Methods and systems for analyzing and utilizing cancer testicular antigen loading.

Abstract

This provides a method for characterizing the patient's tumor response to immune checkpoint blockade therapy. [Solution] A method is provided comprising: (a) obtaining tissue from the tumor; (b) measuring the expression of a panel of cancer testicular antigen (CTA) gene markers in the tissue; (c) determining the cancer testicular antigen load (CTAB) based on the expression of the CTA gene markers; (d) predicting, based on the determined CTAB, that when the CTAB is ≥ 171, the tumor is expected to have a favorable response to immune checkpoint blockade therapy; (e) determining immune checkpoint blockade therapy for the tumor based on the predicted response of the tumor to immune checkpoint blockade therapy; and (f) administering the determined immune checkpoint blockade therapy to the patient.

Description

Technical Field

[0001] The present disclosure generally relates to methods and systems for characterizing cancer testicular antigen burden (“CTAB”), predicting cancer survival outcomes using CTAB analysis, and recommending and / or treating cancer using CTAB analysis.

Background Art

[0002] Cancer testicular antigens (CTAs) have highly tissue-restricted expression under normal gene regulation and are expressed almost exclusively in male germ cells. When normal gene regulation is disrupted, such as in malignancies, CTAs can be expressed in a variety of somatic tissues. When expressed in cancer cells, CTAs are highly immunogenic and have the ability to induce cancer-specific immune responses in a variety of malignancies. As a result, CTAs have become major targets for natural T cell responses, immune cell-based therapies, immune checkpoint inhibitors (ICIs) or immune checkpoint blockade (ICB), and cancer vaccines.

[0003] ICIs have emerged as effective treatments in a variety of cancers, including lung cancers such as breast cancer, colorectal cancer, and non-small cell lung cancer (NSCLC). NSCLC accounts for approximately 50% of brain metastases. The clinical utility of single-agent ICIs or in combination with chemotherapy has been well established, but ICI-based immunotherapy is estimated to cost between $100,000 and $250,000 per patient. As a result, the need for the development of biomarkers that can better predict patient response to ICI treatment remains unmet.

Summary of the Invention

Means for Solving the Problems

[0004] This disclosure relates to a method for characterizing patient responses to ICI treatment. Co-expression of multiple CTA genes occurs in many tumor types and can be reliably detected using a targeted RNA-seq approach. The use of this co-expression pattern to calculate cancer-testicular antigen loading (CTAB) reveals tumor-type signatures associated with overall survival (OS) in a small NSCLC cohort. These immunogenic antigens expose tumor cells to innate or immunotherapy-enhanced cell-based immune responses, and therefore CTAB is a predictive marker of therapeutic response to ICI.

[0005] In various embodiments, a method is provided for characterizing a patient's tumor response to immune checkpoint blockade therapy. The method includes (a) obtaining tissue from a tumor; (b) measuring the expression of a panel of cancer-testis antigen (CTA) gene markers in the tissue; (c) determining the cancer-testis antigen load (CTAB) based on the measured expression of the CTA gene markers; (d) predicting the tumor's response to immune checkpoint blockade therapy based on the determined CTAB, wherein the determined CTAB is associated with a predicted favorable response of the tumor to immune checkpoint blockade therapy when the CTAB is ≥ 171; (e) determining the immune checkpoint blockade therapy for the tumor based on the predicted response of the tumor to immune checkpoint blockade therapy; and (f) administering the determined immune checkpoint blockade therapy to the patient.

[0006] In various embodiments, a diagnostic test is provided for characterizing the CTAB of a tumor using a panel of CTA genes. The diagnostic test includes (a) obtaining tissue from the tumor; (b) measuring the expression of a panel of cancer-testicular antigen (CTA) gene markers in the tissue; (c) determining the CTAB based on the measured expression of the CTA gene markers; (d) characterizing the tumor as high CTAB when the CTAB is ≥171 and as low CTAB when the CTAB is <170; and (e) predicting the favorable response of the tumor to immune checkpoint blockade therapy when the tumor is high CTAB and the less favorable response of the tumor to immune checkpoint blockade therapy when the tumor is low CTAB.

