Abstract 2508: A multiscale framework combining mechanistic models and LSTM-GCN networks for predicting drug sensitivity

Abstract Understanding tumor evolution and treatment resistance requires an integrative approach that combines cellular dynamics with molecular interactions. While mechanistic models excel in capturing physiological processes and predicting behaviors beyond the original data, their specificity limits their generalizability. Conversely, deep learning enables universal predictions but often lacks interpretability, posing challenges for application in cancer research. To address these limitations, we propose a novel computational framework that integrates mechanistic simulations, graph-based modeling, Long Short-Term Memory (LSTM) networks, and Bayesian inference to predict the sensitivity of six cancer types to targeted therapies, chemotherapy agents, immunotherapies, and their combinations. In this framework, mechanistic models provide an individualized baseline simulation for different cancer types, while deep learning integrates these outputs with drug-related information to generate robust and interpretable predictions tailored to specific types. Our hybrid framework leverages a mechanistic pan-cancer model that simulates molecular signaling pathways, cell cycle dynamics, and apoptosis across four biological compartments. The model is parameterized using published gene expression profiles and mutation data from xenograft models, including key mutations such as KRAS, EGFR, and TP53. Simulations are performed for six cancer types, including breast carcinoma, colorectal cancer, and pancreatic carcinoma, to approximate patient-specific tumor biology. Tumor cell populations generated from these simulations are represented as graphs, with each node corresponding to an individual cell characterized by proliferation status and dynamic molecular states (e.g., protein concentrations and phosphorylation levels). Edges are weighted and defined based on molecular similarity between cells. The graph is processed using a Graph Convolutional Network (GCN), which generates embeddings that capture tumor population heterogeneity. These embeddings are then fed into an LSTM to extract the temporal dynamics of the tumor population. The final hidden states computed by the LSTM are further combined with embeddings of drug chemical structure and drug-target interactions to make drug sensitivity predictions. To enhance the robustness of predictions, Bayesian inference is incorporated to quantify uncertainty in estimated drug responses. This framework bridges the gap between mechanistic modeling and machine learning, offering a scalable and interpretable approach to studying tumor evolution under the selective power of drug treatment. By integrating the physiological insights of mechanistic models with the predictive power of deep learning, it enables the identification of molecular drivers of resistance and informs the development of personalized therapies. Citation Format: Chenhui Ma, Evren Gurkan-Cavusoglu. A multiscale framework combining mechanistic models and LSTM-GCN networks for predicting drug sensitivity [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 2508.