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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Abbreviations
- ccRCC:
-
Clear-cell renal cell carcinoma
- CIBERSORT:
-
Cell-type identification by estimating relative subsets of RNA transcripts
- CMS:
-
Consensus molecular subgroups
- CRC:
-
Colorectal cancer
- GSEA:
-
Gene set enrichment analysis
- MCP-counter:
-
Microenvironment cell population-counter
- MSI:
-
Microsatellite instable
- NGS:
-
Next-generation sequencing
- NIBIT:
-
Network Italiano per la Bioterapia dei Tumori (the Italian network for cancer biotherapy)
- TCGA PANCAN:
-
The cancer genome atlas pan-cancer cohort
- Th17:
-
T helper lymphocyte producing interleukin 17
- TME:
-
Tumor microenvironment
- Treg:
-
Regulatory T cell
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Acknowledgements
We wish to thank Benoît Beuselinck, Laetitia Lacroix, Pierre Laurent-Puig, Stéphane Oudard, Jessica Zucman-Rossi, and all other members of the teams who contributed to the data.
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This work was supported by the Institut National de la santé et de la Recherche Medicale (INSERM), University Paris-Descartes, University Pierre and Marie Curie, the Site de Recherche Integrée sur le Cancer (SIRIC) Cancer Research for Personalized Medicine (CARPEM) program, the LabEx Immuno-Oncology (LAXE62_9UMRS972 FRIDMAN), the Institut National Du Cancer (INCa), and the Cancéropôle Ile-de-France, O. Lecomte. Florent Petitprez is recipient of a CARPEM fellowship.
Conflict of interest
Yann A. Vano has received speaker honoraria from Novartis, Pfizer, Bristol-Myers-Squibb, and Astellas Sanofi, and has received financial support for attending symposia from Bristol-Myers-Squibb and Novartis. Aurélien de Reyniès holds intellectual property rights for patents related to immune cell population abundance estimation through transcriptomic analysis. Wolf H. Fridman is a consultant for Pierre Fabre Medicament, Sanofi, Bristol-Myers-Squibb, Novartis, Curetech, Servier, Efranet, Efralys, and Adaptimmune. All other authors declare no conflicts of interest.
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Petitprez, F., Vano, Y.A., Becht, E. et al. Transcriptomic analysis of the tumor microenvironment to guide prognosis and immunotherapies. Cancer Immunol Immunother 67, 981–988 (2018). https://doi.org/10.1007/s00262-017-2058-z
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DOI: https://doi.org/10.1007/s00262-017-2058-z