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

Nature Medicine volume 21, pages 938–945 (2015)Cite this article

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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.

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Figure 1: Prognostic landscape of gene expression across human cancers.
Figure 2: Genes globally associated with adverse and favorable survival.
Figure 3: Inferred leukocyte frequencies and prognostic associations in 25 human cancers.
Figure 4: Ratio of infiltrating PMN cells to PCs is prognostic in diverse solid tumors.

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Acknowledgements

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.).

Author information

Author notes

  1. Scott V Bratman & Chuong D Hoang

    Present address: Present addresses: Department of Radiation Oncology, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada (S.V.B.); Thoracic and Gastrointestinal Oncology Branch, National Cancer Institute, Bethesda, Maryland, USA (C.D.H.).,

  2. Andrew J Gentles and Aaron M Newman: These authors contributed equally to this work.

  3. Sylvia K Plevritis and Ash A Alizadeh: These authors jointly directed this work.

Authors and Affiliations

  1. Center for Cancer Systems Biology (CCSB), Stanford University, Stanford, California, USA

    Andrew J Gentles, Sylvia K Plevritis & Ash A Alizadeh

  2. Department of Radiology, Stanford University, Stanford, California, USA

    Andrew J Gentles & Sylvia K Plevritis

  3. Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, USA

    Aaron M Newman, Chih Long Liu, Scott V Bratman, Weiguo Feng, Dongkyoon Kim, Maximilian Diehn & Ash A Alizadeh

  4. Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, California, USA

    Aaron M Newman, Chih Long Liu & Ash A Alizadeh

  5. Department of Radiation Oncology, Stanford University, Stanford, California, USA

    Scott V Bratman, Weiguo Feng & Maximilian Diehn

  6. Department of Medicine, Division of Pulmonary and Critical Care Medicine, Stanford University, Stanford, California, USA

    Viswam S Nair

  7. Department of Cardiothoracic Surgery, Division of Thoracic Surgery, Stanford University, Stanford, California, USA

    Yue Xu, Amanda Khuong & Chuong D Hoang

  8. Stanford Cancer Institute, Stanford University, Stanford, California, USA

    Maximilian Diehn & Ash A Alizadeh

  9. Department of Pathology, Stanford University, Stanford, California, USA

    Robert B West

  10. Department of Medicine, Division of Hematology, Stanford Cancer Institute, Stanford University, Stanford, California, USA

    Ash A Alizadeh

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  1. Andrew J Gentles
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Contributions

A.J.G., S.K.P. and A.A.A. conceived PRECOG, and A.M.N. and A.A.A. conceived immune-PRECOG. A.J.G., A.M.N. and A.A.A. designed the framework, collected and curated the primary data, and developed strategies for implementation and optimizations in related experiments, analyzed the data, and wrote the paper. A.M.N. and A.J.G. wrote all bioinformatics software for PRECOG and related analyses. A.J.G. and C.L.L. implemented web infrastructure for hosting PRECOG. S.V.B., V.S.N., R.B.W. and M.D. curated the NSCLC tumor GEP and TMA data, including clinical annotations. Y.X., A.K. and C.D.H. identified and provided viable NSCLC patient specimens. D.K. and W.F. assisted with flow cytometry characterizations of primary NSCLC tumor specimens and enumeration of corresponding TALs. V.S.N. and R.B.W. constructed the NSCLC TMA and R.B.W. performed in situ hybridizations and immunohistochemical characterizations for TALs. A.A.A. and S.K.P. contributed equally as senior authors to supervising and funding the project. All authors discussed the results and their implications, and commented on the manuscript at all stages.

Corresponding author

Correspondence to Ash A Alizadeh.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 (PDF 16275 kb)

Supplementary Data 1

PRECOG meta-z matrix and source data (XLSX 10437 kb)

Supplementary Data 2

Prognostic genes shared across multiple cancers or specific to individual cancers, and related analyses (XLSX 88 kb)

Supplementary Data 3

Clusters of prognostic genes and corresponding functional annotations (XLSX 616 kb)

Supplementary Data 4

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)

Supplementary Data 5

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)

Supplementary Data 6

CIBERSORT-inferred fractions of tumor-associated leukocytes across 25 malignancies (XLSX 48 kb)

Supplementary Data 7

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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