T cell phenotypes associated with adoptive cell therapy response

Abstract

1. A method for obtaining a cell population enriched for T cells having a phenotype comprising: (a) obtaining a bulk population of T cells from a patient tumor sample; (b) detecting from the bulk population the marker CD3 + , CD39 - , and CD69 - and (c) separating the cells selected in (b) from cells not having the phenotype to obtain a cell population enriched for T cells having the phenotype. Also disclosed are related methods for treating or preventing cancer, selecting a therapy for a cancer patient, and predicting the clinical response of immunotherapy in a cancer patient. Also disclosed are isolated or purified cell populations obtained according to the methods, and related pharmaceutical compositions.

Description

[Technical Field]

[0001] Cross-reference of related applications This patent application claims the benefit of U.S. Provisional Patent Application No. 63 / 075,536, filed September 8, 2020, which is incorporated herein by reference in its entirety.

[0002] Description of federally supported research or development This invention was made with government support from the National Institutes of Health and the National Cancer Institute under project numbers ZIA BC 010984 and ZIA BC 011726. The government has certain rights in this invention.

[0003] References to electronically submitted literature The computer-readable nucleotide / amino acid sequence listing submitted concurrently with this specification and identified below is incorporated herein by reference in its entirety: a 134,069-byte ASCII (text) file named "756715_ST25.txt" dated 7 September 2021. [Background technology]

[0004] Adoptive cell therapy (ACT) using T cells can produce positive clinical responses in some patients. Nevertheless, several obstacles remain to the successful use of ACT to treat cancer and other conditions. For example, the influence of T cell phenotype on the clinical success of ACT in humans is not yet understood. Therefore, improved methods for preparing cell populations for ACT are needed. [Overview of the Initiative]

[0005] Embodiments of the present invention are methods for obtaining a cell population enriched with phenotypic T cells, comprising: (a) obtaining a bulk population of T cells from a patient's tumor sample; and (b) obtaining the marker CD3 from the bulk population. + CD39 - , and CD69 -Specifically selecting T cells having a phenotype comprising; (c) separating the cells selected in (b) from cells not having the phenotype to obtain a cell population enriched with T cells having the phenotype, and providing a method comprising.

[0006] Another embodiment of the present invention is a method for obtaining a cell population enriched with T cells having a phenotype, comprising: (a) obtaining a bulk population of T cells from a patient's tumor sample; (b) isolating tumor-reactive T cells from the tumor sample; (c) from the isolated tumor-reactive T cells, marker CD3 + , CD39 - , and CD69 - Specifically selecting T cells having a phenotype comprising; (d) separating the cells selected in (c) from cells not having the phenotype to obtain a cell population enriched with T cells having the phenotype, and providing a method comprising.

[0007] Yet another embodiment of the present invention is a method for obtaining a cell population enriched with T cells having a phenotype, comprising: (a) obtaining a bulk population of T cells from a patient's tumor sample; (b) isolating tumor-reactive T cells from the tumor sample; (c) modifying the isolated tumor-reactive T cells to provide a phenotype comprising marker CD39 - and CD69 - to obtain a cell population enriched with T cells having the phenotype, and providing a method comprising.

[0008] Another embodiment of the present invention is a method for preparing a population of cells enriched with T cells having a CD3 + CD39 - CD69 - phenotype, comprising: (a) isolating tumor-reactive T cells from a patient's tumor sample; (b) (i) the following markers: AHNAK + , AL592183.1 + , ANXA1 + , ANXA4 + , AQP3 + , ATM + , BIN1 + , C10orf54 +、C11orf2 + 、C16orf54 + 、CCDC109B + 、CD2 + 、CD5 + 、CD55 + 、CD8B + 、CDC25B + 、CDC42SE1 + 、CLEC2B + 、CLICK3 + 、CLUAP1 + 、CRBN + 、CTD-3184A7.4 + 、DDI2 + 、DND1 + 、EMP3 + 、EPB41 + 、ERN1 + 、FAIM3 + 、FAM65B + 、FBXL8 + 、FGFBP2 + 、GADD45B + 、GIMAP4 + 、GIMAP7 + 、GPR155 + 、GZMM + 、HSD17B1 + 、IL10RA + 、IL7R + 、ISG20 + 、KANSL1-AS1 + 、KLF2 + 、KLHL24 + 、LDLRAP1 + 、LEF1 + 、LGALS3 + 、LINC00861 + 、LITAF + 、LYAR + 、MAPKAPK5-AS1 + 、MED1 + 、MIAT + 、MIR142 + 、MXI1 + 、MYC + 、NEAT1 + 、NOSIP + 、ODF2L + 、P2RY8+ 、PDE6G + 、PIK3IP1 + 、PLEC + 、PLP2 + 、PPP2R5C + 、PXN + 、R3HDM4 + 、RAMP1 + 、RASA3 + 、RASGRP2 + 、RCBTB2 + 、RNASET2 + 、RP11-395B7.4 + 、RP11-539L10.2 + 、RP11-640M9.1 + 、S100A10 + 、S100A4 + 、S100A6 + 、S1PR1 + 、S1PR4 + 、SAMD3 + 、SELL + 、SH3BP5 + 、SIGIRR + 、SLAMF6 + 、SLCO3A1 + 、SORL1 + 、STK38 + 、SYNJ2 + 、TCF7 + 、TIMP1 + 、TRADD + 、TSC22D3 + 、TSPAN32 + 、TXNIP + 、UBXN11 + 、VCL + 、VNN2 + 、YPEL3 + 、ZFP36L2 + 、ZNF276 + 、and ZNF683 + induce the expression of one or more of the following; and / or (ii) the following markers: ACOT7 + 、ADAM19 + 、AGPAT9 + 、AGTRAP + 、AIF1 + 、ASPM +、ATAD2 + 、AGAINST + 、AURKB + 、BIRC3 + 、BIRC5 + 、BRCA1 + 、C15orf48 + 、CASC5 + 、CCL3 + 、CCNA2 + 、CCNB1 + 、CCNB2 + 、CCND2 + 、CD38 + 、CD40LG + 、CD69 + 、CD8B + 、CDC20 + 、CDCA3 + 、CDCA8 + 、CDK1 + 、CDKN3 + 、CDT1 + 、CENPA + 、CENPE + 、CENPF + 、CENPM + 、CENPW + 、CEP55 + 、CISH + 、CKS1B + 、CKS2 + 、CLSPN + 、CRTAM + 、CSF2 + 、DLGAP5 + 、DUSP5 + 、DUSP6 + I HAVE + 、EGR1 + 、ENTPD1 + 、FEN1 + 、GINS2 + 、GTSE1 + 、H2AFX + 、HIST1H4C + 、HLA + DQA2 + 、HMMR + 、IFI27 + 、IFNG + 、IL2RA + 、IL5 +KIAA0101 + KIF11 + KIF23 + , KPNA2 + , KRT7 + ,MAD2L1 + , MCM7 + MKI67 + MX1 + , MYBL2 + NCAPG + NDC80 + NDFIP2 + NUDT1 + NUSAP1 + ORC6 + , PBK + PCNA + PLK1 + RPL39L + , RRM2 + SGOL2 + SHCBP1 + SMC2 + SPC25 + STMN1 + TESC + , TK1 + TNF + TNFRSF18 + , TNFSF10 + TOP2A + , TPM4 + , TPX2 + , TUBA1B + , TUBA1C + , TUBB + TYMS + UBE2C + UBE2S + UBE2T + XCL1 + , and ZWINT + Modifying isolated tumor-reactive T cells to inhibit the expression of one or more of the following markers: (i) induction of the expression of one or more of the markers and / or (ii) inhibition of the expression of one or more of the markers, thereby enabling the T cells to express CD3 + CD39 - CD69 - The present invention provides a method that includes inducing a person to have a phenotype.

[0009] Further embodiments of the present invention provide isolated or purified cell populations obtained according to any of the methods of the present invention, and related pharmaceutical compositions containing the same.

[0010] Another embodiment of the present invention provides a method for treating or preventing a disease in a mammal, comprising: obtaining a cell population enriched with T cells having a phenotype according to any of the methods of the present invention; and administering to the mammal an amount effective to treat or prevent the disease in the mammal, wherein the disease is cancer.

[0011] A further embodiment of the present invention is a method for selecting a therapy for a cancer patient, comprising: (a) obtaining a bulk population of T cells from a tumor sample of the patient; and (b) measuring the proportion of (i) phenotypic T cells or (ii) non-phenotypic T cells in the bulk population, wherein the phenotypic T cells are marker CD3 + CD39 - , and CD69 - The present invention provides a method comprising: (c) comparing the proportion of phenotypic T cells or (ii) the proportion of non-phenotypic T cells with the control; (d) selecting a non-immunotherapy for the patient if (i) the proportion of phenotypic T cells is lower than that of the control or (ii) the proportion of non-phenotypic T cells is equal to or greater than that of the control; or (e) selecting immunotherapy for the patient if (i) the proportion of phenotypic T cells is equal to or greater than that of the control or (ii) the proportion of non-phenotypic T cells is lower than that of the control.

[0012] Another embodiment of the present invention provides a method for treating cancer in a patient, comprising: receiving identification information of a therapy selected for the cancer patient, wherein the therapy has been selected by any of the methods of the present invention; (I)(i) treating the patient by administering to the patient an effective amount of non-immunotherapy if the proportion of T cells having the phenotype is lower than that of the control or (ii) the proportion of T cells not having the phenotype is equal to or greater than that of the control; or (II)(i) treating the patient by administering to the patient an effective amount of immunotherapy if the proportion of T cells having the phenotype is equal to or greater than that of the control or (ii) the proportion of T cells not having the phenotype is lower than that of the control.

[0013] A further embodiment of the present invention is a method for predicting a clinical response to immunotherapy in a cancer patient, comprising: (a) obtaining a bulk population of T cells from a tumor sample from the cancer patient; and (b) measuring the proportion of (i) phenotypic T cells or (ii) non-phenotypic T cells in the bulk population, wherein the phenotypic marker CD3 + CD39 - , and CD69 - The present invention provides a method comprising: (c) comparing the proportion of T cells having the phenotype or (ii) the proportion of T cells not having the phenotype with a control; (d) identifying the patient as likely to have a negative clinical response to the immunotherapy if (i) the proportion of T cells having the phenotype is lower than that of the control or (ii) the proportion of T cells not having the phenotype is equal to or greater than that of the control; or (e) identifying the patient as likely to have a positive clinical response to the immunotherapy if (i) the proportion of T cells having the phenotype is equal to or greater than that of the control or (ii) the proportion of T cells not having the phenotype is lower than that of the control. [Brief explanation of the drawing]

