TANK: A Variance-Based Framework for Identifying Heterogeneous Therapeutic Targets in Gastric Cancer

Abstract Background Identifying therapeutically relevant tumor antigens from large-scale transcriptomic data remains a central challenge in precision oncology. Complex computational methods are often employed, yet simple statistical approaches remain underexplored. Methods We developed TANK (Tumor Antigen prioritization by variance-based raNKing), a simple variance-based framework for identifying highly heterogeneous tumor antigens. Using raw RNA-seq count data from TCGA-STAD (n = 448, 60,660 genes), genes were ranked by expression variance across patients. Results were validated in an independent microarray dataset (GSE26942, n = 217). Results CLDN18.2 ranked 89th out of 60,660 genes (top 0.15%) in TCGA-STAD, and 24th out of 36,157 probes (top 0.07%) in GSE26942. Mean-variance analysis confirmed CLDN18.2 as a consistent outlier across both platforms. Kaplan-Meier and multivariate Cox regression analyses adjusting for stage and age showed no significant association between CLDN18.2 expression and overall survival (HR = 1.37, p = 0.11). CLDN18.2 expression remained stable across pathologic stages I-IV (ANOVA p = 0.71). Conclusions TANK identifies clinically validated therapeutic targets through variance alone. The dissociation between high variance and survival significance positions CLDN18.2 as a companion diagnostic target rather than a prognostic marker - a distinction critical for precision patient selection in targeted therapy.