Detecting 5’ UTR Mutations and Predicting Their Impact on Gene Translation in Lung Cancer

Jon Akutagawa, PhD Student, Biomolecular Engineering & Bioinformatics

Tuesday, October 30, 2018 – 9:00am
Location – Biomedical Sciences, Room 200
Advisor – Assistant Professor Angela Brooks

Abstract
Next generation sequencing studies of lung cancer, the leading cause of cancer-related deaths globally, have revealed a strikingly high rate of somatic mutations. Unfortunately, half of lung cancers lack mutations in a known driver oncogene. Considering that protein-coding regions consist of less than 2% of the entire genome, we hypothesize that many mutations that contribute to lung cancer development lie in noncoding regions such as the 5’ untranslated region (UTR). 5’ UTR mutations have been shown to affect translational efficiency of oncogenes and tumor suppressor genes, making them possible therapeutic targets; however, there has not been a global study of these mutations in a lung cancer model. To identify putative 5’ UTR somatic mutations that affect translation, I propose a new parallelized pipeline to detect novel somatic variants in RNA sequencing data. I will then develop computational methods to examine how these mutatio! ns alter sequence-specific elements in the 5’ UTR and predict their functional impact. I will further investigate the effects of these 5’ UTR mutations on gene translation with the creation of a high throughput genetic assay in a lung adenocarcinoma cell line to validate these predicted functional changes. My project seeks to demonstrate how noncoding mutations in lung cancer can dysregulate gene translation and provide opportunities for an emergent class of cancer therapeutics.