Molecular profiles of tumors and tumor-associated cells hold great promise as biomarkers of clinical outcomes. However, existing data sets are fragmented and difficult to analyze systematically. Here we present a pan-cancer resource and meta-analysis of expression signatures from ∼18,000 human tumors with overall survival outcomes across 39 malignancies. By using this resource, we identified a forkhead box MI (FOXM1) regulatory network as a major predictor of adverse outcomes, and we found that expression of favorably prognostic genes, including KLRB1 (encoding CD161), largely reflect tumor-associated leukocytes. By applying CIBERSORT, a computational approach for inferring leukocyte representation in bulk tumor transcriptomes, we identified complex associations between 22 distinct leukocyte subsets and cancer survival. For example, tumor-associated neutrophil and plasma cell signatures emerged as significant but opposite predictors of survival for diverse solid tumors, including breast and lung adenocarcinomas. This resource and associated analytical tools (http://precog.stanford.edu) may help delineate prognostic genes and leukocyte subsets within and across cancers, shed light on the impact of tumor heterogeneity on cancer outcomes, and facilitate the discovery of biomarkers and therapeutic targets.
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We would like to thank S. Galli, I. Weissman, P. Brown, R. Levy and H. Kohrt for critically reading the manuscript, and members of the Center for Cancer Systems Biology and the Plevritis, Diehn, Levy, and Alizadeh laboratories for valuable guidance and suggestions. This work was supported by grants from the Doris Duke Charitable Foundation (A.A.A.), Damon Runyon Cancer Research Foundation (A.A.A.), V-Foundation (A.A.A.); by the US Public Health Service/National Institutes of Health U01 CA194389 (A.A.A.), R01 CA188298 (M.D. and A.A.A.), U54 CA149145 (S.K.P.), U01CA154969 (S.K.P.), and 5T32 CA09302-35 (A.M.N.); by the Bent & Janet Cardan Oncology Research Fund (A.A.A.); by the Ludwig Institute for Cancer Research (A.A.A.); by a Department of Defense grant W81XWH-12-1-0498 (A.M.N.); and by a grant from the Siebel Stem Cell Institute and the Thomas and Stacey Siebel Foundation (A.M.N.).
The authors declare no competing financial interests.
Supplementary Figures 1–8 (PDF 16275 kb)
PRECOG meta-z matrix and source data (XLSX 10437 kb)
Prognostic genes shared across multiple cancers or specific to individual cancers, and related analyses (XLSX 88 kb)
Clusters of prognostic genes and corresponding functional annotations (XLSX 616 kb)
Bivariate models incorporating FOXM1 and KLRB1 expression levels across cancer types, and significance of a FOXM1-KLRB1 score in multivariate models with clinical parameters (XLSX 26 kb)
Protein-protein association data for the top pan-cancer prognostic genes in PRECOG; analysis of transcription factors and their target genes in PRECOG (XLSX 186 kb)
CIBERSORT-inferred fractions of tumor-associated leukocytes across 25 malignancies (XLSX 48 kb)
Lung adenocarcinoma TMA analyses, including clinical data and marker quantification, multivariate survival analysis with clinical covariates, and comparison of TAL levels with circulating leukocytes (XLSX 36 kb)
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Gentles, A., Newman, A., Liu, C. et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat Med 21, 938–945 (2015). https://doi.org/10.1038/nm.3909
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