Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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

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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

Accession codes

Accessions

Gene Expression Omnibus

References

  1. Coussens, L.M. & Werb, Z. Inflammation and cancer. Nature 420, 860–867 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Zhang, L. et al. Intratumoral T cells, recurrence, and survival in epithelial ovarian cancer. N. Engl. J. Med. 348, 203–213 (2003).

    Article  CAS  PubMed  Google Scholar 

  3. Topalian, S.L. et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N. Engl. J. Med. 366, 2443–2454 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Fan, J.B., Chee, M. & Gunderson, K. Highly parallel genomic assays. Nat. Rev. Genet. 7, 632–644 (2006).

    Article  CAS  PubMed  Google Scholar 

  5. Koscielny, S. Why most gene expression signatures of tumors have not been useful in the clinic. Sci. Transl. Med. 2, ps2 (2010).

    Article  Google Scholar 

  6. Dupuy, A. & Simon, R.M. Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J. Natl. Cancer Inst. 99, 147–157 (2007).

    Article  PubMed  Google Scholar 

  7. Subramanian, J. & Simon, R. Gene expression–based prognostic signatures in lung cancer: ready for clinical use? J. Natl. Cancer Inst. 102, 464–474 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Dalton, W.S. & Friend, S.H. Cancer biomarkers: an invitation to the table. Science 312, 1165–1168 (2006).

    Article  CAS  PubMed  Google Scholar 

  9. Ein-Dor, L., Zuk, O. & Domany, E. Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proc. Natl. Acad. Sci. USA 103, 5923–5928 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ransohoff, D.F. Rules of evidence for cancer molecular-marker discovery and validation. Nat. Rev. Cancer 4, 309–314 (2004).

    Article  CAS  PubMed  Google Scholar 

  11. Varmus, H. Ten years on: the human genome and medicine. N. Engl. J. Med. 362, 2028–2029 (2010).

    Article  CAS  PubMed  Google Scholar 

  12. Lee, H.K., Hsu, A.K., Sajdak, J., Qin, J. & Pavlidis, P. Coexpression analysis of human genes across many microarray data sets. Genome Res. 14, 1085–1094 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Mizuno, H., Kitada, K., Nakai, K. & Sarai, A. PrognoScan: a new database for meta-analysis of the prognostic value of genes. BMC Med. Genomics 2, 18 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Hebestreit, K. et al. Leukemia Gene Atlas: a public platform for integrative exploration of genome-wide molecular data. PLoS ONE 7, e39148 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Yuan, Y. et al. Assessing the clinical utility of cancer genomic and proteomic data across tumor types. Nat. Biotechnol. 32, 644–652 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Newman, A.M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Hanahan, D. & Weinberg, R.A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    Article  CAS  PubMed  Google Scholar 

  18. Mantovani, A. et al. Chemokines in the recruitment and shaping of the leukocyte infiltrate of tumors. Semin. Cancer Biol. 14, 155–160 (2004).

    Article  CAS  PubMed  Google Scholar 

  19. Coussens, L.M., Zitvogel, L. & Palucka, A.K. Neutralizing tumor-promoting chronic inflammation: a magic bullet? Science 339, 286–291 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Gerstein, M.B. et al. Architecture of the human regulatory network derived from ENCODE data. Nature 489, 91–100 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Leek, J.T. et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11, 733–739 (2010).

    Article  CAS  PubMed  Google Scholar 

  22. Beroukhim, R. et al. The landscape of somatic copy-number alteration across human cancers. Nature 463, 899–905 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Newman, A.M. & Cooper, J.B. AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number. BMC Bioinformatics 11, 117 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Gentles, A.J., Plevritis, S.K., Majeti, R. & Alizadeh, A.A. Association of a leukemic stem cell gene expression signature with clinical outcomes in acute myeloid leukemia. J. Am. Med. Assoc. 304, 2706–2715 (2010).

    Article  CAS  Google Scholar 

  25. Zeuner, A., Todaro, M., Stassi, G. & De Maria, R. Colorectal cancer stem cells: from the crypt to the clinic. Cell Stem Cell 15, 692–705 (2014).

    Article  CAS  PubMed  Google Scholar 

  26. Myatt, S.S. & Lam, E.W.-F. The emerging roles of forkhead box (Fox) proteins in cancer. Nat. Rev. Cancer 7, 847–859 (2007).

    Article  CAS  PubMed  Google Scholar 

  27. Scholzen, T. & Gerdes, J. The Ki-67 protein: from the known and the unknown. J. Cell. Physiol. 182, 311–322 (2000).

    Article  CAS  PubMed  Google Scholar 

  28. Fergusson, J.R. et al. CD161 defines a transcriptional and functional phenotype across distinct human T cell lineages. Cell Rep. 9, 1075–1088 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Lachmann, A. et al. ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics 26, 2438–2444 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Chen, X. et al. The forkhead transcription factor FOXM1 controls cell cycle-dependent gene expression through an atypical chromatin binding mechanism. Mol. Cell. Biol. 33, 227–236 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Galon, J. et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313, 1960–1964 (2006).

    Article  CAS  PubMed  Google Scholar 

  33. Fridman, W.H., Pagès, F., Sautès-Fridman, C. & Galon, J. The immune contexture in human tumours: impact on clinical outcome. Nat. Rev. Cancer 12, 298–306 (2012).

    Article  CAS  PubMed  Google Scholar 

  34. Lewis, C.E. & Pollard, J.W. Distinct role of macrophages in different tumor microenvironments. Cancer Res. 66, 605–612 (2006).

    Article  CAS  PubMed  Google Scholar 

  35. de Visser, K.E., Eichten, A. & Coussens, L.M. Paradoxical roles of the immune system during cancer development. Nat. Rev. Cancer 6, 24–37 (2006).

    Article  CAS  PubMed  Google Scholar 

  36. Beyer, M. & Schultze, J.L. Regulatory T cells in cancer. Blood 108, 804–811 (2006).

    Article  CAS  PubMed  Google Scholar 

  37. Girardi, M. et al. Regulation of cutaneous malignancy by γδ T cells. Science 294, 605 (2001).

    Article  CAS  PubMed  Google Scholar 

  38. Haas, W., Pereira, P. & Tonegawa, S. Gamma/delta cells. Annu. Rev. Immunol. 11, 637–685 (1993).

    Article  CAS  PubMed  Google Scholar 

  39. Fridlender, Z.G. & Albelda, S.M. Tumor-associated neutrophils: friend or foe? Carcinogenesis 33, 949–955 (2012).

    Article  CAS  PubMed  Google Scholar 

  40. Di Carlo, E. et al. The intriguing role of polymorphonuclear neutrophils in antitumor reactions. Blood 97, 339–345 (2001).

    Article  CAS  PubMed  Google Scholar 

  41. Vakkila, J. & Lotze, M.T. Inflammation and necrosis promote tumour growth. Nat. Rev. Immunol. 4, 641–648 (2004).

    Article  CAS  PubMed  Google Scholar 

  42. Sica, A., Schioppa, T., Mantovani, A. & Allavena, P. Tumour-associated macrophages are a distinct M2 polarised population promoting tumour progression: potential targets of anti-cancer therapy. Eur. J. Cancer 42, 717–727 (2006).

    Article  CAS  PubMed  Google Scholar 

  43. Teramukai, S. et al. Pretreatment neutrophil count as an independent prognostic factor in advanced non-small-cell lung cancer: an analysis of Japan Multinational Trial Organisation LC00–03. Eur. J. Cancer 45, 1950–1958 (2009).

    Article  PubMed  Google Scholar 

  44. de Visser, K.E., Korets, L.V. & Coussens, L.M. De novo carcinogenesis promoted by chronic inflammation is B lymphocyte dependent. Cancer Cell 7, 411–423 (2005).

    Article  CAS  PubMed  Google Scholar 

  45. Liyanage, U.K. et al. Prevalence of regulatory T cells is increased in peripheral blood and tumor microenvironment of patients with pancreas or breast adenocarcinoma. J. Immunol. 169, 2756–2761 (2002).

    Article  CAS  PubMed  Google Scholar 

  46. Minárik, I. et al. Regulatory T cells, dendritic cells and neutrophils in patients with renal cell carcinoma. Immunol. Lett. 152, 144–150 (2013).

    Article  CAS  PubMed  Google Scholar 

  47. Yamanaka, T. et al. The baseline ratio of neutrophils to lymphocytes is associated with patient prognosis in advanced gastric cancer. Oncology 73, 215–220 (2007).

    Article  PubMed  Google Scholar 

  48. Paik, S. et al. A multigene assay to predict recurrence of Tamoxifen-treated, node-negative breast cancer. N. Engl. J. Med. 351, 2817–2826 (2004).

    Article  CAS  PubMed  Google Scholar 

  49. Chen, X., Sun, X. & Hoshida, Y. Survival analysis tools in genomics research. Hum. Genomics 8, 21 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Alizadeh, A.A. et al. Prediction of survival in diffuse large B-cell lymphoma based on the expression of 2 genes reflecting tumor and microenvironment. Blood 118, 1350–1358 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Bindea, G. et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 39, 782–795 (2013).

    Article  CAS  PubMed  Google Scholar 

  52. Rooney, M.S., Shukla, S.A., Wu, C.J., Getz, G. & Hacohen, N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160, 48–61 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Gabrilovich, D.I. & Nagaraj, S. Myeloid-derived suppressor cells as regulators of the immune system. Nat. Rev. Immunol. 9, 162–174 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Pardoll, D.M. The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12, 252–264 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Ribas, A. Tumor immunotherapy directed at PD-1. N. Engl. J. Med. 366, 2517–2519 (2012).

    Article  CAS  PubMed  Google Scholar 

  56. Day, A., Carlson, M.R., Dong, J., O'Connor, B.D. & Nelson, S.F. Celsius: a community resource for Affymetrix microarray data. Genome Biol. 8, R112 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Dai, M. et al. Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 33, e175 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Gautier, L., Cope, L., Bolstad, B.M. & Irizarry, R.A. affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 307–315 (2004).

    Article  CAS  PubMed  Google Scholar 

  59. Gentleman, R.C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Irizarry, R.A. et al. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31, e15 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Wu, Z., Irizarry, R.A., Gentleman, R., Martinez-Murillo, F. & Spencer, F. A model-based background adjustment for oligonucleotide expression arrays. J. Am. Stat. Assoc. 99, 909–917 (2004).

    Article  Google Scholar 

  62. McCall, M.N., Bolstad, B.M. & Irizarry, R.A. Frozen robust multiarray analysis (fRMA). Biostatistics 11, 242–253 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Piccolo, S.R. et al. A single-sample microarray normalization method to facilitate personalized-medicine workflows. Genomics 100, 337–344 (2012).

    Article  CAS  PubMed  Google Scholar 

  64. Zhu, Y., Qiu, P. & Ji, Y. TCGA-Assembler: open-source software for retrieving and processing TCGA data. Nat. Methods 11, 599–600 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Lipták, T. On the combination of independent tests. Magyar Tud. Akad. Mat. Kutato Int. Közl 3, 171–196 (1958).

    Google Scholar 

  66. Stouffer, S., DeVinney, L. & Suchmen, E. The American Soldier: Adjustment During Army Life (Princeton University Press, 1949).

  67. Zaykin, D.V. Optimally weighted Z-test is a powerful method for combining probabilities in meta-analysis. J. Evol. Biol. 24, 1836–1841 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Johnson, W.E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

    Article  PubMed  Google Scholar 

  69. Storey, J.D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 100, 9440–9445 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Szklarczyk, D. et al. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 39, D561–D568 (2011).

    Article  CAS  PubMed  Google Scholar 

  71. Fiedler, M. Algebraic connectivity of graphs. Czech. Math. J. 23, 298–305 (1973).

    Google Scholar 

  72. Chen, J., Bardes, E.E., Aronow, B.J. & Jegga, A.G. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 37, W305–W311 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Zhong, Y. & Liu, Z. Gene expression deconvolution in linear space. Nat. Methods 9, 8–9 (2012).

    Article  CAS  Google Scholar 

  76. Xu, L. et al. Gene expression changes in an animal melanoma model correlate with Aggressiveness of Human Melanoma Metastases. Mol. Cancer Res. 6, 760–769 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Su, L.-J. et al. Selection of DDX5 as a novel internal control for Q-RT-PCR from microarray data using a block bootstrap re-sampling scheme. BMC Genomics 8, 140 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Landi, M.T. et al. Gene expression signature of cigarette smoking and its role in lung adenocarcinoma development and survival. PLoS ONE 3, e1651 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Brunner, A.L. et al. Transcriptional profiling of long non-coding RNAs and novel transcribed regions across a diverse panel of archived human cancers. Genome Biol. 13, R75 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Holmes, S., Kapelner, A. & Lee, P.P. An interactive java statistical image segmentation system: Gemident. J. Stat. Softw. 30, i10 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

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

Authors and Affiliations

Authors

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.

Ethics declarations

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)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nm.3909

This article is cited by

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer