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

Nature Genetics volume 52, pages 582–593 (2020)Cite this article

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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.

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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.

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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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Author notes

  1. These authors contributed equally: Alejandro Jiménez-Sánchez, Paulina Cybulska, Katherine LaVigne Mager.

Authors and Affiliations

  1. Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK

    Alejandro Jiménez-Sánchez, Simon Koplev, Oliver Cast, Dominique-Laurent Couturier, Danish Memon, Geoff Macintyre, Lena Morrill Gavarró, Ruben M. Drews, Michael B. Gill, Florian Markowetz, James D. Brenton & Martin L. Miller

  2. Department of Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Alejandro Jiménez-Sánchez

  3. Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Paulina Cybulska, Katherine LaVigne Mager & Dennis S. Chi

  4. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK

    Danish Memon & Pedro Beltrao

  5. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Pier Selenica, Felipe C. Geyer, Anastasios D. Papanastasiou, Robert A. Soslow, Kay J. Park, Travis Hollmann, Jorge S. Reis-Filho & Britta Weigelt

  6. Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Ines Nikolovski, Ramon E. Sosa, Hebert Alberto Vargas & Evis Sala

  7. Department of Medical Physics and Radiology, Memorial Sloan Kettering Cancer Centre, New York, NY, USA

    Yousef Mazaheri

  8. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Yonina Bykov, Tyler Walther, Dmitriy Zamarin & Alexandra Snyder

  9. Spanish National Cancer Research Centre (CNIO), Madrid, Spain

    Geoff Macintyre

  10. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Ronglai Shen

  11. Department of Radiology and Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK

    Evis Sala

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  1. Alejandro Jiménez-Sánchez
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Contributions

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.

Corresponding author

Correspondence to Martin L. Miller.

Ethics declarations

Competing interests

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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