[0014] [Figure 1A]Figure 1A is a schematic diagram showing the ACT injection product (IP) and scheme from the melanoma cohort patients used for the study described in the Examples. [Figure 1B] Figure 1B shows t-SNE (t-distribution stochastic neighbor embedding) plots of live CD45+CD3+ cell clusters from all patients' IPs (left), clusters from CR IPs only (middle), and clusters from NR IPs only (right). Cluster 1 represents the CD39-CD69-DN population. [Figure 1C] Figure 1C is a plot showing the percentage of CR and NR IP cells in each cluster. *P=0.0264 from the two-tailed Wilcoxon rank-sum test adjusted for Bonferroni for all clusters. [Figure 1D] Figure 1D is a scatter plot showing the results of flow-based independent validation of CR IP and NR IP samples (N=38), representing the percentage of CD39-CD69-(DN) cells among total CD8+(1E) and total CD3+(1F). [Figure 1E] Figure 1E is a scatter plot showing the results of flow-based independent validation of CR IP and NR IP samples (N=38), showing the percentage of CD39-CD69-(DN) cells among total CD8+(1E) and total CD3+(1F). [Figure 1F] Figure 1F is a scatter plot showing the total number of injected cells in CR IP and NR IP (N=38). [Figure 1G] Figure 1G is a scatter plot showing the total number of CD8+CD39-CD69-(DN) cells in CR IP and NR IP (N=38). Two-sided Wilcoxon rank-sum test showed *P<0.05**P<0.01. [Figure 1H] Figure 1H shows the progression-free survival (PFS) for all IP samples (N=54), separated into the median total number of injected cells (left) and the median number of injected CD8+CD39-CD69-(DN) cells (right). [Figure 1I]Figure 1I shows the melanoma-specific survival rate (MSS) for all IP samples (N=54), separated into the median number of injected cells (left) and the median number of injected CD39-CD69 cells (right). "Low" indicates patients with fewer IP cells than the median of the analyzed subgroup, and "High" indicates patients with IP as shown in Figures 1H-1I. Cells with a higher number of cells than the median of the subgroup were analyzed. P-values ​​calculated using the Log-Rank-Mantel-Cox test are shown in Figures 1H-1I. [Figure 2A] Figure 2A shows the t-SNE plots of all CD8+TIL from CR and NR IPs (5CR, 5NR). [Figure 2B] Figure 2B is a box plot showing the percentages of S cluster A (top) and S cluster B (bottom) within the CR and NR IP. S cluster A includes clusters C0, C2, C5, C6, and C7, and S cluster B includes clusters C1, C3, C4, and C8. A two-tailed Wilcoxon rank-sum test shows *P<0.05. [Figure 2C] Figure 2C is a bar graph showing the analysis results, in which each patient's IP was scored using DN and DP gene signature scores, and the mean scGSEA score was plotted on the y-axis. A two-tailed Wilcoxon rank-sum test comparing the mean scores of DN and DP signatures between CR and NR showed ****P<0.0001. [Figure 2D] Figure 2D shows a scatter plot of flow cytometry analysis results for inhibitory markers and memory markers within each subset (DN[CD39-CD69-], SP[CD39-CD69+, CD39+CD69-], and DP[CD39+CD69+]) of patient IP in the validation set (n=38), expressed as a percentage of each subset (parent gate). When comparing DN with the other subsets using a two-sided Wilcoxon rank-sum test corrected by Bonferroni multiple comparisons, the results were *P<0.05**P<0.01***P<0.001****P<0.0001. Flow cytometry analysis indicates that the expression of T cell exhaustion markers is low in the CD39-CD9- subset. [Figure 2E]Figure 2E is a graph showing the results of flow cytometry analysis of intracellular TCF7 expression in the DN and DP subsets. The histogram shows representative patient IP samples (top), and the box plot shows the quantitative analysis of 18 IPs (bottom). Two-sided Wilcoxon rank-sum test ***P<0.001. Flow cytometry analysis indicates that the CD39-CD69 subset has high expression of the T cell stem cell factor TCF7. [Figure 2F] Figure 2F shows flow cytometry plots (left) illustrating the phenotypes of DN, SP, and DP states before and after 48 hours of anti-CD3 / anti-CD28 stimulation in representative patient samples, and dot plots quantifying the phenotypes of daughter cells after stimulation of FACS-selected DN parents (circles) or FACS-selected DP parents (squares). P<0.001 samples, N=6 I.P. were analyzed using a two-sided Wilcoxon rank-sum test. [Figure 2G] Figure 2G shows a graph. TILs before ICB therapy are scored using the scores of the upper DN and DP gene signatures, and the mean scGSEA score is plotted on the y-axis. TILs in responding lesions are represented by A, and cells in progressive lesions are represented by B. Cell counts and ****P<0.0001 by two-sided Wilcoxon rank-sum test are shown. [Figure 3A] Figure 3A shows representative CR IPs illustrating the detection of neoantigen-specific tetramers (top) and the CD39 / CD69 phenotypes of bulk CD8+TILs and CD8+ tetramer+TILs (bottom). The numbers in the quadrants below the tetramer classification represent the percentage of subpopulations within each CD8+ tetramer+gate. [Figure 3B] Figure 3B shows NR IPs with an equal number of neoantigens (top) and CD39 / CD69 phenotypes of bulk CD8+TIL and CD8+tetramer+TIL (bottom). The numbers in the quadrants listed below the tetramer classification represent the percentage of subpopulations within each CD8+tetramer+gate. [Figure 3C]Figure 3C shows the DN, SP, and DP phenotypes within a total population of 26 neoantigen-specific T cells, expressed as the percentage of tetramers + cells from each individual tetramer from 11 patients. For this data analysis, CR and NR were combined (CR+NR). Following a two-tailed Wilcoxon rank-sum test, Bonferroni corrections for multiple comparisons were performed for *P<0.05 and ****P<0.0001. [Figure 3D] Figure 3D is a graph showing the DN, SP, and DP phenotypes of 26 neoantigen-specific TILs subdivided by response state (CR vs. NR). For each subset, **P<0.01, ***P<0.001 by a two-tailed Wilcoxon rank-sum test between CR tetramer+ cells and NR tetramer+ cells. [Figure 3E] Figure 3E is a graph showing CD39-negative neoantigen-specific tetramers + TILs (CD39- as a single marker) subdivided by response state (CR vs. NR). Two-tailed Wilcoxon rank-sum test showed ****P<0.0001. [Figure 3F] Figure 3F is a t-SNE plot of CD8+TILs from 3713-CR IP showing projections of stem-like DN and differentiated DP gene signatures. The dotted lines indicate the analyzed stem-like and differentiated clusters. [Figure 3G] Figure 3G is a graph showing Pt.3713 NeoTCR+ cells from stem-like C0 clusters and differentiated C1 clusters, expressed as the percentage of total NeoTCR+ cells for each neoantigen specificity. [Figure 3H] Figure 3H is a graph showing each NeoTCR+ clone type scored by the mean fitness score (the value obtained by subtracting the scGSEA score of DP from the scGSEA score of DN). Positive values ​​indicate enrichment of the stem-like phenotype clone type, while negative scores indicate enrichment of the differentiated state clone type. [Figure 3I] Figure 3I is a graph showing the persistence of immunodominant SRPXmutNeoTCR clones from Pt.3713-CR IP in peripheral blood after ACT, normalized to their initial frequency (day 0) in the infusion products. [Figure 3J] Figure 3J is a t-SNE plot of CD8+ TILs from Pt.4000-NR IP, showing projections of stem-like DNs and differentiated DP gene signatures (left) and projections of neoantigen-specific TCRs shaded by antigen specificity (right). The dotted lines indicate the analyzed stem-like and differentiated clusters. [Figure 3K] Figure 3K is a graph showing each NeoTCR+ clone type (HIVEP2mut, AMPHmut) scored by its average fitness score, as described in Figure 3H. [Figure 3L] Figure 3L is a graph showing the post-ACT persistence of HIVEP2mut and AMPHmutNeoTCR clones from Pt.4000-NR in peripheral blood, normalized for their frequencies in the infusion products. Patient follow-up was stopped at 150 days due to disease progression. [Figure 4A] Figure 4A shows a scheme using endogenous human TCR to track NY-ESO-1 TCR transduction (ESO.TCR+) IP clones in peripheral blood after treatment of patients who achieved a complete response to NY-ESO-1 TCR therapy (ESO.CR). [Figure 4B] Figure 4B is a graph showing the persistence of peripheral blood after treatment for the top 20 ESO.TCR+IP clones using endogenous human TCRs, according to the enrichment of DN (circles) and DP (squares) phenotypes in the TCR infusion product. Clones with (frequency in DN / frequency in DP) > 1 are defined as having enriched DN status, and clones with (frequency in DN / frequency in DP) < 1 are defined as having enriched DP status. [Figure 4C] Figure 4C is a violin plot comparing undetectable clones (non-persistent) at 7 days post-ACT with long-lasting persistent clones at 1846 days post-ACT, using the ratio of the frequency of clones in the CD39-CD69-stem-like state to the frequency of clones in the CD39+CD69+ differentiated state in IP. A ratio > 1 indicates enrichment of the DN state, while a ratio < 1 indicates enrichment of the DP state. P < 0.01 by two-sided Wilcoxon rank-sum test. [Figure 4D] Figure 4D shows a scheme for adoptively transferring selected DN and DP pmel transgenic T cells into mice with established B16 melanoma tumors. [Figure 4E] Figure 4E is a graph showing the tumor growth curves of mice with B16 tumors treated with two doses of pmel DN or DP T cells. N=6 mice / group. *P<0.05**P<0.01 for tumor growth dynamics calculated by Wilcoxon rank-sum test. A represents untreated mice. B represents mice treated with CD39+CD69+(3e5, 5e5 doses). C represents mice treated with CD39-CD69-(3e5 dose). D represents mice treated with CD39-CD69-(5e5 dose). [Figure 4F] Figure 4F is a graph showing the survival curves of mice with B16 tumors treated with two doses of pmel DN or DP T cells. N=6 mice / group. *P<0.05**P<0.01 for tumor growth dynamics calculated by Wilcoxon rank-sum test. The dotted line represents untreated mice. A (square) represents treatment with CD39+CD69+(3e5, 5e5 doses). B represents treatment with CD39-CD69-(3e5 dose). C represents treatment with CD39-CD69-(5e5 dose). [Figure 4G] Figure 4G illustrates the role of stem-like T cells in immunotherapy and explains the success and paradoxical nature of ACT (Acquisition Therapy) of tumor mutation-responsive T cells in stem-like and terminally differentiated states. [Figure 5A] Figure 5A shows manual gating of CyTOF for t-SNE projection of CyTOF data (left: live cell gate, right: CD45+DNA(2n) singlet gate). [Figure 5B]Figure 5B is a schematic diagram showing the data processing pipeline for CyTOF data: Activation / exhaustion markers (CTLA-4, PD-1, CD28, LAG3, OX40, CD25, FAS, 4-1BB, HLA-DR, CD39, CD69, ICOS) were identified from differentiation markers (e.g., CD2, CD3, CD4, CD8, etc.) for data processing. Cells were classified into 22 clusters using a hierarchical aggregate learning algorithm (HAL-X) built on a k-nearest neighbor (kNN) classification trained on "pure differentiation clusters" (PDCs) expressing only differentiation markers. Next, the expression levels of activation / exhaustion markers were measured for these 22 clusters to create a feature matrix. Highly discriminative features were selected to construct a patient classification index based on clinical response (CR: complete response, NR: disease progression). [Figure 5C] Figure 5C shows the receiver operating characteristics (ROC) for patient classification based on activation / exhaustion markers: the AUC (area under the curve) for the 17 patients analyzed is over 89%. Here, we present five iterations ("splits") corresponding to randomly dividing the patient sample into training and trial subsets in an 80% / 20% ratio. "Mean" represents the mean ROC over the five splits, and "Baseline" represents the ROC for random classification. [Figure 6A] Figure 6A shows the expression dot plot (left) of CD39 and CD69 gated with CD8+ T cells from a representative CR and NR IP in the discovery set, and the quantification of 16 IPs (right). The upper panel shows CyTOF data from two patients, and the lower panel shows flow cytometry analysis from the same patients. The numbers indicate p-values ​​calculated using the two-tailed Wilcoxon rank-sum test. [Figure 6B] Figure 6B shows the linear regression between flow cytometry and CyTOF analysis for 16 IP samples analyzed for the CD39-CD69 subset. [Figure 6C-1] Figure 6C shows the expression profiles of various subsets in 38 validation IPs compared between responders and non-responders. The numbers indicate the p-values ​​obtained by the two-sided Wilcoxon rank-sum test, analyzed individually without multiple correction. [Figure 6C-2] Figure 6C shows the expression profiles of various subsets in 38 validation IPs compared between responders and non-responders. The numbers indicate the p-values ​​obtained by the two-sided Wilcoxon rank-sum test, analyzed individually without multiple correction. [Figure 6C-3] Figure 6C shows the expression profiles of various subsets in 38 validation IPs compared between responders and non-responders. The numbers indicate the p-values ​​obtained by the two-sided Wilcoxon rank-sum test, analyzed individually without multiple correction. [Figure 7A] Figures 7A-7C are graphs showing progression-free survival and melanoma-specific survival according to the number of cells in the TIL subpopulation received as injection products. 7A: PFS and MSS of injected IP cells are classified into high and low based on the median number of CD8+ cells and CD39+CD69+(DP) cells received. [Figure 7B] Figures 7A–7C are graphs showing progression-free survival and melanoma-specific survival in relation to the number of cells in the TIL subpopulation received by the injection product. 7B: Lack of dose-dependency of patient response to total cell number and CD8+ cell number in IP, indicated by tertiles showing low (dashed line), medium, and high cell tertiles. [Figure 7C] Figures 7A–7C are graphs showing progression-free survival and melanoma-specific survival in relation to the number of cells in the TIL subpopulation received by the injection product. 7C: Dose-dependent response to patient response to CD39-CD69-(DN) cell count and lack of dose-dependent response to CD39+CD69+(DP) cell count in IP, indicated by tertiles showing low (dashed line), medium, and high cell tertiles. Arrows indicate statistically significant tertile analysis from injected DN cells. Numbers in all plots indicate p-values ​​by the Mantel-Cox log-rank test. [Figure 8A]Figure 8A shows the CD39 / CD69 transcriptome analysis of 9335 single cells (circles) from 4 IPs (2CR, 2NR) that defined 4420 CD39-CD69- cells (cells having the two lower quartiles of co-expression of CD39 and CD69) and CD39+CD69+ cells (cells co-expressing the two upper quartiles of CD39 and CD69). [Figure 8B] Figure 8B shows a volcano plot of differentially expressed genes between the DN subset and the DP subset from Figure 8A, highlighting the statistically significant genes in question. A complete list of these genes is shown in Table 3. [Figure 8C] Figure 8C shows the overlap in the number of genes observed between the DEG of DN vs. DP shown in Figure 8B and the S. cluster A-specific genes defined by unsupervised clustering of the responder cluster shown in Figure 2B. [Figure 9A] Figure 9A shows representative flow cytometry plots for inhibitory and memory markers from CD8+ TILs derived from patient IP (3733-CR) in the CD39-CD69-(DN), CD39+CD69+(DP), and CD39-CD69+, CD39+CD69-(SP) states. The numbers indicate the percentage of the parent gate (DN, SP, and DP states). [Figure 9B] Figure 9B shows a summary of paired analysis of specified cell surface markers (n=38 I.P.). Paired t-tests showed ***P<0.001****P<0.0001. [Figure 10] Figure 10 is a graph showing cytokines secreted from DN and DP subsets after CD3 / CD28 stimulation. DN and DP subsets were isolated from patient intracellular immobilization (IP) cells (N=5), stimulated with anti-CD3 / CD28 beads for 48 hours, and then subjected to multicytokine analysis of cytokines secreted into the supernatant of daughter cells. Paired Wilcoxon assay showed **P<0.05** and **P<0.01**. [Figure 11A]Figures 11A–11B are graphs showing the mean phenotypic fitness scores of TILs (N=10) from ACT IP and CD8+ TILs before ICB treatment from the Sade-Feldman sample cohort. The mean fitness score is defined as the difference between the scGSEA score of normalized DN and the scGSEA score of DP from each cell in patient TILs (DN minus DP). Figure 11A shows ACT IP from CR and NR shown in Figure 2C. Two-sided Wilcoxon test shows ****P<0.0001. [Figure 11B] Figures 11A-11B are graphs showing the mean phenotypic fitness scores of TILs (N=10) from ACT IP and CD8+ TILs before ICB treatment from the Sade-Feldman sample cohort. The mean fitness score is defined as the difference between the scGSEA score of normalized DN and the scGSEA score of DP from each cell in patient TILs (DN minus DP). Figure 11B shows the pre-treatment TILs from the Sade-Feldman cohort shown in Figure 2G. Two-sided Wilcoxon test showed ****P<0.0001. [Figure 12A] Figure 12A shows the definition of HLA constraint for neoantigens screened in this study. [Figure 12B-1] Figure 12B shows neoantigen specificity defined by titration of TIL or TCR from IP against mutant (square) and wild-type (circle) peptides by IFNγ ELISpot in patients screened in this study. Additional neoantigens were obtained from previously published studies. Wild-type CDKN2A fs is not present. The last two figures represent the NeoTCRs of HIVEP2 and AMPH from 4000-NR patients transduced into healthy PBLs and titrated against mutant and wild-type peptides using 4-1BB upregulation of CD8+mTCRβ+ subsets to demonstrate NeoTCR specificity (used in Figures 3J-3L). [Figure 12B-2]Figure 12B shows neoantigen specificity defined by titration of TIL or TCR from IP against mutant (square) and wild-type (circle) peptides by IFNγ ELISpot in patients screened in this study. Additional neoantigens were obtained from previously published studies. Wild-type CDKN2A fs is not present. The last two figures represent the NeoTCRs of HIVEP2 and AMPH from 4000-NR patients transduced into healthy PBLs and titrated against mutant and wild-type peptides using 4-1BB upregulation of CD8+mTCRβ+ subsets to demonstrate NeoTCR specificity (used in Figures 3J-3L). [Figure 12B-3] Figure 12B shows neoantigen specificity defined by titration of TIL or TCR from IP against mutant (square) and wild-type (circle) peptides by IFNγ ELISpot in patients screened in this study. Additional neoantigens were obtained from previously published studies. Wild-type CDKN2A fs is not present. The last two figures represent the NeoTCRs of HIVEP2 and AMPH from 4000-NR patients transduced into healthy PBLs and titrated against mutant and wild-type peptides using 4-1BB upregulation of CD8+mTCRβ+ subsets to demonstrate NeoTCR specificity (used in Figures 3J-3L). [Figure 13A] Figure 13 shows the identification of neoantigen T cells from patient IPs using tetramers. All 26 neoantigens from the IPs of the 11 patients shown in Figures 3A-C were detected by two-color tetramers (Materials and Methods). The numbers indicate neoantigen-specific TILs as a percentage of CD8+ T cells in each patient's IP. Note that Pt.3713 had other neoantigens that could not be tetramerized but whose NeoTCR was confirmed. [Figure 13B]Figure 13 shows the identification of neoantigen T cells from patient IPs using tetramers. All 26 neoantigens from the IPs of the 11 patients shown in Figures 3A-C were detected by two-color tetramers (Materials and Methods). The numbers indicate neoantigen-specific TILs as a percentage of CD8+ T cells in each patient's IP. Note that Pt.3713 had other neoantigens that could not be tetramerized but whose NeoTCR was confirmed. [Figure 14A] Figure 14A shows 3713 NeoTCR+ cells, expressed as the percentage of stem-like C0 clusters and differentiated C1 clusters for each NeoAg. [Figure 14B] Figure 14B shows the post-ACT persistence of other subdominant NeoTCR clone types from Pt.3713-CR in peripheral blood, normalized to the frequency in IP, related to the SRPX-NeoTCR data shown in Figure 3I. [Figure 15A] Figure 15A shows 4000 NeoTCR+ cells, expressed as the percentage of stem-like C1 / C5 clusters and differentiated C0 clusters for each NeoTCR specificity. [Figure 15B] Figure 15B shows Pt.4000-NR NeoTCR+ cells from stem-like C1 / C5 clusters and differentiated C0 clusters, expressed as a percentage of total NeoTCR+ cells for each neoantigen specificity. [Figure 16A] Figure 16A shows the scheme for the in vitro proliferation experiment. CD39-CD69- and CD39+CD69+CD8 T cells were FACS isolated from 3733-CR IP and co-cultured overnight with autologous 3733-mel tumor cell lines. Tumor-reactive CD8+4-1BB+ cells were FACS isolated from DN and DP subsets (DN 4-1BB+, DP 4-1BB+) and subjected to repeated rapid amplification (REP, materials and methods) using 105 input T cells with irradiated feeders to mimic ACT TIL injection products. Tumor reactivity and cell count were evaluated upon completion of the REP culture experiment. [Figure 16B]Figure 16B shows the absolute number of cells obtained at the end of each REP (left) and the theoretical total tumor-responsive cell yield (right), assuming all cells are subsequently used as input. [Figure 16C] Figure 16C shows the tumor reactivity of each REP subset, as assessed by CD8+4-1BB upregulation after co-culture with autologous 3733-mel tumor cells from each population. [Figure 16D] Figure 16D shows a flow cytometry plot from one of the biological replicas shown in Figure S14C. [Figure 17] Figure 17 is a schematic diagram showing various embodiments of the method of the present invention. [Figure 18A] Figure 18A is a schematic diagram illustrating a strategy for identifying precise markers for neoantigen-specific tumor-reactive stem-like T cells that differentiate bystander stem-like T cells in the stem-like CD39-CD69-(DN) cluster shown in Figure 3F. [Figure 18B] Figure 18B shows a volcano plot of differential gene expression between bystander DN T cells and neoantigen-specific T cell subsets, as shown in Figure 3F, highlighting the statistically significant genes in question. A complete list of these genes is shown in Table 4. [Modes for carrying out the invention]

[0015] CD39 administered to cancer patients - CD69 - The T cell population may be associated with complete regression of cancer and / or persistence of T cells, and CD39 administered to the patient. + CD69 + The T cell population was found to be potentially associated with low persistence. T cells administered to both ACT responders and non-responders showed antitumor neoantigen-responsive CD39 + CD69 + CD39 may be present in ACT responders, but was hardly present in the T cells administered to non-ACT responders. - CD69 -It was discovered that a pool of neoantigen-specific T cells had been administered. Tumor-responsive CD39 - CD69 - T cells are CD39 + CD69 + Compared to T cells, it can provide one or more of the following in the patient's body: self-regeneration, amplification, persistence, and increased antitumor response.

[0016] Embodiments of the present invention provide a method for obtaining a cell population enriched with phenotypic T cells. For example, this cell population may be administered to a patient to treat or prevent cancer.

[0017] The method may include obtaining a bulk population of T cells from a patient's tumor sample. The tumor sample may be, for example, tissue from a primary tumor or tissue from a site of a metastatic tumor. Thus, the tumor sample can be obtained by any preferred means, including but not limited to aspiration, biopsy, or excision.

[0018] The method involves extracting the marker CD3 from the bulk population. + CD39 - , and CD69 - This may further include specifically selecting T cells having a phenotype that includes [specific markers]. Selecting phenotypic T cells may include sorting T cells into separate single T cell samples and separately detecting the expression and / or non-expression of one or more phenotypic markers by one or more single T cells. In embodiments of the present invention, specifically selecting phenotypic T cells includes performing single-cell transcriptome analysis.

[0019] The detection of the expression and / or non-expression of one or more markers by one or more single T cells may be performed, for example, using the CHROMIUM Single Cell Gene Expression Solution system (10x Genomics, Pleasanton, CA) ("CHROMIUM system"). The CHROMIUM system performs deep profiling of complex cell populations with high-throughput digital gene expression at the cellular level. The CHROMIUM system barcodes the cDNA of individual cells for 5' transcription or T cell receptor (TCR) analysis. For example, a sample starting with an input of 10,000 cells can yield data for approximately 3,000 cells / sample, with an average of approximately 500 genes / cell.

[0020] In embodiments of the present invention, specifically selecting phenotypic T cells includes performing Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) analysis. CITE-Seq is described, for example, in Stoeckius et al., Nat. Methods, 14(9):865-868 (2017). Briefly, CITE-Seq performs antibody-based detection of protein markers in parallel with transcriptome profiling of numerous single cells. Antibodies labeled with oligonucleotides are used to integrate cellular protein and transcriptome measurements into an efficient single-cell readout.

[0021] Because the data obtained by single-cell transcriptome analysis is dimensionally large (e.g., approximately 3000 cells / sample and approximately 500 genes / cell), dimensionality reduction may be performed for the analysis of marker expression data. Accordingly, in embodiments of the present invention, specifically selecting T cells having a phenotype includes performing t-distribution type stochastic neighbor embedding (t-SNE) analysis. t-SNE visualizes high-dimensional data by assigning a location on a two-dimensional or three-dimensional map to each data point. t-SNE is described, for example, in Van der Maaten and Hinton, J. Machine Learning Res., 9:2579-2605 (2008). Briefly, t-SNE is performed in two stages. In stage 1, a probability distribution that defines the relationships between various neighboring points is created in a high-dimensional space. In stage 2, a low-dimensional space that follows the probability distribution as closely as possible is recreated. The "t" in t-SNE comes from the t-distribution, which is the distribution used in stage 2. The terms "S" and "N" ("stochastic" and "neighborhood") derive from the use of probability distributions between neighboring points.

[0022] The phenotype may include (i) positive expression of one or more markers, (ii) negative expression of one or more markers, or (iii) positive expression of one or more markers in combination with negative expression of one or more markers. When used herein, the term "positive" (" +Upregulation (sometimes abbreviated as "upregulation") means that T cells upregulate the expression of a specified marker compared to other T cells in a tumor sample from a cancer patient. Upregulated expression can include, for example, a quantitative increase in the expression of the specified marker by approximately 0.2, 0.2, 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, or any two of the aforementioned ranges, or a mean logarithmic increase (base 2) of more than that. When used herein in relation to the expression of a specified marker, the term “negative” (which may be abbreviated as “-”) means that a T cell downregulates the expression of the specified marker compared to other T cells in a tumor sample from a cancer patient. Downregulated expression can include, for example, a quantitative decrease in the expression of a specified marker by approximately -0.2, -0.5, -1, -2, -3, -4, -5, -6, -7, -8, -9, -10, -11, -12, -13, -14, -15, -16, -17, -18, -19, -20, -21, -22, -23, -24, -25, -26, -27, -28, -29, -30, -31, -32, -33, -34, -35, or any two of the aforementioned ranges, or a mean logarithmic multiplier change (base 2) of more than that. Downregulated expression may include the absence of expression of a specified marker, but it also includes the presence of expression of a specified marker, albeit at a low level compared to other T cells in tumor samples from cancer patients.

[0023] Specific selection of phenotypic T cells may include detecting the presence or absence of the product(s) of marker expression in the phenotype described herein, or measuring the amount thereof. In this regard, specific selection of phenotypic T cells may include detecting the presence of the protein(s) encoded by the phenotypic positively expressed marker(s). Alternatively, specific selection of phenotypic T cells may further include detecting the absence of the protein(s) encoded by the phenotypic negatively expressed marker(s). Alternatively, specific selection of phenotypic T cells may further include measuring the amount of the protein(s) encoded by the phenotypic negatively expressed marker(s). Alternatively, specific selection of phenotypic T cells may further include measuring the amount of the protein(s) encoded by the phenotypic positively expressed marker(s). Alternatively, specific selection of phenotypic T cells may further include detecting the presence of RNA encoded by the phenotypic positively expressed marker(s). Alternatively, specifically selecting phenotypic T cells may include detecting the absence of RNA encoded by marker(s) whose expression is negative in the phenotype. Alternatively, specifically selecting phenotypic T cells may include measuring the amount of RNA encoded by marker(s) whose expression is positive in the phenotype. Alternatively, specifically selecting phenotypic T cells may include measuring the amount of RNA encoded by marker(s) whose expression is negative in the phenotype. In embodiments of the present invention, specifically selecting phenotypic T cells includes detecting the presence and / or absence of cell surface expression of one or more markers in the phenotype. In embodiments of the present invention, specifically selecting phenotypic T cells includes measuring the amount of cell surface expression of one or more markers in the phenotype.

[0024] In embodiments of the present invention, the phenotype is the marker(s): AHNAK + AL592183.1 + ANXA1 + ANXA4 + , AQP3 + ATM + , BIN1 + , C10orf54 + , C11orf21 + , C16orf54 + CCDC109B + CD27 + CD52 + CD55 + CD8B + CDC25B + CDC42SE1 + CLEC2B + CLICK3 + , CLUAP1 + CRBN + , CTD-3184A7.4 + , DDI2 + , DND1 + EMP3 + , EPB41 + , ERN1 + FAIM3 + FAM65B + FBXL8 + FGFBP2 + GADD45B + GIMAP4 + GIMAP7 + GPR155 + GZMM + HSD17B11 + IL10RA + IL7R + ISG20 + KANSL1-AS1 + , KLF2 + KLHL24 + ,LDLRAP1 + , LEF1 + , LGALS3 + LINC00861 + LITAF + , LYAR +、MAPKAPK5-AS1 + 、MED15 + 、MIAT + 、MIR142 + 、MXI1 + 、MYC + 、NEAT1 + 、NOSIP + 、ODF2L + 、P2RY8 + 、PDE6G + 、PIK3IP1 + 、PLEC + 、PLP2 + 、PPP2R5C + 、PXN + 、R3HDM4 + 、RAMP1 + 、RASA3 + 、RASGRP2 + 、RCBTB2 + 、RNASET2 + 、RP11-395B7.4 + 、RP11-539L10.2 + 、RP11-640M9.1 + 、S100A10 + 、S100A4 + 、S100A6 + 、S1PR1 + 、S1PR4 + 、SAMD3 + 、SELL + 、SH3BP5 + 、SIGIRR + 、SLAMF6 + 、SLCO3A1 + 、SORL1 + 、STK38 + 、SYNJ2 + 、TCF7 + 、TIMP1 + 、TRADD + 、TSC22D3 + 、TSPAN32 + 、TXNIP + 、UBXN11 + 、VCL + 、VNN2 + 、YPEL3 + 、ZFP36L2 + 、ZNF276+ , and ZNF683 + The phenotype further includes one or more of the markers listed in this paragraph. In embodiments of the present invention, the phenotype includes any two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty The phenotype further includes 1, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, or more. In embodiments of the present invention, the phenotype further includes all of the markers listed in this paragraph.

