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01-06-2018 | Focussed Research Review

Transcriptomic analysis of the tumor microenvironment to guide prognosis and immunotherapies

Authors: Florent Petitprez, Yann A. Vano, Etienne Becht, Nicolas A. Giraldo, Aurélien de Reyniès, Catherine Sautès-Fridman, Wolf H. Fridman

Published in: Cancer Immunology, Immunotherapy | Issue 6/2018

Abstract

Tumors are highly heterogeneous tissues where malignant cells are surrounded by and interact with a complex tumor microenvironment (TME), notably composed of a wide variety of immune cells, as well as vessels and fibroblasts. As the dialectical influence between tumor cells and their TME is known to be clinically crucial, we need tools that allow us to study the cellular composition of the microenvironment. In this focused research review, we report MCP-counter, a methodology based on transcriptomic markers that assesses the proportion of several immune and stromal cell populations in the TME from transcriptomic data, and we highlight how it can provide a way to decipher the complex mechanisms at play in tumors. In several malignancies, MCP-counter scores have been used to show various prognostic impacts of the TME, which we also show to be linked with the mutational burden of tumors. We also compared established molecular classifications of colorectal cancer and clear-cell renal cell carcinoma with the output of MCP-counter, and show that molecular subgroups have different TME profiles, and that these profiles are consistent within a given subgroup. Finally, we provide insights as to how knowing the TME composition may shape patient care in the near future.

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Becht E, Giraldo NA, Germain C et al (2016) Immune contexture, immunoscore, and malignant cell molecular subgroups for prognostic and theranostic classifications of cancers. Adv Immunol 130:95–190. doi:10.1016/bs.ai.2015.12.002 CrossRefPubMed

Fridman WH, Pagès F, Sautès-Fridman C, Galon J (2012) The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cancer 12:298–306. doi:10.1038/nrc3245 CrossRefPubMed

Hanahan D, Coussens LM (2012) Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell 21:309–322. doi:10.1016/j.ccr.2012.02.022 CrossRefPubMed

Becht E, Giraldo NA, Dieu-Nosjean M-C et al (2016) Cancer immune contexture and immunotherapy. Curr Opin Immunol 39:7–13. doi:10.1016/j.coi.2015.11.009 CrossRefPubMed

Pagès F, Berger A, Camus M et al (2005) Effector memory T cells, early metastasis, and survival in colorectal cancer. N Engl J Med 353:2654–2666. doi:10.1056/NEJMoa051424 CrossRefPubMed

Remark R, Alifano M, Cremer I et al (2013) Characteristics and clinical impacts of the immune environments in colorectal and renal cell carcinoma lung metastases: influence of tumor origin. Clin Cancer Res 19:4079–4091CrossRefPubMed

Postow MA, Callahan MK, Wolchok JD (2015) Immune checkpoint blockade in cancer therapy. J Clin Oncol 33:1974–1982. doi:10.1200/JCO.2014.59.4358 CrossRefPubMedPubMedCentral

Kontermann RE, Brinkmann U (2015) Bispecific antibodies. Drug Discov Today 20:838–847. doi:10.1016/j.drudis.2015.02.008 CrossRefPubMed

Guo Y, Wang Y, Han W (2016) Chimeric antigen receptor-modified T cells for solid tumors: challenges and prospects. J Immunol Res 2016:3850839. doi:10.1155/2016/3850839 PubMedPubMedCentralCrossRef

10.

Galon J, Costes A, Sanchez-Cabo F et al (2006) Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313:1960–1964. doi:10.1126/science.1129139 CrossRefPubMed

11.

Giraldo NA, Becht E, Pagès F et al (2015) Orchestration and prognostic significance of immune checkpoints in the microenvironment of primary and metastatic renal cell cancer. Clin Cancer Res 21:3031–3040. doi:10.1158/1078-0432.CCR-14-2926 CrossRefPubMed

12.

Granier C, Dariane C, Combe P et al (2017) Tim-3 Expression on tumor-infiltrating PD-1(+)CD8(+) T cells correlates with poor clinical outcome in renal cell carcinoma. Cancer Res 77:1075–1082. doi:10.1158/0008-5472.CAN-16-0274 CrossRefPubMed

13.

Shen-Orr SS, Gaujoux R (2013) Computational deconvolution: extracting cell type-specific information from heterogeneous samples. Curr Opin Immunol 25:571–578. doi:10.1016/j.coi.2013.09.015 CrossRefPubMed

14.

Venet D, Pecasse F, Maenhaut C, Bersini H (2001) Separation of samples into their constituents using gene expression data. Bioinformatics 17:S279–S287. doi:10.1093/bioinformatics/17.suppl_1.S279 CrossRefPubMed

15.

Abbas AR, Wolslegel K, Seshasayee D et al (2009) Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus. PLoS One 4:e6098. doi:10.1371/journal.pone.0006098 CrossRefPubMedPubMedCentral

16.

Bindea G, Mlecnik B, Tosolini M et al (2013) Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 39:782–795. doi:10.1016/j.immuni.2013.10.003 CrossRefPubMed

17.

Şenbabaoğlu Y, Gejman RS, Winer AG et al (2016) Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures. Genome Biol 17:231. doi:10.1186/s13059-016-1092-z CrossRefPubMedPubMedCentral

18.

