. 2021 Mar 9;54(3):586-602.e8.

doi: 10.1016/j.immuni.2021.02.014.

Global analysis of shared T cell specificities in human non-small cell lung cancer enables HLA inference and antigen discovery

Shin-Heng Chiou¹, Diane Tseng², Alexandre Reuben³, Vamsee Mallajosyula¹, Irene S Molina⁴, Stephanie Conley⁵, Julie Wilhelmy⁶, Alana M McSween¹, Xinbo Yang⁷, Daisuke Nishimiya⁷, Rahul Sinha⁵, Barzin Y Nabet⁸, Chunlin Wang¹, Joseph B Shrager⁹, Mark F Berry¹⁰, Leah Backhus⁹, Natalie S Lui⁹, Heather A Wakelee¹¹, Joel W Neal¹¹, Sukhmani K Padda², Gerald J Berry¹², Alberto Delaidelli¹³, Poul H Sorensen¹³, Elena Sotillo¹⁴, Patrick Tran¹⁴, Jalen A Benson¹⁰, Rebecca Richards¹⁵, Louai Labanieh¹⁶, Dorota D Klysz¹⁴, David M Louis¹, Steven A Feldman¹⁴, Maximilian Diehn¹⁷, Irving L Weissman⁵, Jianjun Zhang¹⁸, Ignacio I Wistuba¹⁹, P Andrew Futreal²⁰, John V Heymach³, K Christopher Garcia²¹, Crystal L Mackall²², Mark M Davis²³

Affiliations

¹ Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA.
² Department of Medicine, Division of Oncology, Stanford University, Stanford, CA 94305, USA.
³ Department of Thoracic Head and Neck Medical Oncology, Division of Cancer Medicine, MD Anderson Cancer Center, Houston, TX 77030, USA.
⁴ Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA.
⁵ Institute for Stem Cell Biology and Regenerative Medicine Institute, Stanford University, Stanford, CA 94305, USA.
⁶ Stanford Genome Technology Center, Stanford University, Stanford, CA 94305, USA.
⁷ Department of Molecular and Cellular Physiology and Structural Biology, Stanford University, Stanford, CA 94305, USA.
⁸ Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA.
⁹ Department of Cardiothoracic Surgery - Thoracic Surgery, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford, CA 94305, USA.
¹⁰ Department of Cardiothoracic Surgery - Thoracic Surgery, Stanford University, Stanford, CA 94305, USA.
¹¹ Department of Medicine, Division of Oncology, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford, CA 94305, USA.
¹² Department of Pathology, Stanford University, Stanford, CA 94305, USA.
¹³ Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC V5Z 1L3, Canada.
¹⁴ Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA.
¹⁵ Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA; Department of Pediatrics, Stanford University, Stanford, CA 94305, USA.
¹⁶ Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
¹⁷ Institute for Stem Cell Biology and Regenerative Medicine Institute, Stanford University, Stanford, CA 94305, USA; Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford, CA 94305, USA.
¹⁸ Department of Thoracic Head and Neck Medical Oncology, Division of Cancer Medicine, MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genomic Medicine, Division of Cancer Medicine, MD Anderson Cancer Center, Houston, TX 77030, USA.
¹⁹ Department of Translational Molecular Pathology, Division of Pathology and Laboratory Medicine, MD Anderson Cancer Center, Houston, TX 77030, USA.
²⁰ Department of Genomic Medicine, Division of Cancer Medicine, MD Anderson Cancer Center, Houston, TX 77030, USA.
²¹ Department of Molecular and Cellular Physiology and Structural Biology, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA.
²² Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA; Department of Pediatrics, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Stanford University, Stanford, CA 94305, USA.
²³ Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305, USA. Electronic address: mmdavis@stanford.edu.

PMID: 33691136
PMCID: PMC7960510
DOI: 10.1016/j.immuni.2021.02.014

Global analysis of shared T cell specificities in human non-small cell lung cancer enables HLA inference and antigen discovery

Shin-Heng Chiou et al. Immunity. 2021.

. 2021 Mar 9;54(3):586-602.e8.

doi: 10.1016/j.immuni.2021.02.014.

