Abstract 6566: Clinical evaluation of functional combinatorial precision medicine platform to predict treatment outcomes and enhance combination therapy design in soft tissue sarcomas

Abstract

Abstract Purpose: Soft tissue sarcomas (STS) are a collection of heterogenous cancers with limited range of treatment options and poor outcomes. First line treatment for most STS is anthracycline-based, with gemcitabine-based combinations, pazopanib or ifosfamide as treatment options in refractory/relapsed STS. There is a clinical unmet need to improve treatment options for STS as median time to progression following second- and third-line treatments is less than 4 months. We have previously demonstrated in a 71-patient relapsed/refractory non-Hodgkin’s lymphoma clinical study that a functional precision medicine platform can effectively guide treatment, with several exceptional responders (Science Translational Medicine, 2022). This ex vivo drug sensitivity platform, quadratic phenotypic optimization platform (QPOP), analyses a predesigned array of 155 test combinations performed on a primary patient sample to rank and compare all possible therapeutic combinations from a 12-drug set. In this study, we evaluated clinical concordance of QPOP to predict treatment outcomes in an STS cohort. We also explored QPOP’s combination therapy ranking function to identify novel combinations that may benefit larger groups of STS patients when appropriately guided. Experimental Detail: A 12-drug set comprised of STS standard of care, FDA approved drugs and promising investigational drugs was evaluated in QPOP analysis of primary STS tumor samples. Ex vivo cultures of primary STS samples were treated with a predesigned array of 155 test combinations to provide a quantitative phenotypic output for each test combination. This dataset was analyzed by QPOP to rank all possible drug combinations towards a predicted treatment outcome. Concordance analysis was then performed comparing patient sample results with either parallel treatment initiated at time of sample collection or physician choice to use QPOP-guided treatment. Validation of QPOP-derived top-ranked drug combinations was also performed. Results: 12-drug, 3-level QPOP was successfully performed on 45 of 51 primary patient samples. Clinical concordance analysis showed QPOP had a total predictive value (TPV) of 76.9% and an AUCROC of 85.7%. Exceptional responders to guided treatment with pazopanib or eribulin were observed. Additionally, top-ranked epigenetic-based combinations were identified as effective across multiple STS subtypes and validated preclinically. Conclusion: The results show the potential for QPOP to predict clinical response in solid cancers, such as STS. Furthermore, QPOP can improve drug development pipelines through patient samples-based design of effective combinations with investigational drugs or underutilized drugs that may benefit larger populations of patients or specific subgroups of patients. Citation Format: Edward Kai-Hua Chow, Sharon P. Chan, Tan Boon Toh, Masturah Rashid, Valerie S. Yang. Clinical evaluation of functional combinatorial precision medicine platform to predict treatment outcomes and enhance combination therapy design in soft tissue sarcomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6566.