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B cells are associated with survival and immunotherapy response in sarcoma


Soft-tissue sarcomas represent a heterogeneous group of cancer, with more than 50 histological subtypes1,2. The clinical presentation of patients with different subtypes is often atypical, and responses to therapies such as immune checkpoint blockade vary widely3,4. To explain this clinical variability, here we study gene expression profiles in 608 tumours across subtypes of soft-tissue sarcoma. We establish an immune-based classification on the basis of the composition of the tumour microenvironment and identify five distinct phenotypes: immune-low (A and B), immune-high (D and E), and highly vascularized (C) groups. In situ analysis of an independent validation cohort shows that class E was characterized by the presence of tertiary lymphoid structures that contain T cells and follicular dendritic cells and are particularly rich in B cells. B cells are the strongest prognostic factor even in the context of high or low CD8+ T cells and cytotoxic contents. The class-E group demonstrated improved survival and a high response rate to PD1 blockade with pembrolizumab in a phase 2 clinical trial. Together, this work confirms the immune subtypes in patients with soft-tissue sarcoma, and unravels the potential of B-cell-rich tertiary lymphoid structures to guide clinical decision-making and treatments, which could have broader applications in other diseases.

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Fig. 1: The SICs exhibit strongly different TMEs.
Fig. 2: SICs and B cells are predictive of the survival of patients with STS.
Fig. 3: TLSs are a distinguishing feature of the immune-high class of STS.
Fig. 4: SICs are strongly associated with STS response to PD1 blockade therapy.

Data availability

The transcriptomic datasets analysed in this study can be accessed on the GDC Portal (, cohort TCGA SARC) and the GEO repository under accession numbers GSE21050, GSE21122 and GSE30929. FSG cohort data are publicly available from ArrayExpress for gastrointestinal stromal tumour with accession code E-MTAB-373, and from the GEO for synovial sarcomas with accession number GSE40021. Myxoid liposarcomas from the FSG cohort are available from the corresponding authors upon reasonable request. Immunohistochemistry and gene expression data related to the NTUH cohort (Fig. 3, Extended Data Figs. 7, 8) are available upon reasonable request to W.H.F. ( The data that support the findings related to Fig. 4 are available from SARC but restrictions apply to the availability of these data, which were used under license for the study. Data are, however, available from H.A.T. ( upon reasonable request and with permission of SARC.

Code availability

All code used in this study is available from the corresponding author upon reasonable request.


  1. Helman, L. J. & Meltzer, P. Mechanisms of sarcoma development. Nat. Rev. Cancer 3, 685–694 (2003).

    Article  CAS  Google Scholar 

  2. Fletcher, C., Bridge, J., Hogendoorn, P. & Mertens, F. WHO Classification of Tumours of Soft Tissue and Bone (World Health Organization, 2013).

  3. D’Angelo, S. P. et al. Nivolumab with or without ipilimumab treatment for metastatic sarcoma (Alliance A091401): two open-label, non-comparative, randomised, phase 2 trials. Lancet Oncol. 19, 416–426 (2018).

    Article  Google Scholar 

  4. Tawbi, H. A. et al. Pembrolizumab in advanced soft-tissue sarcoma and bone sarcoma (SARC028): a multicentre, two-cohort, single-arm, open-label, phase 2 trial. Lancet Oncol. 18, 1493–1501 (2017).

    Article  CAS  Google Scholar 

  5. Beck, A. H. et al. Discovery of molecular subtypes in leiomyosarcoma through integrative molecular profiling. Oncogene 29, 845–854 (2010).

    Article  CAS  Google Scholar 

  6. Gibault, L. et al. New insights in sarcoma oncogenesis: a comprehensive analysis of a large series of 160 soft tissue sarcomas with complex genomics. J. Pathol. 223, 64–71 (2011).

    Article  CAS  Google Scholar 

  7. Pollack, S. M. et al. T-cell infiltration and clonality correlate with programmed cell death protein 1 and programmed death-ligand 1 expression in patients with soft tissue sarcomas. Cancer 123, 3291–3304 (2017).

