PatientProfiler: from multi-omic data to actionable and patient-specific networks of intracellular signaling in Pancreatic Cancer

Motivation. With a 11% 5-yr survival rate, PDAC is one of the deadliest cancer types. No effective treatment can be offered to patients diagnosed with PDAC as most patients remain refractory to current regimens. A major factor that contributes to PDAC resistance to treatment is its dense fibrotic stroma intertwined with the extracellular matrix, which, together, provide a physical barrier that protects the tumor and promotes its growth and invasiveness. Uncovering these mechanisms at the patient- resolution level will be crucial to underpin mechanisms of PDAC tumorigenesis and progression and to develop efficient therapies. Methods. We recently implemented SignalingProfiler, a generally applicable modelling strategy that combines transcriptomics and phosphoproteomics datasets with prior knowledge annotated in our in-house resource, SIGNOR, to produce context-specific mechanistic models representing the remodeling of the signal transduction cascade at the PTM-resolution level. We recently applied SignalingProfiler to genomics, transcriptomics, proteomics and phosphoproteomics data from 105 treatment-na¨ıve Pancreatic Ductal Adenocarcinoma (PDAC) biopsies, extracted from the CPTAC portal. Results. By this approach we were able to generate patient-specific mechanistic models of PDAC tumorigenesis and progression. These mechanistic models can recapitulate, in a genotype-specific context, the activation status of key signal transduction proteins basing on the regulatory interac- tions that affect their activities. Integration of these models with clinical data made it possible to stratify patients according to the key activity of signal transduction effectors, revealing the transla- tional impact of the approach.