Abstract LB-423: Whole genome comparisons of pre- and post- aromatase inhibitor treatment in estrogen receptor positive breast cancer
Authors: Matthew J. Ellis, Ding Li, Dong Shen, Jingqin Luo, Vera J. Suman, John W. Wallis, Brian A. Van Tine, Jeremy Hoog, Robert J. Crowder, Jacqueline E. Snider, Karla Ballman, Ken Chen, Daniel C. Koboldt, William S. Schierding, Joshua F. McMichael, Christopher A. Miller, Cyriac Kandoth, Charles Lu, Christopher C. Harris, Michael D. McLellan, Michael C. Wendl, Katherine DeSchryver, D. Craig Allred, Laura Esserman, Gary Unzeitig, Julie Margenthaler, G.V. Babiera, P. Kelly Marcom, J.M. Guenther, Marilyn Leitch, Kelly Hunt, John Olson, Yu Tao, Lucinda L. Fulton, Robert S. Fulton, Michelle Harrison, Ben Oberkfell, Feiyu Du, Ryan Demeter, Malachi Griffith, Tammi L. Vickery, Sandra A. McDonald, Mark Watson, David J. Dooling, David Ota, Li-Wei Chang, Ron Bose, Timothy J. Ley, Richard K. Wilson, Elaine R. Mardis
Journal: Cancer Research (2012)

Abstract Background: Estrogen receptors are over-expressed in around 70% of breast cancer cases. The genetic changes that occur during aromatase inhibitor (AI) treatment are not well understood and may differ depending upon the patient's response phenotype. Methods: We performed whole genome sequencing (WGS) of matched blood, pre-treatment, and post-treatment biopsy samples from 22 estrogen receptor positive breast cancer patients treated with neoadjuvant aromatase inhibitors. For 5 cases, we performed the whole genome sequencing (WGS) on patients’ matched normal, two pre AI-treatment, and two post AI-treatment DNA isolates from biopsy samples. We validated all putative coding and non-coding somatic mutations using deep sequencing. By comparing the validated somatic mutations from pre- and post- AI treatment biopsy samples, we were able to determine the alterations in the tumor genomes. In every case we defined the clonal architecture of each pair of pre-treatment and post-treatment biopsy samples by comparing the variant allele frequencies from thousands of validated somatic mutations. Results: Comparisons of the two pre AI-treatment biopsy samples from the same patient indicates that the variant allele frequencies of mutations showed high concordances in all 5 cases, 0.74 to 0.95 range of correlation coefficient. Only a small percentage of somatic mutations were detected in one pre-treatment sample and not the other (4.65% overall). In comparing the somatic variations between pre-treatment and matched post-treatment biopsy samples in 22 cases, we found that patients with good clinical response to AI treatment retained known driver mutations only in their pre-treatment tumors. Conversely, those patients with poor clinical response presented new driver mutations in their post-treatment samples. Furthermore, the variant allele frequency for most mutated genes decreased in post AI treatment samples for patients with good AI treatment response; on the contrary, the variant allele frequency increased for patients with poor clinical response. Conclusions: From WGS of matched normal, pre-treatment, and post-treatment biopsy samples, we identified new driver genes mutated in patients with poor clinical response, while patients with good clinical response had lost mutated driver genes in their post-treatment biopsy samples. The genetic landscape revealed by WGS of pre-treatment and post-treatment biopsy samples reveals mutational repertoires are remodeled by AI therapy. This finding suggests deep sequencing of AI treated samples will be necessary to reveal the complete complement of mutations present in a patient's tumor. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-423. doi:1538-7445.AM2012-LB-423