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BioMed Central, Genome Biology, 1(16), 2015

DOI: 10.1186/s13059-015-0658-5

BioMed Central, Genome Biology, 10(14), p. R110

DOI: 10.1186/gb-2013-14-10-r110

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Modeling precision treatment of breast cancer.

Journal article published in 2013 by Anneleen Daemen, Obi L. Griffith ORCID, Laura M. Heiser, Nicholas J. Wang, Oana M. Enache, Zachary Sanborn, Francois Pepin, Steffen Durinck, James E. Korkola, Malachi Griffith, Joe S. Hur, Nam Huh, Jongsuk Chung, Leslie Cope, Mary Jo Fackler and other authors.
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Abstract Background First-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets. Results We used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples. Conclusions These results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified.