PANACEA: Towards Influence-driven Profiling of Drug Target Combinations in Cancer Signaling Networks

Abstract

Data profiling has garnered increasing attention within the data science community, primarily focusing on structured data. In this paper, we introduce a novel framework called panacea, designed to profile known cancer target combinations in cancer type-specific signaling networks. Given a large signaling network for a cancer type, known targets from approved anticancer drugs, a set of cancer mutated genes, and a combination size parameter k, panacea automatically generates a delta histogram that depicts the distribution of k-sized target combinations based on their topological influence on cancer mutated genes and other nodes. To this end, we formally define the novel problem of influence-driven target combination profiling (i-TCP) and propose an algorithm that employs two innovative personalized PageRank-based measures, PEN distance and PEN-diff, to quantify this influence and generate the delta histogram. Our experimental studies on signaling networks related to four cancer types demonstrate that our proposed measures outperform several popular network properties in profiling known target combinations. Notably, we demonstrate that panacea can significantly reduce the candidate k-node combination exploration space, addressing a longstanding challenge for tasks such as in silico target combination prediction in large cancer-specific signaling networks.