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Genome Biol. 2016 Nov 17;17(1):231.

Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures.

Author information

1
Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA. shenbaba@gmail.com.
2
Present address: Swim Across America/Ludwig Collaborative Laboratory, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA. shenbaba@gmail.com.
3
Molecular Pharmacology and Chemistry Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
4
Weill Cornell Medical College, New York, NY, USA.
5
Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
6
Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
7
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
8
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
9
Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
10
Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
11
Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
12
Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
13
Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
14
Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia.
15
Genitourinary Oncology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
16
Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
17
Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA. hakimia@mskcc.org.
18
Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA. hakimia@mskcc.org.

Abstract

BACKGROUND:

Tumor-infiltrating immune cells have been linked to prognosis and response to immunotherapy; however, the levels of distinct immune cell subsets and the signals that draw them into a tumor, such as the expression of antigen presenting machinery genes, remain poorly characterized. Here, we employ a gene expression-based computational method to profile the infiltration levels of 24 immune cell populations in 19 cancer types.

RESULTS:

We compare cancer types using an immune infiltration score and a T cell infiltration score and find that clear cell renal cell carcinoma (ccRCC) is among the highest for both scores. Using immune infiltration profiles as well as transcriptomic and proteomic datasets, we characterize three groups of ccRCC tumors: T cell enriched, heterogeneously infiltrated, and non-infiltrated. We observe that the immunogenicity of ccRCC tumors cannot be explained by mutation load or neo-antigen load, but is highly correlated with MHC class I antigen presenting machinery expression (APM). We explore the prognostic value of distinct T cell subsets and show in two cohorts that Th17 cells and CD8+ T/Treg ratio are associated with improved survival, whereas Th2 cells and Tregs are associated with negative outcomes. Investigation of the association of immune infiltration patterns with the subclonal architecture of tumors shows that both APM and T cell levels are negatively associated with subclone number.

CONCLUSIONS:

Our analysis sheds light on the immune infiltration patterns of 19 human cancers and unravels mRNA signatures with prognostic utility and immunotherapeutic biomarker potential in ccRCC.

KEYWORDS:

Cancer immunotherapy; Checkpoint blockade; Clear cell renal cell carcinoma (ccRCC); Computational deconvolution; Tumor immune microenvironment

PMID:
27855702
PMCID:
PMC5114739
DOI:
10.1186/s13059-016-1092-z
[Indexed for MEDLINE]
Free PMC Article

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