Authors

Affiliations

¹ Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA.
² Department of Medicine, Division of Oncology, Stanford University, Stanford, CA 94305, USA.
³ Department of Thoracic Head and Neck Medical Oncology, Division of Cancer Medicine, MD Anderson Cancer Center, Houston, TX 77030, USA.
⁴ Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA.
⁵ Institute for Stem Cell Biology and Regenerative Medicine Institute, Stanford University, Stanford, CA 94305, USA.
⁶ Stanford Genome Technology Center, Stanford University, Stanford, CA 94305, USA.
⁷ Department of Molecular and Cellular Physiology and Structural Biology, Stanford University, Stanford, CA 94305, USA.
⁸ Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA.
⁹ Department of Cardiothoracic Surgery - Thoracic Surgery, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford, CA 94305, USA.
¹⁰ Department of Cardiothoracic Surgery - Thoracic Surgery, Stanford University, Stanford, CA 94305, USA.
¹¹ Department of Medicine, Division of Oncology, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford, CA 94305, USA.
¹² Department of Pathology, Stanford University, Stanford, CA 94305, USA.
¹³ Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC V5Z 1L3, Canada.
¹⁴ Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA.
¹⁵ Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA; Department of Pediatrics, Stanford University, Stanford, CA 94305, USA.
¹⁶ Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
¹⁷ Institute for Stem Cell Biology and Regenerative Medicine Institute, Stanford University, Stanford, CA 94305, USA; Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford, CA 94305, USA.
¹⁸ Department of Thoracic Head and Neck Medical Oncology, Division of Cancer Medicine, MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genomic Medicine, Division of Cancer Medicine, MD Anderson Cancer Center, Houston, TX 77030, USA.
¹⁹ Department of Translational Molecular Pathology, Division of Pathology and Laboratory Medicine, MD Anderson Cancer Center, Houston, TX 77030, USA.
²⁰ Department of Genomic Medicine, Division of Cancer Medicine, MD Anderson Cancer Center, Houston, TX 77030, USA.
²¹ Department of Molecular and Cellular Physiology and Structural Biology, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA.
²² Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA; Department of Pediatrics, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Stanford University, Stanford, CA 94305, USA.
²³ Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305, USA. Electronic address: mmdavis@stanford.edu.

PMID: 33691136
PMCID: PMC7960510
DOI: 10.1016/j.immuni.2021.02.014

Abstract

To identify disease-relevant T cell receptors (TCRs) with shared antigen specificity, we analyzed 778,938 TCRβ chain sequences from 178 non-small cell lung cancer patients using the GLIPH2 (grouping of lymphocyte interactions with paratope hotspots 2) algorithm. We identified over 66,000 shared specificity groups, of which 435 were clonally expanded and enriched in tumors compared to adjacent lung. The antigenic epitopes of one such tumor-enriched specificity group were identified using a yeast peptide-HLA A^∗02:01 display library. These included a peptide from the epithelial protein TMEM161A, which is overexpressed in tumors and cross-reactive epitopes from Epstein-Barr virus and E. coli. Our findings suggest that this cross-reactivity may underlie the presence of virus-specific T cells in tumor infiltrates and that pathogen cross-reactivity may be a feature of multiple cancers. The approach and analytical pipelines generated in this work, as well as the specificity groups defined here, present a resource for understanding the T cell response in cancer.

Keywords: EBV; EntS; GLIPH2; LMP2A; NSCLC; T cell receptor repertoire; T cell specificity; TCR; TMEM161A; cancer; cross-reactivity; tumor-infiltrating lymphocyte.

