Combine genetic, pathologic, and lifestyle factors for pan-cancer survival assessment
Principal Investigator
Name
Rong Zhong
Degrees
Ph.D.
Institution
Huazhong University of Science and Technology
Position Title
Professor
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-1701
Initial CDAS Request Approval
Oct 11, 2024
Title
Combine genetic, pathologic, and lifestyle factors for pan-cancer survival assessment
Summary
Tumors, especially lung cancer and colorectal cancer, are a major public health problem with heavy disease burden. Enhancing the precision of survival prediction models is critical for reducing cancer-related deaths and improving patient outcomes. The Prostate, Lung, Colon, Ovary Screening Trial (PLCO) offers a rich and integrative database. It encompasses genetic, pathologic, and lifestyle factors data of about 150,000 participants in prostate cancer, lung cancer, colorectal cancer and breast cancer screening. Previous studies have shown that clinical and pathologic characteristics and some lifestyles are important prognostic factors for predicting cancer survival. Polygenic risk score (PRS) based on cancer-related genome-wide association study (GWAS) is demonstrated to be effective in identifying individuals at high risk of developing cancer. However, it remains uncertain whether genetic variants (single nucleotide polymorphisms, SNPs) offer superior predictive power for cancer survival. Therefore, we plan to systematically investigate the correlations between SNPs and pan-cancer survival, and then develop polygenic prognostic score for cancer survival using significant SNPs. Besides, pathologic characteristics (clinical stage and grade), and lifestyle factors (eg., smoking and drinking status) will be integrated additionally to ascertain the joint effects. This project is expected to contribute to more precise prognosis prediction and provide insights into precision clinical management for cancer patients.
Aims
The aim of this study is to:
1.Identify SNPs significantly associated with pan-cancer survival and develop an accurate polygenic prognostic score model to stratify cancer patients according to survival risk.
2. Integrate polygenic prognostic score, pathologic characteristics, and lifestyle factors to predict the prognosis of pan-cancer patients to ascertain the joint effects
Collaborators
Shanshan Zhang, Huazhong University of science and technology
Qian Shen, Huazhong University of science and technology
Siyue Wang, Huazhong University of science and technology
Pengcheng Liu, Huazhong University of science and technology
Chenhui Zhang, Huazhong University of science and technology
Huan Liu, Huazhong University of science and technology