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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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
Kitamura, T., Qian, B.-Z. & Pollard, J. W. Immune cell promotion of metastasis. Nat. Rev. Immunol. 15, 73–86 (2015).
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).
Robinson, D. R. et al. Integrative clinical genomics of metastatic cancer. Nature 548, 297–303 (2017).
Bowtell, D. D. et al. Rethinking ovarian cancer II: reducing mortality from high-grade serous ovarian cancer. Nat. Rev. Cancer 15, 668–679 (2015).
Zhang, A. W. et al. Interfaces of malignant and immunologic clonal dynamics in ovarian cancer. Cell 173, 1755–1769. (2018).
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).
Jiménez-Sánchez, A. et al. Heterogeneous tumor-immune microenvironments among differentially growing metastases in an ovarian cancer patient. Cell 170, 927–938 (2017).
Reuben, A. et al. Genomic and immune heterogeneity are associated with differential responses to therapy in melanoma. NPJ Genom. Med. 2, 10 (2017).
Wang, W. et al. Effector T cells abrogate stroma-mediated chemoresistance in ovarian cancer. Cell 165, 1092–1105 (2016).
Zhang, L. et al. Intratumoral T cells, recurrence, and survival in epithelial ovarian cancer. N. Engl. J. Med. 348, 203–213 (2003).
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).
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).
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).
Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013).
Liberzon, A. et al. The molecular signatures database (MsigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
Yoshihara, K. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 4, 2612 (2013).
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).
Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615 (2011).
Jiménez-Sánchez, A., Cast, O. & Miller, M. L. Comprehensive benchmarking and integration of tumour microenvironment cell estimation methods. Cancer Res. 79, 6238–6246 (2019).
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).
Kortlever, R. M. et al. Myc cooperates with Ras by programming inflammation and immune suppression. Cell 171, 1301–1315 (2017).
Topper, M. J. et al. Epigenetic therapy ties MYC depletion to reversing immune evasion and treating lung cancer. Cell 171, 1284–1300 (2017).
Spranger, S., Bao, R. & Gajewski, T. F. Melanoma-intrinsic β-catenin signalling prevents anti-tumour immunity. Nature 523, 231–235 (2015).
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).
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).
Van Loo, P. et al. Allele-specific copy number analysis of tumors. Proc. Natl Acad. Sci. USA 107, 16910–16915 (2010).
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).
Downward, J. Targeting RAS signalling pathways in cancer therapy. Nat. Rev. Cancer 3, 11–22 (2003).
Shukla, S. A. et al. Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat. Biotechnol. 33, 1152–1158 (2015).
Wang, Y. K. et al. Genomic consequences of aberrant DNA repair mechanisms stratify ovarian cancer histotypes. Nat. Genet. 49, 856–865 (2017).
Patch, A.-M. et al. Whole-genome characterization of chemoresistant ovarian cancer. Nature 521, 489–494 (2015).
Macintyre, G. et al. Copy number signatures and mutational processes in ovarian carcinoma. Nat. Genet. 50, 1262–1270 (2018).
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).
Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).
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).
Saito, T. et al. PTCH1 mutation is a frequent event in oesophageal basaloid squamous cell carcinoma. Mutagenesis 30, 297–301 (2015).
Larraguibel, J. et al. Wnt ligand-dependent activation of the negative feedback regulator Nkd1. Mol. Biol. Cell 26, 2375–2384 (2015).
Scarlett, U. K. et al. Ovarian cancer progression is controlled by phenotypic changes in dendritic cells. J. Exp. Med. 209, 495–506 (2012).
Roby, K. F. et al. Development of a syngeneic mouse model for events related to ovarian cancer. Carcinogenesis 21, 585–591 (2000).
Pielou, E. C. Species-diversity and pattern-diversity in the study of ecological succession. J. Theor. Biol. 10, 370–383 (1966).