[0007] In various embodiments, a method is provided that includes: (a) measuring the expression of a panel of cancer-testis antigen (CTA) gene markers in tumor-forming tissue; (b) determining a cancer-testis antigen load (CTAB) based on the measured expression of CTA gene markers; (c) predicting the tumor's response to immune checkpoint blockade therapy based on the determined CTAB, wherein the determined CTAB is associated with a predicted favorable response of the tumor to immune checkpoint blockade therapy when the CTAB is ≥ 171; and (d) determining immune checkpoint blockade therapy for the tumor based on the predicted response of the tumor to immune checkpoint blockade therapy.

[0008] In some embodiments, the expression of the CTA gene marker is measured by RNA-seq.

[0009] In some embodiments, the tumor is non-small cell lung cancer (NSCLC).

[0010] In some embodiments, the panel of CTA gene markers includes XAGE1B, SSX2, MLANA, MAGEC2, MAGEA12, MAGEA10, MAGEA4, MAGEA3, MAGEA1, GAGE13, GAGE12J, GAGE10, GAGE2C, CTAG2, CTAG1B, and BAGE.

[0011] In some embodiments, immune checkpoint blockade therapy includes one or more of nivolumab, pembrolizumab, ipilimumab, atezolizumab, and durvalumab.

[0012] In some embodiments, a system is provided comprising one or more data processors and a non-temporary computer-readable medium for storing instructions, wherein when an instruction is executed on one or more data processors, the system causes one or more data processors to execute some or all of one or more methods or processors disclosed herein.

[0013] In some embodiments, a computer program product is provided which includes instructions that are tangibly embodied in a non-temporary machine-readable medium and configured to cause one or more data processors to perform some or all of the methods disclosed herein.

[0014] The terms and expressions used are for illustrative purposes only and are not intended to be limiting, and the use of such terms and expressions is not intended to exclude the illustrated and described features or equivalents thereof, but it should be recognized that various modifications are possible within the scope of the claimed invention. Therefore, although the present invention is specifically disclosed by embodiments and optional features, it should be understood that modifications and variations of the concepts disclosed herein may be used by those skilled in the art, and such modifications and variations shall be deemed to be within the scope of the present invention as defined by the appended claims.

[0015] The present invention will be better understood with reference to the following non-limiting drawings. [Brief explanation of the drawing]

[0016] [Figure 1] This document presents representations of 5,450 samples from clinically tested FFPE tumors across 39 different cancers, using various embodiments. [Figure 2] A flowchart is shown illustrating the calculation of nRPMs (in millions) of raw absolute read counts from discovery cohort data using various embodiments. [Figure 3] This document shows gene expression across all tumors for 17 CTAs classified as positive (nRPM ≥ 20) or negative (nRPM < 20) in various embodiments. [Figure 4] The CTAB distributions in A) discovery, B) TCGA, and C) retrospective cohorts are shown in various embodiments. [Figure 5] This shows the CTAB cancer type distribution in A) discovery, B) TCGA, and C) retrospective cohorts in various embodiments. [Figure 6] This paper presents CoxPH regression analyses of the effects of CTAB, age, and sex in various embodiments, A) TCGA, and B) retrospective cohorts. [Figure 7] This paper presents Kaplan-Meier survival analyses comparing CTAB-positive (≧171) and CTAB-negative (<171) groups in A) TCGA and B) retrospective cohorts under various embodiments. [Figure 8] This chart shows the co-expression patterns of 15 cancer testicular antigens across 5450 tumors of DCs in various embodiments. Pairwise-Pearson correlation values ​​are shown in each grid square, indicated by the shaded bar on the right, and are colored according to the correlation value. Black "X"s passing through the squares indicate non-significant (p>0.05) correlations, and black squares around the chart diagonal indicate clusters of co-expressing CTAs. [Figure 9] Violin plots detailing the distribution of cancer testicular antigen load (CTAB) in each of 39 histories represented by 5450 sample DCs, according to various embodiments, are shown. [Figure 10] Shows the PCA and hierarchical clustering results. A) Eigenvector plot showing in detail the influence of five constituent biomarkers on the first two principal components, B) Classification of 110 patients in the NSCLC validation cohort into four phenotypes, and C) Distribution of the four phenotypes within the validation cohort according to various embodiments. [Figure 11] Shows the Kaplan - Meyer survival analysis of the NSCLC validation cohort (n = 110) stratified by the following. A) PD - L1 IHC status, B) TMB status, C) Cell proliferation, D) Tumor immunogenic signature (TIGS), E) CTAB, and F) Phenotype according to various embodiments. [Figure 12] Shows the CoxPH survival analysis of phenotype, five constituent biomarkers, and gender and race as survival predictors according to various embodiments. [Figure 13] Shows the disease control rate (DCR) of 110 NSCLC patient validation cohorts differentiated into four phenotypes of tumor dominance, proliferation, inflammation, and checkpoint according to various embodiments.

