GastroAlert: a multimodal epigenetic blood test for early detection of gastric cancer using cell-free DNA

Gastric cancer remains a leading cause of cancer-related mortality worldwide, frequently diagnosed at advanced stages due to the limitations of current diagnostic approaches. While cell-free DNA (cfDNA)-based epigenetic profiling has emerged as a promising avenue for early cancer detection, comprehensive investigations into the epigenetic landscape of cfDNA in gastric cancer remain scarce. To address this gap, we aimed to develop and validate a multimodal epigenetic blood test for the non-invasive detection of gastric cancer.

We developed GastroAlert, a multimodal epigenetic blood test that integrates cfDNA methylation, nucleosome footprinting, and fragmentation features into a single diagnostic approach for gastric cancer detection. A custom-designed capture probe panel was utilized to target gastric cancer-associated differentially methylated CpG sites and functionally relevant genes. Targeted enzymatic methylation sequencing was then performed on a discovery dataset consisting of 136 participants, including 53 gastric cancer patients, 55 individuals with benign gastric conditions, and 28 healthy controls. Subsequently, the diagnostic performance of GastroAlert was rigorously validated in an independent external dataset of 149 participants, comprising 79 gastric cancer cases and 70 non-cancer controls.

In cross-validation of the discovery dataset, GastroAlert achieved an area under the receiver operating characteristic curve (AUC) of 0.950 [95% confidence interval (CI) 0.874-0.995], with observed AUCs for conventional protein biomarkers ranging from 0.507 to 0.687. Notably, the locked GastroAlert model exhibited robust and reproducible performance in the independent validation dataset, yielding an AUC of 0.965 (95% CI 0.940-0.989) for distinguishing gastric cancer patients from non-cancer controls. For the clinically critical subset of early-stage gastric cancer cases, the model still maintained high diagnostic efficacy with an AUC of 0.921 (95% CI 0.862-0.980) in the discovery dataset and 0.948 (95% CI 0.910-0.985) in the validation dataset. Among all 79 gastric cancer cases in the validation dataset, the model attained an overall sensitivity of 0.898 (95% CI 0.813-0.948) at a specificity of 0.900 in the validation dataset.

The findings demonstrate that multimodal epigenetic analysis of cfDNA provides a robust, non-invasive strategy for early gastric cancer detection, with potential clinical utility to improve patient outcomes.