Modeling precision treatment of breast cancer

Daemen, Anneleen; Griffith, Obi L.; Heiser, Laura M.; Wang, Nicholas J.; Enache, Oana M.; Sanborn, Zachary; Pepin, Francois; Durinck, Steffen; Korkola, James E.; Griffith, Malachi; Hur, Joe S.; Huh, Nam; Chung, Jongsuk; Cope, Leslie; Fackler, Mary; Umbricht, Christopher; Sukumar, Saraswati; Seth, Pankaj; Sukhatme, Vikas P.; Jakkula, Lakshmi R.; Lu, Yiling; Mills, Gordon B.; Cho, Raymond J.; Collisson, Eric A.; van’t Veer, Laura J.; Spellman, Paul T.; Gray, Joe W.

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

Title: Modeling precision treatment of breast cancer

Journal Article · Tue Jan 01 00:00:00 EST 2013 · GenomeBiology.com

DOI:https://doi.org/10.1186/gb-2013-14-10-r110· OSTI ID:1626741

Daemen, Anneleen ^[1]; Griffith, Obi L. ^[2]; Heiser, Laura M. ^[3]; Wang, Nicholas J. ^[3]; Enache, Oana M. ^[4]; Sanborn, Zachary ^[5]; Pepin, Francois ^[4]; Durinck, Steffen ^[4]; Korkola, James E. ^[3]; Griffith, Malachi ^[6]; Hur, Joe S. ^[7]; Huh, Nam ^[8]; Chung, Jongsuk ^[8]; Cope, Leslie ^[9]; Fackler, Mary ^[9]; Umbricht, Christopher ^[9]; Sukumar, Saraswati ^[9]; Seth, Pankaj ^[10]; Sukhatme, Vikas P. ^[10]; Jakkula, Lakshmi R. ^[4] more »

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Life Sciences Division. Dept. of Cancer and DNA Damage Responses; Univ. of California, San Francisco, CA (United States). Lab. Medicine
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Life Sciences Division. Dept. of Cancer and DNA Damage Responses; Oregon Health and Science Univ., Portland, OR (United States). Dept. of Molecular and Medical Genetics; Washington Univ., St. Louis, MO (United States). School of Medicine. The Genome Inst.
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Life Sciences Division. Dept. of Cancer and DNA Damage Responses; Oregon Health and Science Univ., Portland, OR (United States). Knight Cancer Inst. Center for Spatial Systems Biomedicine. Dept. of Biomedical Engineering
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Life Sciences Division. Dept. of Cancer and DNA Damage Responses
Five3 genomics, Santa Cruz, CA (United States)
Washington Univ., St. Louis, MO (United States). School of Medicine. The Genome Inst.
Samsung Electronics Headquarters, Seoul (Korea, Republic of)
Samsung Advanced Inst. of Technology, Kyunggi-do (Korea, Republic of)
Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Oncology
Harvard Medical School, Boston, MA (United States). Beth Israel Deaconess Medical Center. Dept. of Medicine
MD Anderson Cancer Center, Houston, TX (United States). Dept. of Systems Biology
Univ. of California, San Francisco, CA (United States). Dept. of Dermatology
Univ. of California, San Francisco, CA (United States). Lab. Medicine
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Life Sciences Division. Dept. of Cancer and DNA Damage Responses; Oregon Health and Science Univ., Portland, OR (United States). Dept. of Molecular and Medical Genetics

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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1626741

Journal Information:: GenomeBiology.com, Vol. 14, Issue 10; ISSN 1465-6906

Publisher:: BioMed CentralCopyright Statement

Country of Publication:: United States

Language:: English

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