Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Landscape of helper and regulatory antitumour CD4+ T cells in melanoma


Within the tumour microenvironment, CD4+ T cells can promote or suppress antitumour responses through the recognition of antigens presented by human leukocyte antigen (HLA) class II molecules1,2, but how cancers co-opt these physiologic processes to achieve immune evasion remains incompletely understood. Here we performed in-depth analysis of the phenotype and tumour specificity of CD4+ T cells infiltrating human melanoma specimens, finding that exhausted cytotoxic CD4+ T cells could be directly induced by melanoma cells through recognition of HLA class II-restricted neoantigens, and also HLA class I-restricted tumour-associated antigens. CD4+ T regulatory (TReg) cells could be indirectly elicited through presentation of tumour antigens via antigen-presenting cells. Notably, numerous tumour-reactive CD4+ TReg clones were stimulated directly by HLA class II-positive melanoma and demonstrated specificity for melanoma neoantigens. This phenomenon was observed in the presence of an extremely high tumour neoantigen load, which we confirmed to be associated with HLA class II positivity through the analysis of 116 melanoma specimens. Our data reveal the landscape of infiltrating CD4+ T cells in melanoma and point to the presentation of HLA class II-restricted neoantigens and direct engagement of immunosuppressive CD4+ TReg cells as a mechanism of immune evasion that is favoured in HLA class II-positive melanoma.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Melanoma HLA class II expression and phenotype of CD4+ TIL TCR clonotypes.
Fig. 2: Tumour reactivity of TCRs from CD4+ TIL.
Fig. 3: Antigen specificity of TCRs from CD4+ TILs.
Fig. 4: Relationship between HLA expression and mutation burden in melanoma.

Data availability

The scRNA-seq, TCR-seq and CITE-seq sequencing results are available through the dbGaP portal (study ID 26121, accession number phs001451.v3.p1). All other data and codes are presented in the main text or in the supplementary materials and are available from the corresponding authors upon reasonable request.

Code availability

Code used for data analysis included the Broad Institute Picard Pipeline (WES and RNA-seq), GATK4 v4.0, Mutect2 v2.7.0 (single nucleotide variants (SNV) and indel identification), NetMHCpan 4.0 and NetMHCIIpan 4.0 (neoantigen binding prediction), ContEst v1 (contamination estimation), ABSOLUTE v1.1 (purity/ploidy estimation), STAR v2.6.1c (sequencing alignment), RSEM v1.3.1 (gene expression quantification), Seurat v3.2.0 (single-cell sequencing analysis), Harmony v1.0 (single-cell data normalization), SingleR v3.22 (scater package), Scanpy v1.5.1, Python v3.7.4 (for comparison with other single-cell datasets), EdgeR (to compare bulk RNA-seq expression), EnrichR (to analyse enrichment of gene ontologies) that are each publicly available. Custom computer code used to generate the analyses is available at


  1. Sallusto, F. & Lanzavecchia, A. Heterogeneity of CD4+ memory T cells: functional modules for tailored immunity. Eur. J. Immunol. 39, 2076–2082 (2009).

    CAS  PubMed  Article  Google Scholar 

  2. Swain, S. L., McKinstry, K. K. & Strutt, T. M. Expanding roles for CD4+ T cells in immunity to viruses. Nat. Rev. Immunol. 12, 136–148 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. Sade-Feldman, M. et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175, 998–1013.e20 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. Yost, K. E. et al. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat. Med. 25, 1251–1259 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. Wu, T. D. et al. Peripheral T cell expansion predicts tumour infiltration and clinical response. Nature 579, 274–278 (2020).

    CAS  PubMed  Article  ADS  Google Scholar 

  6. Oh, D. Y. et al. Intratumoral CD4+ T cells mediate anti-tumor cytotoxicity in human bladder cancer. Cell 181, 1612–1625.e13 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. Cachot, A. et al. Tumor-specific cytolytic CD4 T cells mediate immunity against human cancer. Sci. Adv. 7, eabe3348 (2021).

    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

  8. Quezada, S. A. et al. Tumor-reactive CD4+ T cells develop cytotoxic activity and eradicate large established melanoma after transfer into lymphopenic hosts. J. Exp. Med. 207, 637–650 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. Tanaka, A. & Sakaguchi, S. Regulatory T cells in cancer immunotherapy. Cell Res. 27, 109–118 (2017).

    CAS  PubMed  Article  Google Scholar 

  10. Borst, J., Ahrends, T., Bąbała, N., Melief, C. J. M. & Kastenmüller, W. CD4+ T cell help in cancer immunology and immunotherapy. Nat. Rev. Immunol. 18, 635–647 (2018).

    CAS  PubMed  Article  Google Scholar 

  11. Rodig, S. J. et al. MHC proteins confer differential sensitivity to CTLA-4 and PD-1 blockade in untreated metastatic melanoma. Sci. Transl. Med. 10, eaar3342 (2018).

    PubMed  Article  CAS  Google Scholar 

  12. Johnson, D. B. et al. Melanoma-specific MHC-II expression represents a tumour-autonomous phenotype and predicts response to anti-PD-1/PD-L1 therapy. Nat. Commun. 7, 10582 (2016).

    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

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

    CAS  PubMed  Article  ADS  Google Scholar 

  14. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. Miller, B. C. et al. Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol. 20, 326–336 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. Wolfl, M. et al. Activation-induced expression of CD137 permits detection, isolation, and expansion of the full repertoire of CD8+ T cells responding to antigen without requiring knowledge of epitope specificities. Blood 110, 201–210 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. Kawakami, Y. et al. Identification of a human melanoma antigen recognized by tumor-infiltrating lymphocytes associated with in vivo tumor rejection. Proc. Natl Acad. Sci. USA 91, 6458–6462 (1994).

    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

  18. Hu, Z. et al. Personal neoantigen vaccines induce persistent memory T cell responses and epitope spreading in patients with melanoma. Nat. Med. 27, 515–525 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. Anagnostou, V. et al. Integrative tumor and immune cell multi-omic analyses predict response to immune checkpoint blockade in melanoma. Cell Rep. Med. 1, 100139 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  20. Sade-Feldman, M. et al. Resistance to checkpoint blockade therapy through inactivation of antigen presentation. Nat. Commun. 8, 1136 (2017).

    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

  21. Garrido, F. MHC/HLA class I loss in cancer cells. Adv. Exp. Med. Biol. 1151, 15–78 (2019).

    CAS  PubMed  Article  Google Scholar 

  22. Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).

    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

  23. Landsberg, J. et al. Melanomas resist T-cell therapy through inflammation-induced reversible dedifferentiation. Nature 490, 412–416 (2012).

    CAS  PubMed  Article  ADS  Google Scholar 

  24. Boshuizen, J. et al. Reversal of pre-existing NGFR-driven tumor and immune therapy resistance. Nat. Commun. 11, 3946 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. Guo, X. et al. Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing. Nat. Med. 24, 978–985 (2018).

    CAS  PubMed  Article  Google Scholar 

  26. Ahmadzadeh, M. et al. FOXP3 expression accurately defines the population of intratumoral regulatory T cells that selectively accumulate in metastatic melanoma lesions. Blood 112, 4953–4960 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. De Simone, M. et al. Transcriptional landscape of human tissue lymphocytes unveils uniqueness of tumor-infiltrating T regulatory cells. Immunity 45, 1135–1147 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  28. Marangoni, F. et al. Expansion of tumor-associated Treg cells upon disruption of a CTLA-4-dependent feedback loop. Cell 184, 3998–4015.e19 (2021).

    CAS  PubMed  Article  Google Scholar 

  29. Li, H. et al. Dysfunctional CD8 T cells form a proliferative, dynamically regulated compartment within human melanoma. Cell 176, 775–789.e18 (2019).

    CAS  PubMed  Article  Google Scholar 

  30. Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  31. Jerby-Arnon, L. et al. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell 175, 984–997.e24 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. Jhunjhunwala, S., Hammer, C. & Delamarre, L. Antigen presentation in cancer: insights into tumour immunogenicity and immune evasion. Nat. Rev. Cancer 21, 298–312 (2021).

    CAS  PubMed  Article  Google Scholar 

  33. Vagaska, B. et al. MHC-class-II are expressed in a subpopulation of human neural stem cells in vitro in an IFNγ-independent fashion and during development. Sci Rep. 6, 24251 (2016).

    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

  34. Diener, J. & Sommer, L. Reemergence of neural crest stem cell-like states in melanoma during disease progression and treatment. Stem Cells Transl. Med. 10, 522–533 (2021).

