Abstract
Soft-tissue sarcomas are group of rare but highly aggressive malignancies. It is a tremendously heterogeneous group of tumors. Characterizing inter-tumor heterogeneity is crucial for selecting suitable cancer therapy as the presence of diverse molecular subgroups of patients can be associated with disease outcome or response to treatment. However, no methods have been developed to characterize heterogeneity based on genome-wide patient-specific regulatory networks. In this work, we propose a simple but efficient approach to characterize inter-tumor regulatory network heterogeneity, which we call PORCUPINE (Principal Components Analysis to Obtain Regulatory Contributions Using Pathway-based Interpretation of Network Estimates). PORCUPINE uses as input individual patient regulatory networks, represented by estimated regulatory interactions between transcription factors and their target genes, and a list of genes assigned to biological pathways in order to identify key pathways that drive heterogeneity among individuals. We used PORCUPINE to model regulatory heterogeneity in leiomyosarcoma, one of the most common softtissue sarcomas subtypes. We applied it to 80 genome-wide leiomyosarcoma regulatory networks modeled on data from The Cancer Genome Atlas and validated the results in an independent dataset of 37 leiomyosarcoma cases from the German Cancer Research Center. PORCUPINE identified 37 pathways, including pathways that represent potential targets for treatment of subgroups of leiomyosarcoma patients, such as FGFR and CTLA4 inhibitory signaling. PORCUPINE thereby provides a robust way of analyzing and interpreting patient-specific regulatory networks and is the first step towards implementing network-informed personalized medicine in leiomyosarcoma.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
↵† Shared authorship