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Nature Medicine
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Stromal gene expression predicts clinical outcome in breast cancer

Nature Medicinevolume 14, pages518–527 (2008)Cite this article

Abstract

Although it is increasingly evident that cancer is influenced by signals emanating from tumor stroma, little is known regarding how changes in stromal gene expression affect epithelial tumor progression. We used laser capture microdissection to compare gene expression profiles of tumor stroma from 53 primary breast tumors and derived signatures strongly associated with clinical outcome. We present a new stroma-derived prognostic predictor (SDPP) that stratifies disease outcome independently of standard clinical prognostic factors and published expression-based predictors. The SDPP predicts outcome in several published whole tumor–derived expression data sets, identifies poor-outcome individuals from multiple clinical subtypes, including lymph node–negative tumors, and shows increased accuracy with respect to previously published predictors, especially for HER2-positive tumors. Prognostic power increases substantially when the predictor is combined with existing outcome predictors. Genes represented in the SDPP reveal the strong prognostic capacity of differential immune responses as well as angiogenic and hypoxic responses, highlighting the importance of stromal biology in tumor progression.

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Figure 1: Class discovery in tumor stroma.
Figure 2: Class distinction of tumor stroma.
Figure 3: Construction and performance of the SDPP.
Figure 4: Performance of the SDPP in publicly available breast cancer gene expression data sets.

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Acknowledgements

We thank: D. Fleiszer, A. Loutfi, C. Milne, D. Owen, G. Pearl, R. Salasidis, F. Tremblay, M. Wexler (surgeons); F. Halwani, K. Khetani (pathologists); H. Barwick, A. Cuellar, D. Hori, S. Eng, L. Pasyuk, T. Vilhena, C. Palko-Condron (Pathology staff); C. Loiselle (Nursing); the MUHC Anaesthesia Department; A. Dedhar and A. Viquez (tissue and data collectors) for their assistance. We also thank C. Mihalcioiu, P. Siegel and members of the Park lab for their critical review of this manuscript. This work was supported by grants to M.P. from the Québec Breast Cancer Foundation, Genome Canada–Génome Québec, Valorisation-Recherche Québec and Fonds de la Récherche en Santé du Québec and a Canadian Institutes of Health Research (CIHR) Team Grant; a National Science and Engineering Research Council of Canada Discovery Grants Program grant to M.H.; a CIHR McGill University Cancer Consortium Training Award to G.F.; a US Department of Defense Breast Cancer Predoctoral Traineeship Award to F.P.; MUHC Research Institute and MUHC Department of Medicine Fellowships to N.B.; and Cedars Cancer Institute Fellowships to S.S. and N.B. M.P. holds the Diane and Sal Guerrera Chair in Cancer Genetics at McGill University.

Author information

Author notes

  1. Nicholas Bertos and Francois Pepin: These authors contributed equally to this work.

Authors and Affiliations

  1. McGill Centre for Bioinformatics, 3775 University Street, McGill University, Québec, H3A 2B4, Canada

    Greg Finak, Francois Pepin & Michael Hallett

  2. Molecular Oncology Group, 687 Pine Avenue West, McGill University Health Centre, Québec, H3A 1A1, Canada

    Greg Finak, Nicholas Bertos, Francois Pepin, Svetlana Sadekova, Margarita Souleimanova, Hong Zhao, Haiying Chen, Gulbeyaz Omeroglu & Morag Park

  3. Department of Biochemistry, 3655 Promenade Sir William Osler, McGill University, Québec, H3G 1Y6, Canada

    Greg Finak, Francois Pepin, Michael Hallett & Morag Park

  4. Department of Surgery, 687 Pine Avenue West, McGill University, Québec, H3A 1A1, Canada

    Sarkis Meterissian

  5. Department of Oncology, 687 Pine Avenue West, McGill University, Québec, H3A 1A1, Canada

    Morag Park

  6. Department of Pathology, 3775 University Street, McGill University, Québec, H3A 2B4, Canada

    Atilla Omeroglu

Authors
  1. Greg Finak

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  5. Margarita Souleimanova

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  6. Hong Zhao

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  8. Gulbeyaz Omeroglu

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  12. Morag Park

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Contributions

G.F. designed and implemented the data analysis pipeline for the data generated for this study, developed methods and software for data analysis, analyzed and interpreted the data, and contributed to manuscript preparation. N.B. coordinated experiments, supervised the quantitative RT-PCR and immunohistochemical validation aspects of this study, participated in discussions of data analysis and interpretation, and contributed to manuscript preparation. F.P. contributed to methods and software development and participated in discussions of data analysis and interpretation. S.S. developed protocols for tissue storage, LCM, linear amplification and labeling, and supervised these applications. M.S. performed LCM and immunohistochemistry. H.Z. performed quantitative RT-PCR and isolated RNA after LCM. H.C. prepared samples and conducted gene expression profiling. G.O. performed pathological and histological analysis of samples and gave advice regarding immunohistochemistry. S.M. contributed to clinical analyses and tissue procurement. A.O. performed pathological and histological analyses on tissue samples before LCM. M.H. supervised the bioinformatics and biostatistics aspects of the project, designed and coordinated analyses, and contributed to manuscript preparation. M.P. initiated and supervised the tissue collection and microarray preparation, supervised the expression profiling aspect of this project, designed and coordinated experiments and contributed to manuscript preparation.

Corresponding author

Correspondence toMorag Park.

Supplementary information

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Supplementary Figs. 1–4, Supplementary Tables 1 and 2, Supplementary Results and Supplementary Methods (PDF 1644 kb)

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Finak, G., Bertos, N., Pepin, F.et al. Stromal gene expression predicts clinical outcome in breast cancer.Nat Med14, 518–527 (2008). https://doi.org/10.1038/nm1764

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