    CAS  PubMed  Article  Google Scholar 

  35. Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221 (2017).

    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

  36. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. Solé-Boldo, L. et al. Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming. Commun. Biol. 3, 188 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  39. Cohen, C. J., Zhao, Y., Zheng, Z., Rosenberg, S. A. & Morgan, R. A. Enhanced antitumor activity of murine-human hybrid T-cell receptor (TCR) in human lymphocytes is associated with improved pairing and TCR/CD3 stability. Cancer Res. 66, 8878–8886 (2006).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. Haga-Friedman, A., Horovitz-Fried, M. & Cohen, C. J. Incorporation of transmembrane hydrophobic mutations in the TCR enhance its surface expression and T cell functional avidity. J. Immunol. 188, 5538–5546 (2012).

    CAS  PubMed  Article  Google Scholar 

  41. Bialer, G., Horovitz-Fried, M., Ya’acobi, S., Morgan, R. A. & Cohen, C. J. Selected murine residues endow human TCR with enhanced tumor recognition. J. Immunol. 184, 6232–6241 (2010).

    CAS  PubMed  Article  Google Scholar 

  42. Hu, Z. et al. A cloning and expression system to probe T-cell receptor specificity and assess functional avidity to neoantigens. Blood 132, 1911–1921 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. Reynisson, B. et al. Improved prediction of MHC II antigen presentation through integration and motif deconvolution of mass spectrometry MHC eluted ligand data. J. Proteome Res. 19, 2304–2315 (2020).

    CAS  PubMed  Article  Google Scholar 

  44. Sarkizova, S. et al. A large peptidome dataset improves HLA class I epitope prediction across most of the human population. Nat. Biotechnol. 38, 199–209 (2020).

    CAS  PubMed  Article  Google Scholar 

  45. Abelin, J. G. et al. Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction. Immunity 46, 315–326 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. Roemer, M. G. M. et al. Classical Hodgkin lymphoma with reduced β2M/MHC class I expression is associated with inferior outcome independent of 9p24.1 status. Cancer Immunol. Res. 4, 910–916 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. Restivo, G. et al. low neurotrophin receptor CD271 regulates phenotype switching in melanoma. Nat. Commun. 8, 1988 (2017).

    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

  48. Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. Paul, P. et al. A Genome-wide multidimensional RNAi screen reveals pathways controlling MHC class II antigen presentation. Cell 145, 268–283 (2011).

    CAS  PubMed  Article  Google Scholar 

Download references


We thank O. Olive, K. Shetty, S. Pollock, C. Patterson, J. L. Weirather and A. Giobbie-Hurder for expert assistance; F. S. Hodi (DFCI Department of Medical Oncology) for help in collection and management of samples and data; and D. Y. Oh, L. Fong, D. M. Pardoll, K. N. Smith, A. Dykema and all members of the Wu laboratory for productive discussions. The authors thank Bristol-Myers Squibb for supporting the work through its International Immuno-Oncology Network. This research was made possible by a generous gift from the Blavatnik Family Foundation, and was supported by grants from the National Institutes of Health (NCI-1R01CA155010; NCI-U24CA224331 (to C.J.W.); NIH/NCI R21 CA216772-01A1 and NCI-SPORE-2P50CA101942-11A1 (to D.B.K); NCI-1R01CA229261 (to P.A.O.); NIH/NCI P01CA229092, NIH/NIAID U19 AI082630 (to K.J.L.); NCI R50CA251956 (to S.L.), R01 CA208756 (to N.H.); NCI K12CA090354 (J.B.I.)), a Conquer Cancer Foundation/Sontag Foundation grant (to J.B.I.), and a Team Science Award from the Melanoma Research Alliance (to C.J.W. and P.A.O.). G.O. was supported by the American Italian Cancer Foundation fellowship. D.A.B. is supported by the DF/HCC Kidney Cancer SPORE (NCI P50CA101942-15) and the DOD CDMRP Academy of Kidney Cancer Investigators (KC190128). 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. conceived the project. G.O. and C.J.W. directed the overall study. G.O. designed and performed experimental and data analysis. G.O. and K.S. analysed single-cell and bulk sequencing data. G.O. performed TCR reconstruction and screening experiments. W.Z. contributed to plasmid production. G.O., K.S. and D.N. designed and performed statistical analyses. S.L. and K.J.L. performed and analysed scRNA-seq and bulk and scTCR-seq. S.K., S.R., V.C., D.B.K. and S.A.C. generated and analysed mass spectrometry results. S.S.F. performed TMB analysis of the MGH cohort. K.K., D.T.F., C.H.Y., G.M.B., M.W.-R., M.S.-F. and P.A.O. provided patient samples and clinical information and discussed clinical data. J.O.W. generated IHC and immunofluorescence imaging data. S.J.R. and J.B.I. scored IHC stainings. N.C., D.A.B., 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 authors

Correspondence to Giacomo Oliveira or Catherine J. Wu.

Ethics declarations

Competing interests

E.F.F. is an equity holder and consultant for BioNTech, and equity holder and scientific advisory board 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, Xencor, Oncorus, Phio Pharmaceuticals, Evaxion 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 subjects or otherwise participate directly in the Institutional Review Board-approved protocol reported herein. C.J.W.’s contributions 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 neoantigen vaccine. S.J.R. has research funding from Bristol-Myers-Squibb and KITE/Gilead. S.J.R. is a member of the scientific advisory board of Immunitas Therapeutics. D.A.B. reports personal fees from LM Education and Exchange, Adnovate Strategies, MDedge, Cancer Network, Cancer Expert Now, OncLive, Catenion, AVEO, and grants and personal fees from Exelixis, outside the submitted work. M.S.-F. receives research funding from Bristol-Myers Squibb. M.W.-R. was an employee of Bristol-Myers Squibb and holds equity in Bristol-Myers Squibb. D.B.K. has previously advised Neon Therapeutics and has received consulting fees from Neon Therapeutics. D.B.K. owns equity in Affimed N.V., Armata Pharmaceuticals, Breakbio, BioMarin Pharmaceutical, Bristol-Myers Squibb, Celldex Therapeutics, Editas Medicine, Exelixis, Gilead Sciences, Immunitybio, ImmunoGen, IMV, Lexicon Pharmaceuticals, Moderna, Neoleukin Therapeutics, 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.

Peer review

Peer review information

Nature thanks Jacques Neefjes, Aude Chapuis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Characterization of HLA class II expression in melanoma biopsies.

a, UMAP of scRNA-seq data from melanoma-enriched CD45neg cells isolated from tumors obtained from 4 patients with melanoma (Pt-A,B,C,D). Patients’ origin of cells is denoted with colors. Cell types are inferred based on the expression of markers depicted in panels b-c. EC: endothelial cells. b, Single-cell RNA expression of markers associated with non-tumor subpopulations, as reported in38. c, Single-cell RNA expression of melanoma-associated markers, which allow identification of melanoma cells within patient-specific clusters. d, e, Single-cell expression of canonical HLA class I (d) and HLA class II (e) transcripts, which demonstrate aberrant HLA class II expression in Pt-C and Pt-D parental tumors. f, Levels of HLA-DR surface expression detected on melanoma cells from 4 patients of the discovery cohorts (Pt-A,B,C,D, corresponding to different symbol shapes). HLA-DR expression is detected on primary melanoma cells through CITE-seq (left, as shown also in Fig. 1c) and on patient-derived melanoma cell lines through flow cytometry (right). Melanomas are classified in HLA class IIpos (magenta) and HLA class IIneg (grey). For Pt-C, two primary biopsies (Pt-C and Pt-C Rel) collected longitudinally have been analyzed. g, Dual chromogenic immunohistochemical (IHC) staining of formalin-fixed paraffin-embedded (FFPE) tumor samples from Pt-B/C/D for HLA class II (brown) and melanoma transcription factor SOX10 (red-pink). For each specimen, the whole slide was assessed for HLA class II expression. Representative low-magnification images of tumors are provided in top images, with blue boxes identifying representative areas (n = 2 repeated acqusitions), as depicted in bottom panels with 20X magnifications. For Pt-C, the same analysis was performed on 2 tumor biopsies collected at 8 months interval, demonstrating the stability of HLA class II pattern of expression in melanoma cells. h, i, Tumor HLA class II expression on independent melanoma specimens (Supplementary Table 3). HLA class II expression was measured by flow cytometry on patient-derived tumor cell lines generated from 17 different patients with melanoma (h). Cell lines are classified as HLA class IIpos (magenta) and HLA class IIneg (grey). For cell lines with available FFPE tissue blocks from parental tumors (Mel-1/7), Dual chromogenic IHC staining was performed (i) using HLA class II (brown) and melanoma SOX10 (red-pink) antibodies. For each specimen, the whole slide was assessed for HLA class II expression. Representative images are displayed. The IHC-stainings document patterns of melanoma HLA class II expression similar to what observed in corresponding cell lines, demonstrating that such feature was constitutive and stably perpetuated in culture in the absence of an inflammatory milieu.

