Identification of a Gene Signature That Predicts Dependence upon YAP/TAZ-TEAD.
Ryan KanaiEmily NortonPatrick SternRichard O HynesJanine S A WarrenPublished in: Cancers (2024)
Targeted therapies are effective cancer treatments when accompanied by accurate diagnostic tests that can help identify patients that will respond to those therapies. The YAP/TAZ-TEAD axis is activated and plays a causal role in several cancer types, and TEAD inhibitors are currently in early-phase clinical trials in cancer patients. However, a lack of a reliable way to identify tumors with YAP/TAZ-TEAD activation for most cancer types makes it difficult to determine which tumors will be susceptible to TEAD inhibitors. Here, we used a combination of RNA-seq and bioinformatic analysis of metastatic melanoma cells to develop a YAP/TAZ gene signature. We found that the genes in this signature are TEAD-dependent in several melanoma cell lines, and that their expression strongly correlates with YAP/TAZ activation in human melanomas. Using DepMap dependency data, we found that this YAP/TAZ signature was predictive of melanoma cell dependence upon YAP/TAZ or TEADs. Importantly, this was not limited to melanoma because this signature was also predictive when tested on a panel of over 1000 cancer cell lines representing numerous distinct cancer types. Our results suggest that YAP/TAZ gene signatures like ours may be effective tools to predict tumor cell dependence upon YAP/TAZ-TEAD, and thus potentially provide a means to identify patients likely to benefit from TEAD inhibitors.
Keyphrases
- papillary thyroid
- end stage renal disease
- single cell
- rna seq
- squamous cell
- genome wide
- clinical trial
- chronic kidney disease
- ejection fraction
- small cell lung cancer
- squamous cell carcinoma
- newly diagnosed
- endothelial cells
- dna methylation
- copy number
- gene expression
- high resolution
- machine learning
- randomized controlled trial
- poor prognosis
- childhood cancer
- prognostic factors
- patient reported outcomes
- peritoneal dialysis
- electronic health record
- skin cancer
- open label
- big data
- deep learning
- genome wide analysis