Kirsch, I., Vignali, M. & Robins, H. T-cell receptor profiling in cancer. Mol. Oncol. 9, 2063–2070 (2015).
Johnson, B. E. et al. Mutational analysis reveals the origin and therapy-driven evolution of recurrent glioma. Science 343, 189–193 (2014).
Yates, L. R. et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat. Med. 21, 751–759 (2015).
McPherson, A. et al. Divergent modes of clonal spread and intraperitoneal mixing in high-grade serous ovarian cancer. Nat. Genet. 48, 758–767 (2016).
Stanske, M. et al. Dynamics of the intratumoral immune response during progression of high-grade serous ovarian cancer. Neoplasia 20, 280–288 (2018).
Sharma, P., Hu-Lieskovan, S., Wargo, J. A. & Ribas, A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 168, 707–723 (2017).
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).
Casey, S. C. et al. MYC regulates the antitumor immune response through CD47 and PD-L1. Science 352, 227–231 (2016).
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).
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).
Patel, S. A. & Minn, A. J. Combination cancer therapy with immune checkpoint blockade: mechanisms and strategies. Immunity 48, 417–433 (2018).
Grabosch, S. et al. Cisplatin-induced immune modulation in ovarian cancer mouse models with distinct inflammation profiles. Oncogene 38, 2380–2393 (2019).
Yarilin, D. et al. Machine-based method for multiplex in situ molecular characterization of tissues by immunofluorescence detection. Sci. Rep. 5, 9534 (2015).
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).
Martelotto, L. G. et al. Genomic landscape of adenoid cystic carcinoma of the breast. J. Pathol. 237, 179–189 (2015).
Pareja, F. et al. The genomic landscape of mucinous breast cancer. J. Natl Cancer Inst. 111, 737–741 (2019).
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).
Pedregosa, F. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
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).
Barbie, D. A. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462, 108–112 (2009).
Bindea, G. et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 39, 782–795 (2013).
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).
Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).
Ritchie, M. E. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Huber, W. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 12, 115–121 (2015).
Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).
Gene Ontology Consortium. Expansion of the Gene Ontology knowledgebase and resources. Nucleic Acids Res. 45, D331–D338 (2017).
Iglewicz, B. & Hoaglin, D. C. How to Detect and Handle Outliers (ASQC Quality Press, 1993).
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
Becht, E. et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 17, 218 (2016).
Li, B. et al. Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biol. 17, 174 (2016).
Aran, D., Hu, Z. & Butte, A. J. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 18, 220 (2017).
Li, T. et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 77, e108–e110 (2017).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
Adalsteinsson, V. A. et al. Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. Nat. Commun. 8, 1324 (2017).
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).
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).
Selenica, P. et al. Solid pseudopapillary neoplasms of the pancreas are dependent on the Wnt pathway. Mol. Oncol. 13, 1684–1692 (2019).
Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).
Saunders, C. T. et al. Strelka: accurate somatic small-variant calling from sequenced tumor–normal sample pairs. Bioinformatics 28, 1811–1817 (2012).
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).
Narzisi, G. et al. Genome-wide somatic variant calling using localized colored de Bruijn graphs. Commun. Biol. 1, 20 (2018).
Narzisi, G. et al. Accurate de novo and transmitted indel detection in exome-capture data using microassembly. Nat. Methods 11, 1033–1036 (2014).
Oliphant, T. E. Python for scientific computing. Comput. Sci. Eng. 9, 10–20 (2007).
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).
Sheskin, D. J. Handbook of Parametric and Nonparametric Statistical Procedures 3rd edn (CRC Press, 2003).
Dunn, O. J. Multiple comparisons among means. J. Am. Stat. Assoc. 56, 52–64 (1961).
Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).
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).
Taylor, J. & Tibshirani, R. Post-selection inference for ℓ 1-penalized likelihood models. Can. J. Stat. 46, 41–61 (2018).
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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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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DOI: https://doi.org/10.1038/s41588-020-0630-5
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