Genetic dependencies associated with transcription factor activities in human cancer cell lines

Area of Science:

• Oncology
• Molecular Biology
• Genomics

Background:

• Transcription factors (TFs) drive abnormal gene expression in cancer.
• TF activity (TFa) can serve as a biomarker for predicting anti-cancer effects.
• Genetic dependencies (GDs) represent genes essential for cancer cell survival when TF activity is high.

Purpose of the Study:

• To identify associations between TF activity and genetic dependencies across various cancer types.
• To explore the potential of TFa-GD associations as biomarkers for drug discovery in oncology.
• To validate identified TFa-GD associations using experimental evidence.

Main Methods:

• Developed a linear-regression framework to analyze TF activity and genetic dependencies in cancer cell line data.
• Identified 3,047 pan-cancer and 3,952 cancer-type-specific candidate TFa-GD associations.
• Cross-examined identified associations for their impact on patient survival using cohort data.

Main Results:

• Discovered numerous TFa-GD associations, highlighting TEAD1 activity as a prominent biomarker.
• Validated TEAD1 activity's association with its predicted genetic dependencies experimentally.
• Found that high TFa predicts sensitivity to the loss of function of specific genes.

Conclusions:

• TFa-GD associations provide a valuable resource for identifying novel, biomarker-driven hypotheses in oncology drug discovery.
• The findings support the use of TF activity as a predictive biomarker for anti-cancer therapies.
• Experimental validation of TEAD1-GD links demonstrates the framework's utility.