Extended Data Fig. 2 Single-cell profiling of CD4+ tumor infiltrating lymphocytes.

a, Size and patient distribution of the 10 clusters identified from CD4+TIL scRNA-seq. Left: per cluster, patient origin is denoted by color. Right: UMAPs depicting cluster distribution of patient-specific CD4+TILs. b, Violin plots quantifying relative transcriptional expression of genes (columns) with high differential expression among CD4+TIL clusters (rows). c, 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. 1d; markers (rows) are grouped based on their biological function. Grey - unevaluable markers (CD45 isoforms for scRNA-seq) or which were not assessed (for CITE-seq). CITE-seq CD3 surface expression was poorly detected because of the presence of competing anti-CD3 sorting antibody. d, Characterization of the CD4+TIL clusters using independent reference gene-signatures4,5,6. Heatmaps show cross-labelling of T cell clusters defined in the present study (columns, reported as in Fig. 1d) versus reference gene-signatures (rows) derived from the analyses in Yost et al.4, Wu et al.5 and Oh et al.6, with intensities indicating normalized frequency. e, UMAPs depicting the single-cell expression of representative T cell markers among CD4+TILs either through detection of surface protein expression with CITE-seq (Ab), or through scRNA-seq (RNA).

Extended Data Fig. 3 Clonality of CD4+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 intra-cluster TCR clonality, calculated for CD4+ T cells in each cluster (x axis) using normalized Shannon index. Symbols - individual TCR clonality for the 3 patients with high numbers of TILs (Pt-A, Pt-C, Pt-D). Bars - the overall TCR clonality measured within each cluster. c, UMAPs of the cluster distribution of representative dominant TCR clonotype families among CD4+TILs from TIL-rich patients (n = 3). For each patient, numbers denote the ranking of each TCR clonotype (see Fig. 1e), while colors identify their primary cluster (see Fig. 1d). d, Matrix displaying Spearman correlation of normalized cluster distribution of dominant TCR clonotype families composed by ≥4 cells. Colors and area of the circles indicate the strength and significance of the correlation, respectively.

Extended Data Fig. 4 Antitumor 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]), with controls (PBMCs, B cells and EBV-LCLs [blue]), or with EBV-LCLs pulsed with tumor lysate (purple). b, Representative flow cytometry plots depicting CD137 upregulation measured on CD4+ T cells transduced with TCRs isolated from CD4+ melanoma TILs and cultured with melanoma or control targets. Background reactivity was estimated by measuring CD137 upregulation on CD4+ T cells transduced with an irrelevant TCR. c, TCR reactivities from Pt-B, measured on TCR transduced (mTRBC+) CD4+ lymphocytes cultured alone or with patient-derived target cells. Background activation measured on CD4+ T cells transduced with an irrelevant TCR was subtracted. Each dot-line set represents the behavior of a single TCR; dot colors denote cell states of TCR clonotypes, as delineated in Fig. 1c; white dots show background signal of untransduced CD4+ T cells. d, Cytotoxic potential provided by TCRs isolated from all 4 studied melanoma patients and reconstructed from memory clusters (blue), from TTE and TProl exhausted clusters (red), from TFH/TPE exhausted clusters (yellow) or from TReg clusters (magenta). Degranulation (CD107a/b+) and concomitant production of cytokines (IFNγ, TNFα and IL-2) were assessed through intracellular staining, gating on TCR-transduced (mTRBC+) CD4+ T cells cultured alone or in the presence of autologous melanoma (pdMel-CLs). Each dot represents the result for a single TCR isolated from CD4+TILs, color-coded based to its primary phenotypic cluster (as defined in Fig. 1d). For each analyzed TCR, background cytotoxicity from CD4+ T cells transduced with an irrelevant TCR was subtracted. White dots - basal level of activation of untransduced cells. Overall, these data indicate that antitumor cytotoxicity mainly resides among TCR clonotypes with exhausted and regulatory primary clusters.

