Abstract A176: Quantitative parabolic optimization platform for the design of epigenetic-based drug combinations against bortezomib-resistant multiple myeloma

Abstract

Abstract Proteasome inhibitor-based drug combinations have improved multiple myeloma median survival beyond 5 years. Multiple myeloma, however, remains incurable as patients inevitably relapse and become resistant to proteasome inhibitors, such as bortezomib. Thus, there is a need to identify improved drug combinations that may serve as later lines of treatment against bortezomib-resistant multiple myeloma. Epigenetic inhibitors have been explored in treating advanced multiple myeloma; however, clinical trials have shown mixed results. There is increasing evidence from these clinical trials that epigenetic-based therapies may see greater benefit as part of drug combinations rather than as monotherapies. Rationally identifying optimal epigenetic-based drug combinations against aggressive cancers, such as bortezomib-resistant multiple myeloma, however, remains a challenge. Utilizing a quantitative parabolic optimization platform (QPOP), optimal drug combinations against bortezomib-resistant multiple myeloma were identified. QPOP analysis uses quantifiable phenotypic outputs to identify significant effects amongst a set of drugs using a minimal number of experiments through parabolic response surface mapping. By comparing therapeutic response of bortezomib-resistant multiple myeloma cells to bortezomib-sensitive multiple myeloma cells as well as normal control cells, a series of drug combinations were identified from an initial pool of 129 FDA approved oncology drugs. QPOP does not rely on molecular mechanism modeling but rather uses experimental data to rationally identify optimal drug combinations. As a result, targetable molecular mechanisms of cancer progression can be identified without any previous assumptions or bias. Thus, QPOP revealed that drug resistance in multiple myeloma may involve specific epigenetic changes as identified by top-ranked epigenetic inhibitor containing drug combinations. While it is known that DNA hypermethylation accumulates during multiple myeloma progression, reversal of DNA hypermethylation by DNA methyltransferase inhibitors as monotherapies had no significant therapeutic effect in vitro or in vivo. DNA hypermethylation can be targeted, however, with the appropriate drug combinations to effectively treat in vitro and in vivo models of bortezomib-resistant multiple myeloma, as well as ex vivo primary multiple myeloma cells from a wide range of patients. QPOP analysis also revealed that optimal drug combinations for other classes of epigenetic inhibitors, such as HDAC inhibitors, change from naïve multiple myeloma to drug-resistant multiple myeloma. Thus, QPOP-based drug combination optimization not only revealed effective epigenetic-based drug combinations but also the molecular mechanisms by which epigenetic modifiers affect multiple myeloma disease progression. This work highlights the need for rational identification of different epigenetic-based drug combinations throughout the progression of multiple myeloma. Beyond multiple myeloma, this platform can be used to identify optimal drug combinations for other cancers as well as provide insight into therapeutically targetable molecular mechanisms that drive cancer progression in various cancer subtypes. Citation Format: Edward Kai-Hua Chow. Quantitative parabolic optimization platform for the design of epigenetic-based drug combinations against bortezomib-resistant multiple myeloma [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 A176.