Charoentong P, Finotello F, Angelova M et al (2017) Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep 18:248–262. doi:10.1016/j.celrep.2016.12.019 CrossRefPubMed

19.

Newman AM, Liu CL, Green MR et al (2015) Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 12:453–457. doi:10.1038/nmeth.3337 CrossRefPubMedPubMedCentral

20.

Becht E, Giraldo NA, Lacroix L et al (2016) Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol 17:218. doi:10.1186/s13059-016-1070-5 CrossRefPubMedPubMedCentral

21.

Quon G, Haider S, Deshwar AG et al (2013) Computational purification of individual tumor gene expression profiles leads to significant improvements in prognostic prediction. Genome Med 5:29. doi:10.1186/gm433 CrossRefPubMedPubMedCentral

22.

Giraldo NA, Becht E, Vano Y et al (2017) Tumor-infiltrating and peripheral blood T cell immunophenotypes predict early relapse in localized clear cell renal cell carcinoma. Clin Cancer Res. doi:10.1158/1078-0432.CCR-16-2848 PubMedCrossRef

23.

Choueiri TK, Motzer RJ (2017) Systemic therapy for metastatic renal-cell carcinoma. N Engl J Med 376:354–366. doi:10.1056/NEJMra1601333 CrossRefPubMed

24.

Bauman TM, Huang W, Lee MH, Abel EJ (2016) Neovascularity as a prognostic marker in renal cell carcinoma. Hum Pathol 57:98–105. doi:10.1016/j.humpath.2016.07.005 CrossRefPubMed

25.

Dieu-Nosjean M-C, Goc J, Giraldo NA et al (2014) Tertiary lymphoid structures in cancer and beyond. Trends Immunol 35:571–580. doi:10.1016/j.it.2014.09.006 CrossRefPubMed

26.

Dieu-Nosjean M-C, Giraldo NA, Kaplon H et al (2016) Tertiary lymphoid structures, drivers of the anti-tumor responses in human cancers. Immunol Rev 271:260–275. doi:10.1111/imr.12405 CrossRefPubMed

27.

Schumacher TN, Schreiber RD (2015) Neoantigens in cancer immunotherapy. Science 348:69–74. doi:10.1126/science.aaa4971 CrossRefPubMed

28.

Alizadeh AA, Eisen MB, Davis RE et al (2000) Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403:503–511. doi:10.1038/35000501 CrossRefPubMed

29.

Ando T, Suguro M, Kobayashi T et al (2003) Multiple fuzzy neural network system for outcome prediction and classification of 220 lymphoma patients on the basis of molecular profiling. Cancer Sci 94:906–913CrossRefPubMed

30.

Guedj M, Marisa L, de Reynies A et al (2012) A refined molecular taxonomy of breast cancer. Oncogene 31:1196–1206. doi:10.1038/onc.2011.301 CrossRefPubMed

31.

Perou CM, Sørlie T, Eisen MB et al (2000) Molecular portraits of human breast tumours. Nature 406:747–752. doi:10.1038/35021093 CrossRefPubMed

32.

Guinney J, Dienstmann R, Wang X et al (2015) The consensus molecular subtypes of colorectal cancer. Nat Med 21:1350–1356. doi:10.1038/nm.3967 CrossRefPubMedPubMedCentral

33.

Becht E, de Reyniès A, Giraldo NA et al (2016) Immune and stromal classification of colorectal cancer is associated with molecular subtypes and relevant for precision immunotherapy. Clin Cancer Res 22:4057–4066. doi:10.1158/1078-0432.CCR-15-2879 CrossRefPubMed

34.

Le DT, Uram JN, Wang H et al (2015) PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med 372:2509–2520. doi:10.1056/NEJMoa1500596 CrossRefPubMedPubMedCentral

35.

Dienstmann R, Vermeulen L, Guinney J et al (2017) Consensus molecular subtypes and the evolution of precision medicine in colorectal cancer. Nat Rev Cancer 17:79–92. doi:10.1038/nrc.2016.126 CrossRefPubMed

36.

Beuselinck B, Job S, Becht E et al (2015) Molecular subtypes of clear cell renal cell carcinoma are associated with sunitinib response in the metastatic setting. Clin Cancer Res 21:1329–1339. doi:10.1158/1078-0432.CCR-14-1128 CrossRefPubMed

37.

Becht E, Giraldo NA, Beuselinck B et al (2015) Prognostic and theranostic impact of molecular subtypes and immune classifications in renal cell cancer (RCC) and colorectal cancer (CRC). Oncoimmunology 4:e1049804. doi:10.1080/2162402X.2015.1049804 CrossRefPubMedPubMedCentral

Title: Transcriptomic analysis of the tumor microenvironment to guide prognosis and immunotherapies
Authors: Florent Petitprez
Yann A. Vano
Etienne Becht
Nicolas A. Giraldo
Aurélien de Reyniès
Catherine Sautès-Fridman
Wolf H. Fridman
Publication date: 01-06-2018
Publisher: Springer Berlin Heidelberg
Published in: Cancer Immunology, Immunotherapy / Issue 6/2018
Print ISSN: 0340-7004
Electronic ISSN: 1432-0851
DOI: https://doi.org/10.1007/s00262-017-2058-z

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