[0025] In embodiments of the present invention, the phenotype is defined as the marker(s): ACOT7 + ADAM19 + AGPAT9 + AGTRAP + , AIF1 + ASPM + , ATAD2 + AURKA + AURKB + BIRC3 + BIRC5 + BRCA1 + , C15orf48 + CASC5 + , CCL3 + CCNA2 + CCNB1 + CCNB2 + CCND2 + CD38 + CD40LG + CD69+ 、CD8B + 、CDC20 + 、CDCA3 + 、CDCA8 + 、CDK1 + 、CDKN3 + 、CDT1 + 、CENPA + 、CENPE + 、CENPF + 、CENPM + 、CENPW + 、CEP55 + 、CISH + 、CKS1B + 、CKS2 + 、CLSPN + 、DRAW + 、CSF2 + 、DLGAP5 + 、DUSP5 + 、DUSP6 + 、DUT + 、EGR1 + ,ENTPD1 + 、FEN1 + 、GINS2 + 、GTSE1 + 、H2AFX + 、HIST1H4C + 、HLA + DQA2 + ,HMMR + 、IFI27 + 、IFNG + 、IL2RA + 、IL5 + 、KIAA0101 + 、KIF11 + 、KIF23 + 、KPNA2 + 、KRT7 + 、MAD2L1 + 、MCM7 + 、MKI67 + 、MX1 + 、MYBL2 + 、NCAPG + 、NDC80 + 、NDFIP2 + 、NUDT1 + 、NUSAP1 + 、ORC6+ , PBK + , PCNA + , PLK1 + , RPL39L + , RRM2 + , SGOL2 + , SHCBP1 + , SMC2 + , SPC25 + , STMN1 + , TESC + , TK1 + , TNF + , TNFRSF18 + , TNFSF10 + , TOP2A + , TPM4 + , TPX2 + , TUBA1B + , TUBA1C + , TUBB + , TYMS + , UBE2C + , UBE2S + , UBE2T + , XCL1 + , and ZWINT +In embodiments of the present invention, the phenotype may include any two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty It does not include 1, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, or more. In embodiments of the present invention, the phenotype does not include any of the markers listed in this paragraph.

[0026] In embodiments of the present invention, the phenotype is defined as marker(s): AHI1 + ALOX5AP + ANXA5 + CD68 + CD74 + CD99 + CDC27 + ,CDCA7 + , CISH + COTL1 + CTSH + CTSW + DDX60 + , GTSF1 + HIST1H2AG + HLA-DPA1 + HLA-DRB1 + HLA-DRB5 + HMGN3 + IGFBP3 + IL32 + INTS4 + ,ITGAE +ITGB1 + , KLRC3 + , LGALS1 + LIME1 + , PDCD1 + PPM1M + PRSS57 + RAB34 + , RBPMS + S100A11 + S100A4 + SNAP47 + TNFRSF10A + VSIR + ZBP1 + , and ZNF683 + The phenotype further includes one or more of the following. In embodiments of the present invention, the phenotype further includes any two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty As used herein, the term "bystander T cell" refers to a T cell that exhibits antigen specificity towards an unrelated antigen (not a neoantigen).

[0027] In embodiments of the present invention, the phenotype is defined as marker(s): AQP3 + ASXL2 + CD6 + ,CLDND1 + , EEF1A1 + , EPB41 + , ERN1 + FCMR + GIMAP4 + GIMAP7 + , GNLY + GZMK + IL27RA+ LINC01943 + , MRPL57 + , MT-CO1 + , MT-CO2 + RGS10 + RPL13A + RPL18 + RPL18A + RPL37 + RPL41 + RPLP0 + , SFMBT2 + , TPT1 + , and TRGV10 + It does not include one or more of the markers listed in this paragraph. In embodiments of the present invention, the phenotype does not include any two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty, twenty-one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, or more of the markers listed in this paragraph. In embodiments of the present invention, the phenotype does not include all of the markers listed in this paragraph. The presence of one or more of the markers in this paragraph may be characteristic of bystander T cells, so bystander T cells can be distinguished from neoantigen-specific T cells by the presence of one or more of the markers in this paragraph. Conversely, a phenotype that does not include one or more of the markers described in this paragraph may be characteristic of neoantigen-specific T cells, so neoantigen-specific T cells can be distinguished from bystander T cells.

[0028] The phenotype is marker(s) CD8. + and CD4 + It may further include one or both of the following. Alternatively, the phenotype may include the marker(s) CD8 - and CD4 - It further includes one or both of the above.

[0029] The method may further include separating cells having a selected phenotype from cells without the phenotype to obtain a cell population enriched with phenotypic T cells. In this regard, selected cells can be physically separated from unselected cells, i.e., cells without the phenotype. Selected cells can be separated from unselected cells by any preferred method, such as sorting.

[0030] A cell population enriched with phenotypic T cells by the method of the present invention may itself be useful for a variety of applications, such as adoptive cell therapy for treating conditions like cancer. Alternatively, the cell population enriched with phenotypic T cells may further serve as a source of tumor-responsive T cells. Therefore, in embodiments of the present invention, the method may further include isolating tumor-responsive T cells from the isolated phenotypic T cells. Exemplary examples of such embodiments of the method of the present invention are shown by the collection of items 1A, 1B, and 1C in Figure 17.

[0031] Tumor-reactive T cells can be isolated from T cells with an isolated phenotype by any of the various methods known in the art. Examples of techniques for isolating tumor-reactive T cells are described, for example, in Passetto et al., Cancer Immunol. Res., 4:734-743 (2016); Parkhurst et al., Clin. Cancer Res., 23:2491-2505 (2017); Cohen et al., J. Clin. Invest., 125:3981-3991 (2015); Lu et al., Mol. Ther., 26(2):1-10 (2018); U.S. Patent Application Publication No. 2020 / 0056237; U.S. Patent Application Publication No. 2017 / 0218042; International Publication No. 2017 / 048614; and U.S. Patent Application Publication No. 2020 / 0095548.

[0032] Tumor-reactive T cells may exhibit antigen specificity for neoantigens. The terms “antigen-specific” and “antigen-specific” as used herein mean that a T cell can specifically bind to and immunologically recognize an antigen or its epitope, and as a result, the binding of the T cell to the antigen or its epitope triggers an immune response. Neoantigens are a type of cancer antigen resulting from cancer-specific mutations in expressed proteins. The term “neoantigen” refers to a peptide or protein expressed by cancer cells that contains one or more amino acid modifications compared to the corresponding wild-type (non-mutant) peptide or protein expressed by normal (non-cancer) cells. Neoantigens may be patient-specific. “Cancer-specific mutation” refers to a somatic mutation that is present in the nucleic acids of tumor or cancer cells but not in the nucleic acids of the corresponding normal cells, i.e., non-tumor or non-cancer cells.

[0033] In another embodiment of the present invention, tumor-reactive T cells may first be isolated from a tumor sample, and then T cells exhibiting a specific phenotype may be selected from the isolated tumor-reactive T cells. Exemplary examples of such embodiments of the method of the present invention are shown by the collection of items 1A, 2A, and 2B in Figure 17.

[0034] In this regard, embodiments of the present invention provide a method for obtaining a cell population enriched with phenotypic T cells, the method comprising obtaining a bulk population of T cells from a patient's tumor sample. The bulk population of T cells can be obtained from a tumor sample as described herein in relation to other embodiments of the present invention.

[0035] The method may further include isolating tumor-reactive T cells from a tumor sample. This can be carried out by any of the various methods known in the art. Examples of techniques for isolating tumor-reactive T cells from a tumor sample are described, for example, in Passetto et al., Cancer Immunol. Res., 4:734-743 (2016); Parkhurst et al., Clin. Cancer Res., 23:2491-2505 (2017); Cohen et al., J. Clin. Invest., 125:3981-3991 (2015); Lu et al., Mol. Ther., 26(2):1-10 (2018); U.S. Patent Application Publication No. 2020 / 0056237; U.S. Patent Application Publication No. 2017 / 0218042; International Publication No. 2017 / 048614; and U.S. Patent Application Publication No. 2020 / 0095548.

[0036] The method involves extracting the marker CD3 from isolated tumor-reactive T cells. + CD39 - , and CD69 - The invention may further include specifically selecting T cells having a phenotype and separating the selected cells from phenotypic cells to obtain a cell population enriched with phenotypic T cells. The phenotype may be as described herein in relation to other aspects of the invention. The specific selection of phenotypic T cells and the separation of the selected cells from phenotypic cells may be carried out as described herein in relation to other aspects of the invention.

[0037] Phenotypic tumor-reactive T cells can offer one or more of the following advantages in a patient's body compared to phenotypic T cells: self-renewal, amplification, persistence, and increased anti-tumor response. Therefore, it is conceivable that by modifying tumor-reactive T cells to be phenotypic, a T cell population can be provided that offers one or more of these advantages in a patient's body. Exemplary examples of such embodiments of the method of the present invention are shown by the collection of items 1A, 2A, and 2B in Figure 17.

[0038] Accordingly, embodiments of the present invention provide a method for obtaining a cell population enriched with phenotypic T cells, comprising obtaining a bulk population of T cells from a patient's tumor sample and isolating tumor-reactive T cells from the tumor sample. Obtaining a bulk population of T cells from a patient's tumor sample and isolating tumor-reactive T cells from the tumor sample can be carried out as described herein in relation to other embodiments of the present invention.

[0039] The method is marker CD39 - and CD69 - This may further include modifying isolated tumor-reactive T cells to provide a phenotype including CD39, thereby obtaining a cell population enriched with T cells possessing the phenotype. - CD69 - Modifying isolated tumor-reactive T cells to provide a specific phenotype can be carried out by any preferred method known in the art. For example, isolated tumor-reactive T cells may be treated with CD39 inhibitors and CD69 inhibitors.

[0040] In embodiments of the present invention, the CD39 inhibitor is any preferred agent that inhibits the expression of one or both of the CD39 mRNA and / or CD39 protein. The CD39 inhibitor may be a nucleic acid of at least about 10 nucleotides in length that is specifically binding to and complementary to a target nucleic acid encoding one or both of the CD39 mRNA and / or CD39 protein, or their complements. The CD39 inhibitor may be introduced into isolated tumor-reactive T cells capable of expressing one or both of the CD39 and / or CD39 protein in an effective amount, for a sufficient time and under conditions to interfere with the expression of one or both of the CD39 and / or CD39 protein, respectively.

[0041] In embodiments of the present invention, the CD69 inhibitor is any preferred agent that inhibits the expression of one or both of the CD69 mRNA and / or the CD69 protein. The CD69 inhibitor may be a nucleic acid of at least about 10 nucleotides in length that is specifically binding to and complementary to a target nucleic acid encoding one or both of the CD69 mRNA and / or the CD69 protein or their complements. The CD69 inhibitor may be introduced into isolated tumor-reactive T cells capable of expressing one or both of the CD69 mRNA and / or the CD69 protein in an effective amount, for a sufficient time and under conditions to interfere with the expression of one or both of the CD69 mRNA and / or the CD69 protein, respectively.

[0042] In embodiments of the present invention, one or both of the CD39 inhibitor and the CD69 inhibitor may be artificially engineered nucleases that inhibit the expression of CD39 or CD69, respectively. For example, genome editing technology can be used to inhibit the expression of one or both of CD39 and CD69 in isolated tumor-reactive T cells. Genome editing technology can modify gene expression in target cells by inserting, replacing, or removing DNA in the genome using artificially engineered nucleases. Examples of such nucleases include zinc finger nucleases (ZFNs) (Gommans et al., J.Mol.Biol., 354(3):507-519 (2005)), activator-like effector nucleases (TALENs) (Zhang et al., Nature Biotechnol., 29:149-153 (2011)), the CRISPR / Cas system (Cheng et al., Cell Res., 23:1163-71 (2013)), and engineered meganucleases (Riviere et al., Gene Ther., 21(5):529-32 (2014)). Nucleases create specific double-strand breaks (DSBs) at target sites in the genome and repair the induced breaks through homologous recombination (HR) and non-homologous end joining (NHEJ) using endogenous intracellular mechanisms. Using such techniques, inhibition of one or both CD39 and CD69 in isolated tumor-reactive T cells can be achieved. Accordingly, in embodiments of the present invention, one or both of the CD39 inhibitor and the CD69 inhibitor are CRISPR-Cas agents(s), zinc finger agents(s), or TALEN agents(s). TALEN agents(s) may include a transcription activator-like effector (TALE) that binds to the CD39 or CD69 gene and a TALEN. Zinc finger agents(s) may include a zinc finger nuclease that binds to the CD39 or CD69 gene.

[0043] In embodiments of the present invention, the method employs a CRISPR / Cas system. Accordingly, the method of the present invention may include introducing a nucleic acid encoding a Cas endonuclease and a nucleic acid encoding a single guide RNA (sgRNA) molecule into isolated tumor-reactive T cells, wherein the sgRNA hybridizes to the CD39 or CD69 gene in the isolated tumor-reactive T cells, and a complex is formed between the sgRNA and the Cas endonuclease such that the Cas endonuclease introduces a double-strand break into the CD39 or CD69 gene. Non-limiting examples of Cas endonucleases include Casl B, Cas2, Cas3, Cas4, Cas5, Cas6, Cas7, Cas8, Cas9 (also known as Csnl and Csxl2), CaslO, Csyl, Csy2, Csy3, Csel, Cse2, Cscl, Csc2, Csa5, Csn2, Csm2, Csm3, Csm4, Csm5, Csm6, Cmrl, Cmr3, Cmr4, Cmr5, Cmr6, Csbl, Csb2, Csb3, and Csxl7. Preferably, the Cas endonuclease is Cas9. Preferably, the sgRNA hybridizes specifically to the CD39 or CD69 gene, respectively, but not to any other gene that is not the CD39 or CD69 gene. Accordingly, in embodiments of the present invention, one or both of the CD39 inhibitor and the CD69 inhibitor may be a CRISPR-Cas agent(s). The CRISPR-Cas agent(s) may include a nucleic acid encoding a Cas endonuclease and a nucleic acid encoding a single guide RNA (sgRNA) molecule to isolated tumor-reactive T cells, the sgRNA hybridizing to the CD39 or CD69 gene in the isolated tumor-reactive T cells.

[0044] The method may further include deleting all or part of one or both of the CD39 and CD69 genes in order to reduce the expression of one or both of the CD39 and CD69 genes, respectively. The expression of one or both of the CD39 and CD69 genes may be reduced by any amount, for example, about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, or about 90%. Preferably, the expression of one or both of the CD39 and CD69 genes is reduced to the point where there is no detectable expression of one or both of the CD39 and CD69 genes, respectively.

[0045] In some embodiments, RNA interference (RNAi) is employed. In this regard, one or both of the CD39 inhibitor and the CD69 inhibitor may contain an RNAi agent. In embodiments, the RNAi agent may include small interfering RNA (siRNA), small hairpin miRNA (shMIR), microRNA (miRNA), or antisense nucleic acid.

[0046] sgRNA or RNAi agents, such as siRNA, shRNA, miRNA, and / or antisense nucleic acids, may contain overhangs; that is, not all nucleotides need to be bound to the target sequence. The sgRNA or RNAi nucleic acids employed may be at least about 19 nucleotides long, at least about 40 nucleotides long, at least about 60 nucleotides long, at least about 80 nucleotides long, at least about 100 nucleotides long, at least about 120 nucleotides long, at least about 140 nucleotides long, at least about 160 nucleotides long, at least about 180 nucleotides long, at least about 200 nucleotides long, at least about 220 nucleotides long, at least about 240 nucleotides long, about 19 to about 250 nucleotides long, about 40 to about 240 nucleotides long, about 60 to about 220 nucleotides long, about 80 to about 200 nucleotides long, about 60 to about 180 nucleotides long, about 80 to about 160 nucleotides long, and / or about 100 to about 140 nucleotides long.

[0047] sgRNA or RNAi agents, such as siRNA or shRNA, may be encoded by a nucleotide sequence contained within a larger nucleic acid construct, such as a cassette or a suitable vector. Examples of such vectors include lentiviral vectors and adenoviral vectors, as well as other vectors described herein in relation to other aspects of the present invention. Examples of suitable vectors are described in Aagaard et al. Mol. Ther., 15(5):938-45 (2007). When present as part of a larger nucleic acid construct, the resulting nucleic acid may be longer than the contained sgRNA or RNAi nucleic acid, for example, longer than about 70 nucleotides. In some embodiments, the RNAi agent employed cleaves the target mRNA. In other embodiments, the RNAi agent employed does not cleave the target mRNA.

[0048] Any type of suitable sgRNA, siRNA, miRNA, and / or antisense nucleic acid can be employed. In embodiments, the antisense nucleic acid includes a nucleotide sequence complementary to at least about 8 nucleotides, at least about 15 nucleotides, at least about 19 nucleotides, or about 19 to about 22 nucleotides of a nucleic acid encoding one or both of the mRNA and / or protein of CD39 or their complement (or one or both of the mRNA and / or protein of CD69 or their complement). In embodiments, the siRNA may include, for example, trans-acting siRNA (tasiRNA) and / or repeat-associated siRNA (rasiRNA). In another embodiment, the miRNA may include, for example, a small hairpin-type miRNA (shMIR).

[0049] In embodiments of the present invention, one or both of the CD39 inhibitor and the CD69 inhibitor may inhibit or downregulate to some extent the expression of the encoded proteins, for example, at the DNA, RNA, or other regulatory levels. In this regard, isolated tumor-reactive T cells containing a CD39 inhibitor either do not express one or both of the CD39 mRNA and / or the CD39 protein, or express only lower levels of one or both of the CD39 mRNA and / or the CD39 protein, compared to T cells without a CD39 inhibitor. Similarly, isolated tumor-reactive T cells containing a CD69 inhibitor either do not express one or both of the CD69 mRNA and / or the CD69 protein, or express only lower levels of one or both of the CD69 mRNA and / or the CD69 protein, compared to T cells without a CD69 inhibitor. According to embodiments of the present invention, the CD39 inhibitor or the CD69 inhibitor may target the nucleotide sequences of the CD39 gene or the CD69 gene, or the mRNA encoded by them, respectively. Examples of human CD39 and CD69 sequences are shown in Table 1. Other CD39 and CD69 sequences may also be employed in accordance with the present invention. Human and mouse antisense nucleic acids are commercially available (e.g., from OriGene Technologies, Inc. (Rockville, MD) or Sigma-Aldrich (St. Louis, MO)) and can be prepared using the nucleic acid sequences encoding the CD39 and CD69 proteins disclosed herein and routine techniques.

[0050] [Table 1]

[0051] According to embodiments of the present invention, CD39 inhibitors and CD69 inhibitors can target nucleotide sequences selected from the group consisting of the 5' untranslated region (5'UTR), 3' untranslated region (3'UTR), and coding sequence of CD39 and CD69, respectively, their complements, and any combination thereof. Any suitable target sequence of CD39 or CD69 can be employed. In embodiments of the present invention, the sequence of a CD39 inhibitor can be designed for human CD39 having the sequence of SEQ ID NO: 1, but can also recognize the sequence of SEQ ID NO: 3 (and vice versa). In embodiments of the present invention, the sequence of a CD39 inhibitor can be designed for any one of the seven CD39 nucleotide sequences listed in Table 1, but can also recognize any one or more of the other six nucleotide sequences listed in Table 1. CD39 and CD69 inhibitors can be designed for any suitable sequence of CD39 or CD69 DNA or mRNA, respectively.

[0052] In yet another embodiment of the present invention, one or both of the CD39 inhibitor and the CD69 inhibitor are agents(s) that epigenetically inhibit the expression of CD39 or CD69, respectively. Epigenetic regulation of gene expression includes changes in gene expression without changes in the underlying DNA sequence. Epigenetic regulation includes changing the phenotype without changing the genotype, which then affects how cells read genes.

[0053] Of course, the method of the present invention may use two or more CD39 inhibitors, and any of them may be the same or different from each other. Similarly, the method of the present invention may use two or more CD69 inhibitors, and any of them may be the same or different from each other.

[0054] Alternatively, by modifying isolated tumor-reactive T cells to induce and / or inhibit the expression of one or more of various markers, CD39 can be expressed in isolated tumor-reactive T cells. - CD69 -Phenotypes can be induced. In this regard, embodiments of the present invention include CD3 + CD39 - CD69 - The present invention provides a method for preparing a cell population enriched with phenotypic T cells, comprising isolating tumor-reactive T cells from a patient's tumor sample. Isolating tumor-reactive T cells from a patient's tumor sample can be carried out as described herein in relation to other aspects of the present invention.

