Abstract A005: Multi-cancer clinical concordance analysis of Optim.AITM, a combinatorial functional precision medicine platform
Authors: Masturah Mohamed Abdul Rashid, Jhin Jieh Lim, Weng Tong Ho, Chin Hin Ng, Hugo Saavedra, Edward Kai-Hua Chow
Journal: Cancer Research (2025)

Abstract PURPOSE: Precision medicine aims to improve effective treatment selection based on patient-specific features such malignant genomic or transcriptional alterations. Functional precision medicine approaches, which include ex vivo phenotypic drug response data, can provide additional information to further improve individualized treatment selection. We have previously reported the clinical feasibility of a combinatorial functional precision medicine platform, Optim.AITM, to accurately identify both effective and resistant treatment options in non-Hodgkin lymphoma. This study now explores the clinical application of Optim.AITM towards predicting drug sensitivity and resistance across a range of both hematological and solid cancers. METHODS: Optim.AITM employs a hybrid experimental-analytical approach that searches 531,441 possible permutations derived from 100-200 ex vivo test combinations to predictively rank all clinically actionable treatments within an FDA-approved 12-drug panel. As of November 11, 2024, Optim.AITM reports have been successfully processed for 20 cancer types in KYAN’s clinical laboratory licensed by the Ministry of Health Singapore. In this study, we interrogated 64 cases across 5 hematological cancer types and 15 solid tumor types. Sample sources included peripheral blood, bone marrow aspirate, tissue biopsy/resection and pleural/peritoneal fluid. Retrospective concordance was evaluated by comparing the normalized cell viability (NCV) results generated through Optim.AITM against the prior lines of treatment for each sample, as indicated in sample requisition forms. Clinical laboratory test performance was also evaluated. RESULTS: 81% of samples collected had enough cancer cells for testing and reports were successfully generated for 92% of these samples. Median time-to-report from samples collection was 4.4 ± 3 working days for hematological cancers and 6.3 ± 4 working days for solid cancers. With an established NCV cut-off of 0.5, Optim.AITM indicated approximately 88% correlation with retrospective therapies, for cases where clinical information was available. From 35 solid cancer cases evaluated, 85.7% (p-value &amp;lt; 0.0001) demonstrated concordance to prior line(s) of treatment. For hematological cancers, 89.7% of the 29 cases (p-value = 0.0005) were concordant with previously observed resistance. CONCLUSION: Retrospective concordance analysis further validates the clinical robustness of the platform, increasing confidence of the predictive capability of Optim.AITM. Expanding on prior prospective clinical results, this study importantly highlights Optim.AITM’s capability to provide treatment guidance across a wider range of both hematological and solid malignancies. Collectively, these findings demonstrate the utility of functional assays, particularly for patients who lack traditional biomarkers, have failed standard of care treatments or those with fast-progressing/aggressive cancers. Citation Format: Masturah Mohamed Abdul Rashid, Jhin Jieh Lim, Weng Tong Ho, Chin Hin Ng, Hugo Saavedra, Edward Kai-Hua Chow. Multi-cancer clinical concordance analysis of Optim.AITM, a combinatorial functional precision medicine platform [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr A005.