Abstract

Therapeutic antibodies that block the programmed death-1 (PD-1)–programmed death-ligand 1 (PD-L1) pathway can induce robust and durable responses in patients with various cancers, including metastatic urothelial cancer1,2,3,4,5. However, these responses only occur in a subset of patients. Elucidating the determinants of response and resistance is key to improving outcomes and developing new treatment strategies. Here we examined tumours from a large cohort of patients with metastatic urothelial cancer who were treated with an anti-PD-L1 agent (atezolizumab) and identified major determinants of clinical outcome. Response to treatment was associated with CD8+ T-effector cell phenotype and, to an even greater extent, high neoantigen or tumour mutation burden. Lack of response was associated with a signature of transforming growth factor β (TGFβ) signalling in fibroblasts. This occurred particularly in patients with tumours, which showed exclusion of CD8+ T cells from the tumour parenchyma that were instead found in the fibroblast- and collagen-rich peritumoural stroma; a common phenotype among patients with metastatic urothelial cancer. Using a mouse model that recapitulates this immune-excluded phenotype, we found that therapeutic co-administration of TGFβ-blocking and anti-PD-L1 antibodies reduced TGFβ signalling in stromal cells, facilitated T-cell penetration into the centre of tumours, and provoked vigorous anti-tumour immunity and tumour regression. Integration of these three independent biological features provides the best basis for understanding patient outcome in this setting and suggests that TGFβ shapes the tumour microenvironment to restrain anti-tumour immunity by restricting T-cell infiltration.

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Acknowledgements

We thank the patients and their families; all of the investigators and their staff involved in IMvigor210 study; C. Ahearn, S. Lau, C. Havnar, Z. Boyd, S. Sampath, D. Wilson, J. Doss and medical writers at Health Interactions. J.E.R. acknowledges support from P30 CA008748. L.F. acknowledges support from NCI 1R01CA194511.

Author information

Author notes

    • Sanjeev Mariathasan
    • , Shannon J. Turley
    •  & Dorothee Nickles

    These authors contributed equally to this work.

Affiliations

  1. Genentech, South San Francisco, California 94080, USA

    • Sanjeev Mariathasan
    • , Shannon J. Turley
    • , Dorothee Nickles
    • , Alessandra Castiglioni
    • , Kobe Yuen
    • , Yulei Wang
    • , Edward E. Kadel III
    • , Hartmut Koeppen
    • , Jillian L. Astarita
    • , Rafael Cubas
    • , Suchit Jhunjhunwala
    • , Romain Banchereau
    • , Yagai Yang
    • , Yinghui Guan
    • , Cecile Chalouni
    • , James Ziai
    • , Yasin Şenbabaoğlu
    • , Stephen Santoro
    • , Daniel Sheinson
    • , Jeffrey Hung
    • , Jennifer M. Giltnane
    • , Andrew A. Pierce
    • , Kathryn Mesh
    • , Steve Lianoglou
    • , Johannes Riegler
    • , Richard A. D. Carano
    • , Ira Mellman
    • , Daniel S. Chen
    • , Marjorie Green
    • , Christina Derleth
    • , Gregg D. Fine
    • , Priti S. Hegde
    •  & Richard Bourgon
  2. Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Lund, Skåne 223 81, Sweden

    • Pontus Eriksson
    •  & Mattias Höglund
  3. Fios Genomics, Edinburgh EH16 4UX, UK

    • Loan Somarriba
    •  & Daniel L. Halligan
  4. Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands

    • Michiel S. van der Heijden
  5. Department of Cancer Medicine, Institut Gustave Roussy, University of Paris Sud, 94800 Villejuif, France

    • Yohann Loriot
  6. Genitourinary Oncology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA

    • Jonathan E. Rosenberg
  7. University of California San Francisco, Helen Diller Family Comprehensive Cancer Center, San Francisco, California 94158, USA

    • Lawrence Fong
  8. Barts Experimental Cancer Medicine Centre, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK

    • Thomas Powles

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Contributions

S.M., S.J.T., D.N., L.F., I.M., D.S.C., J.E.R., Y.L., M.S.H., M.G., C.D., G.D.F., P.S.H., R.Bo and T.P. contributed to the overall study design. S.M., S.J.T., D.N., Y.W., E.E.K., K.Y., R.Ba., Y.G., Y.S¸ ., S.J., S.L., P.E., M.H., L.S., D.L.H., P.S.H. and R.Bo. performed the biomarker and statistical analyses. H.K., C.C., J.Z., S.S., D.S., J.H., J.M.G., A.A.P. and K.M. conducted microscopy studies. S.J.T., A.C., J.L.A. and R.C. designed all the preclinical experiments and S.J.T., A.C., J.L.A., R.C., Y.Y., C.C., J.Z., Y.S¸ ., S.S., D.S., J.H., J.M.G., A.A.P., K.M., J.R. and R.A.D.C. analysed the corresponding preclinical data. S.M., S.J.T., D.N., I.M., P.S.H., R.Bo. and T.P. wrote the paper. All authors contributed to data interpretation, discussion of results and commented on the manuscript.

