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Nat Med. 2015 Aug;21(8):938-945. doi: 10.1038/nm.3909. Epub 2015 Jul 20.

The prognostic landscape of genes and infiltrating immune cells across human cancers.

Author information

1
Center for Cancer Systems Biology (CCSB), Stanford University, Stanford, California, USA.
2
Department of Radiology, Stanford University, Stanford, California, USA.
3
Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, USA.
4
Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, California, USA.
5
Department of Radiation Oncology, Stanford University, Stanford, California, USA.
6
Department of Medicine, Division of Pulmonary and Critical Care Medicine, Stanford University, Stanford, California, USA.
7
Department of Cardiothoracic Surgery, Division of Thoracic Surgery, Stanford University, Stanford, California, USA.
8
Stanford Cancer Institute, Stanford University, Stanford, California, USA.
9
Department of Pathology, Stanford University, Stanford, California, USA.
10
Department of Medicine, Division of Hematology, Stanford Cancer Institute, Stanford University, Stanford, California, USA.
#
Contributed equally

Abstract

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.

PMID:
26193342
PMCID:
PMC4852857
DOI:
10.1038/nm.3909
[Indexed for MEDLINE]
Free PMC Article

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