Abstract GMM-051: A CLIA-CERTIFIED HIGH-THROUGHPUT DRUG SCREENING PLATFORM FOR OVARIAN CANCER TO INFORM PERSONALIZED CANCER CARE AND DISCOVER NOVEL COMBINATIONS (2019)

Abstract BACKGROUND: Metastatic disease in ovarian cancer is difficult to treat and patients often exhaust standard-of-care regimens. To gain a better understanding of potential treatments, genomic data can be used. However, the interpretation of the genomic data for ovarian cancer is challenging given complex mutation patterns and the absence of recurrent druggable alterations beyond DNA repair deficiency. Here we present the first CLIA certified high-throughput functional assay employing organoid cultures derived from primary patient specimens to directly aid oncologists for personalized treatment selection in combination with genomic data (P.A.R.I.S® Assay, SEngine Precision Medicine, Seattle, WA). EXPERIMENTAL PROCEDURES: Organoids are exposed to a library of 123 clinically relevant drugs. The library was developed to include chemotherapies and FDA approved drugs currently used in the clinic, as well as promising drugs undergoing clinical trials. Compounds are evaluated at a multi-dose response curve and ranked by SPM™ score, which weighs both the sensitivity (degree of cell death) and specificity, which compares the response of the patient's tumor cells to the drug relative to all prior patients. In addition to single agent screens, optimal combinations can also be tested. The results are further integrated with genomic data when available, and drug-responses are reported to the clinician. SEngine has performed &amp;gt;200 drug screens across &amp;gt;20 different tumor types and established high reproducibility of the high-throughput platform. Ovarian samples were derived from either ascites or biopsies. RESULTS: Of the 7 samples directly derived from patients, 1 was from ascites, 4 were from biopsies or surgical excisions, and 2 had both. In addition, 13 cell lines were screened with the SEngine drug library. While every patient exhibited a unique pattern of response consistent with the heterogeneity of the complex genetic landscape of ovarian cancer, sensitivities for certain drugs such as HDAC, PI3K and tyrosine-kinase inhibitors were frequently found. Several n=1 cases will be presented to highlight correlations with retrospective clinical responses as well as with genomic alterations. These results highlight how genomic data and functional testing can be combined to optimize personalized cancer care. To identify optimal drug combinations with PARP inhibitors, we have performed an unbiased screen where patient derived ovarian cancer cell lines were challenged with SEngine drug library in the presence of rucaparib. The screen indicated multiple drugs, such as bromodomain, BCL2 and cyclin dependent kinase (CDK) inhibitors as well as dasatinib as potential sensitizers to rucaparib. Confirmation of these results in additional patient derived organoids and PDX models are in progress. IMPACT: We developed a robust ex vivo screening platform to objectively quantify patient specific sensitivity to a panel of more than 123 oncology drugs and potential novel combinations. SEngine is compiling a registry capturing clinical data, outcome following the P.A.R.I.S® test, and genomic data. Combining the power of high-throughput technology and organoid isolation with genomic data will enable the rapid selection of optimal individualized therapies as single agents or in combination and guide design of future clinical trials. Citation Format: Franz X Schaub, Sabrina Maisel, Reid Shaw, Rachele Rosati, Caroline Bridgwater, Michael Churchill, Robert Diaz, Stephanie Tatem Murphy, Shalini Pereira, Christopher Kemp, VK Gadi, and Carla Grandori. A CLIA-CERTIFIED HIGH-THROUGHPUT DRUG SCREENING PLATFORM FOR OVARIAN CANCER TO INFORM PERSONALIZED CANCER CARE AND DISCOVER NOVEL COMBINATIONS [abstract]. In: Proceedings of the 12th Biennial Ovarian Cancer Research Symposium; Sep 13-15, 2018; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2019;25(22 Suppl):Abstract nr GMM-051.