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Phenotype, specificity and avidity of antitumour CD8+ T cells in melanoma


Interactions between T cell receptors (TCRs) and their cognate tumour antigens are central to antitumour immune responses1,2,3; however, the relationship between phenotypic characteristics and TCR properties is not well elucidated. Here we show, by linking the antigenic specificity of TCRs and the cellular phenotype of melanoma-infiltrating lymphocytes at single-cell resolution, that tumour specificity shapes the expression state of intratumoural CD8+ T cells. Non-tumour-reactive T cells were enriched for viral specificities and exhibited a non-exhausted memory phenotype, whereas melanoma-reactive lymphocytes predominantly displayed an exhausted state that encompassed diverse levels of differentiation but rarely acquired memory properties. These exhausted phenotypes were observed both among clonotypes specific for public overexpressed melanoma antigens (shared across different tumours) or personal neoantigens (specific for each tumour). The recognition of such tumour antigens was provided by TCRs with avidities inversely related to the abundance of cognate targets in melanoma cells and proportional to the binding affinity of peptide–human leukocyte antigen (HLA) complexes. The persistence of TCR clonotypes in peripheral blood was negatively affected by the level of intratumoural exhaustion, and increased in patients with a poor response to immune checkpoint blockade, consistent with chronic stimulation mediated by residual tumour antigens. By revealing how the quality and quantity of tumour antigens drive the features of T cell responses within the tumour microenvironment, we gain insights into the properties of the anti-melanoma TCR repertoire.

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Fig. 1: Distinct cell states among CD8+ TCR clonotype families in melanoma.
Fig. 2: Target specificity and phenotype of tumour-specific CD8+ TCRs.
Fig. 3: Antigenic specificity and avidity of tumour-specific TCRs.
Fig. 4: Peripheral blood dynamics of CD8+ TIL-TCRs.

Data availability

scRNA-seq, scTCR-seq and CITE-seq data are available through the dbGaP portal (study ID: 26121, accession number: phs001451.v3.p1). All other data are available from the corresponding author on reasonable request.

Code availability

The code used for data analysis included the Broad Institute Picard Pipeline (for whole-exome sequencing/RNA-seq), GATK4 v4.0, Mutect2 v2.7.0 (for single-nucleotide variant and indel identification), NetMHCpan 4.0 (for NeoAg-binding prediction), ContEst (for contamination estimation), ABSOLUTE v1.1 (for purity/ploidy estimation), STAR v2.6.1c (for sequencing alignment), RSEM v1.3.1 (for gene expression quantification), Seurat v3.2.0 (for single-cell sequencing analysis), Harmony v1.0 (for single-cell data normalization), SingleR v3.22, Scanpy v1.5.1 and Python v3.7.4 (for comparison with other single-cell datasets), which are each publicly available. The computer code used to generate the analyses is available at


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We are grateful for expert assistance from O. Olive and K. Shetty from the DFCI Center for Personal Cancer Vaccines; M. Manos and M. Severgnini and the staff of the DFCI Center for Immuno-Oncology (CIO); S. Pollock and C. Patterson (the Broad Institute’s Biological Samples, Genetic Analysis, and Genome Sequencing Platform) for their help in sample collection and management; and D. Braun, S. Gohil, S. Sarkizova and all of the members of the Wu laboratory for productive discussions and critical reading of the manuscript. This research was made possible by a generous gift from the Blavatnik Family Foundation, and was supported by grants from the US National Institutes of Health (NCI-1R01CA155010 and NCI-U24CA224331 to C.J.W.; NIH/NCI R21 CA216772-01A1 and NCI-SPORE-2P50CA101942-11A1 to D.B.K.; NCI-1R01CA229261-01 to P.A.O.; NIH/NCI P01CA229092 and NIH/NIAID U19 AI082630 to K.J.L.; NCI R50CA211482-01 to S.A.S.; NCI R50CA251956 to S.L.; and R01 CA208756 to N.H.) and a Team Science Award from the Melanoma Research Alliance (to C.J.W., P.A.O. and K.J.L.). G.O. was supported by the American Italian Cancer Foundation fellowship. This work was further supported by The G. Harold and Leila Y. Mathers Foundation, and the Bridge Project, a partnership between the Koch Institute for Integrative Cancer Research at MIT and the Dana-Farber/Harvard Cancer Center.

