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Related Experiment Video

Updated: Jan 31, 2026

Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research
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Expression-driven genetic dependency reveals targets for precision oncology.

Abdulkadir Elmas1, Hillary M Layden2, Jacob D Ellis2

  • 1Department of Genetics and Genomic Sciences, Department of Artificial Intelligence and Human Health, Center for Transformative Disease Modeling, Tisch Cancer Institute, Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029, USA.

Gigascience
|January 29, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces BEACON, a method to find new cancer drug targets by looking at gene expression, not just mutations. It identifies expression-driven dependencies to expand precision oncology options for more patients.

Keywords:
BEACONBayesian statisticscancer cell linescancer vulnerabilitydrug targetsexpression-driven dependencymulti-omicsprecision oncologyproteomicstranscriptomics

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Area of Science:

  • Genomics
  • Cancer Biology
  • Computational Biology

Background:

  • Cancer cells exhibit heterogeneity with diverse molecular alterations.
  • Targeted therapies based on DNA mutations are limited by the absence of druggable targets in many tumors.

Purpose of the Study:

  • To identify novel precision oncology targets through expression-driven dependency.
  • To explore vulnerabilities in cancer cells based on high gene expression levels.

Main Methods:

  • Developed BEACON, a Bayesian approach integrating transcriptomic, proteomic, and genetic dependency data.
  • Analyzed data from cancer cell lines across 17 tissue lineages.

Main Results:

  • BEACON identified known druggable genes (e.g., BCL2, MYC) and novel targets.
  • Enriched for approved drug targets (3.8-fold) and druggable oncology targets (7-10 fold).
  • Experimental validation confirmed GRHL2, TP63, and PAX5 as effective targets.

Conclusions:

  • The approach systematically identifies precision oncology targets via expression-driven dependency.
  • Integrates multi-omics and dependency screens for a comprehensive target catalog.
  • Expands therapeutic options beyond mutation-based strategies for cancer treatment.