Abstract 3308: Use of biosimulation to predict immune checkpoint inhibitor resistance in patients with high microsatellite instability

Abstract Background: Immune checkpoint inhibitors (ICI) have become a standard treatment option in patients with high microsatellite instability (MSI-H). Although immune checkpoint inhibitors are largely effective in these patients, MSI is an imperfect biomarker and other therapies may be warranted. A mechanistic Computational Biology Model (CBM) was developed by Cellworks that can be personalized based on a patient’s tumor-based genomic profile, revealing signaling pathway dysregulation and patient-specific drug response. Model output was used to identify MSI-H patients who may have a poorer response to ICIs. Design: Computational biosimulation was performed using real-world retrospective cohorts of 423 STAD patients and 534 CRC patients (TCGA). MSI measurements were provided by TCGA. Efficacy scores (ES) based on biosimulated composite cell growth in response to disease and therapy were generated on all patients for pembrolizumab. Molecular rationales for ICI resistance were identified for MSI-H patients with low pembrolizumab ES (ES-L). Results: Model efficacy scores for pembrolizumab were significantly higher in MSI-H patients for both STAD (average ES 20.5 vs 3.2 respectively, p-value < 0.001) as well as CRC (average ES 13.4 vs 2.4 respectively, p-value < 0.001). Of the MSI-H patients, 59% and 81% were ES-L (STAD and CRC respectively) MSI-H/ES-L STAD patients showed a significantly higher percentage of NOTCH2, EGFR, and EZH2 amplifications as well as a higher percentage of TP53-SOF mutations, whereas MSI-H/ES-L CRC patients showed a significantly higher percentage of MYC amplifications (p-value < 0.05) Conclusions: In this study, a therapy efficacy score produced through biosimulation was significantly associated with microsatellite instability levels, and markers of ICI resistance were identified in MSI-H/ES-L patients. Future studies will be performed to confirm the hypothesis that biosimulation has utility in deciding which MSI-H patients might benefit from therapies other than ICI. Citation Format: Adity Ghosh, Mamatha Patil, Anuj Tyagi, Ansu Kumar, Chandan Kumar, Muthiyah MR, Rahul KR, Swati Khandelwal, Jyoti Chauhan, Michael Castro, Shweta Kapoor, James Wingrove. Use of biosimulation to predict immune checkpoint inhibitor resistance in patients with high microsatellite instability [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 3308.