Abstract PR01: Systems analysis of signaling pathway adaptation to design effective PI3K-based combination therapies using ovarian cancer patient-derived xenografts
Authors: Zervantonakis Ioannis K. , Iavarone Claudia , Chen Hsing-Yu , Leverson Joel , Sampath Deepak , Palakurthi Sangeetha , Drapkin Ronny , Liu Joyce F. , Matulonis Ursula , Mills Gordon , Brugge Joan S. 
Published: March 2017

Despite recent advances in understanding the biology of ovarian tumors and the development of targeted therapies, primary or acquired resistance to therapy remains the major cause of treatment failure.

Improvements in “omics” measurement technologies offer new opportunities for cancer systems biology approaches to model and predict drug resistance. Here, we investigate the adaptation of ovarian cancer cells to PI3K/mTOR inhibition in order to design synergistic drug combinations by using proteomic and phenotypic measurements together with computational modeling. We employ a panel of high-grade serous ovarian (HGS-Ov) patient-derived xenografts (PDX) as a clinically relevant platform to model intertumoral heterogeneity and to discover biomarkers that predict sensitivity for future translation in clinical trials.

We evaluated the efficacy of PI3K/mTOR inhibition in ascites cells derived from 14 HGS-Ov PDX models and also performed in vitro proteomic analysis. To identify commonly altered signaling pathways among the PDX models, we performed a systems analysis of key signaling pathways and found that the apoptotic pathway was commonly altered in all models, despite the significant intertumoral heterogeneity. To inhibit pro-survival proteins in the apoptotic pathway we tested the efficacy of BCL-2/XL inhibition as a single agent, and in combination with PI3K/mTOR inhibition in the PDX models. In vitro drug sensitivity analysis to single-agents and combination treatment showed that combined PI3K/mTOR (GNE-493) and BCL-2/XL inhibition (ABT-737) acts synergistically to reduce tumor cell numbers (combination index ranged from 0.2-0.8 for 12 out of 14 PDX models). In addition, synergistic effects in tumor growth inhibition and cell death (2-5 fold increase compared to single-agent treatment) were observed in vivo for two PDX models that exhibited increased drug sensitivity in vitro (IC50 &lt; 0.5μM).

To gain insight into the underlying response mechanisms and identify biomarkers that predict sensitivity we developed data-driven computational models. By employing multivariate partial least squares regression, we analyzed relationships between apoptotic signaling and drug sensitivity, and examined the relative importance of apoptotic proteins. We found that levels of the pro-apoptotic protein Bim, and the pro-survival proteins BCL-XL, MCL-1 and XIAP predicted drug sensitivity (Pearson correlation coefficient r = 0.81). siRNA knockdown experiments and validation using an independent set of established cell lines confirmed the critical role of Bim as a biomarker of drug sensitivity. Using a panel of BCL-XL, MCL-1 and XIAP inhibitors, we confirmed the functional role of these anti-apoptotic (pro-survival) proteins in ovarian cancer cell survival, and showed that BCL-XL inhibition is the most potent in inducing cell death. Interestingly, we found that triple therapy with MCL-1, BCL-2/XL and PI3K/mTOR inhibitor was effective in sensitizing the resistant PDX models in vitro (99% reduction in viability).

Our studies highlight the critical role of targeting apoptotic vulnerabilities in high-grade serous ovarian carcinoma to enhance PI3K-based therapy efficacy and induce synergistic tumor cell death. Furthermore, our integrated proteomic and computational approach enables the rational design of drug combination therapies and the identification of predictive biomarkers for translational studies.

This abstract is also being presented as Poster A35.

Citation Format: Ioannis K. Zervantonakis, Claudia Iavarone, Hsing-Yu Chen, Joel Leverson, Deepak Sampath, Sangeetha Palakurthi, Ronny Drapkin, Joyce F. Liu, Ursula Matulonis, Gordon Mills, Joan S. Brugge. Systems analysis of signaling pathway adaptation to design effective PI3K-based combination therapies using ovarian cancer patient-derived xenografts. [abstract]. In: Proceedings of the AACR Special Conference on Engineering and Physical Sciences in Oncology; 2016 Jun 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2017;77(2 Suppl):Abstract nr PR01.