Abstract 3706: Identifying first-in-class targets for anti-cancer therapy in KRAS mutant NSCLC using the systems biology-based drug discovery platform

Abstract Background: Activating mutations in KRAS are the most common oncogenic drivers, occurring in approximately 30% of non-small cell lung cancer (NSCLC). The G12C mutation, a glycine-to-cysteine mutation at residue 12, is particularly prevalent in NSCLC patients. Developing KRAS inhibitors has long been challenging, but recent FDA approvals of sotorasib and adagrasib for treating KRAS G12C-mutant NSCLC patients represent significant breakthroughs, establishing a new treatment paradigm. However, they are still limited in their clinical applications by low efficacy, side effects, and resistance. Moreover, it is currently unclear how to predict responsive patient subsets and suggest effective therapeutic strategies for clinical application. Methods: For this, we used our drug discovery platform, MObyDiCK (Mechanistic target Optimization by Discovering and Controlling networK), which incorporates systems biology and AI-based computational analysis to identify first-in-class targets and their regulatory mechanisms. MObyDiCK includes (a) analysis of single-cell omics data, (b) construction of simulation-capable network models, (c) identification of first-in-class targets, and (d) elucidation of their mechanisms based on network control technologies. In addition, we predicted the efficacy and side effects of the identified targets using different public data, and experimentally validated that regulating the identified targets can effectively overcome KRAS inhibitor resistance. Results: In this study, we utilized public patient single-cell datasets of NSCLC with KRAS mutations to construct corresponding network models using our MObyDiCK platform. We explored specific pseudo-time trajectories to understand the dynamics of KRAS inhibitor resistance, tailoring the analysis to the distinct cancer subpopulations exhibiting resistance potential in each patient. Next, we binarized single-cell transcriptome data by examining the distribution of gene expression values for each individual. We then inferred gene regulatory networks and constructed their mathematical models. Finally, by analyzing dynamic changes within the mathematical models of NSCLC, we identified the combinatorial targets and their mechanisms to overcome KRAS inhibitor resistance in NSCLC. We further predicted the effectiveness of the targets in NSCLC with public data analysis and validated their efficacy through in vitro experiments. Conclusions: By analyzing single-cell datasets of NSCLC patients with the MObyDiCK platform, we gained a comprehensive understanding of the dynamics of regulatory interactions in KRAS inhibitor-resistant cells and ultimately identified the prominent target genes. With these findings, we will further expand this study on how these targets are related to the normal developmental processes or immune system in NSCLC and other cancer types. Citation Format: Namhee Kim, Jae Il Joo, Sea Rom Choi, Kwang-Hyun Cho. Identifying first-in-class targets for anti-cancer therapy in KRAS mutant NSCLC using the systems biology-based drug discovery platform [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 3706.