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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Herbst, R. S. et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 515, 563–567 (2014)
Powles, T. et al. MPDL3280A (anti-PD-L1) treatment leads to clinical activity in metastatic bladder cancer. Nature 515, 558–562 (2014)
Bellmunt, J. et al. Pembrolizumab as second-line therapy for advanced urothelial carcinoma. N. Engl. J. Med. 376, 1015–1026 (2017)
Rosenberg, J. E. et al. Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial. Lancet 387, 1909–1920 (2016)
Balar, A. V. et al. Atezolizumab as first-line treatment in cisplatin-ineligible patients with locally advanced and metastatic urothelial carcinoma: a single-arm, multicentre, phase 2 trial. Lancet 389, 67–76 (2017)
Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013)
Chalmers, Z. R. et al. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med. 9, 34 (2017)
Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014)
Rizvi, N. A. et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015)
Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015)
Chen, D. S. & Mellman, I. Oncology meets immunology: the cancer–immunity cycle. Immunity 39, 1–10 (2013)
Cancer Genome Atlas Research Network. Comprehensive molecular characterization of urothelial bladder carcinoma. Nature 507, 315–322 (2014)
Burns, M. B., Temiz, N. A. & Harris, R. S. Evidence for APOBEC3B mutagenesis in multiple human cancers. Nat. Genet. 45, 977–983 (2013)
Gao, J. et al. Loss of IFN-γ pathway genes in tumor cells as a mechanism of resistance to anti-CTLA-4 therapy. Cell 167, 397–404 (2016)
Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016)
Ribas, A. Adaptive immune resistance: how cancer protects from immune attack. Cancer Discov. 5, 915–919 (2015)
Benci, J. L. et al. Tumor interferon signaling regulates a multigenic resistance program to immune checkpoint blockade. Cell 167, 1540–1554 (2016)
Lin, R.-L. & Zhao, L.-J. Mechanistic basis and clinical relevance of the role of transforming growth factor-β in cancer. Cancer Biol. Med. 12, 385–393 (2015)
Massagué, J. TGFβ in cancer. Cell 134, 215–230 (2008)
Calon, A. et al. Stromal gene expression defines poor-prognosis subtypes in colorectal cancer. Nat. Genet. 47, 320–329 (2015)
Derynck, R. & Zhang, Y. E. SMAD-dependent and SMAD-independent pathways in TGF-β family signalling. Nature 425, 577–584 (2003)
Morikawa, M., Derynck, R. & Miyazono, K. TGF-β and the TGF-β family: context-dependent roles in cell and tissue physiology. Cold Spring Harb. Perspect. Biol. 8, a021873 (2016)
Flavell, R. A., Sanjabi, S., Wrzesinski, S. H. & Licona-Limón, P. The polarization of immune cells in the tumour environment by TGFb. Nat. Rev. Immunol. 10, 554–567 (2010)
Hegde, P. S., Karanikas, V. & Evers, S. The where, the when, and the how of immune monitoring for cancer immunotherapies in the era of checkpoint inhibition. Clin. Cancer Res. 22, 1865–1874 (2016)
Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014)
Dobson, A. J. & Barnett, A. G. An Introduction to Generalized Linear Models 3rd edn. (Chapman & Hall/CRC, 2008)
Sjödahl, G. et al. A molecular taxonomy for urothelial carcinoma. Clin. Cancer Res. 18, 3377–3386 (2012)
Sjödahl, G. et al. Toward a molecular pathologic classification of urothelial carcinoma. Am. J. Pathol. 183, 681–691 (2013)
R Core Team. R: a Language and Environment for Statistical Computing http://www.r-project.org (R Foundation for Statistical Computing, Vienna, Austria, 2016)
Rusinova, I. et al. Interferome v2.0: an updated database of annotated interferon-regulated genes. Nucleic Acids Res. 41, D1040–D1046 (2013)
Spranger, S., Bao, R. & Gajewski, T. F. Melanoma-intrinsic β-catenin signalling prevents anti-tumour immunity. Nature 523, 231–235 (2015)
Damrauer, J. S. et al. Intrinsic subtypes of high-grade bladder cancer reflect the hallmarks of breast cancer biology. Proc. Natl Acad. Sci. USA 111, 3110–3115 (2014)
Hugo, W. et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165, 35–44 (2016)
Hedegaard, J. et al. Comprehensive Transcriptional Analysis of Early-Stage Urothelial Carcinoma. Cancer Cell 30, 27–42 (2016)
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.
