Abstract A049: Application of a combinatorial functional precision medicine platform to predict immunotherapy response in DLBCL

Abstract PURPOSE: With expanding immunotherapy options available in many cancers, particularly diffuse large B-cell lymphoma (DLBCL), methods for identifying individualized and subtype-specific immunotherapy sensitivity are increasingly important. Evaluation of immunotherapy within ex vivo settings is challenging due to the complexity in conserving tumor heterogeneity and its surrounding tumor microenvironment (TME). We have developed a combinatorial functional precision medicine platform, Optim.AI, a hybrid experimental-analytics platform that predictively ranks all actionable treatments within a drug set of interest. In prior clinical studies, Optim.AI has been demonstrated to guide cytotoxic and targeted treatments in lymphomas, including DLBCL. However, these clinical studies did not interrogate immunotherapy responses. In this study, we explored the integration of high-content screening workflows with Optim.AI to evaluate immunotherapy-focused drug sets in physiologically relevant multicellular models of DLBCL. METHODS: PBMCs and DLBCL cells were fluorescently labelled to facilitate multicellular tracking for high-content analysis. Optim.AI combinatorial drug sensitivity testing plates were then used to evaluate DLBCL cells, co-cultured with PBMCS, with up to 12 FDA-approved chemotherapies, immunomodulatory agent, monoclonal antibodies, antibody-drug conjugates, and bispecific antibodies. Utilizing real-time fluorescent apoptosis signal as the phenotypic readout, high-content analysis of tumor-specific cell death and apoptosis was evaluated at specific timepoints and used as the dataset of Optim.AI analytics and predictive ranking of combinatorial immunotherapy sensitivities in DLBCL cells. RESULTS: Optim.AI could confirm rituximab (CD20 mAb) sensitivity and optimal antibody-dependent cellular cytotoxicity (ADCC) lysis on CD20+ DLBCL cells at an Effector to Target ratio of 5:1 with a quantifiable phenotypic output at 48h post-treatment. Cytotoxicity studies performed across PBMCs from four different donors also showed negligible potential toxicity from drug candidates on immune cells, indicating that the platform can effectively and stably quantify ADCC. Furthermore, low ranking of tafasitamab (CD19 mAb) based combinations in a CD20+ cell line with low CD19 expression showed low target engagement with tafasitamab, confirming the platform’s ability to detect appropriate immunotherapy specificity. These findings collectively demonstrate the feasibility of pairing Optim.AI with an integrated co-culture model and image analysis pipeline to evaluate and rank immunotherapy-based combinations. CONCLUSIONS: We have demonstrated the integration of a high-content screening cancer-immune cell co-culture system with Optim.AI to evaluate immune-based combination therapies. Application of this approach towards ex vivo patient samples as well as appropriate cancer models will allow for guided immunotherapy-based treatment as well as identification of more effective cancer-specific and subtype-specific immunotherapy combinations. Citation Format: Sharon Pei Yi Chan, Jhin Jieh Lim, Masturah Rashid, Edward Kai-Hua Chow. Application of a combinatorial functional precision medicine platform to predict immunotherapy response in DLBCL [abstract]. In: Proceedings of the AACR IO Conference: Discovery and Innovation in Cancer Immunology: Revolutionizing Treatment through Immunotherapy; 2025 Feb 23-26; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(2 Suppl):Abstract nr A049.