Author information

Authors and Affiliations



G.O. and C.J.W. conceived the project and directed the overall study. G.O. designed and performed the experimental and data analysis together with K.S., S.K., P.M.L., W.Z., J.F., T.H., S.L., K.J.L., N.C. and D.B.K. G.O. and K.S. analysed the single-cell sequencing data. J.F., S.A.S. and K.J.L. analysed the bulk sequencing data. K.S. and D.N. designed and performed the statistical analyses. S.K., D.B.K., K.R.C. and S.A.C. generated and analysed the mass spectrometry results. S.J. provided the peptide-stability measurements. T.K., Q.X. and G.M. supported TCR cloning and screening. D.F.T., G.M.B., M.S.-F. and P.A.O. provided patient samples and clinical information. N.C., M.S.-F., E.F.F. and N.H. contributed to data discussion and interpretation. G.O. and C.J.W. wrote the manuscript. All authors discussed the results and read the manuscript.

Corresponding author

Correspondence to Catherine J. Wu.

Ethics declarations

Competing interests

E.F.F. is an equity holder and consultant for BioNTech, and equity holder and SAB member of BioEntre. N.H. and C.J.W. are equity holders of BioNTech. N.H. is an advisor and equity holder for Related Sciences, and receives research funding from Bristol-Myers Squibb. P.A.O. has received research funding from and has advised Neon Therapeutics, Bristol-Myers Squibb, Merck, CytomX, Pfizer, Novartis, Celldex, Amgen, Array, AstraZeneca/MedImmune, Armo BioSciences and Roche/Genentech. C.J.W. is subject to a conflict of interest management plan for the reported studies because of her former competing financial interests in Neon Therapeutics, which was acquired by BioNTech. Under this plan, C.J.W. may not access identifiable data for human participants or otherwise participate directly in the IRB-approved protocol reported herein; the contributions by C.J.W. to the overall strategy and data analyses occurred on a de-identified basis. Patent applications have been filed on aspects of the described work entitled as follows: ‘Compositions and methods for personalized neoplasia vaccines’ (N.H., E.F.F. and C.J.W.), ‘Methods for identifying tumour specific neo-antigens’ (N.H. and C.J.W.), ‘Formulations for neoplasia vaccines’ (E.F.F.) and ‘Combination therapy for neoantigen vaccine’ (N.H., C.J.W. and E.F.F.). The Dana-Farber Cancer Institute has a proprietary and financial interest in the personalized NeoAg vaccine. S.J. is chief scientific officer of Immunitrack. S.A.S. reported non-financial support from Bristol-Myers Squibb outside the submitted work, previously advised and has received consulting fees from Neon Therapeutics, and reported non-financial support from Bristol-Myers Squibb and equity in Agenus Inc., Agios Pharmaceuticals, Breakbio Corp., Bristol-Myers Squibb and Lumos Pharma, outside the submitted work. T.K. and G.M. are employees of TScan Therapeutics and hold equity in TScan Therapeutics. T.K. is a founder of TScan Therapeutics. D.B.K. has previously advised Neon Therapeutics and has received consulting fees from Neon Therapeutics, and owns equity in Aduro Biotech, Agenus, Armata Pharmaceuticals, Breakbio, BioMarin Pharmaceutical, Bristol-Myers Squibb, Celldex Therapeutics, Editas Medicine, Exelixis, Gilead Sciences, IMV, Lexicon Pharmaceuticals, Moderna and Regeneron Pharmaceuticals. BeiGene, a Chinese biotech company, supports unrelated research at the DFCI Translational Immunogenomics Laboratory (TIGL). S.A.C. is a member of the scientific advisory boards of Kymera, PTM BioLabs and Seer, and a scientific advisor to Pfizer and Biogen. The remaining authors declare no competing interests.

Additional information

Peer review information Nature thanks Benny Chain, Aude Chapuis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Clinical course of patients with melanoma analysed for single-cell sequencing and TCR specificity.

Schematic representation of the clinical histories of the four patients with melanoma profiled in this study. Triangles indicate the time of collection of tumour biopsies (red) analysed with single-cell sequencing or of peripheral blood samples (blue) used for isolation of tumour-reactive T cells at serial time points (TP). NED, no evidence of disease.

Extended Data Fig. 2 Single-cell profiling of CD8+ TILs.

a, Flow-cytometry plots quantifying the proportion of T lymphocytes (defined as CD45+CD3+) infiltrating five tumour biopsies subjected to single-cell sequencing. Tissue origin for each tumour sample is indicated. b, Density plots identifying CD8+ TILs through CITE-seq antibody signals for CD4+ (orange) and CD8+ (blue). Data are reported as CD4 and CD8 CITE-seq signals relative to isotype controls for all sequenced cells that were identified as T cells after flow sorting and computational filtering. c, Size and patient distribution of the 13 clusters identified from CD8+ TIL scRNA-seq. Left: per cluster, sample origin is denoted by colour. The analysed CD8+ dataset is predominantly composed by cells from three patients (Pt-A (green), Pt-C (red) and Pt-D (blue)). Two clusters were found to be patient-specific (clusters 8 and 11). Right: UMAPs depicting cluster distribution of patient-specific CD8+ TILs. d, Heatmaps depicting the mean cluster expression of a panel of T cell-related genes, measured by scRNA-seq (left panel) and the mean surface expression of the corresponding proteins measured through CITE-seq (right panel). Clusters (columns) are labelled using the annotation provided in Fig. 1b; markers (rows) are grouped based on their biological function. Grey, unevaluable markers (CD45 isoforms for scRNA-seq) or those that were not assessed (for CITE-seq). CITE-seq CD3 surface expression was poorly detected because of the presence of competing CD3 sorting antibody. e, Violin plots quantifying relative transcriptional expression of genes (columns) with high differential expression among CD8+ TIL clusters (rows). f, UMAPs depicting the single-cell expression of representative T cell markers among CD8+ TILs either through detection of surface protein expression with CITE-seq (Ab) or through scRNA-seq (RNA). g, Characterization of the CD8+ TIL clusters using independent reference gene signatures8,9,10. Heatmaps show cross-labelling of T cell clusters defined in the present study (columns, reported as in Fig. 1b) versus reference gene signatures (rows) derived from the analyses in Sade-Feldman et al.8, Yost et al.9 and Oh et al.10, with intensities indicating normalized frequency.

Extended Data Fig. 3 Clonality of CD8+ TILs and cell states of TCR clonotypes.

a, Histograms depicting the number (bottom panel) and overall frequency (top panel) of patients’ TCR clonotype families divided in categories based on their size (x axis). b, Histograms showing the intracluster TCR clonality, calculated for CD8+ T cells in each cluster (x axis) using normalized Shannon index. The symbols indicate individual TCR clonality for the three patients with high numbers of TILs (Pt-A, Pt-C and Pt-D). The bars indicate the overall TCR clonality measured within each cluster. c, UMAPs of the cluster distribution of representative dominant TCR clonotype families among CD8+ TILs from TIL-rich patients (n = 3). For each patient, the numbers denote the ranking of each TCR clonotype (Fig. 1c), while colours identify their primary cluster (Fig. 1b).