PubMed Disclaimer

Conflict of interest statement

Declaration of interests C.L.M. is a founder of, holds equity in, and receives consulting fees from Lyell Immunopharma and receives consulting fees from NeoImmuneTech, Nektar, Apricity, and Roche. J.W.N. reports research support from Genentech/Roche, Merck, Novartis, Boehringer Ingelheim, Exelixis, Takeda Pharmaceuticals, Nektar Therapeutics, Adaptimmune, and GSK and has served in a consulting or advisory role for AstraZeneca, Genentech/Roche, Exelixis Inc., Jounce Therapeutics, Takeda Pharmaceuticals, Eli Lilly and Company, Calithera Biosciences, Amgen, Regeneron Pharmaceuticals, Natera, and Iovance Biotherapeutics. H.A.W. has received research support from Celgene, Clovis Oncology, Genentech/Roche, Arrys Therapeutics, Novartis, Merck, BMS, Exelixis, Lilly, Pfizer, and has participated on the advisory boards of Helsinn, Mirati, Cellworks, Genentech/Roche, Merck, and ITMIG. N.S.L. has received research funding from Intuitive Foundation and Auspex Diagnostics. E.S. is a consultant for Lyell Immunopharma. L.L. is a consultant for Lyell Immunopharma. S.A.F. is consulting for Lonza PerMed and Samsara BioCapital. M.D. reports research funding from Varian Medical Systems and Illumina; ownership interest in CiberMed and Foresight Diagnostics; patent filings related to cancer biomarkers; paid consultancy from Roche, AstraZeneca, BioNTech, Genentech, Novartis, and Gritstone Oncology; and travel/honoraria from Reflexion. K.C.G. is founder of 3T therapeutics. I.I.W. has received honoraria from Genentech/Roche, Bayer, Bristol-Myers Squibb, AstraZeneca/Medimmune, Pfizer, HTG Molecular, Asuragen, Merck, GlaxoSmithKline, Guardant Health, Oncocyte, and MSD. I.I.W. is also supported by Genentech, Oncoplex, HTG Molecular, DepArray, Merck, Bristol-Myers Squibb, Medimmune, Adaptive, Adaptimmune, EMD Serono, Pfizer, Takeda, Amgen, Karus, Johnson & Johnson, Bayer, Iovance, 4D, Novartis, and Akoya. J.Z. reports grants from Merck and Johnson & Johnson, as well as adversary/consulting/Hornoraria fees from Bristol Myers Squibb, AstraZeneca, GenePlus, Innovent, OrigMed, and Roche outside the submitted work. This study was supported in part by a Cancer Prevention Research Institute of Texas Multi-Investigator Research Award (grant number RP160668) and the University of Texas Lung Specialized Programs of Research Excellence grant (grant number P50CA70907). S.-H.C., D.T., C.L.M., and M.M.D have a patent related to this work.

Figures

**Figure 1**
Establishing specificity groups with CDR3β sequences from lung cancer patients (A) Analysis of shared T cell specificities with the GLIPH2 algorithm. Step 1: 778,938 CDR3β sequences from the MDACC cohort as input for GLIPH2 analysis. Step 2: establish 66,094 specificity groups with multiple criteria (Figure S1A). Step 3: establish 4,226 clonally expanded specificity groups. Step 4: establish 435 clonally expanded, tumor-enriched specificity groups. (B) Clinical relevance of tumor-enriched specificity groups in lung cancer. The most clonally expanded CDR3β sequences from tumors belonged to the 435 tumor-enriched specificity groups, whereas those from lung tissues of healthy donors and COPD patients did not. The trend was validated with tumors from a second NSCLC cohort (the TRACERx consortium, n = 202, validation). ^∗∗∗p < 0.001; ^∗p < 0.05 by paired t test. NS, not significantly different. (C) Network analysis of 396 specificity groups annotated with CDR3β sequences from HLA tetramers with flu (red), EBV (green), and CMV (blue) antigens. Each dot is a specificity group, edges indicate the presence of identical CDR3β sequence(s) shared across two specificity groups. (D) Percentage (%) of HLA-A^∗02 or HLA-B^∗08 tetramer-annotated specificity groups with significantly enriched the *A^∗02* (purple, left plot) or *B^∗08* (blue, right plot) supertype alleles, respectively. Specificity groups annotated with tetramers of other HLA alleles (other tetramer) were included for comparisons. (E) Percentage of shared specificity between any two given MDACC NSCLC patients (% shared between any 2 patients, total n = 178) based on CDR3β membership in total specificity groups regardless of clonal expansion (n = 66,094), membership in clonally expanded specificity groups (n = 4,226), or comparison of identical CDR3β sequences. Boxes represent medians with the first (25^th) and third (75^th) quartiles. (F and G) Bootstrapping of specificity group numbers (y axis, specificity group #) with varying sampling sizes (individuals sampled) for either HLA-A^∗02⁺ or HLA-A^∗02⁻ NSCLC patients (F) or healthy donors (G, Emerson study). Data represent means with 3× standard errors from repeated sampling.

