Abstract A125: Clinical evaluation of a functional combinatorial precision medicine platform to predict combination immunotherapy responses in hematological malignancies

Abstract

Abstract Purpose: Proliferation of immune checkpoint inhibitors and lineage-specific antigen-based therapy have made identifying the most appropriate immunotherapy for specific patients or subgroups of patients increasingly difficult. Furthermore, combination of these therapeutic antibodies with other targeted agents or chemotherapy is often required to achieve durable response. To address the large search space of possible combinations of these immunotherapies with available targeted and cytotoxic drug partners, we applied a hybrid experimental-analytical platform, Flow Cytometry Immunophenotyping–Quadratic Phenotypic Optimization Platform (FCI-QPOP), on primary patient samples from acute myeloid leukemia (AML) and multiple myeloma (MM) cases to predict patient and subtype-specific combination immunotherapy responses. Successful development of this functional combinatorial precision medicine platform could help guide combination therapy treatment while also providing a tool for improving combination therapy design for emerging immunotherapies. Experimental Detail: Single drug dosage response analysis of a set of immunotherapies, targeted small molecules and cytotoxic agents were performed from initial patient samples and used to inform subsequent dosing levels for 8-drug, 3-level FCI-QPOP. In FCI-QPOP, primary AML or MM patient samples composed of both cancer cells and normal cells were treated with an array of test combinations and analyzed by flow cytometry to provide a quantitative phenotypic output for each test combination. This OACD dataset was then analyzed by QPOP to rank all possible drug combinations towards a predicted treatment outcome. Concordance analysis was then performed comparing patient sample results with parallel treatment initiated at time of sample collection. Results: Drug-dose response analysis by flow cytometry could accurately identify samples-specific response, including IC50 determination, to both therapeutic antibodies and small molecule drugs. Following this, 8-drug, 3-level FCI-QPOP was successfully performed on 14 of 17 primary patient samples. Concordance analysis could be performed on 9 cases, with 5 cases still pending clinical follow-up. Concordance to both antibody therapies as well as small molecule treatments were analyzed following FCI-QPOP. FCI-QPOP had a sensitivity of 80% and specificity of 75% and a total predictive value (TPV) of 77.78%. Additionally, FCI-QPOP provided potentially effective combination rankings for a diverse range of therapeutic antibodies, including gemtuzumab-based combination therapies. Conclusion: The results show the potential for FCI-QPOP to predict clinical response to immunotherapy-based treatments. Furthermore, FCI-QPOP can improve drug development pipelines through patient samples-based design of effective immunotherapy-based combinations that may benefit larger populations of patients or specific subgroups of patients. Citation Format: Noor Rashidha Bte Meera Sahib, Masturah Rashid, Sanjay de Mel, Wei Ying Jen, Melissa Ooi, Edward K Chow. Clinical evaluation of a functional combinatorial precision medicine platform to predict combination immunotherapy responses in hematological malignancies [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A125.