Unraveling tumor–immune heterogeneity in advanced ovarian cancer uncovers immunogenic effect of chemotherapy

Abstract

In metastatic cancer, the degree of heterogeneity of the tumor microenvironment (TME) and its molecular underpinnings remain largely unstudied. To characterize the tumor–immune interface at baseline and during neoadjuvant chemotherapy (NACT) in high-grade serous ovarian cancer (HGSOC), we performed immunogenomic analysis of treatment-naive and paired samples from before and after treatment with chemotherapy. In treatment-naive HGSOC, we found that immune-cell-excluded and inflammatory microenvironments coexist within the same individuals and within the same tumor sites, indicating ubiquitous variability in immune cell infiltration. Analysis of TME cell composition, DNA copy number, mutations and gene expression showed that immune cell exclusion was associated with amplification of Myc target genes and increased expression of canonical Wnt signaling in treatment-naive HGSOC. Following NACT, increased natural killer (NK) cell infiltration and oligoclonal expansion of T cells were detected. We demonstrate that the tumor–immune microenvironment of advanced HGSOC is intrinsically heterogeneous and that chemotherapy induces local immune activation, suggesting that chemotherapy can potentiate the immunogenicity of immune-excluded HGSOC tumors.

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Fig. 1: Immune-related gene signatures contribute to most of the transcriptional variance observed across multiple tumor samples from patients with treatment-naive HGSOC.
Fig. 2: T-cell infiltration variation across patients, within patients and within tumors.
Fig. 3: Unbiased analysis of TME heterogeneity in treatment-naive HGSOC tumors.
Fig. 4: Mutation patterns in immune-excluded tumors are associated with Wnt pathway genes and Myc target genes.
Fig. 5: Unbiased signaling pathway and TME cell decomposition analysis of chemotherapy-treated HGSOC tumor samples.
Fig. 6: Chemotherapy-induced enrichment of NK cells is evident in site-matched samples and is supported by preclinical data.
Fig. 7: Oligoclonal expansion of T cells and enrichment of shared TCRs after chemotherapy.

Data availability

Data for this work can be accessed at GitHub (https://github.com/cansysbio/HGSOC_TME_Heterogeneity). Requests for additional data should be directed to the corresponding author. The IF images discussed in this study will be provided by the corresponding author upon request. Microarray data are available through the GEO database (accession number GSE146965). Mutation data are available in Supplementary Table 4a. TITAN copy-number-segment data are available in Supplementary Table 4f. The TCR sequencing data discussed in this study will be provided by the corresponding author upon request.

Code availability

Software used for this work can be accessed at GitHub (https://github.com/cansysbio/HGSOC_TME_Heterogeneity). Requests for additional custom code should be directed to the corresponding author.

References

  1. 1.

    Kitamura, T., Qian, B.-Z. & Pollard, J. W. Immune cell promotion of metastasis. Nat. Rev. Immunol. 15, 73–86 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    Janssen, L. M. E., Ramsay, E. E., Logsdon, C. D. & Overwijk, W. W. The immune system in cancer metastasis: friend or foe? J. Immunother. Cancer 5, 79 (2017).

  3. 3.

    Robinson, D. R. et al. Integrative clinical genomics of metastatic cancer. Nature 548, 297–303 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. 4.

    Bowtell, D. D. et al. Rethinking ovarian cancer II: reducing mortality from high-grade serous ovarian cancer. Nat. Rev. Cancer 15, 668–679 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Zhang, A. W. et al. Interfaces of malignant and immunologic clonal dynamics in ovarian cancer. Cell 173, 1755–1769. (2018).

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Sridharan, V. et al. Immune profiling of adenoid cystic carcinoma: PD-L2 expression and associations with tumor-infiltrating lymphocytes. Cancer Immunol. Res. 4, 679–687 (2016).

    CAS  PubMed  Article  Google Scholar 

  7. 7.

    Jiménez-Sánchez, A. et al. Heterogeneous tumor-immune microenvironments among differentially growing metastases in an ovarian cancer patient. Cell 170, 927–938 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  8. 8.

    Reuben, A. et al. Genomic and immune heterogeneity are associated with differential responses to therapy in melanoma. NPJ Genom. Med. 2, 10 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  9. 9.

    Wang, W. et al. Effector T cells abrogate stroma-mediated chemoresistance in ovarian cancer. Cell 165, 1092–1105 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Zhang, L. et al. Intratumoral T cells, recurrence, and survival in epithelial ovarian cancer. N. Engl. J. Med. 348, 203–213 (2003).