**Figure 2**
The TCR members of the tumor-enriched specificity group with the motif “S%DGMNTE” are inferred to recognize tumor antigen in the context of HLA-A^∗02 (A) Left: volcano plot showing the comparison of the 4,226 clonally expanded specificity groups between tumor (T) and the adjacent lung (N) by Poisson test. The y axis represents the negative log₁₀ converted p values of the Poisson test, and the x axis represents the log₂ converted fold difference between tumor and adjacent lung (T/N). Dot size represents levels of clonal expansion. Tumor-enriched specificity groups (n = 435) are highlighted in red. Right: volcano plot of T/N comparison for CDR3β clonotypes. CDR3β clones of the 435 tumor-enriched specificity groups (left) are highlighted in red. (B) Pearson correlations and the corresponding p values between the signature scores for the hallmark GSEA gene sets (n = 50) and the percentages of CDR3β clones belonging to the 435 tumor-enriched specificity groups. Significant comparisons are highlighted in red (p < 0.05). (C) Heatmap showing the −log₁₀ p values of top-enriched HLA allele(s) of the 435 tumor-enriched specificity groups. Top, number of MDACC patients carrying each indicated HLA alleles. (D) Number of top-enriched HLA allele(s) found in each of the 435 tumor-enriched specificity groups. (E) Volcano plot for the 4,226 NSCLC specificity groups as in (A, left). The tumor-enriched specificity groups significantly enriched with *HLA-A^∗02* alleles (p < 0.05 by Fisher’s exact test) are colored in green. The specificity group “S%DGMNTE” is highlighted. (F) The distinct CDR3β sequence members of the “S%DGMNTE” specificity group. For each CDR3β sequence, the Vβ gene usage (Vβ), number of patients with each sequence (patient counts), number of HLA-A^∗02⁺ patients (counts of HLA-A^∗02⁺ cases/total), and the average clonal frequencies (% by patient) found in the adjacent lung, tumor, and peripheral blood are shown. ND, not detected. Bottom: p values for the enrichment of Vβ gene usage, *HLA-A^∗02* alleles, and the level of clonal expansion are shown.

**Figure 3**
Identification of tumor and pathogen-derived antigens recognized by a tumor-enriched TCR in human lung cancer (A) Top: top-20 mimotopes from the 4^th round of selection on an 11-mer yeast library are used to stimulate Jurkat-TCR2 cells. CD69 fold change is shown compared to unstimulated control. Bottom: ranked raw counts (log₁₀) of the enriched mimotopes from the selection. (B) Alignment of the top-two mimotopes with peptides from the human *TMEM161A* locus, EBV *LMP-2A*, and *E. coli EntS*. All peptides were 9-mers and predicted to bind HLA-A^∗02 with high affinities. (C) Left: representative FACS plots showing the stimulation of the Jurkat-TCR2 cells with 9-mers from the human *TMEM161A* locus (TMEM9-mer), *LMP-2A* of EBV (LMP9-mer), and *EntS* from *E. coli* (EntS9-mer); right: results of Jurkat-TCR2 cell stimulation in triplicate. Control PP, control peptide (GILGFVFTL); No PP, no peptide. (D) Stimulation of primary T cells ectopically expressing TCR2 TCRα/β chains with either 9-mers (left) or full-length proteins (right). Stimulation of primary T cells expressing TCR14 by 293T-A^∗02 cells expressing full-length FluM1 protein was shown as control. ^∗p < 0.05; ^∗∗p < 0.01; ^∗∗∗p < 0.001 by t test. Control PP, control peptide (GILGFVFTL). (E) The binding of TCR2 to the indicated A^∗02/9-mers was determined by biolayer interferometry. An overlay of binding traces over a concentration series of the indicated A^∗02/9-mers from one representative experiment is shown. The data points are represented as open circles and the fits from a simple 1:1 Langmuir interaction model are indicated by solid lines. Each binding experiment was repeated three times. (F) The equilibrium association constants (KA) of the surface plasma resonance as in (E). The flu M1 peptide showed no detectable binding (n.b.) to TCR2. Significance was determined by t test after one-way ANOVA. The reported p values were corrected for multiple comparisons. ^∗∗p < 0.01. ND, not different. All error bars represent standard deviation of the mean.

