Use of T cell tolerant fraction as a predictor of immune-related adverse events

Abstract

This paper provides a method for predicting the risk of immune-related adverse events (irAEs) from immune checkpoint inhibitor (ICI) therapy. ICIs have emerged as promising treatments for many cancer types. However, these therapies can induce unpredictable and potentially serious autoimmune toxicities, termed irAEs. The method involves classifying T cell receptor β genes as productive or repair TCR β genes and calculating a tolerant fraction (TF) score. TCR β gene classification indicates the relative presence of non-tolerant T cells, which are more likely to recognize autoantigens after treatment with ICI therapy. The relative presence of tolerant T cells indicates the risk of irAEs.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority under 35 U.S.C. §119(e) of U.S. Provisional Application No. 63 / 382,257, filed November 3, 2022, the disclosure of which is considered part of the disclosure of this application in its entirety and is incorporated herein by reference.

[0002] Government Funding Statement This invention was made with government support under Grant No. 1U01AI156189-01 awarded by the National Institute of Allergy and Infectious Diseases. The government has certain rights in this invention. [Background technology]

[0003] Field of the Disclosure The present invention relates generally to immune checkpoint inhibitors (ICIs), and more particularly to toxicities associated with ICIs.

[0004] Background information ICIs have emerged as promising treatments for many cancer types. However, these therapies can induce unpredictable and potentially serious autoimmune toxicities, termed immune-related adverse events (irAEs). As ICI regimens transition from advanced cancers to earlier, potentially curable stages, concerns about rare, potentially permanent or fatal irAEs have grown. Furthermore, the increasing use of currently approved combination ICI regimens in melanoma, lung cancer, and mesothelioma has raised concerns about the increasing incidence and severity of ICI-related toxicities. Complicating these considerations is that optimal monitoring for irAEs remains unknown. Furthermore, diagnosing irAEs can be more challenging than diagnosing toxicity from conventional chemotherapy or targeted therapies.

[0005] Approved ICIs, including those targeting cytotoxic T-lymphocyte antigen 4 (CTLA4), programmed death 1 (PD1) and PD1 ligand (PDL1), and lymphocyte antigen 3 (LAG3), mediate their effects through T cells. Therefore, T cell characteristics, particularly T cell receptor (TCR) sequencing, have been investigated as a means of predicting the risk of irAEs. The clonality and diversity of TCRβ can predict irAEs.

[0006] Immune tolerance refers to the unresponsiveness of the immune system to a substance that would otherwise elicit an immune response. This state results from prior exposure to an antigen and can be centrally (in the thymus or bone marrow) or peripherally (in lymph nodes or other tissues). Immune tolerance is a key principle of normal physiology, enabling the immune system to distinguish between self and non-self. Conversely, a lack of tolerance can contribute to autoimmune disease. Because irAEs represent autoimmune diseases associated with ICIs, this disclosure determined whether T cell tolerance, characterized by either a producing or non-producing TCRβ gene, is associated with these toxicities. Summary of the Invention

[0007] Provided herein are tolerant fraction scores, methods for their calculation, and methods for their use to predict the risk of developing autoimmune toxicity from immune checkpoint inhibitor therapy.

[0008] Certain embodiments provide methods for predicting the risk of developing an immune-related adverse event (irAE) from immune checkpoint inhibitor (ICI) therapy in a subject.

[0009] The method includes a) classifying the T cell receptor beta (TCRβ) genes of cells from the subject as either productive or repaired TCRβ genes, b) classifying the TCRβ genes of cells from a donor pool as either productive or repaired TCRβ genes, and c) calculating a tolerizing fraction (TF) score, where the TF score is a productive fraction (F PROD ) and total fraction (F TOTAL ) and F PROD is the number of TCRβ genes identified as produced TCRβ genes from the subject that overlap with TCRβ genes identified as produced TCRβ genes from the donor pool, and F TOTAL is the sum of (i) the number of TCRβ genes identified as produced TCRβ genes from the subject that overlap with the TCRβ genes identified as produced TCRβ genes from the donor pool, and (ii) the number of TCRβ genes identified as produced TCRβ genes from the subject that overlap with the TCRβ genes identified as repair TCRβ genes from the donor pool, thereby predicting the risk of developing irAEs from ICI therapy.

[0010] The engineered TCR β gene can produce a T cell receptor (TCR) that is tolerant to self-antigens. The repaired TCR β gene can produce a TCR that is intolerant to self-antigens. A TCR that is intolerant to self-antigens can induce autoimmune toxicity. The method can further include comparing the TF score with a TF threshold, where a TF score greater than the TF threshold indicates a lower risk of developing irAEs from ICI therapy, and a TF score less than the TF threshold indicates a higher risk of developing irAEs from ICI therapy. The TF threshold can be about 82.5, or about 70, 75, 80, 81, 82, 82.5, 83, 85, or 90%. The risk can be predicted before treatment with ICI therapy. The method can further include administering ICI therapy to subjects with a lower risk of developing irAEs.

[0011] Classifying a TCR β gene can include: (a) obtaining a TCR β gene sequence including multiple gene segments and somatic alterations; (b) translating at least one of the multiple gene segments or somatic alterations into an amino acid sequence; (c) identifying a TCR β gene encoding an amino acid sequence capable of antigen recognition as a producing TCR β gene; (d) identifying a TCR β gene that does not have an amino acid sequence capable of antigen recognition as a non-producing TCR β gene; (e) repairing the amino acid sequence of the TCR β gene identified as non-producing to generate a repaired TCR β gene capable of antigen recognition; and (f) classifying the TCR β gene as a producing TCR β gene or a repaired TCR β gene. The gene segment can be selected from the group consisting of a variable (V) gene segment, a diversity (D) gene segment, a joining (J) gene segment, and any combination thereof. The non-producing TCR β gene can be a TCR β gene having an out-of-frame gene segment or a TCR β gene having a stop codon at the somatic junction between gene segments. Repairing a non-producing TCR β gene can include adding or removing one or more nucleotides at the somatic junction between gene segments to bring the gene segments into the same reading frame and / or mutating nucleotides in the somatic region between gene segments to convert a stop codon to an amino acid. The TCR β gene sequence can include the complementarity determining region 1 (CDR1) sequence of the TCR β gene, the CDR2 sequence of the TCR β gene, the CDR3 sequence of the TCR β gene, a combination thereof, or the sequence of the complete TCR β gene. The TCR β gene sequence can include the CDR3 sequence of the TCR β gene. The method can further include removing the first three amino acids and the last three amino acids of the CDR3 sequence from the TCR β gene sequence. Obtaining the TCR β gene sequence can include sequencing the TCR β gene from a peripheral blood mononuclear cell sample from the subject. Obtaining the TCR β gene sequence can further include isolating T cells from the sample.Isolating the T cells can be by cell sorting and / or RNA expression. The T cells can be non-regulatory T cells.