Extended Data Fig. 5 Peripheral blood phenotype and distribution of CD4+ intratumoral TCR clonotypes.

a, Schema for analysis of peripheral blood distribution and phenotype of CD4+ intratumoral clonotypes. PBMCs collected at pre-treatment timepoints from 4 melanoma patients in the discovery cohort were FACS-sorted to isolate CD4+ fractions enriched in T Naïve, T Memory, T Effectors and T Regulatory cells. FACS-plots show the sorting strategy for a representative patient (Pt-A). In parallel, an aliquot of PBMCs was analyzed with intracellular flow-cytometry using the same panel of antibodies with the addition of FOXP3 marker, to confirm enrichment of FOXP3+ cells within the CD4+TReg fraction. Bulk sequencing of TCRβ-chains was performed on sorted CD4+ subpopulation to detect the TCRβ-chains of CD4+TIL-TCR clonotype families (with >1 cell). This allowed to trace in the peripheral blood the TCR clonotypes with different intratumoral phenotypes. b, Peripheral blood frequencies of CD4+TIL-TCR clonotypes. TCR clonotype families detected amongst CD4+TILs and with different primary clusters (TNExM, TEx: TTE+TProl, TEx: TFH/TPE, TRegs) were traced in peripheral blood, as depicted in a. The frequency of each TCR clonotype among circulating CD4+ T cells was reconstructed by taking into account the frequencies of CD4+ subpopulations (determined by FACS-sorting) and the frequencies of each TCRβ-chain within each CD4 fraction (determined by bulk TCRseq). For each patient, the proportion of CD4+TIL-TCRs detected in blood is summarized with pies below each graph. c, Peripheral blood phenotype of CD4+TIL TCRs. The graphs show the distribution among 4 FACS-sorted fractions of circulating CD4+ T cells for each CD4+TIL-TCR traced in peripheral blood by bulk TCRseq. The TIL-TCR clonotypes are divided based on their intratumoral primary cluster (rows: TNExM, TEx: TTE+TProl, TEx: TFH/TPE, TRegs) and based on the patient of origin (x axis). TCRβ-chains corresponding to TCR clonotypes reconstructed in vitro are pointed with arrows and labeled based on classification of their reactivity as reported in the legend.

Extended Data Fig. 6 Antigen specificity of CD4+ tumor-reactive TCRs.

a, Antigen specificity screening of 76 CD4+TCRs with direct or indirect tumor recognition. Upregulation of CD137 was assessed by flow-cytometry on CD4+ T cells transduced with previously identified tumor-specific or APC-tumor antigen reactive TCRs. Antigen recognition was tested upon culture with autologous EBV-LCLs pulsed with pools of peptides corresponding to immunogenic neoantigens (NeoAgs) predicted to bind patients’ HLA class I or II restrictions or detected through Mass-spectrometry (MS) within the HLA class II immunopeptidome (see Supplementary Table 6), known melanoma associated antigens (MAAs) or immunogenic viral epitopes (CEF pools). Reactivity was also assessed against an irrelevant long peptide (Ova II) or in the presence of polyclonal stimulators (PMA/ionomycin) as negative and positive controls, respectively. Background, assessed using DMSO-pulsed target cells, was subtracted from each condition. Colored dots – confirmed antigen-reactive TCRs, colored based on highest reactivity against a particular antigen, as per the legend, compared to the other tested antigens; white dots –TCRs reactive against an antigen which was not the highest of the panel of antigens tested, and hence considered a cross-reactive response; grey dots - negative responses. b, Deconvolution of antigen specificity of TCRs reactive to NeoAg-peptide pools. Colored dots indicate the deconvoluted cognate antigens, with antigens corresponding to colors represented in panel c. c, Distribution of antigen specificities of tumor reactive CD4+TCRs per patient successfully deorphanized after screening. Colors denote the distinct peptides recognized by individual TCRs.

