Boughey, Judy C.

Purpose The majority of patients with cancer receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework, PANOPLY (Precision Cancer Genomic Report: Single Sample Inventory), to identify and prioritize drug targets and cancer therapy regimens. Materials and Methods The PANOPLY approach integrates clinical data with germline and somatic features obtained from multiomics platforms and applies machine learning and network analysis approaches in the context of the individual patient and matched controls. The PANOPLY workflow uses the following four steps: selection of matched controls to the patient of interest; identification of patient-specific genomic events; identification of suitable drugs using the driver-gene network and random forest analyses; and provision of an integrated multiomics case report of the patient with prioritization of anticancer drugs. Results The PANOPLY workflow can be executed on a stand-alone virtual machine and is also available for download as an R package. We applied the method to an institutional breast cancer neoadjuvant chemotherapy study that collected clinical and genomic data as well as patient-derived xenografts to investigate the prioritization offered by PANOPLY. In a chemotherapy-resistant patient-derived xenograft model, we found that that the prioritized drug, olaparib, was more effective than placebo in treating the tumor ( P < .05). We also applied PANOPLY to in-house and publicly accessible multiomics tumor data sets with therapeutic response or survival data available. Conclusion PANOPLY shows promise as a means to prioritize drugs on the basis of clinical and multiomics data for an individual patient with cancer. Additional studies are needed to confirm this approach.

533 Background: Current guidelines define TNBC as complete absence of estrogen (ER) and progesterone receptor (PR), without HER2 amplification. However, the prognostic impact of clinical and histopathological factors, sTILs, and AdjCT in TNBC meeting these strict criteria is unknown. Methods: From a cohort of 9985 women who underwent upfront surgery for M0 breast cancer (BC) at Mayo Clinic Rochester from 1985-2012, 1159 pts with ER negative or low (≤10%) BC were identified for central ER/PR/HER2 staining and HER2 FISH (IHC2+ only) to select those with TNBC by modern definitions. Cox proportional hazards models were used to assess the impact of clinicopathological variables on invasive disease-free (IDFS) and overall survival (OS). Results: Tumors from 605 pts (median age 56.3 yrs) met criteria for TNBC (ER < 1%, PR < 1% and HER2 0, 1 or 2+ and FISH negative). 51% were T1, 65% N0, 88% grade 3, and 75% had Ki67 > 15%. Histologically, 39% were anaplastic, 26% invasive ductal, 16% medullary, 8% metaplastic, 6% apocrine and 5% others. Median sTILs was 20% (0-90%). 55% pts received AdjCT [21% anthracycline (A), 19% A + taxane, and 15% other]. Median follow-up for IDFS and OS were 7.4 and 10.6 yrs, respectively. Multivariate analyses demonstrated that higher N stage (p < 0.01), lower sTILs (p = 0.01) and no AdjCT (p < 0.01) were independently associated with worse IDFS and OS. Histology (medullary subtype) was associated with better IDFS in univariate (HR 0.56, 95% CI, 0.35-0.89) but not in multivariate analyses, once sTILs were accounted for. Among systemically untreated pts (n = 182), higher N (p < 0.01) and lower sTILs (p = 0.04) were associated with worse IDFS. For systemically untreated T1N0 pts (n = 111), the 5-yr IDFS was 70% (95% CI, 61-81) [T1a: 83% (95% CI, 63-100), T1b: 68% (95% CI, 52-88) and T1c: 67% (95% CI, 55-83)], compared to 78% (95% CI, 68-84) for T1N0 pts treated with AdjCT. Conclusions: In TNBC pts, N stage, sTILs and receipt of AdjCT were independently prognostic for IDFS and OS. sTILs remained prognostic for IDFS in systemically untreated TNBC. In N0 TNBC, the risk of recurrence or death was substantial in the absence of chemotherapy, even for those with T1 tumors.