[0055] The method is (i) the following marker: AHNAK + AL592183.1 + ANXA1 + ANXA4 + , AQP3 + ATM + , BIN1 + , C10orf54 + , C11orf21 + , C16orf54 + CCDC109B + CD27 + CD52 + CD55 + CD8B + CDC25B + CDC42SE1 + CLEC2B + CLICK3 + , CLUAP1 + CRBN + , CTD-3184A7.4 + , DDI2 + , DND1 + EMP3 + , EPB41 + , ERN1 + FAIM3 + FAM65B + FBXL8 + FGFBP2 + GADD45B + GIMAP4 + GIMAP7 + GPR155 + GZMM + HSD17B11 + IL10RA+ 、IL7R + 、ISG20 + 、KANSL1-AS1 + 、KLF2 + 、KLHL24 + 、LDLRAP1 + 、LEF1 + 、LGALS3 + 、LINC00861 + 、LITAF + 、LYAR + 、MAPKAPK5-AS1 + 、MED15 + 、MIAT + 、MIR142 + 、MXI1 + 、MYC + 、NEAT1 + 、NOSIP + 、ODF2L + 、P2RY8 + 、PDE6G + 、PIK3IP1 + 、PLEC + 、PLP2 + 、PPP2R5C + 、PXN + 、R3HDM4 + 、RAMP1 + 、RASA3 + 、RASGRP2 + 、RCBTB2 + 、RNASET2 + 、RP11-395B7.4 + 、RP11-539L10.2 + 、RP11-640M9.1 + 、S100A10 + 、S100A4 + 、S100A6 + 、S1PR1 + 、S1PR4 + 、SAMD3 + 、SELL + 、SH3BP5 + 、SIGIRR + 、SLAMF6 + 、SLCO3A1 + 、SORL1 + 、STK38 + 、SYNJ2+ , TCF7 + TIMP1 + TRADD + , TSC22D3 + ,TSPAN32 + TXNIP + , UBXN11 + VCL + , VNN2 + YPEL3 + ZFP36L2 + ZNF276 + , and ZNF683 + (ii) Induce the expression of one or more of the following; and / or (ii) the following marker: ACOT7 + ADAM19 + AGPAT9 + AGTRAP + , AIF1 + ASPM + , ATAD2 + AURKA + AURKB + BIRC3 + BIRC5 + BRCA1 + , C15orf48 + CASC5 + , CCL3 + CCNA2 + CCNB1 + CCNB2 + CCND2 + CD38 + CD40LG + CD69 + CD8B + CDC20 + ,CDCA3 + ,CDCA8 + CDK1 + CDKN3 + CDT1 + , CENPA + CENPE + , CENPF + CENPM + CENPW + , CEP55 + , CISH + CKS1B +、CKS2 + 、CLSPN + 、CRTAM + 、CSF2 + 、DLGAP5 + 、DUSP5 + 、DUSP6 + 、DUT + 、EGR1 + 、ENTPD1 + 、 FEN1 + 、GINS2 + 、GTSE1 + 、H2AFX + 、HIST1H4C + 、HLA + DQA2 + 、HMMR + 、IFI27 + 、IFNG + 、IL2RA + 、IL5 + 、KIAA0 + 、KIF1 + 、KIF2 + 、KPNA2 + 、KRT7 + 、MAD2L1 + 、MCM7 + 、MKI6 + 、MX1 + 、MYBL2 + 、NCAPG + 、NDC80 + 、NDFIP2 + 、NUDT1 + 、NUSAP1 + 、ORC6 + 、PBK + 、PCNA + 、PLK1 + 、RPL39L + 、RRM2 + 、SGOL2 + 、SHCBP1 + 、SMC2 + 、SPC25 + 、STMN1 + 、TESC + 、TK1 + 、TNF + 、TNFRSF1 + 、TNFSF10 +TOP2A + , TPM4 + , TPX2 + , TUBA1B + , TUBA1C + , TUBB + TYMS + UBE2C + UBE2S + UBE2T + XCL1 + , and ZWINT + Modifying isolated tumor-reactive T cells to inhibit the expression of one or more of the following markers: (i) induction of the expression of one or more of the markers and / or (ii) inhibition of the expression of one or more of the markers, thereby enabling the T cells to express CD3 + CD39 - CD69 -The method may further include inducing the phenotype to be present. In embodiments of the present invention, the method involves any two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty This may further include modifying isolated tumor-reactive T cells to induce the expression of 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100 cells. In embodiments of the present invention, the method may further include modifying isolated tumor-reactive T cells to induce the expression of all of the markers listed in paragraph (i) of this paragraph.In embodiments of the present invention, the method involves any two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty , 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100 isolated tumor-responsive T cells may further include modifying isolated tumor-responsive T cells to inhibit the expression of 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100. In embodiments of the present invention, the method may further include modifying isolated tumor-responsive T cells to inhibit all of the markers listed in (ii) of this paragraph. The induction and inhibition of the expression of one or more markers can be carried out by any of the various methods known in the art. For example, the expression of one or more markers in isolated tumor-reactive T cells can be induced and / or inhibited using genome editing techniques described herein in relation to other aspects of the present invention. In another embodiment of the present invention, isolated tumor-reactive T cells may be modified (e.g., by electroporation, transduction, or transfect) to contain nucleic acids encoding one or more markers. Nucleic acids encoding one or more markers may be contained in a recombinant expression vector.

[0056] In embodiments of the present invention, the method further comprises introducing nucleic acids encoding an exogenous TCR into cells in a population enriched with phenotypic T cells, under conditions that the cells express the exogenous TCR. “Exogenous” means that the TCR is not native to T cells (not naturally present in T cells). The exogenous TCR may be a recombinant TCR. A recombinant TCR is a TCR produced by recombinant expression of genes encoding the α, β, γ, and / or δ chains of one or more exogenous TCRs. A recombinant TCR may consist entirely of a polypeptide chain derived from a single mammalian species, or it may be a chimeric or hybrid TCR composed of amino acid sequences derived from TCRs from two different mammalian species. For example, an antigen-specific TCR may consist of a variable region derived from a mouse TCR and a constant region from a human TCR, resulting in the TCR being “humanized.” Any exogenous TCR having antigen specificity for a cancer antigen (e.g., a neoantigen) may be useful in the method of the present invention. TCRs generally consist of two polypeptides (i.e., polypeptide chains), such as the α-chain, β-chain, γ-chain, δ-chain, or a combination thereof. Such TCR polypeptide chains are known in the art. Cancer antigen-specific TCRs may contain any amino acid sequence, as long as they specifically bind to and immunologically recognize the cancer antigen or its epitope. Examples of exogenous TCRs that may be useful in the method of the present invention include, but are not limited to, those disclosed in U.S. Patent Nos. 7,820,174; 7,915,036; 8,088,379; 8,216,565; 8,431,690; 8,613,932; 8,785,601; 9,128,080; 9,345,748; 9,487,573; 9,879,065, and U.S. Patent Publication Nos. 2013 / 016167 and 2014 / 0378389, respectively, which are incorporated herein by reference.

[0057] In embodiments of the present invention, the method further comprises introducing a nucleic acid encoding a chimeric antigen receptor (CAR) into a population of T cells enriched with the phenotype, under conditions that the cells express the CAR. Typically, a CAR includes an antigen-binding domain of an antibody, such as a single-chain variable fragment (scFv), fused to the transmembrane and intracellular domains of the TCR. Thus, the antigen specificity of a CAR can be encoded by an scFv that specifically binds to a cancer antigen or its epitope. Any CAR having antigen specificity for a cancer antigen may be useful in the method of the present invention. Examples of CARs that may be useful in the method of the present invention include, but are not limited to, those disclosed in U.S. Patents No. 8,465,743, No. 9,266,960, No. 9,765,342, No. 9,359,447, No. 9,765,342, No. 9,868,774, No. 10,072,078, and No. 10,287,350, which are incorporated herein by reference.

[0058] The term "cancer antigen," as used herein, refers to any molecule (e.g., protein, polypeptide, peptide, lipid, carbohydrate, etc.) that is expressed or overexpressed solely or primarily by tumor cells or cancer cells, so that the antigen is associated with a tumor or cancer. Cancer antigens may also be expressed by normal cells, non-tumor cells, or non-cancer cells. However, in such cases, the expression of cancer antigens by normal cells, non-tumor cells, or non-cancer cells is not as robust as that by tumor cells or cancer cells. In this regard, tumor cells or cancer cells can overexpress or express antigens at significantly higher levels compared to the expression of antigens by normal cells, non-tumor cells, or non-cancer cells. Furthermore, cancer antigens may also be expressed by cells in different developmental or maturational stages. For example, cancer antigens may also be expressed by embryonic or fetal cells not typically found in adult hosts. Alternatively, cancer antigens may also be expressed by stem cells or progenitor cells not typically found in adult hosts. Examples of cancer antigens include mesoserine, CD19, CD22, CD30, CD70, CD276 (B7H3), gp100, MART-1, epidermal growth factor receptor variant III (EGFRVIII), vascular endothelial growth factor receptor 2 (VEGFR-2), TRP-1, TRP-2, tyrosinase, human papillomavirus (HPV) 16 E6, and HPV 16. Examples of cancer antigens include, but are not limited to, E7, NY-BR-1, NY-ESO-1 (also known as CAG-3), SSX-2, SSX-3, SSX-4, SSX-5, SSX-9, SSX-10, MAGE-A1, MAGE-A2, BRCA, MAGE-A3, MAGE-A4, MAGE-A5, MAGE-A6, MAGE-A7, MAGE-A8, MAGE-A9, MAGE-A10, MAGE-A11, MAGE-A12, HER-2, etc. In embodiments of the present invention, the cancer antigen may be a mutant antigen that is expressed or overexpressed by tumor cells or cancer cells, but not expressed by normal cells, non-tumor cells, or non-cancer cells. Examples of such cancer antigens include, but are not limited to, mutant KRAS and mutant p53.T cells with antigen specificity for cancer antigens can, advantageously, reduce or avoid cross-reactivity with normal tissues, which may occur when, for example, T cells with antigen specificity for rare histocompatibility antigens are used. In embodiments of the present invention, the cancer antigen is a neoantigen.

[0059] A cancer antigen may be an antigen expressed by any cell of any cancer or tumor, including the cancers and tumors described herein. A cancer antigen may be the cancer antigen of only one type of cancer or tumor, and as a result, the cancer antigen may be associated with or characteristic of only one type of cancer or tumor. Alternatively, a cancer antigen may be the cancer antigen of more than one type of cancer or tumor (for example, characteristic of ). For example, a cancer antigen may be expressed in both breast cancer cells and prostate cancer cells, and not at all in normal cells, non-tumor cells, or non-cancerous cells.

[0060] A cell population enriched with T cells having a phenotype containing an endogenous cancer antigen-specific TCR (e.g., a cancer neoantigen-specific TCR) can also be transformed, e.g., transduced or transfected, with one or more nucleic acids encoding an exogenous (e.g., recombinant) TCR or other recombinant receptor. Such exogenous receptors, e.g., TCRs, can confer further antigen specificity to the transformed T cells, in addition to the antigen to which the endogenous TCR is naturally specific. This can, but is not required, generate T cells with dual antigen specificity.

[0061] In embodiments of the present invention, an exogenous TCR or CAR-coding nucleic acid is introduced into any suitable recombinant expression vector. For the purposes of this specification, the term “recombinant expression vector” means a genetically modified oligonucleotide or polynucleotide construct comprising a nucleotide sequence encoding mRNA, protein, polypeptide, or peptide, such that when the vector is brought into contact with a host cell under conditions sufficient to cause the cell to express the mRNA, protein, polypeptide, or peptide, the cell can express the mRNA, protein, polypeptide, or peptide. The vectors of the present invention do not exist in nature as a whole. However, parts of the vectors may exist in nature. The recombinant expression vectors of the present invention may contain any type of nucleotide, including but not limited to DNA and RNA, such DNA and RNA may be single-stranded or double-stranded, may be synthesized or partially obtained from natural sources, and may contain natural, non-natural, or modified nucleotides. The recombinant expression vectors may contain nucleotide linkages that are present in nature or not present in nature, or both types of linkages. Preferably, nucleotides or internucleotide bonds that are not naturally present or have been modified do not interfere with the transcription or replication of the vector. Examples of recombinant expression vectors that may be useful in the method of the present invention include, but are not limited to, plasmids, viral vectors (retroviral vectors, gamma-retroviral vectors, or lentiviral vectors), and transposons. The vector is then, for example, Green and Sambrook, Molecular Cloning: A Laboratory Manual (4 thAs described in Ed., Cold Spring Harbor Laboratory Press (2012), the phage or viral vector can be introduced into an isolated population of T cells by any suitable technique, such as gene editing, transfection, transformation, or transduction. Many transfection techniques are known in the art, including, for example, calcium phosphate DNA coprecipitation; DEAE-dextran; electroporation; cationic liposome-mediated transfection; tungsten particle-enhanced microparticle guns; and strontium phosphate DNA coprecipitation. In many cases, infectious particles may be grown in suitable commercially available packaging cells, after which the phage or viral vector may be introduced into host cells.

[0062] In embodiments of the present invention, any of the methods of the present invention described herein further comprises increasing the number of cells in a population enriched with T cells having the phenotype obtained by the method. Increased T cell numbers can be achieved by any of numerous methods known in the art, such as those described in, for example, U.S. Patent No. 8,034,334; U.S. Patent Publication No. 8,383,099; U.S. Patent Application Publication No. 2012 / 0244133; Dudley et al., J. Immunother., 26:332-42 (2003); and Riddell et al., J. Immunol. Methods, 128:189-201 (1990). In embodiments, increased T cell numbers are achieved by culturing the T cells with one or more nonspecific T cell stimuli and one or more cytokines. Examples of nonspecific T cell stimulation include, but are not limited to, one or more of the following: irradiated allogeneic feeder cells, irradiated autologous feeder cells, anti-CD3 antibodies, anti-4-1BB antibodies, and anti-CD28 antibodies. In preferred embodiments, nonspecific T cell stimulation may be anti-CD3 antibodies and anti-CD28 antibodies conjugated on beads. The method of the present invention may use any one or more cytokines. Exemplary cytokines that may be useful for increasing cell numbers include interleukin (IL)-2, IL-7, IL-21, and IL-15. In embodiments, T cell number expansion is carried out by culturing the T cells with OKT3 antibody, IL-2, and feeder PBMCs (e.g., irradiated allogeneic PBMCs).

[0063] Embodiments of the present invention further provide isolated or purified cell populations obtained according to any of the methods of the present invention. Cell populations enriched with T cells having the phenotype produced by the methods of the present invention may provide one or more of the following advantages. CD39 produced by the methods of the present invention - CD69 - A cell population enriched with T cells exhibiting the phenotype is CD39 + CD69 +Compared to T cells, it can result in one or more of the following in the patient's body: self-regeneration, expansion, persistence, and increased antitumor response.

[0064] The term "isolated," as used herein, means extracted from its natural environment. The term "purified," as used herein, means increased purity, and "purity" is a relative term and is not necessarily to be interpreted as absolute purity. For example, purity may be at least about 50%, and may be greater than about 60%, greater than about 70%, or greater than about 80%, greater than about 90%, and may be about 100%.

[0065] A cell population enriched with T cells having the phenotype produced by the method of the present invention may be a substantially homogeneous population mainly consisting of T cells produced by any of the methods of the present invention described herein. A cell population enriched with T cells having the phenotype produced by the method of the present invention may also be a clonal population of cells in which all cells in the population are clones of single T cells. In one embodiment of the present invention, the cell population is a clonal population comprising T cells containing a recombinant expression vector encoding an exogenous TCR or CAR as described herein.

[0066] A cell population enriched with phenotypic T cells prepared by any of the methods of the present invention described herein is intended to be included in a composition such as a pharmaceutical composition. In this regard, embodiments of the present invention provide a method for obtaining a pharmaceutical composition comprising a cell population enriched with phenotypic T cells, comprising: obtaining a cell population enriched with phenotypic T cells according to any of the methods of the present invention described herein; and combining the cell population enriched with phenotypic T cells with a pharmaceutically acceptable carrier to obtain a pharmaceutical composition comprising the cell population enriched with phenotypic T cells.

[0067] Preferably, the carrier is a pharmaceutically acceptable carrier. With respect to the pharmaceutical composition, the carrier may be any of those conventionally used for administering cells. Such pharmaceutically acceptable carriers are well known to those skilled in the art and are readily available to the public. Preferably, the pharmaceutically acceptable carrier does not have harmful side effects or toxicity under the conditions of use.

[0068] The choice of carrier may be partially determined by the specific method used to administer the cell population enriched with phenotypic T cells. Therefore, a variety of suitable formulations of the pharmaceutical composition of the present invention exist. Suitable formulations may include those administered parenterally, subcutaneously, intravenously, intramuscularly, intra-arterially, subarachnoidally, intratumorally, or intraperitoneally. More than one route may be used to administer the cell population enriched with phenotypic T cells, and in certain cases, a particular route may provide a more immediate and effective response than another.

[0069] Preferably, the cell population enriched with phenotypic T cells is administered by injection, for example, intravenously. Suitable pharmaceutically acceptable carriers for cells for injection may include any isotonic carrier, for example, ordinary saline (about 0.90% w / v NaCl in water, about 300 mOsm / L NaCl in water, or about 9.0 g NaCl per liter of water), NORMOSOL R electrolyte solution (Abbott, Chicago, IL), PLASMA-LYTE A (Baxter, Deerfield, IL), about 5% dextrose in water, or Ringer's lactate solution. In embodiments, the pharmaceutically acceptable carrier is supplemented with human serum albumen.

[0070] For the purposes of the present invention, the administered dose, for example, the number of T cells, must be sufficient to produce, for example, a therapeutic or prophylactic response in a mammal over an appropriate time frame. For example, the number of T cells administered must be sufficient to bind to cancer antigens or treat or prevent cancer for a period of about two hours or more from the time of administration, for example, 12 to 24 hours or longer. In certain embodiments, the period may be even longer. The number of T cells administered is determined, for example, by the potency of a particular population of T cells administered, the condition of the mammal (e.g., human), and the body weight of the mammal being treated (e.g., human).

[0071] Many assays for determining the number of T cells to be administered are known in the art. For the purposes of the present invention, the starting number to be administered to a mammal can be determined using an assay that involves comparing the extent to which target cells lyse or one or more cytokines, such as IFN-γ and IL-2, are secreted when a given number of such T cells are administered to a mammal, between sets of mammals each given different numbers of T cells. The extent to which target cells lyse or cytokines, such as IFN-γ and IL-2, are secreted when a particular number is administered can be assayed by methods known in the art. The secretion of cytokines such as IL-2 can also provide an indicator of the quality (e.g., phenotype and / or efficacy) of the T cell preparation.

[0072] The number of T cells administered is also determined by the presence, nature, and extent of any adverse side effects that may accompany the administration of a particular population of T cells. Typically, the attending physician determines the number of T cells to treat each individual patient by considering various factors, such as age, weight, overall health, diet, sex, route of administration, and the severity of the condition being treated. As an example, and not intended to limit the present invention, the number of T cells administered per infusion is approximately 10 × 10 6 ~About 10×10 11 Cells, approximately 10 x 10 per injection 9 Cells ~ approx. 10×10 11 Cells, or 10 x 10 per injection. 7~About 10×10 9 It can be a cell.

[0073] A cell population enriched with T cells having the phenotype produced according to the method of the present invention is intended to be used in methods for treating or preventing cancer in mammals. In this regard, the present invention provides a method for treating or preventing cancer in a mammal, comprising administering to the mammal an amount effective for treating or preventing cancer in the mammal either of the pharmaceutical composition described herein or a population of T cells.

[0074] Embodiments of the present invention further include lymphocyte depletion in mammals before administering T cells. Examples of lymphocyte depletion include, but are not limited to, non-myeloablative lymphocyte depletion chemotherapy, myeloablative lymphocyte depletion chemotherapy, and total body irradiation.

[0075] The terms “treat” and “prevent,” and any terms derived therefrom, as used herein, do not necessarily imply 100%, i.e., complete treatment or prevention. Rather, there are varying degrees of treatment or prevention that those skilled in the art would recognize as having potential benefits or therapeutic effects. In this regard, the methods of the present invention can provide treatment or prevention of any amount or level of cancer in mammals. Furthermore, the treatment or prevention provided by the methods of the present invention may include treatment or prevention of one or more conditions or symptoms of the disease being treated or prevented, for example, cancer. Also, for the purposes of this specification, “prevention” may include delaying the onset or recurrence of the disease or its symptoms or conditions.

[0076] For the purposes of the method of the present invention, in which a population of T cells is administered, the T cells may be allogeneic or self cells to the mammal. Preferably, the cells are self to the mammal.

