Abstract 5803: Computational modeling of immune cell phenotypes enhances prediction of clinical anti-cancer drug response

Abstract Preclinical cancer model systems, such as large-scale drug screens in cancer cell lines, are pivotal for identifying drug-sensitive indications and biomarkers of drug efficacy. However, these systems often lack the tumor microenvironment's complexity, including interactions with immune cells, which play a critical role in modulating drug response. Investigating drug-induced effects on immune cells is experimentally challenging due to the diversity of immune cell types and the complexity of co-culture systems. Computational models of immune cells can help to overcome these limitations and to guide further experiments. Leveraging public post-perturbation single-cell RNA sequencing datasets from various immune cells (including CD8 and CD4 T cells), we trained Turbine’s Simulated Cell engine to predict transcription factor (TF) activity changes in 17 immune cell types post-drug treatment. We compared the predicted, drug induced TF activity changes to immune killing effects from public co-culture experiments. Predicted activity changes of CD8 T-cell activator TFs, such as FOSL and MYC, positively correlated with tumor-killing effects (Pearson r = 0.35 and 0.42, respectively). Conversely, inhibitor TFs such as NR4A1 and IRF4 exhibited inverse correlations (Pearson r = -0.27 and -0.47, respectively).To assess clinical relevance, we analyzed oncology clinical trial data from ACTT. We calculated mean overall response rates (ORRs) for indication - drug pairs and compared it to in vitro indication specific relative IC50 values of drugs. Using a linear model to predict ORRs from in vitro cancer cell line drug response data alone, we predicted ORRs with a Pearson correlation of 0.28. Integrating simulated drug induced TF activities of CD8 T-cells improved this correlation to 0.48, showing that combining tumor-intrinsic mechanisms with immune-mediated cell killing enhances the predictive performance of the model. Our findings suggest that augmenting traditional in vitro drug screening with simulated immune cell activity significantly enhances the clinical translatability of preclinical drug response models, paving the way for improved therapeutic strategies. Citation Format: Gema Sanz, Réka Böröcz, Bence Czakó, Gerold Csendes, Csaba Gór, Gábor Kovács, László Mérő, Kristóf Szalay, Daniel Veres, Bence Szalai. Computational modeling of immune cell phenotypes enhances prediction of clinical anti-cancer drug response [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 5803.