[0012] The cells can be peripheral blood mononuclear cells from a subject with cancer.The cancer can be selected from the group consisting of melanoma, prostate cancer, non-small cell lung cancer, mesothelioma, bladder cancer, and renal cancer.The ICI therapy can be selected from the group consisting of ipilimumab, tremelimumab, atezolizumab, avelumab, durvalumab, nivolumab, pembrolizumab, ipilimumab, and nivolumab, and combinations thereof. [Brief explanation of the drawings]

[0013] The accompanying drawings are included to provide a further understanding of the methods and compositions of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate one or more embodiments of the present disclosure and, together with the description, serve to explain the concepts and operation of the present disclosure.

[0014] [Figure 1A] Figures 1A-1B illustrate the study hypotheses and methods. Figure 1A is a schematic representation of the study hypotheses. Patients with a higher proportion of tolerant T cells are expected to have a lower risk of developing ICI-induced irAEs because their T cells do not recognize self-antigens even after ICI therapy. Conversely, patients with a lower proportion of tolerant T cells are expected to have a higher risk of developing ICI-induced irAEs. Figure 1B is a schematic representation of the study methods. Non-producing and producing TCRβ genes from 786 subjects without known disease are separated. The pool of non-producing TCRβ genes is non-expressible and therefore non-tolerant. The pool of producing TCRβ genes is expressible and therefore tolerant. We characterize each cancer patient according to the relative overlap between their produced TCRβ genes and those of the tolerant pool (from 786 subjects), termed the tolerant fraction. [Figure 1B] Same as above.

[0015] [Figure 2A]Figures 2A-2C show that the sequence of a TCR β gene reveals germline-encoded V, D, and J gene segments, as well as somatic changes that occur during V(D)J recombination. Figure 2A shows that a productive TCR β gene found in peripheral blood can be translated into an amino acid sequence. Figure 2B shows that a TCR β gene with out-of-frame V and J gene segments found in peripheral blood does not express a functional receptor for T cell selection. This example of a non-productive TCR β gene can be computationally repaired by removing somatic nucleotides. Figure 2C shows that a TCR β gene found in peripheral blood that encodes a stop codon at the somatic junction also does not express a functional receptor for T cell selection. This example of a non-productive TCR β gene can be computationally repaired by modifying somatic nucleotides. [Figure 2B] Same as above. [Figure 2C] Same as above.

[0016] [Figure 3A]Figures 3A-3F show the steps for processing the CDR3 of each TCR β. Figure 3A shows that the TCR consists of a TCR α chain and a TCR β chain, each of which contributes a CDR3 for antigen recognition. Because only the TCR β chain has been sequenced, we excluded any TCR β chains with "short" CDR3s, based on the hypothesis that TCR β chains with short CDR3s cannot contribute to antigen recognition. Figure 3B shows the truncation of the first and last three amino acid residues from each CDR3, based on previously published findings from three-dimensional X-ray crystal structures that these residues do not directly contact the antigen and are therefore not expected to contribute to antigen recognition. Figure 3C shows the overlap between two sets of TCR β sequences determined by placing TCR β sequences with identical truncated CDR3 sequences in the overlapping region. Figure 3D shows plots of CDR3s from pairs of chains from individual cells revealing no correlation between CDR3 lengths (Pearson correlation coefficient r = 0.0062 ≈ 0.0). Cells with more than one of either chain were excluded from the analysis. Figure 3E shows a rendering of the 3D X-ray crystal structure using PDB ID 4jrx, showing the specific TCR in antigen contact, with longer chains reaching more antigen contact. The CDR3 of the TRA chain (blue) is 14 residues long, with 8 (57%) antigen (red) contact residues (defined as within 5 Å of the antigen), whereas the CDR3 of the TRB chain (green) is 11 residues long, with 3 (27%) antigen (red) contact residues. Figure 3F shows the area under the receiver operating characteristic (AUROC) curves, measuring the ability of the TRB tolerant fraction to predict irAEs, for different length cutoffs to exclude short CDR3 sequences. Performance is best when short CDR3 sequences are excluded. [Figure 3B] Same as above. [Figure 3C] Same as above. [Figure 3D] Same as above. [Figure 3E] Same as above. [Figure 3F] Same as above.

[0017] [Figure 4A]Figures 4A-4C demonstrate that the tolerant fraction is a predictor of irAEs. Figure 4A shows the tolerant fraction for each patient with grade ≥2 irAEs (red squares) versus grade 0-1 irAEs (blue triangles) for different ICI therapies. The threshold (dashed line) was chosen to maximize the mean sensitivity and specificity, achieving a sensitivity of 18 / 24 = 75% and a specificity of 39 / 53 = 73.6%. Figure 4B shows the receiver operating characteristic (ROC) plot. The area under the curve (AUC) is 0.79. Figure 4C shows T cells enriched for napsin A, an antigen that may contribute to irAEs in lung cancer patients, demonstrating a reduced tolerant fraction score. This is consistent with the expectation that napsin A-specific T cells are not tolerant. [Figure 4B] Same as above. [Figure 4C] Same as above.

[0018] [Figure 5A] Figures 5A-5D show that TCRβ diversity and clonality are weak predictors of irAEs. Figure 5A shows the TCRβ diversity for each patient with grade ≥2 irAEs (red squares) versus grade 0-1 irAEs (blue triangles) for different ICI therapies. The threshold (dashed line) was chosen to maximize the mean sensitivity and specificity, achieving a sensitivity of 43.4% and a specificity of 83.3%. Figure 5B shows the ROC plot. The area under the curve (AUC) is 0.60. Figure 5C shows the TCRβ clonality for each patient with grade ≥2 irAEs (red squares) versus grade 0-1 irAEs (blue triangles) for different ICI therapies. The threshold (dashed line) was chosen to maximize the mean sensitivity and specificity, achieving a sensitivity of 75.0% and a specificity of 58.5%. Figure 5D shows the ROC plot. The area under the curve (AUC) is 0.62. [Figure 5B] Same as above. [Figure 5C] Same as above. [Figure 5D] Same as above.

