Expression-Driven Genetic Dependency Reveals Targets for Precision Medicine

Area of Science:

• Genomics
• Proteomics
• Cancer Biology

Background:

• Cancer cells exhibit heterogeneity with diverse molecular alterations.
• Targeted therapies based on DNA mutations are limited in tumors lacking druggable mutations.
• New precision oncology targets are needed for patients with limited treatment options.

Purpose of the Study:

• To identify novel precision oncology targets through expression-driven dependency.
• To develop a computational approach for discovering genes essential for cancer cell survival based on their expression levels.
• To create a catalog of expression-driven dependency targets for therapeutic development.

Main Methods:

• A Bayesian approach, BEACON, was developed to analyze transcriptomic and proteomic data alongside genetic dependency profiles.
• Data from cancer cell lines across 17 tissue lineages were jointly analyzed.
• Experimental validation was performed for identified novel targets.

Main Results:

• BEACON identified known druggable genes (e.g., BCL2, ERBB2, EGFR, ESR1, MYC).
• New targets were revealed, validated by both mRNA and protein expression-driven dependency.
• Identified genes showed significant enrichment for approved drug targets (3.8-fold) and druggable oncology targets (7-10 fold).
• Depletion of GRHL2, TP63, and PAX5 reduced tumor cell growth and survival in dependent cells.

Conclusions:

• Expression-driven dependency is a viable strategy for identifying novel precision oncology targets.
• BEACON provides a powerful framework for discovering therapeutic targets in diverse cancer types.
• The catalog of identified targets serves as a valuable resource for advancing precision oncology treatments.