Extended Data Fig. 7 Characterization of HLA class I restricted CD4+TILs.

a, Levels of CD4/CD8 transcripts (left, detected by scRNA-seq) or surface proteins (right, detected by CITE-seq) expressed by single cells with CD4+ HLA class I restricted TCRs specific for MLANA (n = 1) or TYR (n = 2) isolated from Pt-A. The data indicate that the 3 clonotype families belong to CD4+TILs, despite their reactivity to HLA class I restricted epitopes. b, Flow cytometric plot depicting peptide-HLA binding of T cells transduced with MLANA27-35-specific TCR (red), isolated in CD4+TILs from Pt-A. Transduced T cells comprising CD4+ and CD8+ (CD4) lymphocytes were stained using HLA-A*02:01 dextramers loaded with MLANA26-35 heteroclitic peptide (ELAGIGILTV). Untransduced T cells (grey) are shown as control. The similar level of binding exhibited by CD4+ and CD8+ T cells demonstrates that tested HLA class I TCR act independently from CD4/CD8 co-stimulation. c, Flow cytometry tracing of MLANA-specific HLA class I restricted CD4+ T cells in Pt-A TILs. Plots depict identification of CD4+ or CD8+ cells within CD3+ TILs from Pt-A (left) and binding to HLA-A*02:01 dextramers loaded with MLANA26-35 heteroclitic peptide among CD4+ or CD8+ TILs (right). Frequencies of each population are provided (blue), demonstrating reliable detected of HLA class I restricted CD4+TILs. d, Phenotype of MLANA-specific HLA class I restricted CD4+TILs from Pt-A. Heatmaps show transcript levels detected by scRNA-seq (top), surface protein expression measured by CITE-seq (middle) or flowcytometry (bottom) for CD4+ T cells with TCRs specific for HLA-A*02:01-MLANA26/27-35 complexes. As comparison, the same analysis was performed on CD8+ TILs specific for the same antigen-HLA (as identified in13) or detected by flow cytometry (as depicted in panel c). Markers detected with all the 3 technologies are shown (exhaustion markers: PD-1, CD39, LAG3; regulatory marker: CD25; memory markers: CD127, CD45RA, CD127). For scRNA-seq (top) transcripts related to effector molecules are reported. Grey color: values not available. This analysis shows that HLA class I restricted CD4+TILs specific for MLANA display an exhausted and cytotoxic phenotype similar to the one of CD8+ TILs. e, Cytotoxicity of HLA-class I-restricted CD4+TILs. Degranulation (CD107a/b upregulation) measured in vitro on MLANA-specific HLA-A*01:02 restricted CD4+ or CD8+ T cells, identified in TILs from Pt-A as depicted in c. Cells were cultured in the absence (left) or presence (right) of autologous melanoma for 6 h. HLA-class I restricted CD4+TILs specific for MLANA antigen (bottom raw) exhibited degranulation levels comparable to those observed for CD8+ counterparts (top raw), demonstrating their ability to mediate cytotoxicity.

Extended Data Fig. 8 Avidities of CD4+ MAA/NeoAg-TCRs.

a, Avidity of de-orphanized TCRs with MAA or NeoAg specificity (n = 3 and n = 23 respectively). TCR-dependent CD137 upregulation was measured on TCR-transduced (mTRBC+) CD4+ cells upon culture with patient-derived EBV-LCLs pulsed with increasing concentrations of the cognate antigen (MAAs in the top panels, NeoAgs in bottom panels). Background activation measured on CD4+ T cells transduced with an irrelevant TCR was subtracted. Reactivity to DMSO-pulsed targets (0) and autologous melanoma (pdMel-CL) are reported on the left; for NeoAg-specific TCRs, the dashed lines report reactivity against wildtype peptides. A color legend depicts the different cognate antigens targeted by the deorphanized TCRs and specifies the number of TCRs able to recognize each cognate antigen and the patient the TCR belongs to. b, EC50 calculated from titration curves of MAA or NeoAg-specific TCRs (n = 3 and n = 23 respectively); note that high EC50 values correspond to low TCR avidities. Means with SD are reported, with TCR numbers corresponding to those reported in a. c, Expression levels of MAA or NeoAg transcripts (from bulk RNAseq of pdMel-CLs) from which the analyzed epitopes are generated, as a measure of cognate peptide abundance in tumor cells. Columns show means values with s.d. of values from n = 4 pdMel-CLs (symbols). d, Correlation between the avidity of antitumor CD4+TCRs (y axis) and target abundance (x axis), as measured by RNA expression of TCR-targeted genes detected in the autologous pdMel-CLs and expressed as transcripts per million mapped reads (TPM). Values for TCR avidity represent averages of EC50 data presented in b. The correlation is measured on de-orphanized antitumor CD4+TCR specificities with intratumoral TEx (top) or TReg (bottom) primary clusters, as showed in Fig. 3b, f. Significance of each linear regression is reported within each panel. Symbols: patients from whom TCRs were identified. Colors: cognate antigens of antitumor TCRs, as depicted in a.