[0019] [Figure 6A]Figures 6A–C show that the tolerant fraction is a predictor of irAEs. Figure 6A: Quartiles of 0%, 25%, 50%, 75%, and 100% of the tolerant fraction are shown using boxplots of the tolerant fraction for patients with grade ≥2 irAEs (red) versus grade 0–1 irAEs (blue). Dots represent outliers (exceeding 1.5 interquartile range). Figure 6B: ROC plot showing the true and false positive rates resulting from different thresholds of the tolerant fraction for distinguishing the two outcomes (grade ≥2 irAEs versus grade 0–1 irAEs). The area under the curve (AUC) is 0.79. Figure 6C: T cells enriched against napsin A, an antigen that may contribute to irAEs in lung cancer patients, show reduced tolerant fraction scores. This is consistent with the expectation that napsin A-specific T cells are not tolerant. [Figure 6B] Same as above. [Figure 6C] Same as above.

[0020] [Figure 7] Figure 7 shows the tolerance fraction as a predictor of irAEs for different types of ICIs. Quartiles of 0%, 25%, 50%, 75%, and 100% are shown using a boxplot of the tolerance fraction for patients with grade ≥2 irAEs (red) versus grade 0-1 irAEs (blue). Dots represent outliers (exceeding an interquartile range of 1.5). Because only three patients in the combination therapy group experienced grade 0-1 irAEs, there are no error bars on this plot.

[0021] [Figure 8A]Figures 8A-B show the calculated TRB tolerance fractions for various T cell populations. Figure 8A: TRB tolerance fractions for peripheral blood (blue circles) and thymus (red squares) in an immunologically healthy population. Samples were collected from pediatric patients undergoing corrective surgery for congenital heart defects. Patients 1 through 4 had peripheral blood and thymus samples available, while patients 5 through 8 only had thymus samples available. Figure 8B: Disease-specific TRBs from patients with infectious diseases (influenza, coronavirus) (blue circles) and autoimmune diseases (type 1 diabetes mellitus [T1DM], and multiple sclerosis [MS]) (red squares). TRBs represent the aggregate of multiple patients / experiments within a disease group. [Figure 8B] Same as above.

[0022] [Figure 9A] Figures 9A-D show TRB diversity and clonality as predictors of irAEs. Figure 9A: 0%, 25%, 50%, 75%, and 100% quartiles of TRB diversity for patients with grade ≥2 irAEs (red) versus grade 0-1 irAEs (blue) are shown using boxplots. Dots represent outliers (exceeding an interquartile range of 1.5). See the Methods section for p-value interpretation. Figure 9B: ROC plot showing the true and false positive rates resulting from different thresholds of TRB diversity for distinguishing the two outcomes (grade ≥2 irAEs versus grade 0-1 irAEs). The area under the curve (AUC) is 0.60. Figure 9C: 0%, 25%, 50%, 75%, and 100% quartiles of TRB clonality for patients with grade ≥2 irAEs (red) versus grade 0-1 irAEs (blue) are shown using boxplots. Dots represent outliers (exceeding an interquartile range of 1.5). See the Methods section for how to interpret p-values. Figure 9D. ROC plot showing the true and false positive rates resulting from different thresholds of TRB clonality to distinguish between the two outcomes (grade ≥2 irAEs vs. grade 0-1 irAEs). The area under the curve (AUC) is 0.62. [Figure 9B] Same as above. [Figure 9C] Same as above. [Figure 9D] Same as above.

[0023] [Figure 10] Figure 10 is a diagram of T cell selection showing why producer TCRβ genes are presumed to be tolerant, while non-producer TCRβ genes are not. Producer (P) TCRβ genes express receptors (shown as protrusions from each cell). The receptor determines which developing T cells are eliminated during T cell selection (e.g., red and blue are eliminated, purple are not). Therefore, producer TCRβ genes must express receptor chains that are tolerant (e.g., purple). Non-producer (NP) TCRβ genes do not express receptors. Non-expressing TCRβ genes do not determine which T cells are eliminated during T cell selection. Consequently, non-producer TCRβ genes do not need to be tolerant. Both producer and non-producer TCRβ genes are simultaneously captured during TCRβ gene sequencing.

[0024] [Figure 11] FIG. 11 is a diagram summarizing the steps for calculating the tolerant fraction. DETAILED DESCRIPTION OF THE INVENTION

[0025] Detailed Description of the Invention The present disclosure provides a method for predicting immune-related adverse events (irAEs) resulting from immune checkpoint inhibitor therapy, comprising calculating a tolerant fraction (TF) score.

[0026] overview

[0027] Immune checkpoint inhibitor (ICI) therapy is one of the most promising cancer therapies, but it can cause unpredictable and potentially serious autoimmune toxicities called immune-related adverse events (irAEs). Because T cells mediate the effects of ICIs, T cell profiling may provide insight into irAE risk. Herein, we evaluated a novel marker, the T cell tolerance fraction, as a predictor of future irAEs.

[0028] To identify biomarkers predictive of future irAEs, patients scheduled for ICI therapy were enrolled in a prospective study. The T-cell receptor β locus (TRB; formerly known as the T-cell receptor β chain) was examined in this study. Pretreatment blood samples were collected and TRB sequenced. Each patient was characterized by calculating the T-cell tolerant fraction, as defined herein, from the patient's TRB sequence. The tolerant fraction was then evaluated as a predictor of future irAEs. The tolerant fraction was compared with the clonality and diversity of TRB, which have been found to be predictors of irAEs in previous studies.

[0029] T cell receptor (TCR) β gene sequencing from baseline pretreatment blood samples was performed by AdapTive BioTechnologies. Productive TCR β genes that were in-frame and contained no stop codons were considered tolerant, whereas non-productive TCR β genes (containing stop codons out of frame or in rearrangements) were considered non-tolerant. The tolerance fraction was calculated by dividing the number of tolerant TCR β genes by the total number of TCR β genes measured. irAEs were characterized by the Common Terminology Criteria for Adverse Events and classified as grade 0-1 (not clinically significant) or grade ≥ 2 (clinically significant). P values ​​were calculated using a one-sided Mann-Whitney U test and adjusted for repeated testing using the BonFerroni correction.

[0030] A total of 77 cases from our prospective institutional cohort and three published studies were included, of which 43 (56%) received anti-CTLA4 therapy, 19 (25%) received anti-PD1 / PDL1 therapy, and 15 (19%) received anti-CTLA4 and anti-PD1 / PDL1 combination therapy. The tolerance fraction was significantly lower in clinically significant irAE cases (P<0.001). Using a cutoff of 85.2% tolerance fraction, the sensitivity was 75%, the specificity was 74%, and the area under the receiver operating curve (AUC) was 0.79. In the same cohort, T cell clonality had an AUC of 0.62, and T cell diversity had an AUC of 0.60.

