Differentiating Gastric Cancers from Acid Peptic Diseases through Integrative Targeted Proteomics and Machine Learning Approaches

Gastric cancers (GCs) are often diagnosed
in advanced stages owing
to nonspecific early symptoms resembling Acid Peptic Diseases (APDs).
Despite recent efforts, a simple, liquid biopsy-based multiprotein
panel prediagnostic assay capable of differentiating GCs from APDs
is lacking. Mass spectrometry (MS)-based targeted proteomics methods,
including Multiple Reaction Monitoring (MRM), are utilized as the
method of choice to develop Laboratory Developed Tests (LDTs) that
revolutionize GC early diagnosis and screening. In this study, a 22-min
MS-MRM LDT was developed and tested to quantify a serum protein panel
in 135 serum samples from treatment-naive cases of GCs, APDs, and
healthy individuals. Notably, a novel Deep Neural Network (DNN)-based
pattern recognition scoring architecture, integrated with a model
explainability tool (SHAP), was developed to score and categorize
GCs. The MRM-MS assay produced minimal carryover and matrix effects,
with adequate limits of detection/quantification. Quantities of SAA1
and IGFBP2, as determined through ELISA, demonstrated similar sensitivity
compared to the LDT. Importantly, the DNN-based scoring architecture
efficiently differentiated GCs from the rest of the samples (AUROC
= 0.95), with average precision marking >0.90 and minimal bias
in
protein expression affecting model performance. This LDT can serve
as a prediagnostic screening method to distinguish GCs from APDs,
guiding clinicians and patients in proceeding with a confirmatory
diagnosis.