Abstract A070: Clinical application of functional precision medicine for pancreatic cancer enables personalized treatment prioritization and identification of RAS-targeted combinations
TL;DR
The successful clinical implementation of Optim.AI™ for pancreatic cancer within a CAP/CLIA laboratory is demonstrated, providing actionable treatment recommendations within seven business days and high concordance with observed clinical resistance supports its potential to improve treatment selection.
Abstract
Treatment of advanced pancreatic cancer relies on empirically selected combinations yielding response rates of 20 to 40%. Although KRAS mutations are present in over 90% of these cases, responses to KRAS-targeted therapies vary, highlighting the need for functional approaches to identify optimal therapies. Optim.AI™, a functional precision medicine platform using small data AI, has previously demonstrated clinical utility for hematological cancers and sarcoma. In this study, we evaluated the real-world implementation of Optim.AI™ in pancreatic cancer within a CAP/CLIA laboratory while demonstrating its analytical robustness and ability to identify effective RAS-targeted treatment combinations. Tumor cells were isolated from solid tissues or body fluids of pancreatic cancer samples. Short-term patient-derived organoids were formed before combinatorial treatment with a customized 12-drug panel of chemotherapeutic and targeted agents. Based on this small dataset, Optim.AI™ predicts all possible combinations and ranks them by sensitivity for individual patients. To evaluate inter-laboratory robustness, proficiency testing on pancreatic cancer cell lines, AsPC-1 and MIA PaCa-2, was independently tested at two laboratories. Assay quality parameter, Z' factor and Optim.AI™-predicted single-drug rankings between the two laboratories were compared. Approximately 77% of the samples processed had sufficient viable cells to proceed with Optim.AI™ testing, where reports were successfully generated for 92% of these samples. The mean turnaround time from sample receipt to dissemination of clinical reports was seven business days. Aggregated analyses of Optim.AI™ reports demonstrate differential sensitivities towards standard of care options, with incremental decrease in sensitivity observed for both off-label and novel treatments. Optim.AI™ demonstrated a negative concordance of 75%, accurately predicting resistance to prior or ongoing therapies. Inter-laboratory proficiency testing met all pre-defined acceptance criteria, with both laboratories achieving Z' factors >0.5 and concordant identification of three of the four highest-ranked single agents across both cell lines. Optim.AI™ analyses from these preclinical models also identified Gemcitabine-containing RAS-targeted combinations being consistently top-ranked, a trend that was similarly observed in a KRAS G12V patient-derived sample. Synergistic interactions were also observed between RAS- and EGFR- inhibitors. This study demonstrates the successful clinical implementation of Optim.AI™ for pancreatic cancer within a CAP/CLIA laboratory, providing actionable treatment recommendations within seven business days. High concordance with observed clinical resistance supports its potential to improve treatment selection, while functional drug profiling identified promising RAS-based combinations that may accelerate development of combinatorial therapies. Further validation in larger cohorts representing diverse KRAS mutational backgrounds is warranted. Edward Kai-Hua. Chow, Masturah Mohamed Abdul Rashid, Lydia Ogrodzinski, Jhin Jieh Lim, Sharon Chan, Ralph Garippa. Clinical application of functional precision medicine for pancreatic cancer enables personalized treatment prioritization and identification of RAS-targeted combinations [abstract]. In: Proceedings of the AACR Conference on Pancreatic Cancer: New Frontiers in Biology and Therapeutic Development; 2026 Sep 25-28; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(18_Suppl_2):Abstract nr A070.