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Updated: Mar 20, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

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Data integration to prioritize drugs using genomics and curated data

Riku Louhimo1, Marko Laakso1, Denis Belitskin2

  • 1Genome Scale Biology Research Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, P.O. Box 63 (Haartmaninkatu 8), Helsinki, FI-00014 Finland.

Biodata Mining
|May 28, 2016
PubMed
Abstract

Insights

This study developed a data mining algorithm to integrate diverse molecular data for prioritizing targeted cancer therapies. The approach identified potential drug sensitivities in breast and ovarian tumors, aiding precision medicine and drug repositioning.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genomic alterations in drug targets are crucial for personalized cancer treatments.
  • Patient selection for targeted therapies relies on molecular alterations.
  • Integrating diverse molecular data enhances precision therapy selection but existing methods are limited.

Purpose of the Study:

  • To develop a novel data mining algorithm for integrating heterogeneous molecular data.
  • To facilitate drug repositioning by stratifying patient samples and prioritizing drug targets.
  • To enhance the selection of precision therapies for cancer patients.

Main Methods:

  • Constructed a knowledge base integrating public databases, tumor molecular data, signaling pathways, and drug-target information.
  • Developed a data mining algorithm to utilize this heterogeneous knowledge base for drug prioritization.
  • Applied the framework to analyze 797 breast and ovarian cancer tumors from The Cancer Genome Atlas.

Main Results:

  • The algorithm successfully integrated diverse data types for drug prioritization.
  • FGFR, CDK, and HER2 inhibitors were prioritized for breast and ovarian cancer datasets.
  • Estrogen receptor-positive breast tumors showed potential sensitivity to FGFR inhibitors due to FGFR3 activation.

Conclusions:

  • Computational sample stratification effectively identifies potentially sensitive patient populations for targeted therapies.
  • The developed framework aids in precision medicine and drug repositioning strategies.
  • Source code is publicly available for broader research application.
Keywords:
Breast cancerCancerData integrationDrug prioritizationGene ontology

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