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Ovarian Tumor Tissue Analysis (OTTA) Consortium. Dose–response association of CD8+ tumor-infiltrating lymphocytes and survival time in high-grade serous ovarian cancer. JAMA Oncol. 3, e173290 (2017).

    Article  Google Scholar 

  12. 12.

    Böhm, S. et al. Neoadjuvant chemotherapy modulates the immune microenvironment in metastases of tubo-ovarian high-grade serous carcinoma. Clin. Cancer Res. 22, 3025–3036 (2016).

    PubMed  Article  CAS  Google Scholar 

  13. 13.

    Weigelt, B. et al. Radiogenomics analysis of intratumor heterogeneity in a patient with high-grade serous ovarian cancer. JCO Precis. Oncol. 3, 1–9 (2019).

  14. 14.

    Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Liberzon, A. et al. The molecular signatures database (MsigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Yoshihara, K. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 4, 2612 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  17. 17.

    Ha, G. et al. TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data. Genome Res. 24, 1881–1893 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615 (2011).

    Article  CAS  Google Scholar 

  19. 19.

    Jiménez-Sánchez, A., Cast, O. & Miller, M. L. Comprehensive benchmarking and integration of tumour microenvironment cell estimation methods. Cancer Res. 79, 6238–6246 (2019).

    PubMed  Article  Google Scholar 

  20. 20.

    Stevens, J. R., Herrick, J. S., Wolff, R. K. & Slattery, M. L. Power in pairs: assessing the statistical value of paired samples in tests for differential expression. BMC Genomics 19, 953 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Kortlever, R. M. et al. Myc cooperates with Ras by programming inflammation and immune suppression. Cell 171, 1301–1315 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Topper, M. J. et al. Epigenetic therapy ties MYC depletion to reversing immune evasion and treating lung cancer. Cell 171, 1284–1300 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Spranger, S., Bao, R. & Gajewski, T. F. Melanoma-intrinsic β-catenin signalling prevents anti-tumour immunity. Nature 523, 231–235 (2015).

    CAS  PubMed  Article  Google Scholar 

  24. 24.

    Spranger, S., Bao, R. & Gajewski, T. Melanoma-intrinsic β-catenin signaling prevents T cell infiltration and anti-tumor immunity. J. Immunother. Cancer 2, O15 (2014).

    PubMed Central  Article  Google Scholar 

  25. 25.

    Luke, J. J., Bao, R., Sweis, R. F., Spranger, S. & Gajewski, T. F. WNT/β-catenin pathway activation correlates with immune exclusion across human cancers. Clin. Cancer Res. 25, 3074–3083 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Van Loo, P. et al. Allele-specific copy number analysis of tumors. Proc. Natl Acad. Sci. USA 107, 16910–16915 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Ahmed, A. A. et al. Driver mutations in TP53 are ubiquitous in high grade serous carcinoma of the ovary. J. Pathol. 221, 49–56 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Downward, J. Targeting RAS signalling pathways in cancer therapy. Nat. Rev. Cancer 3, 11–22 (2003).

    CAS  PubMed  Article  Google Scholar 

  29. 29.

    Shukla, S. A. et al. Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat. Biotechnol. 33, 1152–1158 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Wang, Y. K. et al. Genomic consequences of aberrant DNA repair mechanisms stratify ovarian cancer histotypes. Nat. Genet. 49, 856–865 (2017).

    CAS  PubMed  Article  Google Scholar 

  31. 31.

    Patch, A.-M. et al. Whole-genome characterization of chemoresistant ovarian cancer. Nature 521, 489–494 (2015).

    CAS  PubMed  Article  Google Scholar 

  32. 32.

    Macintyre, G. et al. Copy number signatures and mutational processes in ovarian carcinoma. Nat. Genet. 50, 1262–1270 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G. & Barceló-Vidal, C. Isometric logratio transformations for compositional data analysis. Math. Geol. 35, 279–300 (2003).

    Article  Google Scholar 

  34. 34.

    Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Niida, A. et al. DKK1, a negative regulator of Wnt signaling, is a target of the β-catenin/TCF pathway. Oncogene 23, 8520–8526 (2004).

    CAS  PubMed  Article  Google Scholar 

  36. 36.

    Saito, T. et al. PTCH1 mutation is a frequent event in oesophageal basaloid squamous cell carcinoma. Mutagenesis 30, 297–301 (2015).

    CAS  PubMed  Article  Google Scholar 

  37. 37.

    Larraguibel, J. et al. Wnt ligand-dependent activation of the negative feedback regulator Nkd1. Mol. Biol. Cell 26, 2375–2384 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Scarlett, U. K. et al. Ovarian cancer progression is controlled by phenotypic changes in dendritic cells. J. Exp. Med. 209, 495–506 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Roby, K. F. et al. Development of a syngeneic mouse model for events related to ovarian cancer. Carcinogenesis 21, 585–591 (2000).

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Pielou, E. C. Species-diversity and pattern-diversity in the study of ecological succession. J. Theor. Biol. 10, 370–383 (1966).

    CAS  PubMed  Article  Google Scholar 

  41. 41.

    Kirsch, I., Vignali, M. & Robins, H. T-cell receptor profiling in cancer. Mol. Oncol. 9, 2063–2070 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Johnson, B. E. et al. Mutational analysis reveals the origin and therapy-driven evolution of recurrent glioma. Science 343, 189–193 (2014).

    CAS  PubMed  Article  Google Scholar 

  43. 43.

    Yates, L. R. et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat. Med. 21, 751–759 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    McPherson, A. et al. Divergent modes of clonal spread and intraperitoneal mixing in high-grade serous ovarian cancer. Nat. Genet. 48, 758–767 (2016).

    CAS  PubMed  Article  Google Scholar 

  45. 45.

    Stanske, M. et al. Dynamics of the intratumoral immune response during progression of high-grade serous ovarian cancer. Neoplasia 20, 280–288 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Sharma, P., Hu-Lieskovan, S., Wargo, J. A. & Ribas, A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 168, 707–723 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Hirata, E. et al. Intravital imaging reveals how BRAF inhibition generates drug-tolerant microenvironments with high integrin β1/FAK signaling. Cancer Cell 27, 574–588 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Casey, S. C. et al. MYC regulates the antitumor immune response through CD47 and PD-L1. Science 352, 227–231 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Spranger, S. et al. Density of immunogenic antigens does not explain the presence or absence of the T-cell-inflamed tumor microenvironment in melanoma. Proc. Natl Acad. Sci. USA 113, E7759–E7768 (2016).

    CAS  PubMed  Article  Google Scholar 

  50. 50.

    Spranger, S., Dai, D., Horton, B. & Gajewski, T. F. Tumor-residing Batf3 dendritic cells are required for effector T cell trafficking and adoptive T cell therapy. Cancer Cell 31, 711–723 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Patel, S. A. & Minn, A. J. Combination cancer therapy with immune checkpoint blockade: mechanisms and strategies. Immunity 48, 417–433 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Grabosch, S. et al. Cisplatin-induced immune modulation in ovarian cancer mouse models with distinct inflammation profiles. Oncogene 38, 2380–2393 (2019).

    CAS  PubMed  Article  Google Scholar 

  53. 53.

    Yarilin, D. et al. Machine-based method for multiplex in situ molecular characterization of tissues by immunofluorescence detection. Sci. Rep. 5, 9534 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Dowsett, M. et al. Assessment of Ki67 in breast cancer: recommendations from the International Ki67 in Breast Cancer working group. J. Natl Cancer Inst. 103, 1656–1664 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Martelotto, L. G. et al. Genomic landscape of adenoid cystic carcinoma of the breast. J. Pathol. 237, 179–189 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. 56.

    Pareja, F. et al. The genomic landscape of mucinous breast cancer. J. Natl Cancer Inst. 111, 737–741 (2019).

    PubMed Central  Article  PubMed  Google Scholar 

  57. 57.

    Gautier, L., Cope, L., Bolstad, B. M. & Irizarry, R. A.Affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 307–315 (2004).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  58. 58.

    Pedregosa, F. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  59. 59.

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Barbie, D. A. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462, 108–112 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. 61.

    Bindea, G. et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 39, 782–795 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. 62.

    Davoli, T., Uno, H., Wooten, E. C. & Elledge, S. J. Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science 355, eaaf8399 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  63. 63.

    Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. 64.

    Ritchie, M. E. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  65. 65.

    Huber, W. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 12, 115–121 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. 66.

    Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. 67.

    Gene Ontology Consortium. Expansion of the Gene Ontology knowledgebase and resources. Nucleic Acids Res. 45, D331–D338 (2017).

    Article  CAS  Google Scholar 

  68. 68.

    Iglewicz, B. & Hoaglin, D. C. How to Detect and Handle Outliers (ASQC Quality Press, 1993).

  69. 69.

    Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  70. 70.

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  71. 71.

    Li, B. et al. Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biol. 17, 174 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  72. 72.

    Aran, D., Hu, Z. & Butte, A. J. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 18, 220 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  73. 73.

    Li, T. et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 77, e108–e110 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  74. 74.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  75. 75.

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  76. 76.

    Adalsteinsson, V. A. et al. Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. Nat. Commun. 8, 1324 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  77. 77.

    Shen, R. & Seshan, V. E. FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing. Nucleic Acids Res. 44, e131 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  78. 78.

    Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  79. 79.

    Selenica, P. et al. Solid pseudopapillary neoplasms of the pancreas are dependent on the Wnt pathway. Mol. Oncol. 13, 1684–1692 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  80. 80.

    Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  81. 81.

    Saunders, C. T. et al. Strelka: accurate somatic small-variant calling from sequenced tumor–normal sample pairs. Bioinformatics 28, 1811–1817 (2012).

    CAS  PubMed  Article  Google Scholar 

  82. 82.

    Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  83. 83.

    Narzisi, G. et al. Genome-wide somatic variant calling using localized colored de Bruijn graphs. Commun. Biol. 1, 20 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  84. 84.

    Narzisi, G. et al. Accurate de novo and transmitted indel detection in exome-capture data using microassembly. Nat. Methods 11, 1033–1036 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  85. 85.

    Oliphant, T. E. Python for scientific computing. Comput. Sci. Eng. 9, 10–20 (2007).

    CAS  Article  Google Scholar 

  86. 86.

    Friedman, M. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32, 675–701 (1937).

    Article  Google Scholar 

  87. 87.

    Sheskin, D. J. Handbook of Parametric and Nonparametric Statistical Procedures 3rd edn (CRC Press, 2003).

  88. 88.

    Dunn, O. J. Multiple comparisons among means. J. Am. Stat. Assoc. 56, 52–64 (1961).

    Article  Google Scholar 

  89. 89.

    Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  90. 90.

    Lee, J. D., Sun, D. L., Sun, Y. & Taylor, J. E. Exact post-selection inference, with application to the lasso. Ann. Stat. 44, 907–927 (2016).

    Article  Google Scholar 

  91. 91.

    Taylor, J. & Tibshirani, R. Post-selection inference for 1-penalized likelihood models. Can. J. Stat. 46, 41–61 (2018).

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Acknowledgements

We thank D. Pe’er for her support to A.J.-S. in finalizing this project. We thank J. Conejo-Garcia for providing the UPK10 cell line. We acknowledge T. Wu for his insightful comments on the manuscript. A.S. was supported by grants from the Marsha Rivkin Organization, Memorial Sloan Kettering Cancer Center, Translational and Integrative Medicine Research Fund (MSKCC) and Kaleidoscope of Hope. This work has been supported in part by a Kaleidoscope of Hope grant (H.A.V. and B.W.) and an MSK Cancer Center Support Grant of the National Cancer Institute at the National Institutes of Health (P30 CA008748). D.Z. received funding from the Ovarian Cancer Research Alliance Liz Tilberis Award. L.M.G. was supported by a Wellcome Trust grant through the Mathematical Genomics and Medicine programme. M.L.M. was supported by a Cancer Research UK core grant (C14303/A17197), a Brown Performance Innovation in Cancer Informatics Discovery Award (BD523775) and the Target Ovarian Cancer Translational Project Grant (Cambridge-MM18). J.D.B. was supported by a Cancer Research UK core grant (A22905). J.D.B. and E.S. were supported by the Mark Foundation for Cancer Research and Cancer Research UK Cambridge Centre (C9685/A25177). A.J.-S. was supported by a doctoral fellowship from the Cancer Research UK Cambridge Institute and the Mexican National Council of Science and Technology (CONACyT). Research by A.J.-S. was funded in part through the NIH/NCI Cancer Center Support Grant (P30 CA008748). J.S.R.-F. is funded in part by a Breast Cancer Research Foundation grant and by Department of Defense Congressionally Directed Medical Research Programs (W81XWH-15-1-0547; GC229671). D.M. was supported by the joint EMBL-EBI and NIHR Cambridge Biomedical Research Centre (EBPOD) postdoctoral program. F.M. was supported by a Cancer Research UK core grant (A19274). R.M.D. is supported by a doctoral fellowship from the Cancer Research UK Cambridge Institute. O.C. was supported by a doctoral fellowship from the University of Cambridge Harding Distinguished Postgraduate Scholars Programme. D.-L.C. was supported by a Cancer Research UK core grant (C14303/A17197). B.W. is funded in part by Breast Cancer Research Foundation and Cycle for Survival grants. G.M. would like to acknowledge the support of the University of Cambridge, Cancer Research UK and Hutchison Whampoa Limited. G.M. was funded by Cancer Research UK grants (C14303/A17197, A19274 and A15973) and by the Instituto de Salud Carlos III (ISCIII), Spanish Ministry of Science and Innovation.

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Contributions are specified according to CRediT (contributor roles taxonomy; http://dictionary.casrai.org/Contributor_Roles). Conceptualization (ideas; formulation or evolution of overarching research goals and aims): A.J.-S., A.S., E.S. and M.L.M. Data curation (management activities to annotate (produce metadata), scrub data and maintain research data (including software code, where necessary for interpreting the data itself) for initial use and later reuse): A.J.-S., K.L.M., P.C., S.K., O.C. and P.S. Formal analysis (application of statistical, mathematical, computational or other formal techniques to analyze or synthesize study data): A.J.-S., D.-L.C., S.K., O.C., P.S. and D.M. Funding acquisition (acquisition of the financial support for the project leading to this publication): H.A.V., B.W., J.S.R.-F., D.Z., E.S., A.S. and M.L.M. Investigation (conducting a research and investigation process, specifically performing the experiments or data collection): P.C., K.L.M., T.W., Y.M., Y.B., I.N., J.S.R.-F., P.B., B.W., T.H., K.J.P., D.S.C., R.S., E.S., F.C.G., A.D.P., R.A.S., R.E.S. and P.S. Methodology (development or design of methodology; creation of models): A.J.-S. and E.S. Project administration (management and coordination responsibility for the research activity planning and execution): A.J.-S., A.S., E.S. and M.L.M. Resources (provision of study materials, reagents, materials, patients, laboratory samples, animals, instrumentation, computing resources or other analysis tools): A.S., J.S.R.-F., B.W., D.S.C., D.Z., M.L.M. and E.S. Software (programming, software development; designing computer programs, implementation of the computer code and supporting algorithms; testing of existing code components): A.J.-S., D.-L.C., S.K., O.C., R.M.D., L.M.G. and G.M. Supervision (oversight and leadership responsibility for the research activity planning and execution, including mentorship external to the core team); A.S., J.S.R.-F., B.W., E.S. and M.L.M. Validation (verification, whether as a part of the activity or separate, of the overall reproducibility of results and other research outputs): A.J.-S., D.-L.C., R.M.D. and O.C. Visualization (preparation, creation and/or presentation of the published work, specifically visualization/data presentation): A.J.-S., S.K., D.-L.C. and O.C. Writing the original draft (preparation, creation and/or presentation of the published work, specifically writing the initial draft): A.J.-S., A.S. and M.L.M. Writing the review and editing (preparation, creation and/or presentation of the published work by those from the original research group, specifically critical review, commentary or revision): A.J.-S., M.L.M., A.S., K.L.M., P.C., E.S., J.D.B., M.B.G., L.M.G., G.M., R.M.D., B.W., J.S.R.-F., S.K., F.M., P.B., O.C. and D.-L.C.

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Correspondence to Martin L. Miller.

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A.S. is a current employee of, and owns stock in, Merck. D.S.C. is a member of two medical advisory boards and invested in two surgical companies, none of which are related to this research. J.S.R.-F. is a paid consultant of Goldman Sachs Merchant Banking, Paige.AI and REPARE Therapeutics; a member of the scientific advisory board with paid honoraria of Paige.AI and Volition Rx; and an ad hoc member of the scientific advisory boards of Roche Tissue Diagnostics, Ventana, InVicro, Genentech, Novartis, GRAIL and Roche, outside the scope of the submitted work. D.Z. reports personal/consultancy fees from Merck, Synlogic Therapeutics, Biomed Valley Discoveries, Trieza Therapeutics, Tesaro and Agenus, outside of the scope of the submitted work. M.L.M. has received honoraria from GSK not related to this research. E.S. is a cofounder and shareholder of Cambridge AI Health and a consultant for Amazon and has received honoraria from GSK; none of these are related to this research. G.M. is founder and CTO of Pinpoint Oncology Ltd. A.J.-S. has owned and sold stocks while this work was in progress, none of which is directly related to this publication.

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Jiménez-Sánchez, A., Cybulska, P., Mager, K.L. et al. Unraveling tumor–immune heterogeneity in advanced ovarian cancer uncovers immunogenic effect of chemotherapy. Nat Genet 52, 582–593 (2020). https://doi.org/10.1038/s41588-020-0630-5

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