[0031] Among patients receiving multiple ICIs, baseline T cell tolerance fraction predicts future irAEs and achieves better outcomes than T cell clonality or diversity.

[0032] Methods for predicting the risk of developing irAEs from ICI therapy

[0033] Provided herein are methods for predicting the risk of developing an immune-related adverse event (irAE) from immune checkpoint inhibitor (ICI) therapy in a subject.

[0034] The method includes a) classifying the T cell receptor beta (TCRβ) genes of cells from the subject as either producing or repaired TCRβ genes, b) classifying the TCRβ genes of cells from a donor pool as either producing or repaired TCRβ genes, and c) calculating a tolerizing fraction (TF) score, where the TF score is the tolerizing fraction (TF). PROD ) and total fraction (F TOTAL ) and F PROD is the number of TCRβ genes identified as produced TCRβ genes from the subject that overlap with TCRβ genes identified as produced TCRβ genes from the donor pool, and F TOTALis the sum of (i) the number of TCRβ genes identified as subject-produced TCRβ genes that overlap with TCRβ genes identified as produced TCRβ genes from the donor pool, and (ii) the number of TCRβ genes identified as subject-produced TCRβ genes that overlap with TCRβ genes identified as repaired TCRβ genes from the donor pool, thereby predicting the risk of developing irAEs from ICI therapy.

[0035] The engineered TCR β gene can produce a T cell receptor (TCR) that is tolerant to self-antigens. The repaired TCR β gene can produce a TCR that is intolerant to self-antigens. A TCR that is intolerant to self-antigens can induce autoimmune toxicity. The method can further include comparing the TF score with a TF threshold, where a TF score greater than the TF threshold indicates a lower risk of developing irAEs from ICI therapy, and a TF score less than the TF threshold indicates a higher risk of developing irAEs from ICI therapy. The TF threshold can be about 82.5%, or about 70, 75, 80, 81, 82, 82.5, 83, 85, or 90%. The risk can be predicted before treatment with ICI therapy. The method can further include administering ICI therapy to a subject with a lower risk of developing irAEs.

[0036] Classifying a TCR β gene can include: (a) obtaining a TCR β gene sequence consisting of multiple gene segments and somatic variations; (b) translating at least one of the multiple gene segments or somatic variations into an amino acid sequence; (c) identifying a TCR β gene encoding an amino acid sequence capable of antigen recognition as a producing TCR β gene; (d) identifying a TCR β gene that does not have an amino acid sequence capable of antigen recognition as a non-producing TCR β gene; (e) repairing the amino acid sequence of the TCR β gene identified as non-producing to generate a repaired TCR β gene capable of antigen recognition; and (f) classifying the TCR β gene as a producing TCR β gene or a repaired TCR β gene. The gene segment can be selected from the group consisting of a variable (V) gene segment, a diversity (D) gene segment, a joining (J) gene segment, and any combination thereof. The non-producing TCR β gene can be a TCR β gene having an out-of-frame gene segment or a TCR β gene having a stop codon at the somatic junction between the gene segments. Repairing a non-producing TCR β gene can include adding or removing one or more nucleotides at the somatic junction between gene segments to bring the gene segments into the same reading frame and / or mutating nucleotides in the somatic region between gene segments to convert a stop codon to an amino acid. The TCR β gene sequence can include the complementarity determining region 1 (CDR1) sequence of the TCR β gene, the CDR2 sequence of the TCR β gene, the CDR3 sequence of the TCR β gene, a combination thereof, or the complete TCR β gene sequence. The TCR β gene sequence can include the CDR3 sequence of the TCR β gene. The method can further include removing the first three amino acids and the last three amino acids of the CDR3 sequence from the TCR β gene sequence. Obtaining the TCR β gene sequence can include sequencing the TCR β gene from a peripheral blood mononuclear cell sample from the subject. Obtaining the TCR β gene sequence can further include isolating T cells from the sample. Isolating the T cells can be by cell sorting and / or RNA expression.The T cells can be non-regulatory T cells.

[0037] The cells may be peripheral blood mononuclear cells from a subject with cancer. The cancer may be selected from the group consisting of melanoma, prostate cancer, non-small cell lung cancer, mesothelioma, bladder cancer, and renal cancer. The ICI therapy may be selected from the group consisting of ipilimumab, tremelimumab, atezolizumab, avelumab, durvalumab, nivolumab, pembrolizumab, ipilimumab, and nivolumab, and combinations thereof.

[0038] The compositions and methods are described in more detail below, and the examples shown herein are intended to be illustrative only, since numerous modifications and variations will be apparent to those skilled in the art. The terms used herein generally have their ordinary meanings in the art within the context of the compositions and methods described herein, and in the specific context in which each term is used. Some terms are more specifically defined herein to provide additional guidance to those skilled in the art regarding the description of the compositions and methods.

[0039] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items. As used throughout this specification and the claims that follow, the meanings of "a," "an," and "the" include the singular as well as the plural, unless the context clearly requires otherwise. The term "about" in reference to a numerical value means that the numerical value varies within a range of 5% above and below. For example, a value of about 100 means 95 to 105 (or any value between 95 and 105).

[0040] All patents, patent applications, and other scientific or technical literature mentioned anywhere in this specification are incorporated herein by reference in their entirety. The embodiments illustratively described herein can preferably be practiced in the absence of elements and / or limitations specifically and / or not specifically disclosed herein. Thus, for example, in each example herein, any of the terms "comprising," "consisting essentially of," and "consisting of" can be substituted for either of the other two terms while retaining their ordinary meaning. The terms and expressions employed are used as terms of description and not as terms of limitation, and there is no intention in the use of such terms and expressions to exclude equivalents of any of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the claims. Thus, while the present methods and compositions have been specifically disclosed by embodiments and optional features, it should be understood that those skilled in the art can make modifications and variations of the concepts disclosed herein, and that such modifications and variations are considered to be within the scope of the compositions and methods defined by this specification and the appended claims.

[0041] Any single term, single element, single phrase, group of terms, group of phrases, or group of elements described herein may each be specifically excluded from the claims.

[0042] Whenever a range is given herein, such as a temperature range, time range, composition range, or concentration range, all intermediate ranges and subranges, as well as all individual values ​​included in the given range, are intended to be included in the disclosure. It will be understood that any subrange or individual value within a range or subrange included in the description herein may be excluded from the embodiments of the present specification. It will be understood that any element or step included in the description herein may be excluded from the claimed composition or method.