Extended Data Fig. 4 Characterization of patient-derived melanoma cell lines.

a, Purity of tumour cultures, originating from patient biopsies, was assessed by flow cytometry by staining cells with isotype controls (top panels) or surface markers (bottom panels) identifying melanoma (using melanoma chondroitin sulfate proteoglycan (MCSP); y axis) or fibroblast lineages (fibroblast antigen; x axis). Consistent with previous reports36, MCSP was expressed in three of four tumour cultures, with each lacking substantive fibroblast contamination. b, Flow-cytometric assessment of HLA class I surface expression on the established melanoma cell lines. Surface expression was measured with a pan-HLA class I antibody (top panels) or with an HLA-A:02-specific antibody (bottom panels) at basal culture conditions (magenta) or upon exposure to IFNγ for 72 h (purple), compared with isotype control (grey). c, Comparison of the mutation burden of patient-derived melanoma cell lines versus corresponding parental tumours. For all patients, mutation calling from whole-exome sequencing (WES) of tumour biopsies and cell lines was performed through comparison with autologous PBMCs serving as germline controls. Venn diagrams show the numbers and frequencies of mutations unique to parental tumours (red) or melanoma cell lines (blue) or shared between the two (black). Corresponding dot plots, using the same colour code, show the variant allele frequencies (VAF) of mutations detected in the parental tumours (x axis) and derived cell lines (y axis). For both, tumour purity inferred from single-cell data (parental tumours) or detected by flow cytometry (cell lines) is indicated. The high concordance between the genomic mutations detected in paired specimens demonstrates that the melanoma cell lines are reflective of the corresponding parental tumours. d, Gene expression profiles of HLA class I genes and MAA genes in pdMel-CLs (columns, black) or in matched parental tumours (columns, white), compared with control tumour-derived fibroblast cell lines (n = 3) originating from unrelated melanoma biopsies (right columns). Tumour purity is reported in red. Gene expression was measured by RNA-seq and normalized as logarithmic transcripts per million mapped reads (TPM). e, HLA class I immunopeptidome of patient-derived melanoma cell lines cultured with or without IFNγ. Bars indicate the numbers of unique peptides detected by mass spectrometry (MS) after immunoprecipitation of peptide–HLA class I complexes (bottom panel) and of unique genes from which the detected peptides were derived (top panel), and are grouped based on their origin from MAAs or NeoAgs and coloured by patient. The number of MS acquisitions for each condition is indicated.

Extended Data Fig. 5 Antitumour reactivity of in vitro-reconstructed TCRs.

a, Schema for classification of TCR reactivities based on CD137 upregulation of TCR-transduced T cell lines upon challenge with patient-derived melanoma cells (pdMel-CLs; with or without IFNγ pre-treatment (red)) or controls (PBMCs, B cells and EBV-LCLs (blue)). A TCR was defined as tumour-specific if it recognized only the autologous melanoma cell line, but did not upregulate CD137 when challenged with autologous controls. b, Representative flow cytometry plots depicting CD137 upregulation measured on CD8+ T cells transduced with TCRs isolated from Pt-A and cultured with melanoma or control targets. Background reactivity was estimated by measuring CD137 upregulation on CD8+ T cells transduced with an irrelevant TCR. c, Cytotoxic potential provided by TCRs with exhausted (left) or non-exhausted (right) primary clusters isolated from all four studied patients. Degranulation (CD107a/b+) and concomitant production of cytokines (IFNγ, TNF and IL-2) were assessed through intracellular staining, gating on TCR-transduced (mTRBC+) CD8+ T cells cultured alone or in the presence of autologous pdMel-CLs. Each dot represents the result for a single TCR isolated from CD8+ TILs, colour-coded based on its primary phenotypic cluster (as defined in Fig. 1b). For each analysed TCR, background cytotoxicity from CD8+ T cells transduced with an irrelevant TCR was subtracted. White dots depict the basal level of activation of untransduced cells. Overall, these data indicate that antitumour cytotoxicity mainly resides among TCR clonotypes with exhausted primary clusters.

