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Predicting features of breast cancer with gene expression patterns

  • Preclinical Study
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Abstract

Data from gene expression arrays hold an enormous amount of biological information. We sought to determine if global gene expression in primary breast cancers contained information about biologic, histologic, and anatomic features of the disease in individual patients. Microarray data from the tumors of 129 patients were analyzed for the ability to predict biomarkers [estrogen receptor (ER) and HER2], histologic features [grade and lymphatic-vascular invasion (LVI)], and stage parameters (tumor size and lymph node metastasis). Multiple statistical predictors were used and the prediction accuracy was determined by cross-validation error rate; multidimensional scaling (MDS) allowed visualization of the predicted states under study. Models built from gene expression data accurately predict ER and HER2 status, and divide tumor grade into high-grade and low-grade clusters; intermediate-grade tumors are not a unique group. In contrast, gene expression data is inaccurate at predicting tumor size, lymph node status or LVI. The best model for prediction of nodal status included tumor size, LVI status and pathologically defined tumor subtype (based on combinations of ER, HER2, and grade); the addition of microarray-based prediction to this model failed to improve the prediction accuracy. Global gene expression supports a binary division of ER, HER2, and grade, clearly separating tumors into two categories; intermediate values for these bio-indicators do not define intermediate tumor subsets. Results are consistent with a model of regional metastasis that depends on inherent biologic differences in metastatic propensity between breast cancer subtypes, upon which time and chance then operate.

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Acknowledgements

Supported by the Breast Cancer Research Foundation (BCRF) and by the Dana-Faber/Harvard SPORE in Breast Cancer from the National Cancer Institute (J.D.I., A.R.), grants ACS-IRG 70-002 and CA23100-22 (X.L), NSFC grant 30625012 and the National Basic Research Program (2004CB518605) of China (X.Z.).

Author information

Author notes

  1. Xin Lu

    Present address: Department of Family and Preventive Medicine, University of California San Diego, San Diego, CA, 92093, USA

Authors and Affiliations

  1. Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University, Beijing, 100084, China

    Xuesong Lu & Xuegong Zhang

  2. Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA

    Xin Lu

  3. Department of Biostatistics, Dana-Farber Cancer Institute, Boston, MA, USA

    Xin Lu

  4. Department of Surgery, Brigham and Women’s Hospital, Boston, MA, USA

    Zhigang C. Wang & J. Dirk Iglehart

  5. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA

    Zhigang C. Wang & J. Dirk Iglehart

  6. Department of Pathology, Brigham and Women’s Hospital, 75 Francis Street, Boston, MA, 02115, USA

    Andrea L. Richardson

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  1. Xuesong Lu
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  2. Xin Lu
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  3. Zhigang C. Wang
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  4. J. Dirk Iglehart
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  5. Xuegong Zhang
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Corresponding authors

Correspondence to Xuegong Zhang or Andrea L. Richardson.

Additional information

Xuesong Lu and Xin Lu contributed equally to this work.

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Lu, X., Lu, X., Wang, Z.C. et al. Predicting features of breast cancer with gene expression patterns. Breast Cancer Res Treat 108, 191–201 (2008). https://doi.org/10.1007/s10549-007-9596-6

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  • DOI: https://doi.org/10.1007/s10549-007-9596-6

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