Competing interests

D.H. and L.S. are employees of Fios Genomics Ltd., a contract research organisation contracted to provide bioinformatics services to Genentech Inc. M.S.H., Y.L. and T.P. have advisory roles for Roche/Genentech. J.E.R. is a consultant for Roche/Genentech, BMS, Merck, AstraZeneca and EMD-Serono, and Roche/Genentech have provided research funding to his institution. P.E., M.H., L.F. and S.S. declare no competing interests. All other authors are employees and stockholders of Genentech/Roche.

Corresponding authors

Correspondence to Sanjeev Mariathasan or Shannon J. Turley or Richard Bourgon.

Reviewer Information Nature thanks D. McConkey and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains Supplementary Methods, a Supplementary Discussion, Supplementary References and the flow gating strategy (Supplementary Figure 1).

  2. 2.

    Life Sciences Reporting Summary

Excel files

  1. 1.

    Supplementary Table 1

    Pathways discovered to be associated with TMB. The table lists KEGG gene sets significantly (FDR < 0.05) enriched in genes correlated with TMB. “Direction” indicates whether the category was enriched in genes positively (“Up”) or negatively (“Down”) correlated with TMB. “Identified genes” lists all genes within a given category that were found to be correlated with TMB. “S” indicates the number of these genes, “N” gives the total number of genes in a category, while “P (adj.)” holds the adjusted enrichment p values (hypergeometric test). KEGG, Kyoto Encyclopedia of Genes and Genomes. TMB, tumour mutation burden.

  2. 2.

    Supplementary Table 2

    Single-gene expression association with tumour mutation burden (TMB). Each detected gene is annotated with its official symbol, Entrez Gene ID, gene description and chromosome. The statistics for a test of association with TMB are given: log fold change (FC), average expression (“AveExpr”) across the data set, as well as nominal and adjusted p values.

  3. 3.

    Supplementary Table 3

    Single-gene expression association with response. Each detected gene is annotated with its official symbol, Entrez Gene ID, gene description and chromosome. The statistics for a test of differential expression by response (CR/PR vs. SD/PD) are given: log fold change (FC), average expression (“AveExpr”) across the data set, as well as nominal and adjusted p values. CR, complete response; PD, progressive disease; PR, partial response; SD, stable disease.

  4. 4.

    Supplementary Table 4

    Mutation status of DNA repair and cell cycle regulation pathways and association with response and TMB. DNA repair and cell cycle regulation gene sets were tested for association with response (CR/PR vs. SD/PD) and TMB (“category”), with or without inclusion of TP53. The number of patients with at least one mutation in the genes belonging to a gene set (“n mutant”), the effect size (“estimate”) as well as nominal p values are reported. CR, complete response; PD, progressive disease; PR, partial response; SD, stable disease; TMB, tumour mutation burden.

  5. 5.

    Supplementary Table 5

    Mutation status of single genes and association with response and TMB. Symbols and the number of mutant patients are given for each tested gene. Association with mutation status was tested for both response (CR/PR vs. SD/PD) and TMB (“category”). Effect size (“estimate”) as well as nominal and adjusted p values are reported. The last two columns indicate whether a given gene is member of the DDR and/or the cell cycle regulator gene set. CR, complete response; PD, progressive disease; PR, partial response; SD, stable disease; TMB, tumour mutation burden.

  6. 6.

    Supplementary Table 6

    Pathways discovered to be associated with response. The table lists KEGG gene sets significantly (FDR < 0.1) enriched in genes differentially expressed by response (CR/PR vs. SD/PD). “Direction” indicates whether the category was enriched in genes up- (“Up”) or down-regulated (“Down”) in responders. “Identified genes” lists all genes within a given category that were found to be associated with response. “S” indicates the number of these genes, “N” gives the total number of genes in a category, while “P (adj.)” holds the adjusted enrichment p values (hypergeometric test). CR, complete response; KEGG, Kyoto Encyclopedia of Genes and Genomes; PD, progressive disease; PR, partial response; SD, stable disease.

  7. 7.

    Supplementary Table 7

    Characteristics of patients with molecular profiling vs. intent to treat population (ITT). Patients for which RNA sequencing data was generated were chosen as representative biomarker evaluable population (BEP) and distribution of key clinical covariates are listed as compared to the ITT; efficacy evaluable patients only were assessed. Both the number of patients as well as percentages (in parentheses) are given. BCG, Bacille Calmette Guerin; ECOG, Eastern Cooperative Oncology Group.

  8. 8.

    Supplementary Table 8

    Gene sets used for signature analyses. For each gene set, the platform (i.e. whether the gene set was used for gene expression or mutation analysis), the genes within the set that were detected in our data set as well as the source for the set are listed.

  9. 9.

    Supplementary Table 9

    Genes and centroids used for Lund subtype label assignment.

  10. 10.

    Supplementary Table 10

    Single-gene expression association with IC PD-L1 positivity. Each detected gene is annotated with official symbol, Entrez Gene ID, gene description and chromosome. The statistics for a test of association with log2 transformed raw PD-L1 staining (ICp) are given: log fold change (FC), average expression (“AveExpr”) across the data set, as well as nominal and adjusted p-values. IC, tumour-infiltrating immune cell.

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https://doi.org/10.1038/nature25501

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