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.
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 figures and tables
a, Overlap of the efficacy-evaluable patient populations with assays used in this study (n = 326 for one or more of these assays). For gene expression analyses with respect to response, the complete RNA-seq dataset was used (n = 298). For gene expression analyses in the context of TMB or immune phenotype, the intersect between RNA-seq and FoundationOne (FMOne, n = 237) or immune phenotype (n = 244) were used, respectively. For mutation analysis around immune phenotypes, the intersect between FoundationOne and immune phenotype was used (n = 220). For associations between response or genes mutation status with TMB, the complete FoundationOne dataset was used (n = 251). b, PD-L1 protein expression on tumour cells (TC), in contrast to expression on immune cells (Fig. 1a), was not associated with response to atezolizumab (two-sided Fisher’s exact test, P = 0.72). c, d, Transcriptional correlates of PD-L1 protein expression on immune cells. c, Genes associated with PD-L1 immunohistochemistry positivity on immune cells. Normalized log2-transformed gene expression was compared with PD-L1 protein expression on immune cells. Interferon-stimulated genes30 and previously reported CD8 Teff and immune checkpoint-molecule gene sets4,5 were among the most upregulated (complete list of associated genes is given in Supplementary Table 10). d, Association between CD8 Teff-signature score and PD-L1-staining on tumour-infiltrating immune cells. There is a significant positive relationship between the signature score and PD-L1 staining on immune cells (likelihood ratio test, P = 4.2 × 10−35). e, f, Tumour neoantigen burden (TNB) is associated with outcome. e, Box plots showing the relationship between response status and TNB. Shown are the number of mutations based on whole-exome sequencing, filtering for those mutations that are predicted to be expressed neoantigens. TNB is positively associated with response to atezolizumab (two-tailed t-test, P = 2.7 × 10−9). f, TNB, split into quartiles, is also associated with overall survival (likelihood ratio test, P = 0.00015). g, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways enriched in genes whose expression is correlated with TMB. Shown are adjusted −log10P values for enrichment of KEGG gene sets that are significantly (false discovery rate < 0.05) enriched in genes that are correlated with TMB (272 samples analysed). Gene sets that are inferred to reflect key underlying biological processes are coloured. Only the top seven genes per set (ranked by single-gene P value) are shown. ATP5G1 is also known as ATP5MC1, ATP5O is also known as ATP5PO. h, Relationship between different gene-expression signatures as well as the single-gene expression values for MKI67, a marker for proliferation. Dot size and colour show correlation between signature scores and gene expression (348 samples analysed). Numbers are the Pearson correlation coefficients. Gene-set membership is shown in Supplementary Table 8. i, j, APOBEC3A and APOBEC3B gene expression and their association with response and TMB. Both APOBEC3A (two-tailed t-test, P = 0.015; i) and APOBEC3B (two-tailed t-test, P = 0.0025; j) exhibit higher mean expression in responders. For TMB, Pearson correlation coefficients and P values are given. In j, the two extreme expression outliers were excluded when calculating correlation between gene expression and TMB. k, Mutations in cell-cycle-regulator genes are associated with TMB. Rows represent genes and columns represent patients (n = 293); patients with a mutation are indicated by a black rectangle. The top bar plot depicts TMB in each patient. The final rows represent the mutation status of the pathway with or without TP53. Percentages on the left of the plot indicate prevalence. Genes with significant single-gene associations with TMB are marked by an asterisk. Mutations in cell-cycle-regulator genes are associated with TMB with inclusion of TP53 (two-tailed t-test, P = 4.01 × 10−8), but not without inclusion of TP53 (two-tailed t-test, P = 0.0652156; Supplementary Table 4). l, Mutation status in the cell-cycle-regulation (CCR) pathway by response. For each patient, it is determined whether they have any mutations in genes belonging to the CCR pathway, excluding TP53. Excluding TP53, there is no association between mutation status in the CCR pathway and response (two-sided Fisher’s test, P = 0.31104; Supplementary Table 4). Sample sizes are given in parentheses. For box plots, centre mark is median, whiskers are minimum/maximum excluding outliers, and circles are outliers.