Extended Data Fig. 9 Quantification of TILs and clinical outcome of patients with melanoma.

a, b, Quantification of CD8+ (a) and CD4+ (b) TILs detected in tumor specimens from patients reported in cohorts from published studies3,11,18. TILs were quantified in tumor areas using immunohistochemical or immunofluorescence stainings (see Supplementary Information). Only patients with available tumor FFPE were analyzed (n indicated in the legends). Values from patients included in the discovery cohort are labeled (specimens from Pt-A were not available for this analysis). Melanoma were classified based on HLA class II expression, scored on tumor cells by IHC (≥ or < 10% of tumor cells with positive staining). Patients from each study are denoted by different symbol shapes (as reported in each legend, with number of patients for each cohort). Horizontal lines show median (bold) and quartiles values; p-values indicate the significance of comparisons calculated using Mann-Whitney test. c–f, Clinical outcome of patients with melanoma, classified based on tumor HLA class II expression (c, d) or based on classes of tumor mutation burden (e, f), as defined in Fig. 4a and indicated in each color legend. Kaplan-Meier curves estimate the overall survival (c, e) and progression-free survival (d, f) of patients with melanoma from 4 published studies3,11,18,19. Only patients with available overall survival or progression-free survival after treatment with immunotherapies have been analyzed (see Supplementary Table 8). For each category, number of analyzed patients is reported within the legends. P-values indicate the significance of comparison between Kaplan-Meier curves, as obtained using Log-rank Mantel-Cox test between 2 groups (c, d) or 4 groups (e, f).

Extended Data Fig. 10 Analysis of HLA class II expression in cancer cell lines.

a, Tumor mutation burden (top panel) with HLA class I and HLA class II expression scores (middle and bottom panels) of 927 human cancer cell lines22. HLA class I and II scores have been calculated from RNAseq as described in the Material and Methods section. Colored areas denote downregulation of HLA class I (turquoise) and upregulation of HLA class II (pink). Each tumor type is indicated using the corresponding abbreviation from TCGA (x axis). The number of tested cancer cell lines for each tumor type is reported (top). Bars depict mean values with s.d. Compared to hematopoietic tumors, melanoma (SKCM) represents the sole solid cancer with a relevant number of HLA class IIpos cell lines (red arrow). b, Volcano plot showing RNA transcripts deregulated upon comparison of HLA class IIpos (n = 18) and HLA class IIneg (n = 36) melanoma cell lines (Mel-CLs). Significantly upregulated or downregulated genes (log2FC≥ 2 or log2FC≤ −2, adj-p.value ≤ 0.01) are colored in red or blue respectively. HLA class II related genes are labeled; Upregulated genes previously associated with de-differentiated melanoma23,24,47 are highlighted in green. c, Top 20 gene ontology processes enriched in transcripts upregulated in HLA class IIpos Mel-CLs. HLA class II related genes were excluded from this analysis. Bars depict enrichment, as calculated using two-sided Fisher’s exact test48. Processes related to differentiation of central nervous system are colored in green, while processes related to remodeling of interactions with cells or extracellular matrix are highlighted in yellow. Enrichment in such gene-ontologies, characteristic of neural-crest-like tumors23,24,47, in HLA class IIpos melanoma confirm that HLA class II expression is peculiar of de-differentiated melanoma. No significant deregulation of genes derived from HLA class II RNA-interference screens49 was observed.

Supplementary information

Supplementary Information

This file contains Supplementary Results and Supplementary Methods

Reporting Summary

Supplementary Tables

This file contains Supplementary Tables 1-10

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Oliveira, G., Stromhaug, K., Cieri, N. et al. Landscape of helper and regulatory antitumour CD4+ T cells in melanoma. Nature 605, 532–538 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing