PatientProfiler: building patient-specific signaling models from proteogenomic data

Deciphering patient-specific mechanisms of cancer cell reprogramming remains a crucial challenge in systems oncology, as it is key to improving patient diagnosis and treatment. For this reason, comprehensive and patient-specific multi-omic characterization of tumor specimens has become increasingly common in clinical practice. Here, we developed PatientProfiler, a computational workflow that integrates proteogenomic data with curated causal interaction networks to generate mechanistic models of signal transduction for individual patients. PatientProfiler allows multi-omic data analysis and standardization, generation of patient-specific mechanistic models of signal transduction, and extraction of network-based prognostic biomarkers. We successfully benchmarked the tool on proteogenomic and clinical data derived from 122 biopsies of treatment-naïve breast cancer, available through the CPTAC portal. We identified patient-specific mechanistic models that recapitulate oncogenic signaling pathways. In-depth topological exploration of these networks revealed seven subgroups of patients, associated with unique transcriptomic signatures and distinct prognostic values. We identified well-known Basal-like 1 and 2 subtypes, while also highlighting distinct mechanistic drivers such as the MYC-CDK4/6 axis or NF-kappaB-mediated inflammatory programs. Beyond breast cancer, PatientProfiler offers a generalizable framework to transform cohort-level multi-omic data into interpretable mechanistic models, making it applicable across diverse cancer types and other complex diseases.