[0077] With respect to the method of the present invention, cancers include leukemia (e.g., B-cell leukemia), sarcoma (e.g., synovial sarcoma, osteogenic sarcoma, uterine leiomyosarcoma, and alveolar rhabdomyosarcoma), lymphoma (e.g., Hodgkin lymphoma and non-Hodgkin lymphoma), hepatocellular carcinoma, glioma, head and neck cancer, acute lymphoblastic carcinoma, acute myeloid leukemia, bone cancer, brain cancer, breast cancer, cancer of the anus, anal canal, or anorectum, eye cancer, intrahepatic bile duct cancer, joint cancer, cancer of the neck, gallbladder, or pleura, cancer of the nose, nasal cavity, or middle ear, and oral cavity. Any cancer may include any of the following: cancer of the genitals, cancer of the vulva, chronic lymphocytic leukemia, chronic myeloid cancer, colon cancer (e.g., colon carcinoma), esophageal cancer, cervical cancer, gastrointestinal carcinoid tumor, hypopharyngeal cancer, laryngeal cancer, liver cancer, lung cancer, malignant mesothelioma, melanoma, multiple myeloma, nasopharyngeal cancer, ovarian cancer, pancreatic cancer, cancer of the peritoneum, retinoplasm, and mesentery, pharyngeal cancer, prostate cancer, rectal cancer, kidney cancer, small intestine cancer, soft tissue cancer, stomach cancer, testicular cancer, thyroid cancer, ureteral cancer, and bladder cancer.

[0078] CD39 as described in this specification - CD69 - The T cell phenotype may be useful for selecting a therapy for cancer patients. Accordingly, embodiments of the present invention provide a method for selecting a therapy for cancer patients. This method may include obtaining a bulk population of T cells from a patient's tumor sample. The bulk population of T cells can be obtained from a tumor sample as described herein with respect to other embodiments of the present invention.

[0079] The method involves measuring the proportion of T cells exhibiting a phenotype in a bulk population, wherein the phenotype is identified by the marker CD3. + CD39 - , and CD69 - The method may further include: including; comparing the proportion of T cells having the phenotype with a control. Alternatively, the method may further include: measuring the proportion of T cells without the phenotype in a bulk population, wherein the phenotype is marker CD3 + CD39 - , and CD69 -This may further include; (comparing the proportion of phenotypic T cells to a control. The control may be, for example, the results of a previous study to determine the proportion of phenotypic (or phenotypic) T cells in the infusion product administered to responders and non-responders to ACT in a specific cohort, e.g., a specific cancer type. For example, as shown in Figure 1E, a median of 10% or more (of phenotypic T cells in the whole population) can be defined as a precursor to a potential response to ACT (as seen in complete responders) for melanoma. As shown in Figures 5A-5C, the patient's infusion product can be classified with an accuracy of over 80%. The proportion of phenotypic T cells in the bulk population can be measured using any preferred method known in the art, e.g., flow cytometry.

[0080] The method may further include (i) selecting a non-immunotherapy therapy for the patient if the proportion of phenotypic T cells is lower than that of the control or (ii) selecting a non-phenotypic T cell therapy for the patient if (i) the proportion of phenotypic T cells is higher than that of the control or (ii) the proportion of non-phenotypic T cells is lower than that of the control.

[0081] Another embodiment of the present invention provides a method for treating cancer in a patient. The method may include receiving identification information of a therapy selected for a cancer patient, wherein the therapy is selected by any of the methods described herein in relation to other aspects of the present invention. The method may further include (I) treating the patient by administering to the patient an effective amount of non-immunotherapy therapy to treat cancer in the patient if (i) the proportion of phenotypic T cells is lower than that of the control or (ii) the proportion of non-phenotypic T cells is equal to or greater than that of the control; or (II) treating the patient by administering to the patient an effective amount of immunotherapy to treat cancer in the patient if (i) the proportion of phenotypic T cells is equal to or greater than that of the control or (ii) the proportion of non-phenotypic T cells is lower than that of the control. The control may be as described herein in relation to other aspects of the present invention.

[0082] Immunotherapy encompasses the treatment of cancer by activating or suppressing the immune system. Examples of immunotherapy include, but are not limited to, NK cell therapy, T cell therapy, B cell therapy, immune checkpoint inhibitor therapy, chimeric antigen receptor (CAR) therapy, antibody therapy, immune system modulator therapy, anti-cancer vaccine therapy, or any combination thereof. In embodiments of the present invention, immunotherapy is ACT, immune checkpoint inhibitor therapy, immune system modulator therapy, T cell therapy, and / or CAR therapy.

[0083] Examples of non-immunotherapy therapies include, but are not limited to, surgical resection, chemotherapy, radiotherapy, stem cell therapy, hormone therapy, targeted drug inhibitor therapy, or any combination thereof.

[0084] Another embodiment of the present invention provides a method for predicting the clinical response to immunotherapy in cancer patients. The method may include obtaining a bulk population of T cells from a tumor sample from a cancer patient. The bulk population of T cells can be obtained from a tumor sample as described herein in relation to other aspects of the present invention. The method involves measuring the proportion of (i) phenotypic T cells or (ii) phenotypic T cells in the bulk population, wherein the phenotypic marker CD3 + CD39 - , and CD69 - It may further include the following, which can be implemented as described herein in relation to other aspects of the present invention.

[0085] The method may further include comparing (i) the proportion of phenotypic T cells or (ii) the proportion of non-phenotypic T cells with a control. The method may further include identifying that a patient is likely to have a negative clinical response to immunotherapy if (i) the proportion of phenotypic T cells is lower than that of the control or (ii) the proportion of non-phenotypic T cells is equal to or greater than that of the control. Conversely, the method may include identifying that a patient is likely to have a positive clinical response to immunotherapy if (i) the proportion of phenotypic T cells is equal to or greater than that of the control or (ii) the proportion of non-phenotypic T cells is lower than that of the control. The control may be as described herein with respect to other aspects of the present invention.

[0086] Methods for predicting the clinical response to immunotherapy in cancer patients may be performed before administering immunotherapy to the patient, after administering immunotherapy to the patient, or both before administering immunotherapy and after administering immunotherapy again to the patient.

[0087] The following embodiments further illustrate the present invention, but should not be construed as limiting its scope in any way. [Examples]

[0088] The experiments described in Examples 1 to 5 used the following materials and methods.

[0089] TIL fabrication Metastatic melanoma tumor deposits are surgically removed, as described above, 2-3 mm 3 Tumor-infiltrating lymphocytes (TILs) were prepared by enzymatic digestion of tumor fragments or by plate culture using single-cell suspensions (Tran et al., J.Immunother., 31:742-751 (2008); Dudley et al., J.Immunother., 26:332-342 (2003)). Clinical injection products were prepared by rapidly amplifying lymphocytes in culture with irradiated PBMCs, anti-CD3 antibody, and IL-2, as described above (Jin et al., J.Immunother., 35:283-292 (2012)).

[0090] Clinical Protocol All patients were enrolled in one of the following experimental TIL protocols using IL-2 ACT approved by the National Cancer Institute (NIH) Institutional Review Board (NCT00001832, NCT00513604, NCT01319565, NCT01468818, NCT01585415, or NCT01993719) (Goff et al., J. Clin. Oncol., 34:2389-2397 (2016); Dudley et al., Clin. Cancer Res., 16:6122-6131 (2010)). Informed consent was obtained and documented in accordance with the principles of the Declaration of Helsinki. Patients were required to be at least 18 years of age, have measurable metastatic melanoma, have a good performance status, be free from systemic infections, have not previously received anti-PD-1 targeted therapy, and be eligible for high-dose IL-2 therapy. Furthermore, for NCT01319565, patients were required to be eligible for total body irradiation (TBI). The protocol consisted of a non-myeloablative chemotherapy regimen of cyclophosphamide and fludarabine followed by a single infusion of autologous TILs and high-dose IL-2 for tolerance. A subset of patients received TBI during chemotherapy prior to cell infusion. Baseline cross-sectional imaging was performed, and disease progression was assessed at regular intervals using the Guidelines for Assessment of Treatment Response in Solid Tumors (RECIST) 1.0. Outcomes were classified as complete response (CR), partial response (PR), or disease progression (NR).

[0091] Explanation of the study cohort Patient samples were collected from clinical protocols based on clinical response and sample availability. To eliminate the influence of previous immunotherapy on the differentiation state of CD8 TILs, patients with a history of anti-PD-1 immunotherapy or genetically engineered T-cell or TCR therapy were excluded. Patients with a history of anti-CTLA-4 therapy were not excluded. In this study, any patient who experienced a complete response according to the RECIST 1.0 criteria was defined as a complete responder (CR). Among patients whose disease progressed after ACT (NR, non-response), non-response patients were strictly identified as a subset of patients with disease progression whose target lesion did not decrease by 10% or more at any point after ACT, in order to exclude tumor-endogenous treatment failure (e.g., acquired resistance due to treatment). Accordingly, patients with stable disease and partial responses were excluded from this study according to these criteria, resulting in 54 patients with viable TIL injection product samples available for analysis. The clinical endpoints used in the analysis were progression-free survival (PFS) or melanoma-specific survival (MSS), defined as the length of time from the start of treatment to an event (melanoma, death due to melanoma, or censoring).

[0092] Mass cytometry staining Frozen patient ACT injection product samples were thawed in warm medium and incubated overnight in TIL medium (RPMI containing 10% human serum, L-glutamine, and Anti-Anti) with DNAse, without cytokines. Unless otherwise noted, all reagents were from Fluidigm. First, cells were incubated with 5 μM cisplatin in PBS and dead cells were marked. Then, the cells were washed and resuspended in MAXPAR cell staining buffer (MCSB) (Fluidigm (South San Francisco, CA)). Human Fc receptor blocking solution (BioLegend (San Diego, CA)) was added to each sample and incubated at room temperature for 10 minutes. All metal-labeled antibodies were purchased from Fluidigm. Biotin anti-human CD39 (BioLegend) was detected using Qdot-streptavidin conjugate (Thermo Fisher Scientific (Waltham, MA)). Antibodies were diluted with MCSB at verified concentrations to minimize channel overflow, added to each sample, and incubated at room temperature for 30 minutes. Primary staining consisted of anti-CD39, and secondary staining consisted of all other metal coupling antibodies + streptavidin (QDOT-streptavidin conjugate). After each staining, cells were washed twice with MCSB. The cells were then fixed in a 1.6% solution of paraformaldehyde (MilliporeSigma (Burlington, MA)) in PBS for 15 minutes. A cell intercalation solution was prepared by adding CELL-ID Intercalator-Ir to MAXPAR Fix and Perm Buffer (Fluidigm) to a final concentration of 125 nM. The cells were incubated with the cell intercalation solution overnight at 4°C. The cells were then washed once with MCSB and twice with MAXPAR Water (Fluidigm). Finally, the cells were immersed in an aqueous solution containing EQ Four Element Calibration Beads (Fluidigm) for 10 minutes. 6The cells were resuspended at a cell concentration of cells / mL, filtered immediately before acquiring CyTOF data, and placed in a cell strainer cap tube. As previously described (Bendall et al., Science. 332:687-696 (2011)), data were acquired using a HELIOS mass cytometer (Fluidigm).

[0093] Calculation methods and statistical analysis of mass cytometry data The raw mass cytometry data was normalized using the Normalizer algorithm as recommended by the software developers (Finck et al., Cytometry A., 83:483-494 (2013)). The data was analyzed using a custom Python script pipeline (available at github.com / Immunodynamics / CyTOF-Processing). First, [EQ4 - cisplatin - ] and [CD45 + Data were gated as live singlet cells using a manual DNA(2n) gate (Figure 5A). Hierarchical aggregate learning (HAL-x) was used for automated identification of surface antigens based on surface marker expression. A custom-programmed algorithm (Figure 5B) relied on cross-validated identification of high-density regions, followed by fast random forest classification in a 35-dimensional space of surface markers to identify 22 leukocyte populations with characteristic patterns of surface marker expression. All cells in all samples were then labeled with HAL-x, and the inverse hyperbolic sine and normalized frequencies of each leukocyte population were calculated. A support vector machine (SVM) algorithm used TIL features to determine whether samples were taken from patients with complete response (CR) or disease progression (NR) (Figure 5C and Table 2). Table 2 shows the confusion matrix for classifying patients for clinical outcome based on the phenotype of T cell injection products (n=1700 runs). True positive (TP), true negative (TN), false positive (FP), false negative (FN). Subsequent analysis prioritized the 10 activation features best suited to classifying patients by SVM according to clinical outcome.

[0094] [Table 2]

[0095] Visualization of mass cytometry clusters To visualize CyTOF data obtained from 4.8 million cells acquired from patient-injected products, FLOWJO v10.5.3 software (Ashland, OR) was used. The data was then processed using raw DNA. + cisplatin - CD45 + Gating is performed on the cells, and then CD3 + Cellular gating was performed to obtain 2.5 million ligated T cells. Ligated T cells from all patient samples were downsampled to 100,000 cells using FLOWJO software, and the gate and sample ID were retransmitted to the downsampled population. Dimensionality reduction and clustering were performed using the Barnes-Hut approximation plugin of the program with the following parameters: perplexity: 90, learning speed (ETA): 200, iterations: 1000, theta: 0.5, and visualized in t-SNE space (van der Maaten et al., Machine Learning, 87: pp.33-55 (2012)). Clusters were defined using the automatic gating function of FLOWJO software. Cluster frequencies and protein expression for each marker were exported, z-scaled, and analyzed using the R v3.5.2 statistical environment (RCTeam, R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing (2014)).

[0096] Flow cytometry phenotyping Frozen patient ACT IP samples were thawed and left to stand overnight in TIL medium (RPM1 containing 10% human serum) and DNAse without cytokines. IP cells were washed once with staining buffer (1×PBS, 0.5% BSA, 2 mM EDTA) and stained with the following antibodies: anti-CD3-APC-Cy7 (SK7; 1:25, BD Biosciences (Franklin Lakes, NJ)), anti-CD8-PE-Cy7 (RPA-T8; 1:300, BD Biosciences), anti-CD4-BV785 (OKT4; 1:66, BioLegend (San Diego, CA)), anti-CD39-FITC (A1; 1:200, BioLegend), anti-CD69-APC (FN50; 1:25, BD Biosciences (Franklin Lakes, NJ)), anti-PD-1-AF700 (EH12.2H7; 1:66, BioLegend), anti-TIM3-BV650 (7D3; 1:100, BD Anti-CD62L-BV421 (DREG-56; 1:50, BioLegend), anti-SLAMF6-PE (hSF6.4.20, 1:50, BD Biosciences), anti-CD27-BV421 (O323, 1:50, BioLegend). Samples were incubated on ice for 30 minutes, then washed twice and resuspended in a staining buffer containing propidium iodide to remove dead cells. For intracellular staining of TCF7, IPTIL was washed twice and fixed using TRUE-NUCLEAR Transcription Factor Buffer Set (BioLegend) according to the manufacturer's instructions. Fixed and permeabilized cells were washed once, then anti-TCF7-PE antibody (1:50) was added over 20 minutes, washed twice, and subsequently stained with antibodies for CD3, CD8, CD39, and CD69 as described above. For neoantigen tetramer phenotyping, samples were first stained with the tetramer, and then the antibodies described above were added. Samples were acquired using a BD LSRFORTESSA cell analyzer (BD Biosciences) and analyzed with FLOWJO v10.5.3 software. The frequencies of DN, DP, SP, and individual markers were obtained for each patient in two independent biological replications, and CR and NR were compared using the median %.

[0097] statistical analysis In the visual cluster comparison of CyTOF, the normality of the response and non-response clusters was first assessed using the Shapiro-Wilk test, and then their frequencies were compared using the Wilcoxon rank-sum test. The p-values ​​were corrected using Bonferroni multiple correction, and statistical significance was examined with α set to 0.05. All flow cytometry comparisons between CR and NR IP were similarly analyzed using the two-sided Wilcoxon rank-sum test at the standard statistical significance level. * P<0.05 ** P<0.01 *** P<0.001 **** P<0.0001. Survival analysis (PFS and MSS) was performed using the Kaplan-Meier estimator. Briefly, the median number of T cells injected in IP was first calculated by considering all reported injected cells as CD3. + The total number of CD8 cells, DN cells, and DP cells injected for each patient was estimated by adjusting for the percentage of cells, and then calculating the CD8, DN, and DP percentages of CD3. High cell counts were stratified against low cell counts using the median (Figures 1A-1I), and low, medium, and high cell counts were stratified for each population using cell count ternaries (Figures 6A-6C). The statistical significance of survival distributions between patients was determined by calculating the hazard ratio (HR) using the Mantel-Cox log-rank test. Marker expression and precursor-daughter populations of DN vs. DP were compared using paired t-tests, and multicytokine comparisons were performed using paired Wilcoxon tests. In tetramer analysis, all neoantigen-specific TILs were tetramerized. + Subgroup analysis of CR vs. NR was performed using a Wilcoxon rank-sum test adjusted for Bonferroni correction to compare multiple groups, with values ​​expressed as a percentage. In differential gene expression analysis, the DEG gene was determined using a false detection rate (FDR) of 0.1. Since the scRNA data is nonparametric, all CR vs. NR comparisons were performed using a Wilcoxon rank-sum test corrected for the total number of cells being compared.

[0098] TCR CDR3 was compared among patients using the Wilcoxon rank-sum test. All statistical analyses were performed using Graphpad v7.05 or R Statistical Computing environment v3.5.2 (rproject.org) (RCTeam, R, listed above).

[0099] Identification of neoantigens Whole exome sequencing (WES) Genomic DNA (gDNA) was purified from fresh tumor (FrTu) and corresponding normal cells from autologous apheresis samples using the Qiagen ALLPREP DNA / RNA kit (Qiagen (Venlo, Netherlands)) as suggested by the manufacturer. Whole exome libraries were prepared and sequenced by Novogene (Novogene Corporation Inc. (Sacramento, CA)) using the Agilent SureSelectXT2 Human All Exon V6 Kit for exome capture and the Illumina platform for paired-end sequencing of 2 × 150 bp with approximately 200X on-target coverage. Additional WES for patient tumors was performed on tumor tissue and normal peripheral blood cells by Personal Genome Diagnostics, Broad Institute, and Surgery Branch, NCI, as previously described (Parkhurst et al., Cancer Discov., 9:1022-1035 (2019)).

[0100] Exome variant callingAlignment was performed on the human genome build hg19 using Novocraft's Novoalign MPI. Duplicates were marked using Picard's MarkDuplicates tool. Indel realignment and base recalibration were performed according to the GATK best practice workflow. Variants were called using Varscan2, SomaticSniper, Strelka, and Mutect. After variant calling, the VCF files were merged using the GATK CombineVariants tool and annotated with Annovar. The parameters for retaining called variants are as follows: tumor and normal coverage >10, variant allele frequency >7%, variant read count >4, and mutation identified in 2 out of 4 callers.

[0101] RNA-seq alignment, processing, and variant calling: Alignment was performed on the human genome build hg19 using the STAR 2-pass method. Duplicates were marked and sorted using Picard's MarkDuplicates tool. Next, reads were split and trimmed using the GATK SplitNTrim tool, and indel realignment and base recalibration were performed as was done for the whole exome data. Finally, a pileup file was created using the recalibrated bam file, and variants were called using only Varscan2.

[0102] Tandem mini-genetic screening:Minigenes designed as 25mers containing tumor mutations, centered on mutant amino acids, were linked to TMGs (10-12 TMGs per patient, 15-20 minigenes per TMG), and synthesized in pcRNA6SL plasmids (Genscript, NJ) for in vitro RNA transcription (mMESSAGE IVT Kit, Thermo Fisher Scientific) used to identify neoantigens, as previously described (Robbins et al., Nat. Med., 19:747-752 (2013); Parkhurst et al., Cancer Discov., 9:1022-1035 (2019); Tran et al., Science, 344:641-645 (2014); Zacharakis et al., Nat. Med., 24:724-730 (2018)). In short, as previously described (Parkhurst et al., Cancer Discov., 9:1022-1035 (2019); Zacharakis et al., Nat. Med., 24:724-730 (2018)), immature dendritic cells (DCs) or CD40L-activated B cells derived from the patient were generated, and individual TMG RNAs (BTX or Lonza 4D) were electroporated and treated to display one of the patient's class I HLA molecules. The patient's IP or pre-injection TILs were thawed, left to stand overnight in TIL medium containing IL-2, and then co-cultured with autologous APCs on which RNA had been electroporated the following day. Recognition was evaluated as previously described (Parkhurst et al., Cancer Discov., 9:1022-1035 (2019)) via interferon-gamma cytokine release by ELISpot and TIL activation by 4-1BB expression. If a positive TMG "hit" was obtained in the preliminary screening, the minimal peptides of candidate HLA class I predicted from each promising TMG hit were synthesized in-house using fmoc chemistry. Self-APCs were peptide-pulsed in independent deconvolution experiments (10 μg / mL), and candidate minimal neoepitopes were identified by evaluating the activation of IFNγ ELISpot and 4-1BB TIL.Next, the most promising minimal peptides were resynthesized with HPLC purity (Genscript, NJ) and used for HLA-restricted mapping (described later) and mutation specificity. Mutation specificity was tested by peptide pulses of HPLC-grade wild-type and mutant peptides at different concentrations into autologous APCs, followed by co-culture with TIL or TCR transducer cells to determine the titration of the mutant and wild-type peptides (Figures 11A-11B).