Mode for Carrying Out the Invention

[0017] In the accompanying figures, similar components and / or features can have the same reference labels. Further, various components of the same type can be distinguished by a dash and a second label following the reference label to distinguish between similar components. If only the first reference label is used herein, this specification is applicable to any one of the similar components having the same first reference label regardless of the second reference label.

[0018] The following description provides only preferred exemplary embodiments and is not intended to limit the scope, applicability, or configuration of the present disclosure. Rather, the following description of the preferred exemplary embodiments will provide those skilled in the art with a possible description for implementing various embodiments. It should be understood that various changes can be made to the functions and arrangements of elements without departing from the spirit and scope recited in the appended claims.

[0019] Specific details are set forth in the following description to provide a thorough understanding of the embodiments. However, it is understood that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagrams in order not to obscure the embodiments with unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.

[0020] Also, note that individual embodiments may be described as a process depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. A flowchart or diagram may describe the operations as a sequential process, but many operations may be performed in parallel or concurrently. In addition, the order of the operations may be rearranged. A process terminates when its operations are completed, but may have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination may correspond to the return of the function to the calling function or the main function.

[0021] Techniques for characterizing CTAB The present invention is directed to methods and systems for predicting ICI response and survival in patients having tumors spanning multiple histotypes, particularly in NSCLC.

[0022] In one embodiment, a pan-cancer discovery cohort (DC) was studied to develop low-CTAB and high-CTAB cutoffs. Comprehensive genomic and immunoprofiling of standard treatments was performed on 5450 FFPE tumors representing 39 histological types, and the expression levels of 395 immunogenes and over 500 tumor-related genes were assessed. Sample inclusion criteria were based on clinical QC parameters from RNA-seq. As shown in Figure 1, the DCs consisted mainly of lung cancer (40.4%), followed by colorectal cancer (10.6%), breast cancer (8.64%), and ovarian cancer (4.73%), with less significant proportions of other cancer types. Targeted RNA-seq was performed on the 5450 FFPE tumors. From the gene expression data of the DCs, three gene expression signatures were calculated: cell proliferation (CP), tumor immunogenicity signature (TIGS), and cancer-testicular antigen loading (CTAB). The PD-L1 status of each tumor was assessed by IHC, and the tumor mutation burden (TMB) was calculated.

[0023] In a second embodiment, a cohort from The Cancer Genome Atlas (TCGA) was used to validate a classifier based on the CTAB distribution and define a non-ICB-treated population. The TCGA contained 19,923 tumors across 32 tumor types. Subsequently, in a third embodiment, the nearest neighbor method was used to classify a retrospective validation cohort (RC) of 242 patients who had received ICI treatment, including 110 NSCLC patients, as well as patients with melanoma and renal cell carcinoma.

[0024] An amplicon-based NGS approach was used to rank the expression levels of 17 CTAs against a pan-cancer reference population. Figure 2 shows the method for calculating gene expression normalized reads from raw absolute read counts and then calculating gene expression (GEX) ranks. As shown in Figure 3, the measured CTAs were XAGE1B, SSX2, MLANA, MAGEC2, MAGEA12, MAGEA10, MAGEA4, MAGEA3, MAGEA1, GAGE13, GAGE12J, GAGE10, GAGE2C, CTAG2, CTAG1B, and BAGE. The prevalence of positive CTA expression ranged from 3% (GAGE13) to 31.5% (XAGE1B) across the 5450 tumors studied. Figure 8 shows the 15 co-expression patterns of CTAs across the 5450 tumors within the DCs. CTA co-expression was observed across several histological types, with the highest levels of co-expression observed within the MAGEA and GAGE ​​families.

[0025] CTAB was calculated for each sample, cohort, and tumor type as the sum of the expression ranks of 17 CTA genes. The results for each cohort's CTAB composition are shown in Table 1 below. As shown in Figure 4, each of the three cohorts demonstrated a left-leaning CTAB distribution curve with overlapping single peaks centered around CTAB values ​​between 171 for 256 DCs in the RC. Using the median CTAB of 171 DCs, all three cohorts were classified into high-CTAB and low-CTAB groups. [Table 1]

[0026] As shown in Figures 5 and 9, when grouped by tumor type and ordered by median CTAB, the CTAB distribution of tumor types within the three cohorts was similar. CTAB values ​​ranged from 0 to 1700, with renal cancer demonstrating the lowest mean CTAB overall (110) and the highest (550) for melanoma. The mean CTAB for NSCLC was 283.

[0027] As shown in Figure 6, OS analysis was performed on the TCGA and ICB-treated cohorts using the CoxPH regression model, and hazard ratios (HRs) were determined. The CoxPH regression analysis revealed an association between the CTAB threshold classifier and OS in both the ICB-treated RC and the non-ICB-treated TCGA. However, the direction of this association differed between the two cohorts, with high-CTAB samples having better survival in the ICB-treated RC (HR=0.936, p=0.076) and worse survival in the non-ICB-treated TCGA (HR=1.007, p=0.084).

[0028] A Kaplan-Meier survival analysis comparing the CTAB-positive (≧171) and CTAB-negative (<171) groups in TCGA and RC Kaplan-Meier studies revealed a strong association (p<0.000) between CTAB positivity and poor survival in TCGA, as shown in Figure 7. This association was not present in RC (p=0.64), but a tendency toward better survival was observed in those with CTAB positivity. This difference suggests that advances in CTA-targeted immunotherapy have largely eliminated the survival disadvantage observed in TCGA before immunotherapy.

[0029] The CTAB distribution is maintained across DC and TCGA, and across a wide range of tumor types, supporting CTAB as an effective and histologically agnostic classifier. Furthermore, when evaluating ICB-treated and non-ICB-treated cohorts, CTAB demonstrated its ability to predict OS, demonstrating the usefulness of ICB in supporting CTA-specific innate immune responses.

[0030] DCs were further evaluated by comprehensive genomic and immunoprofiling of the tumor immune microenvironment. Individual and combined biomarker assessments included PD-L1 IHC, TMB, tumor inflammation (TIGS), cell proliferation (CP), and cancer-testicular antigen loading (CTAB). From DCs, molecular and immunobiometric combinations were identified and applied to a subcohort of RCs (recurrent disease) from 110 metastatic NSCLC patients treated with pembrolizumab plus chemotherapy or pembrolizumab monotherapy to correlate with responses. Objective response rates (ORRs) were compared using the chi-square test. Kaplan-Meier analysis was performed to examine differences in overall survival (OS) and 1-year OS.

[0031] Example 1. In 110 NSCLC patients treated with ICB from a 242-sample subcohort, high CTAB was associated with better OS compared to low CTAB (HR: 0.55, p=0.07). Furthermore, when combined with tumor inflammation and cell proliferation biomarkers, highly inflammatory but low-proliferative tumors with high CTAB showed improved OS (HR: 0.27, p=0.05). A significant association with a higher response (HR=1.84, p=0.05) was detected across all 242 RC samples.

[0032] [Table 2]

[0033] As shown in Figure 10, the analysis of DCs revealed four distinct biomarker combination groups that explain the underlying tumor immunobiology: tumor-dominant (high CTAB, TMB, CP), proliferative (high CP), inflammatory (high TIGS), and checkpoint (high PDL1, TIGS, and TMB). When these biomarker groups were applied to RC, significant differences in response to ICI regimens were demonstrated between groups (p=0.04). Patients in the proliferative group treated with pembrolizumab monotherapy (35.1%, 79 / 225, median PD-L1=20% TPS) showed a significantly higher ORR (59%, 16 / 27), a significantly improved 1-year OS (p=0.03), and a tendency toward better OS (p=0.14) compared to pembrolizumab + chemotherapy (27%, 14 / 52, p=0.005). Importantly, in the inflammatory group (16%, 36 / 225, median PD-L1=1%TPS), pembrolizumab plus chemotherapy (ORR 26.1%, 6 / 23) was not associated with ORR compared to pembrolizumab alone (ORR 31%, 4 / 13, p=0.76), or OS (p=0.37) and 1-year OS (p=0.57). As a result, while pembrolizumab monotherapy may be beneficial for NSCLC patients with a proliferative phenotype and low PD-L1 levels, both monotherapy and combination therapy with pembrolizumab may be beneficial for patients with an inflammatory phenotype and low PD-L1 levels.