Extended Data Fig. 6 Isolation, single-cell sequencing and screening of tumour-reactive TCRs from peripheral blood samples.

a, b, PBMCs collected at serial time points (TP1, TP2 and TP3; Extended Data Fig. 1) were cultured with autologous melanoma cell lines to enrich for antitumour TCRs. After two rounds of stimulation, the reactivity of effector CD8+ T cells was assessed by measuring: degranulation and cytokine production (a) or CD137 upregulation upon re-challenge with melanoma (blue line) (b). The specificity of the response was supported by the low recognition of HLA-mismatched unrelated melanoma (grey dashed line). Negative controls (cultured in the absence of target cells) and positive controls (polyclonal stimulators, PHA or PMA-ionomycin) are displayed as solid grey and black lines, respectively. c, FACS sorting strategy for the isolation of tumour-reactive T cells. CD8+ effectors with active degranulation and concomitant cytokine production were identified using cytokine secretion assays (Supplementary Methods) upon stimulation without any target (top panel) or in the presence of autologous melanoma (bottom panels). CD107a/b+ cells secreting at least one of the measured cytokines (IFNγ, TNF and IL-2) were single-cell sorted and sequenced. Gates depict the detection and quantification of reactive (black) or sorted cells (magenta) from a representative sample (TP3 PBMCs from Pt-A). d, TCR clonotypes identified upon single-cell sorting and scTCR-seq of melanoma-reactive CD8+ T cells from the four studied patients. Bars indicate cell counts of clonotype families, defined as CD8+ cells bearing identical TCRα and TCRβ chains, divided on the basis of their detection at specific time points (TP1, TP2 and TP3) or across multiple time points (shared). The presence of multiple TCRα or TCRβ chains is indicated with black or orange borders, respectively. eh, TCRs isolated and sequenced from anti-melanoma cultures were reconstructed, expressed in CD8+ T cells and screened against melanoma (pdMel-CL; with or without IFNγ pre-treatment in red) or controls (PBMCs, B cells and EBV-LCLs in blue). TCRs were classified as reported in Extended Data Fig. 5a, to identify: tumour-specific TCRs (e), non-tumour-reactive TCRs (f) and tumour/control reactive TCRs (g). Reactivity was calculated by subtracting the background of lymphocytes transduced with an irrelevant TCR from CD137 expression of CD8+ cells transduced with the reconstructed TCR. Floating boxes show minimum to maximum measurements, with mean values depicted as horizontal lines; white dots denote the basal level of activation measured on untransduced cells. The pie charts in h summarize the classification of TCR reactivity for all reconstructed TCRs. i, Cytotoxicity mediated by TCRs classified as tumour-specific (left panel), non-tumour-reactive (middle panel) or tumour/control-reactive (right panel). Degranulation (CD107a/b+) and concomitant production of cytokines (IFNγ, TNF and IL-2) were measured through intracellular flow cytometry on TCR transduced (mTRBC+) CD8+ T cells cultured alone or in the presence of autologous pdMel-CLs. Each dot represents the results of a single TCR isolated from CD8+ TILs (upon subtraction of background activation measured on CD8+ lymphocytes transduced with an irrelevant TCR). White dots denote the basal level of cytotoxicity of untransduced cells. j, Bar plots showing intratumoural cluster distribution of cells bearing tumour-specific (left) or non-tumour-reactive (right) TCRs isolated from blood and traced within the tumour microenvironment (Fig. 2e). For each patient, the numbers denote the ranking of each TCR among the top 100 clonotype families (Fig. 1c), while the colours identify their primary cluster (Fig. 1b).

Extended Data Fig. 7 Cell states of tumour-specific CD8+ TILs.