[0043] Furthermore, where features or aspects of compositions and methods are described in terms of a Markush group or other grouping of alternatives, those skilled in the art will recognize that the compositions and methods are thereby also described in terms of any individual member or subgroup of members of the Markush group or other group.

[0044] The following are provided for illustrative purposes only and are not intended to limit the scope of the embodiments described in broad terms above. [Example]

[0045] Example 1: Materials and Methods

[0046] Clinical Data Sources and Collection

[0047] Clinical data were collected from patients enrolled in an institutional prospective immunotherapy cohort and from cases from three published studies. As previously described, our cohort included cancer patients for whom ICI therapy was planned but not yet initiated. Collected data included demographics, tumor characteristics, treatment information, and irAEs. Due to the difficulty in determining the occurrence, type, timing, and severity of irAEs, two independent clinicians experienced in ICI administration and monitoring reviewed each case for toxicity, and any discrepancies were reviewed and adjudicated by a third experienced clinician. Enrolled patients underwent blood sampling at baseline before treatment during ICI therapy. Published cohorts were selected for this analysis according to publicly available clinical and biomarker data. For comparison, available T cell receptor sequencing data from healthy human subjects in another published study were used.

[0048] irAEs were categorized as clinically significant (Common Terminology Criteria for Adverse Events [CTCAE] grade ≥ 2) or clinically insignificant (CTCAE grade ≤ 1). This threshold was chosen because grade 2 or greater toxicity generally indicates the need for medical intervention, whereas grade 1 toxicity is generally asymptomatic and does not require ICI modification or specific treatment. Previous studies have used similar cutpoints to predict irAEs after a single treatment.

[0049] TCRβ gene sequencing

[0050] All TCRβ gene sequencing was performed by Adaptive Biotechnologies (Seattle, WA). In this study, the focus was on identifying irAE predictors from unsorted T cells derived from peripheral blood samples. However, in one of the study cohorts included in this analysis, T cells were sorted into CD4 and CD8 populations, and each subset was sequenced independently. Because the ratio of TCRβ genes from sorted CD4 and CD8 T cells was approximately 2:1 (the expected ratio of CD4 to CD8 T cells in peripheral blood), the TCRβ genes from the separately sequenced CD4 and CD8 T cell populations were combined into one sample. This allowed us to compare the TCRβ genes from sorted T cells with unsorted T cells from other studies.

[0051] Single-cell sequencing

[0052] Baseline peripheral blood samples from five cancer patients were subjected to single-cell RNA sequencing using the 10x Genomics platform, which includes a library for capturing TCR sequences containing V(D)J recombination events. After collecting the sequences, we excluded all cells that did not have exactly one TRA sequence and one TRB sequence per cell. We then measured the lengths of CDR3a and CDR3b.

[0053] Producing and non-producing TCRβ genes

[0054] Sequencing of the TRB gene revealed the presence of both producing and non-producing TRB genes in peripheral blood. Produced TRB genes, defined as those in-frame in the rearrangement and not containing a stop codon, can express TRB. Because produced TRB genes can express receptors, we speculate that produced TRB genes are tolerated (Figure 1B). In contrast, non-producing TRB genes, defined as those out-of-frame in the rearrangement or containing a stop codon, cannot express TRB. Because non-producing TRB genes cannot express receptors, we cannot necessarily speculate that non-producing TRB genes are tolerated (Figure 1B).

[0055] T cell selection, also known as thymic selection, represents a biological process that ensures that produced TRB sequences are tolerized (Figure 10). During T cell selection, developing T cells can contain both produced and non-produced TRB sequences on opposing chromosomes. However, only produced TRB sequences can express TRB protein chains. Developing T cells that express non-tolerant TRB chains are eliminated by T cell selection. Therefore, surviving T cells are those that can express tolerant TRB protein chains. Because the expressed TRB protein chains must be tolerized, the produced TRB sequences that express TRB protein chains must also be tolerized. However, non-produced TRB sequences are not subject to such constraints and will not be expressed, so they will pass through T cell selection regardless of whether the sequence is tolerized or not. After completing T cell selection, surviving T cells enter the peripheral blood, where they can be sequenced for TRB sequences.

[0056] Tolerant fraction

[0057] To define the parameters of T cell tolerance, we separated non-producing and producing TCRβ genes from 786 subjects without known disease (Figure 1B). The pool of non-producing TCRβ genes cannot be expressed and therefore may be non-tolerant, while the pool of producing TCRβ genes can be expressed and therefore tolerant. Each cancer patient was characterized according to the relative overlap between their produced TCRβ genes and those of the tolerant pool (from 786 subjects).

[0058] Specifically, f PROD and f NON was used to represent the fraction of tolerant and non-tolerant pools that overlapped with the TCRβ gene from cancer patients (the definition of overlap is described below) (Figure 1B). For each cancer patient, the fraction of tolerant T cells, termed the tolerant fraction, was calculated as follows: [ka]

[0059] A value of 1 indicates that all TCRβs can be predicted to be tolerant, while a value of 0 indicates that none of the TCRβs can be predicted to be tolerant.

[0060] The TRB sequences are subjected to in-silico processing and filtering steps before being used to calculate the tolerance fraction. These steps are summarized in Figure 11 and described in the sections above.

[0061] Translation of the TCRβ gene into protein sequence

[0062] T cell specificity is based on the expressed TCR protein, not the nucleotide sequence of the gene, motivating us to compare the protein sequences of producing and non-producing TCR genes. Therefore, all TCRβ genes were translated into protein sequences in silico. Producing TCRβ genes can be translated into protein sequences, while non-producing TCRβ genes cannot. An algorithm was previously developed to computationally repair non-producing TCRβ genes, allowing the repaired genes to be translated into producing protein sequences. To maximize preservation of the original biological sequence, including the complex and intricate biases resulting from V(D)J recombination, our algorithm repairs each non-producing TCRβ gene using the fewest modifications necessary to obtain a producing copy (Figure 2).

[0063] Our repair algorithm treats each non-producing TRB gene as one of three cases, based on the type of repair required. 1. In the case of non-producing TRB genes, the open reading frame of the J segment is located one position before the open reading frame of the V segment, so our algorithm removes (in silico) any single nucleotide at the soma junction to bring the segments into the same open reading frame (Figure 2B). 2. In the case of non-producing TRB genes, since the open reading frame of the J segment is two positions before the open reading frame of the V segment, our algorithm removes (in silico) any two nucleotides of the soma junction to bring the segments into the same open reading frame. 3. In the case of TRB genes that are not productive due to the presence of a stop codon in the cell body junction, we mutate (in silico) any nucleotide in the cell body junction that encodes the stop codon, aiming to convert the amino acid residue (Figure 2C).

[0064] There are multiple methods for repairing non-producing TCRβ genes (Figure 3). In this study, all protein sequences obtained by repairing the same non-producing TCRβ gene are treated as valid. Therefore, all protein sequences obtained by repairing the same non-producing TCRβ gene are used to calculate the tolerance fraction.

[0065] Exclude TCRβ with "short" TCRβ sequences

[0066] T cell receptors (TCRs) are heterodimers of TCRα and TCRβ chains, each of which contributes a complementary determining region 3 (CDR3) for antigen recognition. We hypothesized that TCR chains with longer CDR3s contribute more to antigen recognition than TCR chains with shorter CDR3s. Because only TCRβ sequences were available in this study, we excluded TCRβ sequences with "short" CDR3s based on the assumption that "short" CDR3s do not contribute to antigen recognition and are therefore not associated with T cell tolerance (Figure 3A). In this study, we considered length cutoffs of 2, 9, 10, 11, 12, 13, 14, 15, and 16, measured by the number of amino acid residues in the CDR3. We used a cutoff of 15 amino acid residues in our analysis because it demonstrated the best discriminatory power. All reported p values ​​were adjusted using the Bonferroni correction.

[0067] CDR3 sequence truncation

[0068] Although the complementarity-determining region 3 (CDR3) of each TCRβ is involved in antigen recognition, not all amino acid residues within CDR3 directly contact the antigen. A published 3D X-ray crystal structure analysis of TCRβ in contact with antigen demonstrated that the first and last three CDR3 amino acid residues do not directly contact the antigen. Therefore, to determine the tolerance fraction, we removed the first and last three amino acid residues from each CDR3 because they do not contribute to specificity and therefore would not be expected to contribute to tolerance (Figure 2B).

[0069] Define duplicates

[0070] In this study, we investigated the overlap between the two sets of TCRβ genes by comparing the TCRβ genes from each set. The overlapping region was defined as a region with equally truncated CDR3 sequences. The size of the overlapping region was weighted not by the number of TCR β gene templates, but by the number of times the same TCR β chain (protein sequence) was found in different subjects (pooled from 786 subjects).

[0071] We did not incorporate the number of TRB sequence templates when calculating relative overlap, but did include replicate TRB sequences in the tolerant and non-tolerant pools from the aggregated results of 786 healthy subjects. It is also noteworthy that the number of TRB sequence templates from ICI-treated cancer patients (Fig. 1b) did not change the calculation of relative overlap, which was confirmed by performing calculations with and without the number of TRB sequence templates from cancer patients.

[0072] statistical analysis

[0073] Our null hypothesis is that the tolerated fraction of patients with grade 0-1 irAEs is not higher than that of patients with grade ≥ 2 irAEs. The alternative hypothesis is that the tolerated fraction of patients with grade 0-1 irAEs is higher than that of patients with grade ≥ 2 irAEs. Because we do not test whether the tolerated fraction of patients with grade 0-1 irAEs is lower, we use a one-sided test of our null hypothesis. Specifically, we calculate p-values ​​using a one-sided Mann-Whitney U test assuming the null hypothesis. Because we considered multiple cutoffs for the length of the CDR3 sequence, we adjusted the p-values ​​using a Bonferroni correction.

[0074] T cell enrichment against irAE antigens

[0075] Napsin A is a potential T cell antigen that may help induce irAEs in lung cancer patients. Starting from peripheral blood from four lung cancer patients, a recent study enriched napsin A-specific T cells by co-culturing T cells with napsin A and then selecting CD8 interferon (IFN)-γ-positive / tumor necrosis factor (TNF)-positive T cells.

[0076] TRB sequences from thymus and peripheral blood samples

[0077] To further evaluate the tolerant fraction, publicly available TRB sequences were analyzed from matched thymus and peripheral blood samples from pediatric patients who underwent corrective cardiac surgery. The samples were subjected to TRB sequencing by AdapTive BioTechnologies (Seattle, WA), and the results were downloaded to calculate the tolerant fraction of these TRB sequences. We hypothesized that because the thymus is enriched for developing T cells that have not yet been eliminated by T cell selection, thymus samples would have a lower tolerant fraction than peripheral samples.

[0078] Disease-specific TRB sequences

[0079] We also examined TRB sequences from various clinical disease scenarios, including infectious diseases (influenza, coronavirus) and autoimmune diseases (type 1 diabetes mellitus (T1DM) and multiple sclerosis (MS)). In these analyses, TRB sequences were not derived from individual patients but instead collected and curated from pooled published literature available through the Immune Epitope Database (IEDB). Generally, each study from the literature pool contributed a small number of TRB sequences, which were sourced from tissue samples and peripheral blood using diverse experimental methods. For each clinical disease state, we aggregated TRB sequences and were able to calculate the tolerant fraction.

[0080] Example 2: Results

[0081] A total of 77 patients (including 22 from our institutional cohort and 55 from three published studies) were analyzed in this study (Table 1). The types of ICIs included anti-CTLA4 (n = 43), anti-PD1 / PDL1 (n = 19), and a combination of anti-CTLA4 and anti-PD1 / PDL1 (n = 15), as follows: ipilimumab (n = 22), tremelimumab (n = 21), atezolizumab (n = 2), avelumab (n = 1), durvalumab (n = 2), nivolumab (n = 9), pembrolizumab (n = 5), and ipilimumab and nivolumab (n = 15). Cancer types included the following: melanoma (n=45), prostate cancer (n=10), non-small cell lung cancer (n=4), small cell lung cancer (n=1), mesothelioma (n=1), bladder cancer (n=1), and renal cell carcinoma (n=1). Overall, 53 patients (68.8%) had grade ≥2 irAEs, and 24 patients (31.2%) had grade 0-1 irAEs.

[0082] Table 1: Patient Sources. This study includes patients with baseline samples from three published studies as well as patients from this study. The table shows the study, number of patients, cancer type, and ICI therapy. [Table 1]

[0083] To evaluate our hypothesis that longer TCR CDR3s are most involved in antigen interactions and therefore most relevant to autoimmune phenomena, including irAEs, we examined various TCR length features (Figure 3). No significant correlation was found between the length of CDR3a and CDR3b in individual cells (Pearson correlation coefficient r = 0.0062) (Figure 3D). Figure 3E demonstrates a representative 3D X-ray crystal structure, showing that longer TCR chains reach greater antigen contacts (approximately 55% of amino acid residues) than shorter TCR chains (approximately 25% of residues). We then examined TCR chain length cutoffs of 2, 9, 10, 11, 12, 13, 14, 15, and 16 amino acid residues (Figure 3F) and observed a clear association between higher TCR chain length cutoffs and the ability of the tolerant fraction to predict irAEs. A length of 15 amino acid residues showed the best discrimination and was therefore selected as the cutoff.