    Article  CAS  Google Scholar 

  8. Cancer Genome Atlas Research Network. Comprehensive and integrated genomic characterization of adult soft tissue sarcomas. Cell 171, 950–965.e28 (2017).

    Article  CAS  Google Scholar 

  9. Becht, E. et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 17, 218 (2016).

    Article  CAS  Google Scholar 

  10. Burgess, M. A. et al. Clinical activity of pembrolizumab (P) in undifferentiated pleomorphic sarcoma (UPS) and dedifferentiated/pleomorphic liposarcoma (LPS): final results of SARC028 expansion cohorts. JCO 37, 11015–11015 (2019).

    Article  Google Scholar 

  11. Kroeger, D., Milne, K. & H Nelson, B. Tumor-infiltrating plasma cells are associated with tertiary lymphoid structures, cytolytic T-cell responses, and superior prognosis in ovarian cancer. Clinical Cancer Res. 22, 3005–3015 (2016).

    Article  CAS  Google Scholar 

  12. Sautès-Fridman, C., Petitprez, F., Calderaro, J. & Fridman, W. H. Tertiary lymphoid structures in the era of cancer immunotherapy. Nat. Rev. Cancer 19, 307–325 (2019).

    Article  CAS  Google Scholar 

  13. Coppola, D. et al. Unique ectopic lymph node-like structures present in human primary colorectal carcinoma are identified by immune gene array profiling. Am. J. Pathol. 179, 37–45 (2011).

    Article  Google Scholar 

  14. Dieu-Nosjean, M.-C., Goc, J., Giraldo, N. A., Sautès-Fridman, C. & Fridman, W. H. Tertiary lymphoid structures in cancer and beyond. Trends Immunol. 35, 571–580 (2014).

    Article  CAS  Google Scholar 

  15. Posch, F. et al. Maturation of tertiary lymphoid structures and recurrence of stage II and III colorectal cancer. OncoImmunology 7, e1378844 (2017).

    Article  Google Scholar 

  16. Siliņa, K. et al. Germinal centers determine the prognostic relevance of tertiary lymphoid structures and are impaired by corticosteroids in lung squamous cell carcinoma. Cancer Res. 78, 1308–1320 (2018).

    Article  CAS  Google Scholar 

  17. Gu-Trantien, C. et al. CD4+ follicular helper T cell infiltration predicts breast cancer survival. J. Clin. Invest. 123, 2873–2892 (2013).

    Article  CAS  Google Scholar 

  18. Dorfman, D. M., Brown, J. A., Shahsafaei, A. & Freeman, G. J. Programmed death-1 (PD-1) is a marker of germinal center-associated T cells and angioimmunoblastic T-cell lymphoma. Am. J. Surg. Pathol. 30, 802–810 (2006).

    Article  Google Scholar 

  19. D’Angelo, S. P. et al. Prevalence of tumor-infiltrating lymphocytes and PD-L1 expression in the soft tissue sarcoma microenvironment. Hum. Pathol. 46, 357–365 (2015).

    Article  CAS  Google Scholar 

  20. Sorbye, S. W. et al. Prognostic impact of peritumoral lymphocyte infiltration in soft tissue sarcomas. BMC Clin. Pathol. 12, 5 (2012).

    Article  CAS  Google Scholar 

  21. Sorbye, S. W. et al. High expression of CD20+ lymphocytes in soft tissue sarcomas is a positive prognostic indicator. OncoImmunology 1, 75–77 (2012).

    Article  Google Scholar 

  22. Bertucci, F. et al. PDL1 expression is a poor-prognosis factor in soft-tissue sarcomas. OncoImmunology 6, e1278100 (2017).

    Article  CAS  Google Scholar 

  23. Kim, J. R. et al. Tumor infiltrating PD1-positive lymphocytes and the expression of PD-L1 predict poor prognosis of soft tissue sarcomas. PLoS One 8, e82870 (2013).

