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Pan-cancer gene-environment interaction studies

Principal Investigator
Name
jianbo Tian
Degrees
Ph.D.
Institution
School of Health Public Wuhan University
Position Title
Associate Professor
Email
About this CDAS Project
Study
PLCO (Learn more about this study)
Project ID
PLCO-900
Initial CDAS Request Approval
Jan 18, 2022
Title
Pan-cancer gene-environment interaction studies
Summary
The occurrence and development of cancers are a multi-step process involving multiple cell types, what’s more its prevention remains challenging in the world. Tumors is closely related to environmental factors such as diet and lifestyle, as well as genetic factors. However, we would incorrectly estimate the risk if we only considered the separate contributions of genetic or environmental factors. The gene-environment interaction is needed to be considered. Therefore, due to the high heterogeneity of tumor phenotype and the complexity of tumor etiology, the construction of risk prediction model has always been one of the hot spots and difficulties in research. Previous research by our research group found that the incorporation of genetic information on the basis of traditional tumor risk prediction models has significant public health transformational significance for early identification, precise prevention, and individualized intervention of people at high risk of cancer. The PLCO database stores about 150,000 participants in prostate cancer, lung cancer, colorectal cancer and breast cancer screening and diet, lifestyle and other environmental factors data, most of which have not been verified to be related to cancer, even if some have been proven in other populations with the risk of cancer, but its biological mechanism interpretation still needs more in-depth study. Besides, the PLCO database also contains relevant GWAS data, which makes it possible for us to interpret the biological mechanism from the root of genetic factors. In short, the rich resources of the PLCO database will provide data support for us to build tumor polygenic risk prediction models and interpret biological mechanisms.
Aims

The purpose of our application for PLCO database is to make fully integrated use of the existing database of our research group as well as PLCO database to conduct research on the relationship between tumors and tumor-related risk factors. Then try to build a tumor polygenic risk prediction model suitable for multi-ethnic populations and interpret the biological mechanism. We mainly want to study the following three directions. The first is using the genotype and phenotype data in the UK Biobank that our research group has applied for as the discovery data set, and use the phenotype-related loci as a variable tool to discover that it is related to the risk of pan-cancer Phenotype. Then the data in the PLCO database was used as the verification data set, and at the same time, it was re-verified in our GWAS data (981 patients and 1991 controls of Chinese ancestry). The second is using the genetic susceptibility sites discovered by GWAS and traditional risk factors to try to construct a tumor risk prediction model, the prediction performance of this model will be in our own Beijing，China early screening data set (867 cases of normal people, 621 cases of non-advanced adenoma, 242 cases of advanced adenoma, 24 cases of colorectal cancer) were verified. Of course, we are also interested in doing some cancer-related genetic environment interaction studies. For example, we initially want to study the relationship between long-term drinking with age, gender, weight, etc. as covariates on the occurrence and development of cancer. At present, our research group is also building an alcohol mouse model, which can be used to interpret the mechanism of pan-cancer as an environmental factor of alcohol. In addition, our alcohol mouse model will also combine genomics, transcriptomics, and single-cell data analysis to interpret potential functional mechanisms.

Collaborators

Miao Xiaoping, Professor, School of Health Public Wuhan University；
Tian Jianbo, Associate Professor, School of Health Public Wuhan University；
Zhu Ying, Researcher, School of Health Public Wuhan University；
Li Bin, PhD, School of Health Public Wuhan University;
Zhang Ming, PhD, School of Health Public Wuhan University；
Chen Can, PhD, School of Health Public Wuhan University;