[0084] To evaluate the hypothesis that baseline T cell tolerance is associated with a lower risk of autoimmune toxicity, we measured the TCRβ tolerance fraction in patients with and without clinically significant irAEs (Figures 4A and 6A). As hypothesized, tolerance fraction values ​​were higher in patients with grade 0-1 irAEs compared with those in patients with grade ≥2 irAEs (P < 0.001). Among patients with grade 0-1 irAEs, 18 of 24 had tolerance fractions greater than 85.2% (sensitivity 75%). Among patients with grade ≥2 irAEs, 39 of 53 had tolerance fractions less than 85.2% (specificity 74%). The predictive power of the tolerance fraction appeared stronger for anti-CTLA-4 therapy and anti-CTLA-4 and anti-PD1 / PDL1 combination therapy than for PD-1 / PD-L1 monotherapy (Figures 4A and 6A). As shown in Figures 4B and 6B, the true positive rate was almost always greater than the false positive rate at all possible cutoffs of the tolerance fraction. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.79 (Figures 4B and 6B).

[0085] We also determined the tolerant fraction of T cells capable of recognizing antigens associated with irAEs. For this analysis, we measured the tolerant fraction of T cells enriched against napsin A. Napsin A is an antigen expressed in more than 80% of lung adenocarcinoma cases, and napsin A-enriched T cells have been associated with pulmonary inflammation leading to pulmonary irAEs. The tolerant fraction values ​​for three napsin A-enriched samples were 75.0%, 68.8%, and 77.6% (Figures 4C and 5C), well within the range associated with irAEs (<82.5%). (The tolerant fraction for the fourth sample could not be calculated due to the lack of overlap between the TCRβ gene from this sample and the 786 subjects.)

[0086] To place our findings in the context of other predictive parameters, we also assessed TCRβ diversity and clonality in the current 77 cases. For TCRβ diversity, the ROC AUC was 0.60 (P = 0.073) (Figures 5A and 5B). For TCRβ clonality, the ROC AUC was 0.62 (P = 0.046) (Figures 5C and 5D).

[0087] We also examined the predictive ability of tolerance fractions for specific ICI treatment types (CTLA4, PD1 / PDL1, and the combination of CTLA4 and PD1 / PDL1) (Figure 7). For all three categories, the tolerance fraction was lower in grade ≥2 irAE cases. This difference reached statistical significance for CTLA4 (n = 43; P < 0.001) and tended to be non-significant for PD1 / PDL1 (n = 19; P = 0.21) and the combination of CTLA4 and PD1 / PDL1 (n = 15; P = 0.18).

[0088] To assess the TRB tolerant fraction in various clinical disease states, we examined publicly available individual and pooled TRB sequences (Figure 8). In an immunologically healthy population of pediatric patients undergoing surgery for congenital heart defects, matched thymic and peripheral samples demonstrated substantially lower TRB tolerant fractions for thymic TRB sequences (Figure 8A). TRBs specific for infectious diseases (influenza and coronavirus) demonstrated tolerant fractions ranging from 85–86%, while T1DM-specific TRBs had higher tolerant fractions (approximately 87%) and MS-specific TRBs had lower tolerant fractions (approximately 84%) (Figure 8B).

[0089] To place our findings in the context of other predictive parameters, we also evaluated TRB diversity and clonality in 77 ICI-treated cancer patients (Figure 9). For TRB diversity, the ROC AUC was 0.60 (P = 0.07) (Figures 9A-9B). For TRB clonality, the ROC AUC was 0.62 (P = 0.05) (Figures 9C-9D).

[0090] These data show the following: (i) Patients with MS have a worse (lower) tolerance fraction than patients without the disease, indicating that the above method can be used for the diagnosis or prognosis of MS. (ii) Patients with covid have a slightly worse (lower) tolerance fraction than patients with influenza, indicating that the above method can be used for diagnosing or prognosing long covid.

[0091] Example 3: Discussion

[0092] Immune-related adverse events remain a major concern in immuno-oncology. These autoimmune toxicities can affect almost every organ system. In rare cases, these autoimmune toxicities can be permanent or even fatal. Our lack of understanding of these clinical phenomena is evident through a relatively crude approach to patient selection and monitoring. Aside from the observation that patients with pre-existing autoimmune disease may face a higher risk of autoimmune disease recurrence and irAEs, and that patients who have undergone organ transplants may face a higher risk of organ rejection, there is still no clear way to identify high-risk populations. Similarly, recommendations for irAE monitoring range from tracking only thyroid, liver, and kidney function to an extensive panel that includes assessment of these parameters as well as cardiac, pulmonary, pituitary, adrenal, and pancreatic function.

[0093] In response to clear clinical needs, characterization of patient T cell function through parameters such as TCR diversity and clonality has emerged as a potential approach to irAE prediction. Based on the principle that tolerance suppresses anti-autoimmunity, this study proposes a new T cell metric, the tolerant fraction, as a potential means of identifying patients at high risk for future irAEs. Using this approach, we identified a specific tolerant fraction threshold (e.g., 82.5%) that could distinguish between low and high risk for future irAEs in a cohort containing both published and unpublished TCR and clinical data from patients with diverse cancer types treated with various immunotherapies. Consistent with the overall concept of tolerance, patients with a higher number of tolerant T cells, exhibiting a higher tolerant fraction, were less likely to develop clinically significant irAEs. In this study, the receiver operating characteristic curve (ROC) AUC was approximately 0.8, and the tolerant fraction performed better for irAE prediction than both TCR diversity and clonality (ROC AUC approximately 0.6). There was also a clear association between low tolerance and the presence of T cell populations enriched against irAE-associated antigens.

[0094] The association between immune tolerance and irAEs is consistent with previous findings linking immune dysregulation with immunotherapy toxicity. Specifically, a signature characterized by a low baseline but significant increase in IFNγ-inducible cytokines / chemokines involved in T cell recruitment and activation was associated with a higher risk of future irAEs. Clinical correlates of this relationship include a subset of primary immune deficiencies called immune dysregulation, in which patients with impaired immunity develop autoimmune and inflammatory disorders. Similar findings have been made in cases of acquired immune deficiencies, such as HIV.