    Article  ADS  CAS  Google Scholar 

  24. Honda, Y. et al. Infiltration of PD-1-positive cells in combination with tumor site PD-L1 expression is a positive prognostic factor in cutaneous angiosarcoma. OncoImmunology 6, e1253657 (2016).

    Article  CAS  Google Scholar 

  25. Paydas, S., Bagir, E. K., Deveci, M. A. & Gonlusen, G. Clinical and prognostic significance of PD-1 and PD-L1 expression in sarcomas. Med. Oncol. 33, 93 (2016).

    Article  CAS  Google Scholar 

  26. Nielsen, J. S. et al. CD20+ tumor-infiltrating lymphocytes have an atypical CD27- memory phenotype and together with CD8+ T cells promote favorable prognosis in ovarian cancer. Clin. Cancer Res. 18, 3281–3292 (2012).

    Article  CAS  Google Scholar 

  27. Montfort, A. et al. A strong B-cell response is part of the immune landscape in human high-grade serous ovarian metastases. Clin. Cancer Res. 23, 250–262 (2017).

    Article  CAS  Google Scholar 

  28. Hennequin, A. et al. Tumor infiltration by Tbet+ effector T cells and CD20+ B cells is associated with survival in gastric cancer patients. OncoImmunology 5, e1054598 (2015).

    Article  CAS  Google Scholar 

  29. Wouters, M. C. A. & Nelson, B. H. Prognostic significance of tumor-infiltrating B cells and plasma cells in human cancer. Clin. Cancer Res. 24, 6125–6135 (2018).

    Article  Google Scholar 

  30. Helmink, B. et al. B cells and tertiary lymphoid structures promote immunotherapy response. Nature (2020).

    Article  Google Scholar 

  31. Chibon, F. et al. Validated prediction of clinical outcome in sarcomas and multiple types of cancer on the basis of a gene expression signature related to genome complexity. Nat. Med. 16, 781–787 (2010).

    Article  CAS  Google Scholar 

  32. Barretina, J. et al. Subtype-specific genomic alterations define new targets for soft-tissue sarcoma therapy. Nat. Genet. 42, 715–721 (2010).

    Article  CAS  Google Scholar 

  33. Gobble, R. M. et al. Expression profiling of liposarcoma yields a multigene predictor of patient outcome and identifies genes that contribute to liposarcomagenesis. Cancer Res. 71, 2697–2705 (2011).

    Article  CAS  Google Scholar 

  34. Lagarde, P. et al. Mitotic checkpoints and chromosome instability are strong predictors of clinical outcome in gastrointestinal stromal tumors. Clin. Cancer Res. 18, 826–838 (2012).

    Article  CAS  Google Scholar 

  35. Lagarde, P. et al. Chromosome instability accounts for reverse metastatic outcomes of pediatric and adult synovial sarcomas. J. Clin. Oncol. 31, 608–615 (2013).

    Article  CAS  Google Scholar 

  36. McCall, M. N., Bolstad, B. M. & Irizarry, R. A. Frozen robust multiarray analysis (fRMA). Biostatistics 11, 242–253 (2010).

    Article  Google Scholar 

  37. Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

    Article  Google Scholar 

  38. Petitprez, F. et al. Transcriptomic analysis of the tumor microenvironment to guide prognosis and immunotherapies. Cancer Immunol. Immunother. 67, 981–988 (2017).

    Article  Google Scholar 

  39. Beuselinck, B. et al. Molecular subtypes of clear cell renal cell carcinoma are associated with sunitinib response in the metastatic setting. Clin. Cancer Res. 21, 1329–1339 (2015).

    Article  CAS  Google Scholar 

  40. Giraldo, N. A. et al. Orchestration and prognostic significance of immune checkpoints in the microenvironment of primary and metastatic renal cell cancer. Clin. Cancer Res. 21, 3031–3040 (2015).

