A Predictive Model of Noncardia Gastric Adenocarcinoma Risk Using Antibody Response to Helicobacter pylori Proteins and Pepsinogen.

Journal Article (Journal Article)

BACKGROUND: Blood-based biomarkers for gastric cancer risk stratification could facilitate targeting screening to people who will benefit from it most. The ABC Method, which stratifies individuals by their Helicobacter pylori infection and serum-diagnosed chronic atrophic gastritis status, is currently used in Japan for this purpose. Most gastric cancers are caused by chronic H. pylori infection, but few studies have explored the capability of antibody response to H. pylori proteins to predict gastric cancer risk in addition to established predictors. METHODS: We used the least absolute shrinkage and selection operator (Lasso) to build a predictive model of noncardia gastric adenocarcinoma risk from serum data on pepsinogen and antibody response to 13 H. pylori antigens as well as demographic and lifestyle factors from a large international study in East Asia. RESULTS: Our best model had a significantly (P < 0.001) higher AUC of 73.79% [95% confidence interval (CI), 70.86%-76.73%] than the ABC Method (68.75%; 95% CI, 65.91%-71.58%). At 75% specificity, the new model had greater sensitivity than the ABC Method (58.67% vs. 52.68%) as well as NPV (68.24% vs. 66.29%). CONCLUSIONS: Along with serologically defined chronic atrophic gastritis, antibody response to the H. pylori proteins HP 0305, HP 1564, and UreA can improve the prediction of gastric cancer risk. IMPACT: The new risk stratification model could help target more invasive gastric screening resources to individuals at high risk.

Full Text

Duke Authors

Cited Authors

  • Murphy, JD; Olshan, AF; Lin, F-C; Troester, MA; Nichols, HB; Butt, J; Qiao, Y-L; Abnet, CC; Inoue, M; Tsugane, S; Epplein, M

Published Date

  • April 1, 2022

Published In

Volume / Issue

  • 31 / 4

Start / End Page

  • 811 - 820

PubMed ID

  • 35131882

Pubmed Central ID

  • PMC8983566

Electronic International Standard Serial Number (EISSN)

  • 1538-7755

Digital Object Identifier (DOI)

  • 10.1158/1055-9965.EPI-21-0869

Language

  • eng

Conference Location

  • United States