[0103] HLA constraint mapping Rapid transfection of COS7 cells was performed using patient-specific HLA class I alleles. Gene plasmids for individual HLA alleles (100 ng / well) and tandem minigenes (TMG) containing mutant neoantigens (50 ng / well) were combined with LIPOFECTAMINE 2000 transfection reagent (Thermo Fisher Scientific) in flat-bottom 96-well plates. 3 × 10⁶ cells were added to culture medium containing DMEM, 10% fetal bovine serum, L-glutamine, penicillin / streptomycin, and amphotericin B. 4 COS7 cells were added to plates at a cell / well ratio. Cells were incubated overnight at 37°C. The following day, cells were washed with PBS, triedpsinized, and T cells (2 × 10⁶) were treated with RPMI1640, 10% human serum, HEPES, L-glutamine, and penicillin / streptomycin. 4 The cells were transferred to ELISpot plates coated with an IFNγ capture antibody for co-culture with cells (per well). After co-culture overnight at 37°C, T cell responses were measured using flow cytometry to assess IFNγ secretion and CD137 / 4-1BB expression.

[0104] Neoantigen TIL phenotyping After demonstrating mutation specificity by peptide titration of T cell populations between mutants and wild-types, neoantigen-specific T cells were detected in the IP as described above (Kvistborg et al., Sci.Transl.Med., 6:254ra128 (2014)), and phenotyping was performed using UV-exchangeable HLA monomers for the corresponding HLA. Briefly, neopeptides with confirmed mutation specificity were generated in-house, and 45 μg of neopeptide in a 50 μL volume was co-incubated with 10 μg of UV-exchangeable biotinylated HLA monomer under 366 nm UV light for 90 minutes in the dark on ice. After neopeptide exchange, 10 μL of streptavidin-PE (Thermo Fisher Scientific) and 10 μL of streptavidin-APC or streptavidin-BV421 (BioLegend) were added over 2 hours, increasing by 2 μL every 15 minutes, to produce HLA tetramers. Next, the neoepitope tetramer was centrifuged at 2000 rpm for 5 minutes before use. For tetramer staining, the cells were first resuspended over 30 minutes in 100 μL of AIM-V medium (Gibco) and 1 mM dasatinib (Thermo Fisher Scientific), followed by 30 minutes on ice in 0.5-2 μL of tetramer in PE and APC per 100 μL of staining buffer. The tetramer-stained cells were washed once, and then the phenotyping antibody was stained and obtained as described above. Using a dichromatic tetramer, the tetramer was obtained. + The event and neoantigen gate settings were obtained. (Tetramer) - PE was used for phenotyping along with other antibodies. Although Pt.3713 contained other neoantigens that could not be tetramerized, the identified NeoTCR is shown in S11. To normalize the neoantigen TILs of CR and NR for phenotypic comparison, the phenotype of the neoantigen TIL was tetramerized. + It was expressed as a percentage of the event.

[0105] Identification of neoantigen-specific TCRs (NeoTCRs) Neoantigen-specific tetramers from IP +TILs were sorted using an SH800S cell sorter or an MA900 multi-application cell sorter (Sony Biotechnologies (San Jose, CA)) and placed in 96-well plates with lysis buffer. NeoTCRs were identified using one of the following two methods.

[0106] scRT-PCR :tetramer + Alternatively, TMG-reactive TILs were directly sorted into an RT-PCR buffer containing gene-specific primers targeting the constant regions of human TCRα and TCRβ, as well as a forward primer pool of the TCRα and TCRβ families, as previously reported (Pasetto et al., Cancer Immunol. Res., 4:734-743 (2016)). Briefly, an RT-PCR master mix was developed using the CELLSDIRECT one-step qRT-PCR kit (Thermo Fisher Scientific) with 20 cycles of 15 minutes at 50°C, 2 minutes at 95°C, 15 seconds at 95°C, and 30 seconds at 60°C. A second PCR was performed individually for TCRA and TCRB using nested TCRA and TCRB family primers in a touchdown PCR program. Using the PLATINUM II Hot-Start PCR Master Mix (Thermo Fisher Scientific), 3 μL of the initial RT-PCR product was added as a template to a total of 25 μL of PCR mix. The touchdown cycling conditions were 95°C for 5 minutes; a higher temperature annealing cycle (94°C for 30 seconds, 60°C for 15 seconds, 72°C for 30 minutes x 5 cycles); a high temperature annealing cycle (94°C for 30 seconds, 55°C for 15 seconds, 72°C for 30 minutes x 5 cycles); a low temperature annealing cycle (94°C for 30 seconds, 50°C for 15 seconds, 72°C for 30 minutes x 40 cycles); 72°C for 10 minutes; and 4°C. The PCR products were purified and sequenced by Sanger sequencing using internally nested Cα and Cβ region primers by Beckman Coulter (Schaumburg, IL).

[0107] scTCR Profiling Kit :Following the manufacturer's instructions, use either the SMARTER Human scTCR a / b Profiling Kit (Takara Bio (Shiga, Japan), catalog number 634432) or the Takara SMARTER Human scTCR a / b Profiling Kit-96 (Takara Bio, USA) to tetramerize + Alternatively, TMG-reactive TILs were selected and placed in plates. Briefly, single cells were selected from the target population and placed in the wells of a 96-well plate, and subjected to cDNA synthesis and amplification using SMART technology to incorporate cell barcodes. cDNA corresponding to TCRα and TCRβ transcripts was further amplified and prepared for sequencing using an Illumina MiSeq instrument. Sequencing was performed using paired-end 2×300bp reads with MiSeq Reagent Kit v3 (600 cycles) (Illumina, San Diego, CA, MS-102-3003). Read extraction and clonality count were determined using the MiXCR package (milaboratory.com / software / mixcr / ). From both methods, as described above (Pasetto et al., Cancer Immunol. Res., 4:734-743 (2016); Parkhurst et al., Clin. Cancer Res. 23:2491-2505 (2017)), reconstituted TCRs were obtained in pMSGV1 vectors with constant TCRs in mice transduced into healthy donor PBLs for in vitro testing of wild-type and mutant neoepitopes.

[0108] Single-cell transcriptome analysis Single-cell capture and library preparationCells were pelleted at 300 g for 5 minutes at 4°C in a swing-bucket centrifuge, and the TILs were washed twice with PBS containing 0.04% BSA. Cell viability was counted using an AO-PI with a LUNAFL automated fluorescence cell counter (Logos Biosystems (South Korea)). Cells were fractionated and lysed, and then reverse transcription of mRNA was performed with single-cell barcoding using a Chromium controller equipped with either 3' gene expression chemistry v3.0 or 5' immunocell profiling chemistry v1.0 and v1.1 (10x Genomics (Pleasanton, CA)). Single-cell cDNA amplification, TCR enrichment, and library preparation were performed according to their respective user guides. For transcriptome analysis, the following patient IPs were sequenced using 3'GEX v3.0: 2984-NR, 2990-NR, 3504-NR, 3408-NR, 3870-NR, 3905-CR, 3418-CR, 3664-CR, 3733-CR, and 3835-CR. To perform combined RNA transcriptome and TCR identification, the following patient IPs were sequenced using 5'GEX+TCR v1.0: 3713-CR and 4000-NR.

[0109] Single-cell sequencing Single-cell gene expression libraries were sequenced using a NEXTSEQ 550 sequencer with an insert read length of 98 base pairs. For samples containing TCR-enriched libraries, the VDJ region was sequenced by paired-end 150 base pair sequencing using either a MiSeq or a NEXTSEQ 550 sequencer.

[0110] scRNA data processingThe sequencing output was processed using the Cell Ranger 3.0 pipeline (10x Genomics). In short, the sequencing output was demultiplexed and converted into a set of fastq files. Fastq files related to the gene expression library were aligned to the GRCh38 reference provided by 10x Genomics (refdata-cellranger-GRCh38-3.0.0), and for the TCR library, to refdata-cellranger-vdj-GRCh38-alts-ensembl-3.1.0. Read counts, with unique molecular identifiers (UMIs) removed, were assigned to individual cell barcodes to construct single-cell gene expression matrices, and for the TCR library, a single-cell TCR clone type list including alpha and beta sequences was created. To ensure reliable gene expression and TCR clone type results, the performance of sequencing and single-cell assays was evaluated, with target read depths of over 30,000 reads per single cell for gene expression and over 5,000 reads per single cell for the VDJ dataset. For each cell barcode, a gene expression matrix was created representing the read count with UMI removed for each annotated gene. Cells with fewer than 200 genes and genes with fewer than 5 read counts across all cells were excluded from the expression matrix. TCR clone types were defined based on TCR variable CDR3β nucleotide sequences, and single cells (possibly doublets) with two different TCR variable CDR3β nucleotide sequences were excluded. The expression matrix was converted to TPM and uniquely matched to the relevant TCR clone type using the cell barcode. CD4 cells defined by transcriptome were excluded from scRNA analysis. Cells were first normalized using convolution (Lun et al., Genome Biol., 17:75 (2016)). Next, the normalized data was decomposed using randomized principal component analysis (Halko et al., SIAM Review, 53:217-288 (2011)).

[0111] scRNA analysisAll scRNAs and scTCRs were analyzed using the R package Seurat v2.4 (Butler et al., Nat. Biotechnol., 36:411-420 (2018)). A single batch-corrected gene expression matrix was used as input for scRNA transcriptome analysis of responders and non-responders. Briefly, the gene expression matrix was subjected to standard pretreatment by regression after removing highly variable and mitochondrial genes (regressing out), followed by normalization and scaling. Principal components (PCs) were created, and significant PCs for clustering were defined using elbow and jackstraw plots. A t-SNE plot was created using the clustered PCs. Clusters and superclusters were defined to compare CRs and NRs. Cluster markers for S. clusters A and B were obtained by comparing individual clusters using default parameters. For combined scRNA / scTCR IP analysis (3713-CR and 4000-NR), separate gene expression matrices were created for each patient. Before processing with Seurat, all TRAV / TRBV genes were excluded to remove endogenous TCR expression, which can cause clustering bias. In the two-cluster solution, low-resolution clustering was performed on the first and second principal components, followed by analysis of the frequency distribution of all CR and NR cells to determine superclusters A and B. Individual cluster markers were obtained for each cluster using Seurat's default parameters.

[0112] Composite scRNA and scTCR analysis: Independent metadata files were created from neoepitope-responsive TCR clones (NeoTCRs) associated with individual cell barcodes using TCRB and TCRA CDR3 sequences. Cells with good read quality for TCRA or TCRB were included in subsequent analyses. +The metadata of the cell barcodes was backprojected onto the t-SNE transcriptome clusters identified within the patient cluster, and the DN / DP gene signatures were scored as follows. As mentioned above, endogenous cellular TCRs were removed to reduce TRAV / TRBV gene expression, which can cause clustering bias. Cells that failed transcriptome pretreatment were excluded from the analysis, even if they had intact TCRB / TCRA reads.

[0113] Differential gene expression (DEG) analysis Using the expression of ENTPD1 (CD39) and CD69 transcripts from single cells in patient IPs (3664-CR, 3733-CR, 3408-NR, 3504-NR), CD39 - CD69 - Cells (the two lower quartiles of CD39 and CD69 TPM) and CD39 + CD69 + Cells (the top two quartiles of CD39 and CD69 TPM) were defined (Figure 6A). DEG comparisons between single DN and single DP cells were created using the edgeR package with FDR < 0.1 and magnification change of 0.5. For publicly available gene signature analysis, gene signature matrices were created from ImmunesigDB, selected by human gene sets constrained to CD8 T cells and other gene sets, for a total of 568 gene sets (Godec et al., Immunity, 44:194-206 (2016)). GSEA was performed in R using default parameters, as previously described at (gsea-msigdb.org / gsea / index.jsp). CD39 - CD69 - and CD39 + CD69 + The top 100 statistically significant genes that were enriched were considered the gene signatures of DN and DP, respectively.

[0114] Single-cell gene signature analysis:Single-cell gene set enrichment analysis (scGSEA), an application of single-sample gene set enrichment analysis (ssGSEA), is a rank-based gene signature criterion that calculates the expression score of a gene list relative to all other genes in RNA expression for each sample (bioconductor.org / packages / release / bioc / html / GSVA.html) (Senbabaoglu et al., Genome Biol., 17:231 (2016)). Briefly, normalized scRNA TIL IP data with barcodes was used as input along with the gene set list. The scGSEA scores for all gene signatures were z-scaled for cross-signature comparisons between cells and samples. A clustering correlation matrix was created between various gene signatures from single T cells using the R package Corrplot. The top two quartiles of the scGSEA scores for DN and DP were projected onto a t-SNE plot to identify clusters enriched by each gene signature. Using mean DN and DP scGSEA scores, responders and non-responders were evaluated using the Wilcoxon rank-sum test. The phenotypic fitness score was calculated as the difference between the DN and DP scGSEA scores of each cell (DN minus DP), indicating a relatively high concentration of the stem-like phenotype at the single-cell level. Mean fitness scores were calculated for each patient's IP and compared between responders and non-responders of ACT and ICB. For cross-research comparisons of subsets of DN and DP with genetic signatures, correlation matrices between various genetic signatures from single T cells were constructed using the Corrplot package and presented after hierarchical clustering. For comparisons of TCR clone types, mean scGSEA scores were calculated for all cells expressing a specific NeoTCR, projected onto t-SNE plots, and compared between clone types.

[0115] Short-term TIL stimulation The patient IP was defrosted and left to stand overnight without cytokines, and DN(CD39 - CD69 - ) and DP (CD39 +CD69 + A subset of ) was flow-sorted and left to stand for 1 hour. Then, 10 for each TIL group. 5 Individual cells were stimulated for 48 hours in a round-bottom 96-well plate with plate-bound anti-CD3 / CD28 (Invitrogen (Waltham, MA)).

[0116] Multi-cytokine analysis of stimulated TILs After 48 hours of CD3 / CD28 stimulation, 96-well plates containing stimulated TILs were centrifuged at 300g for 5 minutes. 25 μL of clear supernatant was subjected to a CD8 / NK panel LEGENDPLEX (catalog no. 740267, BioLegend) assay using V-bottom plates with technical replication according to the manufacturer's instructions. Captured cytokines were analyzed by flow cytometry using a BD LSRFortessa cell analyzer (BD Biosciences). Standard curves were created for each cytokine, and cytokine concentrations secreted from the stimulated TIL population were calculated using the manufacturer's software (VigeneTech v8, BioLegend).

[0117] In vitro antitumor TIL amplification experiments The 3733-CR IP was thawed and left to stand overnight without cytokines. The next day, CD39 - CD69 - (DN) and CD39 + CD69 + CD8 T cells from (DP) were isolated by flow sorting, and DN, DP, and bulk IPT cells were co-cultured overnight with autologous 3733-mel tumor cell lines in a 1:1 ratio. Tumor-responsive cells were isolated from DN, DP, and bulk populations using CD8 T cells. + 4-1BB + Sort from flow sort (DN 4-1BB + DP 4-1BB + , and bulk 4-1BB + ), 10 5The cells were subjected to rapid amplification (REP) as previously described (Dudley et al., J.Immunother., 26:332-342 (2003)). The cells were counted at the end of each REP, and 10 of the obtained cells were selected. 5 One sample was subjected to an additional REP, while the remainder was cryopreserved. Upon completion of the third REP, the cryopreserved samples were thawed and collected, and tumor reactivity was evaluated by co-culturing the autologous 3733-mel tumor cell line in a 1:1 ratio with a biological triplicate, resulting in 4-1BB cells. + This was used as the readout information for tumor responsiveness. The number of viable cells was evaluated at the end of each REP. To calculate the theoretical cell amplification yield (Figure 16B), 4-1BB + The percentage of tumor-responsive fractions is calculated from the total CD8 + The total cell yield from 1e5 cells was used to estimate the theoretical tumor-specific cell yield, which was expressed as a percentage of viable cells.

[0118] CDR3β survey sequencing and tracking Frozen injection products or PBL samples were thawed, tested for viability, and counted. First, cells were flow-sorted or directly pelletized for markers including CD39 and CD69 as specified, rapidly frozen, and sent to Adaptive Biotechnologies (Seattle, WA) for genomic DNA extraction and IMMUNOSEQ TCRB survey sequencing (v4) (Pasetto et al., Cancer Immunol. Res., 4:734-743 (2016)). Subsequently, the frequencies of known neoantigen-reactive CDR3β were compared among different samples from a given patient.

[0119] Phenotyping of NY-ESO-1-reactive TCRs For experiments involving the sequencing of T cells from patients treated with NY-ESO-1 specific TCR transducers (ESO.CR), thawed IP was used to synthesize NY-ESO-1 TCRs (HLA-A) conjugated to CD8;APC, CD39-PE, and CD69-FITC. *Staining was performed for 02:01-SLLMWITQC (SEQ ID NO: 17) peptide tetramer (ProImmune, UK), and three populations were isolated for CDR3β survey sequencing:CD8 + NY-ESO-1 TCR + (Bulk), CD8 + NY-ESO-1 TCR + CD39 - CD69 - (DN), and CD8 + NY-ESO-1 TCR + CD39 + CD69 + (DP). The true endogenous CDR3β frequency in TCR transduced cells was estimated by normalizing the endogenous CDR3β sequence to the frequency of CDR3β (CASSYVGNTGELFF) (SEQ ID NO: 18) in the NY-ESO-1 specific TCR construct. In Figure 4B, using the normalized NY-ESO-1 TCR-expressed IPCDR3β data, the top 20 clones in the infusion bag were divided into DN enriched and DP enriched groups based on the ratio of the frequency of a given clone in selected DN cells to the frequency of the given clone in selected DP cells. Any clone with a DN / DP ratio > 1 was considered DN enriched, and any clone with a DN / DP ratio < 1 was considered DP enriched. In Figure 4C, the top 1311 common clones were defined as clones present in the bulk, DN, and DP populations. They were analyzed for short-term (7 days) and long-term (51 months) persistence according to the frequency ratios of the DN and DP populations of cells selected from the IP.

[0120] Culture of mouse T cells Pmel spleen cells were isolated and pulsed with human gp100(25-33) peptide as described above (24). (Klebanoff et al., Clin. Cancer Res., 17:5343-5352 (2011)), and amplified for 5 days in the presence of 60 IU IL2 and complete mouse T cell medium. Cells were harvested on day 5 and restimulated with plate-bound anti-CD3 (2 μg / mL) and soluble CD28 (1 μg / mL) in the presence of IL2, and amplified until day 10. After 10 days of culture, cells were subjected to DN(CD39) for tumor therapy experiments. - CD69 - ) and DP (CD39 + CD69 + It was divided into )

[0121] Mouse tumor treatment Adult female B6 NCR (B6; Ly5.2) 6-8 weeks old + The mice were purchased from Charles River Laboratories (NCI Frederick). The B6.SJL-Ptprca Pepcb / BoyJ (Ly5.1) mice were obtained from The Jackson Laboratory (Bar Harbor, ME). + The mice were maintained under specific pathogen elimination conditions. For tumor treatment experiments, 3.5 × 10¹⁶ female B6 mice overexpressing the chimeric human / mouse gp100 antigen KVPRNQDWL (SEQ ID NO: 19) (aa25-33) were used in B16 melanoma cell lines. 5 Individual cells were injected (Hanada et al., JCI Insight, 4(2019), doi:10.1172 / jci.insight.124405). Tumor cells were established for 10 days, and tumor-bearing mice were irradiated whole-body with 6 Gy before T cell injection. One day later, FACS-selected DN (CD39) cells were injected at two doses (3e5 and 5e5 per mouse). - CD69 - ) and DP (CD39 + CD69 +Animals were treated with [specified treatment]. Pmel T cells were activated in vitro and amplified at specified doses for 10 days. Control mice administered 1×PBS were included as controls. All treated animals received 12 μg of IL-2 via daily IP injection for 3 days. All tumor measurements were performed by independent investigators in a double-blind manner.

