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

Baihui Xu, Sourav S Bhowmick, Jiancheng Hu

TL;DR

It is demonstrated 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 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.

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

TL;DR

It is demonstrated 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 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.

Paper Structure

This paper contains 23 sections, 6 equations, 13 figures, 6 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Profiling Known Target Combinations
  3. Overview & Contributions
  4. Background
  5. Human Signaling Network
  6. Cancer Mutated Genes
  7. Cancer Drug Targets
  8. Personalized PageRank (PPR)
  9. The $i$-TCP Problem
  10. The PANACEA Framework
  11. PEN Distance
  12. PEN-Diff
  13. Profiling Drug Target Combinations
  14. An Application of Delta Histograms
  15. Performance Study
  16. ...and 8 more sections

Figures (13)

  • Figure 1: [Best viewed in color] The PANACEA framework.
  • Figure 2: [Best viewed in color] A small subnetwork of the reduced human signaling network. The red edges are positive links and the blue edges are negative links. Nodes in yellow are drug targets. Nodes in green are cancer genes. Red nodes are both a cancer gene and a drug target.
  • Figure 3: PEN distance (top row) and PPR (bottom row) distributions of different target-aware cancer-specific signaling networks.
  • Figure 4: [Best viewed in color] Target-aware cancer-specific signaling network for breast cancer.
  • Figure 5: [Best viewed in color] Delta histograms of breast, bladder, colorectal, and prostate cancers.
  • ...and 8 more figures

Theorems & Definitions (3)

  • definition 1
  • definition 2
  • definition 3