**Figure 4**
TMEM161A protein is highly expressed in human lung cancer (A) Representative images of TMEM161A immunohistochemistry on tumor (top) and the adjacent lung (bottom) sections from four patients. Scale bar, 100 μm. Rightmost panels: zoomed in images of patient A16 tumor with TMEM161A immunohistochemistry (top) and H&E staining on a serial section (bottom). Scale bar, 40 μm. (B) Quantification of TMEM161A immunohistochemistry on sections from the Stanford NSCLC cohort (n = 11). Boxplots show medians with the first (25^th) and third (75^th) quartiles with individual data points. ^∗∗∗p < 0.001. (C) *TMEM161A* expression quantified by bulk RNA-seq of the indicated samples from TCGA (n = 958) is shown in boxplots. Adj-Ctrl, the adjacent lung control. *TMEM161A* expression normalized against Adj-Ctrl is shown. p values were calculated with the Wilcoxon Rank Sum test. ND, not significantly different. Boxplots represent medians with the first (25^th) and third (75^th) quartiles. (D) Gene set enrichment analysis of the ranked gene list based on Pearson correlation with *TMEM161A* abundance in the pan-lung cancer TCGA dataset (n = 958). Left: hallmark gene sets with highest (blue) and lowest (red) normalized enrichment scores are indicated, and their enrichment curves are shown (right). (E) Single-sample GSEA signature scores (Sig score) of two most and two least enriched hallmark signatures are plotted against *TMEM161A* expression. Pearson correlation coefficients are shown in plots (cor coef).

**Figure 5**
Isolation and characterization of cross-reactive TMEM161A-specific T cells from peripheral blood of healthy donors and lung cancer patients (A) Schematic showing the procedure used to capture antigen-specific T cell clones from HLA-A^∗02⁺ healthy donors and NSCLC patients. Cells were sorted by FACS directly into 96-well plates for scRNA-seq and scTCR-seq. (B) Representative FACS plots of T cells sorted with indicated tetramers from the PBMC of HLA-A^∗02⁺ healthy donors (He65 and He66) or HLA-A^∗02⁺ NSCLC patients (A6 and A17). (C) Percentage of tetramer⁺ T cells from healthy donors (n = 11) and NSCLC patients (n = 7). Boxes represent medians with the first (25^th) and third (75^th) quartiles. NS, not significantly different. (D) Percentage of distinct CDR3β sequences in tetramer-sorted T cells from healthy donors and NSCLC patient. Numbers in plots represent the cell counts. (E) Indicated TCR clonotypes identified with tetramers were expressed in Jurkat cells and co-cultured with T2 cells pulsed with indicated 9-mers. y axis (fold stimulated) shows activation by CD69 fold change compared to unstimulated control. ^∗∗∗p < 0.001. Ctrl peptide, control peptide (GILGFVFTL). (F and G) Cell-mediated cytotoxicity of H1395 lung cancer cells. Primary T cells ectopically expressing TCR2α/β chains were co-cultured with the A^∗02⁺ H1395 cancer cells and pulsed with either no peptide, TMEM9-mer, or LMP9-mer. Representative images (F) and results using cells from two different donors (G) are shown. ^∗∗p < 0.01; ^∗∗∗p < 0.001 by t test. Error bars represent standard deviation of the mean.

**Figure 6**
Phenotypic characterization of the TMEM161A-specific CD8⁺ T cells (A and B) Dimension reduction by Uniform Manifold Approximation and Projection (UMAP) of the scRNA-seq data from 2,950 sorted tumor-infiltrating T cells from 10 NSCLC patients (Stanford cohort). The identified cell clusters (n = 14) are labeled with distinct colors (A) and shown with varying dot sizes representing the level of clonal expansion (B). (C) Clonality of the 2,950 sorted T cells as in (B) quantified as 1 - Pielou’s evenness. (D) Breakdown of cell states for T cell clones of the 4,226 specificity groups defined in Figure S1A (top), viral-related specificity groups (second from top), the 435 tumor-enriched specificity groups (third from top), and TMEM9-mer/A^∗02 tetramer-sorted CD8 T cells from tumor (bottom, patient A6). (E) Heatmap showing differentially expressed genes for each cell cluster defined in (A). Select differential genes for cluster c5, c6, and c7 are highlighted. (F) Stacked violin plot showing the expression of highlighted differential genes in (E) in all cell clusters. (G) Pseudotime trajectory of CD8⁺ single cells by Monocle (v2.10.1). (H) Exhaustion score versus activation score for CD8⁺ T cells sorted by the HLA-A^∗02/TMEM9-mer tetramer (top right) and those that belong to tumor-enriched specificity groups (bottom right), colored by the cluster identity. Exhausted CD8⁺ T cells (c11) and activated CTL (c12) are shown for comparison.

**Figure 7**
Virus-specific CD8 T cell clones expanded in patients responding to anti-PD1 treatment (A) Comparisons of pre- and post-treatment CDR3β clonal frequencies (in log₁₀ percent) in the peripheral blood of patient M1 (left) and M2 (right). CDR3β clones inferred to recognize viral antigens are highlighted. (B) Specificity groups containing expanded CDR3β clones post-treatment (column 5, CDR3β sequence) from patients M1 or M2 (column 6, Patient ID) that are annotated with viral tetramer CDR3β sequences (column 2–4, antigen and HLA alleles of the tetramers). Enrichment of the *A^∗02:01* or *B^∗35:01* allele is shown (last two columns, p values from the hypergeometric tests are shown). CDR3α/β sequences of the two EBV-related expanded clones from patient M2 are shown at the bottom. (C) TCR27- (CDR3β: CASSTGDSNQPQHF, top panels) and TCR28- (CDR3β: CASSARTGELFF, bottom panels) Jurkat cell lines were created and tested for their reactivities to the predicted EBV antigens in the context of B^∗35 as shown in (B). TCR27- and TCR28-Jurkat cells were co-cultured with T2-B^∗35 cells pulsed with indicated peptides (above each plot). Level of activation was quantified with CD69 expression. Control peptide: LPFDFTPGY.

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References

1. Altman J.D., Davis M.M. MHC-peptide tetramers to visualize antigen-specific T cells. Curr Protoc Immunol. 2003;Chapter 17:Unit 17.13. - PubMed
1. Anagnostou V., Forde P.M., White J.R., Niknafs N., Hruban C., Naidoo J., Marrone K., Sivakumar I.K.A., Bruhm D.C., Rosner S. Dynamics of Tumor and Immune Responses during Immune Checkpoint Blockade in Non-Small Cell Lung Cancer. Cancer Res. 2019;79:1214–1225. - PMC - PubMed
1. Anders S., Pyl P.T., Huber W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–169. - PMC - PubMed
1. Andersen R.S., Thrue C.A., Junker N., Lyngaa R., Donia M., Ellebæk E., Svane I.M., Schumacher T.N., Thor Straten P., Hadrup S.R. Dissection of T-cell antigen specificity in human melanoma. Cancer Res. 2012;72:1642–1650. - PubMed
1. Arstila T.P., Casrouge A., Baron V., Even J., Kanellopoulos J., Kourilsky P. A direct estimate of the human alphabeta T cell receptor diversity. Science. 1999;286:958–961. - PubMed

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Global analysis of shared T cell specificities in human non-small cell lung cancer enables HLA inference and antigen discovery

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Global analysis of shared T cell specificities in human non-small cell lung cancer enables HLA inference and antigen discovery

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Conflict of interest statement

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