[0095] The potential clinical application of the tolerant fraction requires consideration of certain factors. First, the range between the extreme values ​​of the tolerant fraction in our cohort was relatively narrow. A possible explanation for this finding is that only a small fraction of pretreatment T cells is mechanistically associated with subsequent irAEs. Better predictive performance of the tolerant fraction in patients treated with anti-CTLA4-containing regimens, rather than solely PD1 / PDL1-based therapy, is also needed. Some patients in this study received prior chemotherapy, which may affect T-cell populations.

[0096] Strengths of this study include the total number of patients, which exceeds the combined total of numerous previously published studies of TCR-based irAE-correlated biomarkers. Furthermore, this study included a wide variety of cancers and ICI treatment types. From a clinical perspective, the T cell tolerant fraction has the advantageous feature of being a pretreatment baseline parameter. In contrast, assessment of T cell clonal expansion or diversification requires assessment of immune cell populations over time. Indeed, the availability of pretreatment guidance may be particularly useful in areas of greatest need: patient selection and monitoring.

[0097] In conclusion, we identified a novel parameter, the T cell tolerant fraction, associated with the future development of clinically significant irAEs. Unlike dynamic T cell clonal expansion or diversification, the tolerant fraction can be determined before ICI initiation, thereby informing patient selection and monitoring in advance. Furthermore, in our cohort, the tolerant fraction had better predictive power than pretreatment TCR clonality or diversity.

[0098] References [ka] [ka] [ka]

[0099] Although the invention has been described with reference to the above examples, it will be understood that modifications and variations are encompassed within the spirit and scope of the invention. Accordingly, the invention is limited only by the following claims.

Claims

1. 1. A method for predicting a risk of developing an immune-related adverse event (irAE) from immune checkpoint inhibitor (ICI) therapy in a subject, comprising: a) classifying the T cell receptor beta (TCRβ) gene of cells from the subject as a productive TCRβ gene or a repaired TCRβ gene; b) classifying the TCRβ gene of cells from the donor pool as a production TCRβ gene or a repair TCRβ gene; and c) calculating a tolerizing fraction (TF) score, said TF score being calculated based on the productive fraction (F ＰＲＯＤ ) and total fraction (F ＴＯＴＡＬ ) and F ＰＲＯＤ is the number of TCRβ genes identified as produced TCRβ genes from the subject that overlap with TCRβ genes identified as produced TCRβ genes from the donor pool; and F ＴＯＴＡＬ teeth, (i) the number of TCRβ genes identified as the produced TCRβ genes from the subject that overlap with the TCRβ genes identified as the produced TCRβ genes from the donor pool; (ii) the number of TCRβ genes identified as product TCRβ genes from the subject that overlap with the TCRβ genes identified as repair TCRβ genes from the pool of donors; is the sum of Thereby, the risk of developing irAEs due to ICI therapy can be predicted. A method comprising:

2. The method of claim 1, wherein the produced TCRβ gene is likely to produce a T cell receptor (TCR) that is tolerant to self-antigens.

3. The method of claim 1, wherein the repaired TCRβ gene is likely to produce a TCR that is not tolerant to self-antigens.

4. The method of claim 3, wherein a TCR that is not tolerant to a self-antigen is likely to induce autoimmune toxicity.

5. The method of claim 1, further comprising comparing the TF score to a TF threshold, wherein a TF score greater than the TF threshold indicates a lower risk of developing an irAE from ICI therapy, and a TF score less than the TF threshold indicates a higher risk of developing an irAE from ICI therapy.

6. The method of claim 5 , wherein the TF threshold is about 82.5%.

7. The method of claim 1, wherein the risk is predicted prior to treatment with ICI therapy.

8. 10. The method of claim 1, further comprising administering ICI therapy to the subject having a lower risk of developing an irAE.

9. Sorting the TCRβ gene a) obtaining a TCRβ gene sequence comprising multiple gene segments and cell body alterations; b) translating at least one of said plurality of gene segments or cell somatic variations into an amino acid sequence; c) identifying a TCRβ gene encoding an amino acid sequence capable of antigen recognition as the produced TCRβ gene; d) Identifying TCRβ genes that do not have an amino acid sequence capable of antigen recognition as non-producing TCRβ genes; e) repairing the amino acid sequence of the TCRβ gene identified as non-productive to generate a repaired TCRβ gene capable of antigen recognition; and f) classifying the TCRβ gene as a productive TCRβ gene or a repaired TCRβ gene; The method of claim 1 , comprising:

10. 10. The method of claim 9, wherein the gene segment is selected from the group consisting of a variable (V) gene segment, a diversity (D) gene segment, a joining (J) gene segment, and any combination thereof.

11. The method of claim 9, wherein the non-producing TCRβ gene is a TCRβ gene having an out-of-frame gene segment or a TCRβ gene having a stop codon at the somatic junction between the gene segments.

12. repairing the non-producing TCRβ gene by adding or removing one or more nucleotides at the somatic junctions between gene segments to bring the gene segments into the same reading frame, and / or mutating nucleotides in the somatic regions between gene segments to convert a stop codon to an amino acid; 10. The method of claim 9, comprising:

13. 10. The method of claim 9, wherein the TCRβ gene sequence comprises the complementarity determining region 1 (CDR1) sequence of the TCRβ gene, the CDR2 sequence of the TCRβ gene, the CDR3 sequence of the TCRβ gene, a combination thereof, or the sequence of the complete TCRβ gene.

14. 10. The method of claim 9, wherein the TCRβ gene sequence comprises a CDR3 sequence of the TCRβ gene.

15. 15. The method of claim 14, further comprising removing the first three amino acids and the last three amino acids of the CDR3 sequence from the TCR beta gene sequence.

16. 10. The method of claim 9, wherein obtaining the TCRβ gene sequence comprises sequencing the TCRβ gene from a peripheral blood mononuclear cell sample from the subject.

17. 17. The method of claim 16, wherein obtaining a TCRβ gene sequence further comprises isolating T cells from the sample.

18. 18. The method of claim 17, wherein isolating the T cells is by cell sorting and / or RNA expression.

19. 19. The method of claim 18, wherein the T cells are non-regulatory T cells.

20. 10. The method of claim 1, wherein the cells are peripheral blood mononuclear cells from a subject with cancer.

21. 21. The method of claim 20, wherein the cancer is selected from the group consisting of melanoma, prostate cancer, non-small cell lung cancer, mesothelioma, bladder cancer, and renal cancer.

22. 2. The method of claim 1, wherein the ICI therapy is selected from the group consisting of ipilimumab, tremelimumab, atezolizumab, avelumab, durvalumab, nivolumab, pembrolizumab, ipilimumab and nivolumab, and combinations thereof.

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