Integrating knowledge, omics and AI to develop patient-specific virtual avatars

We propose a method for creating personalized regulatory networks, enabling the development of virtual avatars for cancer patients, with patient-derived xenograft (PDX) models as a test case. Starting from a Prior Knowledge Network (PKN) based on the hallmarks of cancer, we constructed gene networks that are contextualized to each sample by integrating sample-specific gene expression data. These networks were optimized using a genetic algorithm to align with individual molecular profiles, focusing on key cancer-related processes. Following network optimization, we employed Graph Convolutional Networks (GCNs) to classify samples based the structures and interactions of their individualized network models and molecular profiles. This personalized approach provides insights into drug responses and helps predict treatment outcomes, offering a path toward more targeted cancer therapies. Author summary Cancer treatment can be more effective when therapies are personalized to each patients unique molecular profile. In this study, we introduce a method to create virtual avatars of cancer patients by personalizing regulatory networks using patient-derived xenograft (PDX) models as a proof of concept. Starting from known cancer hallmarks, we developed individualized gene networks for each sample by leveraging their specific gene expression data. These networks were refined with an optimization process to match the distinct molecular characteristics of each sample. By applying advanced machine learning, specifically Graph Convolutional Networks (GCNs), we classified these personalized models to better understand likely drug responses and predict treatment outcomes. This approach brings us closer to tailoring cancer therapies to individual patients, potentially improving treatment success by targeting key cancer pathways unique to each person.