Yu, Jyh-Cherng

Abstract Introduction: The vast majority of cancer patients continue to receive treatments that are minimally informed by omics data. In the case of breast cancer, only ER and HER2 are routinely used for treatment selection. There is a particular need for personalized treatment in individuals with primary and secondary drug resistance or aggressive breast cancers. Emerging bioinformatics and statistical methods have made a fundamental impact on cancer research. However, challenges remains with regard to patient-centric data analysis and providing genomic data guidance to oncologists. There exists a large number of FDA approved anti-neoplastic drugs used to treat cancers other than breast and the development of innovative informatics methods and algorithms to repurpose those drugs should benefit breast cancer patients. Methods and Results: We have developed precision care systems (such as PANOPLY and CORPUS) to identify personalized therapies for an individual patient and to deliver genomic reports in a standard, searchable format so that a researcher or an oncologist can quickly navigate through molecular data and obtain prioritized drugs and targets.The PANOPLY (Precision cancer genomics report: single sample inventory) algorithm applies machine learning and topology-based network analysis methods to integrate multi-omics profiles and clinical data; individual-specific molecular alterations are identified and compared with a set of matched-controls having similar clinical data. Since there is a lack of a “gold standard” dataset to test such algorithms, we simulated 500 case-control sets and evaluated drug predictions across multiple simulation scenarios. We applied the PANOPLY algorithm to The Cancer Genome Atlas (TCGA) breast cancer cohort, which consists of multi-omics data and clinical data. In addition, PANOPLY was also applied to an in-house neoadjuvant breast cancer study (BEAUTY) that consists of multi-omics data, clinical data, and patient-derived xenografts (PDXs). In the TCGA breast cancer study we obtained survival data to determine the cases and matched-controls; and in the BEAUTY, we used pathologic complete response (pCR) as an outcome to determine responders and non-responders. Recurrent targetable alterations were not enriched in patients without pCR in the BEAUTY study. We have applied the PANOPLY to non-responder patients to identify individual specific alterations, dysregulated networks, drug targets, and drugs for each patient and stored them as case reports in CORPUS (Computational Oncology Reports and Precision therapeUticS), a web-based repository that allows clinicians to review genomic reports. Using comprehensive “omic” data derived from a triple negative breast cancer patient who had pre and post-neoadjuvant chemotherapy PDXs, PANOPLY prioritized the PARP inhibitors as the top class of drug. Using the PDX models available from this patient, we tested olaparib and confirmed the in vivo antitumor activity (more effective than vehicle with a p-value < 0.05 in the PDXs). Further studies to confirm PANOPLY findings are currently underway. Conclusions: In summary, the PANOPLY and CORPUS systems incorporate molecular data together with clinical data to provide genomic reports with proposed drug targets to advance or enable precision breast cancer care. Citation Format: Kalari KR, Sinnwell JP, Thompson KJ, Tang X, Carlson EE, Alaparthi T, Yu J, Vedell PT, Kalmbach MT, Bockol MA, Hossain A, Weinshilboum RM, Boughey JC, Wang L, Suman VJ, Goetz MP. Multiscale modeling of omics data for precision breast cancer treatment [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-06-10.

Abstract The 19p13.1 breast cancer susceptibility locus is a modifier of breast cancer risk in BRCA1 mutation carriers and is also associated with the risk of ovarian cancer. Here, we investigated 19p13.1 variation and risk of breast cancer subtypes, defined by estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2) status, using 48,869 breast cancer cases and 49,787 controls from the Breast Cancer Association Consortium (BCAC). Variants from 19p13.1 were not associated with breast cancer overall or with ER-positive breast cancer but were significantly associated with ER-negative breast cancer risk [rs8170 OR, 1.10; 95% confidence interval (CI), 1.05–1.15; P = 3.49 × 10−5] and triple-negative (ER-, PR-, and HER2-negative) breast cancer (rs8170: OR, 1.22; 95% CI, 1.13–1.31; P = 2.22 × 10−7). However, rs8170 was no longer associated with ER-negative breast cancer risk when triple-negative cases were excluded (OR, 0.98; 95% CI, 0.89–1.07; P = 0.62). In addition, a combined analysis of triple-negative cases from BCAC and the Triple Negative Breast Cancer Consortium (TNBCC; N = 3,566) identified a genome-wide significant association between rs8170 and triple-negative breast cancer risk (OR, 1.25; 95% CI, 1.18–1.33; P = 3.31 × 10−13]. Thus, 19p13.1 is the first triple-negative–specific breast cancer risk locus and the first locus specific to a histologic subtype defined by ER, PR, and HER2 to be identified. These findings provide convincing evidence that genetic susceptibility to breast cancer varies by tumor subtype and that triple-negative tumors and other subtypes likely arise through distinct etiologic pathways. Cancer Res; 72(7); 1795–803. ©2012 AACR.