Abstract 675: Network modeling of drug resistance mechanisms and drug combinations in breast cancer

Abstract

Abstract Durable control of invasive solid tumors is thwarted by the lack of knowledge of effective drug combinations and of the acquired and intrinsic resistance mechanisms of drugs. In an effort to tackle this problem, the SU2C-NSF-TVF Drug Combination Convergence Team is using mechanistic models of cancer cell signaling based on therapeutic and cell line data in order to identify elements within cancer cells that might eventually be exploited through therapeutic combinations. Here we present a comprehensive mechanistic network model of signal transduction in ER+ PIK3CA-mutant breast cancer. Focusing on PI3K inhibitors, the model recapitulates known resistance mechanisms and predicts other possibilities for resistance: loss of RB1, FOXO3, P27, or PRAS40. To test these predictions, we analyzed genome-wide CRISPR screens of two breast cell lines in the presence of PI3K inhibitors (BYL719 and GDC0032) and found that the predicted genes (RB1, FOXO3, P27, and PRAS40) were significantly enriched in the screens. Some of these resistance genes (e.g. loss of RB1) were found to be cell-line specific and follow-up experiments in RB1-KO cells confirmed the cell-line-specific nature of PI3K-inhibitor resistance. The model also reveals known and novel combinatorial interventions that are more effective than PI3K inhibition alone. For example, the model predicts that the combination of PI3K inhibitors with inhibitors of anti-apoptotic protein MCL1 would be effective. Follow up experiments in cell lines using cell viability assays, cell death analyses, and dynamic BH3 profiling experiments to determine increases in apoptotic priming upon treatment confirmed that MCL1 inhibitors (S63845) enhance the effect of PI3K inhibitors (BYL719) and that this combinatorial effect is cell-line-specific, similarly to what was found in the resistance genes case. In conclusion, the model predicted drug resistance mechanisms and effective drug combinations, some of which were verified experimentally and found to be cell-line-specific. Next iterations of the model will incorporate the identified discrepancies and newly identified resistance mechanisms to drugs of clinical interest. Citation Format: Jorge Gómez Tejeda Zañudo, Pingping Mao, Joan Montero, Guotai Xu, Kailey J. Kowalski, Gabriela N. Johnson, José Baselga, Maurizio Scaltriti, Anthony G. Letai, Nikhil Wagle, Reka Albert. Network modeling of drug resistance mechanisms and drug combinations in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 675.