkpoint inhibitor (ICI); and / or (iii) the individual is classified as a candidate for treatment comprising an immune checkpoint inhibitor (ICI). (Item 89) A method for predicting the survival of an individual having cancer treated with an immune checkpoint inhibitor (ICI), comprising: (a) acquiring knowledge of loss of heterozygosity (LOH) of a human leukocyte antigen (HLA) gene in a sample from the individual, wherein the LOH of the HLA gene is detected according to the method described in any one of items 36 to 66; and (b) acquiring knowledge of high tumor mutagenesis (TMB) in a sample from the individual, wherein, in response to the acquisition of the knowledge in (a) and (b), the individual is predicted to have a longer survival after treatment with the ICI compared to the survival of an individual treated with the ICI in which the cancer has LOH of the HLA gene without high TMB. (Item 90) A method for treating cancer or a method for slowing the progression of cancer, (1) Detecting loss of heterozygosity (LOH) of the human leukocyte antigen (HLA) gene in a sample obtained from an individual, wherein the LOH of the HLA gene is a) Receiving allele frequencies observed for an HLA allele in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA allele detected among a plurality of sequence reads corresponding to the HLA gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule. b) Receiving the relative binding tendency of the HLA allele to the bait molecule in one or more processors, wherein the relative binding tendency of the HLA allele corresponds to the tendency of at least a portion of the nucleic acid encoding the HLA allele to bind to the bait molecule in the presence of nucleic acids encoding portions of one or more other HLA alleles. c) Using one or more processors, execute the objective function to measure the difference between the relative binding tendency of the HLA alleles and the observed allele frequency. d) Executing the optimization model using one or more processors to minimize the objective function. e) Using one or more processors, determine the adjusted allele frequency of the HLA allele based on the optimization model and the observed allele frequency, and f) The detection of LOH by determining that LOH has occurred when the adjusted allele frequency of the HLA allele is less than a predetermined threshold by one or more processors, (2) To detect high tumor mutational burden (TMB) in the sample obtained from the said individual, (3) A method comprising administering to the individual an effective amount of a treatment comprising an immune checkpoint inhibitor (ICI) based at least in part on the detection of LOH and high TMB of the HLA gene. (Item 91) Using one or more processors, Each reaction corresponds to a bait molecule that binds to a different allele of the polymorphic gene, and each reaction results in the capture of the corresponding allele fraction. Identifying the multiple chemical reactions such that the multiple chemical reactions consist of a first subset of reactions and a second subset of reactions, the first and second subsets do not share a common reaction, and each of the first and second subsets contains at least one chemical reaction, Using one or more of the aforementioned processors, identify multiple equations that collectively relate the bonding tendencies of each chemical reaction and the allele fractions of each captured allele, The one or more processors receive the empirically identified relative coupling tendencies of the first subset of the plurality of chemical reactions, A non-temporary computer-readable storage medium comprising one or more programs executable by one or more computer processors for performing a method including identifying the relative coupling tendency of the second subset by minimizing the total error using one or more of the processors. (Item 92) The non-temporary computer-readable storage medium described in item 91, wherein minimizing the total error is constrained by the median of the relative coupling tendencies being equal to 1. (Item 93) A non-temporary computer-readable storage medium as described in item 91, wherein one relative binding tendency is set to equal 1. (Item 94) A non-temporary computer-readable storage medium as described in item 91, wherein minimizing the total error includes performing a least-squares procedure. (Item 95) The method described above is The one or more processors receive the measured raw allele frequencies in a patient's DNA sample, wherein the measured raw allele frequencies are received as measured by performing a hybrid capture process. A non-temporary computer-readable storage medium according to item 91, further comprising scaling the measured raw allele frequencies using the first and second subsets of relative coupling tendencies in one or more processors, thereby reducing sampling bias. (Item 96) The non-temporary computer-readable storage medium described in item 91, wherein the polymorphic gene includes a human leukocyte antigen gene. (Item 97) The aforementioned polymorphic genes are ST7 / RAY1, ARH1 / NOEY2, TSLC1, RB, PTEN, SMAD2, SMAD4, DCC, TP53, ATM, miR-15a, miR-16-1, NAT2, BRCA1, BRCA2, hOGG1, CDH1, IGF2, CDKN1C / P57, MEN1, PRKAR1A, H19, KRAS, BAP1, PTCH1, SMO, SUFU, NOTCH1, PPP6C, LATS1, CASP8, PTPN14, ARID1A, FBXW7, M6P / IGF2R, IFN-α, olfactory receptor genes, CBFA2T3, DUTT1, FHIT, APC, P16, FCMD, TSC2, miR-3 4, c-MPL, RUNX3, DIRAS3, NRAS, miR-9, FAM50B, PLAGL1, ER, FLT3, ZDBF2, GPR1, c-KIT, NAP1L5, GRB10, EGFR, PEG10, BRAF, MEST, JAK2, DAPK1, LIT1, WT1, NF-1, PR, c-CBL, DLK1, AKT1, SNURF, cytochrome P450 gene (CYP), ZNF587, SOCS1, TIMP2, RUNX1, AR, CEBPA, C19MC, EMP3, ZNF331, CDKN2A, PEG3, NNAT, GNAS, or GATA5, as described in item 91. (Item 98) The non-temporary computer-readable storage medium according to item 95, further comprising the method of determining, using one or more processors, whether the patient has experienced a loss of heterozygosity. (Item 99) An immune checkpoint inhibitor (ICI) for use in a method to treat or slow the progression of cancer in an individual, wherein the absence of loss of heterozygosity (LOH) of the human leukocyte antigen (HLA) gene is present in a sample obtained from the individual. a) Receiving allele frequencies observed for an HLA allele in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA allele detected among a plurality of sequence reads corresponding to the HLA gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule. b) Receiving the relative binding tendency of the HLA allele to the bait molecule in one or more processors, wherein the relative binding tendency of the HLA allele corresponds to the tendency of at least a portion of the nucleic acid encoding the HLA allele to bind to the bait molecule in the presence of nucleic acids encoding a portion of one or more other HLA alleles. c) Using one or more processors, execute the objective function to measure the difference between the relative binding tendency of the HLA alleles and the observed allele frequency, d) Executing the optimization model using one or more processors to minimize the objective function, e) Using one or more processors, determine the adjusted allele frequency of the HLA allele based on the optimization model and the observed allele frequency, f) An immune checkpoint inhibitor (ICI) detected by one or more processors, which determines that LOH did not occur if the adjusted allele frequency of the HLA allele is greater than a predetermined threshold. (Item 100) An immune checkpoint inhibitor (ICI) for use in a method to treat or slow the progression of cancer in an individual, wherein loss of heterozygosity (LOH) and high tumor mutagenesis (TMB) of the human leukocyte antigen (HLA) gene are present in a sample obtained from the individual. a) Receiving allele frequencies observed for an HLA allele in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA allele detected among a plurality of sequence reads corresponding to the HLA gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule. b) Receiving the relative binding tendency of the HLA allele to the bait molecule in one or more processors, wherein the relative binding tendency of the HLA allele corresponds to the tendency of at least a portion of the nucleic acid encoding the HLA allele to bind to the bait molecule in the presence of nucleic acids encoding a portion of one or more other HLA alleles. c) Using one or more processors, execute the objective function to measure the difference between the relative binding tendency of the HLA alleles and the observed allele frequency, d) Executing the optimization model using one or more processors to minimize the objective function, e) Using one or more processors, determine the adjusted allele frequency of the HLA allele based on the optimization model and the observed allele frequency, f) If the adjusted allele frequency of the HLA allele is less than a predetermined threshold, one or more processors determine that LOH has occurred, g) Acquiring or detecting knowledge of high TMB in samples obtained from the aforementioned individuals, and detecting immune checkpoint inhibitors (ICIs). (Item 101) An immune checkpoint inhibitor (ICI) for use in the manufacture of a drug to treat or slow the progression of cancer in an individual, wherein loss of heterozygosity (LOH) and high tumor mutagenesis (TMB) of the human leukocyte antigen (HLA) gene are present in a sample obtained from the individual, a) Receiving allele frequencies observed for an HLA allele in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA allele detected among a plurality of sequence reads corresponding to the HLA gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule. b) Receiving the relative binding tendency of the HLA allele to the bait molecule in one or more processors, wherein the relative binding tendency of the HLA allele corresponds to the tendency of at least a portion of the nucleic acid encoding the HLA allele to bind to the bait molecule in the presence of nucleic acids encoding a portion of one or more other HLA alleles. c) Using one or more processors, execute the objective function to measure the difference between the relative binding tendency of the HLA alleles and the observed allele frequency, d) Executing the optimization model using one or more processors to minimize the objective function, e) Using one or more processors, determine the adjusted allele frequency of the HLA allele based on the optimization model and the observed allele frequency, f) If the adjusted allele frequency of the HLA allele is less than a predetermined threshold, one or more processors determine that LOH has occurred, g) Acquiring or detecting knowledge of high TMB in samples obtained from the aforementioned individuals, and detecting immune checkpoint inhibitors (ICIs). (Item 102) An immune checkpoint inhibitor (ICI) for use in the manufacture of a drug to treat or slow the progression of cancer in an individual, wherein the absence of loss of heterozygosity (LOH) of the human leukocyte antigen (HLA) gene is present in a sample obtained from the individual, a) Receiving allele frequencies observed for an HLA allele in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA allele detected among a plurality of sequence reads corresponding to the HLA gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule. b) Receiving the relative binding tendency of the HLA allele to the bait molecule in one or more processors, wherein the relative binding tendency of the HLA allele corresponds to the tendency of at least a portion of the nucleic acid encoding the HLA allele to bind to the bait molecule in the presence of nucleic acids encoding a portion of one or more other HLA alleles. c) Using one or more processors, execute the objective function to measure the difference between the relative binding tendency of the HLA alleles and the observed allele frequency, d) Executing the optimization model using one or more processors to minimize the objective function, e) Using one or more processors, determine the adjusted allele frequency of the HLA allele based on the optimization model and the observed allele frequency, f) An immune checkpoint inhibitor (ICI) detected by one or more processors, which determines that LOH did not occur if the adjusted allele frequency of the HLA allele is greater than a predetermined threshold. (Item 103) It is a system, One or more processors, A memory configured to store one or more computer program instructions, wherein when the one or more computer program instructions are executed by the one or more processors, Each reaction corresponds to a bait molecule that binds to a different allele of the polymorphic gene, and each reaction results in the capture of the corresponding allele fraction. Identifying the multiple chemical reactions such that the multiple chemical reactions consist of a first subset of reactions and a second subset of reactions, the first and second subsets do not share a common reaction, and each of the first and second subsets contains at least one chemical reaction, Identifying multiple equations that collectively relate the bonding tendencies of each chemical reaction and the allele fractions of each captured allele, Receiving the empirically identified relative bonding tendencies of the first subset of the aforementioned multiple chemical reactions, A system configured to identify the relative coupling tendency of the second subset by minimizing the total error. (Item 104) The system described in item 103, wherein minimizing the aforementioned total error is constrained by the median of the relative coupling tendencies being equal to 1. (Item 105) The system described in item 103, in which one relative bonding tendency is set to equal 1. (Item 106) The system according to item 103, wherein minimizing the total error includes performing a least-squares procedure. (Item 107) When one or more computer program instructions are executed by one or more processors, Receiving measured raw allele frequencies in a patient's DNA sample, wherein the measured raw allele frequencies are received after being measured by performing a hybrid capture process. The system according to item 103, further configured to scale the measured raw allele frequencies using the first and second subsets of relative binding tendencies, thereby reducing sampling bias. (Item 108) The system described in item 103, wherein the aforementioned polymorphic gene includes a human leukocyte antigen gene. (Item 109) The aforementioned polymorphic genes are ST7 / RAY1, ARH1 / NOEY2, TSLC1, RB, PTEN, SMAD2, SMAD4, DCC, TP53, ATM, miR-15a, miR-16-1, NAT2, BRCA1, BRCA2, hOGG1, CDH1, IGF2, CDKN1C / P57, MEN1, PRKAR1A, H19, KRAS, BAP1, PTCH1, SMO, SUFU, NOTCH1, PPP6C, LATS1, CASP8, PTPN14, ARID1A, FBXW7, M6P / IGF2R, IFN-α, olfactory receptor genes, CBFA2T3, DUTT1, FHIT, APC, P16, FCMD, TSC2 The system described in item 103, which is miR-34, c-MPL, RUNX3, DIRAS3, NRAS, miR-9, FAM50B, PLAGL1, ER, FLT3, ZDBF2, GPR1, c-KIT, NAP1L5, GRB10, EGFR, PEG10, BRAF, MEST, JAK2, DAPK1, LIT1, WT1, NF-1, PR, c-CBL, DLK1, AKT1, SNURF, cytochrome P450 gene (CYP), ZNF587, SOCS1, TIMP2, RUNX1, AR, CEBPA, C19MC, EMP3, ZNF331, CDKN2A, PEG3, NNAT, GNAS, or GATA5. (Item 110) The system according to item 107, further configured to determine whether the patient has experienced a loss of heterozygosity when one or more computer program instructions are executed by one or more processors. (Item 111) Receiving observed allele frequencies for an HLA allele using one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA allele detected among a plurality of sequence reads corresponding to the HLA gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule. Receiving the relative binding tendency of the HLA allele to the bait molecule using one or more processors, wherein the relative binding tendency of the HLA allele corresponds to the tendency of at least a portion of the nucleic acid encoding the HLA allele to bind to the bait molecule in the presence of nucleic acids encoding portions of one or more other HLA alleles. Using one or more of the aforementioned processors, the objective function is executed to measure the difference between the relative binding tendency of the HLA alleles and the observed allele frequency. Using one or more of the aforementioned processors, the optimization model is executed to minimize the objective function. Using one or more of the aforementioned processors, the adjusted allele frequency of the HLA allele is determined based on the optimization model and the observed allele frequency. A non-temporary computer-readable storage medium comprising one or more programs executable by one or more computer processors for performing a method including determining that a Low-Level Hypothesis (LOH) has occurred if the adjusted allele frequency of the HLA allele is less than a predetermined threshold, using one or more processors. (Item 112) The non-temporary computer-readable storage medium described in item 111, wherein the HLA gene is a human HLA-A, HLA-B, or HLA-C gene. (Item 113) The non-temporary computer-readable storage medium according to item 111 or 112, wherein the plurality of sequence reads are obtained by sequencing nucleic acids obtained from a sample containing tumor cells and / or tumor nucleic acids. (Item 114) The non-temporary computer-readable storage medium described in item 113, wherein the sample further comprises non-tumor cells. (Item 115) A non-temporary computer-readable storage medium as described in item 113, wherein the sample is from a tumor biopsy or tumor specimen. (Item 116) The sample is a non-temporary computer-readable storage medium as described in item 113, containing tumor cell-free DNA (cfDNA). (Item 117) The sample is a non-temporary computer-readable storage medium as described in item 113, comprising a fluid, cells, or tissue. (Item 118) The sample is a non-temporary computer-readable storage medium as described in item 117, comprising blood or plasma. (Item 119) The non-temporary computer-readable storage medium described in item 113, wherein the sample includes a tumor biopsy or circulating tumor cells. (Item 120) The non-temporary computer-readable storage medium described in item 113, wherein the sample is a nucleic acid sample. (Item 121) A non-temporary computer-readable storage medium as described in item 120, wherein the nucleic acid sample comprises mRNA, genomic DNA, circulating tumor DNA, cell-free DNA, or cell-free RNA. (Item 122) The method described above is A non-temporary computer-readable storage medium according to any one of items 111 to 121, further comprising using one or more processors to determine tumor mutational burden (TMB) from a plurality of sequence reads, wherein the plurality of sequence reads are obtained by sequencing at least a portion of nucleic acids of a genome. (Item 123) A non-transient computer-readable storage medium as described in item 122, wherein the TMB is determined based on the number of non-driver somatic coding mutations per megabase of the sequenced genome. (Item 124) It is a system, One or more processors, A memory configured to store one or more computer program instructions, wherein when the one or more computer program instructions are executed by the one or more processors, Determining the observed allele frequency for an HLA allele, wherein the observed allele frequency corresponds to the frequency of nucleic acids encoding at least a portion of the HLA allele detected among a plurality of sequence reads corresponding to the HLA gene, and the determination is obtained by sequencing the nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule. The determination of the relative binding tendency of the HLA allele to the bait molecule, wherein the relative binding tendency of the HLA allele corresponds to the tendency of at least a portion of the nucleic acid encoding the HLA allele to bind to the bait molecule in the presence of nucleic acids encoding a portion of one or more other HLA alleles. The objective function is executed to measure the difference between the relative binding tendency of the HLA allele and the observed allele frequency, The optimization model is executed to minimize the aforementioned objective function, Based on the optimization model and the observed allele frequencies, the adjusted allele frequencies of the HLA alleles are determined. A system configured to determine that LOH has occurred if the adjusted allele frequency of the HLA allele is less than a predetermined threshold. (Item 125) The system described in item 124, wherein the HLA gene is a human HLA-A, HLA-B, or HLA-C gene. (Item 126) The system according to item 124 or 125, wherein the plurality of sequence reads are obtained by sequencing nucleic acids obtained from a sample containing tumor cells and / or tumor nucleic acids. (Item 127) The system according to item 126, wherein the sample further comprises non-tumor cells. (Item 128) The system described in item 126, wherein the sample is from a tumor biopsy or tumor specimen. (Item 129) The system described in item 126, wherein the sample contains tumor cell-free DNA (cfDNA). (Item 130) The system according to item 126, wherein the sample comprises body fluids, cells, or tissues. (Item 131) The system described in item 130, wherein the sample includes blood or plasma. (Item 132) The system according to item 126, wherein the sample includes a tumor biopsy or circulating tumor cells. (Item 133) The system described in item 126, wherein the sample is a nucleic acid sample. (Item 134) The system according to item 133, wherein the nucleic acid sample comprises mRNA, genomic DNA, circulating tumor DNA, cell-free DNA, or cell-free RNA. (Item 135) When one or more computer program instructions are executed by one or more processors, The system according to any one of items 124 to 134, further configured to use one or more of the processors to acquire or detect knowledge of tumor mutational burden (TMB) from a plurality of sequence reads, wherein the plurality of sequence reads are obtained by sequencing nucleic acids of at least a portion of a genome. (Item 136) The system described in item 135, wherein the TMB is determined based on the number of non-driver somatic coding mutations per megabase of the sequenced genome. [Brief explanation of the drawing]

[0046] [Figure 1] This is a schematic diagram of the hybrid capture process. [Figure 2] The results of the bias removal process are shown. [Figure 3A] This shows an exemplary device according to some embodiments. [Figure 3B] An exemplary system according to some embodiments is shown. [Figure 4A] The characteristics and methods of HLA-I detection are described. Figure 4A is a schematic diagram of tumor mutational burden (TMB) in association with somatic HLA-I LOH, PD-L1 expression, and other oncogenic processes (quoted from Hegde, P.S. and Chen, DS (2020) Immunity 52:17-35). Figure 4B shows the relationship of HLA-I LOH to the immune response. HLA-I LOH is associated with TMB via neoantigens and with PD-L1 as an evasion mechanism (see McGranahan, N. et al. (2017) Cell 171:1259-1271). Figure 4C is a schematic diagram of the computational pipeline used to detect heterozygous somatic loss and germline homozygosity of HLA-I loci from mixed tumor-normal next-generation sequencing results. Figure 4D shows a methodological consideration of HLA-I LOH detection using the bait effect, including the bait / target sequence dispersion effect in BAF (top) and a modeled BAF (bottom) that explains sequencing. [Figure 4B] Same as above. [Figure 4C] Same as above. [Figure 4D] Same as above. [Figure 4E]The effect of hybridization on HLA bait efficiency is shown. Figure 4E provides an example of how hybrid capture can pull down HLA target sequences with different efficiencies. Shown are the allele frequencies (AF) of the HLA-A*31:01 allele before (top; median AF=0.3) and after (bottom; median AF=0.5) adjusting for the hybrid capture binding affinity bias of samples containing the HLA-A*31:01 and HLA-A*11:01 alleles. Figure 4F shows dendrograms of representative sequences for each known two-digit haplotype of HLA-A. A matrix of all pairwise sequence distances was used to cluster the haplotypes. The k-affinity constants of the haplotypes on the left were all greater than or equal to 1, while the k-constants of the sequences on the right were all less than 1. Figure 4G shows dendrograms of representative sequences for each known two-digit haplotype of HLA-A. A matrix of all pairwise sequence distances was used to cluster the haplotypes. The k-affinity constants for all haplotypes on the left were greater than 1, while the k-constants for the sequences on the right were greater than 0.7 or between 0.7 and 0.9. The dots on the left axis represent sequences of specific bait molecules used to capture various HLA alleles. [Figure 4F] Same as above. [Figure 4G] Same as above. [Figure 5A] Figure 5A shows known genome-related survival probabilities in the Clinical Genome Database (CGDB). Figure 5A shows survival curves for high (≧10 mutations / Mb) and low (<10 mutations / Mb) tumor mutation burden (TMB). High TMB was positively associated with survival (HR=0.76, P=0.007). Figure 5B shows survival curves for STK11 or KEAP1 loss. STK11 or KEAP1 loss was negatively associated with survival in a previous analysis of a non-squamous non-small cell lung cancer (NSCLC) cohort (N=652) treated with second-line checkpoint inhibitor monotherapy (HR=1.3, P=0.009). [Figure 5B] Same as above. [Figure 6A]Somatic HLA-I LOH and TMB are independent and significantly predict patient survival in NSCLC treated with immune checkpoint inhibitors (ICIs). Figure 6A shows enrichment of sample attributes (including genomic driver changes) in non-squamous NSCLC samples with evidence of HLA-I LOH (right, n=2,769) and without evidence of HLA-I LOH (left, n=10,471). High TMB: ≥10 mutations / Mb; PD-L1 positive: ≥1% tumor percentage score. Statistics performed by Fisher's exact test and only very significant (P<0.01) associations are labeled. Figure 6B shows overall survival of non-squamous NSCLC patients from initiation of second-line ICI monotherapy, stratified by HLA-I LOH status. The median overall survival (mOS) for HLA-I intact patients (n=180) was 11.3 months [8.2–15.3], while for HLA-I LOH patients (n=60) it was 8.0 months [5.2–13.1]. The HR for HLA-I intact patients was 0.68 [0.49–0.95], P=0.02. Figure 6C shows the lack of effect of biopsy timing in CGDB. Figure 6D shows overall survival of non-squamous NSCLC patients from the initiation of second-line ICI monotherapy, stratified by HLA-I LOH and TMB status (high TMB: ≥10 mutations / Mb, low TMB: <10 mutations / Mb). High TMB, HLA-I intact patients (n=82) had a mOS of 14.09 months [9.0–21.1]. High TMB, HLA-I LOH (n=31): mOS 10.87 months [6.60~20.0]. Low TMB, HLA-I intact (n=98): mOS 9.59 months [6.18~14.8]. Low TMB, HLA-I LOH (n=29): mOS 4.83 months [2.86~12.6]. HR for high TMB = 0.74 [0.54~0.99], P = 0.046. HR for HLA-I intact = 0.65 [0.47~0.91], P = 0.013. Somatic HLA-I LOH and TMB as independent significant predictors of patient survival in NSCLC treated with ICI. [Figure 6B] Same as above. [Figure 6C] Same as above. [Figure 6D] Same as above. [Figure 7A]Shows the impact of HLA-I germline heterozygosity on the survival of NSCLC patients treated with ICI. Figure 7A shows the overall survival from the start of second-line ICI monotherapy stratified by the number of germline HLA-I alleles in all non-squamous NSCLC patients. mOS was the same in both cohorts regardless of the number of germline HLA-I alleles (number of germline HLA-I alleles = 6 (n = 182): mOS 10.8 [7.49 - 14.0]; number of germline HLA-I alleles < 6: mOS 10.8 [4.80 - 18.3]; P = 0.6). Figure 7B shows the overall survival from the start of second-line ICI monotherapy in non-squamous NSCLC patients without evidence of somatic HLA-I LOH stratified by the number of germline HLA-I alleles. The mOS of patients with a germline HLA-I allele number of 6 (n = 141) was 11.9 months [8.84 - 15.90], and the mOS of patients with a germline allele number of less than 6 (n = 39) was 7.1 months [3.68 - 19.20], P = 0.9]. HLA-I germline heterozygosity on the survival of patients with NSCLC treated with ICI. [Figure 7B] Same as above. [Figure 7C] Shows the overall survival of non-squamous NSCLC patients in the actual clinical genomics cohort from the start of second-line ICI monotherapy. Figure 7C shows the overall survival stratified by the most statistically significant combination of TMB (mutations / Mb) and HLA-I status. The mOS of patients with either TMB, HLA-I intact, or TMB ≥ 13, HLA-I LOH (n = 203) was 12.2 months [9.1 - 15.3]. The mOS of patients with TMB < 13, HLA-I LOH (n = 37) was 6.0 months [2.9 - 8.9]. Hazard ratio for either TMB, HLA-I intact. TMB ≥ 13, HLA-I LOH = 0.45 [0.31 - 0.66], P = 0.00004. Figure 7D shows the overall survival stratified by HLA-I LOH and TMB status across multiple TMB thresholds (1 - 20 mutations / Mb). For each threshold, high TMB ≥ TMB threshold and low TMB < TMB threshold. Hazard ratios were derived from a multivariate Cox proportional hazard model controlled for TMB at each TMB threshold. [Figure 7D] Same as above. [Figure 8A]This shows the pan-cancer landscape of somatic HLA-I LOH. Figure 8A shows the prevalence of HLA-I LOH across 59 different solid tumor types in 83,664 unique patient samples. Table 2 summarizes the number of patients within each tumor type. Figure 8B shows the prevalence of HLA-I LOH in microsatellite-stable (MSS) samples compared to microsatellite-unstable (MSI-H) samples in tumor types with high frequency (≧3%) microsatellite instability. Sample size (MSS, MSI): small intestine (n=420, n=21), stomach (n=1100, n=49), large intestine (n=9787, n=332), endometrium (n=1883, n=330), uterus (n=385, n=20). Statistics were performed using Fisher's exact test. Figure 8C shows the prevalence of HLA-I LOH within breast cancer molecular subtypes: whole breast (n=9686), HER+ (n=281), ER+ / HER2- (n=731), and triple-negative (n=631). Statistics were performed using the chi-square test. Figure 8D shows the prevalence of HLA-I LOH in PD-L1-positive (≥1% tumor proportion score, n=3271) samples compared to PD-L1-negative (<1% tumor proportion score, n=9920) samples (top). Statistics were performed using Fisher's exact test. Figure 8D also shows the association between the prevalence of HLA-I LOH within each tumor type and the prevalence of PD-L1 positivity (bottom). The association was fitted using linear regression. Figure 8E shows the prevalence of HLA-I LOH in high TMB (≥10 mutations / Mb, n=13393) samples compared to low TMB (<10 mutations / Mb, n=70263) samples (top). Statistics were performed by Fisher's exact test. Figure 8E also shows the association between the prevalence of HLA-I LOH within each tumor type and the prevalence in high TMB samples (bottom). Associations were fitted using regression models (LOESS regression, quadratic regression, etc.). For tumor types with high microsatellite instability (small intestine, stomach, large intestine, endometrium, and uterus), MSS and MSI-H samples are shown separately in the graphs below Figures 8D and 8E. Significant (P<0.05) associations are labeled with asterisks. Figure 8F shows the association between HLA-I LOH and TMB and PD-L1. [Figure 8B] Same as above. [Figure 8C] Same as above. [Figure 8D] Same as above. [Figure 8E] Same as above. [Figure 8F] Same as above. [Figure 9A] This shows the association between DAXX loss-of-function mutations and HLA-I LOH in tumor types with low PD-L1 positivity and low TMB. Figure 9A shows enrichment of sample attributes (including genomic driver changes) in islet cell samples with HLA-I LOH (right, n=97) and without evidence of HLA-I LOH (left, n=157). Figure 9B shows enrichment of sample attributes (including genomic driver changes) in non-adrenocortical carcinoma samples with evidence of HLA-I LOH (right, n=62) and without evidence of HLA-I LOH (left, n=110). Only statistically significant (P<0.05) associations performed by Fisher's exact test are labeled. [Figure 9B] Same as above. [Figure 10A]The results show a correlation between somatic HLA-I LOH and immune evasion in samples with tumor antigen presentation. Figure 10A shows neoantigen prediction of recurrent driver mutations performed by NetMHC pan. Predicted neoantigens are listed as gene:protein effects, and the percentage of times the predicted presentation allele was lost or retained during heterozygous loss events is shown. Only neoantigens involved in >5 events are included. Statistics are performed by binomial tests, and significance is determined as P<0.05. Figure 10B shows the prevalence of HLA-I LOH in tumor types with known oncovirus associations. HPV: Human papillomavirus, EBV: Epstein-Barr virus, HBV: Hepatitis B virus. Number of samples (virus-positive, virus-negative): Head and neck squamous cell carcinoma (SqCC) (n=363, n=771), cervix (n=141, n=121), stomach (n=189, n=1018), nasopharyngeal (n=50, n=38), hepatocytes (n=64, n=506). Statistical and significant (P<0.05) associations, performed by Fisher's exact test, are labeled with an asterisk. Somatic HLA-I LOH is a potential mechanism of immune evasion in samples presenting tumor antigens. [Figure 10B] Same as above. [Figure 11A] Figure 11A shows the enrichment of genomic alterations in samples with somatic HLA-I LOH. It also shows the enrichment of genomic alterations in tumor types with evidence of HLA-I LOH ("HLA-I LOH enriched samples") and without evidence of HLA-I LOH ("HLA-I intact enriched samples"). The overall top quartile tumor types in prevalence for high TMB samples (≥10 mutations / Mb), PD-L1 positivity (≥1% tumor ratio score), APOBEC mutation signature, tobacco mutation signature, and UV mutation signature are shown. Only enriched genes in at least six different tumor types are included. Figure 11B shows the enrichment of tumor types in samples with selected genomic mutations, stratified by HLA-I LOH status. Statistics shown in Figures 11A and 11B were performed by Fisher's exact test, and only significant associations (P<0.05) are shown. [Figure 11B] Same as above. [Figure 12] A block diagram shows an exemplary process for detecting loss of heterozygosity (LOH) in human leukocyte antigen (HLA) genes, according to some embodiments. [Figure 13] A block diagram of an exemplary process for identifying the relative binding tendencies of different alleles of polymorphic genes to a bait molecule, according to some embodiments, is shown. [Figure 14] A block diagram of an exemplary process for determining allele frequencies according to some embodiments is shown. [Modes for carrying out the invention]

[0047] Assessing whether a subject (e.g., human or other animal) has experienced loss of heterozygosity ("LOH") in one or more genes is often an important clinical objective. One method for determining LOH in a subject's specific genes is to use a hybrid capture process. As used herein, LOH may refer to loss-of-copy LOH and / or copy-neutral LOH.

[0048] Figure 1 shows a prior art hybrid capture process. Further details of this and other hybrid capture processes can be found in U.S. Patent No. 9,340,830 (in its entirety, incorporated herein by reference).

[0049] A population of DNA fragments 104 is prepared from the subject, some of which correspond to the target gene 100 in the subject's genome 102. If the subject is heterozygous for the target gene 100, the population of DNA fragments 104 contains approximately equal amounts of different alleles (one from each parent). On the other hand, if the subject has undergone LOH, one of the parental alleles will either be absent from the population of on-target fragment 104a, or significantly reduced.

[0050] Therefore, consistent with the hybrid capture approach, a population of bait molecules 106 corresponding to the target gene 100 is introduced into a population of target DNA fragments 104. Bait molecules 106 bind to "on-target" fragments 104a (i.e., DNA fragments 104 derived from the target gene 100). Conversely, bait molecules 106 do not bind to "off-target" fragments 104b.

[0051] After allowing sufficient time for such binding to occur, the fragment / bait hybrid is captured, and the remaining fragments are discarded. The captured hybrid is then sequenced to determine which alleles are present and their relative frequencies. If the allele frequencies are sufficiently equal, the patient can be determined to be heterozygous. If one allele frequency is sufficiently low, the patient can be determined to have received LOH in the target gene 100.

[0052] This relatively simple process can be complicated by several factors. Firstly, patient samples may be of mixed nature. For example, if a sample is obtained from a tumor biopsy, it may contain both normal, healthy cells from the patient and cancer cells derived from the tumor. Secondly, some cancer cells may exhibit aneuploidy, meaning they have more or fewer duplicate chromosomes than typical. If one or both of these factors are present, the expected allele frequencies for either heterozygous subjects or subjects that have experienced LOH may be altered.

[0053] Despite the presence of these factors, techniques for evaluating LOH have been developed. One approach involves introducing additional parameters, including tumor purity (i.e., the ratio of tumor cells to healthy cells in the sample) and tumor ploidy (i.e., the number of duplicate chromosomes in tumor cells), into mathematical calculations similar to those described above. An example of this approach is described, for example, in McGranahan et al., Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution (Cell, 2017 Nov 30;171(6):1259-1271.e11), which is incorporated herein by reference in its entirety.

[0054] However, the techniques disclosed therein are still prone to statistical bias, which can distort the evaluation of measured allele frequencies, particularly for the 100 target genes with a high degree of polymorphism.

[0055] One such gene family is the human leukocyte antigen ("HLA") genes. These genes are partially involved in regulating the human immune system. Their functions are spread across several locations within the human genome 102, but even within any given location, there may be up to several thousand possible alleles.

[0056] Therefore, in the hybrid capture process described above, a particular bait molecule 106 does not enjoy complete complementarity with the most likely allele. Furthermore, different alleles may compete with each other to bind to the same bait molecule. These phenomena can then (sometimes seriously) affect the tendency of the on-target fragment 104a to successfully bind to bait molecule 106. This can lead to undersampling or oversampling of that particular allele by the capture process, artificially inflating or undersampling the measured allele frequency, and in some cases, misjudging whether the subject experienced LOH.

[0057] One approach to mitigate this sampling error is to empirically determine the relative binding tendencies of various alleles to a particular bait molecule. For example, if a sample of the target DNA fragment 104 truly contains equal proportions of on-target fragments 104a from two different alleles, it is possible to empirically determine what actual allele frequencies will result from a hybrid capture process using a particular bait molecule 106. Since the binding tendencies are at least partially due to competition between alleles, this determination can be made on an allele-pair basis rather than on an allele-by-allele basis. If their relative binding tendencies are known, the sampling bias of the subsequent hybrid capture process between those alleles and the bait molecule can be corrected by scaling the observed allele frequencies. For example, an objective function can be applied to measure the difference between the relative binding tendencies of a given allele and the observed allele frequencies.

[0058] However, for highly polymorphic genes like HLA, this approach may not be practical because there are too many allele pairs to determine all relative binding tendencies. However, if only a subset of relative binding tendencies is known, the following techniques may be useful.

[0059] In the following, we assume that the target gene 100 is a polymorphic gene with n possible alleles. The relative binding tendencies of alleles i and j to the bait molecule are given by the following formula.

number

[0060] AF i and AF jNote that since these numerical values are partially determined by the interaction of their corresponding alleles, they should be understood to represent the allele frequencies of alleles i and j only when other alleles are present. In other words, if i, j, and k are different, AF i may be different in the presence of alleles j and k. In some implementations, it is convenient to linearize these equations by taking the logarithm of the above equations, and the following equations are obtained. log(k i ) - log(k j ) = log(AF i ) - log(AF j )

[0061] In the case of n alleles, there are a total of n(n - 1) pairs of alleles. Thus, a system of n(n - 1) linear equations of the above form that represents the relative binding tendencies of alleles with respect to the observed allele frequencies can be obtained. If empirical allele frequency data is available for all possible pairs of alleles, this system can be solved in a straightforward manner.

[0062] However, even if empirical allele frequency data is available only for a subset of the possible pairs, useful estimates can be made by an error minimization approach. The above system of simultaneous equations can be represented in the form Ax = b. Where A is an n(n - 1) by n matrix, and all entries in each row are 0 except for a 1 in one column and a -1 in another column such that no two rows are equal. The vector x is a column vector with the component logk i in the i-th position, and b is a column vector with each component of the form log(AF n ) - log(AF m ), and the values of n and m correspond to the positions of the non-zero terms of A in the corresponding row. In some implementations, the rows of the matrix can be modified such that at a certain position (e.g., column m), only the non-zero terms are equal to 1. This is equivalent to arbitrarily setting the relative binding tendency of the bait molecule to allele m equal to 1, thereby setting the scale by which the other relative binding tendencies are measured.

[0063] all k i Item and / or AF i If the terms are not known, then the following formula

number

[0064] k, which was empirically determined or calculated according to the preceding paragraph. i These can be used to rescale the raw, measured allele frequencies from the hybrid capture process, thereby mitigating the sampling bias that existed as a result of the factors mentioned above.

[0065] Certain aspects of this disclosure relate to methods for determining allele frequencies. In some embodiments, the method includes: a) obtaining an observed allele frequency for an allele of a gene, wherein the observed allele frequency corresponds to the frequency of nucleic acids encoding at least a portion of the allele detected among a plurality of sequence reads corresponding to the gene, and the plurality of sequence reads are obtained by sequencing a gene or a portion thereof captured by hybridization with a bait molecule; b) obtaining the relative binding tendency of the allele to a bait molecule, wherein the relative binding tendency of the allele corresponds to the tendency of nucleic acids encoding at least a portion of the allele to bind to a bait molecule in the presence of nucleic acids encoding one or more other alleles of the gene; c) applying an objective function to measure the difference between the relative binding tendency of the allele and the observed allele frequency; d) applying an optimization model to minimize the objective function; and e) determining an adjusted allele frequency of the allele based on the optimization model and the observed allele frequency.

[0066] Optimization Modeling Optimization refers to methods and processes of working toward, continuously improving, or refining a solution (which may be the best obtainable solution, a preferred solution, or a solution that provides a particular advantage within constraints), or searching for the desired highest or highest point (or lowest or lowest point), or processing a penalty function or cost function to reduce it. In optimization modeling, the goal is often to minimize the model error (also known as the model residual; a residual is the difference between the observed value and the fitted value provided by the model).

[0067] Generally, an optimization model has three main components: a) an objective function, which is the function to be optimized (e.g., minimizing the error in the estimation of the model's parameters); b) a set of variables (the solution to an optimization problem is the set of variable values ​​at which the objective function reaches its optimal value); and c) a set of constraints that restrict the values ​​of the variables. Various optimization models are known in the art and can be used in the methods of this disclosure. Those skilled in the art will be able to identify optimization models suitable for use according to their specific needs and criteria. Examples of optimization models in the art include least-squares regression models, logistic regression models, quadratic regression models, LOESS regression models, Bayes-ridge regression models, LASSO regression models, elastic network regression models, decision tree models, gradient-boosting tree models, neural network models, and support vector machine models. Detailed explanations of optimization modeling can be found in Yang, X. (2008). Introduction to mathematical optimization. From Linear Programming to Metaheuristics; Allaire, G., & Allaire, G. (2007). Numerical analysis and optimization: an introduction to mathematical modelling and numerical simulation. Oxford University Press; Pedregal, P. (2006). Introduction to optimization (Vol. 46). Springer Science & Business Media; Chong, E.K., & Zak, SH. (2004). An introduction to optimization. John Wiley & Sons.

[0068] Allergy frequency In some embodiments, the optimization model includes allele frequency as a model variable. Allele frequency is the frequency of an allele (i.e., a variant of a nucleotide sequence) at a genomic locus within a population of alleles, and is expressed as a fraction or percentage. If the population of alleles refers to the population of alleles of an individual subject, the allele frequency can be calculated as the ratio of the number of allele sequences to the total number of allele sequences at a given genomic locus for that individual subject. In this sense, allele frequency represents the allele composition of an individual at a genomic locus, from which zygosity (e.g., homozygosity or heterozygosity) can be inferred. For example, in the case of individual diploid subjects such as humans: 1) If the allele frequency of an allele is 0 or reasonably close to 0 (e.g., within the statistical confidence interval), then the individual subject is considered homozygous null (also known as nullzygous) for that allele. 2) If the allele frequency of an allele is 0.5 or reasonably close to 0.5 (e.g., within the statistical confidence interval), then the individual subject is considered heterozygous for that allele. 3) If the allele frequency of an allele is 1 or reasonably close to 1 (e.g., within the statistical confidence interval), then the individual subject is considered homozygous for that allele.

[0069] In some embodiments, the allele frequency is the observed allele frequency, corresponding to the relative frequency of nucleic acids encoding at least a portion of the alleles detected among multiple sequence reads, compared to a reference value. In some embodiments, the reference value is the total number of sequence reads. In some embodiments, the reference value is the number of sequence reads, corresponding to a reference gene or a function thereof, e.g., reads per million map reads (RPM) or counts per million map reads (CPM).

[0070] In some embodiments, allele frequency can be expressed as relative binding tendency. In hybrid capture-based sequencing processes, relative binding tendency corresponds to the likelihood that one allele will bind to a bait molecule in the presence of one or more other alleles. Therefore, in some embodiments, an optimization model is applied to an objective function that measures the difference between the relative binding tendency of one allele and the allele frequency of the observed allele.

[0071] As an example, Figure 2 shows the results of such scaling for various HLA-A alleles. The bar graph on the left shows the raw allele frequencies of the HLA-A*31:01 allele from heterozygous subjects in the presence of various other HLA-A alleles shown on the horizontal axis. The median allele frequency is 0.38, indicating that HLA-A*31:01 is typically undersampled in the presence of the other alleles shown. After correcting for bias, the graph on the right shows that the median allele frequency is 0.5, which is more consistent with the population of heterozygous samples.

[0072] Figure 4D shows the effect of adjusted allele frequencies for use in determining loss of heterozygosity (LOH) of the human leukocyte antigen class I (HLA-I) gene in an individual population. The X-axis represents the B allele frequency (BAF) for each individual in the population, where the B allele refers to the non-reference allele or minor allele. The Y-axis represents the number of BAF samples in the population. Figure 4D shows that after adjusting allele frequencies using the method of this disclosure, the median allele frequency is adjusted from approximately 0.32 (upper panel) to approximately 0.5 (lower panel), indicating that most of the individual population is heterozygous for the HLA-I gene.

[0073] As shown in Figure 4G, a particular allele (or fragment thereof) may have a range of relative binding tendencies to a particular bait molecule. To improve the capture of sequences representing complete polymorphic variations in a gene, it may be desirable to select one or more additional bait molecules, particularly those with a low relative binding tendency to the original bait molecule and an improved binding tendency to the allele.

[0074] Accordingly, in some embodiments, the methods of the present disclosure may include obtaining observed allele frequencies for two or more alleles of a gene, obtaining the relative binding tendencies of two or more alleles of a gene to a particular bait molecule, and / or identifying or selecting a sequence of a second bait molecule. In some embodiments, one or more alleles of a gene that have a lower relative binding tendency to the first bait molecule may have a higher binding tendency to the second bait molecule than to the first bait molecule. For example, the second bait molecule may include sequences complementary to at least a portion of one of the low-binding alleles of a gene, or sequences complementary to a sequence (e.g., a consensus sequence) based on complementarity or binding with the sequences of one or more low-binding alleles of a gene. This allows for bait selection based on the sequences of low-binding alleles of polymorphic genes, for example, to sample gene diversity more comprehensively or with less bias (e.g., based on hybrid capture).

[0075] Least squares optimization In some embodiments, the optimization model is a least-squares optimization model. The least-squares optimization model is a regression optimization model, and the objective function is a quadratic function (e.g., a sum of squares function) of the parameter being optimized (e.g., the variable residual / error to be minimized). In some embodiments, the least-squares optimization model is used in the method of this disclosure to minimize an objective function that measures the difference between the relative binding tendency of alleles and the observed allele frequencies. In some embodiments, the optimization model is a quadratic regression. In some embodiments, the optimization model is a LOESS regression.

[0076] In some embodiments, an optimization model can be used to modify or adjust the desired variable (e.g., allele frequency). In some embodiments, the optimization model and the observed allele frequencies are used to determine the adjusted allele frequency of an allele. The adjusted allele frequency can then be used further in downstream operations (e.g., inferring the conjugation status of individual objects for each allele).

[0077] Further details on least-squares optimization can be found, for example, in Wolberg, J. (2006). Data analysis using the method of least squares: extracting the most information from experiments. Springer Science & Business Media; Borowiak, D. (2001). Linear models, least squares and alternatives; Bjorck, Å. (1996). Numerical methods for least squares problems. Society for Industrial and Applied Mathematics; and Luenberger, DG (1997) “Least-Squares Estimation”. Optimization by Vector Space Methods. New York: John Wiley & Sons. pp.78-102.

[0078] Model constraints In some embodiments, the optimization model is subject to one or more constraints. These constraints limit the possible values ​​of the variables in the optimization model. In some embodiments, zero or more constraints require that the median relative binding tendency for multiple alleles of a gene is equal to 1. In some embodiments, 0.5 or more constraints require that the median relative binding tendency for multiple alleles of a gene is equal to 1. In some embodiments, one or more constraints require that the median relative binding tendency for multiple alleles of a gene is equal to 1.

[0079] Sequence determination In some embodiments, multiple sequence reads were obtained by sequencing nucleic acids captured by hybridization with bait molecules. In some embodiments, multiple sequence reads were obtained by whole exome sequencing of nucleic acids captured by hybridization with bait molecules. In some embodiments, multiple sequence reads were obtained by performing next-generation sequencing (NGS), whole exome sequencing, or methylation sequencing on nucleic acids captured by hybridization with bait molecules.

[0080] In some embodiments, the method further comprises sequencing multiple polynucleotides by next-generation sequencing (NGS) to obtain multiple sequence reads before obtaining the observed allele frequencies, wherein the multiple polynucleotides include nucleic acids encoding at least a portion of the alleles. The NGS method is known in the art and is described, for example, in Metzker, M. (2010) Nature Biotechnology Reviews 11:31-46. Platforms for next-generation sequencing include, for example, Roche / 454's Genome Sequencer (GS) FLX System, Illumina / Solexa's Genome Analyzer (GA), Illumina's HiSeq 2500, HiSeq 3000, HiSeq 4000, and NovaSeq 6000 sequencing systems, Life / APG's Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator's G.007 system, Helicos BioSciences' HeliScope Gene sequencing system, and Pacific Biosciences' PacBio RS system. NGS technology involves one or more steps, such as template preparation, sequencing and imaging, and data analysis. Template preparation methods may include steps such as randomly degrading nucleic acids (e.g., genomic DNA) into smaller sizes to generate sequencing templates (e.g., fragment templates or mate-pair templates). Spatially separated templates can be attached to or immobilized on a solid surface or support, allowing for the simultaneous execution of a large number of sequencing reactions. Types of templates that can be used in NGS reactions include, for example, clonal amplification templates derived from single DNA molecules and single DNA molecule templates. Exemplary sequencing and imaging steps for NGS include, for example, cyclic reversible termination (CRT), ligation-based sequencing (SBL), single-molecule addition (pyro-sequencing), and real-time sequencing. After generating NGS reads, they can be aligned to a known reference sequence or assembled de novo.For example, the identification of genetic variations such as single nucleotide polymorphisms and structural variants in a sample (e.g., tumor sample) can be achieved by aligning NGS reads to a reference sequence (e.g., wild-type sequence). Methods for NGS sequence alignment are described, for example, in Trapnell C. and Salzberg SLNature Biotech., 2009, 27:455-457. Examples of de novo assembly are described in Warren R. et al., Bioinformatics, 2007, 23:500-501, Butler J. et al., Genome Res., 2008, 18:810-820, and Zerbino DR and Birney E., Genome Res., 2008, 18:821-829. Sequence alignment or assembly can be performed using read data from one or more NGS platforms (e.g., mixing Roche / 454 and Illumina / Solexa read data).