a, KEGG pathways are significantly associated with response to atezolizumab (adjusted P < 0.10; comparing 68 responders to 230 non-responders). The top seven genes per set are shown; complete lists are given in Supplementary Table 6. SGOL1 is also known as SGO1 b, c, IFNG (b) and IFNGR1 (c) gene expression (y axis) are significantly associated with response (two-tailed t-test, P = 9.1 × 10−5) and non-response (two-tailed t-test, P = 0.00012), respectively. d, e, TGFBR2 gene expression (y axis) is significantly associated with non-response (two-tailed t-test, P = 0.00019, d) and, when split by quartiles, with reduced overall survival (likelihood ratio test, P = 0.022, e). In b–d, the numbers above the graphs specify sample numbers in each bin. 87 samples per quartile (e). f, Histology of tumour–immune phenotypes desert, excluded, and inflamed. g, Explained variance in patient response. Generalized linear models were fitted to all efficacy-evaluable, immune-phenotyped samples (n = 204) using binary response (complete response/partial response versus stable disease/progressive disease) as the dependent variable and scores from single input or input combinations (x axis) as independent variables. Percentage of explained variance of response is plotted on the y axis. Comparisons between different models were made using a likelihood ratio test. Horizontal bars describe likelihood ratio test results for two-biology versus three-biology model comparisons. Stacked significance symbols for two-biology models show results of likelihood ratio test comparison to the first single-biology model and separately to the second single-biology model, in the same order as the x-axis bar labels; for example, the Teff, TMB model achieves three asterisks when compared to the Teff single model, but an NS when compared to the TMB single model—due to dilution of its inflamed-specific signal in this all-sample analysis. A model that includes both DNA (TMB) and RNA markers (CD8+ Teff signature and F-TBRS) as well as interactions between the F-TBRS and both TMB and cancer–immune phenotype explains 50% of the variance observed in response, and it significantly improves on all single- and two-biology models. This final bar is also given on the far right in Fig. 2f. *P < 0.05, **P < 0.01, ***P < 0.001. NS, P > 0.1. Exact likelihood ratio test P values: Teff,TBRS versus Teff, 0.0026; Teff,TBRS versus TBRS, 0.0032; Teff,TMB versus Teff, 4.9 × 10−8; Teff,TMB versus TMB, 0.2; TBRS,TMB versus TBRS, 6.6 × 10−8; TBRS,TMB versus TMB, 0.014; Teff,TBRS,TMB versus Teff,TBRS, 1.9 × 10−7; Teff,TBRS,TMB versus Teff,TMB, 0.0028; Teff,TBRS,TMB versus TBRS,TMB, 0.016. Teff, CD8 T-effector gene signature.
a, Heat map representing all evaluated patients, except for patients without defined response, arranged in columns and sorted first by molecular subtype, then by response to atezolizumab. Left, patients were sorted based on a TCGA-based subtyping scheme. Right, patients were sorted by a Lund-based subtyping scheme (as in Fig. 3). Immune cell and tumour cell PD-L1 status are given. In addition, TMB and mutation status (black, mutated; grey, patients without mutation data) for a few genes of interest are shown. The rows of the heat map show expression (Z scores) of genes of interest, grouped into the following biologies and/or pathways: TCGA, TCGA subtyping genes; A, FGFR3 gene signature; B, CD8 Teff signature; C, antigen-processing machinery; D, immune checkpoint signature; E, MKI67 and cell cycle genes; F, DNA replication-dependent histones; G, DNA damage-repair genes; H, TGFβ receptor and ligand; I, F-TBRS genes; J, angiogenesis signature; K, EMT markers (for details on these signatures see Supplementary Methods). b, FGFR3-related and WNT target genes31 as well as PPARG are significantly differentially expressed by Lund subtype (Wald test; P-values: FGFR3-related, 2.7 × 10−43; WNT target, 1.3 × 10−15; PPARG, 1.2 × 10−53). Gene set membership is given in Supplementary Table 8. c, d, Distribution of Lund subtypes by cancer–immune phenotypes and response status. c, The fraction of patients within the different Lund subtypes (y axis) is plotted by tumour–immune phenotype. There is a significant difference in Lund-subtype composition between cancer–immune phenotypes (χ2 test, P = 1.2 × 10−7). d, For excluded tumours, the fraction of patients within the different Lund subtypes (y axis) is plotted by response status. Responders, complete and partial response (CR + PR); non-responders, stable and progressive disease (SD + PD). There is a significant difference in Lund-subtype composition between response groups (χ2 test, P = 0.00061). The numbers above the graphs specify sample numbers in each bin. e, Assessment of MKI67 expression and signatures of interest as well as TMB relative to molecular subtypes. Biologies of interest were scaled before medians were calculated across the Lund (left) and TCGA (right) molecular subtypes (columns). Red, high; blue, low. DNA rep., DNA replication.
a, TMB (y axis) is plotted against Lund and TCGA subtypes (x axis). The Lund genomically unstable (two-tailed t-test, P = 0.00018) and TCGA luminal II subtypes (P = 0.00024) have a higher median TMB. b, Patients are split into TMB low (grey) and high (black), on the basis of median TMB, and the fraction of patients in these two groups is shown for the Lund and TCGA molecular subtypes. c–e, TGFβ as a probable driver of differential response in the Lund genomically unstable subtype. c, Three patient subgroups: Lund genomically unstable but not TCGA luminal II, both genomically unstable and luminal II, or luminal II but not genomically unstable. d, CD8+ Teff, F-TBRS and TMB by subgroup. e, Response differs significantly by subgroups (two-tailed Fisher’s exact test, P = 0.00062). The numbers above the graphs or in parentheses specify sample numbers in each bin.
Extended Data Figure 5 Efficacy data of anti-TGFβ and anti-PD-L1 treatment in EMT6 and MC38 immune-excluded tumour models.
