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Approved Research

Uncovering the causal mechanism between environmental exposure and cancer by integrating exposome, genomic, clinical phenomic and radiomic data

Principal Investigator: Professor Fangfang Song
Approved Research ID: 76092
Approval date: November 2nd 2021

Lay summary

Cancer is still a leading cause of death and poor quality of life worldwide. Although a large number of studies on tumor etiology based on environmental exposure, genetic characteristics and clinical phenotypes, respectively, have been carried out, few studies have paid attention to the interactions between these biomarkers on the risk of cancer. It is necessary to integrate exposome, genomics/epigenetics, and phenomic markers contributing to tumor pathogenesis, and to explore the causal pathway of tumor development based on multi-dimensional omics markers. On the basis of this theoretical framework and previous studies, this program intends to perform a prospective, comprehensive analysis by using the large-scale, high quality data from UK biobank, and to identify traditional and new environmental factors (including psychosocial factors, physical and chemical factors and biological factors, etc.), and construct a comprehensive environmental exposure scoring system related to cancer. After preliminary analysis of the association between environmental exposure and cancer, with the help of artificial intelligence (AI) and other analytical techniques, we gradually explore the mediating effect of genetic changes and clinical phenotypes on environmental exposure-induced tumors, and delineate the causal association framework of environmental exposure, genetic characteristics, clinical phenotypes with cancer. We will start analyses as soon as data are available and plan to finish this project within 36 months. Our integrated and multi-omics study will help to illustrate how the environmental exposure and genetic background affects the clinical biomarkers, and to improve targeted, early prevention of cancer. Besides, the AI analytical strategies and methods we developed for these big data may also provide some application values for further work.