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

By Y. Şenbabaoğlu, Ron S. Gejman, A. Winer, Ming Liu, E. V. Van Allen, G. de Velasco, Diana Miao, I. Ostrovnaya, E. Drill, Augustin Luna and 19 more. Genome Biology, 2016

623 citations recorded by the source corpus.

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. 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. 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.

Subfields: Molecular Biology, Oncology, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/7a027170711d89760fd957b166e9f515044e6901

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

In Oncology

In Pulmonary and Respiratory Medicine

Citations in the corpus

Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data (2017, background): “Therefore, to correctly interpret these data in particular from an immuno-oncology point of view (Angelova et al., 2015; Gentles et al., 2015; Hackl, Charoentong, Finotello, & Trajanoski, 2016; B. Li et al., 2016; Linsley, Chaussabel, & Speake, 2015; Rooney, Shukla, Wu, Getz, & Hacohen, 2015; Şenbab”
xCell: digitally portraying the tissue cellular heterogeneity landscape (2017, background): “limited number of cell types and a limited number of samples [7, 13].”