Dr.Nod: computational framework for discovery of regulatory non-coding drivers in tissue-matched distal regulatory...

Area of Science:

• Genomics
• Cancer Research
• Molecular Biology

Background:

• Cancer driver mutations are key to understanding cancer.
• Most known drivers are in protein-coding regions, with limited success in non-coding regions.
• Non-coding regions, particularly enhancers, are under-explored for cancer drivers.

Purpose of the Study:

• To develop a comprehensive framework, Dr.Nod, for detecting non-coding cis-regulatory candidate driver mutations.
• To associate these mutations with dysregulated gene expression using tissue-matched enhancer-gene annotations.
• To investigate the role of non-coding mutations in cancer development.

Main Methods:

• Developed the Dr.Nod framework for non-coding driver mutation detection.
• Utilized tissue-matched enhancer-gene annotations.
• Applied the framework to over 1500 tumors across eight tissues.

Main Results:

• Identified a 4.4-fold enrichment of candidate driver mutations in regulatory regions of known cancer genes.
• Demonstrated that non-coding mutations alter transcription factor binding sites, affecting oncogene and tumor suppressor gene expression.
• Found that over half of detected mutations are >20 kb from the genes they regulate.

Conclusions:

• Non-coding enhancer mutations play a significant, previously underappreciated role in cancer.
• Dr.Nod framework highlights the importance of tissue-matched maps and functional mutation impact.
• These findings advance the prediction of non-coding regulatory drivers and understanding of cancer genetics.