PatientProfiler: building patient-specific signaling models from proteogenomic data.

Area of Science:

• Systems oncology
• Computational biology
• Cancer genomics

Background:

• Understanding patient-specific cancer cell reprogramming is vital for diagnosis and treatment.
• Multi-omic tumor characterization is increasingly used in clinical settings.

Purpose of the Study:

• To develop a computational workflow, PatientProfiler, for generating patient-specific mechanistic models of signal transduction.
• To integrate proteogenomic data with causal interaction networks for mechanistic modeling.
• To identify network-based prognostic biomarkers.

Main Methods:

• Developed PatientProfiler, a workflow for multi-omic data analysis and standardization.
• Integrated proteogenomic data with curated causal interaction networks.
• Benchmarked on 122 treatment-naïve breast cancer biopsies from the CPTAC portal.

Main Results:

• Generated patient-specific mechanistic models recapitulating oncogenic signaling pathways.
• Identified seven patient subgroups with distinct transcriptomic signatures and prognostic values.
• Highlighted mechanistic drivers like the MYC-CDK4/6 axis and NF-kappaB inflammatory programs.

Conclusions:

• PatientProfiler provides a generalizable framework for transforming cohort-level multi-omic data into interpretable mechanistic models.
• The tool can be applied across diverse cancer types and complex diseases.
• Enables deeper understanding of cancer heterogeneity and personalized therapeutic strategies.