[0034] The association between these groups and the effect of ICI treatment was determined by overexpression analysis, and overall survival was assessed by Kaplan-Meier and CoxPH analyses, as shown in Figures 11 and 12. Kaplan-Meier survival analysis suggested a significant relationship between these groups and overall survival [p=0.035], demonstrating that the proliferation and checkpoint groups had extended survival compared to the tumor-dominant and inflammation groups. Phenotypic stratification demonstrated a greater increase in median survival than stratification by any constituent biomarker. CoxPH analysis showed that the checkpoint group had a significantly lower hazard ratio for ICI treatment [HR=0.10, p=0.038]. In all Kaplan-Meier and CoxPH analyses, this checkpoint group was superior as a survival predictor compared to any of its constituent biomarkers.

[0035] Furthermore, as shown in Figure 13, classifying RC into four groups revealed a significant association with the ICI response, yielding the following results: tumor-dominant [DCR=0.200], proliferative [DCR=0.273], inflammatory [DCR=0.154], and checkpoint [DCR=0.417]. The checkpoint group had the highest rate of disease control [p=0.0313].

[0036] An integrated approach combining comprehensive tumor profiling with novel biomarkers better predicts ICI response and survival across multiple histological types. The resulting differences between groups are likely due to different modes of interaction between tumors and the immune system.

[0037] Additional considerations Specific details are provided in the above description to provide a complete understanding of the embodiments. However, it is understood that embodiments can be carried out without these specific details. For example, circuits can be shown in block diagrams to avoid obscuring the embodiments with unnecessary details. In other cases, well-known circuits, processes, algorithms, structures, and techniques can be shown without unnecessary details to avoid obscuring the embodiments.

[0038] The implementation of the above techniques, blocks, steps, and means can be carried out in various ways. For example, these techniques, blocks, steps, and means can be implemented in hardware, software, or a combination thereof. In the case of hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, other electronic units designed to perform the functions described above, and / or a combination thereof.

[0039] Furthermore, it should be noted that embodiments can be described as processes depicted as flowcharts, flow diagrams, data flow diagrams, structural diagrams, or block diagrams. While flowcharts can describe operations as sequential processes, many operations can be performed in parallel or concurrently. In addition, the order of operations can be rearranged. A process terminates when its operations are complete, but it may have additional steps not shown in the diagram. A process can correspond to a method, function, procedure, subroutine, subprogram, etc. When a process corresponds to a function, its termination corresponds to the function's return to the calling function or main function.

[0040] Furthermore, embodiments can be implemented by hardware, software, scripting languages, firmware, middleware, microcode, hardware description languages, and / or any combination thereof. When implemented in software, firmware, middleware, scripting languages, and / or microcode, program code or code segments for performing the required tasks can be stored in a machine-readable medium such as a storage medium. Code segments or machine-executable instructions can represent procedures, functions, subprograms, programs, routines, subroutines, modules, software packages, scripts, classes, or any combination of instructions, data structures, and / or program statements. Code segments can be coupled to other code segments or hardware circuits by passing and / or receiving information, data, arguments, parameters, and / or memory contents. Information, arguments, parameters, data, etc., can be passed, transferred, or transmitted via any preferred means, including memory sharing, message passing, ticket passing, network transmission, etc.

[0041] In firmware and / or software implementations, the methodology may be implemented in modules (e.g., procedures, functions, etc.) that perform the functions described herein. Any machine-readable medium that tangibly embodies instructions may be used when implementing the methodology described herein. For example, software code may be stored in memory. Memory may be implemented within or outside the processor. As used herein, the term “memory” means any type of storage medium, whether long-term, short-term, volatile, non-volatile, or otherwise, and is not limited to any particular type of memory, or number of memories, or type of medium in which the memory is stored.

[0042] Furthermore, as disclosed herein, the terms “storage medium,” “storage,” or “memory” may refer to one or more memories for storing data, including read-only memory (ROM), random access memory (RAM), magnetic RAM, core memory, magnetic disk storage medium, optical storage medium, flash memory device, and / or other machine-readable media for storing information. The term “machine-readable medium” includes, but is not limited to, portable or fixed storage devices, optical storage devices, wireless channels, and / or various other storage media capable of containing or carrying instructions and / or data.

