Abstract 206: Organoid models for predicting drug response in high grade serous and triple negative breast cancer

Abstract

Abstract While most patients with high grade serous ovarian cancer (HGSC) respond to platinum-based chemotherapy, the response is rarely durable and recurrence almost inevitable. Women with triple negative breast cancer (TNBC) urgently require effective therapeutic options. In women with HGSC and TNBC (with mutations in BRCA1/2) treatment with a Poly-ADP-ribose inhibitor (PARPi) has emerged as a standard of care. In models of HGSC and TNBC senescence may play a role in PARPi resistance with i Bcl-xL, a member of the Bcl-2 family of proteins, preventing apoptosis. Therefore, we have initiated a clinical trial whereby a PARPi and then, an inhibitor of Bcl-xL, Navitoclax, will be added to their course of treatment [NCT05358639]. Bcl-xL is only one of the five known inhibitors of apoptosis, at present we do not know in vivo how these will impact the efficacy of the PARPi/Navitoclax combination or if they will provide other targets for effective therapy. Our hypothesis is that patient derived organoids can be used as a pragmatic way to assess the efficacy of new drugs and to identify for individual patients which drug(s) and drug combinations are likely to be most efficacious. In particular, with reference to the clinical trial, identify for individual patients which Bcl-2 protein inhibitor will synergize with a PARPi to optimize treatment. Currently we are using organoids to test novel drugs and drug combinations and to develop biomarkers for HGSC and TNBC treatment response. A limitation to the use of organoids generated from biopsy samples is the lack of efficient and reliable experimental models that recapitulate in vitro patient tumors faithfully enough to facilitate translation to therapeutic decisions for patients. By adapting the relatively new technique of conditional reprogramming and combining it with novel hydrogel based synthetic ECM supports we can reproducibly generate HGSC and TNBC patient-specific tumor organoids models in two weeks with greater than 90% success. Organoids grown in 384 or 1536 well format are stained with novel non-toxic dyes and chemoresponses to drugs are inferred from automatically acquired 3D confocal image stacks using deep learning AI algorithms that enable automated analyses. Our data suggest that this approach captures the inherent heterogeneity of the disease, albeit local to the sampled site. We are now employing deep learning AI algorithms to enable automated analyses of 3D confocal image stacks of organoids and infer drug responses that will be compared to patient responses in the clinical trial. Citation Format: Helen J. MacKay, Alla Buzina, Betty Li, Lilian Gien, David Andrews. Organoid models for predicting drug response in high grade serous and triple negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 206.