[0081] In some embodiments, the method further comprises sequencing multiple polynucleotides by whole exome sequencing to obtain multiple sequence reads before obtaining observed allele frequencies, wherein the multiple polynucleotides include nucleic acids encoding at least a portion of the alleles.

[0082] In some embodiments, the method further comprises contacting a mixture of polynucleotides with a bait molecule under conditions suitable for hybridization, prior to sequencing the polynucleotides, wherein the mixture contains a plurality of polynucleotides that can hybridize with the bait molecule; and isolating the plurality of polynucleotides hybridized with the bait molecule, wherein the isolated plurality of polynucleotides hybridized with the bait molecule are sequenced by NGS. Figure 1 illustrates such a hybrid capture process. Further details of this and other hybrid capture processes can be found in U.S. Patent No. 9,340,830 (in whole, incorporated herein by reference). In some embodiments, the method further comprises obtaining a sample from an organism prior to contacting a mixture of polynucleotides with a bait molecule, wherein the sample contains tumor cells and / or tumor nucleic acids; and extracting a mixture of polynucleotides from the sample, wherein the mixture of polynucleotides is from tumor cells and / or tumor nucleic acids. In some embodiments, the sample further comprises non-tumor cells.

[0083] In some embodiments, the method involves subjecting multiple polynucleotides to methylation sequencing in order to obtain multiple sequence reads. In some embodiments, the multiple polynucleotides include nucleic acids encoding at least a portion of the allele.

[0084] In some embodiments, nucleic acids are obtained from a sample containing, for example, tumor cells and / or tumor nucleic acids. For example, the sample may include tumor cells, circulating tumor cells, tumor nucleic acids (e.g., circulating tumor DNA, cfDNA, or cfRNA), part or all of a tumor biopsy, body fluids, cells, tissues, mRNA, genomic DNA, RNA, cell-free DNA, and / or cell-free RNA. In some embodiments, the sample is from a tumor biopsy or tumor specimen. In some embodiments, the sample further includes non-tumor cells and / or non-tumor nucleic acids. In some embodiments, the fluid includes blood, serum, plasma, saliva, semen, cerebrospinal fluid, amniotic fluid, ascites, interstitial fluid, etc.

[0085] In some embodiments, the sample is or contains biological tissue or biological fluid. The sample may contain compounds that do not naturally mix with tissue in nature, such as preservatives, anticoagulants, buffers, fixatives, nutrients, and antibiotics. In one embodiment, the sample is stored as a frozen sample or as a formaldehyde or paraformaldehyde-fixed paraffin-embedded (FFPE) tissue preparation. For example, the sample can be embedded in a matrix (e.g., an FFPE block or frozen sample). In another embodiment, the sample is a blood or blood component sample. In yet another embodiment, the sample is a bone marrow aspiration sample. In yet another embodiment, the sample contains cell-free DNA (cfDNA). While we do not wish to be bound by theory, in some embodiments, cfDNA is considered to be DNA from apoptotic or necrotic cells. Typically, cfDNA is bound to proteins (e.g., histones) and protected from nucleases. cfDNA can be used, for example, as a biomarker for non-invasive prenatal testing (NIPT), organ transplantation, cardiomyopathy, microbiome, and cancer. In another embodiment, the sample contains circulating tumor DNA (ctDNA). While we do not wish to be bound by theory, ctDNA is cfDNA that has genetic or epigenetic alterations (e.g., somatic alterations or methylation signatures) that can distinguish it from that which originates from tumor cells. In another embodiment, the sample contains circulating tumor cells (CTCs). While we do not wish to be bound by theory, in some embodiments, CTCs are considered to be cells released into circulation from primary or metastatic tumors. In some embodiments, CTC apoptosis is a source of ctDNA in the blood / lymph.

[0086] In some embodiments, the biological sample may be or include bone marrow, blood, blood cells, ascites, tissue or fine-needle biopsy specimens, cell-containing bodily fluids, free suspended nucleic acids, sputum, saliva, urine, cerebrospinal fluid, ascites, pleural fluid, feces, lymph, gynecological fluid, skin swabs, vaginal swabs, oral swabs, nasal swabs, lavage fluids or lavage fluids such as tube lavage fluid or bronchoalveolar lavage fluid, aspirates, scrapes, bone marrow specimens, tissue biopsy specimens, surgical specimens, feces, other bodily fluids, secretions, and / or excretions, and / or cells therefrom. In some embodiments, the biological sample may be or include cells obtained from an individual. In some embodiments, the obtained cells may be or include cells derived from the individual from which the sample was obtained.

[0087] Figure 12 shows an exemplary process 1200 for detecting loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes, according to some embodiments. Process 1200 is performed, for example, using one or more electronic devices implementing a software program. In some embodiments, process 1200 is performed using a client-server system, and blocks of process 1200 are divided in any way between the server and client devices. In other embodiments, blocks of process 1200 are divided between the server and multiple client devices. Thus, while parts of process 1200 are described herein as being performed by specific devices in a client-server system, it should be understood that process 1200 is not limited in this way. In other embodiments, process 1200 is performed using only one client device, or only multiple client devices. In process 1200, some blocks are optionally combined, the order of some blocks is optionally changed, and some blocks are optionally omitted. In some embodiments, additional steps can be performed in combination with process 1200. Therefore, the operations illustrated (and described in more detail below) are essentially illustrative and should not be considered limiting.

[0088] In block 1202, multiple nucleic acids obtained from an individual-derived sample are provided, the multiple nucleic acids including nucleic acids encoding HLA genes. Optionally, in block 1204, one or more adapters are ligated to one or more nucleic acids from the multiple nucleic acids. In block 1206, nucleic acids are amplified from the multiple nucleic acids. In block 1208, multiple nucleic acids corresponding to HLA genes are captured from the amplified nucleic acids by hybridization with bait molecules. In block 1210, an exemplary sequencer sequences the captured nucleic acids to obtain multiple sequence reads corresponding to HLA genes. In block 1212, an exemplary system (e.g., one or more electronic devices) fits one or more values ​​associated with one or more of the multiple sequence reads into a model. In block 1214, the system detects the LOH of the HLA gene and the relative binding tendencies for the HLA alleles of the HLA gene based on the model.

[0089] Figure 13 shows an exemplary process 1300 for identifying the relative binding tendencies of different alleles of polymorphic genes to a bait molecule, according to some embodiments. Process 1300 is performed, for example, using one or more electronic devices implementing a software program. In some embodiments, process 1300 is performed using a client-server system, and blocks of process 1300 are divided in any way between the server and client devices. In other embodiments, blocks of process 1300 are divided between the server and multiple client devices. Thus, while parts of process 1300 are described herein as being performed by specific devices in a client-server system, it should be understood that process 1300 is not limited in this way. In other embodiments, process 1300 is performed using only one client device, or only multiple client devices. In process 1300, some blocks are optionally combined, the order of some blocks is optionally changed, and some blocks are optionally omitted. In some embodiments, additional steps can be performed in combination with process 1300. Therefore, the operations illustrated (and described in more detail below) are essentially illustrative and should not be considered limiting.

[0090] In block 1302, an exemplary system (e.g., one or more electronic devices) identifies a plurality of chemical reactions such that, for example, each reaction corresponds to a bait molecule that binds to a different allele of a polymorphic gene, and each reaction results in the capture of the corresponding allele fraction. The plurality of chemical reactions consist of a first subset of reactions and a second subset of reactions, the first and second subsets do not share a common reaction, and the first and second subsets each contain at least one chemical reaction. In block 1304, the system identifies a plurality of equations that collectively relate the binding tendencies of each chemical reaction and the allele fractions of each captured allele. In block 1306, the system empirically identifies the relative binding tendencies of the first subset of the plurality of chemical reactions. In block 1308, the system identifies the relative binding tendencies of the second subset by minimizing the total error.

[0091] Figure 14 shows an exemplary process 1400 for determining allele frequencies according to some embodiments. In some embodiments, allele frequencies of one or more HLA alleles are determined, for example, to detect LOH. Process 1400 is performed using, for example, one or more electronic devices implementing a software program. In some embodiments, process 1400 is performed using a client-server system, and blocks of process 1400 are divided in any way between the server and client devices. In other embodiments, blocks of process 1400 are divided between the server and multiple client devices. Thus, while parts of process 1400 are described herein as being performed by specific devices in a client-server system, it should be understood that process 1300 is not limited in this way. In other embodiments, process 1400 is performed using only one client device, or only multiple client devices. In process 1400, some blocks are optionally combined, the order of some blocks is optionally changed, and some blocks are optionally omitted. In some embodiments, additional steps can be performed in combination with process 1400. Therefore, the operations illustrated (and described in more detail below) are essentially illustrative and should not be considered limiting.

[0092] In block 1402, an exemplary system (e.g., one or more electronic devices) receives observed allele frequencies for an allele of a gene. In some embodiments, the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the allele detected among a plurality of sequence reads corresponding to the gene, the plurality of sequence reads obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule. In some embodiments, the gene is a human HLA gene and the allele is a human HLA allele (e.g., as described herein). In block 1404, the system receives the relative binding tendency of the allele to the bait molecule. In some embodiments, the relative binding tendency of the allele corresponds to the tendency of nucleic acids encoding at least a portion of the allele to bind to the bait molecule in the presence of nucleic acids encoding portions of one or more other alleles of the gene. In block 1406, the system runs an objective function to measure the difference between the relative binding tendency of the allele and the observed allele frequencies. In block 1408, the system runs an optimization model to minimize the objective function. In block 1410, the system determines the adjusted allele frequency of an allele based on the optimized model and the observed allele frequency.

[0093] Software and devices In some other embodiments, non-temporary computer-readable storage media are provided herein. In some embodiments, the non-temporary computer-readable storage media includes one or more programs executed by one or more processors of the device, the one or more programs including instructions that, when executed by one or more processors, cause the device to perform a method according to any of the embodiments described herein.

[0094] Figure 3A shows an example of a computing device according to one embodiment. Device 300 may be a host computer connected to a network. Device 300 may be a client computer or a server. As shown in Figure 3A, device 300 may be any suitable type of microprocessor-based device, such as a personal computer, workstation, server, or handheld computing device (portable electronic device, e.g., telephone or tablet). The device may include, for example, one or more of a processor 310, an input device 320, an output device 330, storage 340, and a communication device 360. The input device 320 and the output device 330 may generally correspond to those described above and may be connectable to or integrated with the computer.

[0095] The input device 320 may be any suitable device that provides input, such as a touchscreen, keyboard or keypad, mouse, or voice recognition device. The output device 330 may be any suitable device that provides output, such as a touchscreen, haptic device, or speaker.

[0096] Storage 340 may be any suitable device that provides storage (e.g., electrical, magnetic, or optical memory including RAM, cache, hard drive, or removable storage disk). Communication device 360 ​​may include any suitable device that can send and receive signals over a network, such as a network interface chip or device. Computer components may be connected in any suitable manner, for example, via wired media (e.g., physical bus, Ethernet, or any other wired transfer technology) or wirelessly (e.g., Bluetooth®, Wi-Fi®, or any other wireless technology).

[0097] The HLA module 350 is stored in the storage 340 as executable instructions and can be executed by the processor 310, and may include, for example, a process that embodies the functions of the present disclosure (for example, embodied in the above-mentioned device).

[0098] The HLA module 350 can also be stored and / or transferred to any non-temporary computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device (e.g., those described above), and can fetch and execute software-associated instructions from the instruction execution system, apparatus, or device. In the context of this disclosure, the computer-readable storage medium may be any medium, such as storage 340, and may contain or store processes for use by or in connection with an instruction execution system, apparatus, or device. Examples of computer-readable storage media include hard drives, flash drives, and memory units such as distribution modules, which operate as single functional units. Furthermore, the various processes described herein may be embodied as modules configured to operate according to the embodiments and techniques described above. In addition, although processes may be shown and / or described separately, those skilled in the art will understand that the above processes may be routines or modules within other processes.

[0099] The HLA module 350 can also be propagated by or in any transmission medium for use with instruction execution systems, apparatus, or devices (e.g., those described above) to fetch and execute software-associated instructions from the instruction execution systems, apparatus, or devices. In the context of this disclosure, the transmission medium can be any medium that can communicate, transmit, or transmit transmission programming for use with or in connection with the instruction execution systems, apparatus, or devices. Transmission-readable media may include, but are not limited to, wired or wireless transmission media of electronic, magnetic, optical, electromagnetic, or infrared.

[0100] Device 300 can be connected to a network (for example, the network shown in Figure 3B, and / or network 404 below), which may be any preferred type of interconnected communication system. The network may implement any preferred communication protocol and may be protected by any preferred security protocol. The network may include network links of any preferred configuration that can implement the transmission and reception of network signals, such as wireless network connections (T1 or T3 lines), cable networks, DSL, or telephone lines.

[0101] Device 300 can implement any operating system suitable for running over a network. The HLA module 350 can be written in any suitable programming language such as C, C++, Java, or Python. In various embodiments, application software embodying the functionality of this disclosure can be deployed in different configurations (e.g., in a client / server configuration, or via a web browser as a web-based application or web service).

[0102] Figure 3B shows an example of a computation system according to one embodiment. In system 400, device 300 (e.g., as shown above and in Figure 3A) is connected to network 404, which in turn is connected to device 406. In some embodiments, device 406 is a sequencer. Exemplary sequencers include, but are not limited to, the Roche / 454 Genome Sequencer (GS) FLX System, the Illumina / Solexa Genome Analyzer (GA), the Illumina HiSeq 2500, HiSeq 3000, HiSeq 4000, and NovaSeq 6000 sequencing systems, the Life / APG Support Oligonucleotide Ligation Detection (SOLiD) system, the Polonator G.007 system, the Helicos BioSciences HeliScope Gene sequencing system, or the Pacific Biosciences PacBio RS system. Devices 300 and 406 can communicate using a preferred communication interface over a network 404, such as a local area network (LAN), a virtual private network (VPN), or the internet. In some embodiments, network 404 may be, for example, the internet, an intranet, a virtual private network, a cloud network, a wired network, or a wireless network. Devices 300 and 406 can communicate partially or entirely over wireless or wired communication such as Ethernet or IEEE 802.11b wireless. Furthermore, devices 300 and 406 can communicate over a second network, such as a mobile / cellular network, using a preferred communication interface. Communication between devices 300 and 406 may further include, or communicate with, various servers such as mail servers, mobile servers, media servers, and telephone servers.In some embodiments, devices 300 and 406 can communicate directly (instead of, or in addition to, communication via network 404) via wireless or wired communication such as Ethernet or IEEE 802.11b wireless.

[0103] One or all of devices 300 and 406 are generally programmed to include logic (e.g., HTTP web server logic) or to format data accessed from local or remote databases or other sources of data and content, in order to provide and / or receive information over the network 404, according to the various embodiments described herein.

[0104] Human leukocyte antigen (HLA) and loss of heterozygosity (LOH) In some embodiments according to any of the embodiments described herein, the gene is a human leukocyte antigen (HLA) gene encoding a major histocompatibility (MHC) class I molecule. In some embodiments, the method further includes determining the adjusted allele frequency and then determining, at least in part, that the gene has undergone loss of heterozygosity (LOH). In other embodiments, the genes include ST7 / RAY1, ARH1 / NOEY2, TSLC1, RB, PTEN, SMAD2, SMAD4, DCC, TP53, ATM, miR-15a, miR-16-1, NAT2, BRCA1, BRCA2, hOGG1, CDH1, IGF2, CDKN1C / P57, MEN1, PRKAR1A, H19, KRAS, BAP1, PTCH1, SMO, SUFU, NOTCH1, PPP6C, LATS1, CASP8, PTPN14, ARID1A, FBXW7, M6P / IGF2R, IFN-α, olfactory receptor genes, CBFA2T3, DUTT1, FHIT, APC, P16, These are FCMD, TSC2, miR-34, c-MPL, RUNX3, DIRAS3, NRAS, miR-9, FAM50B, PLAGL1, ER, FLT3, ZDBF2, GPR1, c-KIT, NAP1L5, GRB10, EGFR, PEG10, BRAF, MEST, JAK2, DAPK1, LIT1, WT1, NF-1, PR, c-CBL, DLK1, AKT1, SNURF, cytochrome P450 gene (CYP), ZNF587, SOCS1, TIMP2, RUNX1, AR, CEBPA, C19MC, EMP3, ZNF331, CDKN2A, PEG3, NNAT, GNAS, or GATA5.

[0105] Furthermore, in some other embodiments, methods for detecting loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes are provided herein. In some embodiments, the method includes: a) obtaining an observed allele frequency for an HLA allele, wherein the observed allele frequency corresponds to the frequency of nucleic acids encoding at least a portion of the HLA allele detected among a plurality of sequence reads corresponding to an HLA gene, and the plurality of sequence reads are obtained by sequencing a gene or a portion thereof captured by hybridization with a bait molecule; b) obtaining the relative binding tendency of the HLA allele to the bait molecule, wherein the relative binding tendency of the HLA allele corresponds to the tendency of nucleic acids encoding at least a portion of the HLA allele to bind to the bait molecule in the presence of nucleic acids encoding one or more other HLA alleles; c) applying an objective function to measure the difference between the relative binding tendency of the HLA allele and the observed allele frequency; d) applying an optimization model to minimize the objective function; e) determining an adjusted allele frequency of the HLA allele based on the optimization model and the observed allele frequency; and f) determining that LOH has occurred if the adjusted allele frequency of the HLA allele is smaller than a predetermined threshold. In some embodiments, the HLA gene is a human HLA-A, HLA-B, or HLA-C gene. In some embodiments, multiple sequence reads are obtained by sequencing nucleic acids obtained from a sample containing tumor cells and / or tumor nucleic acids. In some embodiments, the sample further includes non-tumor cells. In some embodiments, the method is for detecting loss of heterozygosity (LOH) of the polymorphic gene of interest.In some embodiments, the method includes: a) obtaining an observed allele frequency for an allele of a target gene, wherein the observed allele frequency corresponds to the frequency of nucleic acids encoding at least a portion of the allele detected among a plurality of sequence reads corresponding to the gene, and the plurality of sequence reads are obtained by sequencing a gene or nucleic acid encoding a portion thereof captured by hybridization with a bait molecule; b) obtaining the relative binding tendency of the allele to the bait molecule, wherein the relative binding tendency of the allele corresponds to the tendency of nucleic acids encoding at least a portion of the allele to bind to the bait molecule in the presence of nucleic acids encoding one or more other alleles; c) applying an objective function to measure the difference between the relative binding tendency of the allele and the observed allele frequency; d) applying an optimization model to minimize the objective function; e) determining an adjusted allele frequency of the allele based on the optimization model and the observed allele frequency; and f) determining that a LOH has occurred if the adjusted allele frequency of the allele is smaller than a predetermined threshold.In some embodiments, the polymorphic genes are ST7 / RAY1, ARH1 / NOEY2, TSLC1, RB, PTEN, SMAD2, SMAD4, DCC, TP53, ATM, miR-15a, miR-16-1, NAT2, BRCA1, BRCA2, hOGG1, CDH1, IGF2, CDKN1C / P57, MEN1, PRKAR1A, H19, KRAS, BAP1, PTCH1, SMO, SUFU, NOTCH1, PPP6C, LATS1, CASP8, PTPN14, ARID1A, FBXW7, M6P / IGF2R, IFN-α, olfactory receptor genes, CBFA2T3, DUTT1, FHIT, APC, P1 6, FCMD, TSC2, miR-34, c-MPL, RUNX3, DIRAS3, NRAS, miR-9, FAM50B, PLAGL1, ER, FLT3, ZDBF2, GPR1, c-KIT, NAP1L5, GRB10, EGFR, PEG10, BRAF, MEST, JAK2, DAPK1, LIT1, WT1, NF-1, PR, c-CBL, DLK1, AKT1, SNURF, cytochrome P450 gene (CYP), ZNF587, SOCS1, TIMP2, RUNX1, AR, CEBPA, C19MC, EMP3, ZNF331, CDKN2A, PEG3, NNAT, GNAS, or GATA5.

[0106] In some other embodiments, any of the methods of the Disclosure further includes, for example, measuring TMB in a sample of the Disclosure comprising tumor cells and / or tumor nucleic acids. In some embodiments, the method includes, for example, determining LOH and evaluating TMB in a sample of the Disclosure. As demonstrated herein, HLA LOH and high TMB (and optionally, intact HLA genes) can predict, for example, increased overall survival, increased probability of higher survival, and / or increased likelihood of response to ICI therapy, compared to HLA LOH without high TMB. In some embodiments, high TMB refers to TMB of 10 mutations / Mb or more or 13 mutations / Mb or more. In some embodiments, TMB is obtained from multiple sequence reads, for example, multiple sequence reads obtained by sequencing nucleic acids of at least a portion of a genome (e.g., from an enriched or unenriched sample). In some embodiments, TMB is determined based on the number of non-driver somatic coding mutations per megabase of the sequenced genome.

[0107] In some embodiments, any of the methods of the Disclosure include acquiring knowledge of the LOH of an HLA gene (e.g., in a sample obtained from an individual) and acquiring knowledge of the TMB (e.g., in a sample obtained from an individual). In some embodiments, any of the methods of the Disclosure include detecting the LOH of an HLA gene (e.g., in a sample obtained from an individual) and acquiring knowledge of the TMB (e.g., in a sample obtained from an individual). In some embodiments, any of the methods of the Disclosure include acquiring knowledge of the LOH of an HLA gene (e.g., in a sample obtained from an individual) and detecting or determining the TMB (e.g., in a sample obtained from an individual). In some embodiments, any of the methods of the Disclosure include detecting the LOH of an HLA gene (e.g., in a sample obtained from an individual) and detecting or determining the TMB (e.g., in a sample obtained from an individual). In some embodiments, the samples used to detect / determine the LOH and TMB are the same. In some embodiments, the samples used to detect / determine the LOH and TMB are different.

[0108] Treatment and Therapy Furthermore, in some other embodiments, methods for identifying individuals with cancer are provided herein, where loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes indicates that individuals with certain types of disease tend to respond to certain treatments. In some embodiments, the method comprises detecting LOH of HLA genes in a sample from an individual, where LOH of HLA genes is detected according to a method according to any embodiment described herein. In some embodiments, LOH of HLA genes in a sample indicates that the individual is not likely to benefit from treatments including ICIs. In some embodiments, the detection of the absence of LOH of HLA genes in a sample indicates that the individual is likely to benefit from treatments including ICIs. In some embodiments, the method further comprises detecting tumor mutagenesis (TMB) in a sample obtained from an individual. In some embodiments, the method further comprises obtaining knowledge of high tumor mutagenesis (TMB) in a sample obtained from an individual. In some embodiments, LOH of HLA genes and high TMB indicate that the individual is likely to benefit from treatments including ICIs. In some embodiments, LOH of HLA genes without low TMB or high TMB indicates that individuals are less likely to benefit from treatments including ICIs.

[0109] Furthermore, in some other embodiments, methods for selecting a therapy for an individual with cancer are provided herein. In some embodiments, the method comprises detecting loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from an individual, wherein LOH of the HLA genes is detected according to a method according to any embodiment described herein. In some embodiments, LOH of the HLA genes in a sample indicates that the individual is unlikely to benefit from a therapy including an ICI. In some embodiments, detecting the absence of LOH of the HLA genes in a sample indicates that the individual is likely to benefit from a therapy including an ICI. In some embodiments, the method further comprises detecting tumor mutagenesis (TMB) in a sample obtained from an individual. In some embodiments, the method further comprises obtaining knowledge of high tumor mutagenesis (TMB) in a sample obtained from an individual. In some embodiments, LOH of the HLA genes and high TMB indicate that the individual is likely to benefit from a therapy including an ICI. In some embodiments, LOH of the HLA genes and low TMB, or LOH of the HLA genes without high TMB, indicates that the individual is unlikely to benefit from a therapy including an ICI.

[0110] Furthermore, in some other embodiments, methods for identifying one or more treatment options for an individual with cancer are provided herein. In some embodiments, the method includes (a) acquiring knowledge of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from an individual, wherein the LOH of HLA genes is detected and acquired according to a method according to any embodiment described herein, and (b) generating a report that includes one or more treatment options identified for the individual, at least in part on such knowledge. In some embodiments, the LOH of HLA genes in a sample indicates that the individual is unlikely to benefit from treatments including ICIs. In some embodiments, the one or more treatment options do not include treatments including ICIs. In some embodiments, the method includes (a) acquiring knowledge of the absence of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from an individual, wherein the absence of LOH of HLA genes is detected and acquired according to a method according to any embodiment described herein, and (b) generating a report that includes one or more treatment options identified for the individual, at least in part on such knowledge. In some embodiments, the absence of LOH in an HLA gene in a sample indicates that the individual is likely to benefit from treatment including an ICI. In some embodiments, the method further includes detecting tumor mutational burden (TMB) in a sample obtained from an individual. In some embodiments, LOH and high TMB in an HLA gene indicate that the individual is likely to benefit from treatment including an ICI. In some embodiments, LOH and low TMB in an HLA gene, or LOH in an HLA gene without high TMB, indicates that the individual is not likely to benefit from treatment including an ICI. In some embodiments, the method further includes gaining knowledge of high TMB in a sample from an individual, where one or more treatment options include treatment including an ICI.

[0111] Furthermore, in some other embodiments, methods for selecting treatment for individuals with cancer are provided herein. In some embodiments, the method comprises acquiring knowledge of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from an individual with cancer, wherein LOH of the HLA genes is detected according to a method according to any embodiment described herein. In some embodiments, in response to acquiring such knowledge, (i) the individual is classified as a candidate not to receive treatment with an immune checkpoint inhibitor (ICI), (ii) the individual is identified as not likely to respond to treatment including an immune checkpoint inhibitor (ICI), and / or (iii) the individual is classified as a candidate to receive treatment other than an immune checkpoint inhibitor (ICI). In some embodiments, the method comprises acquiring knowledge of the absence of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from an individual with cancer, wherein the absence of LOH of the HLA genes is detected according to a method according to any embodiment described herein. In some embodiments, in response to acquiring such knowledge, (i) the individual is classified as a candidate for treatment with an immune checkpoint inhibitor (ICI), and / or (ii) the individual is identified as likely to respond to treatment including an immune checkpoint inhibitor (ICI). In some embodiments, the method includes acquiring knowledge of the LOH of the human leukocyte antigen (HLA) gene in a sample obtained from the individual, and acquiring knowledge of high tumor mutagenesis (TMB) in a sample obtained from the individual. In some embodiments, in response to acquiring such knowledge, (i) the individual is classified as a candidate for treatment with an immune checkpoint inhibitor (ICI), and / or (ii) the individual is identified as likely to respond to treatment including an immune checkpoint inhibitor (ICI).

[0112] Furthermore, in some other embodiments, methods for predicting the survival time of individuals with cancer treated with immune checkpoint inhibitors (ICIs) are provided herein. In some embodiments, the method comprises acquiring knowledge of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from the individual, wherein LOH of the HLA genes is detected according to a method according to any embodiment described herein. In some embodiments, in response to acquiring such knowledge, the individual is predicted to have a shorter survival time after ICI treatment compared to the survival time of individuals treated with ICIs in which the cancer does not exhibit LOH of the HLA genes. In some embodiments, the method comprises acquiring knowledge of the absence of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from the individual, wherein the absence of LOH of the HLA genes is detected according to a method according to any embodiment described herein. In some embodiments, in response to acquiring such knowledge, the individual is predicted to have a longer survival time after ICI treatment compared to the survival time of individuals treated with ICIs in which the cancer does not exhibit LOH of the HLA genes. In some embodiments, the method comprises acquiring knowledge of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from an individual and acquiring knowledge of high tumor mutagenesis (TMB) in a sample obtained from an individual, wherein LOH of the HLA gene is detected according to the method of any embodiment described herein. In some embodiments, in response to acquiring such knowledge, it is predicted that the individual will have a longer survival period after treatment with an ICI compared to the survival period of an individual treated with an ICI in which the cancer has LOH of the HLA gene without high TMB.

[0113] In some embodiments according to any of the embodiments described herein, the LOH of an HLA gene is obtained by a) obtaining the allele frequency observed for an HLA allele, wherein the observed allele frequency corresponds to the frequency of nucleic acids encoding at least a portion of the HLA allele detected among a plurality of sequence reads corresponding to the HLA gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule, and b) obtaining the relative binding tendency of the HLA allele to the bait molecule, wherein the relative binding tendency of the HLA allele is The determination is made by: a) obtaining the tendency of nucleic acids encoding at least a portion of an HLA allele to bind to a bait molecule in the presence of nucleic acids encoding a portion of one or more other HLA alleles; c) determining an objective function that measures the difference between the relative binding tendency of the HLA allele and the observed allele frequency; d) determining an optimization model configured to minimize the objective function; e) determining a tuned allele frequency of the HLA allele based on the optimization model and the observed allele frequency; and f) determining that LOH has occurred if the tuned allele frequency of the HLA allele is less than a predetermined threshold.

[0114] Furthermore, in some other embodiments, methods for treating cancer or slowing the progression of cancer are provided herein. In some embodiments, the method is to (1) detect loss of heterozygosity (LOH) of a human leukocyte antigen (HLA) gene in a sample obtained from an individual, wherein the LOH of the HLA gene is obtained by a) obtaining the allele frequency observed for an HLA allele, wherein the observed allele frequency corresponds to the frequency of nucleic acids encoding at least a portion of the HLA allele detected among a plurality of sequence reads corresponding to the HLA gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding a gene or a portion thereof captured by hybridization with a bait molecule, and b) obtaining the relative binding tendency of the HLA allele to the bait molecule, wherein the relative binding tendency of the HLA allele encodes a portion of one or more other HLA alleles (2) Detecting LOH of an HLA gene, which is detected by: (a) obtaining, in the presence of nucleic acids, the tendency of nucleic acids encoding at least a portion of an HLA allele to bind to a bait molecule; (b) applying an objective function to measure the difference between the relative binding tendency of the HLA allele and the observed allele frequency; (d) applying an optimization model to minimize the objective function; (e) determining the adjusted allele frequency of the HLA allele based on the optimization model and the observed allele frequency; and (f) determining that LOH has occurred if the adjusted allele frequency of the HLA allele is less than a predetermined threshold; and (2) administering to the individual an effective dose of a non-immune checkpoint inhibitor (ICI) treatment, at least in part, based on the detection of LOH of an HLA gene.In some embodiments, the method involves (1) detecting the absence of loss of heterozygosity (LOH) of a human leukocyte antigen (HLA) gene in a sample obtained from an individual, wherein the absence of LOH of the HLA gene is obtained by a) obtaining the observed allele frequency for the HLA allele, wherein the observed allele frequency corresponds to the frequency of nucleic acids encoding at least a portion of the HLA allele detected among a plurality of sequence reads corresponding to the HLA gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule, and b) obtaining the relative binding tendency of the HLA allele to the bait molecule, wherein the relative binding tendency of the HLA allele codes for a portion of one or more other HLA alleles (2) Detecting LOH of an HLA gene, which is detected by (1) obtaining, in the presence of nucleic acids, the nucleic acids encoding at least a portion of the HLA alleles, corresponding to the tendency of the nucleic acids to bind to the bait molecule; (2) applying an acquisition objective function to measure the difference between the relative binding tendency of the HLA alleles and the observed allele frequency; (3) applying an optimization model to minimize the objective function; (4) determining the adjusted allele frequency of the HLA allele based on the optimization model and the observed allele frequency; and (5) determining that LOH did not occur if the adjusted allele frequency of the HLA allele is greater than a predetermined threshold; and (6) administering an effective amount of an immune checkpoint inhibitor (ICI) to the individual, at least in part, based on the detection of the absence of LOH of an HLA gene.

[0115] Certain aspects of this disclosure relate to immune checkpoint inhibitors (ICIs). As is known in the art, checkpoint inhibitors target at least one immune checkpoint protein to alter the modulation of the immune response. Immune checkpoint proteins include, for example, CTLA4, PD-L1, PD-1, PD-L2, VISTA, B7-H2, B7-H3, B7-H4, B7-H6, 2B4, ICOS, HVEM, CEACAM, LAIR1, CD80, CD86, CD276, VTCN1, MHC class I, MHC class II, GALS, adenosine, TGFR, CSF1R, MICA / B, arginase, CD160, gp49B, PIR-B, KIR family receptors, TIM-1, TIM-3, TIM-4, LAG-3, BTLA, SIRPα (CD47), CD48, 2B4 (CD244), B7.1, B7.2, ILT-2, ILT-4, TIGIT, LAG-3, BTLA, IDO, OX40, and A2aR.In some embodiments, molecules involved in the modulation of immune checkpoints include, but are not limited to, PD-1 (CD279), PD-L1 (B7-H1, CD274), PD-L2 (B7-CD, CD273), CTLA-4 (CD152), HVEM, BTLA (CD272), killer cell immunoglobulin-like receptor (KIR), LAG-3 (CD223), TIM-3 (HAVCR2), CEACAM, CEACAM-1, CEACAM-3, CEACAM-5, GAL9, VISTA (PD-1H), TIGIT, LAIR1, CD160, 2B4, TGFRβ, A2AR, GITR (CD357), CD80 (B7-1), CD86 (B7-2), CD276 (B7-H3), VTCNI (B7-H4), MHC class I, MHC class II, GALS, adenosine, TGFR, B7-H1, OX40 (CD134), CD94 (KLRD1), CD137 (4-1BB), CD137L (4-1BBL), CD40, IDO, CSF1R, CD40L, CD47, CD70 (CD27L), CD226, HHLA2, ICOS (CD278), ICOSL (CD275), LIGHT (TNFSF14, CD258), NKG2a, NKG2d, OX40L (CD134L), PVR (NECL5, CD155), SIRPa, MICA / B, and / or arginase. In some embodiments, immune checkpoint inhibitors (i.e., checkpoint inhibitors) reduce the activity of checkpoint proteins that negatively modulate immune cell function, for example, to enhance T cell activation and / or anti-cancer immune responses. In other embodiments, checkpoint inhibitors increase the activity of checkpoint proteins that positively modulate immune cell function, for example, to enhance T cell activation and / or anti-cancer immune responses. In some embodiments, the checkpoint inhibitor is an antibody.Examples of checkpoint inhibitors include, but are not limited to, PD-1 axis-coupled antagonists, PD-L1 axis-coupled antagonists (e.g., anti-PD-L1 antibodies, e.g., atezolizumab (MPDL3280A)), antagonists against co-inhibitory molecules (e.g., CTLA4 antagonists (e.g., anti-CTLA4 antibodies), TIM-3 antagonists (e.g., anti-TIM-3 antibodies), or LAG-3 antagonists (e.g., anti-LAG-3 antibodies)), or any combination thereof. In some embodiments, immune checkpoint inhibitors include drugs such as small molecules, recombinant ligands or receptors, or antibodies (e.g., human antibodies) (see, for example, International Patent Publication No. 2015 / 016718, Pardoll, Nat Rev Cancer, 12(4):252-64, 2012; both are incorporated herein by reference). In some embodiments, known inhibitors of immune checkpoint proteins or their analogues may be used, and in particular, chimeric, humanized, or human-shaped antibodies may be used.

[0116] In some embodiments according to any of the embodiments described herein, the ICI comprises a PD-1 antagonist / inhibitor or a PD-L1 antagonist / inhibitor.

[0117] In some embodiments, the checkpoint inhibitor is a PD-L1 axis-binding antagonist (e.g., a PD-1 binding antagonist, a PD-L1 binding antagonist, or a PD-L2 binding antagonist). PD-1 (programmed death 1) is also referred to in the art as "programmed cell death 1," "PDCD1," "CD279," and "SLEB2." An exemplary human PD-1 is shown in UniProtKB / Swiss-Prot accession number Q15116. PD-L1 (programmed cell death ligand 1) is also referred to in the art as "programmed cell death 1 ligand 1," "PDCD1LG1," "CD274," "B7-H," and "PDL1." An exemplary human PD-L1 is shown in UniProtKB / Swiss-Prot accession number Q9NZQ7.1. PD-L2 (programmed cell death ligand 2) is also referred to in the art as "programmed cell death ligand 1-2," "PDCD1LG2," "CD273," "B7-DC," "Btdc," and "PDL2." An exemplary human PD-L2 is shown in UniProtKB / Swiss-Prot accession number Q9BQ51. In some cases, PD-1, PD-L1, and PD-L2 are human PD-1, PD-L1, and PD-L2.

[0118] In some cases, a PD-1 binding antagonist / inhibitor is a molecule that inhibits the binding of PD-1 to its ligand-binding partner. In certain embodiments, the PD-1 ligand-binding partner is PD-L1 and / or PD-L2. In another example, a PD-L1 binding antagonist / inhibitor is a molecule that inhibits the binding of PD-L1 to its binding ligand. In certain embodiments, the PD-L1 binding partner is PD-1 and / or B7-1. In yet another example, a PD-L2 binding antagonist is a molecule that inhibits the binding of PD-L2 to its ligand-binding partner. In certain embodiments, the PD-L2 binding ligand partner is PD-1. The antagonist may be an antibody, its antigen-binding fragment, an immunoadhesin, a fusion protein, or an oligopeptide. In some embodiments, the PD-1 binding antagonist is a small molecule, nucleic acid, polypeptide (e.g., an antibody), carbohydrate, lipid, metal, or toxin.

[0119] In some cases, the PD-1 conjugated antagonist is, for example, one of the following anti-PD-1 antibodies (e.g., human antibody, humanized antibody, or chimeric antibody): MDX-1106 (nivolumab), MK-3475 (pembrolizumab, Keytruda®), MEDI-0680 (AMP-514), PDR001, REGN2810, MGA-012, JNJ-63723283, BI754091, or BGB-108. In other embodiments, the PD-1 conjugated antagonist is an immunoadhesin (e.g., an immunoadhesin containing an extracellular or PD-1 binding moiety of PD-L1 or PD-L2 fused to a constant region (e.g., the Fc region of an immunoglobulin sequence)). In some cases, the PD-1 conjugated antagonist is AMP-224. Other examples of anti-PD-1 antibodies include MEDI-0680 (AMP-514; AstraZeneca), PDR001 (CAS Registry No. 1859072-53-9; Novartis), REGN2810 (LIBTAYO® or semiprimab-rwlc; Regeneron), BGB-108 (BeiGene), BGB-A317 (BeiGene), BI754091, JS-001 (Shanghai Junshi), STI-A1110 (Sorrento), INCSHR-1210 (Incyte), PF-06801591 (Pfizer), TSR-042 (also known as ANB011; Tesaro / AnaptysBio), AM0001 (ARMO Biosciences), and ENUM244C8 (Enumeral Biomedical). Examples include ENUM388D4 (Enumeral Biomedical Holdings) or ENUM388D4 (Enumeral Biomedical Holdings).In some embodiments, the PD-1 axially coupled antagonist is tisrelizumab (BGB-A317), BGB-108, STI-A1110, AM0001, BI754091, cintilimab (IBI308), cetrerimab (JNJ-63723283), tripalimab (JS-001), or camrelizumab (SHR-1210, INCSHR-1210, HR-301210). MEDI-0680 (AMP-514), MGA-012 (INCMGA0012), Nivolumab (BMS-936558, MDX1106, ONO-4538), Spartalizumab (PDR00l), Pembrolizumab (MK-3475, SCH900475, Keytruda®), PF-06801591, Semiprimab (REGN-2810, REGEN2 810), Dostallumab (TSR-042, ANB011), FITC-YT-16 (PD-1 binding peptide), APL-501 or CBT-501 or Genolimuzumab (GB-226), AB-122, AK105, AMG404, BCD-100, F520, HLX10, HX008, JTX-4014, LZM009, Sym021, PSB205, AMP-2 24 (PD-1 targeting fusion protein), CX-188 (PD-1 probody), AGEN-2034, GLS-010, buzigalimab (ABBV-181), AK-103, BAT-1306, CS-1003, AM-0001, TILT-123, BH-2922, BH-2941, BH-2950, ​​ENUM-244C8, ENUM-388D4, HAB-21, H EISCOI11-003, IKT-202, MCLA-134, MT-17000, PEGMP-7, PRS-332, RXI-762, STI-1110, VXM-10, XmAb-23104, AK-112, HLX-20, SSI-361, AT-16201, SNA-01, AB122, PD1-PIK, PF-06936308, RG-7 Includes 769, CABPD-1Abs, AK-123, MEDI-3387, MEDI-5771, 4H1128Z-E27, REMD-288, SG-001, BY-24.3, CB-201, IBI-319, ONCR-177, Max-1, CS-4100, JBI-426, CCC-0701, or CCX-4503, or derivatives thereof.

[0120] In some embodiments, the PD-L1-binding antagonist is a small molecule that inhibits PD-1. In some embodiments, the PD-L1-binding antagonist is a small molecule that inhibits PD-L1. In some embodiments, the PD-L1-binding antagonist is a small molecule that inhibits PD-L1 and VISTA or PD-L1 and TIM3. In some embodiments, the PD-L1-binding antagonist is CA-170 (also known as AUPM-170). In some embodiments, the PD-L1-binding antagonist is an anti-PD-L1 antibody. In some embodiments, the anti-PD-L1 antibody can bind to human PD-L1 (e.g., human PD-L1 as shown in UniProtKB / Swiss-Prot accession number Q9NZQ7.1) or a variant thereof. In some embodiments, the PD-L1-binding antagonist is a small molecule, nucleic acid, polypeptide (e.g., antibody), carbohydrate, lipid, metal, or toxin.

[0121] In some cases, the PD-L1 conjugated antagonist is, for example, the anti-PD-L1 antibody listed below. In some cases, the anti-PD-L1 antibody can inhibit the binding between PD-L1 and PD-1, and / or the binding between PD-L1 and B7-1. In some cases, the anti-PD-L1 antibody is a monoclonal antibody. In some cases, the anti-PD-L1 antibody is an antibody fragment selected from Fab, Fab'-SH, Fv, scFv, or (Fab')2 fragments. In some cases, the anti-PD-L1 antibody is a humanized antibody. In some cases, the anti-PD-L1 antibody is a human antibody. In some cases, the anti-PD-L1 antibody is selected from YW243.55.S70, MPDL3280A (atezolizumab), MDX-1105, MEDI4736 (durvalumab), or MSB0010718C (avelumab).In some embodiments, the PD-L1 axis-coupled antagonist is atezolizumab, avelumab, durvalumab (Imfinzi), BGB-A333, SHR-1316 (HTI-1088), CK-301, BMS-936559, emvafolimab (KN035, ASC22), CS1001, MDX-1105 (BMS-936559), LY3300 054, STI-A1014, FAZ053, CX-072, INCB086550, GNS-1480, CA-170, CK-301, M-7824, HTI-1088(HT I-131, SHR-1316), MSB-2311, AK-106, AVA-004, BBI-801, CA-327, CBA-0710, CBT-502, FPT-155, IKT-201, IKT-703, 10-103, JS-003, KD-033, KY-1003, MCLA-145, MT-5050, SNA-02, BCD-135, AP L-502 (CBT-402 or TQB2450), IMC-001, KD-045, INBRX-105, KN-046, IMC-2102, IMC-2101, KD-005 This includes IMM-2502, 89Zr-CX-072, 89Zr-DFO-6E11, KY-1055, MEDI-1109, MT-5594, SL-279252, DSP-106, Gensci-047, REMD-290, N-809, PRS-344, FS-222, GEN-1046, BH-29xx, or FS-118, or their derivatives.

[0122] In some embodiments, the checkpoint inhibitor is an antagonist / inhibitor of CTLA4. In some embodiments, the checkpoint inhibitor is a small molecule antagonist of CTLA4. In some embodiments, the checkpoint inhibitor is an anti-CTLA4 antibody. CTLA4 is part of the CD28-B7 immunoglobulin superfamily of immune checkpoint molecules and acts to negatively modulate T cell activation (particularly the CD28-dependent T cell response). CTLA4 competes for binding to ligands common to CD28, such as CD80(B7-1) and CD86(B7-2), and binds to these ligands with higher affinity than CD28. Blocking CTLA4 activity (e.g., using an anti-CTLA4 antibody) is thought to enhance CD28-mediated costimulation (leading to increased T cell activation / priming), affect T cell development, and / or deplete Tregs (e.g., intratumor Tregs). In some embodiments, the CTLA4 antagonist is a small molecule, nucleic acid, polypeptide (e.g., antibody), carbohydrate, lipid, metal, or toxin. In some embodiments, the CTLA-4 inhibitor includes ipilimumab (IBI310, BMS-734016, MDX010, MDX-CTLA4, MEDI4736), tremelimumab (CP-675, CP-675, 206), APL-509, AGEN1884, CS1002, AGEN1181, abatacept (Orencia, BMS-188667, RG2077), BCD-145, ONC-392, ADU-1604, REGN4659, ADG116, KN044, KN046, or derivatives thereof.

[0123] In some embodiments, the anti-PD-1 antibody or antibody fragment is MDX-1106 (nivolumab), MK-3475 (pembrolizumab, Keytruda®), MEDI-0680 (AMP-514), PDR001, REGN2810, MGA-012, JNJ-63723283, BI754091, BGB-108, BGB-A317, JS-001, STI-A1110, INCSHR-1210, PF-06801591, TSR-042, AM0001, ENUM244C8, or ENUM388D4. In some embodiments, the PD-1 binding antagonist is an anti-PD-1 immunoadhesin. In some embodiments, the anti-PD-1 immunoadhesin is AMP-224. In some embodiments, the anti-PD-L1 antibody or antibody fragment is YW243.55.S70, MPDL3280A (atezolizumab), MDX-1105, MEDI4736 (durvalumab), MSB0010718C (avelumab), LY3300054, STI-A1014, KN035, FAZ053, or CX-072.

[0124] In some embodiments, the immune checkpoint inhibitor comprises a LAG-3 inhibitor (e.g., an antibody, an antibody conjugate, or its antigen-binding fragment). In some embodiments, the LAG-3 inhibitor comprises a small molecule, nucleic acid, polypeptide (e.g., an antibody), carbohydrate, lipid, metal, or toxin. In some embodiments, the LAG-3 inhibitor comprises a small molecule. In some embodiments, the LAG-3 inhibitor comprises a LAG-3 conjugate. In some embodiments, the LAG-3 inhibitor comprises an antibody, an antibody conjugate, or its antigen-binding fragment. In some embodiments, the LAG-3 inhibitor includes eftilagimod α (IMP321, IMP-321, EDDP-202, EOC-202), relatrimab (BMS-986016), GSK2831781 (IMP-731), LAG525 (IMP701), TSR-033, EVIP321 (soluble LAG-3 protein), BI754111, IMP761, REGN3767, MK-4280, MGD-013, XmAb22841, INCAGN-2385, ENUM-006, AVA-017, AM-0003, iOnctura anti-LAG-3 antibody, Arcus Biosciences LAG-3 antibody, Sym022, its derivatives, or antibodies that compete with any of the above.

