Modeling precision treatment of breast cancer
- Equal contributors
1 Department of Cancer & DNA Damage Responses, Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
2 Laboratory Medicine, University of California San Francisco, San Francisco, CA 94115, USA
3 Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, OR 97239, USA
4 Department of Biomedical Engineering, Center for Spatial Systems Biomedicine, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA
5 Five3 genomics, 101 Cooper St, Santa Cruz, CA 95060, USA
6 The Genome Institute, Washington University School of Medicine, St Louis, MO 63105, USA
7 Samsung Electronics Headquarters, Seocho-gu, Seoul 137-857, Korea
8 Emerging Technology Research Center, Samsung Advanced Institute of Technology, Kyunggi-do 446-712, Korea
9 Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
10 Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
11 Department of Systems Biology, MD Anderson Cancer Center, Houston, TX 77030, USA
12 Department of Dermatology, University of California, San Francisco, CA 94115, USA
13 Present address: Department of Bioinformatics & Computational Biology, Genentech Inc, 1 DNA Way, South San Francisco, CA 94080, USA
14 Present address: Sequenta Inc, South San Francisco, CA 94080, USA
Genome Biology 2013, 14:R110 doi:10.1186/gb-2013-14-10-r110Published: 31 October 2013
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.
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.
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.