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Optimizing drug combination and mechanism analysis based on risk pathway crosstalk in pan cancer

Congxue Hu1, Wanqi Mi1, Feng Li1

  • 1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.

Scientific Data
|January 16, 2024
PubMed

Insights

This study introduces a computational method to identify effective cancer drug combinations by analyzing pathway crosstalk. The approach identified 687 optimized combinations for improved cancer treatment efficacy.

Area of Science:

  • Computational biology
  • Network pharmacology
  • Cancer research

Background:

  • Combination therapy enhances cancer treatment efficacy.
  • Identifying optimal drug combinations and interactions is crucial for accelerating therapeutic development.

Purpose of the Study:

  • To develop a computational network biology approach for identifying effective drug combinations.
  • To screen and optimize drug combinations by targeting cancer pathway crosstalk.
  • To provide a queryable webpage for precise drug combination screening.

Main Methods:

  • Integrated high-throughput pan-cancer and drug data.
  • Constructed miRNA-mediated crosstalk networks among cancer pathways.
  • Developed a drug combination screening method to identify optimized combinations.
  • Performed survival analysis to assess the relationship between drug targets and cancer prognosis.

Main Results:

  • Identified 687 optimized drug combinations from 83 first-line anticancer drugs across pan-cancer.
  • Confirmed that drug combination targets in cancer-specific crosstalk networks are significantly related to cancer prognosis.
  • Developed a web-based tool for querying drug combinations (http://bio-bigdata.hrbmu.edu.cn/oDrugCP/).

Conclusions:

  • The study presents an effective computational method for screening precise drug combinations for cancer treatment.
  • The findings hold significant scientific and clinical value for advancing tumor therapy through optimized combination strategies.

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