[0125] In some embodiments, the anticancer therapy includes immunomodulatory molecules or cytokines. In some embodiments, the methods provided herein include, for example, administering immunomodulatory molecules or cytokines to an individual in combination with another anticancer therapy. The immunomodulatory profile is necessary to induce an efficient immune response and balance the target immune system. Examples of suitable immunomodulatory cytokines include, but are not limited to, interferons (e.g., IFNα, IFNβ, and IFNγ), interleukins (e.g., IL-1, IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12, and IL-20), tumor necrosis factors (e.g., TNFα and TNFβ), erythropoietin (EPO), FLT-3 ligand, gIp10, TCA-3, MCP-1, MIF, MIP-1α, MIP-1β, RANTES, macrophage colony-stimulating factor (M-CSF), granulocyte colony-stimulating factor (G-CSF), or granulocyte-macrophage colony-stimulating factor (GM-CSF), and their functional fragments. In some embodiments, any immunomodulatory chemokine that binds to a chemokine receptor (i.e., CXC, CC, C, or CX3C chemokine receptor) can be used in connection with this disclosure. Examples of chemokines include, but are not limited to, MIP-3α(Lax), MIP-3β, Hcc-1, MPIF-1, MPIF-2, MCP-2, MCP-3, MCP-4, MCP-5, eotaxin, Tarc, Elc, I309, IL-8, GCP-2 Groα, Gro-β, Nap-2, Ena-78, Ip-10, MIG, I-Tac, SDF-1, or BCA-1(Blc), and their functional fragments. In some embodiments, the immunomodulatory molecule is included in any of the treatments provided herein.

[0126] In some embodiments, the immune checkpoint inhibitor is monovalent and / or monospecific. In some embodiments, the immune checkpoint inhibitor is polyvalent and / or polyspecific.

[0127] In some embodiments, the method includes administering a second therapeutic agent. In some embodiments, the second agent is an agent other than an ICI (e.g., as described below) or a second ICI (e.g., as described above).

[0128] In some embodiments, the method includes administering a drug other than an ICI. In some embodiments, the drug includes chemotherapeutic agents, antihormone agents, antimetabolitic chemotherapeutic agents, kinase inhibitors, peptides, gene therapies, vaccines, platinum-based chemotherapeutic agents, immunotherapies, or antibodies.

[0129] In some embodiments, anticancer therapy includes chemotherapy. In some embodiments, the methods provided herein include administering chemotherapy to an individual, for example, in combination with another anticancer therapy. Examples of chemotherapeutic agents include alkylating agents (e.g., thiotepa and cyclophosphamide), alkyl sulfonates (e.g., busulfan, improsulfan, pigosulfan), aziridines (e.g., benzodopa, carbocon, metsuredopa, and uredopa), ethyleneimines and methylamelamines (including altretamine, triethylenemelamine, triethylenephosphoramide, triethylenethiophosphoramide, and trimethylolmelamine), acetogenins (in particular, bratacin and bratacin). Tacinone), Camptothecin (including synthetic analog Topotecan), Briostatin, Callistatin, CC-1065 (including synthetic analogs Adzelesin, Carzelesin, and Bizeresin), Cryptophycin (especially Cryptophycin 1 and Cryptophycin 8), Dorastatin, Duocalmycin (including synthetic analogs KW-2189 and CB1-TM1), Erytherobin, Pancratistatin, Sarcodictin, Spongestatin, Nitrogen Mustard (e.g., Chlorambucil, Chroma Fascin, chlorophosphamide, estramustine, ifosfamide, mechloretamine, mechloretamine oxide hydrochloride, melphalan, nobenbitin, fenestrine, prednimustine, trophosphamide, and uracil mustard), nitrosourea (carmustine, chlorozotocin, fotemustine, lomustine, nimustine, and ranimustine), antibiotics (e.g., engine antibiotics (calicheamicin, especially calicheamicin gamma II and calicheamicin omega II), Dynemicin (e.g., including dynemicin A), bisphosphonates (e.g., clodronate), esperamicin, and neocardinostatin chromophores and related pigment proteins enediin antibiotic chromophores, acrasinomycin, actinomycin, outramycin, azaserin, bleomycin, kactinomycin, carabicin, carminomycin, cardinophilin, chromomycin, dactinomycin, daunorubicin, detorubicin, 6-diazo-5-oxo-L-norleucine,Doxorubicin (including morpholinodoxorubicin, cyanomorpholinodoxorubicin, 2-pyrrolino-doxorubicin and deoxydoxorubicin), epirubicin, esorubicin, idarubicin, marcelomycin, mitomycin (e.g., mitomycin C), mycophenolic acid, nogaramycin, olibomycin, peplomycin, potophyllomycin, puromycin, keramycin, rhodorubicin, streptonigrin, streptozocin, tubercidine, ubenimex, dinostatin, and zolbicin), antimetabolites (e.g., methotrexate), Trexate and 5-fluorouracil (5-FU)), folate analogs (e.g., denopterin, pteropterin, trimethrexate), purine analogs (e.g., fludarabine, 6-mercaptopurine, thiamiprine, thioguanine), pyrimidine analogs (e.g., ancitabine, azacitidine, 6-azauridine, carmofur, cytarabine, dideoxyuridine, doxifluridine, enocitabine, and phloxuridine), androgens (e.g., carsterone, dromostanolone propionate, epithiostanol, mepitiostane, and tes) Tractone), anti-adrenal agents (e.g., mitotane and trilostane), folic acid supplements (e.g., folic acid), acegraton, aldofsphamide glycoside, aminolevulinic acid, enyluracil, amsacrin, bestrabusil, bisanthren, edatraxate, defoamine, demecolsin, diazicone, elformin, eriptinium acetate, epotilon, etoglucide, gallium nitrate, hydroxyurea, lentinan, ronidynin, mytansinoids (e.g., mytansin and anthamitosin), mitogwazone, mitoxantrone, mopidammole, ni Traeline, pentostatin, fenamet, pirarubicin, losoxantrone, podophyllic acid, 2-ethylhydrazide, procarbazine, PSK polysaccharide complex, razoxane, rhizoxin, schizophyllan, spirogermanium, tenuazonic acid, triadicone, 2,2',2"-trichlorotriethylamine, trichothecenes (especially T-2 toxin, beraclin A, loridine A, and angidin), urethane, vindesine, dacarbazine, mannomustine, mitobronitol, mitractol, pipobromane, gasitosine, arabinoside ("Ara-C"),Cyclophosphamide, taxoids (e.g., paclitaxel and docetaxel gemcitabine), 6-thioguanine, mercaptopurine, platinum-coordinated complexes (e.g., cisplatin, oxaliplatin, and carboplatin), vinblastine, platinum, etoposide (VP-16), ifosfamide, mitoxantrone, vincristine, vinorelbine, novantrone, teniposide, edatrexate, daunomycin, aminopterin, xeloda, ibandronate, irinotecan (e.g., CPT-16) l) Examples include the topoisomerase inhibitor RFS2000, difluoromethylornithine (DMFO), retinoids (e.g., retinoic acid), capecitabine, carboplatin, procarbazine, pricomycin, gemcitabine, navelbine, farnesyl protein transferase inhibitors, transplatinum, and any pharmaceutically acceptable salts, acids, or derivatives of any of the above.

[0130] Some non-limiting examples of chemotherapy drugs that can be combined with the anticancer therapies described herein include carboplatin (Paraplatin), cisplatin (Platinol, Platinol-AQ), cyclophosphamide (Cytoxan, Neosar), docetaxel (Taxotere), doxorubicin (Adriamycin), erlotinib (Tarceva), etoposide (VePesid), fluorouracil (5-FU), gemcitabine (Gemzar), imatinib mesylate (Gleevec), irinotecan (Camptosar), methotrexate (Folex, Mexate, Amethopterin), paclitaxel (Taxol, Abraxane), sorafinib (Nexavar), sunitinib (Sutent), topotecan (Hycamtin), and vincristine (Oncovin, Vincasar). These are PFS and vinblastine (Velban).

[0131] In some embodiments, the anticancer therapy includes a kinase inhibitor. In some embodiments, the methods provided herein include, for example, administering a kinase inhibitor to an individual in combination with another anticancer therapy. Examples of kinase inhibitors include those that target one or more receptor tyrosine kinases (e.g., BCR-ABL, B-Raf, EGFR, HER-2 / ErbB2, IGF-IR, PDGFR-a, PDGFR-β, cKit, Flt-4, Flt3, FGFR1, FGFR3, FGFR4, CSF1R, c-Met, RON, c-Ret, or ALK), those that target one or more cytoplasmic tyrosine kinases (e.g., c-SRC, c-YES, Abl, or JAK-2), those that target one or more serine / threonine kinases (e.g., ATM, Aurora A and B, CDK, mTOR, PKCi, PLK, b-Raf, S6K, or STK11 / LKB1), or those that target one or more lipid kinases (e.g., PI3K or SKI). Small molecule kinase inhibitors include PHA-739358, nilotinib, dasatinib, PD166326, NSC743411, lapatinib (GW-572016), canertinib (CI-1033), semaxinib (SU5416), batalanib (PTK787 / ZK222584), sutent (SU11248), sorafenib (BAY43-9006), or leflunomide (SU101). Additional non-exclusive examples of tyrosine kinase inhibitors include imatinib (Gleevec / Glivec) and gefitinib (Iressa).

[0132] In some embodiments, the anticancer therapy includes an anti-angiogenic agent. In some embodiments, the methods provided herein include, for example, administering an anti-angiogenic agent to an individual in combination with another anticancer therapy. Angiogenesis inhibitors prevent the extensive growth of blood vessels (angiogenesis) necessary for tumor survival. Non-limiting examples of angiogenesis-mediated molecules or angiogenesis inhibitors that may be used in the methods of this disclosure include soluble VEGF (e.g., VEGF isoforms (e.g., VEGF121 and VEGF165), VEGF receptors (e.g., VEGFR1, VEGFR2), and co-receptors (e.g., neuropilin-1 and neuropilin-2)), NRP-1, angiopoietin 2, TSP-1 and TSP-2, angiostatins and related molecules, endostatins, vasostatins, calreticulin, platelet factor-4, TIMP and CDAI, Meth-1 and Meth-2, IFNα, IFN-β and IFN-γ, CXCL10, IL-4, IL-12, and IL-18, prothrombin (cringle domain- 2) Examples include antithrombin III fragments, prolactin, VEGI, SPARC, osteopontin, Maspin, canstatin, proliferin-related proteins, restin, and drugs (e.g., bevacizumab, itraconazole, carboxamide triazole, TNP-470, CM101, IFN-a, platelet factor-4, suramin, SU5416, thrombospondin, VEGFR antagonists, anti-angiogenic steroids, and heparin), cartilage-derived anti-angiogenic factors, matrix metalloproteinase inhibitors, 2-methoxyestradiol, tecogalane, tetrathiomolybdate, thalidomide, thrombospondin, prolactin νβ3 inhibitors, linamide, or tascinimod. In some embodiments, known therapeutic candidates that may be used according to the methods of this disclosure include, but are not limited to, naturally occurring anti-angiogenic inhibitors, including angiostatin, endostatin, or platelet factor-4. In another embodiment, therapeutic candidates that may be used according to the methods of this disclosure include, but are not limited to, endothelial cell proliferation-specific inhibitors such as TNP-470, thalidomide, and interleukin-12.Further anti-angiogenic agents that can be used according to the methods of this disclosure include, but are not limited to, those that neutralize angiogenic molecules, and include antibodies against fibroblast growth factor, antibodies against vascular endothelial growth factor, antibodies against platelet-derived growth factor, or antibodies or other types of inhibitors of EGF, VEGF, or PDGF receptors. In some embodiments, anti-angiogenic agents that can be used according to the methods of this disclosure include, but are not limited to, suramin and its analogs, and tecogalan. In other embodiments, anti-angiogenic agents that can be used according to the methods of this disclosure include, but are not limited to, agents that neutralize angiogenic factor receptors, or agents that interfere with the vascular basement membrane and extracellular matrix, and include, but are not limited to, metalloproteinase inhibitors and angiogenesis-inhibiting steroids. Another group of anti-angiogenic compounds that can be used according to the methods of this disclosure include, but are not limited to, anti-adhesion molecules such as antibodies against integrin αvβ3. Further anti-angiogenic compounds or compositions that may be used in accordance with the methods of this disclosure include kinase inhibitors, thalidomide, itraconazole, carboxamide triazole, CM101, IFN-α, IL-12, SU5416, thrombospondin, cartilage-derived angiogenesis inhibitors, 2-methoxyestradiol, tetrathiomolybdate, thrombospondin, prolactin, and linamide. In one particular embodiment, the anti-angiogenic compound that may be used in accordance with the methods of this disclosure is an antibody against VEGF, such as Avastin® / bevacizumab (Genentech).

[0133] In some embodiments, the anticancer therapy includes anti-DNA repair therapy. In some embodiments, the methods provided herein include administering anti-DNA repair therapy to an individual, for example, in combination with another anticancer therapy. In some embodiments, the anti-DNA repair therapy is a PARP inhibitor (e.g., talazoparib, lucaparib, olaparib), a RAD51 inhibitor (e.g., RI-1), or an inhibitor of DNA damage response kinase (e.g., CHCK1 (e.g., AZD7762), ATM (e.g., KU-55933, KU-60019, NU7026, or VE-821), and ATR (e.g., NU7026)).

[0134] In some embodiments, anticancer therapy includes a radiosensitizer. In some embodiments, the methods provided herein include administering a radiosensitizer to an individual, for example, in combination with another anticancer therapy. Exemplary radiosensitizers include hypoxic radiosensitizers (e.g., misonidazole, metronidazole) and sodium trans-crocetinate, which is a compound that helps increase the diffusion of oxygen into hypoxic tumor tissue. Radiosensitizers can also be DNA damage response inhibitors that interfere with base excision repair (BER), nucleotide excision repair (NER), and mismatch repair (MMR), including homologous recombination (HR) and non-homologous end joining (NHEJ) and direct repair mechanisms. Single-strand break (SSB) repair mechanisms include the BER, NER, or MMR pathway, while double-strand break (DSB) repair mechanisms consist of the HR and NHEJ pathways. Radiation causes DNA breaks that, if not repaired, are fatal. SSBs are repaired via a combination of BER, NER, and MMR mechanisms, using an intact DNA strand as a template. The primary pathway for SSB repair is BER, which utilizes a family of related enzymes called poly(ADP-ribose) polymerase (PARP). Therefore, radiosensitizers can include DNA damage response inhibitors such as PARP inhibitors.

[0135] In some embodiments, the anticancer therapy includes an anti-inflammatory agent. In some embodiments, the methods provided herein include, for example, administering an anti-inflammatory agent to an individual in combination with another anticancer therapy. In some embodiments, the anti-inflammatory agent is an agent that blocks, inhibits, or reduces signaling from inflammation or inflammatory signaling pathways. In some embodiments, the anti-inflammatory agent inhibits or reduces the activity of one or more of the following: IL-1, IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12, IL-13, IL-15, IL-18, IL-23, interferon (IFN) (e.g., IFNα, IFNβ, IFNγ, IFN-γ) inducer (IGIF), transforming growth factor-β TGF-β), transforming growth factor-α (TGF-α), tumor necrosis factor (e.g., TNF-α, TNF-β, TNF-RI, TNF-RII), CD23, CD30, CD40L, EGF, G-CSF, GDNF, PDGF-BB, RANTES / CCL5, IKK, NF-κB, TLR2, TLR3, TLR4, TL5, TLR6, TLR7, TLR8, TLR9, and / or their homologous receptors. In some embodiments, the anti-inflammatory agent is IL-1 or an IL-1 receptor antagonist, e.g., anakinra (Kineret®), lilonacept, or canakinumab. In some embodiments, the anti-inflammatory agent is an IL-6 or IL-6 receptor antagonist, for example, an anti-IL-6 antibody or anti-IL-6 receptor antibody (such as tocilizumab (ACTEMRA®), olokizumab, crazakizumab, sarilumab, sirucumab, siltuximab, or ALX-0061). In some embodiments, the anti-inflammatory agent is a TNF-α antagonist, for example, an anti-TNFα antibody (such as infliximab (Remicade®), golimumab (Simponi®), adalimumab (Humira®), certolizumab pegol (Cimzia®), or etanercept). In some embodiments, the anti-inflammatory agent is a corticosteroid.Examples of corticosteroids include cortisone (hydrocortisone, hydrocortisone sodium phosphate, hydrocortisone sodium succinate, Ala-Cort®, Hydrocort Acetate (registered trademark), hydrocortonate (Lanacort (registered trademark), Solu-Cortef (registered trademark)), Decadron (dexamethasone, dexamethasone acetate, dexamethasone sodium phosphate, Dexasone (registered trademark), Diodex (registered trademark), Hexadrol (registered trademark), Maxidex (registered trademark)), methylprednisolone (6-methylprednisolone, methylprednisolone acetate, methylprednisolone sodium succinate, Duralone (registered trademark), Medralone (registered trademark), Medrol (registered trademark), M-Prednisol (registered trademark), Solu-Medrol (registered trademark)), prednisolone (Delta-Cortef (registered trademark), ORAPRED (registered trademark), Pediapred (registered trademark), Prezone (registered trademark)), and prednisone (Deltasone (registered trademark), Liquid Examples include, but are not limited to, Pred®, Meticorten®, Orasone®, and bisphosphonates (e.g., pamidronate (Aredia®) and zoledronic acid (Zometac®)).

[0136] In some embodiments, the anti-cancer therapy includes an anti-hormone agent. In some embodiments, the methods provided herein include, for example, administering an anti-hormone agent to an individual in combination with another anti-cancer therapy. An anti-hormone agent is a drug that acts to modulate or inhibit the hormonal action on a tumor. Examples of antihormone agents include anti-estrogen agents and selective estrogen receptor modulators (SERMs), such as tamoxifen (including NOLVADEX® tamoxifen), raloxifen, doroxifen, 4-hydroxytamoxifen, trioxyfen, keoxyfen, LY117018, onapristone, and FARESTON®, toremifene, and aromatase inhibitors (which inhibit aromatase, an enzyme that regulates estrogen production in the adrenal gland) (for example, 4(5)-imidazole, aminoglutethimide, MEGACE® megestrol acetate, AROMASIN® exemestane, formestan, fadrozol, RIVISOR® borozole, FEMARA® letrozole, and ARIMIIDEX® (anastrozole)). Examples include antiandrogens (e.g., flutamide, nilutamide, bicalutamide, leuprolide, goserelin), troxacitabine (1,3-dioxolane nucleoside cytosine analog); antisense oligonucleotides (particularly those that inhibit the expression of genes in signaling pathways involved in abnormal cell proliferation (e.g., PKC-α, Raf, H-Ras, and epidermal growth factor receptor (EGF-R))); vaccines such as gene therapy vaccines (e.g., ALLOVECTIN® vaccine, LEUVECTIN® vaccine, and VAXID® vaccine); PROLEUKIN® rIL-2; LURTOTECAN® topoisomerase 1 inhibitor; ABARELIX® rmRH; and any pharmaceutically acceptable salts, acids, or derivatives of any of the above.

[0137] In some embodiments, the anticancer therapy includes an antimetabolite chemotherapeutic agent. In some embodiments, the methods provided herein include, for example, administering an antimetabolite chemotherapeutic agent to an individual in combination with another anticancer therapy. Antimetabolite chemotherapeutic agents are drugs that are structurally similar to metabolites but cannot be used productively in the body. Many antimetabolite chemotherapeutic agents interfere with the production of RNA or DNA. Examples of antimetabolites include gemcitabine (GEMZAR®), 5-fluorouracil (5-FU), capecitabine (XELODA®), 6-mercaptopurine, methotrexate, 6-thioguanine, pemetrexed, larcitrexed, arabinosylcytosine, ARA-C, cytarabine (CYTOSAR-U®), dacarbazine (DTIC-DOMED), azocytosine, deoxycytosine, pyridomiden, fludarabine (FLUDARA®), cladrabine, and 2-deoxy-D-glucose. In some embodiments, the antimetabolite is gemcitabine. Gemcitabine hydrochloride is marketed by Eli Lilly under the trademark GEMZAR®.

[0138] In some embodiments, the anticancer therapy comprises a platinum-based chemotherapeutic agent. In some embodiments, the method provided herein comprises, for example, administering a platinum-based chemotherapeutic agent to an individual in combination with another anticancer therapy. The platinum-based chemotherapeutic agent is a chemotherapeutic agent comprising an organic compound containing platinum as an integral part of the molecule. In some embodiments, the chemotherapeutic agent is a platinum agent. In some such embodiments, the platinum agent is selected from cisplatin, carboplatin, oxaliplatin, nedaplatin, triplatin tetranitrate, phenantriplatin, picoplatin, or satraplatin.

[0139] In some embodiments, anticancer therapy includes heat shock protein (HSP) inhibitors, MYC inhibitors, HDAC inhibitors, immunotherapy, neoantigens, vaccines, or cell therapy. In some embodiments, anticancer therapy includes one or more chemotherapeutic agents, VEGF inhibitors, integrin β3 inhibitors, statins, EGFR inhibitors, mTOR inhibitors, PI3K inhibitors, MAPK inhibitors, or CDK4 / 6 inhibitors.

[0140] In some embodiments, the anticancer therapy includes a kinase inhibitor. In some embodiments, the methods provided herein include, for example, administering a kinase inhibitor to an individual in combination with another anticancer therapy. In some embodiments, the kinase inhibitor is crizotinib, alectinib, ceritinib, lorlatinib, brigutinib, ensartinib (X-396), repotrectinib (TPX-005), entrectinib (RXDX-101), AZD3463, CEP-37440, belizatinib (TSR-011), ASP3026, KRCA-0008, TQ-B3139, TPX-0131, or TAE684 (NVP-TAE684). Further examples of ALK kinase inhibitors that may be used according to any of the methods provided herein are described in Examples 3-39 of WO2005016894 (incorporated herein by reference).

[0141] In some embodiments, the anticancer therapy includes a heat shock protein (HSP) inhibitor. In some embodiments, the methods provided herein include, for example, administering an HSP inhibitor to an individual in combination with another anticancer therapy. In some embodiments, the HSP inhibitor is a pan-HSP inhibitor such as KNK423. In some embodiments, the HSP inhibitor is an HSP70 inhibitor such as cmHsp70.1, quercetin, VER155008, or 17-AAD. In some embodiments, the HSP inhibitor is an HSP90 inhibitor. In some embodiments, the HSP90 inhibitor is 17-AAD, Debio0932, ganetespib (STA-9090), letaspimycin hydrochloride (letaspimycin, IPI-504), AUY922, alvespimycin (KOS-1022, 17-DMAG), tanespimycin (KOS-953, 17-AAG), DS2248, or AT13387 (onarespib). In some embodiments, the HSP inhibitor is an HSP27 inhibitor such as apatrusen (OGX-427).

[0142] In some embodiments, the anticancer therapy comprises a MYC inhibitor. In some embodiments, the methods provided herein include, for example, administering a MYC inhibitor to an individual in combination with another anticancer therapy. In some embodiments, the MYC inhibitor is MYCi361 (NUCC-0196361), MYCi975 (NUCC-0200975), Omomyc (dominant-negative peptide), ZINC16293153 (Min9), 10058-F4, JKY-2-169, 7594-0035, or an inhibitor of MYC / MAX dimerization and / or MYC / MAX / DNA complex formation.

[0143] In some embodiments, the anticancer therapy comprises a histone deacetylase (HDAC) inhibitor. In some embodiments, the methods provided herein include, for example, administering an HDAC inhibitor to an individual in combination with another anticancer therapy. In some embodiments, the HDAC inhibitor is bellinostat (PXD101, Beleodac®), SAHA (vorinostat, suberoylanilide hydroxamine, Zolinza®), panobinostat (LBH589, LAQ-824), ACY1215 (Rocilinostat), xinostat (JNJ-26481585), avexinostat (PCI-24781), prasinostat (SB939), gibinostat (ITF2357), resminostat (4SC-201), trichostatin A (TSA), MS-275 (ethinostat), romidepsin (depsipeptide, FK228), MGCD0103 (mosetinostat), BML-210, CAY10603, Valproic acid, MC1568, CUDC-907, CI-994 (Tasejinarin), Pivanex (AN-9), AR-42, Thidamide (CS055, HBI-8000), CUDC-101, CHR-3996, MPT0E028, BRD8430, MRLB-223, Apicidine, RGFP966, BG45, PCI-34051, C149 (NCC14 9) TMP269, Cpd2, T247, T326, LMK235, C1A, HPOB, Nextrustat A, Befexamac, CBHA, Phenylbutyric Acid, MC1568, SNDX275, Scriptide, Merck60, PX089344, PX105684, PX117735, PX117792, PX117245, PX105844, compound 12 listed in Li et al., Cold Spring Harb Perspect Med (2016) 6(10):a026831, or PX117445.

[0144] In some embodiments, the anticancer therapy includes a VEGF inhibitor. In some embodiments, the methods provided herein include, for example, administering a VEGF inhibitor to an individual in combination with another anticancer therapy. In some embodiments, the VEGF inhibitor is bevacizumab (Avastin®), BMS-690514, ramucirumab, pazopanib, sorafenib, sunitinib, golvatinib, vandetanib, cabozantinib, levantinib, axitinib, cejilanib, tivozanib, lusitanib, semaxanib, nindentanib, regorafinib, or aflibercept.

[0145] In some embodiments, the anticancer therapy comprises an integrin β3 inhibitor. In some embodiments, the methods provided herein include, for example, administering an integrin β3 inhibitor to an individual in combination with another anticancer therapy. In some embodiments, the integrin β3 inhibitor is anti-avb3 (clone LM609), sirengitide (EMD121974, NSC, 707544), siRNA, GLPG0187, MK-0429, CNTO95, TN-161, etalacizumab (MEDI-522), intetumumab (CNTO95) (anti-αV subunit antibody), abituzumab (EMD525797 / DI17E6) (anti-αV subunit antibody), JSM6427, SJ749, BCH-15046, SCH221153, or SC56631. In some embodiments, the anticancer therapy comprises an αIIbβ3 integrin inhibitor. In some embodiments, the methods provided herein include, for example, administering an αIIbβ3 integrin inhibitor to an individual in combination with another anticancer therapy. In some embodiments, the αIIbβ3 integrin inhibitor is absiximab, eptifivatide (Integrilin®), or tirofiban (Aggrastat®).

[0146] In some embodiments, the anticancer therapy includes a statin or a statin-based agent. In some embodiments, the method provided herein includes, for example, administering a statin or a statin-based agent to an individual in combination with another anticancer therapy. In some embodiments, the statin or statin-based agent is simvastatin, atorvastatin, fluvastatin, pitavastatin, pravastatin, rosuvastatin, or cerivastatin.

[0147] In some embodiments, the anticancer therapy includes an mTOR inhibitor. In some embodiments, the methods provided herein include, for example, administering an mTOR inhibitor to an individual in combination with another anticancer therapy. In some embodiments, the mTOR inhibitor is temsirolimus (CCI-779), KU-006379, PP242, Trin-1, Trin-2, ICSN3250, Rapalink-1, CC-223, sirolimus (rapamycin), everolimus (RAD001), ductosilib (NVP-BEZ235), GSK2126458, WAY-001, WAY-600, WYE-687, WYE-354, SF1126, XL765, INK128 (MLN012), AZD8055, OSI027, AZD2014, or AP-23573.

[0148] In some embodiments, the anticancer therapy includes a PI3K inhibitor. In some embodiments, the method provided herein includes, for example, administering a PI3K inhibitor to an individual in combination with another anticancer therapy. In some embodiments, the PI3K inhibitor is GSK2636771, buparlisib (BKM120), AZD8186, copanlisib (BAY80-6946), LY294002, PX-866, TGX115, TGX126, BEZ235, SF1126, idelalisib (GS-1101, CAL-101), pictilisib (GDC-094), GDC0032, IPI145, INK1117 (MLN1117), SAR260301, KIN-193 (AZD6482), duvelisib, GS-9820, GSK2636771, GDC-0980, AMG319, pazovanib, or alpelisib (BYL719, Piqray).

[0149] In some embodiments, the anticancer therapy includes a MAPK inhibitor. In some embodiments, the method provided herein includes, for example, administering a MAPK inhibitor to an individual in combination with another anticancer therapy. In some embodiments, the MAPK inhibitor is SB203580, SKF-86002, BIRB-796, SC-409, RJW-67657, BIRB-796, VX-745, RO3201195, SB-242235, or MW181.

[0150] In some embodiments, the anticancer therapy comprises a CDK4 / 6 inhibitor. In some embodiments, the method provided herein comprises, for example, administering a CDK4 / 6 inhibitor to an individual in combination with another anticancer therapy. In some embodiments, the CDK4 / 6 inhibitor is ribociclib (Kisqali®, LEE011), palbociclib (PD0332991, Ibrance®), or abemaciclib (LY2835219).

[0151] In some embodiments, the anticancer therapy includes an EGFR inhibitor. In some embodiments, the methods provided herein include, for example, administering an EGFR inhibitor to an individual in combination with another anticancer therapy. In some embodiments, the EGFR inhibitor is cetuximab, panitumumab, lapatinib, gefitinib, vandetanib, dacomitinib, icotinib, osimertinib (AZD9291), afatanib, olmutinib, EGF816 (nazartinib), abitinib (AC0010), rosiletinib (CO-1686), BMS-690514, YH5448, PF-06747775, ASP8273, PF299804, AP26113, or erlotinib. In some embodiments, the EGFR inhibitor is gefitinib or cetuximab.

[0152] In some embodiments, anticancer therapy includes cancer immunotherapy such as cancer vaccines, cell-based therapies, T-cell receptor (TCR)-based therapies, adjuvant immunotherapy, cytokine immunotherapy, and oncolytic virus therapy. In some embodiments, the methods provided herein include administering cancer immunotherapy, such as cancer vaccines, cell-based therapies, T-cell receptor (TCR)-based therapies, adjuvant immunotherapy, cytokine immunotherapy, and oncolytic virus therapy, to an individual in combination with another anticancer therapy. In some embodiments, cancer immunotherapy includes small molecules, nucleic acids, polypeptides, carbohydrates, toxins, cell-based drugs, or cell binders. Examples of cancer immunotherapy are described in more detail herein but are not intended to be limiting. In some embodiments, cancer immunotherapy activates one or more aspects of the immune system to attack cells (e.g., tumor cells) that express a neoantigen (e.g., a neoantigen expressed by the cancer of this disclosure). The cancer immunotherapy of this disclosure is intended for use as monotherapy or in combination approaches, including any combination or two or more of them, subject to medical judgment. Any of the cancer immunotherapies (optionally, as monotherapy, or in combination with another cancer immunotherapy or other therapeutic agent described herein) can be used in any of the methods described herein.

[0153] In some embodiments, cancer immunotherapy includes cancer vaccines. Various cancer vaccines have been tested using different approaches to enhance the immune response against cancer (see, for example, Emens LA, Expert Opin Emerg Drugs 13(2):295-308 (2008) and US2019 / 0367613). The approaches have been designed to enhance the response of B cells, T cells, or specialized antigen-presenting cells to tumors. Exemplary types of cancer vaccines include, but are not limited to, DNA-based vaccines, RNA-based vaccines, transdextrin vaccines, peptide-based vaccines, dendritic cell vaccines, oncolytic viruses, whole tumor cell vaccines, and tumor antigen vaccines. In some embodiments, cancer vaccines may be prophylactic or therapeutic. In some embodiments, cancer vaccines are formulated as peptide-based vaccines, nucleic acid-based vaccines, antibody-based vaccines, or cell-based vaccines. For example, vaccine compositions may include naked cDNA in cationic lipid preparations, lipopeptides (e.g., Vitiello, A. et al., J. Clin. Invest. 95:341, 1995), naked cDNA or peptides encapsulated in poly(DL-lactide-coglycolide) ("PLG") microspheres (see e.g., Eldridge, et al., Molec. Immunol. 28:287-294, 1991, Alonso et al., Vaccine 12:299-306, 1994, Jones et al., Vaccine 13:675-681, 1995), and peptide compositions contained in immunostimulatory complexes (ISCOMs) (e.g., Takahashi et al., Nature 344:873-875, 1990, Hu et al. This may include al., Clin. Exp. Immunol. 113:235-243, 1998), or multi-antigen peptide systems (MAPs) (see, for example, Tam, JP, Proc. Natl Acad. Sci. USA 85:5409-5413, 1988, Tam, JP, J. Immunol. Methods 196:17-32, 1996).In some embodiments, the cancer vaccine is formulated as a peptide-based vaccine or a nucleic acid-based vaccine (where the nucleic acid encodes a polypeptide). In some embodiments, the cancer vaccine is formulated as an antibody-based vaccine. In some embodiments, the cancer vaccine is formulated as a cell-based vaccine. In some embodiments, the cancer vaccine is a peptide cancer vaccine, and in some embodiments, it is a personalized peptide vaccine. In some embodiments, the cancer vaccine is a polyvalent long-chain peptide, multiple peptides, peptide mixtures, hybrid peptides, or peptide-pulsed dendritic cell vaccine (see, e.g., Yamada et al, Cancer Sci, 104:14-21), 2013). In some embodiments, such a cancer vaccine enhances the anti-cancer response.

[0154] In some embodiments, the cancer vaccine comprises a polynucleotide encoding a neoantigen (e.g., a neoantigen expressed by the cancer of this disclosure). In some embodiments, the cancer vaccine comprises DNA or RNA encoding the neoantigen. In some embodiments, the cancer vaccine comprises a polynucleotide encoding the neoantigen. In some embodiments, the cancer vaccine further comprises one or more additional antigens, neoantigens, or other sequences that facilitate antigen presentation and / or an immune response. In some embodiments, the polynucleotide forms a complex with one or more additional agents, such as liposomes or lipoplexes. In some embodiments, the polynucleotide is taken up and translated by antigen-presenting cells (APCs) and then presents the neoantigen via MHC class I on the surface of the APC cells.

[0155] In some embodiments, the cancer vaccine is selected from sipuleucel-T (Provenge®, Dendreon / Valeant Pharmaceuticals) (approved for the treatment of asymptomatic or minimally symptomatic metastatic castration-resistant (hormone-resistant) prostate cancer) and talimogene laherparepvec (Imlygic®, BioVex / Amgen, formerly known as T-VEC) (a genetically modified oncolytic virus therapy approved for the treatment of unresectable cutaneous, subcutaneous, and lymph node lesions in melanoma). In some embodiments, the cancer vaccine is pexastimogene devacirepvec (PexaVec / JX-594, SillaJen / formerly Jennerex Biotherapeutics) (a thymidine kinase-(TK-) deficient vaccinia virus engineered to express GM-CSF against hepatocellular carcinoma (NCT02562755) and melanoma (NCT00429312)), pelareorep (Reolysin®, OncolyticsBiotech) (a variant of respiratory intestinal orphan virus (reovirus) that does not replicate in cells where the RAS is not activated in many cancers, including colorectal cancer (NCT01622543), prostate cancer (NCT01619813), head and neck squamous cell carcinoma (NCT01166542), pancreatic adenocarcinoma (NCT00998322), and non-small cell lung cancer (NSCLC) (NCT00861627)), enadenotucirev (NG-348, PsiOxus, formerly known as ColoAdl) (ovarian cancer (NCT02028117), metastatic or Adenoviruses engineered to express antibody fragments specific to full-length CD80 and T cell receptor CD3 protein in progressive epithelial tumors (e.g., colorectal cancer, bladder cancer, head and neck squamous cell carcinoma, and salivary gland cancer (NCT02636036)), ONCOS-102 (Targovax / formerly Oncos) (adenoviruses engineered to express GM-CSF in melanoma (NCT03003676), and peritoneal diseases, colorectal cancer, or ovarian cancer (NCT02963831)), GL-ONC1 (GLV-1h68 / GLV-1h153, Genelux) The cancer vaccine is selected from oncolytic virus therapies such as CG0070 (Cold Genesys) (an adenovirus designed to express β-galactosidase (β-gal) / β-gluconidase or β-gal / human sodium-iodine cotransporter (hNIS), respectively, and studied in peritoneal carcinomatosis (NCT01443260), fallopian tube cancer, and ovarian cancer (NCT02759588), or CG0070 (Cold Genesys) (an adenovirus designed to express GM-CSF in bladder cancer (NCT02365818)), anti-gp100, STINGVAX, GVAX, DCVaxL, and DNX-2401. In some embodiments, the cancer vaccine is JX-929 (SillaJen / formerly Jennerex).Biotherapeutics) (TK-deficient and vaccinia growth factor-deficient vaccinia viruses designed to express cytosine deaminase that can convert the prodrug 5-fluorocytosine into the cytotoxic drug 5-fluorouracil), TGO1 and TG02 (Targovax / formerly Oncos) (peptide-based immunotherapies targeting difficult-to-treat RAS mutations), TILT-123 (TILT Biotherapeutics) (a modified adenovirus called Ad5 / 3-E2F-δ24-hTNFα-IRES-hIL20), VSV-GP (ViraTherapeutics) (antigen-specific CD8 + The cancer vaccine is selected from vesicular stomatitis virus (VSV) engineered to express the glycoprotein (GP) of lymphocytic choriomeningitis virus (LCMV), which can be further engineered to express antigens designed to provoke a T cell response. In some embodiments, the cancer vaccine includes vector-based tumor antigen vaccines. Vector-based tumor antigen vaccines can be used as a way to provide a stable supply of antigens to stimulate an anti-tumor immune response. In some embodiments, a vector encoding a tumor antigen is injected into an individual (possibly together with a pro-inflammatory agent or other attractant substance such as GM-CSF) to be taken up by cells in vivo to produce a specific antigen, which then induces the desired immune response. In some embodiments, the vector can be used to deliver multiple tumor antigens at once to increase the immune response. Furthermore, recombinant viruses, bacteria, or yeast vectors may themselves provoke an immune response, which may enhance the overall immune response.

[0156] In some embodiments, cancer vaccines include DNA-based vaccines. In some embodiments, DNA-based vaccines can be used to stimulate an antitumor response. The ability to induce a protective immune response by directly injecting DNA encoding an antigen protein has been demonstrated in numerous experimental systems. Vaccination by directly injecting DNA encoding an antigen protein to induce a protective immune response often elicits both cellular and humoral responses. Furthermore, reproducible immune responses to DNA encoding various antigens have been reported in mice, which essentially persist throughout the animal's lifetime (see, e.g., Yankauckas et al. (1993) DNA Cell Biol., 12:771-776). In some embodiments, plasmid (or other vector) DNA containing a sequence encoding a protein operably linked to regulatory elements necessary for gene expression is administered to an individual (e.g., a human patient, a non-human mammal, etc.). In some embodiments, the individual's cells take up the administered DNA, and the coding sequence is expressed. In some embodiments, the antigen thus produced becomes a target for the immune response.

[0157] In some embodiments, cancer vaccines include RNA-based vaccines. In some embodiments, RNA-based vaccines can be used to stimulate an antitumor response. In some embodiments, RNA-based vaccines include self-replicating RNA molecules. In some embodiments, self-replicating RNA molecules may be RNA replicons derived from alphaviruses. Self-replicating RNA (or "SAM") molecules are well known in the art and can be produced, for example, by using replication elements derived from alphaviruses and replacing the structural viral protein with a nucleotide sequence encoding the protein of interest. Self-replicating RNA molecules are typically +-chain molecules that can be directly translated after delivery to cells, and this translation provides an RNA-dependent RNA polymerase that produces both antisense and sense transcripts from the delivered RNA. Thus, the delivered RNA leads to the production of multiple daughter RNAs. These daughter RNAs, as well as collinear subgenomic transcripts, can be translated themselves to provide insight expression of the encoded polypeptide, or transcribed to provide further transcripts of the same sense strand as the delivered RNA (translated to provide insight expression of the antigen).

[0158] In some embodiments, cancer immunotherapy includes cell-based therapy. In some embodiments, cancer immunotherapy includes T-cell-based therapy. In some embodiments, cancer immunotherapy includes adoptive therapy (e.g., adoptive T-cell-based therapy). In some embodiments, the T cells are autologous or allogeneic to the recipient. In some embodiments, the T cells are CD8+ T cells. In some embodiments, the T cells are CD4+ T cells. Adoptive immunotherapy refers to a therapeutic approach to treating cancer or infection in which immune cells are administered to a host with the aim of mediating direct or indirect specific immunity against cancer cells (i.e., initiating an immune response against them). In some embodiments, the immune response results in inhibition of the growth and / or proliferation of tumor cells and / or metastatic cells, and in relevant embodiments, results in the death and / or reabsorption of tumor cells. The immune cells may originate from a different organism / host (exogenous immune cells) or may be cells obtained from the target organism (autoimmune cells). In some embodiments, immune cells (e.g., autologous or allogeneic T cells (e.g., regulatory T cells, CD4+ T cells, CD8+ T cells, or γδ T cells), NK cells, invariant NK cells, or NKT cells) may be genetically engineered to express antigen receptors such as engineered TCRs and / or chimeric antigen receptors (CARs). For example, host cells (e.g., autologous or allogeneic T cells) may be modified to express T cell receptors (TCRs) that have antigen specificity for cancer antigens. In some embodiments, NK cells may be engineered to express TCRs. NK cells may be further engineered to express CARs. Multiple CARs and / or TCRs for different antigens may be added to a single cell type, such as T cells or NK cells. In some embodiments, the cells include one or more nucleic acids / expression constructs / vectors introduced via genetic engineering encoding one or more antigen receptors, and genetically engineered products of such nucleic acids. In some embodiments, the nucleic acids are heterogeneous. In other words, it is not usually present in cells or samples obtained from cells, such as those obtained from other organisms or cells, and is not usually found in the cells being manipulated and / or the organism from which such cells originate.In some embodiments, the nucleic acids are not naturally occurring, such as nucleic acids not found in nature (e.g., chimeric nucleic acids). In some embodiments, the immune cell population can be obtained from a subject in need of therapy or a subject suffering from a disease associated with reduced immune cell activity. Thus, the cells are autologous to the subject in need of therapy. In some embodiments, the immune cell population can be obtained from a donor (e.g., a histocompatibility-matched donor). In some embodiments, the immune cell population can be collected from peripheral blood, umbilical cord blood, bone marrow, spleen, or any other organ / tissue where immune cells are present in the subject or donor. In some embodiments, immune cells can be isolated from a pool of subject and / or donor (e.g., pooled umbilical cord blood). In some embodiments, if the immune cell population is obtained from a donor different from the subject, the donor may be allogeneic, insofar as the obtained cells are subject-compatible in that they can be introduced into the subject. In some embodiments, allogeneic donor cells may or may not be human leukocyte antigen (HLA) compatible. In some embodiments, allogeneic cells can be treated to reduce their immunogenicity in order to make them subject-compatible.

[0159] In some embodiments, cell-based therapies include T cell-based therapies and include autologous cells, e.g., tumor-infiltrating lymphocytes (TILs); T cells activated ex vivo using autologous DCs, lymphocytes, artificial antigen-presenting cells (APCs) or beads coated with T cell ligands and activating antibodies, or cells isolated by capturing target cell membranes; allogeneic cells that spontaneously express anti-host tumor T cell receptors (TCRs); and non-tumor-specific autologous or allogeneic cells that have been genetically reprogrammed or "redirected" to express tumor-reactive TCRs or chimeric TCR molecules (known as "T bodies" and exhibiting antibody-like tumor recognition ability). Several approaches for the isolation, induction, manipulation or modification, activation, and proliferation of functional antitumor effector cells have been described over the past 20 years and can be used according to any of the methods provided herein. In some embodiments, T cells are derived from blood, bone marrow, lymph, umbilical cord, or lymphoid organs. In some embodiments, the cells are human cells. In some embodiments, the cells are primary cells such as cells isolated directly from the subject and / or cells isolated from the subject and frozen. In some embodiments, cells are defined by their function, activation state, maturity, differentiation potential, proliferative capacity, recirculation capacity, localization capacity, and / or persistence capacity, antigen specificity, antigen receptor type, presence in a specific organ or compartment, marker or cytokine secretion profile, and / or degree of differentiation, and may be T cells or other cell types (e.g., whole T cell population, CD4). + cells, CD8 + It comprises one or more subsets of cells (and their subpopulations). In some embodiments, the cells may be homogeneous and / or self. In some embodiments, such as off-the-shelf technologies, the cells may be pluripotent and / or multipotent, such as stem cells including induced pluripotent stem cells (iPSCs).

[0160] In some embodiments, T cell-based therapies include chimeric antigen receptor (CAR)-T cell-based therapies. This approach involves the manipulation of CARs. CARs specifically bind to an antigen of interest and contain one or more intracellular signaling domains for T cell activation. CARs are then expressed on the surface of manipulated T cells (CAR-Ts), which are administered to a patient, resulting in a T cell-specific immune response against cancer cells expressing the antigen.

[0161] In some embodiments, T cell-based therapies include T cells expressing recombinant T cell receptors (TCRs). This approach involves identifying TCRs that specifically bind to the antigen of interest. These are then used to replace the endogenous or native TCRs on the surface of the engineered T cells. When administered to a patient, this elicits a T cell-specific immune response against cancer cells expressing the antigen.

[0162] In some embodiments, the T cell-based therapy includes tumor-infiltrating lymphocytes (TILs). For example, TILs can be isolated from the tumor or cancer of the Disclosure, then isolated and grown in vitro. Some or all of these TILs can specifically recognize antigens expressed by the tumor or cancer of the Disclosure. In some embodiments, after being isolated in vitro, the TILs are exposed to one or more neoantigens (e.g., one neoantigen). The TILs are then administered to a patient (optionally in combination with one or more cytokines or other immunostimulants).

[0163] In some embodiments, cell-based therapies include natural killer (NK) cell-based therapies. Natural killer (NK) cells are a subpopulation of lymphocytes that spontaneously exhibit cytotoxicity against various tumor cells, virus-infected cells, and some normal cells in the bone marrow and thymus. NK cells are important effectors of the initial innate immune response to transformed and virus-infected cells. NK cells can be detected by specific surface markers such as human CD16, CD56, and CD8. NK cells do not express T cell antigen receptors, the pan-T marker CD3, or surface immunoglobulin B cell receptors. In some embodiments, NK cells are obtained from human peripheral blood mononuclear cells (PBMCs), unstimulated leukocyte depletion products (PBSCs), human embryonic stem cells (hESCs), induced pluripotent stem cells (iPSCs), bone marrow, or umbilical cord blood by methods well known in the art.

[0164] In some embodiments, cell-based therapies include dendritic cell (DC)-based therapies (e.g., dendritic cell vaccines). In some embodiments, the DC vaccine comprises antigen-presenting cells, collected from a patient or donor, that can induce specific T-cell immunization. In some embodiments, the DC vaccine can then be exposed in vitro to a peptide antigen, generating T cells for this purpose in the patient. In some embodiments, the antigen-loaded dendritic cells are then injected back into the patient. In some embodiments, immunization can be repeated multiple times as needed. Methods for collecting, growing, and administering dendritic cells are known in the art (see, for example, WO2019 / 178081). Dendritic cell vaccines (e.g., APC8015 and Sipuleucel-T, also known as PROVENGE®) are vaccines involving the administration of dendritic cells that function as APCs, presenting one or more cancer-specific antigens to the patient's immune system. In some embodiments, the dendritic cells are autologous or allogeneic to the recipient.