a, Fibroblast (PDGFRa, left) and T cell (CD3, right) parametric maps. Left image shows PDGFRa density (per cent positive pixels) and right shows T-cell density (cells mm−2). Scale bar, 1 mm. Representative images of eight biological replicates. b, Collagen (green) and T cells (CD3, red) stained by immunofluorescence. Representative images of five biological replicates. c, Collagen (green), T cells (CD3, white) and PDGFRa (red) in EMT6 tumours stained by immunofluorescence. Scale bar, 200 μm. d, PDGFRa (red) in EMT6 tumours stained by immunofluorescence. Scale bar: 200 μm. Representative images of four biological replicates. e, Quantification of TGFβ and PD-L1 RNA in whole EMT6 tumours by RNA-seq. The tumours were inoculated orthotopically and collected when volume reached 300 mm3 (n = 5 mice; data from one experiment). f, Quantification of TGFβ protein within whole EMT6 tumours by ELISA. Tumours were collected 14 days after inoculation, flash-frozen and lysed for protein quantification (n = 4 mice; data from one experiment). g, BALB/cJ mice were inoculated orthotopically with EMT6 tumour cells. When tumour volumes reached around 160 mm3 approximately nine days after inoculation, mice were treated with isotype control, anti-PD-L1, anti-TGFβ, or a combination of anti-PD-L1 and anti-TGFβ. Tumours were measured two times per week for approximately eight weeks by calliper. When tumour volumes fell below 32 mm3 (lowest limit of detection), they were considered complete response. Percentage of complete regressions across 2–6-independent studies (10 mice per group per study). h, Tumour weights at day seven after initiation of treatment (n = 28 mice per treatment group; data from three independent experiments). i, CD8 depletion experiment. CD8 T cells were depleted before initiation of treatment (n =10 mice per group, data from one experiment). j, Quantification of TGFβ and PD-L1 RNA in whole MC38 tumours by RNA-seq. The tumours were inoculated subcutaneously and collected when volume reached 300 mm3 (n = 5 mice; data from one experiment). k, Quantification of CD8 T cells in the centre and in the periphery of EMT6 and MC38 tumours from immunohistochemistry stains. Data expressed as number of cells per tissue area (periphery is defined as 400–600 μm from the tumour edge, centre is the remaining distance to the centre point). EMT6, n = 5 mice; MC38, n = 4 mice. i, Collagen (green) and T cells (CD3, red) in MC38 tumours stained by immunofluorescence. Scale bar, 1 mm (left); 0.1 mm (right, representative images of 5 biological replicates). m, C57BL/6 mice were inoculated subcutaneously with MC38 tumour cells. When tumour volumes reached around 180 mm3 approximately eight days after inoculation, mice were treated with isotype control, anti-PD-L1, anti-TGFβ, or a combination of anti-PD-L1 and anti-TGFβ. Tumours were measured two times per week for approximately eight weeks by calliper. When tumour volumes fell below 32 mm3 (lowest limit of detection), they were considered complete response. Percentage of complete response across two independent studies (one for anti-TGFβ alone) shown with 10 mice per treatment group for each independent study. n, Tumour growth curves for each individual mouse are shown. The data are representative of two independent experiments with 10 mice per treatment group. For box plots, centre mark is median, and whiskers are minimum and maximum. All statistics are two-sided Mann–Whitney U-tests compared to isotype group. *P < 0.05, **P < 0.01, ***P < 0.001.
a, c, d, Cytofluorimetric analysis of T cells seven days after initiation of the treatment. The abundance of total T cells (a), total CD4+ cells (c) and the percentage of T-regulatory cells (CD25+FOXP3+) in the CD4+ population (d) are shown. n = 15 mice for all treatment groups except for anti-TGFβ alone in which n = 10; data combined from three independent experiments expressed as fold change relative to the isotype mean. b, e, RNA-seq analysis on whole tumours collected seven days after the initiation of treatment. Single-gene expression for Ifng, Gzmb and Zap70 (b) Ikzf2 (also known as Helios) and Foxp3 (e) are shown (n = 8 mice per treatment group; data from one experiment). f, Distribution of tumour-infiltrating lymphocytes in tumours as assessed by immunohistochemistry and digital imaging seven days after the initiation of treatment as above. Representative CD3 staining (brown). Dashed line indicates tumour boundaries. (n = 19 for all groups except anti-PD-L1/anti-TGFβ, in which n = 20; three independent experiments). Scale bar, 500 μm. g, Quantification of pSMAD2 by immunohistochemistry at day seven after initiation of treatment. (n = 9 or 10 mice per treatment group; data from one experiment). h, Phospho-flow analysis of SMAD2/3 in tumours seven days after the initiation of treatment as above. MFI of pSMAD2/3 among total cells, CD45− or CD45+ cells are shown. Data are expressed as fold change (FC) relative to the isotype MFI average. Ten mice per treatment group from two independent experiments. i–k, RNA-seq analysis on whole tumours collected seven days after the initiation of treatment. Three EMT signatures (i), and TGFβ-response signatures for T cells (j) and macrophages (k) are also shown. (n = 8 mice per treatment group; data from one experiment). All statistics in the figure use two-sided Mann–Whitney U-test. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. NS, not significant compared to isotype group.
Generalized linear models were fitted using binary response (complete or partial response versus stable or progressive disease) as the dependent variable and scores from single input or input combinations (x axis) as independent variables (236 samples). Percentage of explained variance of response is plotted on the y axis. Comparisons between different models were made using the likelihood ratio test; a significant P value means that the additional variable contributed some independent information to the model. The association of TMB with response is significantly stronger than that of its proxy measurements (APOBEC3B and MKI67 expression or mutation in members of the DDR set). APOBEC3B and DDR gene set mutation provided no additional explanatory information independent of direct measurement of TMB. Combining TMB with MKI67 expression marginally improved on TMB alone, possibly through the negative association between MKI67 and TFGβ (Extended Data Fig. 1e, f). To test this hypothesis, we added MKI67 to a two-pathway model based on TMB and the F-TBRS, and confirmed that MKI67 does not add independent information to this two-pathway model. Furthermore, there was no benefit from adding MKI67 to our full three-pathway model, shown in Fig. 2f and Extended Data Fig. 2g. #P < 0.1, *P < 0.05. Exact likelihood ratio test P values: TMB,DDR versus TMB, 0.38; TMB,APOBEC3B versus TMB, 0.26; TMB,MKI67 versus TMB, 0.029; TMB,TBRS,MKI67 versus TMB,TBRS, 0.064.
Extended Data Figure 8 Relationship between different TGFβ related gene expression signatures and response.
a, Correlation between different TGFβ related gene expression signatures. Oval size and colour show correlation between signature scores and gene expression), calculated based on the complete RNA-seq dataset of 348 samples. Numbers show Pearson correlation coefficients. Gene-set membership is shown in Supplementary Table 8. See Supplementary Methods for computation of signature scores. b, EMT-signature expression is associated with response to atezolizumab in excluded tumours. Scores of three different EMT signatures, EMT132, EMT233 and EMT334, are significantly higher in non-responders (stable and progressive disease) than in responders (complete and partial response) in excluded tumours (EMT1, P = 0.0102; EMT2, P = 0.0027; EMT3, P = 0.0063); there is no significant difference in signature scores in desert and inflamed tumours (all P = 1; two-tailed t-test, P values for each signature are Bonferroni-corrected for three tests). The numbers above the graphs specify sample numbers in each bin. c, Explained variance in patient response. Generalized linear models were fitted using binary response (complete or partial response versus stable or progressive disease) as the dependent variable and scores from single input or input combinations (x axis) as independent variables (233 samples). Percentage of explained variance of response is plotted on the y axis. Comparisons between different models were made via likelihood ratio test; a significant P value means that the additional variable contributed some independent information to the model. The association of F-TBRS with response is the strongest among its correlates, that is, three different EMT signatures. None of these signatures provided additional explanatory information independent of F-TBRS.
This file contains Supplementary Methods, a Supplementary Discussion, Supplementary References and the flow gating strategy (Supplementary Figure 1). (PDF 1299 kb)
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. (XLSX 76 kb)
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. (XLSX 3013 kb)
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. (XLSX 2290 kb)
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. (XLSX 46 kb)
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. (XLSX 66 kb)
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. (XLSX 74 kb)
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. (XLSX 28 kb)
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. (XLSX 27 kb)
Genes and centroids used for Lund subtype label assignment. (XLSX 154 kb)
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. (XLSX 3032 kb)
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Mariathasan, S., Turley, S., Nickles, D. et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554, 544–548 (2018). https://doi.org/10.1038/nature25501
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