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Prediction of lymph node involvement in breast cancer from primary tumor tissue using gene expression profiling and miRNAs

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Abstract

The aim of this study was to investigate whether lymph node involvement in breast cancer is influenced by gene or miRNA expression of the primary tumor. For this purpose, we selected a very homogeneous patient population to minimize heterogeneity in other tumor and patient characteristics. First, we compared gene expression profiles of primary tumor tissue from a group of 96 breast cancer patients balanced for lymph node involvement using Affymetrix Human U133 Plus 2.0 microarray chip. A model was built by weighted Least-Squares Support Vector Machines and validated on an internal and external dataset. Next, miRNA profiling was performed on a subset of 82 tumors using Human MiRNA-microarray chips (Illumina). Finally, for each miRNA the number of significant inverse correlated targets was determined and compared with 1000 sets of randomly chosen targets. A model based on 241 genes was built (AUC 0.66). The AUC for the internal dataset was 0.646 and 0. 651 for the external datasets. The model includes multiple kinases, apoptosis-related, and zinc ion-binding genes. Integration of the microarray and miRNA data reveals ten miRNAs suppressing lymph node invasion and one miRNA promoting lymph node invasion. Our results provide evidence that measurable differences in gene and miRNA expression exist between node negative and node positive patients and thus that lymph node involvement is not a genetically random process. Moreover, our data suggest a general deregulation of the miRNA machinery that is potentially responsible for lymph node invasion.

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Acknowledgment

AS is supported by a grant from Research Foundation Flanders (FWO) and Pfizer Oncology.

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Authors and Affiliations

  1. Multidisciplinary Breast Centre University Hospital, Herestraat 49, B-3000, Leuven, Belgium

    A. Smeets, H. Wildiers, P. Neven, T. Vandorpe, R. Paridaens & M. R. Christiaens

  2. Department of Electrical Engineering, University of Leuven, Leuven, Belgium

    A. Daemen, O. Gevaert & B. De Moor

  3. Department of Microbiology, University Antwerp, Antwerp, Belgium

    I. Vanden Bempt

  4. Department of Radiology, Stanford University School of Medicine, Stanford, USA

    O. Gevaert

  5. The Vesalius Research Centre, VIB and University of Leuven, Leuven, Belgium

    B. Claes & D. Lambrechts

  6. Department of Pathology, Jessa Hospital, Hasselt, Belgium

    R. Drijkoningen

  7. Center for Cancer Genome Discovery (CCGD), Dana Farber Cancer Institute, 44 Binney St., Boston, USA

    P. Van Hummelen

  8. Department of Medical Oncology, Jules Bordet Institute, Brussels, Belgium

    C. Sotiriou

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  1. A. Smeets
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Correspondence to A. Smeets.

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Smeets, A., Daemen, A., Vanden Bempt, I. et al. Prediction of lymph node involvement in breast cancer from primary tumor tissue using gene expression profiling and miRNAs. Breast Cancer Res Treat 129, 767–776 (2011). https://doi.org/10.1007/s10549-010-1265-5

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  • DOI: https://doi.org/10.1007/s10549-010-1265-5

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