RT Journal Article
SR Electronic
T1 The landscape of T cell infiltration in human cancer and its association with antigen presenting gene expression
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 025908
DO 10.1101/025908
A1 Yasin Şenbabaoğlu
A1 Andrew G. Winer
A1 Ron S. Gejman
A1 Ming Liu
A1 Augustin Luna
A1 Irina Ostrovnaya
A1 Nils Weinhold
A1 William Lee
A1 Samuel D. Kaffenberger
A1 Ying Bei Chen
A1 Martin H. Voss
A1 Jonathan A. Coleman
A1 Paul Russo
A1 Victor E. Reuter
A1 Timothy A. Chan
A1 Emily H. Cheng
A1 David A. Scheinberg
A1 Ming O. Li
A1 James J. Hsieh
A1 Chris Sander
A1 A. Ari Hakimi
YR 2015
UL http://biorxiv.org/content/early/2015/09/01/025908.abstract
AB One sentence summary In silico decomposition of the immune microenvironment among common tumor types identified clear cell renal cell carcinoma as the most highly infiltrated by T-cells and further analysis of this tumor type revealed three distinct and clinically relevant clusters which were validated in an independent cohort.Abstract Infiltrating T cells in the tumor microenvironment have crucial roles in the competing processes of pro-tumor and anti-tumor immune response. However, the infiltration level of distinct T cell subsets and the signals that draw them into a tumor, such as the expression of antigen presenting machinery (APM) genes, remain poorly characterized across human cancers. Here, we define a novel mRNA-based T cell infiltration score (TIS) and profile infiltration levels in 19 tumor types. We find that clear cell renal cell carcinoma (ccRCC) is the highest for TIS and among the highest for the correlation between TIS and APM expression, despite a modest mutation burden. This finding is contrary to the expectation that immune infiltration and mutation burden are linked. To further characterize the immune infiltration in ccRCC, we use RNA-seq data to computationally infer the infiltration levels of 24 immune cell types in a discovery cohort of 415 ccRCC patients and validate our findings in an independent cohort of 101 ccRCC patients. We find three clusters of tumors that are primarily separated by levels of T cell infiltration and APM gene expression. In ccRCC, the levels of Th17 cells and the ratio of CD8+ T/Treg levels are associated with improved survival whereas the levels of Th2 cells and Tregs are associated with negative clinical outcome. Our analysis illustrates the utility of computational immune cell decomposition for solid tumors, and the potential of this method to guide clinical decision-making.