Abstract 7214: Real-world concordance analysis of a combinatorial functional precision medicine platform, Optim.AI™, in both solid and hematological cancers

作者: Kai-Hua Chow, Edward ; Abdul Rashid, Masturah Mohamed ; Lung Tan, Daryl Chen ; Hee Poon, Donald Yew ; Ng, Chin Hin ; Tenggara, Jeffry Beta ; Saavedra, Hugo ; Lim, Jhin Jieh ; Choo, Su Pin ; Loh, Yvonne ; Khee Hwang, William Ying ; Ho, Weng Tong

AbstractPURPOSE:Functional precision medicine aims to expand the landscape of effective drug treatment options beyond standard of care through additional insights from ex vivo phenotypic drug response data. In our previous work, we have demonstrated the clinical feasibility of Optim.AI™, a combinatorial functional precision medicine platform, in identifying effective and resistant treatment options for non-Hodgkin lymphoma and acute myeloid leukemia (AML). This study further investigates the clinical utility and concordance of Optim.AI™ in predicting treatment sensitivity or resistance in a variety of hematological and solid cancers processed at KYAN’s clinical laboratory, with notable case studies highlighted.METHODS:Optim.AI™ utilizes efficiently designed experiments of less than 200 ex vivo combinations to interrogate over 530, 000 possible treatment permutations from a 12-drug panel. Optim.AI™ then ranks all clinically actionable treatments within the tested drug panel based on predicted sensitivity or resistance. In this study, we investigated 66 cases from a total of 20 cancer types (5 hematological and 15 solid cancer) and a range of tissue sample sources, including bone marrow aspirate, peripheral blood, tissue biopsy or resections, and pleural or peritoneal fluid. Retrospective concordance analysis was performed by evaluating Optim.AI™-generated tumor cell viability results against the prior lines of treatment for each sample. Prospective data of patients administered with Optim.AI™-predicted treatment were also collected, where applicable.RESULTS:With an established viability cut-off of 0.5, Optim.AI™ demonstrated a correlation of 88% with prior therapies, which included a range of 36 targeted treatments and chemotherapy agents. Among the 37 solid cancer cases evaluated, 86.5% (p-value < 0.0001) demonstrated concordance to prior line(s) of treatment, while for hematological cancers, 89.7% of the 29 cases (p-value = 0.0005) were concordant with previously observed resistance. Additionally, platform-predicted treatments were associated with positive clinical responses in several cases of hematological and solid cancers, where prospective data were available.CONCLUSIONS:Our retrospective concordance results demonstrate the clinical applicability and robustness of Optim.AI™ in predicting sensitivity or resistance to cancer treatments. In line with previous prospective clinical studies, these findings illustrate the platform’s potential to positively impact treatment recommendations across a broad range of hematological and solid cancers. Future prospective studies to evaluate Optim.AI™-guided treatments are warranted to further confirm the predictive value of the platform and provide evidence for incorporating such functional assays in clinical oncology.Citation Format:Masturah Mohamed Abdul Rashid, Jhin Jieh Lim, Weng Tong Ho, Su Pin Choo, Donald Yew Hee Poon, Daryl Chen Lung Tan, William Ying Khee Hwang, Yvonne Loh, Jeffry Beta Tenggara, Chin Hin Ng, Hugo Saavedra, Edward Kai-Hua Chow. Real-world concordance analysis of a combinatorial functional precision medicine platform, Optim.AI™, in both solid and hematological cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7214.

2025-04-21·Cancer Research

Abstract 5492: Application of Optim.AI™ platform for predictive evaluation of immunotherapy combinations in breast cancer

作者: Kai-Hua Chow, Edward ; Lim, Jhin Jieh ; Abdul Rashid, Masturah Mohamed ; Yi Chan, Sharon Pei

AbstractPURPOSE:Successful pre-clinical evaluation of immunotherapy within ex vivo settings require complex systems to accurately represent tumor heterogeneity and the surrounding tumor microenvironment (TME). With expanding immunotherapy combinations being investigated in clinical trials, particularly for breast cancer (BC), there is a growing need for reliable methodologies for functional immune-oncology drug testing in a patient- and subtype-specific manner. We have developed Optim.AI™, a hybrid experimental-analytics platform designed to predict and rank actionable treatment options, with successful application in clinical settings to guide cytotoxic and targeted therapies. However, its potential for evaluating immunotherapy responses remains underexplored. In this study, we present an integrated platform that combines short-term 3D tumor-immune cell co-culture with high content imaging and Optim.AI™ to evaluate immunotherapy-focused drug sets in HER2-positive and triple-negative BC subtypes.METHODS:BC cells were pre-labeled with cell tracker and seeded in a 384-well format for 3-4 days to allow spheroid formation, and co-cultured with PBMCs. Optim.AI™ combinatorial drug testing was then applied to the co-culture system with up to 12 FDA-approved chemotherapies, targeted agents, monoclonal antibodies, and antibody-drug conjugates. Utilizing real-time fluorescent apoptosis signal as the phenotypic readout, high-content analysis of tumor-specific cell death was evaluated at specific timepoints and used for Optim.AI™ analytics and predictive ranking of combinatorial immunotherapy sensitivities.RESULTS:Quantitative, real-time, image-based analysis allowed for phenotypic measurement of complex tumor-immune cell interactions within the 3D co-culture system, including immune cell migration, infiltration into spheroids, and subsequent tumor-specific killing. Co-culturing BC spheroids with PBMCs at an optimized effector-to-target ratio induced significant antibody-dependent cellular cytotoxicity, with measurable responses observed within 72 hours post-treatment. Through Optim.AI™, we observed distinct sensitivities towards the different antibody-based therapies, in HER2-positive and triple-negative BC spheroids. These findings underscore the potential of Optim.AI™ to effectively evaluate and rank immunotherapy-based treatment combinations in a physiologically relevant, 3D TME model.CONCLUSIONS:We present an integrated platform combining high-content screening of a 3D tumor-immune cell co-culture system with Optim.AI™ and demonstrated both visualization and quantification of tumor-specific responses in the context of the TME. Future applications to ex vivo patient samples hold promise for personalized treatment regimens and identifying more effective immunotherapy combinations for BC and other solid malignancies.Citation Format:Sharon Pei Yi Chan, Jhin Jieh Lim, Masturah Mohamed Abdul Rashid, Edward Kai-Hua Chow. Application of Optim.AI™ platform for predictive evaluation of immunotherapy combinations in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5492.

2025-03-11·Cancer Research

Abstract A005: Multi-cancer clinical concordance analysis of Optim.AITM, a combinatorial functional precision medicine platform

作者: Abdul Rashid, Masturah Mohamed ; Kai-Hua Chow, Edward ; Lim, Jhin Jieh ; Saavedra, Hugo ; Ng, Chin Hin ; Ho, Weng Tong

AbstractPURPOSE: 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 < 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.

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