ac, Antigen specificity screening of 94 TCRs sequenced from clonally expanded CD8+ T cells isolated from tumour biopsies of 7 patients with metastatic melanoma from Sade-Feldman et al.8. a, After TCR reconstruction and expression in T cells, reactivity was measured as CD137 upregulation on TCR-transduced (mTRBC+) CD8+ cells upon culture with autologous EBV-LCLs pulsed with peptide pools covering immunogenic viral epitopes (CEF). Unstimulated cells were analysed as negative control. Results are reported after subtraction of background CD137 expression on T cells transduced with an irrelevant TCR. Five TCRs (black dots) recognized unpulsed EBV-LCLs, thereby documenting specificity for EBV epitopes. b, TCR antitumour reactivity, evaluated upon culture with autologous EBV-LCLs pulsed with peptide pools derived from 12 known MAAs. Background detected upon culture with DMSO-pulsed EBV-LCLs was subtracted. Additional negative and positive controls were an irrelevant peptide (Ova) and polyclonal stimulators (PHA or PMA/ionomycin), respectively. Coloured dots denote MAA-reactive TCRs. c, Table summarizing patient distribution of TCR specificities either discovered from reconstruction and screening of 94 TCRs or present within a database of human TCRs with known specificities (TCRdb12). df, Single-cell phenotype of TILs with antiviral or anti-MAA TCRs identified in the validation cohort from Sade-Feldman et al.8. d, t-SNE plot of CD8+ TILs highlighting the spatial distribution of cells containing TCRs with identified antigen specificity. Each colour denotes a distinct specificity, with crosses representing two cells with identical spatial coordinates. e, Pie charts summarizing the assignment of single cells containing antiviral (top) or anti-MAA (bottom) TCRs to one of the previously reported six clusters8. f, RNA transcripts differentially expressed between antiviral and anti-MAA T cells (log2FC > 1.5, adj. P value < 0.05). The heatmap reports Z scores, calculated from average gene expression of each TCR clonotype family (columns). Antigen specificity is reported on top with the same colour code as in d. g, h, Analysis of deregulated genes in exhausted clusters (TEx), enriched in tumour-reactive T cells, from the discovery cohort. g, Average gene expression, reported as Z scores, for each TCR clonotype family (columns) validated in vitro as tumour-specific (orange, 134 TCRs) or defined as virus-specific (black, 17 TCRs). The heatmap reports 98 RNA transcripts (adj. P < 0.0001, log2FC > 1) and 6 surface proteins (bottom rows, adj. P < 0.0001, log2FC > 0.4) detected trough scRNA-seq and CITE-seq, respectively. h, Plots depicting expression of representative RNA transcripts (top) or surface proteins (bottom) in each TCR clonotype family with antiviral (black) or antitumour (orange) specificity. Dots depict the average gene expression in each TCR clonotype, with size proportional to the frequency of the TCR clonotype within patient-specific CD8+ TILs. i, Heatmap depicting the top 20 overexpressed genes in each tumour-specific (TS) cluster of TS CD8+ cells (columns). Z scores of gene expression among five subpopulations are shown (Z score colour legend as in panel j). Genes important for the classification of each subset are highlighted in blue. j, Heatmaps depicting the expression of a panel of T cell-related transcripts detected through scRNA-seq (left) or surface proteins detected through CITE-seq (right). Z scores document the trends in expression among subpopulations of TS CD8+ cells (columns). k, Enrichment in expression of gene signatures among identified clusters of TS CD8+ cells (columns). Single cells with TS TCRs were divided in clusters as reported in Fig. 2f and scored for the expression of gene signatures defined from analysis of CD8 TILs of the discovery cohort (left), reported in external datasets of sequenced human CD8+ TILs (middle), or defined from published mouse studies (right) (Methods; Supplementary Table 8). The average enrichment score was calculated for each cluster and reported as the Z score.

Extended Data Fig. 8 Antigen specificity of tumour-reactive TCRs.

