Xiang Shu

Memorial Sloan Kettering Cancer Center

Assistant Attending Epidemiologist

PLCO (Learn more about this study)

2020-1017

Jan 13, 2021

Prospective metabolomics study of gastric cancer

Gastric cancer remains one of the most common and deadly cancers worldwide. It is the 5th most common and the 3rd most deadly malignancy, with an estimated one million new cases and 783,000 deaths in 2018. Gastric cancer also imposes an important health threat in US as an estimated 27,600 new cases is diagnosed each year. Of note, the 5-year relative survival is merely 30% for the patients, which is largely due to the fact that a substantial proportion are diagnosed at an advanced stage. Endoscopy is widely used for early detection of gastric cancer. However, the diagnostic efficiency varied by endoscopists due to differences in skills and experience. Identifying blood biomarkers for risk assessment and early detection of cancer represents a long interest in cancer research due to its non-invasive property. Nevertheless, serum biomarkers such as CEA and CA19-9, are not effective in clinical or screening settings because of poor sensitivity or specificity. Metabolomics, which systematically identifies and quantifies thousands of small molecule metabolic products in a biological system, could provide new opportunities to develop new tools. Most previously conducted studies compared metabolite levels between tumor and adjacent normal tissues or between cancer patients and non-cancer counterparts using collected blood or urine samples. Due to large variations in sample size, study population, sample collection and storage, and profiling platform and protocol, inconsistent results were observed across studies for many metabolites. Increased glycolysis activity (Warburg effects), reprogramming of glutamine metabolism (essential for cancer cell survival), and dysregulation of aldehydes and ketones (metabolic products of β-oxidation of fatty acids) are few examples that are consistently reported across studies. Furthermore, no prospective metabolomics study has been conducted for the malignancy. Therefore, new investigations with a more homogeneous design and large sample size are warranted to address the gaps mentioned above.

In the present proposal, we herein are requesting plasma samples from PLCO to conduct a prospective metabolomics study of gastric cancer. We will collaborate with Metabolon to generate global untargeted metabolomics data using the samples from the Shanghai Women’s Health Study, Shanghai men’s Health Study, Southern Community Cohort Study, and PLCO. Association analysis will be performed within individual study using conditional/unconditional logistic regression, which will be determined by the design of each study. Only metabolites passed QC will be analyzed (e.g. CV < 25%). The individual results of metabolite-gastric cancer association will be combined by meta-analysis to generate ancestry-specific and cross-ancestry estimates. Stratified analysis will be conducted by sex, smoking, years between the enrollment and cancer diagnosis (e.g. < 5 years vs >=5 years), fasting status/time, tea/coffee drinking, site of gastric cancer, etc. Interaction effects will also be evaluated for selective variables mentioned above if evidence for heterogeneity is observed in the stratified analysis. Multiple comparisons will be corrected by taking the Benjamini-Hochberg False Discovery Rate procedure.

1) To identify novel metabolic biomarkers for gastric cancer risk. We will perform assays to profile circulating metabolites in ~1200 cases and 1500 controls using the DiscoveryHD4 platform offered by the Metabolon. We request 150ul of plasma samples from each of the cancer cases and their matched controls from the PLCO study.

2) To perform mediation analyses to evaluate inter-relationships of known lifestyle risk factors, blood metabolites, and cancer risk. We will use the metabolomics data described in Aim 1 and exposure data collected in the parent cohort studies for these analyses. We request relevant exposure data obtained in the PLCO for all PLCO cases and controls included in Aim 1.

3) Explore group specific associations and interaction effects by sex, H. pylori infection, and dietary intakes (tea/coffee intake) if the relevant data is available.