Pharmaceutical composition for patients whose tumors carry high passenger gene mutation load

Abstract

To provide a pharmaceutical composition for treating a cancer patient having a tumor having a total passenger gene mutation amount larger than the background mutation amount of the tumor.SOLUTION: A pharmaceutical composition for treating a subject having a tumor with a total passenger gene mutation load that is greater than the background mutation load of the tumor, wherein the background mutation load has been determined based on randomly selected genes of the tumor, comprising antibodies that bind to PD1 as an active ingredient. Antibodies that bind PD1 comprise a heavy chain variable region (HCVR) comprising the amino acid sequence of SEQ ID NO: 21 and / or comprise a light chain variable region (LCVR) comprising the amino acid sequence of SEQ ID NO: 22.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 62 / 560,955, filed September 20, 2017, which is incorporated herein by reference in its entirety.

[0002] The present disclosure relates generally to the field of immunotherapy. More specifically, the present disclosure relates to methods of administering immunotherapy regimens to patients whose tumors have a high passenger gene mutation burden. [Background technology]

[0003] Various publications, including patents, patent applications, published patent applications, accession numbers, technical papers, and journal articles, are cited herein, and each of these cited publications is incorporated by reference in its entirety and for all purposes.

[0004] Recent studies suggest that patients with a high overall tumor mutational burden (TMB) are more likely to benefit from immunotherapy due to the increased presence of neoantigens that can elicit an immune response. However, overall mutational burden also includes driver mutations, which may actually suppress immunogenicity and reduce sensitivity to treatment. Summary of the Invention [Problem to be solved by the invention]

[0005] Methods aimed at identifying passenger genes and their mutations and assessing their immunogenicity have not been developed. The present disclosure addresses these and other deficiencies. [Means for solving the problem]

[0006] It is to be understood that both the following general description and the following detailed description are exemplary and explanatory only and are not restrictive. In a first aspect, the present disclosure provides a method comprising the steps of receiving genetic sequence data, the genetic sequence data comprising multiple genes, derived from multiple biological samples collected from subjects with multiple disease types; identifying multiple mutant genes for each of the multiple biological samples, each mutant gene comprising a gene sequence with at least one non-synonymous somatic mutation; determining the tumor mutation burden of each biological sample for each disease type based on the number of mutant genes in each biological sample; determining the average tumor mutation burden of the multiple mutant genes in the multiple biological samples based on the number of mutant genes determined in each biological sample for each mutant gene and each disease type; determining the proportion of biological samples containing the mutant gene for each mutant gene; and determining a correlation coefficient between the average tumor mutation burden and the proportion of biological samples containing the mutant gene. A higher correlation coefficient suggests that a particular gene is more likely to have acquired somatic mutations in cancer types with a generally high mutation frequency (passenger genes). On the other hand, a low correlation coefficient suggests that a particular gene is unlikely to have acquired a somatic mutation (and is not a passenger gene) in a cancer type with an overall high mutation frequency.

[0007] In another aspect, the disclosure provides a method for selecting a cancer patient for immunotherapy, generally comprising establishing a total passenger mutational burden from a tumor of the cancer patient, creating a background distribution for the mutational burden of the tumor, normalizing the total passenger mutational burden to the background distribution, and categorizing the cancer patient as an immunotherapy responder when the total passenger mutational burden is at least about 1.5 standard deviations higher than the mean of the background distribution.

[0008] Creating a background distribution involves establishing a mutational burden from multiple samples of randomly selected genes obtained from the tumor, where the number of randomly selected genes in each sample preferably equals the number of passenger genes used to calculate the total passenger mutational burden. Normalizing the total passenger mutational burden to the background distribution can include creating a z-score that indicates the number of standard deviations from the mean of the background distribution.

[0009] The method may further include categorizing mutated genes in the tumor as passenger genes. Categorizing mutated genes in the tumor as passenger genes may include selecting mutated genes from the tumor and matching the mutated genes to a data structure containing passenger genes established according to a passenger gene index. The passenger gene index may include a correlation coefficient between the proportion of samples containing a mutated gene obtained from a cancer patient cohort and the median number of mutated genes in each tumor type within the cancer patient cohort.

[0010] The method may further comprise administering to the cancer patient an immunotherapy regimen. The immunotherapy regimen may comprise administering to the patient an inhibitor of a T cell inhibitory receptor. The immunotherapy regimen may comprise administering to the patient an activator of a T cell activation receptor.

[0011] The immunotherapy regimen may include administering to a patient an antibody that binds to PD1. The antibody that binds to PD1 may comprise at least the heavy chain variable region (HCVR) sequence and light chain variable region of SEQ ID NO: 21, or may comprise at least the light chain variable region (LCVR) sequence and heavy chain variable region of SEQ ID NO: 22. The antibody that binds to PD1 may comprise the HCVR or SEQ ID NO: 21 and the LCVR or SEQ ID NO: 22. The antibody that binds to PD1 may be administered in combination with an antibody that binds to LAG3.

[0012] The immunotherapy regimen may comprise administering to a patient an antibody that binds to PDL1. The antibody that binds to PDL1 may comprise at least the HCVR sequence and LCVR of SEQ ID NO: 122, or may comprise at least the HCVR and LCVR sequence of SEQ ID NO: 123. The antibody that binds to PDL1 may comprise the HCVR or SEQ ID NO: 122, and the LCVR or SEQ ID NO: 123. The antibody that binds to PDL1 may be administered in combination with an antibody that binds to LAG3.

[0013] The immunotherapy regimen may comprise administering to a patient an antibody that binds to LAG3. The antibody that binds to LAG3 may comprise at least the HCVR sequence and LCVR of SEQ ID NO: 93, or may comprise at least the HCVR and LCVR sequence of SEQ ID NO: 94. The antibody that binds to LAG3 may comprise the HCVR or SEQ ID NO: 93, and the LCVR or SEQ ID NO: 94. The antibody that binds to LAG3 may be administered in combination with an antibody that binds to PD1 or an antibody that binds to PDL1.

[0014] Additional advantages will be set forth in part in the description that follows or may be learned by practice. These advantages will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims. [Effects of the Invention]

[0015] According to the present invention, an immunotherapeutic agent for patients whose tumors carry a high passenger gene mutation load can be provided. [Brief explanation of the drawings]

[0016] [Figure 1] FIG. 1 shows a flow chart illustrating an example method. [Figure 2] FIG. 2 shows a flowchart illustrating an example method. [Figure 3] FIG. 3 shows a flowchart illustrating another example method. [Figure 4] FIG. 4 shows a flowchart illustrating another example method. [Figure 5] FIG. 5 illustrates a schematic of passenger gene characteristics. [Figure 6] Figure 6 shows a scatter plot of the mean number of total mutated genes (x-axis) versus the proportion of patients with genetic variants (y-axis) for each cancer type. [Figure 7] FIG. 7 shows enrichment along the Passenger Gene Index (PGI) scale for cancer driver genes and various other gene groups. [Figure 8] FIG. 8 shows the highest (left) and lowest (right) PGI CGC genes and the corresponding cancer types with the highest percentage of mutant samples (>2%). [Figure 9A] 9A-C are graphical representations of a) local immune cytolytic activity, b) TCR read counts, and c) clinical outcomes of patient cohorts. [Figure 9B] 9A-C are graphical representations of a) local immune cytolytic activity, b) TCR read counts, and c) clinical outcomes of patient cohorts. [Figure 9C] 9A-C are graphical representations of a) local immune cytolytic activity, b) TCR read counts, and c) clinical outcomes of patient cohorts. [Figure 10] FIG. 10 is a block diagram illustrating an exemplary operating environment for implementing the disclosed methods. [Figure 11] Figure 11 shows the TMB of the patient cohort from the Phase I clinical trial. [Figure 12-1] FIG. 12 shows the top 500 passenger genes—highest passenger gene index (PGI). [Figure 12-2] FIG. 12 shows the top 500 passenger genes—highest passenger gene index (PGI). [Figure 12-3] FIG. 12 shows the top 500 passenger genes—highest passenger gene index (PGI). DETAILED DESCRIPTION OF THE INVENTION

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments and, together with the description, serve to explain the principles of the method and system. Various terms are used throughout the specification and claims in connection with the presently disclosed embodiments. Such terms are to be given their ordinary meaning in the art unless otherwise indicated. Other specifically defined terms are to be construed in a manner consistent with the definitions provided herein.

[0018] As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

[0019] Inhibition includes decreasing, decreasing, interfering with, suppressing, slowing, inactivating, desensitizing, stopping, and / or downregulating the activity or expression of a molecule or pathway of interest. Embodiments of the present methods and systems are described below with reference to block diagrams and flowchart illustrations of methods, systems, apparatuses, and computer program products. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by computer program instructions. These computer program instructions can be loaded into a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to produce machine means for performing the functions identified in the flowchart blocks, whereby the instructions, executing on the computer or other programmable data processing apparatus, create means for performing the functions identified in the flowchart blocks.

[0020] The computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture that includes computer-readable instructions for performing the functions identified in the flowchart blocks. The computer program instructions may also be loaded into a computer or other programmable data processing apparatus and cause the computer or other programmable device to perform a series of operating steps to generate a computer-implemented process, such that the instructions executing on the computer or other programmable device provide steps for performing the functions identified in the flowchart blocks.

[0021] Thus, the blocks in the block diagrams and flowchart diagrams represent combinations of means for performing the specified functions, combinations of steps for performing the specified functions, and program instruction means for performing the specified functions. It should also be understood that each block in the block diagrams and flowchart diagrams, and combinations of blocks in the block diagrams and flowchart diagrams, can be implemented by a special-purpose hardware-based computer system or a combination of special-purpose hardware and computer instructions that performs the specified functions or steps.

[0022] The terms "subject" and "patient" are used interchangeably and include any animal. Mammals preferably include companion (e.g., cats, dogs) and domestic mammals (e.g., pigs, horses, cows), as well as rodents, including mice, rabbits, and rats, guinea pigs, and other rodents. Non-human primates are more preferred, and humans are even more preferred.

[0023] According to the present disclosure, it has been observed that in cancer, the total mutational burden of passenger genes, as opposed to the total mutational burden of all genes, serves as an accurate indicator of the likelihood that a cancer patient will respond positively to immunotherapy. Tumor mutational burden (TMB) can refer to the number of mutations within the coding regions of a tumor genome. Mutated genes are assessed and classified according to their passenger gene status via a passenger gene index. This index has been used as a metric to identify passenger genes from large-scale cancer genome analyses. Identified passenger genes have been observed to be enriched in gene families known to have an excess of passenger mutations, including genes encoding large proteins, genes with low expression levels, and genes in the late DNA replication phase. The total mutational burden of passenger genes positively correlated with tumor immunogenicity and successfully predicted patient clinical outcomes. Accordingly, the present disclosure features a method for classifying patients according to their passenger gene mutational burden as part of an immunotherapy regimen.

[0024] In cancer biology, driver mutations are understood to be at least incidentally involved in the formation and transformation of cancer. Passenger mutations are understood to be neither beneficial for growth nor contribute to the development of cancer. See Stratton MR et al. (2009) Nature. 458:719-24. Therefore, passenger genes include genes that contain passenger mutations. Non-limiting examples of mutations include substitutions, inversions, insertions and deletions of one or more nucleotides, codons, genes or chromosomes, and copy number changes.

[0025] In one aspect, the present disclosure features methods and systems for identifying or classifying passenger genes. The identified passenger genes are enriched in families known for having an excessive number of passenger mutations, such as very large proteins and genes with low expression levels or genes in the late DNA replication phase. In some embodiments, passenger genes can be identified or classified according to a passenger gene index (PGI). Thus, for example, passenger genes can be identified or classified according to a PGI, which includes the correlation coefficient between the proportion of samples containing a mutated gene obtained from a cancer patient cohort and the median number of mutated genes in each tumor type within the cancer patient cohort. Based on the identification of passenger genes, a data structure including the passenger genes can be established.

