Discovery of therapeutic targets in cancer using chromatin accessibility and transcriptomic data

Area of Science:

• Computational biology
• Genomics
• Cancer research

Background:

• Many cancer types lack effective targeted therapies.
• Treatment resistance remains a significant challenge even with available targeted options.
• Advances in high-throughput sequencing technologies like ATAC-seq and RNA-seq offer new molecular insights from patient tissues.

Purpose of the Study:

• To develop a computational strategy for identifying novel drug targets based on tumor lineage.
• To leverage integrated ATAC-seq and RNA-seq data for precision oncology.
• To address therapeutic vulnerabilities in cancers with poor prognoses.

Main Methods:

• Construction of gene regulatory networks for 371 patients across 22 cancer types using machine learning.
• Integration of three-dimensional genomic data, specifically enhancer-to-promoter contacts.
• Identification of key transcription factors (TFs) within these networks as potential therapeutic targets or interactors.

Main Results:

• Successfully built comprehensive gene regulatory networks from patient genomic data.
• Identified key transcription factors and their interacting proteins as potential therapeutic targets.
• Validated four promising drug target candidates for neuroendocrine, liver, and renal cancers.

Conclusions:

• The developed computational approach effectively identifies tumor lineage-specific drug targets.
• This method holds promise for discovering new therapeutic strategies for cancers with limited treatment options.
• Validated targets offer potential avenues to overcome resistance and improve outcomes in challenging cancer types.