TISON: A Web Server for Omics-driven Integrative Cancer Modeling & Simulations towards Personalized Medicine

Next-generation sequencing (NGS) has catalyzed the generation of individualized multi-omics datasets, potentiating precise molecular insights into cancer initiation, progression, and metastasis. The subsequent data analytics have necessitated the development of computational pipelines that couple patient-specific omics with biomolecular network models, enabling personalized simulations for precision oncology. However, to date, no unified webserver supports patient-specific network modeling alongside systematic therapy prioritization and in silico therapeutic evaluation. To address this gap, we present TISON (Theatre for In-silico Systems Oncology; https://tison.lums.edu.pk/), a webserver for in-silico precision oncology. TISON integrates copy-number alterations, exome-derived somatic mutations, and transcriptomic expression data with biomolecular networks to generate patient-specific models. Dynamical analyses of these models help identify attractor states, characterize phenotypic transitions, and quantify oncogenic signaling. TISON further computes patient-specific drug scores and evaluates single and combined regimens on personalized networks to derive therapeutic response indices, including efficacy, cytotoxicity, resistivity, and overall therapeutic response. To demonstrate TISON's translational potential, we evaluated two head and neck squamous cell carcinoma patients from TCGA. Our results show that TISON-prioritized sequential therapies reprogrammed tumor networks from proliferative toward apoptosis-dominant states i.e. reducing propensity for proliferation from ∼0.65-0.88 to <0.15 while increasing apoptosis from ∼0.06-0.32 to >0.85. Next, TISON prioritizes therapeutic regimens by computing a therapeutic response index based on efficacy-cytotoxicity trade-offs. Taken together, these case studies demonstrate TISON as an integrated precision-oncology platform that operationalizes patient multi-omics data to augment clinically interpretable therapy prioritization and decision-support workflows.