Systematic mapping of cancer cell target dependencies using high-throughput drug screening in triple-negative breast

Tianduanyi Wang1,2, Prson Gautam1, Juho Rousu2

  • 1Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.

Insights

A new computational pipeline identifies specific drug targets in triple-negative breast cancer (TNBC) cell lines by analyzing drug responses and interactions. This approach reveals potential group-based therapeutic targets and synthetic lethal interactions for TNBC treatment.

Area of Science:

  • Computational biology
  • Pharmacogenomics
  • Cancer research

Background:

  • High-throughput drug screening identifies potential compounds but requires further analysis for mechanism elucidation.
  • Understanding cell context-specific drug mechanisms is crucial for effective cancer therapy.

Purpose of the Study:

  • To develop a computational pipeline for deconvoluting drug targets.
  • To identify cell line-specific drug target dependencies and vulnerabilities in triple-negative breast cancer (TNBC).

Main Methods:

  • Developed a computational target deconvolution pipeline integrating drug response data with drug-target interaction networks.
  • Applied the pipeline to 310 small molecules across 20 TNBC cell lines.
  • Quantified protein target essentiality using a target addiction score (TAS).

Main Results:

  • Identified cell line-specific drug target mechanisms and vulnerabilities.
  • Discovered potential protein groups acting synergistically, resistant to single gene knockouts.
  • Found novel multi-target synthetic lethal interactions, including among histone deacetylases (HDACs).
  • Validated known TNBC drivers and therapeutic targets like HDACs and cyclin-dependent kinases (CDKs).

Conclusions:

  • The pipeline provides insights into druggable vulnerabilities for TNBC.
  • Reveals opportunities for identifying multi-target synthetic lethal interactions for future therapeutic strategies.