[0026] Individual cancer patients can be screened to determine whether their tumors contain passenger genes, and the total passenger gene mutation burden of the tumor can be determined. Based on the patient's passenger gene mutation burden, patients can be classified according to their ability to respond positively to immunotherapy. Immunotherapy generally enhances the body's natural immune response to cancer, and includes, but is not limited to, enhancing T cell responses to tumors.

[0027] An example of a method by which a cancer patient may be assessed for immunotherapy responsiveness is shown in Figure 1. Generally, the method includes establishing a total passenger mutational burden from the cancer patient's tumor (110), creating a background distribution for the tumor mutational burden (120), normalizing the total passenger mutational burden to the background distribution (130), and categorizing the cancer patient as an immunotherapy responder (140).

[0028] Also disclosed is a method for treating a cancer patient with immunotherapy after the patient has been evaluated for immunotherapy responsiveness. For example, a method for treating a cancer patient with immunotherapy is disclosed, the method comprising: determining whether the cancer patient is an immunotherapy responder, the method comprising: establishing the total passenger mutation burden of the patient's tumor; creating a background distribution for the tumor mutation burden; normalizing the total passenger mutation burden to the background distribution; and categorizing the cancer patient as an immunotherapy responder genotype when the total passenger mutation burden is at least about 1.5 standard deviations higher than the mean of the background distribution; and administering immunotherapy to the cancer patient categorized as an immunotherapy responder.

[0029] Also disclosed is a method of treating a patient with an inhibitor of a T cell inhibitory receptor, or an inhibitor of a receptor on a tumor cell, or a non-immunotherapeutic treatment, wherein the patient is afflicted with cancer, the method comprising the steps of obtaining or obtaining a biological sample from the patient's tumor; sequencing the biological sample to generate sequence data, thereby performing or having performed genotyping on the biological sample to determine whether the patient has a genotype that is a responder to immunotherapy; establishing a total passenger mutation burden in the patient's tumor based on the sequence data; and generating a background distribution for the mutation burden of the tumor based on the sequence data. determining whether the patient is an immunotherapy responder by: normalizing the total passenger mutation load to a background distribution; and categorizing the patient as having an immunotherapy responder genotype when the total passenger mutation load is at least about 1.5 standard deviations higher than the mean of the background distribution, wherein if the patient has an immunotherapy responder genotype, administering a therapeutically effective amount of an inhibitor of a T-cell inhibitory receptor or a receptor on tumor cells, and wherein if the patient does not have an immunotherapy responder genotype, administering a non-immunotherapeutic treatment. In some embodiments, a patient with an immunotherapy responder genotype has a lower risk of poor clinical outcome after administration of a therapeutically effective amount of an inhibitor of a T-cell inhibitory receptor or a receptor on tumor cells than if the patient is administered a non-immunotherapeutic treatment. In some embodiments, a patient with an immunotherapy responder genotype has higher T cell activation and / or immune cytolytic activity after administration of a therapeutically effective amount of an inhibitor of a T cell inhibitory receptor or a receptor on a tumor cell than when the patient is given a non-immunotherapeutic treatment.

[0030] An immunotherapy is disclosed for use in a method of treating a cancer patient, the method comprising determining whether the cancer patient is an immunotherapy responder by establishing a total passenger mutation burden in the patient's tumor; creating a background distribution for the mutation burden of the tumor; normalizing the total passenger mutation burden to the background distribution; categorizing the cancer patient as having an immunotherapy responder genotype when the total passenger mutation burden is at least about 1.5 standard deviations higher than the mean of the background distribution; and administering immunotherapy to the cancer patient categorized as an immunotherapy responder.

[0031] In some preferred embodiments, establishing 110 the total passenger mutation burden from the cancer patient's tumor may include determining the total passenger mutation burden by any sequencing method used to determine the coding regions of the tumor genome ("exome"). Whole genome sequencing methods may be used.

[0032] Exome mutation can be determined by using sequencing methods known in the art.For example, US2013 / 0040863, which is incorporated herein by reference, describes the method for determining the nucleic acid sequence of target nucleic acid molecule for mutation detection, whole genome sequencing and exome sequencing, including sequencing by synthesis, sequencing by ligation or sequencing by hybridization.If desired, various amplification methods can be used to produce a larger amount of particularly small nucleic acid samples, and then carry out sequencing.

[0033] Sequencing by synthesis (SBS) and sequence analysis by ligation can be performed using ePCR, as used by 454 Lifesciences (Bradford, Connecticut) and Roche Diagnostics (Basel, Switzerland). Nucleic acids or other materials, such as genomic DNA, can be fragmented, dispersed in a water / oil emulsion, and diluted to separate single nucleic acid fragments from other materials in the emulsion droplets. Beads containing multiple copies of primers, for example, can be used, and amplification methods can be performed, with each emulsion droplet serving as a reaction vessel for the amplification of multiple copies of a single nucleic acid fragment. Other methods, such as bridging PCR (Illumina, Inc., San Diego, California) or polony amplification (Agencourt / Applied Biosystems), can also be used. Nos. US2009 / 0088327, US2010 / 0028885, and US2009 / 0325172, each of which is incorporated herein by reference.

[0034] Manual or automated sequencing methods are also known in the art and include, but are not limited to, Sanger sequencing, pyrosequencing, sequencing by hybridization, sequencing by ligation, etc. Sequencing methods may be performed manually or using automated methods. Furthermore, the amplification methods described herein may be used to prepare nucleic acids for sequence analysis using commercially available methods, such as automated Sanger sequencing (available from Applied Biosystems, Foster City, CA) or pyrosequencing (available from 454 Lifesciences, Branford, CT, and Roche Diagnostics, Basel, Switzerland), or for sequence analysis by synthesis, commercially available from Illumina (San Diego, CA) or Helicos (Cambridge, MA), or for sequence analysis by ligation on the Agencourt platform developed by Applied Biosystems (Ronaghi et al., Science 281:363 (1998); Dressman et al., Proc. Natl. Acad. Sci. USA 100:8817-8822 (2003); Mitra et al., Proc. Natl. Acad. Sci. USA 100:55926-5931 (2003), incorporated herein by reference.

[0035] In the nucleic acid population, a primer hybridizes to each nucleic acid, thereby forming the nucleic acid as a template, and primer modification occurs in a template-directed manner. The modification can be detected to determine the sequence of the template. For example, the primer can be modified by extension using a polymerase, and primer extension can be monitored under conditions that reveal the identity and location of the specific nucleotide to be determined. For example, extension can be monitored, and the sequence of the template nucleic acid can be determined using pyrosequencing. These are described in US2005 / 0130173, US2006 / 0134633, U.S. Patent No. 4,971,903, U.S. Patent No. 6,258,568, and U.S. Patent No. 6,210,891. Each of these is incorporated herein by reference and is also commercially available. Extension can be monitored by following the addition of labeled nucleotide analogues by polymerase, for example, using the methods described in U.S. Patent No. 4,863,849, U.S. Patent No. 5,302,509, U.S. Patent No. 5,763,594, U.S. Patent No. 5,798,210, U.S. Patent No. 6,001,566; U.S. Patent No. 6,664,079, US2005 / 0037398, and U.S. Patent No. 7,057,026. Each of these is incorporated herein by reference. Polymerases useful in sequence analysis methods are usually polymerase enzymes derived from natural sources. It should be understood that polymerases can be modified to change their specificity for modified nucleotides, for example, as described in WO01 / 23411, U.S. Patent No. 5,939,292, and WO05 / 024010. Each of these is incorporated herein by reference. Furthermore, polymerases do not have to be derived from biological systems. Polymerases useful in the present invention include any substance capable of catalyzing the extension of a nucleic acid primer in a manner directed by the template sequence to which the primer hybridizes. Typically, polymerases are protein enzymes isolated from biological systems.

[0036] Alternatively, exon sequences may be determined using ligation sequencing, as described, for example, in Shendure et al. Science 309:1728-1732 (2005); U.S. Patent No. 5,599,675, and U.S. Patent No. 5,750,341, each of which is incorporated herein by reference. The sequence of the template nucleic acid may be determined using hybridization sequencing, as described, for example, in U.S. Patent No. 6,090,549, U.S. Patent No. 6,401,267, and U.S. Patent No. 6,620,584, each of which is incorporated herein by reference.

[0037] If desired, exon sequence products are detected using a ligation assay, such as an oligonucleotide ligation assay (OLA). Detection using OLA involves template-dependent ligation of two small probes into a single long probe using the target sequence in the amplicon as a template. In certain embodiments, the single-stranded target sequence includes a first target domain and a second target domain, which are adjacent and contiguous. The first and second OLA probes may be hybridized to complementary sequences of each target domain. The two OLA probes are then covalently linked to each other to form a modified probe. In embodiments in which the probes hybridize directly adjacent to each other, the covalent linkage may occur via a ligase. One or both probes may contain a nucleoside bearing a label, such as a peptide-linked label. The presence of the ligated product may then be determined by detecting the label. In certain embodiments, a ligation probe may include a priming site configured to allow amplification of the ligated probe product using a primer that hybridizes to the priming site, for example in a PCR reaction.

[0038] Alternatively, ligation probes may be used in an extension-ligation assay, in which the hybridized probes are discontinuous and one or more nucleotides are added along with one or more substances that bind the probes via the added nucleotides. Additionally, ligation or extension-ligation assays may be performed using a single padlock probe instead of two separate ligation probes.

[0039] In some preferred embodiments, creating 120 the background distribution includes establishing mutational burdens from multiple samples of randomly selected genes obtained from the tumor, where the number of randomly selected genes in each sample is equal to the number of passenger genes used to calculate the total passenger mutational burden.

[0040] In some preferred embodiments, normalizing (130) the total passenger mutation load to the background distribution includes generating a z-score that indicates the number of standard deviations from the mean of the background distribution. In alternative embodiments, p-values ​​may be used. Z-scores may be correlated with p-values. For example, a z-score of 1.65 is equivalent to a p-value of p<0.05, and a z-score of 2.3 is equivalent to a p-value of p<0.01.

[0041] Categorizing a cancer patient as an immunotherapy responder can be based on the correlation between the mean of the background distribution and the total passenger mutation load. For example, a patient can be categorized as an immunotherapy responder if the total passenger mutation load is at least a few standard deviations higher than the mean of the background distribution. The number of standard deviations can be, for example, at least about 1, at least about 1.5, at least about 2, at least about 2.5, at least about 3, or more than 3 standard deviations higher than the mean of the background distribution.

[0042] In some embodiments, the cancer patient may be afflicted with cutaneous squamous cell carcinoma (CSCC), bladder urothelial cell carcinoma (BLCA), invasive breast cancer (BRCA), cervical squamous cell carcinoma and adenocarcinoma (CESC), colon / rectal adenocarcinoma (CORE), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), clear cell renal carcinoma of the kidney (KIRC), papillary cell renal carcinoma of the kidney (KIRP), acute myeloid leukemia (LAML), hepatocellular carcinoma of the liver (LIHC), low grade glioma of the brain (LGG), lung adenoma (LUAD), lung squamous cell carcinoma (LUSC), serous cystadenocarcinoma of the ovary (OV), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), cutaneous melanoma (SKCM), gastric adenocarcinoma.

[0043] In some embodiments, the method further comprises categorizing mutated genes in the tumor as passenger genes. Categorizing mutated genes in the tumor as passenger genes may comprise selecting mutated genes from the tumor and matching the mutated genes to a data structure comprising established passenger genes according to a passenger gene index. The passenger gene index may comprise a correlation coefficient between the proportion of samples from a cancer patient cohort that contain a mutated gene and the median number of mutated genes in each tumor type within the cancer patient cohort.

[0044] If the cancer patient is categorized as an immunotherapy responder, the method may further include administering an immunotherapy regimen to the cancer patient. In some embodiments, the immunotherapy regimen includes administering to the patient an inhibitor of a T cell inhibitory receptor or a receptor on tumor cells. In some embodiments, the inhibitor of a T cell inhibitory receptor or a receptor on tumor cells may comprise an antibody or antigen-binding fragment thereof. In some embodiments, the immunotherapy regimen includes administering to the patient an activator of a T cell receptor that promotes T cell activation and prolongs immune cytolytic activity.