[0165] In some embodiments, cancer immunotherapy includes TCR-based therapy. In some embodiments, cancer immunotherapy includes administration of one or more TCRs or TCR-based therapeutics that specifically bind to antigens expressed by the cancers of this disclosure. In some embodiments, the TCR-based therapeutic may further include a moiety that binds to immune cells (e.g., T cells), such as an antibody or antibody fragment that specifically binds to a T cell surface protein or receptor (e.g., an anti-CD3 antibody or antibody fragment).

[0166] In some embodiments, immunotherapy includes adjuvant immunotherapy. Adjuvant immunotherapy includes the use of one or more agents that activate components of the innate immune system, such as HILTONOL® (imiquimod), which targets the TLR7 pathway.

[0167] In some embodiments, immunotherapy includes cytokine immunotherapy. Cytokine immunotherapy involves the use of one or more cytokines that activate components of the immune system. Examples include, but are not limited to, aldesleukin (PROLEUKIN®, interleukin-2), interferon α-2a (ROFERON®-A), interferon α-2b (INTRON®-A), and PEG interferon α-2b (PEGINTRON®).

[0168] In some embodiments, immunotherapy includes oncolytic virus therapy. In oncolytic virus therapy, a genetically modified virus is used to replicate in cancer cells, kill them, and release an antigen that stimulates an immune response. In some embodiments, the replicable oncolytic virus expressing the tumor antigen includes any naturally occurring (e.g., from a “field source”) or a modified replicable oncolytic virus. In some embodiments, the oncolytic virus may be modified to enhance the virus’s selectivity for cancer cells in addition to expressing the tumor antigen. In some embodiments, the reproducible oncolytic viruses include Myoviridae, Cyphoviridae, Podoviridae, Tesiviridae, Corticoviridae, Plasmaviridae, Liposrixviridae, Fuseroviridae, Poxyiridae, Iridoviridae, Phycodnaviridae, Baculoviridae, Herpesviridae, Adonoviridae, Papovaviridae, Polidnaviridae, Inoviridae, Microviridae, Geminiviridae, Circoviridae, Parvoviridae, Hepadnaviridae, Retroviridae, This includes, but is not limited to, oncolytic viruses belonging to the families Cytoviridae, Reoviridae, Birnaviridae, Paramyxoviridae, Rhabdoviridae, Filoviridae, Orthomyxoviridae, Bunyaviridae, Arenaviridae, Leviviridae, Picornaviridae, Ceciviridae, Comoviridae, Potiviridae, Caliciviridae, Astroviridae, Nodaviridae, Tetraviridae, Tombusviridae, Coronaviridae, Graviviridae, Togaviridae, and Varnaviridae. In some embodiments, replicating tumor lysis viruses include adenoviruses, retroviruses, reoviruses, rhabdoviruses, Newcastle disease virus (NDV), polyomaviruses, vacciniaviruses (VacV), herpes simplex virus, picornaviruses, coxsackieviruses, and parvoviruses. In some embodiments, replicating oncolytic vacciniaviruses expressing tumor antigens may be engineered to lack one or more functional genes to enhance the virus's cancer selectivity.In some embodiments, oncolytic vaccinia viruses are engineered to lack thymidine kinase (TK) activity. In some embodiments, oncolytic vaccinia viruses may be engineered to lack vaccinia virus growth factor (VGF). In some embodiments, oncolytic vaccinia viruses may be engineered to lack both VGF and TK activity. In some embodiments, oncolytic vaccinia viruses may be engineered to lack one or more genes involved in evading the host interferon (IFN) response, such as E3L, K3L, B18R, or B8R. In some embodiments, the replicating oncolytic vaccinia virus is a Western Reserve, Copenhagen, Lister, or Wyeth strain lacking a functional TK gene. In some embodiments, the oncolytic vaccinia virus is a Western Reserve, Copenhagen, Lister, or Wyeth strain lacking functional B18R and / or B8R genes. In some embodiments, a replicating oncolytic vaccinia virus expressing a tumor antigen may be administered locally or systemically to a subject, for example, via intratumoral, intraperitoneal, intravenous, intraarterial, intramuscular, intradermal, intracranial, subcutaneous, or intranasal administration.

[0169] The following exemplary embodiments represent some aspects of the present invention: Embodiment 1. A method for detecting loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes, To provide multiple nucleic acids obtained from individual-derived samples, wherein the multiple nucleic acids include nucleic acids encoding HLA genes. The process of selectively ligating one or more adapters to one or more nucleic acids from multiple nucleic acids, Amplifying nucleic acids from multiple nucleic acids, The capture of multiple nucleic acids corresponding to HLA genes, wherein the multiple nucleic acids corresponding to HLA genes are captured from nucleic acids amplified by hybridization with bait molecules. The sequencer is used to sequence the captured nucleic acids and obtain multiple sequence reads corresponding to the HLA gene, One or more processors fit one or more values ​​associated with one or more of multiple array reads to a model, A method comprising detecting the relative binding tendency of LOH of HLA genes and HLA alleles of HLA genes based on a model. Embodiment 2. The relative binding tendency of the LOH of the HLA gene and the HLA allele of the HLA gene is a) Obtaining the observed allele frequency for an HLA allele, wherein the observed allele frequency corresponds to the frequency of nucleic acids encoding at least a portion of the HLA allele detected among multiple sequence reads corresponding to the HLA gene. b) Obtaining the relative binding tendency of an HLA allele to a bait molecule, wherein the relative binding tendency of an HLA allele corresponds to the tendency of a nucleic acid encoding at least a portion of an HLA allele to bind to a bait molecule in the presence of nucleic acids encoding a portion of one or more other HLA alleles. c) Apply the objective function to measure the difference between the relative binding tendency of HLA alleles and the observed allele frequency, d) Applying an optimization model to minimize the objective function, e) Determining the adjusted allele frequencies of HLA alleles based on the optimized model and observed allele frequencies, f) The method according to Embodiment 1, wherein it is determined that LOH has occurred if the adjusted allele frequency of the HLA allele is less than a predetermined threshold, and is detected by the method described above. Embodiment 3. The method according to Embodiment 1 or 2, further comprising administering an effective amount of a non-immune checkpoint inhibitor (ICI) therapy to an individual, at least partially based on the detection of LOH of the HLA gene. Embodiment 4. The method according to Embodiment 1 or 2, further comprising recommending a non-immune checkpoint inhibitor (ICI) treatment based at least partially on the detection of LOH of the HLA gene. Embodiment 5. The method according to Embodiment 1 or 2, further comprising detecting or acquiring knowledge thereof of a high tumor mutational burden (TMB) in a sample. Embodiment 6. The method according to Embodiment 5, further comprising administering an effective amount of an immune checkpoint inhibitor (ICI) to an individual, at least partially based on the detection of LOH and high TMB of HLA genes. Embodiment 7. The method according to Embodiment 5, further comprising recommending a treatment including an immune checkpoint inhibitor (ICI) to an individual, at least in part, based on the detection of LOH and high TMB of HLA genes. Embodiment 8. The method according to any one of Embodiments 1 to 7, wherein the HLA gene is a human HLA-A, HLA-B, or HLA-C gene. The method according to any one of Embodiments 1 to 8, further comprising extracting a plurality of nucleic acids from a sample prior to Embodiment 9(1). Embodiment 10. The method according to any one of Embodiments 1 to 9, wherein the sample comprises tumor cells and / or tumor nucleic acids. Embodiment 11. The method according to Embodiment 10, wherein the sample further comprises non-tumor cells. Embodiment 12. The method according to Embodiment 10, wherein the sample is from a tumor biopsy or tumor specimen. Embodiment 13. The method according to Embodiment 10, wherein the sample contains tumor cell-free DNA (cfDNA). Embodiment 14. The method according to Embodiment 10, wherein the sample comprises a fluid, cells, or tissue. Embodiment 15. The method according to Embodiment 14, wherein the sample includes blood or plasma. Embodiment 16. The method according to Embodiment 10, wherein the sample includes a tumor biopsy or circulating tumor cells. Embodiment 17. The method according to Embodiment 16, wherein the sample from an individual is a nucleic acid sample. Embodiment 18. The method according to Embodiment 17, wherein the nucleic acid sample comprises mRNA, genomic DNA, circulating tumor DNA, cell-free DNA, or cell-free RNA. Embodiment 19. The method according to any one of Embodiments 5 to 18, wherein the TMB is determined based on the number of non-driver somatic coding mutations per megabase of the sequenced genome. Embodiment 20. Each reaction corresponds to a bait molecule that binds to a different allele of the polymorphic gene, and each reaction results in the capture of the corresponding allele fraction. Identifying multiple chemical reactions such that each of them consists of a first subset of reactions and a second subset of reactions, the first and second subsets do not share a common reaction, and each of the first and second subsets contains at least one chemical reaction. Identifying multiple equations that collectively relate the bonding tendencies of each chemical reaction and the allele fractions of each captured allele, To empirically identify the relative bonding tendencies of a first subset of multiple chemical reactions, A method comprising identifying the relative joining tendency of a second subset by minimizing the total error. Embodiment 21. The method of Embodiment 20, wherein minimizing the total error is constrained by the median of the relative coupling tendencies being equal to 1. Embodiment 22. The method according to Embodiment 20, wherein one relative coupling tendency is set to equal 1. Embodiment 23. The method of Embodiment 20, wherein minimizing the total error includes performing a least-squares procedure. Embodiment 24. Perform a hybrid capture process to measure the raw allele frequency in the patient's DNA sample, The method according to Embodiment 20, further comprising scaling the measured raw allele frequencies using first and second subsets of relative binding tendencies, thereby reducing sampling bias. Embodiment 25. The method according to Embodiment 20, wherein the polymorphic gene includes a human leukocyte antigen gene. Embodiment 26. Polymorphic genes include ST7 / RAY1, ARH1 / NOEY2, TSLC1, RB, PTEN, SMAD2, SMAD4, DCC, TP53, ATM, miR-15a, miR-16-1, NAT2, BRCA1, BRCA2, hOGG1, CDH1, IGF2, CDKN1C / P57, MEN1, PRKAR1A, H19, KRAS, BAP1, PTCH1, SMO, SUFU, NOTCH1, PPP6C, LATS1, CASP8, PTPN14, ARID1A, FBXW7, M6P / IGF2R, IFN-α, olfactory receptor genes, CBFA2T3, DUTT1, FHIT, APC, P16, FCMD, T The method according to Embodiment 20, wherein the gene is SC2, miR-34, c-MPL, RUNX3, DIRAS3, NRAS, miR-9, FAM50B, PLAGL1, ER, FLT3, ZDBF2, GPR1, c-KIT, NAP1L5, GRB10, EGFR, PEG10, BRAF, MEST, JAK2, DAPK1, LIT1, WT1, NF-1, PR, c-CBL, DLK1, AKT1, SNURF, cytochrome P450 gene (CYP), ZNF587, SOCS1, TIMP2, RUNX1, AR, CEBPA, C19MC, EMP3, ZNF331, CDKN2A, PEG3, NNAT, GNAS, or GATA5. Embodiment 27. The method of Embodiment 24, further comprising determining whether the patient has experienced a loss of heterozygosity. Embodiment 28. A system, One or more processors, A memory configured to store one or more computer program instructions, wherein when one or more computer program instructions are executed by one or more processors, Each reaction corresponds to a bait molecule that binds to a different allele of the polymorphic gene, and each reaction results in the capture of the corresponding allele fraction. Identifying multiple chemical reactions such that each of them consists of a first subset of reactions and a second subset of reactions, the first and second subsets do not share a common reaction, and each of the first and second subsets contains at least one chemical reaction. Identifying multiple equations that collectively relate the bonding tendencies of each chemical reaction and the allele fractions of each captured allele, Receiving the empirically identified relative binding tendencies of a first subset of multiple chemical reactions, A system configured to identify the relative coupling trends of a second subset by minimizing the total error. Embodiment 29. The system according to Embodiment 28, wherein minimizing the total error is constrained by the median of the relative coupling tendencies being equal to 1. Embodiment 30. The system according to Embodiment 28, wherein one relative coupling tendency is set to equal 1. Embodiment 31. The system according to Embodiment 28, wherein minimizing the total error includes performing a least-squares procedure. Embodiment 32. When one or more computer program instructions are executed by one or more processors, Receiving measured raw allele frequencies in a patient's DNA sample using one or more processors, wherein the measured raw allele frequencies are received after being measured by performing a hybrid capture process. The system according to Embodiment 28, further configured to scale the measured raw allele frequencies using first and second subsets of relative coupling tendencies with one or more processors, thereby reducing sampling bias. Embodiment 33. The system according to Embodiment 28, wherein the polymorphic gene includes a human leukocyte antigen gene. Embodiment 34. Polymorphic genes include ST7 / RAY1, ARH1 / NOEY2, TSLC1, RB, PTEN, SMAD2, SMAD4, DCC, TP53, ATM, miR-15a, miR-16-1, NAT2, BRCA1, BRCA2, hOGG1, CDH1, IGF2, CDKN1C / P57, MEN1, PRKAR1A, H19, KRAS, BAP1, PTCH1, SMO, SUFU, NOTCH1, PPP6C, LATS1, CASP8, PTPN14, ARID1A, FBXW7, M6P / IGF2R, IFN-α, olfactory receptor genes, CBFA2T3, DUTT1, FHIT, APC, P16, FCMD, TS The system according to Embodiment 28, wherein the genes are C2, miR-34, c-MPL, RUNX3, DIRAS3, NRAS, miR-9, FAM50B, PLAGL1, ER, FLT3, ZDBF2, GPR1, c-KIT, NAP1L5, GRB10, EGFR, PEG10, BRAF, MEST, JAK2, DAPK1, LIT1, WT1, NF-1, PR, c-CBL, DLK1, AKT1, SNURF, cytochrome P450 gene (CYP), ZNF587, SOCS1, TIMP2, RUNX1, AR, CEBPA, C19MC, EMP3, ZNF331, CDKN2A, PEG3, NNAT, GNAS, or GATA5. Embodiment 35. The system according to Embodiment 32, wherein the method further includes determining, using one or more processors, whether a patient has experienced a loss of heterozygosity. Embodiment 36. A method for determining allele frequency, a) Receiving allele frequencies observed for a gene allele in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the allele detected among multiple sequence reads corresponding to the gene, and the multiple sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule. b) Receiving the relative binding tendency of an allele to a bait molecule in one or more processors, wherein the relative binding tendency of an allele corresponds to the tendency of at least one nucleic acid encoding a portion of an allele to bind to a bait molecule in the presence of nucleic acids encoding portions of one or more other alleles of a gene. c) Using one or more processors, execute the objective function to measure the difference between the relative binding tendency of alleles and the observed allele frequency, d) Executing the optimization model using one or more processors to minimize the objective function, e) A method comprising determining the adjusted allele frequency of an allele based on an optimized model and the observed allele frequency using one or more processors. Embodiment 37. The method according to Embodiment 36, wherein the optimization model is a least-squares optimization model. Embodiment 38. The method according to Embodiment 36 or 37, wherein the optimization model is subject to one or more constraints. Embodiment 39. The method according to Embodiment 38, wherein one or more constraints require that the median relative binding tendency for multiple alleles of a gene is equal to 1. Embodiment 40. The method according to any one of Embodiments 36 to 39, wherein the observed allele frequency corresponds to the relative frequency of nucleic acids encoding at least a portion of the alleles detected among multiple sequence reads, compared to a reference value. Embodiment 41. The method according to Embodiment 40, wherein the reference value is the total number of sequence reads. Embodiment 42. The method according to Embodiment 40, wherein the reference value is the number of sequence reads corresponding to the reference gene. Embodiment 43. The method according to any one of Embodiments 36 to 42, wherein the gene is a human leukocyte antigen (HLA) gene encoding a major histocompatibility (MHC) class I molecule. Embodiment 44. The genes are ST7 / RAY1, ARH1 / NOEY2, TSLC1, RB, PTEN, SMAD2, SMAD4, DCC, TP53, ATM, miR-15a, miR-16-1, NAT2, BRCA1, BRCA2, hOGG1, CDH1, IGF2, CDKN1C / P57, MEN1, PRKAR1A, H19, KRAS, BAP1, PTCH1, SMO, SUFU, NOTCH1, PPP6C, LATS1, CASP8, PTPN14, ARID1A, FBXW7, M6P / IGF2R, IFN-α, olfactory receptor gene, CBFA2T3, DUTT1, FHIT, APC, P16, FCMD, TSC2, mi The method according to any one of embodiments 36 to 42, wherein the gene is R-34, c-MPL, RUNX3, DIRAS3, NRAS, miR-9, FAM50B, PLAGL1, ER, FLT3, ZDBF2, GPR1, c-KIT, NAP1L5, GRB10, EGFR, PEG10, BRAF, MEST, JAK2, DAPK1, LIT1, WT1, NF-1, PR, c-CBL, DLK1, AKT1, SNURF, cytochrome P450 gene (CYP), ZNF587, SOCS1, TIMP2, RUNX1, AR, CEBPA, C19MC, EMP3, ZNF331, CDKN2A, PEG3, NNAT, GNAS, or GATA5. Embodiment 45. The method according to any one of Embodiments 36 to 44, further comprising determining, after determining the adjusted allele frequencies, determining, at least in part, that the gene has undergone loss of heterozygosity (LOH). Embodiment 46. The method according to any one of Embodiments 36 to 45, wherein multiple sequence reads are obtained by performing next-generation sequencing (NGS), whole-exome sequencing, or methylation sequencing on nucleic acids captured by hybridization with a bait molecule. Embodiment 47. The method according to any one of Embodiments 36 to 46, further comprising sequencing multiple polynucleotides by next-generation sequencing (NGS), whole exome sequencing, or methylation sequencing to obtain multiple sequence reads before receiving the observed allele frequencies, wherein the multiple polynucleotides include nucleic acids encoding at least a portion of the alleles. Embodiment 48. Before sequencing multiple polynucleotides, Contacting a mixture of polynucleotides with a bait molecule under conditions suitable for hybridization, wherein the mixture contains multiple polynucleotides capable of hybridizing with the bait molecule. The method according to Embodiment 47, further comprising isolating a plurality of polynucleotides hybridized with a bait molecule, wherein the isolated plurality of polynucleotides hybridized with the bait molecule are sequenced. Embodiment 49. Before contacting the polynucleotide mixture with the bait molecule, The process of obtaining a sample from an individual, wherein the sample contains tumor cells and / or tumor nucleic acids. The method according to Embodiment 48, further comprising: extracting a mixture of polynucleotides from a sample, wherein the mixture of polynucleotides is from tumor cells and / or tumor nucleic acids. Embodiment 50. The method according to Embodiment 49, wherein the sample further comprises non-tumor cells. Embodiment 51. The method according to Embodiment 49, wherein the sample is from a tumor biopsy or tumor specimen. Embodiment 52. The method according to Embodiment 49, wherein the sample contains tumor cell-free DNA (cfDNA). Embodiment 53. (1) Receiving allele frequencies observed for each of two or more alleles of a gene in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of each allele detected among a plurality of sequence reads corresponding to the gene, and the plurality of sequence reads are obtained by sequencing nucleic acids encoding the gene or a portion thereof captured by hybridization with a bait molecule. (2) Receiving the relative binding tendency of each of two or more alleles to the bait molecule in one or more processors, wherein the second allele of the two or more alleles has a lower relative binding tendency to the bait molecule than the first allele of the two or more alleles. (3) The method according to any one of embodiments 36 to 52, further comprising: (3) Identifying a second bait molecule by one or more processors such that the second allele of two or more alleles has a higher relative binding tendency to the second bait molecule than to the first bait molecule. Embodiment 54. The method according to Embodiment 53, wherein the second bait molecule contains a sequence complementary to at least a portion of the second alleles of two or more alleles. Embodiment 55. A non-temporary computer-readable storage medium containing one or more programs executed by one or more processors of a device, wherein, when one or more programs are executed by one or more processors, the one or more programs include instructions causing the device to perform the method described in any one of Embodiments 36-46, 53, and 54. Embodiment 56. A method for detecting loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes, a) Receiving allele frequencies observed for an HLA allele in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA allele detected among multiple sequence reads corresponding to the HLA gene, and the multiple sequence reads are obtained by sequencing nucleic acids encoding a gene or a portion thereof captured by hybridization with a bait molecule. b) Receiving the relative binding tendency of an HLA allele to a bait molecule in one or more processors, wherein the relative binding tendency of an HLA allele corresponds to the tendency of a nucleic acid encoding at least a portion of an HLA allele to bind to a bait molecule in the presence of nucleic acids encoding portions of one or more other HLA alleles. c) Using one or more processors, execute the objective function to measure the difference between the relative binding tendency of HLA alleles and the observed allele frequency, d) Executing the optimization model using one or more processors to minimize the objective function, e) Determining the adjusted allele frequencies of HLA alleles based on the optimized model and observed allele frequencies using one or more processors, f) A method comprising determining that a Low-Level Hysteresis (LOH) has occurred if one or more processors determine that the adjusted allele frequency of an HLA allele is less than a predetermined threshold. Embodiment 57. The method according to Embodiment 56, wherein the HLA gene is a human HLA-A, HLA-B, or HLA-C gene. Embodiment 58. The method according to Embodiment 56 or 57, wherein multiple sequence reads are obtained by sequencing nucleic acids obtained from a sample containing tumor cells and / or tumor nucleic acids. Embodiment 59. The method according to Embodiment 58, wherein the sample further comprises non-tumor cells. Embodiment 60. The method according to Embodiment 58, wherein the sample is from a tumor biopsy or tumor specimen. Embodiment 61. The method according to Embodiment 58, wherein the sample contains tumor cell-free DNA (cfDNA). Embodiment 62. The method according to Embodiment 58, wherein the sample comprises a fluid, cells, or tissue. Embodiment 63. The method according to Embodiment 62, wherein the sample includes blood or plasma. Embodiment 64. The method according to Embodiment 58, wherein the sample includes a tumor biopsy or circulating tumor cells. Embodiment 65. The method according to Embodiment 58, wherein the sample is a nucleic acid sample. Embodiment 66. The method according to Embodiment 65, wherein the nucleic acid sample comprises mRNA, genomic DNA, circulating tumor DNA, cell-free DNA, or cell-free RNA. Embodiment 67. A method for identifying an individual with cancer who may benefit from a treatment comprising an immune checkpoint inhibitor (ICI), comprising detecting loss of heterozygosity (LOH) of a human leukocyte antigen (HLA) gene in a sample from the individual, wherein the LOH of the HLA gene is detected according to the method of any one of Embodiments 36 to 66. Embodiment 68. A method for selecting a therapy for an individual having cancer, comprising detecting loss of heterozygosity (LOH) of a human leukocyte antigen (HLA) gene in a sample from the individual, wherein the LOH of the HLA gene is detected according to the method of any one of Embodiments 36 to 66. Embodiment 69. A method for identifying one or more treatment options for an individual with cancer, (a) to acquire knowledge of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from an individual, wherein the LOH of the HLA gene is detected according to the method described in any one of Embodiments 36 to 66, (b) A method comprising generating a report that includes, at least in part, the knowledge, one or more treatment options identified for an individual. Embodiment 70. The method according to any one of Embodiments 67-69, wherein the LOH of the HLA gene in the sample indicates that the individual is not likely to benefit from treatment including ICI. Embodiment 71. The method according to Embodiment 70, wherein one or more treatment options do not include treatments containing an ICI. Embodiment 72. A method for selecting a treatment for an individual with cancer, comprising acquiring knowledge of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from an individual with cancer, wherein LOH of HLA genes is detected according to the method of any one of Embodiments 36 to 66, and in response to acquiring such knowledge, (i) the individual is classified as a candidate not to receive treatment with an immune checkpoint inhibitor (ICI), (ii) the individual is identified as not likely to respond to a treatment including an immune checkpoint inhibitor (ICI), and / or (iii) the individual is classified as a candidate to receive a treatment other than an immune checkpoint inhibitor (ICI). Embodiment 73. A method for predicting the survival time of an individual with cancer treated with an immune checkpoint inhibitor (ICI), comprising acquiring knowledge of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from the individual, wherein LOH of the HLA gene is detected according to the method of any one of Embodiments 36 to 66, and in response to the acquisition of such knowledge, the individual is predicted to have a shorter survival time after treatment with an ICI compared to the survival time of an individual treated with an ICI in which the cancer does not exhibit LOH of the HLA gene. Embodiment 74. A method for monitoring an individual having cancer, comprising acquiring knowledge of loss of heterozygosity (LOH) of a human leukocyte antigen (HLA) gene in a sample from the individual, wherein the LOH of the HLA gene is detected according to the method of any one of Embodiments 36 to 66, and in response to the acquisition of such knowledge, the individual is predicted to have an increased risk of recurrence compared to an individual whose cancer does not exhibit LOH of the HLA gene. Embodiment 75. A method for evaluating an individual having cancer, comprising acquiring knowledge of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from the individual, wherein LOH of the HLA gene is detected according to the method of any one of Embodiments 36 to 66, and the LOH of the HLA gene is identified as indicating that the individual has an increased risk of recurrence compared to an individual that does not exhibit LOH of the HLA gene. Embodiment 76. A method for screening individuals with cancer, comprising acquiring knowledge of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from an individual, wherein LOH of the HLA gene is detected according to the method of any one of Embodiments 36 to 66, and in response to the acquisition of such knowledge, the individual is predicted to have an increased risk of recurrence compared to an individual whose cancer does not exhibit LOH of the HLA gene. Embodiment 77. The LOH of the HLA gene is a) Receiving allele frequencies observed for an HLA allele in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA allele detected among multiple sequence reads corresponding to the HLA gene, and the multiple sequence reads are obtained by sequencing nucleic acids encoding a gene or a portion thereof captured by hybridization with a bait molecule. b) Receiving the relative binding tendency of an HLA allele to a bait molecule in one or more processors, wherein the relative binding tendency of an HLA allele corresponds to the tendency of a nucleic acid encoding at least a portion of an HLA allele to bind to a bait molecule in the presence of nucleic acids encoding portions of one or more other HLA alleles. c) Using one or more processors, determine an objective function that measures the difference between the relative binding tendency of HLA alleles and the observed allele frequency, d) Determining an optimization model configured to minimize the objective function using one or more processors, e) Determining the adjusted allele frequencies of HLA alleles based on the optimized model and observed allele frequencies using one or more processors, f) The method according to any one of embodiments 67 to 76, wherein it is determined by one or more processors that a Low-Level Hysteresis (LOH) has occurred if the adjusted allele frequency of an HLA allele is less than a predetermined threshold. Embodiment 78. A method for treating cancer or a method for slowing the progression of cancer, (1) Detecting loss of heterozygosity (LOH) of the human leukocyte antigen (HLA) gene in a sample obtained from an individual, wherein the LOH of the HLA gene is a) Receiving allele frequencies observed for an HLA allele in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA allele detected among multiple sequence reads corresponding to the HLA gene, and the multiple sequence reads are obtained by sequencing nucleic acids encoding a gene or a portion thereof captured by hybridization with a bait molecule. b) Receiving the relative binding tendency of an HLA allele to a bait molecule in one or more processors, wherein the relative binding tendency of an HLA allele corresponds to the tendency of a nucleic acid encoding at least a portion of an HLA allele to bind to a bait molecule in the presence of nucleic acids encoding portions of one or more other HLA alleles. c) Using one or more processors, execute the objective function to measure the difference between the relative binding tendency of HLA alleles and the observed allele frequency. d) Execute the optimization model using one or more processors to minimize the objective function. e) Determining the adjusted allele frequency of an HLA allele based on an optimized model and observed allele frequencies using one or more processors, and f) Detection by determining that LOH has occurred if the adjusted allele frequency of an HLA allele is less than a predetermined threshold by one or more processors, (2) A method comprising administering to an individual an effective dose of a non-immune checkpoint inhibitor (ICI) treatment, at least in part, based on the detection of LOH of the HLA gene. Embodiment 79. A method for treating cancer or slowing the progression of cancer, (1) Detecting the absence of loss of heterozygosity (LOH) in human leukocyte antigen (HLA) genes in samples obtained from individuals, wherein the absence of LOH in HLA genes is a) Receiving allele frequencies observed for an HLA allele in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA allele detected among multiple sequence reads corresponding to the HLA gene, and the multiple sequence reads are obtained by sequencing nucleic acids encoding a gene or a portion thereof captured by hybridization with a bait molecule. b) Receiving the relative binding tendency of an HLA allele to a bait molecule in one or more processors, wherein the relative binding tendency of an HLA allele corresponds to the tendency of a nucleic acid encoding at least a portion of an HLA allele to bind to a bait molecule in the presence of nucleic acids encoding portions of one or more other HLA alleles. c) Using one or more processors, execute the objective function to measure the difference between the relative binding tendency of HLA alleles and the observed allele frequency. d) Execute the optimization model using one or more processors to minimize the objective function. e) Determining the adjusted allele frequency of an HLA allele based on an optimized model and observed allele frequencies using one or more processors, and f) Detection by determining that LOH did not occur if the adjusted allele frequency of an HLA allele is greater than a predetermined threshold by one or more processors, (2) A method comprising administering an effective amount of an immune checkpoint inhibitor (ICI) to an individual, at least partially based on the detection of a lack of LOH in the HLA gene. Embodiment 80. The method according to any one of Embodiments 67 to 79, wherein the ICI comprises a PD-1 inhibitor, a PD-L1 inhibitor, or a CTLA-4 inhibitor. Embodiment 81. The method according to any one of Embodiments 67 to 80, further comprising detecting tumor mutational burden (TMB) in a sample obtained from an individual. Embodiment 82. The method according to any one of Embodiments 67 to 80, further comprising obtaining knowledge of tumor mutational burden (TMB) in a sample obtained from an individual. Embodiment 83. The method according to any one of Embodiments 67 to 82, wherein the treatment or one or more treatment options further comprises a second therapeutic agent. Embodiment 84. The method according to any one of Embodiments 67-69 and 72-83, wherein LOH and high TMB of HLA genes in a sample indicate that the individual is likely to benefit from treatment including an immune checkpoint inhibitor (ICI). Embodiment 85. The method according to Embodiment 84, wherein one or more treatment options include a treatment comprising an ICI. Embodiment 86. The method according to any one of Embodiments 81 to 85, wherein LOH and high TMB of the HLA gene are detected in the same sample obtained from an individual. Embodiment 87. The method according to any one of Embodiments 81 to 85, wherein LOH and high TMB of the HLA gene are detected in different samples obtained from individuals. Embodiment 88. A method for selecting a treatment for an individual with cancer, comprising: (a) acquiring knowledge of loss of heterozygosity (LOH) of human leukocyte antigen (HLA) genes in a sample from an individual with cancer, wherein LOH of HLA genes is detected according to the method of any one of Embodiments 36 to 66; and (b) acquiring knowledge of high tumor mutagenesis (TMB) in a sample from an individual with cancer, wherein in response to the acquisition of such knowledge in (a) and (b), (i) the individual is classified as a candidate for treatment with an immune checkpoint inhibitor (ICI); (ii) the individual is identified as likely to respond to a treatment comprising an immune checkpoint inhibitor (ICI); and / or (iii) the individual is classified as a candidate for treatment comprising an immune checkpoint inhibitor (ICI). Embodiment 89. A method for predicting the survival time of an individual having cancer treated with an immune checkpoint inhibitor (ICI), comprising: (a) acquiring knowledge of loss of heterozygosity (LOH) of the human leukocyte antigen (HLA) gene in a sample from the individual, wherein the LOH of the HLA gene is detected according to the method of any one of Embodiments 36 to 66; and (b) acquiring knowledge of high tumor mutagenesis (TMB) in a sample from the individual, wherein, in response to the acquisition of such knowledge in (a) and (b), the individual is predicted to have a longer survival time after treatment with an ICI compared to the survival time of an individual treated with an ICI in which the cancer has LOH of the HLA gene without high TMB. Embodiment 90. A method for treating cancer or a method for slowing the progression of cancer, (1) Detecting loss of heterozygosity (LOH) of the human leukocyte antigen (HLA) gene in a sample obtained from an individual, wherein the LOH of the HLA gene is a) Receiving allele frequencies observed for an HLA allele in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA allele detected among multiple sequence reads corresponding to the HLA gene, and the multiple sequence reads are obtained by sequencing nucleic acids encoding a gene or a portion thereof captured by hybridization with a bait molecule. b) Receiving the relative binding tendency of an HLA allele to a bait molecule in one or more processors, wherein the relative binding tendency of an HLA allele corresponds to the tendency of a nucleic acid encoding at least a portion of an HLA allele to bind to a bait molecule in the presence of nucleic acids encoding portions of one or more other HLA alleles. c) Using one or more processors, execute the objective function to measure the difference between the relative binding tendency of HLA alleles and the observed allele frequency. d) Execute the optimization model using one or more processors to minimize the objective function. e) Determining the adjusted allele frequency of an HLA allele based on an optimized model and observed allele frequencies using one or more processors, and f) Detection by determining that LOH has occurred if the adjusted allele frequency of an HLA allele is less than a predetermined threshold by one or more processors, (2) To detect high tumor mutational burden (TMB) in samples obtained from individuals, (3) A method comprising administering to an individual a treatment comprising an effective amount of an immune checkpoint inhibitor (ICI), at least in part, based on the detection of LOH and high TMB of HLA genes. Embodiment 91. Using one or more processors, Each reaction corresponds to a bait molecule that binds to a different allele of the polymorphic gene, and each reaction results in the capture of the corresponding allele fraction. Identifying multiple chemical reactions such that each of them consists of a first subset of reactions and a second subset of reactions, the first and second subsets do not share a common reaction, and each of the first and second subsets contains at least one chemical reaction. Using one or more processors, identify multiple equations that collectively relate the binding tendencies of each chemical reaction and the allele fractions of each captured allele, One or more processors receive the empirically identified relative coupling tendencies of a first subset of multiple chemical reactions, A non-temporary computer-readable storage medium comprising one or more programs executable by one or more computer processors for performing a method including identifying the relative coupling tendency of a second subset by minimizing the total error using one or more processors. Embodiment 92. A non-temporary computer-readable storage medium according to Embodiment 91, wherein minimizing the total error is constrained by the median of the relative coupling tendencies being equal to 1. Embodiment 93. A non-temporary computer-readable storage medium according to Embodiment 91, wherein one relative coupling tendency is set to equal 1. Embodiment 94. A non-temporary computer-readable storage medium according to Embodiment 91, wherein minimizing the total error includes performing a least-squares procedure. Embodiment 95. The method is Receiving measured raw allele frequencies in a patient's DNA sample using one or more processors, wherein the measured raw allele frequencies are received after being measured by performing a hybrid capture process. A non-temporary computer-readable storage medium according to Embodiment 91, further comprising scaling the measured raw allele frequencies using first and second subsets of relative coupling tendencies on one or more processors, thereby reducing sampling bias. Embodiment 96. A non-temporary computer-readable storage medium according to Embodiment 91, wherein the polymorphic gene comprises a human leukocyte antigen gene. Embodiment 97. Polymorphic genes include ST7 / RAY1, ARH1 / NOEY2, TSLC1, RB, PTEN, SMAD2, SMAD4, DCC, TP53, ATM, miR-15a, miR-16-1, NAT2, BRCA1, BRCA2, hOGG1, CDH1, IGF2, CDKN1C / P57, MEN1, PRKAR1A, H19, KRAS, BAP1, PTCH1, SMO, SUFU, NOTCH1, PPP6C, LATS1, CASP8, PTPN14, ARID1A, FBXW7, M6P / IGF2R, IFN-α, olfactory receptor genes, CBFA2T3, DUTT1, FHIT, APC, P16, FCMD, TSC2, miR A non-temporary computer-readable storage medium according to Embodiment 91, which is -34, c-MPL, RUNX3, DIRAS3, NRAS, miR-9, FAM50B, PLAGL1, ER, FLT3, ZDBF2, GPR1, c-KIT, NAP1L5, GRB10, EGFR, PEG10, BRAF, MEST, JAK2, DAPK1, LIT1, WT1, NF-1, PR, c-CBL, DLK1, AKT1, SNURF, cytochrome P450 gene (CYP), ZNF587, SOCS1, TIMP2, RUNX1, AR, CEBPA, C19MC, EMP3, ZNF331, CDKN2A, PEG3, NNAT, GNAS, or GATA5. Embodiment 98. A non-temporary computer-readable storage medium according to Embodiment 95, further comprising a method in which one or more processors determine whether a patient has experienced a loss of heterozygosity. Embodiment 99. An immune checkpoint inhibitor (ICI) for use in a method to treat or slow the progression of cancer in an individual, wherein the absence of loss of heterozygosity (LOH) of the human leukocyte antigen (HLA) gene is present in a sample obtained from the individual. a) Receiving allele frequencies observed for an HLA allele in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA allele detected among multiple sequence reads corresponding to the HLA gene, and the multiple sequence reads are obtained by sequencing nucleic acids encoding a gene or a portion thereof captured by hybridization with a bait molecule. b) Receiving the relative binding tendency of an HLA allele to a bait molecule in one or more processors, wherein the relative binding tendency of an HLA allele corresponds to the tendency of a nucleic acid encoding at least a portion of an HLA allele to bind to a bait molecule in the presence of nucleic acids encoding portions of one or more other HLA alleles. c) Using one or more processors, execute the objective function to measure the difference between the relative binding tendency of HLA alleles and the observed allele frequency, d) Executing the optimization model using one or more processors to minimize the objective function, e) Determining the adjusted allele frequencies of HLA alleles based on the optimized model and observed allele frequencies using one or more processors, f) Immune checkpoint inhibitors (ICIs) detected by one or more processors, which determine that LOH did not occur if the adjusted allele frequency of an HLA allele is greater than a predetermined threshold. Embodiment 100. An immune checkpoint inhibitor (ICI) for use in a method to treat or slow the progression of cancer in an individual, wherein loss of heterozygosity (LOH) and high tumor mutagenesis (TMB) of human leukocyte antigen (HLA) genes are present in a sample obtained from the individual. a) Receiving allele frequencies observed for an HLA allele in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA allele detected among multiple sequence reads corresponding to the HLA gene, and the multiple sequence reads are obtained by sequencing nucleic acids encoding a gene or a portion thereof captured by hybridization with a bait molecule. b) Receiving the relative binding tendency of an HLA allele to a bait molecule in one or more processors, wherein the relative binding tendency of an HLA allele corresponds to the tendency of a nucleic acid encoding at least a portion of an HLA allele to bind to a bait molecule in the presence of nucleic acids encoding portions of one or more other HLA alleles. c) Using one or more processors, execute the objective function to measure the difference between the relative binding tendency of HLA alleles and the observed allele frequency, d) Executing the optimization model using one or more processors to minimize the objective function, e) Determining the adjusted allele frequencies of HLA alleles based on the optimized model and observed allele frequencies using one or more processors, f) If one or more processors determine that a Low-Level Hysteresis (LOH) has occurred when the adjusted allele frequency of an HLA allele is lower than a predetermined threshold, g) Acquiring or detecting knowledge of high TMB in samples obtained from individuals, and detecting immune checkpoint inhibitors (ICIs). Embodiment 101. An immune checkpoint inhibitor (ICI) for use in the manufacture of a drug for treating or slowing the progression of cancer in an individual, wherein loss of heterozygosity (LOH) and high tumor mutagenesis (TMB) of the human leukocyte antigen (HLA) gene are present in a sample obtained from an individual. a) Receiving allele frequencies observed for an HLA allele in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA allele detected among multiple sequence reads corresponding to the HLA gene, and the multiple sequence reads are obtained by sequencing nucleic acids encoding a gene or a portion thereof captured by hybridization with a bait molecule. b) Receiving the relative binding tendency of an HLA allele to a bait molecule in one or more processors, wherein the relative binding tendency of an HLA allele corresponds to the tendency of a nucleic acid encoding at least a portion of an HLA allele to bind to a bait molecule in the presence of nucleic acids encoding portions of one or more other HLA alleles. c) Using one or more processors, execute the objective function to measure the difference between the relative binding tendency of HLA alleles and the observed allele frequency, d) Executing the optimization model using one or more processors to minimize the objective function, e) Determining the adjusted allele frequencies of HLA alleles based on the optimized model and observed allele frequencies using one or more processors, f) If one or more processors determine that a Low-Level Hysteresis (LOH) has occurred when the adjusted allele frequency of an HLA allele is lower than a predetermined threshold, g) Acquiring or detecting knowledge of high TMB in samples obtained from individuals, and detecting immune checkpoint inhibitors (ICIs). Embodiment 102. An immune checkpoint inhibitor (ICI) for use in the manufacture of a drug for treating or slowing the progression of cancer in an individual, wherein the absence of loss of heterozygosity (LOH) of the human leukocyte antigen (HLA) gene is present in a sample obtained from an individual. a) Receiving allele frequencies observed for an HLA allele in one or more processors, wherein the observed allele frequencies correspond to the frequencies of nucleic acids encoding at least a portion of the HLA allele detected among multiple sequence reads corresponding to the HLA gene, and the multiple sequence reads are obtained by sequencing nucleic acids encoding a gene or a portion thereof captured by hybridization with a bait molecule. b) Receiving the relative binding tendency of an HLA allele to a bait molecule in one or more processors, wherein the relative binding tendency of an HLA allele corresponds to the tendency of a nucleic acid encoding at least a portion of an HLA allele to bind to a bait molecule in the presence of nucleic acids encoding portions of one or more other HLA alleles. c) Using one or more processors, execute the objective function to measure the difference between the relative binding tendency of HLA alleles and the observed allele frequency, d) Executing the optimization model using one or more processors to minimize the objective function, e) Determining the adjusted allele frequencies of HLA alleles based on the optimized model and observed allele frequencies using one or more processors, f) Immune checkpoint inhibitors (ICIs) detected by one or more processors, which dete...

Claims

1. A method for detecting loss of heterozygosity (LOH) in human leukocyte antigen (HLA) genes, To provide a plurality of nucleic acids obtained from an individual-derived sample, wherein the plurality of nucleic acids include a nucleic acid encoding an HLA gene. Amplifying nucleic acids from the aforementioned plurality of nucleic acids, The capture of multiple nucleic acids corresponding to the HLA gene, wherein the multiple nucleic acids corresponding to the HLA gene are captured from the amplified nucleic acid by hybridization with a bait molecule. The captured nucleic acid is sequenced using a sequencer to obtain multiple sequence reads corresponding to the HLA gene, Determining the observed allele frequency for an HLA allele, wherein the observed allele frequency is determined based on the ratio of the number of sequence reads for the HLA allele to the total number of sequence reads for the HLA gene. Determining the relative binding tendency of the HLA allele to the bait molecule, wherein the relative binding tendency of the HLA allele is determined based on the ratio of the tendency of nucleic acids encoding at least a portion of the HLA allele to bind to the bait molecule to the tendency of nucleic acids encoding at least a portion of one or more other HLA alleles to bind to the bait molecule. Applying a quadratic objective function, the difference between the relative binding tendency for the HLA allele and the observed allele frequency of the HLA allele is measured. The least-squares optimization model is applied to minimize the quadratic objective function, Based on the least-squares optimization model and the observed allele frequencies of the HLA alleles, the adjusted allele frequencies of the HLA alleles are determined. A method comprising determining that LOH has occurred if the adjusted allele frequency of the HLA allele is less than a predetermined threshold.

2. The method according to claim 1, further comprising detecting a high tumor mutational burden (TMB) in the sample or acquiring knowledge thereof.

3. The method according to claim 2, wherein the TMB is determined based on the number of non-driver somatic coding mutations per megabase of the sequenced genome.

4. The method according to claim 2 or 3, further comprising using one or more processors to generate a report including one or more treatment options identified for the individual, at least in part based on the detection of LOH and high TMB of the HLA gene.

5. The method according to claim 4, wherein the one or more treatment options identified for the individual include treatment with an immune checkpoint inhibitor (ICI).

6. The method according to any one of claims 1 to 5, wherein the HLA gene is a human HLA-A, HLA-B, or HLA-C gene.

7. The method according to any one of claims 1 to 6, wherein the sample comprises tumor cells and / or tumor nucleic acids.

8. The aforementioned sample, (a) further comprising non-tumor cells, (b) From a tumor biopsy or tumor specimen, (c) containing tumor cell-free DNA (cfDNA), (d) including fluids, cells, or tissues, (e) Tumor biopsy or circulating tumor cells, The method according to claim 7.

9. Using one or more processors, receive sequence read data for multiple sequence reads corresponding to HLA genes, Determining the observed allele frequency for an HLA allele using one or more of the aforementioned processors, wherein the observed allele frequency is determined based on the ratio of the number of sequence reads for the HLA allele to the total number of sequence reads for the HLA gene, Determining the relative binding tendency of the HLA allele to the bait molecule using one or more processors, wherein the relative binding tendency of the HLA allele is determined based on the ratio of the tendency of nucleic acids encoding at least a portion of the HLA allele to bind to the bait molecule to the tendency of nucleic acids encoding at least a portion of one or more other HLA alleles to bind to the bait molecule. Using one or more of the aforementioned processors, a quadratic objective function is applied to measure the difference between the relative binding tendency for the HLA allele and the observed allele frequency of the HLA allele. Using one or more of the aforementioned processors, apply a least-squares optimization model to minimize the quadratic objective function. Using one or more of the aforementioned processors, the adjusted allele frequency of the HLA allele is determined based on the least-squares optimization model and the observed allele frequency of the HLA allele. A non-temporary computer-readable storage medium comprising one or more programs executable by one or more computer processors for performing a method including determining that a Low-Level Occurrence (LOH) has occurred if the adjusted allele frequency of the HLA allele is less than a predetermined threshold, using one or more processors.

10. It is a system, One or more processors, A memory configured to store one or more computer program instructions, wherein when the one or more computer program instructions are executed by the one or more processors, Receiving sequence read data for multiple sequence reads corresponding to the HLA gene, Determining the observed allele frequency for an HLA allele, wherein the observed allele frequency is determined based on the ratio of the number of sequence reads for the HLA allele to the total number of sequence reads for the HLA gene. Determining the relative binding tendency of the HLA allele to the bait molecule, wherein the relative binding tendency of the HLA allele is determined based on the ratio of the tendency of nucleic acids encoding at least a portion of the HLA allele to bind to the bait molecule to the tendency of nucleic acids encoding at least a portion of one or more other HLA alleles to bind to the bait molecule. Applying a quadratic objective function to measure the difference between the relative binding tendency for the HLA allele and the observed allele frequency of the HLA allele, The least-squares optimization model is applied to minimize the quadratic objective function, Based on the least-squares optimization model and the observed allele frequencies of the HLA alleles, the adjusted allele frequencies of the HLA alleles are determined. A system configured to determine that LOH has occurred if the adjusted allele frequency of the HLA allele is less than a predetermined threshold.

11. The method according to claim 1, further comprising ligating one or more adapters to one or more nucleic acids from the plurality of nucleic acids.

Images