Abstract LB-A15: An Ex-vivo Platform Predicts Anti-tumor Outcome of Metabolically-Targeted, Algorithm-Driven Combination Therapy in Triple-Negative Breast Cancer

Abstract

Abstract Abstract: Cancer cells undergo phenotypic cell state transitions in response to chemotherapy as a mechanism that can confer transient resistance. However, such cell state transitions can also unlock unique vulnerabilities that can be exploited using temporally-sequenced combination chemotherapy. Here, utilizing a primary breast cancer ex-vivo functional assay (CANScriptTM) that captures tumor heterogeneity, we report that in response to a chemotherapeutic agent, a subset of cancer cells can mount an acutely-induced phenotypic adaptive resistance to future cytotoxic pressure via the transient acquisition of a unique metabolic state defined by augmented glycolysis together with mitochondrial proficiency. These cells activate two complex, temporally-interdependent pathways that enable a glucose shunt towards the pentose phosphate pathway (PPP), which confers an adaptive cross-tolerance to different chemotherapeutic agents. Mathematically modeling these pathways, and simulating drug schedules, we define a rationally-designed 3-drug combination therapy of metabolic inhibitors and cytotoxic agents, which results in improved cancer survival. Our findings highlight a new bioenergetics-based adaptive resistance mechanism through which cancer cells can survive combinations of chemotherapy. Administration of metabolic inhibitors in rational, temporal sequence with existing chemotherapy can emerge as a new paradigm in the treatment of cancer. Citation Format: Aaron J. Goldman. An Ex-vivo Platform Predicts Anti-tumor Outcome of Metabolically-Targeted, Algorithm-Driven Combination Therapy in Triple-Negative Breast Cancer [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr LB-A15.