[0045] In some embodiments, T cell inhibitory receptors or receptors on tumor cells can be targeted by inhibitors for immunotherapy and include one or more of PD1, PDL1, CTLA4, LAG3, and TIM3. Thus, in some embodiments, inhibitors of T cell inhibitory receptors or receptors on tumor cells include antibodies or antigen-binding fragments thereof that specifically bind to one or more of PD1, PDL1, CTLA4, LAG3, and TIM3. As part of an immunotherapy regimen, cancer patients may be administered antibodies or antigen-binding fragments thereof that specifically bind to one or more of PD1, PDL1, CTLA4, LAG3, and TIM3, or any combination of two or more such antibodies or antigen-binding fragments thereof.

[0046] In some embodiments, the immunotherapy regimen comprises administering to the patient an antibody that binds to PD1. In some preferred embodiments, the antibody that binds to PD1 comprises at least the heavy chain variable region (HCVR) sequence of SEQ ID NO: 21 and the light chain variable region (LCVR) sequence of SEQ ID NO: 22. In certain embodiments, the antibody or antigen-binding fragment thereof that binds to PD1 may be any of the antibodies or antigen-binding fragments thereof described in U.S. Patent Application No. 14 / 603,776 (U.S. Patent Application Publication No. 2015-0203579), which is incorporated herein by reference. For example, in some embodiments, the antibody or antigen-binding fragment thereof that binds to PD1 comprises an HCVR and an LCVR having an amino acid sequence from among the sequences listed in Table 1. In some embodiments, the antibody or antigen-binding fragment thereof that binds to PD1 comprises an LCVR and an HCVR having an amino acid sequence from among the sequences listed in Table 1. In some embodiments, the antibody or antigen-binding fragment thereof that binds to PD1 comprises a pair of HCVRs and LCVRs shown in Table 1. Other antibodies (or antigen-binding fragments thereof) that bind to PD1 can be used, including, but not limited to, pembrolizumab, nivolumab, durvalumab, atezolizumab, pidilizumab, camrelizumab, PDR001, MED10680, JNJ-63723283, and MCLA-134.

[0047] [Table 1]

[0048] In some embodiments, the immunotherapy regimen comprises administering to the patient an antibody that binds to the LAG3 protein (also known as CD223). In some embodiments, the antibody that binds to LAG3 comprises at least the HCVR sequence of SEQ ID NO: 93 and the LCVR sequence of SEQ ID NO: 94. In some embodiments, the antibody or antigen-binding fragment thereof that binds to LAG3 may be any of the antibodies or antigen-binding fragments thereof described in U.S. Patent Application No. 15 / 289,032 (U.S. Patent Application Publication No. 2017-0101472), which is incorporated herein by reference. For example, in some embodiments, the antibody or antigen-binding fragment thereof that binds to LAG3 comprises an HCVR and an LCVR having an amino acid sequence from among the sequences listed in Table 2. In some embodiments, the antibody or antigen-binding fragment thereof that binds to LAG3 comprises an LCVR and an HCVR having an amino acid sequence from among the sequences listed in Table 2. In some embodiments, the antibody or antigen-binding fragment thereof that binds to LAG3 comprises a pair of HCVRs and LCVRs shown in Table 2. Other antibodies (or antigen-binding fragments thereof) that bind to LAG3 can be used, including, but not limited to, BMS-986016 and GSK2381781.

[0049] [Table 2]

[0050] In some embodiments, the immunotherapy regimen comprises administering to the patient an antibody that binds to PDL1. In some embodiments, the antibody that binds to PDL1 comprises at least the HCVR sequence of SEQ ID NO: 122 and the LCVR sequence of SEQ ID NO: 123. In some embodiments, the antibody or antigen-binding fragment thereof that binds to PDL1 may be any of the antibodies or antigen-binding fragments thereof described in U.S. Patent Application No. 14 / 603,808 (U.S. Patent Application Publication No. 2015-0203580), which is incorporated herein by reference. For example, in some embodiments, the antibody or antigen-binding fragment thereof that binds to PDL1 comprises an HCVR and an LCVR having an amino acid sequence from among the sequences listed in Table 3. In some embodiments, the antibody or antigen-binding fragment thereof that binds to PDL1 comprises an LCVR and an HCVR having an amino acid sequence from among the sequences listed in Table 3. In some embodiments, the antibody or antigen-binding fragment thereof that binds to PDL1 comprises a pair of HCVRs and LCVRs shown in Table 3. Other antibodies (or antigen-binding fragments thereof) that bind to PDL1 can be used, including, but not limited to, avelumab, atezolizumab, and durvalumab.

[0051] [Table 3]

[0052] In some embodiments, the immunotherapy regimen comprises administering to the patient an antibody that binds CTLA4. In some embodiments, the antibody or antigen-binding fragment thereof that binds CTLA4 may be any of the antibodies or antigen-binding fragments thereof described in U.S. Provisional Patent Application No. 62 / 537,753, filed July 27, 2017, which is incorporated herein by reference. For example, in some embodiments, the antibody or antigen-binding fragment thereof that binds CTLA4 comprises an HCVR and an LCVR having an amino acid sequence from among the sequences listed in Table 4. In some embodiments, the antibody or antigen-binding fragment thereof that binds CTLA4 comprises an LCVR and an HCVR having an amino acid sequence from among the sequences listed in Table 4. In some embodiments, the antibody or antigen-binding fragment thereof that binds CTLA4 comprises a pair of HCVR and LCVR shown in Table 4. Other antibodies (or antigen-binding fragments thereof) that bind to CTLA4 can be used, including, but not limited to, ipilimumab and tremelimumab, as well as one or more of any of the antibodies or antigen-binding fragments thereof disclosed in U.S. Patent Nos. 6,984,720, 7,605,238, or 7,034,121, all of which are incorporated herein by reference.

[0053] [Table 4]

[0054] In some embodiments, an immunotherapy regimen may comprise administering to a patient a combination of one or more inhibitors of a T-cell inhibitory receptor. The combination may comprise a combination of antibodies, or a combination of antigen-binding portions of such antibodies, or a combination of an antibody and an antigen-binding portion. Thus, for example, an immunotherapy regimen may comprise administering to a patient an antibody that binds PD1 in combination with a second immunotherapy regimen, such as an antibody that binds LAG3, or an antibody that binds PDL1, or an antibody that binds CTLA. An immunotherapy regimen may comprise administering to a patient an antibody that binds PDL1 in combination with a second immunotherapy regimen, such as an antibody that binds LAG3, or an antibody that binds PD1, or an antibody that binds CTLA. An immunotherapy regimen may comprise administering to a patient an antibody that binds LAG3 in combination with a second immunotherapy regimen, such as an antibody that binds PD1, or an antibody that binds PDL1, or an antibody that binds CTLA. The immunotherapy regimen may include administering to the patient an antibody that binds to CTLA4 in combination with a second immunotherapy regimen, such as an antibody that binds to LAG3, an antibody that binds to PDL1, or an antibody that binds to PD1. The antibody that binds to PD1 may include any antibody or antigen-binding domain exemplified herein. The antibody that binds to PD1 may include any antibody or antigen-binding domain exemplified herein. The antibody that binds to PDL1 may include any antibody or antigen-binding domain exemplified herein. The antibody that binds to LAG3 may include any antibody or antigen-binding domain exemplified herein. The antibody that binds to CTLA4 may include any antibody or antigen-binding domain exemplified herein.

[0055] In some preferred embodiments, the immunotherapy regimen comprises administering to a patient a combination of an antibody or antigen-binding portion thereof that binds PD1 and an antibody or antigen-binding portion thereof that binds LAG3. In some preferred embodiments, the antibody that binds PD1 comprises at least the heavy chain variable region (HCVR) sequence of SEQ ID NO: 21 and the light chain variable region (LCVR) sequence of SEQ ID NO: 22, and the antibody that binds LAG3 comprises at least the HCVR sequence of SEQ ID NO: 93 and the LCVR sequence of SEQ ID NO: 94.

[0056] In some preferred embodiments, the immunotherapy regimen comprises administering to a patient a combination of an antibody or antigen-binding portion thereof that binds to PDL1 and an antibody or antigen-binding portion thereof that binds to LAG3. In some preferred embodiments, the antibody that binds to PDL1 comprises at least the heavy chain variable region (HCVR) sequence of SEQ ID NO: 122 and the light chain variable region (LCVR) sequence of SEQ ID NO: 123, and the antibody that binds to LAG3 comprises at least the HCVR sequence of SEQ ID NO: 93 and the LCVR sequence of SEQ ID NO: 94.

[0057] In some embodiments, the immunotherapy may be any known immunotherapy for cancer. For example, the immunotherapy may be cemiplimab, nivolumab, pembrolizumab, atezolizumab, durvalumab, avelumab, ipilimumab, IFN-alpha, IL-2, or a combination thereof. In some embodiments, the immunotherapy may be an immune checkpoint inhibitor described herein or commonly known in the art. For example, cemiplimab, nivolumab, pembrolizumab, atezolizumab, durvalumab, and avelumab are known immune checkpoint inhibitors.

[0058] In some alternative embodiments, the immunotherapeutic regimen includes administering to the patient an activator of a T cell activating receptor, hi some preferred embodiments, the T cell activating receptor can be targeted by an activator for immunotherapy and includes one or more of CD28, CD40L, ICOS, and 4-1BB.

[0059] An exemplary method for establishing total passenger mutation burden from a cancer patient's tumor is shown in Figures 2 and 3. A genetic sample may be obtained / received (202). The genetic sample may be derived from the cancer patient. The genetic sample may be derived from the cancer patient's tumor. The genetic sample may be sequenced, thereby obtaining genetic sequence data.

[0060] In some embodiments, sequence data can be obtained or received through any method described herein.For example, sequence data can be obtained directly by performing a sequencing process on a sample.Alternatively, or in addition, sequence data can be obtained indirectly, for example, from a third party, a database, and / or a publication.In some embodiments, sequence data is received by a computer system, for example, from a data storage device or from another computer system.

[0061] In some embodiments, the sequence data can include bulk sequence data. The terms "bulk sequencing," "next-generation sequencing," or "massively parallel sequencing" refer to any high-throughput sequencing technology that parallelizes DNA and / or RNA sequencing processes. For example, bulk sequencing methods can generally produce more than a million polynucleic acid amplicons in a single assay. The terms "bulk sequencing," "massively parallel sequencing," and "next-generation sequencing" refer only to conventional methods and do not necessarily imply the acquisition of more than a million sequence tags in a single run. Any bulk sequencing method can be implemented in the disclosed methods and systems, such as reversible terminator chemistry (e.g., Illumina), pyrosequencing using polony emulsion droplets (e.g., Roche), ion semiconductor sequencing (IonTorrent), single-molecule sequencing (e.g., Pacific Bioscience), and massively parallel signature sequencing.

[0062] In some embodiments, sequence data can be generated by any sequencing method known in the art.For example, in some embodiments, sequence data is generated by chain termination sequencing, ligation sequencing, synthesis sequencing, pyrosequencing, ion semiconductor sequencing, single molecule real-time sequencing, tag-based sequencing, dilute-'N'-go sequencing and / or 454 sequencing.

[0063] In some embodiments, the sequence data is the result of a nucleic acid amplification process performed to amplify at least a portion of one or more genomic loci or transcripts, followed by sequencing of the resulting amplification products. Examples of nucleic acid amplification processes useful in practicing the methods disclosed herein include, but are not limited to, polymerase chain reaction (PCR), LATE-PCR, ligase chain reaction (LCR), strand displacement amplification (SDA), transcript-mediated amplification (TMA), self-sustained sequence replication (3SR), Qβ cyclic amplification, nucleic acid sequence-based amplification (NASBA), repair chain reaction (RCR), boomerang DNA amplification (BDA), and / or rolling circle amplification (RCA).

