PARADYS: Patient-specific Ranking of Genes Driving Dysregulation in
Cancer and Therapeutic Implications
Abstract
In cancer, most somatic mutation are so-called passenger mutations with
no functional impact on disease development. Only a subset of the
mutations act as drivers and are responsible for tumor growth and
progression. Identifying patient-specific driver mutations is one of the
main challenges in precision oncology. Existing computational methods to
identify patient-specific cancer drivers either integrate mutation and
expression data with a single aggregated gene interaction network or use
personalized gene interaction networks without considering mutation
data. As yet, no methods making use of both patient-specific mutation
and gene network data exist. For this reason, we developed PARADYS
(PAtient-specific RAnking of genes driving DYSregulation in cancer), a
computational tool for personalized detection and impact scoring of
genes driving dysregulation in cancer
(https://github.com/bionetslab/PARADYS). On several cancer cohorts, we
show that PARADYS is able to make robust predictions by integrating
patient-specific mutation and dysregulation data. Furthermore, PARADYS’
driver predictions allow for patient stratification into functionally
coherent and biologically distinct subgroups. In particular, a case
study in prostate cancer reveals a subgroup of patients with
infiltration of dendritic cells in the tumor micro-environment and
unexpectedly high survival times, highlighting the potential of
dendritic cell therapy in prostate cancer.