[0043] While the principles of this disclosure have been explained in relation to specific apparatuses and methods, it should be clearly understood that this explanation is for illustrative purposes only and does not limit the scope of this disclosure. In certain embodiments, for example, the following items are provided: The present invention provides, for example, the following items: (Item 1) A method for characterizing a patient's tumor response to immune checkpoint blockade therapy, (a) The step of obtaining tissue from the tumor, (b) The step of measuring the expression of a panel of cancer testicular antigen (CTA) gene markers in the tissue, (c) A step of determining cancer testicular antigen burden (CTAB) based on the measured expression of the CTA gene marker, (d) A step of predicting the response of the tumor to immune checkpoint blockade therapy based on the determined CTAB, A predictive step in which the determined CTAB is associated with a predicted favorable response of the tumor to immune checkpoint block therapy when the CTAB is ≥ 171, (e) Determining an immune checkpoint blockade therapy for the tumor based on the predicted response of the tumor to immune checkpoint blockade therapy; (f) A method comprising the step of administering the determined immune checkpoint blockade therapy to the patient. (Item 2) A diagnostic test for characterizing tumor cancer testicular antigen burden (CTAB) using a panel of cancer testicular antigen (CTA) genes, (a) The step of obtaining tissue from the tumor, (b) The step of measuring the expression of the panel of CTA gene markers in the tissue, (c) A step of determining the CTAB based on the measured expression of the CTA gene marker, (d) The step of characterizing the tumor as high CTAB when the CTAB is ≥ 171 and as low CTAB when the CTAB is < 170, (e) A diagnostic test comprising the step of predicting a favorable response of the tumor to immune checkpoint blockade therapy when the tumor is high CTAB, and a less favorable response of the tumor to immune checkpoint blockade therapy when the tumor is low CTAB. (Item 3) The method according to item 1, wherein the expression of the CTA gene marker is measured by RNA-seq. (Item 4) The method according to item 1, wherein the tumor is non-small cell lung cancer (NSCLC). (Item 5) The panel of CTA gene markers is The method described in item 1, including XAGE1B, SSX2, MLANA, MAGEC2, MAGEA12, MAGEA10, MAGEA4, MAGEA3, MAGEA1, GAGE13, GAGE12J, GAGE10, GAGE2C, CTAG2, CTAG1B, and BAGE. (Item 6) The method according to item 1, wherein the immune checkpoint blockade therapy comprises one or more of nivolumab, pembrolizumab, ipilimumab, atezolizumab, and durvalumab. (Item 7) It is a system, The system comprises one or more data processors and a non-temporary computer-readable medium for storing instructions, and when an instruction is executed on one or more data processors, the system provides the one or more data processors with the instruction. (a) Measuring the expression of a panel of cancer testicular antigen (CTA) gene markers in tumor-forming tissue, (b) Determining cancer testicular antigen burden (CTAB) based on the measured expression of the CTA gene marker, (c) Predicting the response of the tumor to immune checkpoint blockade therapy based on the determined CTAB, wherein the determined CTAB is associated with a predicted favorable response of the tumor to immune checkpoint blockade therapy when the CTAB is ≥ 171. (d) A system that determines the appropriate immune checkpoint blockade therapy for the tumor based on the predicted response of the tumor to the immune checkpoint blockade therapy. (Item 8) The system according to item 7, wherein the expression of the CTA gene marker is measured by RNA-seq. (Item 9) The system described in item 7, wherein the tumor is non-small cell lung cancer (NSCLC). (Item 10) The panel of CTA gene markers is The systems described in item 7, including XAGE1B, SSX2, MLANA, MAGEC2, MAGEA12, MAGEA10, MAGEA4, MAGEA3, MAGEA1, GAGE13, GAGE12J, GAGE10, GAGE2C, CTAG2, CTAG1B, and BAGE. (Item 11) The system described in item 7, wherein the immune checkpoint blockade therapy comprises one or more of nivolumab, pembrolizumab, ipilimumab, atezolizumab, and durvalumab.

Claims

[Claim 1] A method for characterizing the response of a patient's tumor to immune checkpoint blockade therapy, (a) The step of obtaining tissue from the tumor, (b) The step of measuring the expression of a panel of cancer testicular antigen (CTA) gene markers in the tissue, (c) A step of determining cancer testicular antigen burden (CTAB) based on the measured expression of the CTA gene marker, (d) A step of predicting the tumor's response to immune checkpoint blockade therapy based on the determined CTAB, A predictive step in which the determined CTAB is associated with a predicted favorable response of the tumor to immune checkpoint block therapy when the CTAB is ≥ 171, (e) A step of determining an immune checkpoint blockade therapy for the tumor based on the predicted response of the tumor to an immune checkpoint blockade therapy, (f) A method comprising the step of administering the determined immune checkpoint blockade therapy to the patient.

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