    Article  CAS  Google Scholar 

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This work was supported by the Institut National de la Santé et de la Recherche Médicale, the Université de Paris, Sorbonne University, the Programme Cartes d’Identité des Tumeurs (CIT) from the Ligue Nationale Contre le Cancer, grants from Institut National du Cancer (HTE-INSERM plan cancer, C16082DS), Association pour la Recherche sur le Cancer (ARC), Cancer Research for Personalized Medecine programme (CARPEM T8), French Sarcoma Group, the European Connective Tissue Cancer Network (CONTICANET, FP6-018806), ‘FONCER contre le cancer’ programme, Labex Immuno-Oncology (LAXE62_9UMRS972 FRIDMAN), the National Institutes of Health (E.Z.K. is supported by T32CA0095999) and the Moon Shot program at MD Anderson Cancer Center. Grants from the Ministry of Education (NTU-107L9014) and Ministry of Science and Technology (MOST 107-3017-F-002 -002-), Taiwan and from the National Taiwan University (YongLin Chair Grant S-01 and S-03) also supported this study. SARC028 was jointly funded by Merck, Inc., SARC, Sarcoma Foundation of America, and the QuadW Foundation. F.P. supported by CARPEM doctorate fellowship. C.L.R. is recipient of the Paul Calabresi Clinical Oncology Award (K12 CA088084). The slides stained for immunofluorescence were scanned and analysed at the Centre d’Histologie, d’Imagerie et de Cytométrie (CHIC), Centre de Recherche des Cordeliers UMRS1138 (Paris, France). CHIC is a member of the Sorbonne University Flow Cytometry Network (RECYF). We thank C. Klein, K. Garbin and E. Devevre for their support with the imaging. The Nanostring analysis of the NTUH core cohort was performed by the Plateforme Génomique of the Institut Curie (Paris, France). We thank D. Gentien and E. Henry for their support. We acknowledge the help of H. Yan and B. Singh.

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Authors and Affiliations



F.P., W.H.F., C.S.-F., A.d.R., T.W.-W.C., H.A.T. and A.I. designed the study and experiments. F.P., A.d.R., C.L. and Y.L. performed the bioinformatics analysis. L.L., G.L., I.N., L.-P.H., A.B., M.M. and F.P. carried out the immunohistochemistry experiments. J.C., Y.-M.J. and J.A. performed anatomo-pathology revision on the samples. E.Z.K., C.-M.S., W.-L.W. and K.M.W. performed the RNA extraction and Nanostring experiments. T.W.-W.C., A.I., M.T. and H.A.T. provided clinical guidance. T.W.-W.C., M.T., A.I., E.Z.K., A.J.L., C.L.R., M.A.B., V.B., D.R. and H.A.T. cared for the patients and provided patient materials or clinical data. F.P., W.H.F., C.S.-F., H.A.T., A.d.R., E.Z.K., C.L.R., A.J.L., T.W.-W.C., C.-M.S., J.A.W. and A.I. discussed the data and wrote the text. W.H.F., C.S.-F., A.d.R. and H.A.T. supervised the study and all authors commented on the manuscript and approved the submission.

Corresponding authors

Correspondence to Hussein A. Tawbi or Wolf H. Fridman.

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Competing interests

W.H.F. is a consultant for AstraZeneca, Novartis, Servier and Pierre Fabre. A.I. serves in the advisory board of Bayer, Daiichy, Epizyme, Lilly, Novartis, Roche and Springworks, and received research funding from Astra Zeneca, Bayer, Chugai, Merck, MSD, Novartis and Pharmamar. J.A. is a consultant for AstraZeneca, Bayer, BMS, MSD and Roche and received research funding from MSD, Pfizer and Pierre Fabre. J.A.W. participated on advisory boards for Merck, BMS, Novartis, Astra Zeneca, Roche Genentech and Illumina. M.A.B. is a consultant for EMD Serono, Immune Design, Eisai. H.A.T. serves on advisory boards and receives consulting fees from BMS, Merck and Genentech, and received research funding from BMS, Merck, Celgene, GSK, and Genentech. T.W.-W.C. participated in advisory boards for Eisai and Lilly and received research funds from Eisai. C.L.R. received research funding from BMS. The other authors declare no conflict of interest.

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Peer review information Nature thanks Naiyer Rizvi 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 Diagram of analytic workflow.