a, Antitumour TCRs isolated from patients positive for HLA-A*02:01 (Pt-A, Pt-B and Pt-D) were tested for the ability to cross-recognize allogeneic HLA-A*02:01+ melanomas. Melanoma reactivity was measured as CD137 upregulation on TCR-transduced (mTRBC+) CD8+ cells upon culture with autologous or allogeneic HLA-A*02:01-matched melanomas. Tumour specificity was ruled out through parallel detection of CD137 upregulation upon challenge with matched non-tumour controls (PBMCs). The floating boxes show minimum to maximum measurements, with mean values denoted by horizontal lines. All results are shown after subtraction of background CD137 expression on T cells transduced with an irrelevant TCR; the white dots denote the basal level of activation of untransduced CD8+ T cells. Pt-A and Pt-B displayed high melanoma-specific (that is, lack of recognition of autologous PBMCs) cross-reactivity indicating that a substantial proportion of antitumour TCRs recognize public HLA-A*02:01-restricted melanoma antigens. b, c, Antigen specificity screening of 299 antitumour TCRs. Upregulation of CD137 was assessed by flow cytometry on CD8+ T cells transduced with previously identified tumour-specific TCRs upon culture with autologous EBV-LCLs. Background, assessed using DMSO-pulsed target cells, was subtracted from each condition. b, Antigen recognition tested with pools of peptides corresponding to predicted immunogenic NeoAgs (Supplementary Table 9), known MAAs (Supplementary Table 10) or immunogenic viral epitopes. Reactivity was also assessed against an irrelevant peptide (Ova) or in the presence of polyclonal stimulators (PHA or PMA/ionomycin) as negative and positive controls, respectively. The black dots show the activation levels of a control Flu-specific HLA-A*02:01-restricted TCR. The coloured dots show confirmed antigen-reactive TCRs, coloured on the basis of the highest reactivity against a particular antigen, as per the legend, compared with the other tested antigens; white dots indicate TCRs reactive against an antigen that was not the highest of the panel of antigens tested, and hence considered a cross-reactive response; grey dots show negative responses. Deconvolution of antigen specificity of TCRs reactive to NeoAg peptide pools is reported in Supplementary Information—‘Flow-cytometry data’. c, Antigen specificity tested using NeoAg or MAA peptides detected by HLA class I mass spectrometry (MS) immunopeptidome of melanoma cell lines (Supplementary Tables 9, 10) with the addition of the MLANA protein (not retrieved by MS but known as highly immunogenic34). The coloured dots indicate confirmed antigen-reactive TCRs, coloured on the basis of the highest reactivity against a particular antigen (colour legend reported in d), compared with the other tested antigens; the white dots denote TCRs reactive against an antigen that was not the highest of the panel of antigens tested, and hence considered a cross-reactive response. d, Distribution of antigen specificities of antitumour TCRs per patient successfully deorphanized after screening. Colours denote the distinct peptides recognized by individual antitumour TCRs. Note that TCRs classified as specific for antigenic pools (n = 11) represent CD8-restricted specificities showing reactivity against peptide pools (b), but not towards single peptides (c), probably due to the absence of the specific cognate antigen within the tested panels of epitopes in c.

Extended Data Fig. 9 Phenotype of MAA/NeoAg-TCRs and parameters affecting their avidity.

a, Heatmap showing genes differentially expressed between CD8+ TILs with identified MAA-specific, NeoAg-specific or virus-specific TCRs. Comparisons were performed independently for each patient, and only significantly deregulated genes (adj. P < 0.05, log2FC > 1 for scRNA-seq data; log2FC > 0.4 for CITE-seq data) in at least two out of four patients were selected. No deregulated gene was found upon comparison of single cells with MAA-TCRs versus NeoAg-TCRs; 60 RNA transcripts and 2 surface proteins resulted from comparison of MAA and/or NeoAg cells versus viral cells. Heatmap colours depict Z scores of average gene expression within a TCR clonotype (columns). Top tracks: annotations of antigen specificity (colour legend reported in panel b), normalized antitumour TCR reactivity, TCR avidity and patient of origin. b, To define the avidity of antitumour TCRs, TCR-dependent CD137 upregulation was measured on TCR-transduced (mTRBC+) CD8+ cells upon culture with patient-derived EBV-LCLs pulsed with increasing concentrations of the cognate antigen (MAAs in the top panel; NeoAgs in bottom panels). Reactivity to DMSO-pulsed targets (0) and autologous melanoma (pdMel-CLs) are reported on the left; for NeoAg-specific TCRs, the dashed lines report reactivity against wild-type peptides. A colour legend depicts the different cognate antigens targeted by the deorphanized TCRs and report the number of TCRs for each antigenic specificity. c, EC50 calculated from titration curves; note that high EC50 values correspond to low TCR avidities. Means with s.d. are reported, with TCR numbers corresponding to that reported in the legend of b. Most of the NeoAg-specific TCRs display higher avidities than MAA-specific TCRs. d, Expression levels of MAA or NeoAg transcripts (from bulk RNA-seq data) from which the analysed epitopes are generated, as a measure of cognate peptide abundance in tumour cells, as analysed from four patient-derived cell lines (symbols). Columns show means values with s.d. e, Assessment of the affinity (left) and stability (right) of peptide–HLA complexes. The interactions between reported MAA or NeoAg peptides and their HLA restriction (assessed in vitro as shown in Supplementary Information—‘Flow-cytometry data’) were measured as previously described37. Note that high values correspond to low affinity (left) or to stable interactions (right). Columns report mean affinity/stability with standard deviations from repeated measurements; the number of replicates is indicated at the base of the columns. The horizontal grey lines denote affinity levels of reference peptides reported to be strong binders for the analysed HLA alleles that were tested in parallel. In all NeoAg panels, comparisons of mutant (Mut, coloured bars) versus wild-type (WT, white bars) peptides were performed using two-tailed ratio-paired parametric t-tests, and P values are reported. na, not assessed; NB, non-binding; ND, not detectable; ne, not evaluable.

Extended Data Fig. 10 Peripheral blood dynamics of intratumoural T cell specificities.

a, Peripheral blood dynamics of T cells containing TCRs with in vitro-defined antigen specificity (black: virus-specific TCRs; red: MAA-specific TCRs; green: NeoAg-specific TCRs). TCRs were quantified through bulk sequencing of TCRβ chains of sorted CD3+ T cells from serial peripheral blood sampling of the four patients with melanoma within the discovery cohort. The numbers report the median number of TCRs detected longitudinally out of the total number of TCRs within each category. bd, CD8+ TCR clonotypes identified in CD8+ TILs were traced within serial peripheral blood samples collected from an independent cohort of patients with melanoma (n = 14) treated with immune checkpoint blockade therapies and with available scRNA-seq data generated from TILs8. TCRs were classified as exhausted (red) or non-exhausted (blue) based on their phenotypic primary cluster assessed by scRNA-seq. Quantification of circulating TCR clonotypes was performed through bulk sequencing of TCRβ chains on circulating CD3+ cells and reported as percentage of total TCR sequences detected. Patient clinical outcomes were grouped as: survivors who did not experienced post-therapy disease recurrence (panel b, n = 4); survivors who experienced disease progression after immunotherapy (panel c, n = 3); and deceased patients (panel d, n = 7). Per patient, a schematic of the clinical timeline and sample collection is depicted above each panel. ND, not detected; NED, no evidence of disease.

Supplementary information

Supplementary Information

This file contains supplementary methods, sorting strategies, Gating strategies for analysis of flow-cytometry data, Flow-cytometry data and supplementary references.

Reporting Summary

Supplementary Tables

This file contains Supplementary Tables 1 – 11. Supplementary Table 1. Characteristics of discovery cohort. Supplementary Table 2. List of CITEseq Ab used for single cell sequencing. Supplementary Table 3. Metrics of single-cell RNAseq, TCRseq and TCR clonotype information. Supplementary Table 4. Differentially expressed genes among the 12 clusters of CD8+ TILs identified by scRNA-seq. Supplementary Table 5. Characteristics of validation cohort. Supplementary Table 6. Differentially expressed genes in tumor-specific CD8 TILS compared to virus-specific CD8 TILs. Supplementary Table 7. Differentially expressed genes among the 5 clusters of CD8+ TILs with in vitro verified tumor-specific TCRs. Supplementary Table 8. Gene-signature from internal or external datasets. Supplementary Table 9. Neoantigen peptides tested for TCR recognition. Supplementary Table 10. MAA peptides tested for TCR recognition. Supplementary Table 11. Summary of cognate antigens recognized by antitumor deorphanized TCRs.

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Oliveira, G., Stromhaug, K., Klaeger, S. et al. Phenotype, specificity and avidity of antitumour CD8+ T cells in melanoma. Nature 596, 119–125 (2021).

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