[0122] ATAC Sequencing Analysis ATAC-seq fastq files were aligned to genome build 38 using bowtie2 with the --very-sensitive flag. The aligned bam files had duplicates that had been removed using Picard's mark duplicates tool. Mitochondrial reads were removed, and peaks were called using Homer v4.11.1 with the peak finding style DNase setting. Duplicate peaks were merged using Bedtools, and the 5' end counts of reads within each peak were recorded for each sample. This count matrix was used as input to EdgeR, and the difference regions were identified using the estimateGLMTagwiseDisp function. Likelihood ratio tests were then performed using glmFit and glmLRT. Peaks were identified as significant if the FDR q value was <=0.01.

[0123] Transcription factor motif analysisAll human transcription factor motifs were downloaded from the CIS-BP database on July 7, 2020. Joint scores were created for all difference peaks of each motif using GOMER (Sade-Feldman et al., Cell, 176:404 (2019); Liu et al., Genome Res., 16:1517-1528 (2006)). Using the Python scipy.stats hypergeom module, a minimum hypergeometric mean test was calculated for both double-positive and double-negative peaks for each motif, using the top N scoring peaks (up to 3000 peaks). Then, treating each test independently, p-values ​​were corrected using the Benjamin-Hockberg FDR with the Python statsmodels.sandbox.stats.multicomp multipletests module.

[0124] Example 1 This example shows CD39 in administered T cells. - CD69 - This suggests that the T cell phenotype is associated with a positive response to ACT.

[0125] We compared phenotypic differences (Figure 1A) that distinguished ACT infusion products (IPs) administered to patients who achieved a complete response to treatment (complete responders, CR, N=24) from those whose disease progressed after ACT (non-responders, NR, N=30). These ACT IPs were obtained from a cohort of stage IV metastatic melanoma patients who had previously received treatment with autologous in vitro amplified TILs and had not previously received genetically engineered T-cell therapy or immunotherapy in the form of anti-PD-1 blockade (Goff et al., J. Clin. Oncol., 34:2389-2397 (2016); Dudley et al., Clin. Cancer Res., 16:6122-6131 (2010)).

[0126] Initial single-cell analysis of a set of 4.8 million intracellular lesions (IPs) discovered from 7 CRs and 9 NRs using mass cytometry (CyTOF) revealed heterogeneous expression of 34 cell surface markers. Supervised analysis of TIL CyTOF profiles identified clusters that were more prevalent in either CRs or NRs (Figure 1B). Cluster 1 was four times more abundant in CR IPs than in NR IPs (adjusted P=0.0264, Figure 1C), and was characterized by high levels of CD44, CD27, and CD28, and low expression of CD8 with TIM3. + These cells correspond to T cells and have been characterized in previous studies as memory-like (Sade-Feldman et al., Cell, 176:404 (2019)) and stem-like (Jansen et al., Nature, 576:465-470 (2019)) T cells. Notably, cluster 1 also showed low expression of the inhibitory marker CD39 and the T cell activation marker CD69. Unsupervised clustering based on machine learning of T cell activation / exhaustion status to classify patients according to clinical outcomes further confirmed that the expression of CD69 and CD39 were the two most clinically relevant key features (Figures 5A-5C). In particular, the frequency of cluster 2, in which CD39 and CD69 were highly expressed but CD44, CD27, and CD28 were at low levels, tended to be higher in NR IP than in CR IP, although this difference was not statistically significant (Figure 1C).

[0127] CD8 + CD39 - CD69 - Flow cytometry analysis of 16 discovery samples, in which cells were defined as members of cluster 1, reproduced mass cytometry data with high reliability (Figures 6A-6B). Multiparameter flow cytometry evaluated an independent set of 38 IPs (CR n=17, NR n=21), and CD8 was observed in CR IPs compared to NR IPs. + CD39 - CD69 -The frequency of the TIL population was found to be 2.5 times higher (P=0.0096, %), which supports the association between this subset and the ACT response (Figure 1D-E). In this set of 38 patient samples, the total number of injected T cells did not differ significantly between CR IP and NR IP, but the number of injected CD8 + CD39 - CD69 - The total number of cells was four times higher in CR IP than in NR IP (P=0.0031, Figure 1F-G). Examination of individual markers revealed a mild trend in CD8+TIM3+TIL being associated with non-response to ACT (Figure 6C). Analysis of the survival time of all 54 patients showed that CD8 in the patients' infusion products was higher. + The injected CD39 contained the majority of the TIL. + CD69 + The absolute number of T cells (Figure 6C) did not have a significant effect on either melanoma-specific survival (MSS) or progression-free survival (PFS) in this cohort (Figures 7A-7C), but CD8 in IP + CD39 - CD69 - A higher cell count was found to be significantly associated with dose-dependent improvements in progression-free survival (PFS, P<0.0001, HR=0.255, 95% confidence interval (CI) 0.1257 to 0.5186, Figure 1H) and melanoma-specific survival (MSS, P<0.0001, HR=0.217, 95% CI 0.101 to 0.463, Figure 1I) (Tertile analysis, Figures 7A-7C). Furthermore, CD8 in the injection product + CD39 - CD69 - CD8 cells + CD39 + CD69 + The ratio to cells had a significant impact on MSS and PFS, suggesting that the ACT response in this cohort was CD39 + CD69 + It's not about having fewer TILs, but rather having more CD8s. + CD39 - CD69 -This suggests a connection to the injection of cells.

[0128] Example 2 This embodiment relates to CD39 in a positive response. - CD69 - TIL is in a precursor memory stem-like state, but CD39 + CD69 + TIL indicates that the patient is in the terminal differentiation state.

[0129] CD8 + CD39 - CD69 - CD8 is dominant in TIL (Cluster 1, Double Negative: DN) and IP. + CD39 + CD69 + To further investigate the potential significance of TILs (Cluster 2, double-positive: DP), the transcriptome profiles of these two subsets were evaluated (Figure 8A). DN TILs showed increased expression of the quiescent T stem cell markers KLF2, TCF7, S1PR1, LEF1, IL7R, CD27, and SELL (CD62L), while DP TILs expressed CD38, MK167, GITR, GZMA, TNF, and IFNG, which are found in differentiated and activated T cells (Figure 8B, Table 3). Single-cell transcriptome analysis (scRNA) of 20,672 CD8 cells was performed from IPs of 10 patients (5 complete, 5 non-reactive). +Unsupervised clustering of TILs resulted in the definition of eight clusters (Figure 2A). Analysis of the distribution of TILs from IPs for CR and NR revealed a two-cluster solution defined by S. Cluster A (including supercluster A and clusters C0, C2, C5, C6, and C7) and S. Cluster B (including supercluster B and clusters C1, C3, C4, and C8) (Sade-Feldman et al., Cell, 176:404 (2019)). Responsive TILs were more abundant in S. Cluster A (P=0.0317), while S. Cluster B contained almost all non-responsive TILs (P=0.03, Figure 2B). TILs in S. Cluster A showed enrichment of the DN gene signature (81% duplication, Figure 8C), while TILs in S. Cluster B showed enrichment of the DP gene signature. Consistent with these analyses, CR TILs and NR TILs, scored by single-cell gene set enrichment analysis (scGSEA) of DN and DP gene signatures, showed that CR TILs had a higher DN score on average, while NR TILs had a higher DP score (both P<4.5×10⁻⁶). -12 (Figure 2C).

[0130] [Table 3-1]

[0131] [Table 3-2]

[0132] [Table 3-3]

[0133] Cell surface expression of inhibitory and memory markers was analyzed by flow cytometry within the CD39 / CD69 TIL subset from validation samples (n=38 IP, Figure 2D). Single-positive (SP, CD39) + CD69- and CD39 - CD69 + Compared to the DP subset, the DN subset showed decreased cell surface expression and reduced co-expression of exhaustion markers PD-1 and TIM3 (Figure 2D, Figures 9A-9B). Conversely, the expression of memory markers CD62L and CD27, and the precursor marker SLAMF6, was increased in DN compared to the DP subset, and expression was variable within the SP population (Figure 2D, Figures 9A-9B). Protein expression of the memory stem-like T cell marker TCF7 was 5-fold higher in DN TILs than in DP TILs (P=0.0002, n=18 IP, Figure 2E), which was consistent with scRNA analysis. DN TILs in the injection product expressed both SLAMF6 and TCF7 transcripts at high levels, but TILs co-expressing TCF7 and SLAMF6 were not significantly enriched within the DN subset, suggesting that adding SLAMF6 as a surface marker to DN was equivalent to DN alone in describing TILs associated with survival benefit in this patient subset. In contrast, DP TILs showed significantly higher transcription and protein expression of the effector and exhaustion-related transcription factor TOX compared to DN TILs. Notably, the transcriptional profile of DN TILs was similar to that of peripheral blood stem cell memory (SCM) T cells (Gattinoni et al., Nat. Med., 17:1290-1297 (2011); Lugli et al., J. Clin. Invest., 123:594-599 (2013)), but in patient TIL infusion products, cell surface expression of the classical SCM markers CD95 and CCR7 was not associated with the DN phenotype.

[0134] CD39 - CD69 - To assess whether IPTILs are stem-like T cells, subsets of DN and DP were isolated and stimulated with plate-bound anti-CD3 / anti-CD28. Upon stimulation of the T cell receptor (TCR), DN TILs underwent self-renewal and were single-positive and CD39 + CD69 +Both DP daughter populations were generated, but most stimulated DP TILs remained in the same DP state, indicating a DN precursor state (n=6 IP, Figure 2F). From the time-course dynamics of the daughter population, essentially all DN TILs experienced CD69 activation, but a subset of DN TILs (median=17%) showed CD39 activation 5 days after initial stimulation. - CD69 - It was shown that it had returned to a stem-like state. In contrast, CD39 + Both parent populations (CD39 SP and DP) showed stable CD39 after TCR stimulation. + CD69 + It was thought that the final differentiation into the ACT state occurred. Furthermore, while DP and DN TILs secreted similar levels of IL-2, IFNγ, GZMA, GZMB, and perforin in response to polyclonal stimulation, stimulation of DN TIL resulted in the secretion of higher levels of IL-17A, TNF-α, IL-4, sFasL, and granulosin than DP TIL (Figure 10). In summary, these results suggest that CD39 is related to the ACT response. - CD69 - TILs are in a precursor memory stem-like state that can differentiate into other subsets, but CD39 is dominant in the injection product. + CD69 + TIL indicates that the patient is in the terminal differentiation state.

[0135] To understand the key regulators of these two TIL states in injection products, corresponding DN and DP TILs were isolated from injection products of three patients, and their epigenetic profiles were analyzed using ATAC (Assassination for Transposase-Accessible Chromatin) sequencing. The presence of 4314 human transcription factor (TF) motifs in the open chromatin regions of DN and DP TILs was searched. The open chromatin regions within stem-like DN TILs contained numerous binding sites for SOX and C2H2-ZF family TFs, including KLF4, TCF7, LEF1, and TCF7L1, which was consistent with poorly differentiated human T cells (Sade-Feldman et al., Cell, 176:404 (2019); Lynn et al., Nature, 576:293-300 (2019)). In contrast, the chromatin region within DP TILs exhibits widespread and abundant binding motifs for the bZIP TFs FOSL1, FOS, JUNB, and JUND, suggesting that epigenetic imprinting may be involved in maintaining the terminally differentiated state resulting from chronic activation of tumor cells (Lynn et al., Nature, 576:293-300 (2019); Blank et al., Nat. Rev. Immunol., 19:665-674 (2019)).

[0136] To understand whether the TIL status in IP corresponds to the status in fresh tumors, we used 6001 CD8s from a previous melanoma ICB response study (Sade-Feldman et al., Cell, 176:404 (2019)). + The transcriptome profiles of T cells were re-analyzed using the gene signatures of DN and DP described above (Figure 2G). Before immune checkpoint therapy, TILs from ICB-responsive lesions were more CD39-responsive than non-responsive lesions. - CD69 - The signature score was high (P=1.9×10 -10 ) However, TILs from lesions that progressed during ICB are CD39 + CD69 +The signature score was high (P<2.2×10 -16 ) was found. Hierarchical clustering correlation analysis with T cell dysfunction and precursor gene signatures in other recent studies revealed that CD39 - CD69 - The signature of TIL is most similar to memory and precursor fatigue TIL associated with ICB response, and CD39 + CD69 + The genetic signature of TIL is associated with TOX, which is linked to poor ICB response. + Furthermore, it showed a high correlation with final exhausted TILs (Sade-Feldman et al., Cell, 176:404 (2019); Miller et al., Nat.Immunol., 20:326-336 (2019); Scott et al., Nature, 571:270-274 (2019); Tirosh et al., Science, 352:189-196 (2016)). A phenotypic fitness score was developed, defined as the difference between the DN stem-like signature and the DP differentiation signature (DN minus DP), and scRNAs from ACT IP and melanoma ICB TILs were scored. Single-cell fitness scores reaffirmed that responder TILs had, on average, more stem-like fitness than non-responder TILs (Figures 11A-11B). These data suggest that the stem-like TILs observed in ACT responder injection products are similar to those observed in ICB responses.

[0137] Example 3 This example, at least in the context of melanoma ACT, all CD39 - TILs are not bystander T cells; the cells that respond are CD39 - This indicates that the proportion of tumor neoantigen-responsive TILs in a stem-like state is preserved.

[0138] Previous studies on the stem-like and differentiated human TIL subsets have been conducted on bulk TIL populations and lacked specific analysis of anti-tumor T cells (Sade-Feldman et al., Cell, 176:404 (2019); Jansen et al., Nature, 576:465-470 (2019)). Neoantigen-specific TILs are almost exclusively CD39 + only, while CD39 - TILs, in light of recent findings suggesting that they become bystander cells (Simoni et al., Nature, 557:575-579 (2018); Duhen et al., Nat. Commun, 9:2724 (2018)), CD39 - CD69 - stem-like state present within the neoantigen-reactive T cell population in I.P. We attempted to clarify whether it exists. Using the above-mentioned tandem mini-gene neoantigen screening platform (Parkhurst et al., Cancer Discov., 9:1022-1035 (2019); Tran et al., Science, 344:641-645 (2014); Zacharakis et al., Nat. Med., 24:724-730 (2018); Tran et al., N. Engl. J. Med., 375:2255-2262 (2016)), 26 HLA class I-restricted neoantigens from I.P. were defined for phenotypic evaluation (n = 11 patients, Figures 12A - 12B and 13). Stem-like and differentiated TIL subsets defined by CD39 / CD69 expression were readily detectable within HLA neoantigen tetramers + in TILs, as shown in representative CR and NR (Figures 3A - 3B). In the combined analysis of all 26 neoantigen-specific T cell populations, consistent with other studies reporting CD39 enrichment in neoantigen-specific T cells (DP median = 62.7%, Figure 3C) (Simoni et al., Nature, 557:575-579 (2018)), most were CD39 + CD69 +showed that it exists in the final differentiated state. However, when the data were stratified by the ACT response state, the frequency of DN cells was significantly higher in neoantigen-specific TILs from CR I.P. (median = 8.8%) than in NR I.P. (median = 0.5%, P = 1.2×10 -4 , Figure 3D). CD39 as a single marker showed that CD39-negative (CD39 - ) neoantigen-reactive TILs were 20-fold higher in CR TILs than in NR TILs (median = 23.6%, P = 1.86×10 -5 , Figure 3E). When adjusted for tumor-specific cells, these differences between CR and NR injected products became significantly prominent, with neoantigen-specific DN TILs being 23-fold higher in CR (median = 2.3×10 8 cells), and CD39 - TILs being 40.4-fold higher in CR (median = 1.06×10 9 cells). There was no difference in the total number of neoantigen-reactive TILs or neoantigen-reactive CD39 + or DP TILs between CR and NR injected products. These results indicate that, at least in the context of melanoma ACT injected products, not all CD39 - TILs are bystander T cells as previously reported, and a subset of tumor neoantigen-reactive TILs exists in a CD39 - stem-like state in CR. Furthermore, these data indicate that the enrichment of tumor-reactive neoantigen-specific TILs in the differentiated subset does not necessarily correspond to the frequency in the stem-like state.

[0139] To investigate the heterogeneity of the stem-like and differentiated states of neoantigen-specific TILs at the single-cell transcriptome level, composite scRNA and scTCR sequencing was performed on IPs from CR (Pt.3713) and NR (Pt.4000) with defined neoantigen-reactive TCRs (Figure 3F-3L) (Prickett et al., Cancer Immunol. Res., 4:669-678 (2016); Pasetto et al., Cancer Immunol Res., 4:734-743 (2016); Parkhurst et al., Clin. Cancer Res., 23:2491-2505 (2017); Cohen et al., J. Clin. Invest., 125:3981-3991 (2015)).

[0140] In the IP from successful responder Pt.3713, two main clusters, C0 and C1, were dominant, with C0 representing a stem-like DN state and C1 representing a differentiated DP state (Figure 3F). 20 neoantigen-specific TCRs + (NeoTCR + ) Clonal projection showed a broad distribution between the two clusters (Figure 3F-3G). Immunodominant SRPX mutation-specific NeoTCR (SRPX mut ) was abundant in cluster C0, but the proportion of other NeoTCRs differed between the two clusters (Figure 3G). This finding was confirmed by CDR3β sequencing of FACS-selected DN and DP IPTIL (Figure 14A). SRPX mut NeoTCR +Further analysis of single cells revealed that positive phenotypic fitness scores were concentrated in 9 / 10 of these clonal types (and 15 / 20 of all NeoTCR clonal types) (Figure 3H). Previous studies had suggested that T-cell-specific differences might influence the persistence of clonal types after ACT (Gattinoni et al., Nat. Med., 17:1290-1297 (2011); Rosenberg et al., Clin. Cancer Res., 17:4550-4557 (2011)), so we analyzed peripheral blood from 3713 patients after ACT. Sequencing of the TCR repertoire revealed that the majority of NeoTCR clonal types (18 / 20) persisted for up to 75 months, corresponding to the precursor stem-like state SRPX. mut This is consistent with the concept of TIL amplification (Figure 3I, other NeoTCRs: Figure 14B). Interestingly, 10 SRPX targeting the same neoepitope mut -Among the NeoTCRs, the single clonal type with the lowest phenotypic fitness score (TCR-51) decreased in frequency and became undetectable just 3 months after treatment (Figures 3H-3I).

[0141] Composite scRNA and scTCR sequencing of non-responder Pt.4000I.P. cells showed two major stem-like clusters (C1, C5) and one major differentiation cluster (C0) (Figure 3J). However, NeoTCR+ cells (HIVEP2 mut AMPH mutThe majority of these were concentrated in the differentiated C0 cluster (~60%, Figure 3J, 15A-15B). scGSEA scores from Pt.4000 showed that both NeoTCR clone types had negative phenotypic fitness scores (Figure 3K). In stark contrast to the persistent clonal type of 3713-CR, the NeoTCRs from Pt.4000 did not show sustained amplification after ACT; rather, the clonal types of both injected NeoTCRs rapidly decreased in peripheral blood after treatment (Figure 3L). These results support previous findings and other published reports that link cell therapy response to the persistence of post-treatment TCR (Rosenberg et al, Clin. Cancer Res., 17:4550-4557 (2011); Zhou et al., Immunity, 33:229-240 (2010); Kalos et al., Sci. Transl. Med., 3:95ra73 (2011)).

[0142] Example 4 This example is CD39 - CD69 - This study demonstrates that phenotypic T cells mediate antitumor activity and the persistence of TCRs.

[0143] The results described in Examples 1-3 showed that differences in T cell-specific phenotypes are associated with post-ACT persistence, but additional factors such as TCR avidity to variable tumor antigens may also play a role in T cell persistence. To address this issue, single HLA-A * A preliminary analysis of T cell persistence was performed in metastatic synovial cell sarcoma patients who experienced complete response (CR) after adoptive transfer of autologous PBMCs transduced with a TCR targeting the 02:01-restrictive NY-ESO-1 epitope. Endogenous human TCRβ sequences were used as barcodes to track stem-like DN clones and differentiated DP clones within the TCR transducer population in the blood after treatment (Figure 4A) (D'Angelo et al., Cancer Discov., 8:944-957 (2018)). The top 20 ESO TCRs were identified. +Of the IP clonal types, 14 were predominantly present in the stem-like DN state, while 6 were predominantly present in the differentiated DP state. The frequency of stem-like clones gradually decreased over 5 years after TCR transduction, whereas differentiated clones rapidly decreased to very low frequencies in the blood of patients after treatment (Figure 4B). Of the top 1311 clones, long-lasting clones (detectable 51 months after ACT, n=790) were significantly more prevalent in the stem-like state in IP compared to low-lasting clones (undetectable 7 days after treatment, n=122) (P=0.0035, Figure 4C). These analyses suggest that unique stem-like phenotypes can modulate the behavior of T cell clonal types after ACT; however, further research in larger patient cohorts in TCR and CAR trials targeting other antigens is needed to validate these hypotheses.

[0144] This finding is from CD39 - CD69 - The study suggested that treatment with tumor-responsive TILs could lead to superior tumor control. This hypothesis was first tested in vitro by isolating tumor-responsive DN and DP populations from CR IP (Pt.3733) by co-culturing with the autologous 3733-mel tumor cell, selecting tumor-responsive TILs, and then evaluating their proliferative capacity and tumor recognition through multiple rounds of rapid amplification (Figure 16A). The tumor-responsive stem-like DN subset was found to amplify approximately 1000 times more than the differentiated DP subset (Figure 16B). The stem-like DN subset maintained tumor recognition, while the differentiated DP subset lost tumor responsiveness after subsequent amplification rounds (Figures 16C-16D).

[0145] To evaluate the in vivo antitumor effects of stem-like DN and differentiated DP subsets, CD39 was extracted from in vitro amplified Pmel transgenic TCR spleen cells. - CD69 - and CD39 + CD69 + CD8 +A mouse T cell population was isolated, and the isolated subset was adopted into mice transplanted with B16-F10 melanoma cells modified to express human gp100 antigen (Figure 4D) (Hanada et al., JCI Insight, 4(2019), doi:10.1172 / jci.insight.124405). 3×10 5 individual or 5 x 10 5 Adoptive transfer of one differentiated DP Pmel T cell had a moderate effect on tumor control and mouse survival, but transfer of the same number of stem-like DN T cells resulted in dose-dependent significant tumor regression and improved survival (Figures 4E-4F). Furthermore, this data was used to evaluate tumor responsiveness in enriched antitumor neoantigen-specific TIL subsets (e.g., CD39 + CD69 + However, this suggests that it may be a terminally differentiated TIL with relatively low proliferative capacity in vivo (Figure 4G) (van der Leun et al., Nat. Rev. Cancer. 20:218-232 (2020)).

[0146] Example 5 This example demonstrates the identification of gene expression profiles that distinguish neoantigen-specific T cells from bystander T cells.

[0147] We reanalyzed single-cell transcriptome data for the stem-like cluster C0 (Figure 14A) to identify whether there were any selective differences in gene expression between neoantigen-specific T cells and bystander T cells within the stem-like cluster, as shown in Figure 18A.

[0148] The results are shown in Figure 18B. As shown in Figure 18B, differential gene expression was observed in stem-like neoantigen-specific T cells (right side of Figure 18B) compared to bystander T cells (left side of Figure 18B), in which the selected gene was upregulated.

[0149] Table 4 shows the differentially expressed genes and their multiplier changes. In Table 4, positive logFC values ​​indicate that CD39 T cells are expressed differently compared to other T cells in tumor samples from cancer patients.- CD69 - This indicates that the specified gene is highly expressed in neoantigen-specific T cells. A negative logFC value indicates that CD39 is highly expressed compared to other T cells in tumor samples from cancer patients. - CD69 - This shows that a specific gene is highly expressed in bystander T cells.

[0150] [Table 4-1]

[0151] [Table 4-2]

[0152] All references cited herein, including publications, patent applications, and patents, are incorporated herein by reference as if they were included herein, each reference being individually and specifically indicated to be incorporated herein by reference.

[0153] In connection with the description of the present invention (particularly in connection with the following claims), the terms “a,” “an,” “the,” and “at least one,” as well as similar references, should be interpreted as encompassing both singular and plural forms, unless otherwise specified herein or unless clearly contradicted by the context. The use of the term “at least one” after a list of one or more items (e.g., “at least one of A and B”) should be interpreted as meaning one item (A or B) selected from the enumerated items or any combination of two or more enumerated items (A and B), unless otherwise specified herein or unless clearly contradicted by the context. The terms “comprising,” “having,” “including,” and “containing” should be interpreted as open-ended terms (i.e., “including but not limited to these”), unless otherwise specified herein. The enumeration of value ranges in this specification is intended, unless otherwise specified herein, simply to function as an abbreviation for each distinct value within the range, and each distinct value is incorporated into the specification as if it were individually enumerated herein. All methods described herein may be performed in any preferred order unless otherwise specified herein or unless it is clearly inconsistent with the context. The use of any and all examples or exemplary expressions provided herein (e.g., "etc.") is intended, unless otherwise specifically asserted, merely to further elucidate the invention and not to propose any limitation of the scope of the invention. No expression in the specification should be construed as indicating that any unclaimed element is essential for the practice of the invention.

[0154] Preferred embodiments of the Invention, including the best mode known to the inventors for carrying out the Invention, are described herein. Variations of the preferred embodiments may become apparent to those skilled in the art upon reading the foregoing description. The inventors anticipate that those skilled in the art will use such variations as appropriate, and they intend that the Invention may be carried out in ways other than those specifically described herein. Accordingly, the Invention includes all variations and equivalents of the subject matter of the Invention enumerated in the claims appended herein, as permitted by applicable law. Furthermore, any combination of the above elements in all possible variations is encompassed by the Invention unless otherwise specified herein or is clearly inconsistent with the context.

Claims

1. A method for obtaining a cell population enriched with T cells exhibiting a specific phenotype, (a) Obtaining a bulk population of T cells from tumor samples from patients; (b) From the bulk population, marker CD3 ＋ CD39 － , and CD69 － Specifically selecting T cells having a phenotype including; (c) Separating the cells selected in (b) from cells without the phenotype to obtain a cell population enriched with T cells having the phenotype, A method that includes this.

2. The method according to claim 1, further comprising isolating tumor-reactive T cells from T cells having an isolated phenotype.

3. A method for obtaining a cell population enriched with T cells exhibiting a specific phenotype, (a) Obtaining a bulk population of T cells from tumor samples from patients; (b) Isolating tumor-reactive T cells from the tumor sample; (c) Marker CD3 from isolated tumor-reactive T cells ＋ CD39 － , and CD69 － Specifically selecting T cells having a phenotype including; (d) The cells selected in (c) are separated from cells that do not have a phenotype to obtain a cell population enriched with phenotypic T cells, A method that includes this.

4. The method according to any one of claims 1 to 3, wherein the selection of isolated T cells having a specific phenotype includes performing t-distribution stochastic neighbor embedding (t-SNE) analysis.

5. The method according to any one of claims 1 to 4, wherein the selection of isolated T cells having a specific phenotype includes performing sequencing-based transcriptome and epitope cell indexing (CITE-Seq) analysis.

6. The method according to any one of claims 1 to 5, wherein the selection of isolated T cells having a specific phenotype includes detecting the presence or absence of RNA encoding one or more phenotypic markers in the T cells.

7. The method according to any one of claims 1 to 6, wherein the selection of isolated T cells having a specific phenotype includes detecting the presence or absence of cell surface expression of one or more phenotypic markers by the T cells.

8. A method for obtaining a cell population enriched with T cells exhibiting a specific phenotype, (a) Obtaining a bulk population of T cells from tumor samples from patients; (b) Isolating tumor-reactive T cells from the tumor sample; (c) Isolated tumor-reactive T cells, marker CD39 － and CD69 － To modify the cell to provide a phenotype including the above, thereby obtaining a cell population enriched with T cells having the above phenotype, A method that includes this.

9. CD3 ＋ CD39 － CD69 － A method for preparing a population of cells enriched with T cells having a phenotype, comprising: (a) Isolating tumor-reactive T cells from tumor samples from patients; (b) Isolated tumor-reactive T cells (i) The following marker: AHANAK ＋ , AL592183.1 ＋ , ANXA1 ＋ , ANXA4 ＋ , AQP3 ＋ ATM ＋ , BIN1 ＋ , C10orf54 ＋ , C11orf21 ＋ , C16orf54 ＋ CCDC109B ＋ CD27 ＋ CD52 ＋ CD55 ＋ CD8B ＋ CDC25B ＋ CDC42SE1 ＋ CLEC2B ＋ CLIC3 ＋ , CLUAP1 ＋ CRBN ＋ , CTD-3184A7.4 ＋ , DDI2 ＋ , DND1 ＋ EMP3 ＋ , EPB41 ＋ , ERN1 ＋ FAIM3 ＋ FAM65B ＋ FBXL8 ＋ FGFBP2 ＋ GADD45B ＋ GIMAP4 ＋ GIMAP7 ＋ GPR155 ＋ GZMM ＋ HSD17B11 ＋ IL10RA ＋ IL7R ＋ ISG20 ＋ , KANSL1-AS1 ＋ , KLF2 ＋ , KLHL24 ＋ , LDLRAP1 ＋ , LEF1 ＋ , LGALS3 ＋ , LINC00861 ＋ LITAF ＋ , LYAR ＋ , MAPKAPK5-AS1 ＋ 、MED15 ＋ 、MIAT ＋ 、MIR142 ＋ 、MXI1 ＋ 、MYC ＋ 、NEAT1 ＋ 、NOSIP ＋ 、ODF2L ＋ 、P2RY8 ＋ 、PDE6G ＋ 、PIK3IP1 ＋ 、PLEC ＋ 、PLP2 ＋ 、PPP2R5C ＋ 、PXN ＋ 、R3HDM4 ＋ 、RAMP1 ＋ 、RASA3 ＋ 、RASGRP2 ＋ 、RCBTB2 ＋ 、RNASET2 ＋ 、RP11-395B7.4 ＋ 、RP11-539L10.2 ＋ 、RP11-640M9.1 ＋ 、S100A10 ＋ 、S100A4 ＋ 、S100A6 ＋ 、S1PR1 ＋ 、S1PR4 ＋ 、SAMD3 ＋ 、SELL ＋ 、SH3BP5 ＋ 、SIGIRR ＋ 、SLAMF6 ＋ 、SLCO3A1 ＋ 、SORL1 ＋ 、STK38 ＋ 、SYNJ2 ＋ 、TCF7 ＋ 、TIMP1 ＋ 、TRADD ＋ 、TSC22D3 ＋ 、TSPAN32 ＋ 、TXNIP ＋ 、UBXN11 ＋ 、VCL ＋ 、VNN2 ＋ 、YPEL3 ＋ 、ZFP36L2 ＋ 、ZNF276 ＋ , and ZNF683 ＋ Induces the expression of one or more of the following; and / or (fi) the following markers:AAT7 ＋ ,ADAM19 ＋ ,AGPAT9 ＋ 、AGTRAP ＋ 、AIF1 ＋ ,AASPM ＋ 、ATAD2 ＋ ,AURKA ＋ ,AURKB ＋ ,BIRC3 ＋ ,BIRC5 ＋ ,BRCA1 ＋ ,C15orf48 ＋ 、CASC5 ＋ 、CCL3 ＋ 、CCNA2 ＋ 、CCNB1 ＋ 、CCNB2 ＋ 、CCND2 ＋ 、CD38 ＋ ,CD40LG ＋ ,CD69 ＋ ,CD8B ＋ ,CD20 ＋ 、CDCA3 ＋ ,CDCA8 ＋ ,CD1 ＋ ,CDKN3 ＋ ,CD@ ＋ ,CENPA ＋ 、CENPE ＋ ,CENPF ＋ ,CENPM ＋ ,CENPW ＋ ,CEP55 ＋ ,CISH ＋ 、CKS1B ＋ 、CKS2 ＋ ,CLSPN ＋ 、CRTAM ＋ 、CCF2 ＋ ,DLGAP5 ＋ ,DUSP5 ＋ ,DUSP6 ＋ 、DUT ＋ ,EGR1 ＋ ,ENTPD1 ＋ ,FFEN1 ＋ ,GINS2 ＋ 、GTSE1 ＋ 、H2AFX ＋ , HIS1H4C ＋ HLA ＋ DQA2 ＋ HMMR ＋ IFI27 ＋ , IFNG ＋ IL2RA ＋ IL5 ＋ KIAA0101 ＋ KIF11 ＋ KIF23 ＋ , KPNA2 ＋ KRT7 ＋ , MAD2L1 ＋ MCM7 ＋ MKI67 ＋ MX1 ＋ MYBL2 ＋ , NCAPG ＋ , NDC80 ＋ , NDFIP2 ＋ , NUDT1 ＋ , NUSAP1 ＋ ORC6 ＋ PBK ＋ PCNA ＋ , PLK1 ＋ RPL39L ＋ , RRM2 ＋ SGOL2 ＋ SHCBP1 ＋ SMC2 ＋ SPC25 ＋ STMN1 ＋ TESC ＋ , TK1 ＋ TNF ＋ , TNFRSF18 ＋ , TNFSF10 ＋ TOP2A ＋ , TPM4 ＋ , TPX2 ＋ , TUBA1B ＋ , TUBA1C ＋ , TUBB ＋ TYMS ＋ , UBE2C ＋ , UBE2S ＋ , UBE2T ＋ XCL1 ＋ , and ZWINT ＋ inhibit the expression of one or more of the following It is about modifying it in that way, Induction of the expression of one or more markers (i) and / or inhibition of the expression of one or more markers (ii) causes T cells to CD3 ＋ CD39 － CD69 － Being induced to have a phenotype, A method that includes this.

10. A method for obtaining a pharmaceutical composition containing a cell population enriched with T cells exhibiting a specific phenotype, Obtaining a cell population enriched with T cells having the phenotype according to the method described in any one of claims 1 to 9; A method comprising combining a cell population enriched with T cells having the aforementioned phenotype with a pharmaceutically acceptable carrier to obtain a pharmaceutical composition containing the cell population enriched with T cells having the aforementioned phenotype.

11. The method according to any one of claims 1 to 10, further comprising introducing a nucleic acid encoding the exogenous TCR into cells under conditions in which cells in a population enriched with T cells having the phenotype express the exogenous TCR.

12. The method according to any one of claims 1 to 10, further comprising introducing nucleic acids encoding chimeric antigen receptors (CARs) into cells in a population enriched with T cells having the aforementioned phenotype, under conditions in which the cells express chimeric antigen receptors (CARs).

13. The method according to any one of claims 1 to 12, further comprising increasing the number of cells in a population enriched with T cells having the phenotype obtained by the method described above.

14. A method for producing a pharmaceutical product for treating or preventing cancer in mammals, comprising obtaining a cell population enriched with T cells having a specific phenotype according to the method described in any one of claims 1 to 13.

15. The aforementioned phenotype is marker(s): AHANAK ＋ , AL592183.1 ＋ , ANXA1 ＋ , ANXA4 ＋ , AQP3 ＋ ATM ＋ , BIN1 ＋ , C10orf54 ＋ , C11orf21 ＋ , C16orf54 ＋ CCDC109B ＋ CD27 ＋ CD52 ＋ CD55 ＋ CD8B ＋ CDC25B ＋ CDC42SE1 ＋ CLEC2B ＋ CLIC3 ＋ , CLUAP1 ＋ CRBN ＋ , CTD-3184A7.4 ＋ , DDI2 ＋ , DND1 ＋ EMP3 ＋ , EPB41 ＋ , ERN1 ＋ FAIM3 ＋ FAM65B ＋ FBXL8 ＋ FGFBP2 ＋ GADD45B ＋ GIMAP4 ＋ GIMAP7 ＋ GPR155 ＋ GZMM ＋ HSD17B11 ＋ IL10RA ＋ IL7R ＋ ISG20 ＋ , KANSL1-AS1 ＋ , KLF2 ＋ , KLHL24 ＋ , LDLRAP1 ＋ , LEF1 ＋ , LGALS3 ＋ , LINC00861 ＋ LITAF ＋ , LYAR ＋ , MAPKAPK5-AS1 ＋ 、MED15 ＋ 、MIAT ＋ 、MIR142 ＋ 、MXI1 ＋ 、MYC ＋ 、NEAT1 ＋ 、NOSIP ＋ 、ODF2L ＋ 、P2RY8 ＋ 、PDE6G ＋ 、PIK3IP1 ＋ 、PLEC ＋ 、PLP2 ＋ 、PPP2R5C ＋ 、PXN ＋ 、R3HDM4 ＋ 、RAMP1 ＋ 、RASA3 ＋ 、RASGRP2 ＋ 、RCBTB2 ＋ 、RNASET2 ＋ 、RP11-395B7.4 ＋ 、RP11-539L10.2 ＋ 、RP11-640M9.1 ＋ 、S100A10 ＋ 、S100A4 ＋ 、S100A6 ＋ 、S1PR1 ＋ 、S1PR4 ＋ 、SAMD3 ＋ 、SELL ＋ 、SH3BP5 ＋ 、SIGIRR ＋ 、SLAMF6 ＋ 、SLCO3A1 ＋ 、SORL1 ＋ 、STK38 ＋ 、SYNJ2 ＋ 、TCF7 ＋ 、TIMP1 ＋ 、TRADD ＋ 、TSC22D3 ＋ 、TSPAN32 ＋ 、TXNIP ＋ 、UBXN11 ＋ 、VCL ＋ 、VNN2 ＋ 、YPEL3 ＋ 、ZFP36L2 ＋ 、ZNF276 ＋ , and ZNF683 ＋ The method according to any one of claims 1 to 14, further comprising one or more of the above.

16. The aforementioned phenotype is marker(s): ACOT7 － ADAM19 － AGPAT9 － , AGTRAP － , AIF1 － ASPM － , ATAD2 － AURKA － AURKB － BIRC3 － BIRC5 － BRCA1 － , C15orf48 － CASC5 － , CCL3 － CCNA2 － CCNB1 － CCNB2 － CCND2 － CD38 － CD40LG － CD69 － CD8B － CDC20 － , CDCA3 － , CDCA8 － CDK1 － CDKN3 － CDT1 － , CENPA － , CENPE － , CENPF － , CENPM － , CENPW － , CEP55 － CISH － CKS1B － , CKS2 － , CLSPN － , CRTAM － , CSF2 － DLGAP5 － , DUSP5 － , DUSP6 － DUT － EGR1 － ENTPD1 － FEN1 － GINS2 － GTSE1 － H2AFX － HIST1H4C － HLA － DQA2 － HMMR － IFI27 － , IFNG － IL2RA － IL5 － KIAA0101 － KIF11 － KIF23 － , KPNA2 － KRT7 － , MAD2L1 － MCM7 － MKI67 － MX1 － MYBL2 － , NCAPG － , NDC80 － , NDFIP2 － , NUDT1 － , NUSAP1 － ORC6 － PBK － PCNA － , PLK1 － RPL39L － , RRM2 － SGOL2 － SHCBP1 － SMC2 － SPC25 － STMN1 － TESC － , TK1 － TNF － , TNFRSF18 － , TNFSF10 － TOP2A － , TPM4 － , TPX2 － , TUBA1B － , TUBA1C － , TUBB － TYMS － , UBE2C － , UBE2S － , UBE2T － XCL1 － , and ZWINT － The method according to any one of claims 1 to 15, further comprising one or more of the above.

17. The aforementioned phenotype is marker(s): AHI1 ＋ ALOX5AP ＋ ANXA5 ＋ CD68 ＋ CD74 ＋ CD99 ＋ CDC27 ＋ , CDCA7 ＋ CISH ＋ COTL1 ＋ CTSH ＋ CTSW ＋ DDX60 ＋ GTSF1 ＋ ,HIST1H2AG ＋ HLA-DPA1 ＋ HLA-DRB1 ＋ HLA-DRB5 ＋ HMGN3 ＋ IGFBP3 ＋ IL32 ＋ , INTS4 ＋ ITGAE ＋ ITGB1 ＋ KLRC3 ＋ , LGALS1 ＋ , LIME1 ＋ , PDCD1 ＋ , PPM1M ＋ PRSS57 ＋ , RAB34 ＋ , RBPMS ＋ S100A11 ＋ S100A4 ＋ , SNAP47 ＋ , TNFRSF10A ＋ VSIR ＋ , ZBP1 ＋ , and ZNF683 ＋ The method according to any one of claims 1 to 16, further comprising one or more of the above.

18. The aforementioned phenotype is marker(s): AQP3 － ASXL2 － CD6 － ,CLDND1 － , EEF1A1 － , EPB41 － , ERN1 － , FCMR － GIMAP4 － GIMAP7 － , GNLY － GZMK － IL27RA － , LINC01943 － , MRPL57 － , MT － CO1 － , MT － CO2 － , RGS10 － RPL13A － , RPL18 － RPL18A － , RPL37 － , RPL41 － , RPLP0 － , SFMBT2 － , TPT1 － , and TRGV10 － The method according to any one of claims 1 to 17, further comprising one or more of the above.

19. The aforementioned phenotype is marker(s) CD8 ＋ and CD4 ＋ The method according to any one of claims 1 to 18, further comprising one or both of the above.

20. The aforementioned phenotype is marker(s) CD8 － and CD4 － The method according to any one of claims 1 to 18, further comprising one or both of the above.

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