The drawing of the syringe in the bottom left corner originates from Servier Medical Art (, and is distributed under a CC-BY 3.0 Attribution license (

Extended Data Fig. 2 SICs in various STS histologies.

a, Repartition of the SICs in various histologies of TCGA SARC and GSE21050 (LMS, UPS and DDLPS), and FSG cohort (synovial sarcoma, myxoid liposarcoma, gastrointestinal stromal tumour (GIST)). b, Survival of patients from the FSG cohort (n = 136) according to SIC classification. Patients with synovial sarcoma, myxoid liposarcoma and gastrointestinal stromal tumour were pooled. Analysis was performed with Kaplan–Meier estimates and two-sided log-rank tests.

Extended Data Fig. 3 The SICs exhibit strongly different TMEs.

This figure refers to the GSE21050 cohort (n = 283). a, Composition of the GSE21050 cohort by SIC, histology and site of disease. b, Composition of the TME by SIC as defined by the MCP-counter Z-scores. c, Expression of gene signatures related to the functional orientation of the immune TME by SIC. d, Expression of genes related to immune checkpoints by SIC. Adjusted P values are obtained from Benjamini–Hochberg correction of two-sided Kruskal–Wallis test P values. These observations stand for cohorts GSE21122 and GSE30929 (not shown).

Extended Data Fig. 4 Distribution of MCP-counter scores.

ae, MCP-counter scores in TCGA SARC (n = 213) (a, c, e) and GSE21050 (n = 283) (b, d, e), for CD8+ T cells (a, b), cytotoxic lymphocytes (c, d) and B lineage cells (e, f). The blue line indicates the density curve. The red dotted line indicates the cut-off chosen to segregate high or low values, set at the median for CD8+ T cells and at the third quartile for cytotoxic lymphocytes and B lineage, in each cohort. These values were chosen because the CD8 T cells scores present a normal distribution, whereas the cytotoxic lymphocytes and B lineage scores distribution exhibit a long right tail.

Extended Data Fig. 5 B cell infiltration of STS is the key factor associated with overall survival.

This figure refers to TCGA SARC and GSE21050 pooled cohorts (n = 496). a, Overall survival of patients with STS according to MCP-counter scores for cytotoxic lymphocytes. b, Overall survival of patients based on the infiltration level of their tumours by B lineage cells and cytotoxic lymphocytes. ce, Overall survival of patients based on degree of tumour infiltration by B lineage cells and expression of PDCD1 (c), CD274 (d) and FOXP3 (e). The analyses were performed with the Kaplan–Meier estimates and two-sided log-rank tests. Tumours were considered high for expression of PDCD1, CD274 and FOXP3 if their expression was above median, and high for B lineage and cytotoxic lymphocytes if the MCP-counter score was above the third quartile.

Extended Data Fig. 6 The mutational landscape of STS tumours does not vary significantly between SICs.

This figure refers to the TCGA SARC cohort (n = 213). a, Mutational burden according to the SIC of the tumours, expressed in number of non-silent mutations. P value was computed with a Kruskal–Wallis test. Box plots as in Fig. 3d. b, Mutation frequency of all genes that are mutated in greater than 2.5% of tumours. c, Mutation frequency for genes that are mutated in more than 5% of tumours, according to SICs in the TCGA SARC cohort. The dashed lines indicate the overall mutation frequency. P values were obtained through one-sample two-sided t-tests, corrected for multiple testing with the Bonferroni method. This was applied only to samples that had mutations on the considered genes (TP53: n = 75; ATRX: n = 34; TTN: n = 21; RB1: n = 19; MUC16, n = 17; PCLO, n = 13; DNAH5, MUC17 and USH2A: n = 11, PTEN, n = 6; KRAS, n = 2; BRAF, n = 1).

Extended Data Fig. 7 Validation of SIC profiles by immunohistochemistry.