[0064] In some embodiments, the method comprises carrying out sequencing process on sample.As long as the sample contains DNA and / or RNA from patient's tumor, any sample can be used.The source of the sample can be, for example, solid tissue from fresh, frozen and / or preserved organ, tissue sample, biopsy or aspirate; blood or any blood component, serum, blood; body fluid such as cerebrospinal fluid, amniotic fluid, ascites or interstitial fluid.

[0065] Gene sequence data can be analyzed via a computer device to identify driver genes and determine the number of mutations in the driver genes (204). If the number of mutations in the driver genes is high (206), an indication can be made that the patient will likely respond to immunotherapy (210). If the number of mutations in the driver genes is low (206), an indication can be made that the patient will likely respond to immunotherapy (208). Mutations in driver genes can promote "hallmarks of cancer," such as immune evasion.

[0066] In alternative embodiments, the genetic sequence data can be analyzed via a computer device to identify passenger genes and determine the number of mutations in the passenger genes (212). If the number of mutations in the passenger genes is high (216), an indication can be made that the patient will likely respond to immunotherapy (210). If the number of mutations in the passenger genes is low (216), an indication can be made that there will be little or no response to immunotherapy (208). While passenger genes do not have any causal role in cancer, mutations in passenger genes can be used to assess immunogenicity. In some embodiments, the genetic sequence data can be analyzed (212) to identify passenger genes, determine the number of mutations in the passenger genes, and determine the background distribution for the tumor's genetic mutation burden. The number of mutations in the passenger genes can be analyzed with respect to the background distribution to determine how many standard deviations (if any) the number of mutations in the passenger genes is from the mean. If the standard deviation number is high (e.g., at least 1, 1.5, 2, or 2.5) (216), the cancer patient can be categorized as a better immunotherapy responder (210). If the standard deviation number is low (216), the cancer patient can be categorized as a poor immunotherapy responder (208).

[0067] In some embodiments, passenger genes can be identified in large-scale cancer genome analyses according to a metric referred to herein as the passenger gene index (PGI) (212). In some embodiments, PGI is based on the genetic mutation rate (GMR) of passenger genes, which correlates highly with overall cancer gene mutation frequency, also referred to as tumor mutation burden (214). Identified passenger genes are enriched in families known for a disproportionate number of passenger mutations, such as very large proteins and genes with low expression levels or genes in late DNA replication. Cancer samples / cancer types with high mutation rates accumulate more passenger gene mutations. The average number of mutated genes per sample in each cancer type can then serve as a surrogate for the likelihood of passenger mutations in that cancer type. Thus, for each gene X i On the other hand, gene X iPGI can be defined as the correlation between the proportion of samples with somatic mutations and the average number of mutated genes per sample in each cancer type. A higher PGI score suggests that a particular gene is more likely to acquire somatic mutations in cancer types with a high overall mutation frequency. Genes with a low PGI score show a weak association between the two variables (e.g., as can be observed in canonical cancer driver genes such as TP53, PIK3CA, and KRAS). High-ranking PGI genes are enriched in gene families known to have an excess of passenger mutations, such as extremely large proteins (>4,000 amino acids), genes spanning broad genomic loci (>1 MB), genes with low expression levels, and genes in the late DNA replication phase. The cumulative distribution functions (CDFs) of these gene families show a sharp increase with a PGI >0.7, whereas genes in the Catalogue of Somatic Mutations in Cancer (COSMIC) Cancer Gene Census (CGC) are more evenly distributed. A two-sample Kolmogorov-Smirnov test shows a significant difference in the rank distribution of passenger gene families compared to CGC genes (p = 8.3 × 10 for large proteins). -19 ; p=2.9×10 for genomic loci >1MB -12 ; p=6.4×10 for low expression -35 ; p = 2.7 × 10 for late replication -29 ) Similar results are obtained when samples are grouped by mutation rate (instead of cancer type) for PGI calculation.

[0068] The top passenger genes are based on the highest PGI and can be tumor type independent or specific to each tumor type. Thus, in some cases, the top passenger genes can be used globally regardless of tumor type. These top passenger genes do not change regardless of tumor type, but the top passenger genes can change over time due to the availability of additional samples. In some cases, the top passenger genes can vary between tumor types. In some cases, the top passenger genes can be identical between tumor types. Furthermore, if the top passenger genes are identical between tumor types, their ranking in the top passenger gene list can change. For example, the top 50 passenger genes for breast cancer can be the same as the top 50 passenger genes for lung cancer, but the number one passenger gene (meaning the highest PGI) for breast cancer can be the number five passenger gene for lung cancer. In some cases, the top 50 passenger genes of one tumor type may comprise 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, or even 100% of the top 50 passenger genes of a second tumor type. Depending on the range of PGIs included, the top passenger gene list may include a list of the top 25, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, or even 2000 or more passenger genes. In some cases, the top passenger genes do not vary between patients. All patients can use the same passenger gene list, and each patient will have a different TMB score.

[0069] In one embodiment, a method (300) is disclosed as illustrated in Figure 3, which includes receiving (310) genetic sequence data. The genetic sequence data can include multiple genes and can be derived from multiple biological samples collected from subjects with multiple disease types. The multiple disease types can include cancer.

[0070] In some embodiments, the method (300) can identify (320) a plurality of mutant genes for each of a plurality of biological samples, wherein each of the mutant genes comprises a gene sequence having at least one non-synonymous somatic mutation.

[0071] In some embodiments, the method 300 can determine 330 a tumor mutation burden for each biological sample based on the number of mutant genes in each biological sample. In a preferred embodiment, determining 330 a tumor mutation burden for each biological sample based on the number of mutant genes in each biological sample can include adding the number of mutant genes in each patient sample.

[0072] The method (300) can identify mutations in a gene (passenger gene or driver gene), for example, by aligning a mutant sequence with a wild-type or reference sequence. Various programs and alignment algorithms are described in Smith and Waterman (1981) Adv. Appl. Math. 2:482; Needleman and Wunsch (1970) J. Mol. Biol. 48:443; Pearson and Lipman (1988) Proc. Natl. Acad. Sci. USA 85:2444; Higgins and Sharp (1988) Gene 73:237-244; Higgins and Sharp (1989) CABIOS 5:151-153; Corpet et al. (1988) Nucl. Acids Res. 16:10881-90; Huang et al. (1992) Computer Appl. in Biosci. 8:155-65; and Pearson et al. al. (1994). Meth. Mol. Biol. 24:307-31, which are incorporated herein by reference. Altschul et al. (1994) Nature Genet. 6:119-29, incorporated herein by reference, presents a detailed discussion of sequence alignment methods and homology calculations.

[0073] The NCBI Basic Local Alignment Search Tool (BLAST) (Altschul et al. 1990) is available from several sources, including the National Center for Biological Information (NCBI, Bethesda, Md.) and the Internet, and is used in conjunction with the sequence analysis programs blastp, blastn, blastx, tblastn, and tblastx.< / / www.ncbi.nlmn.ih.gov / BLAST / > A description of how to determine sequence identity using this program can be found at< / / www.nebi.rlm.nih.gov / BLAST / blast-help.html> It is available at.

[0074] In some embodiments, for each disease type, the method (300) can determine an average tumor mutation burden for the plurality of mutant genes in the plurality of biological samples based on the number of mutant genes determined in each biological sample (340). In a preferred embodiment, determining an average tumor mutation burden for the plurality of mutant genes in the plurality of biological samples based on the number of mutant genes determined in each biological sample can include adding the tumor mutation burden from each patient sample and, for each disease type, dividing by the number of patient samples.

[0075] In some embodiments, for each mutant gene and each disease type, the method (300) can determine the proportion of biological samples that contain the mutant gene (350). In some embodiments, for each mutated gene, the method (300) can determine (360) a correlation coefficient between the mean tumor mutational burden and the proportion of biological samples containing the mutated gene.

[0076] In some embodiments, the method (300) can determine whether a mutated gene is a passenger gene (370) based on the correlation coefficient. A high correlation coefficient suggests that a particular gene is more likely to have acquired a somatic mutation in a cancer type with a generally high mutation frequency (a passenger gene), whereas a low correlation coefficient suggests that a particular gene is less likely to have acquired a somatic mutation in a cancer type with a generally high mutation frequency (a non-passenger gene).

[0077] In an alternative embodiment, the method (300) can further comprise generating a list of mutant genes identified as passenger genes. In a preferred embodiment, the list can represent an immunogenicity profile for a selected disease.

[0078] In some embodiments, a method of selecting a patient for cancer treatment (400), as illustrated in FIG. 4, is disclosed, comprising determining a plurality of passenger genes present in a tumor sample for a patient with the disease (410).

[0079] In some embodiments, the method (400) can compare a plurality of passenger genes to the immunogenicity profile of a disease (420). In a preferred embodiment, the immunogenicity profile can be generated by performing the following steps: receiving gene sequence data, the gene sequence data including a plurality of genes, from a plurality of biological samples collected from subjects with a plurality of disease types; identifying a plurality of mutant genes for each of the plurality of biological samples, each of the mutant genes including a gene sequence having at least one non-synonymous somatic mutation; determining a tumor mutation burden for each biological sample based on the number of mutant genes in each biological sample for each disease type; determining an average tumor mutation burden for the plurality of mutant genes in the plurality of biological samples based on the number of mutant genes determined in each biological sample for each mutant gene and each disease type; determining, for each mutant gene, the proportion of biological samples containing the mutant gene; and determining a correlation coefficient between the average tumor mutation burden and the proportion of biological samples containing the mutant gene. In some embodiments, the mutant genes can be determined to be passenger genes based on the correlation coefficient. A high correlation coefficient suggests that a particular gene is more likely to have acquired somatic mutations in cancer types with a generally high mutation frequency (a passenger gene), whereas a low correlation coefficient suggests that a particular gene is less likely to have acquired somatic mutations in cancer types with a generally high mutation frequency (a non-passenger gene).

[0080] A list of mutated genes identified as passenger genes can be generated, where the list represents the immunogenicity profile of the selected disease. In a preferred embodiment, determining the tumor mutation burden for each biological sample based on the number of mutant genes in each biological sample can include adding the number of mutant genes in each patient sample. In a preferred embodiment, determining the average tumor mutation burden for a plurality of mutant genes in a plurality of biological samples based on the number of mutant genes determined in each biological sample can include adding the tumor mutation burden from each patient sample and dividing by the number of patient samples for each disease type.

[0081] In some embodiments, PGI can be used to identify passenger genes for a particular cancer, and the TMB of the passenger gene can then be used to identify patients as responders to a particular treatment, such as, but not limited to, anti-PD-1, or a combination of anti-PD-1 and another cancer therapeutic. The TMB of the passenger gene can also be used to identify responders to other cancer antibody treatments, such as, but not limited to, anti-CD20 (chronic lymphocytic leukemia), anti-HER2 (breast cancer), anti-EGFR (colon cancer and head and neck cancer), anti-CD19 (B-cell cancer), and anti-CD20 (lymphoma), or a combination of an antibody treatment and another cancer therapeutic. In some embodiments, the other cancer treatment can be chemotherapy, an immunomodulator (e.g., a second antibody, a cytokine), radiation, or surgery.

[0082] In some embodiments, comparing the plurality of passenger genes to the immunogenicity profile of the disease can include determining the number of matches between the plurality of mutant genes and the list of mutant genes in the profile.

[0083] In some embodiments, if multiple passenger genes match the immunogenicity profile of the disease (430), the method (400) can identify the patient as a candidate for immunotherapy.

[0084] In some embodiments, if the passenger genes do not match the immunogenicity profile of the disease (440), the method (400) can identify the patient as not being a candidate for immunotherapy.

[0085] In an alternative embodiment, the method (400) can further include enrolling the patient in an immunotherapy program if the patient is identified as a candidate for immunotherapy. The disclosed immunotherapies can be used in combination with other antibodies or antigen-binding fragments thereof, as well as other anti-cancer therapies. Combination therapies can be administered simultaneously or sequentially. In some embodiments, multiple therapeutic agents can be formulated with a pharmaceutically acceptable carrier to form a pharmaceutical composition. In some embodiments, multiple therapeutic agents are individually formulated with a pharmaceutically acceptable carrier to form multiple pharmaceutical compositions. "Pharmaceutically acceptable" refers to a material or carrier selected to minimize any degradation of the active ingredient and minimize any adverse side effects in the subject, as known to those skilled in the art. Examples of carriers include dimyristoyl phosphatidylcholine (DMPC), phosphate-buffered saline, or multivesicular liposomes. For example, PG:PC:cholesterol:peptide, or PC:peptide, can be used as a carrier in the present invention. Other suitable pharmaceutically acceptable carriers and their formulations are described in Remington: The Science and Practice of Pharmacy (19th ed.) ed. A.R. Gennaro, Mack Publishing Company, Easton, PA 1995. Typically, an appropriate amount of a pharmaceutically acceptable salt to render the formulation isotonic is used in the formulation. Other examples of pharmaceutically acceptable carriers include, but are not limited to, saline, Ringer's solution, and dextrose solution. The pH of the solution may be about 5 to about 8, or about 7 to about 7.5. Carriers also include sustained-release preparations, such as semipermeable matrices of solid hydrophobic polymers containing the composition, which matrices are in the form of shaped articles, e.g., films, stents (implanted in blood vessels during angioplasty), liposomes, or microparticles. It will be apparent to those skilled in the art that certain carriers may be more preferable depending, for example, on the route of administration and the concentration of the composition being administered. Most typical are standard solutions for drug administration to humans, including, for example, sterile water, saline, and buffered solutions at physiological pH.

[0086] Pharmaceutical compositions can also contain carriers, thickeners, diluents, buffers, preservatives, etc., so long as the intended activity of the immunotherapy of the present invention is not impaired. Pharmaceutical compositions may contain (in addition to the composition of the present invention) one or more active ingredients, such as, for example, antibacterial agents, anti-inflammatory agents, anesthetics, etc. Pharmaceutical compositions can be administered in a number of ways, depending on whether local or systemic treatment is desired and on the area to be treated.

[0087] Preparations for parenteral administration include sterile aqueous or non-aqueous solutions, suspensions, and emulsions. Examples of non-aqueous solvents are propylene glycol, polyethylene glycol, vegetable oils such as olive oil, and injectable organic esters such as ethyl oleate. Aqueous carriers include water, alcoholic / aqueous solutions, emulsions, or suspensions, including saline and buffered media. Parenteral vehicles include sodium chloride solution, Ringer's dextrose, dextrose and sodium chloride, lactated Ringer's, or fixed oils. Intravenous vehicles include fluid and nutrient replenishers, electrolyte replenishers (e.g., Ringer's dextrose-based substances), and the like. Preservatives and other additives may also be present, such as antibacterial agents, antioxidants, chelating agents, and inert gases.

[0088] Formulations for ophthalmic administration include ointments, lotions, creams, gels, drops, suppositories, sprays, liquids and powders. Conventional pharmaceutical carriers, aqueous solutions, powders, oily bases, thickeners and the like may be necessary or desirable.

[0089] Compositions for oral administration include powders or granules, suspensions or solutions in aqueous or non-aqueous media, capsules, sachets, or tablets.Thickeners, flavorings, diluents, emulsifiers, dispersing aids, or binders may be desirable.Some compositions may be administered as pharmaceutically acceptable acid addition salts or base addition salts formed by reaction with inorganic acids such as hydrochloric acid, hydrobromic acid, perchloric acid, nitric acid, thiocyanic acid, sulfuric acid, and phosphoric acid, and organic acids such as formic acid, acetic acid, propionic acid, glycolic acid, lactic acid, pyruvic acid, oxalic acid, malonic acid, succinic acid, maleic acid, and fumaric acid, or inorganic bases such as sodium hydroxide, ammonium hydroxide, potassium hydroxide, and organic bases such as mono-, di-, tri-, and arylamines, and substituted ethanolamines.

[0090] Pharmaceutical compositions of the present invention suitable for injectable use include sterile aqueous solutions or dispersions. Furthermore, the compositions may be in the form of sterile powders for the extemporaneous preparation of such sterile injectable solutions or dispersions. Typically, the final injectable form must be sterile and fluid enough to pass through a needle easily. Pharmaceutical compositions must be stable under the conditions of manufacture and storage, and thus must be preserved against the influence of contaminating microorganisms, such as bacteria and fungi. The carrier may be a solvent or dispersion medium containing, for example, water, ethanol, polyol (e.g., glycerol, propylene glycol, and liquid polyethylene glycol), vegetable oils, and suitable mixtures thereof.

[0091] Injectable solutions, for example, may be prepared in which the carrier contains saline, glucose solution, or a mixture of saline and glucose solution. Injectable suspensions may also be prepared in which case appropriate liquid carriers, suspending agents and the like may be employed. Also included are solid form preparations which are intended to be converted into liquid form preparations shortly before use.

[0092] Preparations for parenteral administration include sterile aqueous or non-aqueous solutions, suspensions, and emulsions. Examples of non-aqueous solvents are propylene glycol, polyethylene glycol, vegetable oils such as olive oil, and injectable organic esters such as ethyl oleate. Aqueous carriers include water, alcoholic / aqueous solutions, emulsions, or suspensions, including saline and buffered media. Parenteral vehicles include sodium chloride solution, Ringer's dextrose, dextrose and sodium chloride, lactated Ringer's, or fixed oils. Intravenous vehicles include fluid and nutrient replenishers, electrolyte replenishers (e.g., Ringer's dextrose-based substances), and the like. Preservatives and other additives may also be present, such as antibacterial agents, antioxidants, chelating agents, and inert gases.

[0093] The pharmaceutical compositions of the present invention may be in a form suitable for topical use, such as, for example, an aerosol, cream, ointment, lotion, dusting powder, mouthwash, or gargle. The compositions may also be in a form suitable for use in transdermal devices. These formulations may be prepared using a compound of the present invention, or a pharmaceutically acceptable salt thereof, by conventional processing methods. For example, a cream or ointment is prepared by mixing about 5% to about 10% by weight of the compound with a hydrophilic material and water to produce a cream or ointment having the desired viscosity.

[0094] In compositions suitable for transdermal administration, the carrier may optionally contain a penetration enhancer and / or a suitable wetting agent, and may optionally contain small amounts of suitable additives of any nature. Such additives do not cause significant adverse effects on the skin. Such additives may facilitate application to the skin and / or aid in the preparation of the desired composition. These compositions may be administered in various ways, for example, as a transdermal patch, as a spot-on, or as an ointment.

[0095] The pharmaceutical composition of the present invention can be in a form suitable for rectal administration, and in this case, the carrier is solid.Preferably, the mixture forms a unit-dose suppository.Suitable carrier includes cocoa butter and other materials commonly used in the art.Suppository can be easily formed by first mixing the composition with softened or melted carrier, then cooling and shaping in molds.

[0096] In addition to the carrier components described above, the above-described pharmaceutical formulations may optionally include one or more additional carrier components, such as diluents, buffers, flavoring agents, binders, surfactants, thickeners, lubricants, preservatives (including antioxidants), etc. Additionally, other adjuvants that render the formulation isotonic with the blood of the intended recipient may be included. Compositions containing the disclosed immunotherapeutic agents, and / or pharmaceutically acceptable salts thereof, may be prepared in powder or liquid concentrate form.

[0097] The exact dose and frequency of administration, as known to those skilled in the art, will depend on the particular disclosed peptide, product of the disclosed production method, its pharmaceutically acceptable salt, solvate, or polymorph, its hydrate, solvate, polymorph, or stereochemical isomer; the particular condition being treated and the severity of the condition being treated; various factors specific to the medical history of the subject to whom the dose is administered, such as age; the weight, sex, extent of disability, and general health of the particular subject, as well as other medications that the individual may be taking. Furthermore, the effective daily amount may be reduced or increased depending on the response of the treated subject and / or depending on the evaluation of the physician prescribing the composition.

[0098] Depending on the mode of administration, the pharmaceutical composition contains 0.05 to 99% by weight, preferably 0.1 to 70% by weight, more preferably 0.1 to 50% by weight, of the active ingredient, and 1 to 99.95% by weight, preferably 30 to 99.9% by weight, more preferably 50 to 99.9% by weight of a pharmaceutically acceptable carrier. All percentages are based on the total weight of the composition.

[0099] In an exemplary embodiment, some or all of the methods and systems may be implemented on one or more computers, such as computer 1001, as shown in FIG. 10 and described below. In some embodiments, the disclosed methods and systems may utilize one or more computers to perform one or more functions at one or more locations. FIG. 10 is a block diagram illustrating an exemplary operating environment for implementing the disclosed methods. This exemplary operating environment is only one example of an operating environment and is not intended to suggest any limitation as to the scope of use or functionality of the operating environment architecture. Neither should any operating environment be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment.

[0100] In some embodiments, the method and system may be operational with numerous other general-purpose or special-purpose computing system environments or configurations. Examples of well-known computing systems, environments, and / or configurations that may be suitable for use with the system and method include, but are not limited to, personal computers, server computers, laptop devices, and multiprocessor systems. Additional examples include set-top boxes, programmable consumer electronics products, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.

[0101] In some embodiments, the processing of the disclosed methods and systems may be performed via software components. The disclosed systems and methods may be described in the general context of computer-executable instructions, such as program modules, executed via one or more computers or other devices. Generally, program modules include computer code, routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types. The disclosed methods may also be practiced in grid-based and distributed computing environments where tasks are performed via remote processing devices linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including memory storage devices.

[0102] Additionally, those skilled in the art will recognize that the systems and methods disclosed herein may be implemented via a general-purpose computing device in the form of a computer 1001. Components of the computer 1001 may include, but are not limited to, one or more processors 1003, a system memory 1012, and a system bus 1013 that couples various system components, including the one or more processors 1003, to the system memory 1012. The system may utilize parallel computing.

[0103] The system bus 1013 represents one or more of several possible types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, or a local bus, using any of a variety of bus architectures. The bus 1013, and all buses specified herein, may also be implemented via a wired or wireless network connection, and each subsystem, including one or more processors 1003, mass storage device 1004, operating system 1005, PGI software 1006, PGI data 1007, network adapter 1008, system memory 1012, input / output interface 1010, display adapter 1009, display device 1011, and human-machine interface 1002, may be housed in one or more remote computing devices 1014a,b,c in physically separate locations connected via this type of bus, effectively implementing a fully distributed system.

[0104] The computer 1001 typically includes a variety of computer-readable media. Exemplary readable media may be any available media that can be accessed by the computer 1001, including, but not limited to, volatile and nonvolatile media, both removable and non-removable media. The system memory 1012 includes computer-readable media in the form of volatile memory, such as random access memory (RAM), and / or nonvolatile memory, such as read-only memory (ROM). The system memory 1012 typically includes data, such as PGI data 1007, and / or program modules, such as the operating system 1005 and PGI software 1006, that are immediately accessible to and / or currently being operated by the one or more processors 1003. The PGI data 1007 may include read coverage data and / or expected read coverage data.

[0105] In some embodiments, computer 1001 may also include other removable / non-removable, volatile / non-volatile computer storage media. By way of example, Figure 10 illustrates a mass storage device 1004 that can provide non-volatile storage of computer code, computer-readable instructions, data structures, program modules, and other data for computer 1001. For example, and without limitation, mass storage device 1004 can be a hard disk, a removable magnetic disk, a removable optical disk, a magnetic cassette or other magnetic storage device, a flash memory card, a CD-ROM, a digital versatile disk (DVD) or other optical storage, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0106] Optionally, any number of program modules can be stored on the mass storage device 1004, including, for example, an operating system 1005 and PGI software 1006. The operating system 1005 and PGI software 1006 (or some combination thereof) can each include elements of programming and PGI software 1006. PGI data 1007 can also be stored on the mass storage device 1004. The PGI data 1007 can be stored in any one or more databases known in the art. Examples of such databases include DB2®, Microsoft® Access, Microsoft® SQL Server, Oracle®, mySQL, PostgreSQL, etc. The databases can be centralized or distributed across multiple systems.

[0107] In alternative embodiments, a user can enter commands and information into computer 1001 through input devices (not shown). Examples of such input devices include, but are not limited to, keyboards, pointing devices (e.g., a "mouse"), microphones, joysticks, scanners, tactile input devices such as gloves, and other body coverings. These and other input devices can be connected to one or more processors 1003 through a human-machine interface 1002 coupled to system bus 1013, but can also be connected by other interface and bus structures, such as a parallel port, a game port, an IEEE 1394 port (also known as a Firewire port), a serial port, or a universal serial bus (USB).

[0108] In an alternative embodiment, a display device 1011 can also be connected to the system bus 1013 via an interface such as a display adapter 1009. It is contemplated that the computer 1001 can be provided with multiple display adapters 1009 and that the computer 1001 can also be provided with multiple display devices 1011. For example, a display device can be a monitor, a liquid crystal display (LCD), or a projector. In addition to the display device 1011, other output peripheral devices can include components such as speakers (not shown) and a printer (not shown), which can be connected to the computer 1001 via the input / output interface 1010. Any process and / or result of the present method can be output to an output device in any format. Such output can be a visual representation in any format, including, but not limited to, text, graphical, animation, audio, tactile, etc. The display 1011 and the computer 1001 can be part of a single device or can be separate devices.

[0109] The computer 1001 can operate in a networked environment using logical connections to one or more remote computing devices 1014a, b, c. By way of example, the remote computing devices can be personal computers, portable computers, smartphones, servers, routers, network computers, peer devices or other common network nodes, etc. The logical connections between the computer 1001 and the remote computing devices 1014a, b, c can be through a network 1015, such as a local area network (LAN) and / or a general wide area network (WAN). Such network connections can be through a network adapter 1008. The network adapter 1008 can be implemented in both wired and wireless environments. Such networking environments are conventional and commonplace in homes, offices, enterprise-wide computer networks, intranets, and the Internet.

[0110] For convenience of illustration, application programs and other executable program components, such as the operating system 1005, are illustrated herein as discrete blocks, although it is recognized that such programs and components may reside at various times in different storage components of the computing device 1001 and be executed via one or more processors 1003 of the computer. In certain aspects, at least a portion of the PGI software 1006 and / or PGI data 1007 may be stored and / or executed on one or more of the computing device 1001, remote computing devices 1014a, b, c, and / or a combination thereof. Thus, the PGI software 1006 and / or PGI data 1007 may operate within a cloud computing environment where access to the PGI software 1006 and / or PGI data 1007 may be performed via a network 1015 (e.g., the Internet). Furthermore, in certain aspects, the PGI data 1007 may be synchronized across one or more of the computing device 1001, remote computing devices 1014a, b, c, and / or a combination thereof.

[0111] An implementation of the PGI software 1006 may be stored on or transmitted via some form of computer-readable media. Any of the methods of this disclosure may be performed by computer-readable instructions embodied on the computer-readable medium. The computer-readable medium may be any available medium accessible by a computer. Examples of computer-readable media include, but are not limited to, “computer storage media” and “communications media.” “Computer storage media” includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data. Exemplary computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVDs) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage devices or other magnetic storage devices, or any other medium that can be used to store the desired information and that can be accessed by a computer.

[0112] Methods and systems can employ artificial intelligence techniques such as machine learning and iterative learning, including, but not limited to, expert systems, case-based reasoning, Bayesian networks, behavior-based AI, neural networks, fuzzy systems, evolutionary computing (e.g., genetic algorithms), swarm intelligence (e.g., ant algorithms), and hybrid intelligence systems (e.g., expert inference rules generated through neural networks or production rules derived from statistical learning).

[0113] Unless otherwise expressly stated, methods described herein should not be construed as requiring that their steps be performed in a particular order. Thus, unless a claim regarding a method does not actually recite the order in which its steps should be followed, or unless the claim or specification otherwise expressly states that it is limited to a particular order, no order is intended to be inferred in any way. This holds true for questions of logic regarding the placement of steps or the sequence of operational flow; the apparent meaning derived from grammatical organization or punctuation; any possible implicit basis for interpreting the number or type of embodiments described herein, etc. [Example]

[0114] The following examples are provided to further illustrate the present disclosure and are intended to illustrate, but not limit, the present disclosure. Example 1 Passenger Gene Index The passenger gene index (PGI) method includes all TCGA samples binned by cancer type, and the median number of mutated genes is determined for each binning. By comparing solid tumors with their blood-borne tumor or normal solid organ counterparts, mutations were limited to non-silent somatic mutations only. The exception is acute myeloid leukemia, where blood-borne tumors were compared with normal solid organs. The mutation profile is constructed as a binary matrix, whereby a 1 bit is set if any locus corresponding to a gene carries a mutation in the patient. Gene X i The Pearson correlation between the proportion of samples with mutations in each gene X and the median number of mutated genes in each cancer type is i Before calculating the correlation, a non-uniformly distributed trace noise was added to the proportion of samples with the mutation to avoid the problem of all-zero entries.

[0115] Example 2 Z-scores for driver and passenger tumor mutation burden The Z-score method for driver / passenger TMB involves the following: To calculate the z-score for TMB, a randomly selected set of 1,000 genes of the same size was used to first establish the background distribution of TMB. The number of mutated driver / passenger genes was then calculated and compared to the background distribution to calculate a Z-score, which indicates how many standard deviations the number is from the background mean. Driver genes were downloaded from the COSMIC Cancer Gene Census on January 22, 2015, and passenger genes were defined as the top n genes ranked by PGI from the TCGA data.

[0116] Example 3 Passenger gene tumor mutation burden and immunotherapy response To calculate the passenger gene index (PGI), we compiled a list of non-silent somatic mutations from 6,685 samples from 20 TCGA tumor types. Somatic mutations were determined by comparing the tumor genome with the germline genome, e.g., the genome of a normal blood sample from the same patient. The median number of altered genes per sample ranged from 9 in acute myeloid leukemia to 289 in cutaneous melanoma, with a >32-fold difference between the cancers with the lowest and highest mutation rates (Figure 5). This is consistent with previous findings that skin and lung cancer samples have the highest mutation rates due to exposure to environmental mutagens. Figure 5 shows the number of non-silent somatic mutations per sample in 6,685 TCGA cancer exomes.

[0117] The 20 cancer types included in this study are bladder urothelial cell carcinoma (BLCA), invasive breast cancer (BRCA), cervical squamous cell carcinoma and adenocarcinoma (CESC), colorectal adenocarcinoma (CORE), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), clear cell renal carcinoma of the kidney (KIRC), papillary cell renal carcinoma of the kidney (KIRP), acute myeloid leukemia (LAML), hepatocellular carcinoma of the liver (LIHC), low grade glioma of the brain (LGG), lung adenoma (LUAD), lung squamous cell carcinoma (LUSC), serous cystadenocarcinoma of the ovary (OV), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), cutaneous melanoma (SKCM), gastric adenocarcinoma (STAD), thyroid carcinoma (THCA), and uterine endometrial carcinoma (UCEC).

[0118] It was hypothesized that cancer types with a high overall mutation rate would accumulate more passenger gene mutations, and that the average number of altered genes per sample in each cancer type could be a surrogate for the likelihood of passenger gene mutations in that cancer type. i On the other hand, gene X i The PGI was defined as the correlation between the proportion of samples with variants and the average number of altered genes per sample in each cancer type. A higher PGI score suggested that a particular gene was more likely to acquire somatic mutations in cancer types with a high overall mutation frequency. Passenger genes showed a strong linear relationship with the two variables. However, a weaker association was observed for canonical cancer genes, such as TP53, PIK3CA, and KRAS (Figure 6). Figure 6 shows a scatter plot of the average number of total mutated genes (x-axis) versus the proportion of patients with genetic variants (y-axis) in each cancer type. The top row shows strong linear relationships among the top passenger genes (MUC16 r = 0.979; ADAM2 r = 0.972; COL5A2 r = 0.968), and the bottom row shows weak bivariate associations among the canonical cancer genes (TP53 r = 0.301; PIK3CA r = 0.120; KRAS r = 0.222).

[0119] Genes ranked highly in the PGI are enriched in gene families known to have an excess of passenger mutations, such as extremely large proteins (>4,000 amino acids), genes spanning broad genomic loci (>1 MB), genes with low expression levels, and genes in the late DNA replication phase. The cumulative distribution functions (CDFs) of these gene families show a sharp increase at PGIs >0.7, whereas driver genes in the Catalogue of Somatic Mutations in Cancer (COSMIC) Cancer Gene Census (CGC) are more evenly distributed (Figure 7). Figure 7 shows enrichment along the PGI scale for cancer driver genes and various other gene groups. The dotted lines (top) indicate the proportion of genes in various PGIs, and the vertical lines (bottom) indicate the rank of individual genes. A two-sample Kolmogorov-Smirnov test was used to examine differences in gene distribution for each group compared to cancer gene distribution. A two-sample Kolmogorov-Smirnov test showed a significant difference in the rank distribution of passenger gene families compared to CGC genes (p = 8.3 × 10 for large proteins). -19 ; p=2.9×10 for genomic loci >1MB -12 ; p=6.4×10 for low expression -35 ; p = 2.7 × 10 for late replication -29Similar results were observed when samples were grouped by mutation rate (instead of cancer type) to calculate the PGI. Some CGC genes also have high PGI scores but are not confirmed in the top altered TCGA cancer types. For example, KDR (kinase insert domain receptor) has the highest mutation rate in melanoma (14% in SKCM), but KDR is only known to be causally linked in non-small cell lung cancer and angiosarcoma. Similarly, none of the top 30 CGC genes were confirmed in the most altered cancer types. In contrast, 16 of the 30 CGC driver genes with the lowest PGI were confirmed in the corresponding altered cancer types (Figure 8). Figure 8 shows that CGC genes with low PGI are more likely to be confirmed in altered cancer types. Figure 8 shows the CGC genes with the highest (left) and lowest (right) PGI, along with the corresponding cancer types and the highest percentage of mutant samples (>2%). Acronyms marked with an asterisk indicate the cancer types confirmed by CGC for the gene. No cancers were identified in the CGC gene with the highest PGI, and 16 / 30 cancer types were identified in the CGC gene with the lowest PGI.

[0120] We applied PGI as a metric to select passenger genes. The tumor mutation burden (TMB) of the selected passenger genes was used to stratify patient cohorts into those likely to respond to immunotherapy. To validate this method, we used local immune cytolytic activity and T cell receptor (TCR) read counts as surrogates for immunogenicity to examine whether any differences in immunogenicity existed between patients with high and low TMB in the TCGA data. For each patient, TMB was calculated using three different methods: (i) traditional total TMB, (ii) driver gene-driven TMB, and (iii) passenger gene-driven TMB. To quantify cytolytic activity, we employed a simple RNA-based metric based on the gene expression levels of two key cytolytic effectors, granzyme A (GZMA) and perforin (PRF1). Cytolytic activity was significantly different (Mann-Whitney U test p<0.05) between high and low passenger TMB patients in seven different cancer types (colon adenocarcinoma p<4.6x10 -11 , invasive breast cancer p<5.0x10 -4 , lung adenoma p<7.7x10 -4 , endometrial cancer p<9.9x10 -4 , cervical squamous cell carcinomap<2.2x10 -3 , lung squamous cell carcinoma p<5.7x10 -3 , prostate adenocarcinoma p<2.1x10 -2 The difference is more pronounced when comparing total TMB and driver gene TMB in the corresponding cancer types (Figure 9A).

[0121] TCRs contribute to the recognition of peptide-MHC complexes, and their diversity is directly related to the number of foreign or mutant proteins, such as neoantigens derived from cancer cells. TCRβ repertoire analysis was performed using TCGA RNA-seq data to compare TCRβ read counts between high and low passenger TMB patients. As shown in Figure 9B, the number of detected TCRβ read counts was significantly different between high and low passenger TMB patients in eight different cancer types (endometrial cancer, p<3.2 × 10). -6 , colon adenocarcinoma p<4.5×10 -6 , cervical squamous cell carcinoma p<2.4×10 -3 , invasive breast cancer p<7.3×10 -3 , cutaneous melanoma p<9.2×10 -3 , lung adenoma p<1.5×10 -2 , ovarian serous cystadenocarcinoma p<2.7×10 -2 , prostate adenocarcinoma p<3.8×10 -2 Consistent with the cytolytic activity experiments, the difference in TCRβ was more pronounced between the groups divided by passenger TMB compared to total TMB and driver TMB.

[0122] Finally, we examined the TCGA data to determine whether there was any survival benefit associated with TMB. Total TMB showed a positive trend toward a favorable survival outcome in cervical and lung squamous cell carcinomas (CESC and LUSC), although this was not statistically significant. Meanwhile, driver TMB was associated with a poor prognosis (Figure 9C). Figure 9C shows clinical outcomes for patient cohorts separated by (i) total, (ii) driver, and (iii) passenger gene mutation burden in cutaneous melanoma (SKCM), cervical squamous cell carcinoma (CESC), and cervical adenocarcinoma, as well as lung squamous cell carcinoma (LUSC). The data show that only the patient cohort separated by passenger TMB yielded significant survival differences, whereas the ratio of total TMB to driver TMB did not. SKCM demonstrates a significant difference in patient survival between the high and low total / passenger TMB groups.

[0123] In both CESC and LUSC, the difference in survival outcomes between high and low TMB patient groups was statistically significant only when passenger TMB was used. In cutaneous melanoma (SKCM), both stratification of patients using passenger TMB and total TMB showed similar significant separation in survival curves. This suggests that there are few or no driver genes that strongly influence immunogenicity suppression in melanoma. In metastatic melanoma, an independent dataset of CTLA-4 blockade was used to divide 110 patients into two groups of equal size based on mutational burden. Stratification using TMB of 200 passenger genes included clinical benefit rates for the selected patient group of 24.55% to 36.36% (Fisher's exact test, p=0.0035). Stratifying patients using total TMB resulted in the same improvement in clinical benefit, further reinforcing experimental findings in cytolytic activity, TCR detection, and survival benefit for melanoma using TCGA data.

[0124] Figure 11 shows the TMB of patient cohorts in a Phase I clinical trial of anti-PD1. Filled circles, squares, and triangles represent patients with partial response (PR), stable disease (SD), and progressive disease (PD), respectively. Open areas represent individual patient data, while solid areas represent the average for each PR / SD / PD group. Total TMB is shown as the total number of mutated genes (left y-axis), and driver / passenger TMB is shown as z-score (right y-axis). Both PR vs. PD and PR vs. PD+SD showed statistically significant differences in passenger TMB, and did not vary by the number of top (50 / 100 / 1000) passenger genes used. The PR group did not achieve significant differences in total or driver TMB.

[0125] Example 4 Passenger gene tumor mutation burden and clinical immunotherapy response To evaluate the clinical response of various malignancies to immunotherapy, we used somatic mutation data from a phase I trial of a human monoclonal antibody against PD-1 (Programmed Death-1) as a single agent and in combination with other anticancer therapeutics. In total, clinical response data were available for 74 patients with advanced malignancies (n=8 partial response PR, n=29 stable disease SD, n=37 progressive disease PD). Total TMB is expressed as the total number of mutated genes. Driver / passenger TMB is expressed as a z-score and normalized to the patient's total TMB. The z-score was calculated by comparing the number of mutated driver / passenger genes to the background distribution using randomly selected genes of the same size. A higher z-score suggests a higher mutation burden for the selected driver / passenger gene set, regardless of the total TMB background. Total TMB and driver TMB z-scores do not distinguish PRs from other patient groups. On the other hand, the z-scores of passenger TMB were significantly higher in PR patients compared to the PD or PD+SD patient groups. The results were consistent for TMB calculated using the top 50, 100, 500 (Figure 12), and 1000 passenger genes.

[0126] The technical concepts that can be understood from the above-described embodiment will be described below as supplementary notes. [Appendix 1] 1. An immunotherapeutic agent for use in treating a cancer patient, the immunotherapeutic agent comprising an antibody that binds to PD-1, and the cancer patient having a cancer with a passenger mutation burden that is greater than a background distribution for mutation burden.

[0127] [Appendix 2] The passenger gene mutation amount is establishing the passenger mutation burden of the cancer; creating a background distribution for the mutation load of the cancer; normalizing the passenger mutation load to the background distribution; is determined to be greater than the background distribution by 2. The immunotherapeutic agent of claim 1, wherein the passenger mutation load is greater than the background distribution when the normalized passenger mutation load is at least about 1, at least about 1.5, at least about 2, at least about 2.5, at least about 3, or more than 3 standard deviations higher than the mean of the background distribution.

[0128] [Appendix 3] 3. The immunotherapeutic agent of claim 2, wherein creating a background distribution comprises establishing the mutation burden from multiple samples of randomly selected genes obtained from the cancer, wherein the number of randomly selected genes in each sample equals the number of passenger genes used to calculate the passenger gene mutation burden.

[0129] [Appendix 4] The immunotherapeutic agent according to any one of appendices 1 to 3, wherein the step of normalizing the passenger gene mutation load to the background distribution comprises the step of generating a z-score indicating the number of standard deviations from the mean of the background distribution, or the step of generating a p-value.

[0130] [Appendix 5] The immunotherapeutic agent according to any one of appendix 1 to 4, further comprising the step of categorizing the mutated gene in the cancer as a passenger gene.

[0131] [Appendix 6] 6. The immunotherapeutic agent of claim 5, wherein the step of categorizing mutated genes in the cancer as passenger genes comprises the steps of selecting mutated genes from the cancer and matching the mutated genes to a data structure comprising passenger genes established according to a passenger gene index.

[0132] [Appendix 7] 7. The immunotherapeutic agent of claim 6, wherein the passenger gene index comprises a correlation coefficient between the proportion of samples containing the mutant gene obtained from a cancer patient cohort and the median number of mutant genes in each tumor type within the cancer patient cohort.

[0133] [Appendix 8] 8. The immunotherapeutic agent of any one of appendices 1 to 7, wherein the cancer patient has cutaneous squamous cell carcinoma (CSCC), bladder urothelial cell carcinoma (BLCA), invasive breast cancer (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), colorectal adenocarcinoma (CORE), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), clear cell renal carcinoma of the kidney (KIRC), papillary renal cell carcinoma of the kidney (KIRP), acute myeloid leukemia (LAML), hepatocellular carcinoma of the liver (LIHC), low grade glioma of the brain (LGG), lung adenoma (LUAD), lung squamous cell carcinoma (LUSC), serous cystadenocarcinoma of the ovary (OV), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), cutaneous melanoma (SKCM), or gastric adenocarcinoma.

[0134] [Appendix 9] The immunotherapeutic agent according to any one of appendices 1 to 7, wherein the cancer is endometrial cancer, colon adenocarcinoma, cervical squamous cell carcinoma, invasive breast cancer, skin melanoma, lung adenoma, ovarian serous cystadenocarcinoma, or prostate adenocarcinoma.

[0135] [Appendix 10] The immunotherapeutic agent according to any one of appendices 1 to 9, wherein the antibody that binds to PD-1 is cemiplimab, pembrolizumab, nivolumab, durvalumab, atezolizumab, pidilizumab, camrelizumab, spartalizumab (PDR001), or cetrelimab (JNJ-63723283).

[0136] [Appendix 11] The immunotherapeutic agent according to any one of appendices 1 to 10, wherein the antibody that binds to PD-1 comprises the heavy chain variable region (HCVR) sequence of SEQ ID NO:21.

[0137] [Appendix 12] The immunotherapeutic agent according to any one of appendices 1 to 11, wherein the antibody that binds to PD-1 comprises the light chain variable region (LCVR) sequence of SEQ ID NO:22.

[0138] [Appendix 13] The immunotherapeutic agent according to any one of appendices 1 to 12, wherein the antibody that binds to PD-1 comprises the HCVR sequence of SEQ ID NO: 21 and the LCVR sequence of SEQ ID NO: 22.

[0139] [Appendix 14] The immunotherapeutic agent according to any one of appendices 1 to 13, wherein the antibody that binds to PD-1 is cemiplimab.

[0140] [Appendix 15] The immunotherapeutic agent according to any one of appendices 1 to 14, wherein the cancer is skin cancer. [Appendix 16] 16. The immunotherapeutic agent according to claim 15, wherein the skin cancer is cutaneous squamous cell carcinoma.

[0141] [Appendix 17] The immunotherapeutic agent according to any one of appendices 1 to 14, wherein the cancer is lung cancer. [Appendix 18] The immunotherapeutic agent according to any one of appendices 1 to 14, wherein the cancer is cervical cancer.

[0142] [Appendix 19] The immunotherapeutic agent according to any one of appendices 1 to 18, wherein the background distribution of the gene mutation amount is determined by performing or having performed sequence analysis on a gene sample obtained from the cancer patient.

[0143] [Appendix 20] 1. Use of an antibody that binds to PD-1 in the manufacture of a medicament for treating a cancer patient, wherein the cancer patient has a cancer with a passenger mutation burden that is greater than a background distribution for mutation burden.

[0144] [Appendix 21] The passenger gene mutation amount is establishing the passenger mutation burden of the cancer; creating a background distribution for the mutation load of the cancer; normalizing the passenger mutation load to the background distribution; is determined to be greater than the background distribution by 21. The use of claim 20, wherein the passenger mutation load is greater than the background distribution when the normalized passenger mutation load is at least about 1, at least about 1.5, at least about 2, at least about 2.5, at least about 3, or more than 3 standard deviations higher than the mean of the background distribution.

[0145] [Appendix 22] 22. The use of claim 21, wherein creating a background distribution comprises establishing the mutational burden from multiple samples of randomly selected genes obtained from the cancer, wherein the number of randomly selected genes in each sample is equal to the number of passenger genes used to calculate the passenger gene mutational burden.

[0146] [Appendix 23] The use according to any one of Appendices 20 to 22, wherein the step of normalizing the passenger gene mutation load to the background distribution comprises the step of generating a z-score indicating the number of standard deviations from the mean of the background distribution, or the step of generating a p-value.

[0147] [Appendix 24] The use according to any one of appendix 20 to 23, further comprising the step of categorizing the mutated gene in the cancer as a passenger gene.

[0148] [Appendix 25] 25. The use of claim 24, wherein categorizing mutated genes in the cancer as passenger genes comprises selecting mutated genes from the cancer and matching the mutated genes to a data structure comprising established passenger genes according to a passenger gene index.

[0149] [Appendix 26] 26. The use of claim 25, wherein the passenger gene index comprises a correlation coefficient between the proportion of samples containing the mutated gene obtained from a cancer patient cohort and the median number of mutated genes in each tumor type within the cancer patient cohort.

[0150] [Appendix 27] 27. The use of any one of appendices 20 to 26, wherein the cancer patient has cutaneous squamous cell carcinoma (CSCC), bladder urothelial cell carcinoma (BLCA), invasive breast cancer (BRCA), cervical squamous cell carcinoma and adenocarcinoma (CESC), colorectal adenocarcinoma (CORE), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), clear cell renal carcinoma of the kidney (KIRC), papillary cell renal carcinoma of the kidney (KIRP), acute myeloid leukemia (LAML), hepatocellular carcinoma of the liver (LIHC), low grade glioma of the brain (LGG), lung adenoma (LUAD), lung squamous cell carcinoma (LUSC), serous cystadenocarcinoma of the ovary (OV), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), cutaneous melanoma (SKCM) or gastric adenocarcinoma.

[0151] [Appendix 28] 27. The use of any one of appendices 20 to 26, wherein the cancer is endometrial cancer, colon adenocarcinoma, cervical squamous cell carcinoma, invasive breast cancer, skin melanoma, lung adenoma, ovarian serous cystadenocarcinoma, or prostate adenocarcinoma.

[0152] [Appendix 29] 29. The use of any one of appendices 20 to 28, wherein the antibody that binds to PD-1 is cemiplimab, pembrolizumab, nivolumab, durvalumab, atezolizumab, pidilizumab, camrelizumab, spartalizumab (PDR001), or cetrelimab (JNJ-63723283).

[0153] [Appendix 30] 30. The use according to any one of Appendices 20 to 29, wherein the antibody that binds to PD-1 comprises the heavy chain variable region (HCVR) sequence of SEQ ID NO: 21.

[0154] [Appendix 31] The use according to any one of Appendices 20 to 30, wherein the antibody that binds to PD-1 comprises the light chain variable region (LCVR) sequence of SEQ ID NO: 22.

[0155] [Appendix 32] The use according to any one of Appendices 20 to 31, wherein the antibody that binds to PD-1 comprises the HCVR sequence of SEQ ID NO: 21 and the LCVR sequence of SEQ ID NO: 22.

[0156] [Appendix 33] The use according to any one of Appendices 20 to 32, wherein the antibody that binds to PD-1 is cemiplimab.

[0157] [Appendix 34] The use according to any one of appendices 20 to 33, wherein the cancer is skin cancer. [Appendix 35] 35. The use of claim 34, wherein the skin cancer is cutaneous squamous cell carcinoma.

[0158] [Appendix 36] The use according to any one of appendices 20 to 33, wherein the cancer is lung cancer. [Appendix 37] The use according to any one of appendices 20 to 33, wherein the cancer is cervical cancer.

[0159] [Appendix 38] 38. The use according to any one of appendices 20 to 37, wherein the background distribution of gene mutation load is performed or determined by performing sequence analysis on genetic samples obtained from the cancer patient.

[0160] [Appendix 39] 1. An immunotherapeutic agent for use in treating a cancer patient, said immunotherapeutic agent comprising an antibody that binds to LAG-3, said cancer patient having a cancer with a passenger mutation burden that is greater than the background mutation burden.

[0161] [Appendix 40] 1. An immunotherapeutic agent for use in treating a cancer patient, the immunotherapeutic agent comprising an antibody that binds to PDL1, and the cancer patient having a cancer with a passenger gene mutation burden that is greater than the background gene mutation burden.

[0162] [Appendix 41] 1. An immunotherapeutic agent for use in treating a cancer patient, said immunotherapeutic agent comprising an antibody that binds to CTLA4, said cancer patient having a cancer with a passenger mutation burden that is greater than the background mutation burden.

[0163] [Appendix 42] 1. An immunotherapeutic agent for use in treating a cancer patient, the immunotherapeutic agent comprising an antibody that binds to TIM3, and the cancer patient having a cancer with a passenger mutation burden that is greater than the background mutation burden.

[0164] [Appendix 43] 10. Use of an antibody that binds to LAG-3 in the manufacture of a medicament for the treatment of a cancer patient, wherein the cancer patient has a cancer with a passenger mutation burden that is greater than the background mutation burden.

[0165] [Appendix 44] 10. Use of an antibody that binds to PDL1 in the manufacture of a medicament for the treatment of a cancer patient, wherein the cancer patient has a cancer with a passenger mutation burden that is greater than the background mutation burden.

[0166] [Appendix 45] 1. Use of an antibody that binds to CTLA4 in the manufacture of a medicament for the treatment of a cancer patient, wherein the cancer patient has a cancer with a passenger mutation burden that is greater than the background mutation burden.

[0167] [Appendix 46] 10. Use of an antibody that binds to TIM3 in the manufacture of a medicament for treating a cancer patient, wherein the cancer patient has a cancer with a passenger mutation burden that is greater than the background mutation burden.

Claims

1. 1. A pharmaceutical composition for treating a cancer patient having a tumor with a total passenger gene mutation burden greater than the background mutation burden of the tumor, wherein the background mutation burden is determined based on randomly selected genes of the tumor, the pharmaceutical composition comprising an antibody that binds to PD1 as an active ingredient.

2. 2. The pharmaceutical composition of claim 1, wherein the antibody that binds to PD1 comprises a heavy chain variable region (HCVR) comprising the amino acid sequence of SEQ ID NO:

21.

3. 3. The pharmaceutical composition of claim 1 or claim 2, wherein the antibody that binds to PD1 further comprises a light chain variable region (LCVR) comprising the amino acid sequence of SEQ ID NO:

22.

4. The pharmaceutical composition according to any one of claims 1 to 3, wherein the antibody that binds to PD1 comprises cemiplimab.

5. The pharmaceutical composition according to any one of claims 1 to 4, further comprising an antibody that binds to PDL1.

6. The pharmaceutical composition of claim 5 , wherein the antibody that binds to PDL1 comprises a heavy chain variable region (HCVR) comprising the amino acid sequence of SEQ ID NO:

122.

7. 7. The pharmaceutical composition of claim 5 or claim 6, wherein the antibody that binds to PDL1 further comprises a light chain variable region (LCVR) comprising the amino acid sequence of SEQ ID NO:

123.

8. The pharmaceutical composition according to any one of claims 1 to 4, further comprising an antibody that binds to CTLA4.

9. 9. The pharmaceutical composition of claim 8, wherein the antibody that binds to CTLA4 comprises a heavy chain variable region (HCVR) comprising an amino acid sequence selected from the group consisting of SEQ ID NO:155, SEQ ID NO:157, SEQ ID NO:159, SEQ ID NO:161, SEQ ID NO:163, SEQ ID NO:165, SEQ ID NO:167, SEQ ID NO:169, SEQ ID NO:171, SEQ ID NO:173, SEQ ID NO:175, SEQ ID NO:177, SEQ ID NO:179, SEQ ID NO:181, SEQ ID NO:183, SEQ ID NO:185, SEQ ID NO:187, SEQ ID NO:189, SEQ ID NO:191, SEQ ID NO:193, SEQ ID NO:195, SEQ ID NO:197, SEQ ID NO:199, SEQ ID NO:201, SEQ ID NO:203, SEQ ID NO:205, SEQ ID NO:207, SEQ ID NO:209, SEQ ID NO:211, SEQ ID NO:213, SEQ ID NO:215, and SEQ ID NO:

217.

10. 10. The pharmaceutical composition of claim 8 or claim 9, wherein the antibody that binds to CTLA4 further comprises a light chain variable region (LCVR) comprising an amino acid sequence selected from the group consisting of SEQ ID NO:156, SEQ ID NO:158, SEQ ID NO:160, SEQ ID NO:162, SEQ ID NO:164, SEQ ID NO:166, SEQ ID NO:168, SEQ ID NO:170, SEQ ID NO:172, SEQ ID NO:174, SEQ ID NO:176, SEQ ID NO:178, SEQ ID NO:180, SEQ ID NO:182, SEQ ID NO:184, SEQ ID NO:186, SEQ ID NO:188, SEQ ID NO:190, SEQ ID NO:192, SEQ ID NO:194, SEQ ID NO:196, SEQ ID NO:198, SEQ ID NO:200, SEQ ID NO:202, SEQ ID NO:204, SEQ ID NO:206, SEQ ID NO:208, SEQ ID NO:210, SEQ ID NO:212, SEQ ID NO:214, and SEQ ID NO:

216.

11. The pharmaceutical composition according to any one of claims 1 to 4, further comprising an antibody that binds to LAG3.

12. The pharmaceutical composition of claim 11, wherein the antibody that binds to LAG3 comprises a heavy chain variable region (HCVR) comprising the amino acid sequence of SEQ ID NO:

93.

13. The pharmaceutical composition of claim 11 or claim 12, wherein the antibody that binds to LAG3 further comprises a light chain variable region (HCVR) comprising the amino acid sequence of SEQ ID NO:

94.

14. 14. The pharmaceutical composition according to any one of claims 1 to 13, wherein the cancer is cutaneous squamous cell carcinoma (CSCC), bladder urothelial cell carcinoma (BLCA), invasive breast cancer (BRCA), cervical squamous cell carcinoma and adenocarcinoma (CESC), colorectal adenocarcinoma (CORE), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), clear cell renal carcinoma of the kidney (KIRC), papillary cell renal carcinoma of the kidney (KIRP), acute myeloid leukemia (LAML), hepatocellular carcinoma of the liver (LIHC), low grade glioma of the brain (LGG), lung adenoma (LUAD), lung squamous cell carcinoma (LUSC), serous cystadenocarcinoma of the ovary (OV), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), cutaneous melanoma (SKCM), endometrial carcinoma of the uterine corpus, or gastric adenocarcinoma.

15. The pharmaceutical composition according to any one of claims 1 to 13, wherein the cancer comprises lung cancer.

16. 16. The pharmaceutical composition of claim 15, wherein the lung cancer comprises non-small cell lung cancer, adenocarcinoma, or squamous cell lung carcinoma.

17. The pharmaceutical composition of claim 15, wherein the lung cancer comprises non-small cell lung cancer.

18. The pharmaceutical composition according to any one of claims 1 to 13, wherein the cancer comprises skin cancer.

19. 19. The pharmaceutical composition of claim 18, wherein the skin cancer comprises cutaneous squamous cell carcinoma, melanoma, or cutaneous squamous cell carcinoma.

20. 19. The pharmaceutical composition of claim 18, wherein the skin cancer comprises melanoma.

21. The pharmaceutical composition according to any one of claims 1 to 13, wherein the cancer comprises cervical cancer.

22. The pharmaceutical composition according to any one of claims 1 to 13, wherein the cancer comprises a blood-borne cancer.

23. 23. The pharmaceutical composition of claim 22, wherein the blood-borne cancer comprises leukemia or acute myeloid leukemia.

24. The total passenger gene mutation load is establishing the total passenger mutation burden of the tumor; generating the background mutation dose; normalizing the total passenger mutation load to the background mutation load; The pharmaceutical composition of any one of claims 1 to 23, wherein the amount of mutation is greater than the background amount determined by

25. 25. The pharmaceutical composition of any one of claims 1 to 24, wherein the background mutation burden is determined based on randomly selected genes of the tumor, the number of randomly selected genes being equal to the number of passenger genes used to determine the total passenger gene mutation burden.

26. 26. The pharmaceutical composition of claim 24 or claim 25, wherein normalizing the total passenger gene mutation load to the background mutation load comprises generating a z-score indicating the number of standard deviations from the mean of the background mutation load.

27. The pharmaceutical composition according to any one of claims 24 to 26, further comprising the step of categorizing the mutated gene of the tumor as a passenger gene.

28. 28. The pharmaceutical composition of claim 27, wherein categorizing the mutated genes of the tumor as passenger genes comprises selecting the mutated genes from the tumor and matching the mutated genes to a data structure comprising passenger genes established according to a passenger gene index.

29. 29. The pharmaceutical composition of claim 28, wherein the passenger gene index comprises a correlation coefficient between the proportion of samples containing a mutated gene obtained from a cancer patient cohort and the median number of mutated genes in each tumor type within the cancer patient cohort.

30. 30. The pharmaceutical composition of any one of claims 24 to 29, further comprising receiving genetic sequence data, said genetic sequence data comprising a plurality of genes and derived from said tumor of said patient.

31. 31. The pharmaceutical composition of any one of claims 24 to 30, wherein the total passenger mutation load is greater than the background mutation load when the normalized passenger mutation load is at least about 1, at least about 1.5, at least about 2, at least about 2.5, at least about 3, or more than 3 standard deviations higher than the mean of the background mutation load.

32. 32. The pharmaceutical composition of any one of claims 1 to 31, wherein the total passenger gene mutation burden is determined based on one or more passenger genes having a gene mutation rate that is highly correlated with total tumor mutation frequency.

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