Colorectal cancer (CRC) is the third most common cancer worldwide and ranks as the second leading cause of cancer-related deaths. Screening plays a key role in early detection and makes CRC one of the most preventable cancers. Developing an accurate risk prediction score is crucial because it helps us identify and focus on those at high risk from a young age, enabling early screening and effective intervention. Research has shown that thousands of genetic mutations can increase the risk of developing CRC. Our goal is to convert these genetic discoveries into useful tools for clinical use. We plan to utilize advanced techniques such as CRISPR screening technology and single-cell sequencing, combined with deep learning models and statistical analysis. This approach will help us understand the whole impact of these genetic mutations better. This work aims to provide deeper insights into how these mutations contribute to the development of CRC, leading to more targeted and efficient screening strategies. Ultimately, our research is directed toward developing a sophisticated method for predicting colorectal cancer risk, focusing specifically on those who are most at risk. This could significantly change how we prevent and treat colorectal cancer.
Xueqiu (Chu) Lin, PhD
Location: Fred Hutchinson Cancer Center - Seattle
Proposal: Mapping and modeling oncogenic non-coding networks for colorectal cancer risk prediction