This figure refers to the NTUH cohort. a, SIC attribution as defined by gene expression using the MCP-counter Z-scores in 73 cases. b, Cell density counts showing the differences in TME composition according to SIC identification of the 73 cases (SIC A: n = 16; SIC C: n = 10; SIC E: n = 11). P values are determined by two-sided Kruskal–Wallis (KW) tests. Pairwise comparisons are derived from the Dunn test. Box plots are as in Fig. 3d. c, Representative images of CD3 (green), CD20 (pink), CD8 (brown) and CD34 (green) expression by immunohistochemistry of SIC A, C and E tumours. The same area of the tumour is represented (0.05 mm2) in each image. Similar results were observed on the other tumours from the same SICs (SIC A: n = 16; SIC C: n = 10; SIC E: n = 11).

Extended Data Fig. 8 Location and maturation of TLSs.

a, Pearson correlation between the expression of CXCL13 and the 12-chemokine signature of TLS in TCGA SARC cohort (n = 213). Samples are coloured according to SICs. b, Intratumoural location of TLSs in three different examples from the NTUH cohort—DDLPS, UPS and LMS, respectively. TLSs are observed by the presence of CD20+ B cells aggregates (brown, surrounded by blue shapes). The red line delineates the tumoral zone. Similar findings were observed on the 11 tumours with TLS. c, Definition of peripheral, medium and central zones, accounting for 25%, 25% and 50% of the total tumour area, respectively. d, Distribution of TLSs in the various zones. Each bar represents one tumour. The letters above bars indicate the SIC of the tumour when the sample passed quality control of Nanostring nCounter hybridization. Dots indicate tumours in which SIC could not be determined because of RNA quality control. Similar images were observed for 66 E-TLS, 23 PFL-TLS and 20 SFL-TLS. e, Illustration of diverse degrees of TLS maturation in STS tumours. Consistent with maturation events occurring in secondary lymphoid organs, three maturation steps have been described for TLS: E-TLS (bottom), PFL-TLS (middle) and SFL-TLS (top), which differ in the presence of follicular dendritic cells (FDC) and their markers. E-TLS contain aggregates of CD20+ B cells and CD3+ T cells without FDC, PFL-TLS contain CD21+ FDC (red dotted zones) and SFL-TLS contain a germinal centre, notably visible through the presence of CD21+CD23+ follicular dendritic cells (yellow dotted zone). DAPI staining is shown in white. DAPI-negative green dots correspond to fluorescent erythrocytes. f, Distribution of TLS maturation steps in a subset of tumours. Each bar represents one tumour. Differences between the number of TLSs observed here and in other figures can be explained by use of non-consecutive slides or a different tumour block for some samples.

Extended Data Fig. 9 Pan-cohort immune classification.

This figure refers to the four discovery cohorts: TCGA SARC (n = 213), GSE21050 (n = 283), GSE21122 (n = 72) and GSE30929 (n = 40). ad, Heat map and unsupervised hierarchical clustering of the MCP-counter scores describing the tumour microenvironment. Each of the population is represented by the Z-scores of the signature. a, TCGA SARC. b, GSE21050. c, GSE21122. d, GSE30929. eh, Evolution of the variance explained by the clusters as a function of the number of clusters. Red dots indicate the number of clusters that was retained in this study. Each graph corresponds to the heat map on its left. i, Heat map of the Pearson correlation of centroids from each SIC class of discovery cohorts (TCGA SARC, GSE21050, GSE21122 and GSE30929, n = 608), with five immune classes and two groups of unclassified samples. j, k, Principal component analysis of samples from the four discovery cohorts (n = 608), based on their normalized and merged MCP-counter scores. j is coloured according to the original classes, k is coloured according to the predicted immune classes, showing a heightened homogeneity within each SIC class. l, m, Composition of the TME with classes defined as in j and k for the four discovery cohorts (n = 608), expressed in cohort-specific row Z-scores.

Extended Data Table 1 Clinicopathological composition of the cohorts included in this study
Extended Data Table 2 Antibodies used for immunohistochemistry and immunofluorescence

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Petitprez, F., de Reyniès, A., Keung, E.Z. et al. B cells are associated with survival and immunotherapy response in sarcoma. Nature 577, 556–560 (2020).

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