TARGEN: A STRUCTURAL SCORE BEYOND MOLECULAR EVIDENCE FOR CANCER TARGET GENE PREDICTION

Cancer-associated gene prediction requires integrating molecular measurements with biological relations. We study whether learning the predictive contributions of a candidate’s connection partners improves ranking beyond molecular and functional scores and their neighborhood propagation. We develop TARGEN, a regularized additive model that combines multi-omics data, optional pretrained GeneRAIN representations, and functional gene sets. Alongside direct scores and their propagation through protein–protein interaction and functional relations, TARGEN learns node coefficients from training labels and aggregates them into a structural score. For the linear special case with fixed relation operators and scale, we derive a stationary-point identity under L2 regularization: the structural score is a weighted sum of training residuals, with weights determined by shared-neighborhood similarity. This identity explains how training labels contribute to a candidate’s structural score. In five repetitions of ten-fold cross-validation, TARGEN achieves pan-cancer average precision (AP) of 0.921 and cancer-specific macro-average AP of 0.841. It obtains the highest mean AP among the evaluated configurations for pan-cancer prediction and in 15 of 16 cancer types. Compared with a control that fixes node coefficients to zero, the full model improves pan-cancer AP by 0.278 percentage points and cancer-specific macro-average AP by 1.663 percentage points. Each model repeats selection and fitting independently; mean AP improves in all 16 cancer types. Public patient-derived organoid data further support growth dependency on the prescreened candidate GRB2 in organoids from 34 of 49 patients with esophageal adenocarcinoma. Together, the score decomposition, linear analysis, and paired ablation explain the structural score and support its complementary predictive value in the evaluated tasks. candidate’s structural score. In five repetitions of ten-fold cross-validation, TARGEN achieves pan-cancer average precision (AP) of 0.921 and cancer-specific macro-average AP of 0.841. It obtains the highest mean AP among the evaluated configurations for pan-cancer prediction and in 15 of 16 cancer types. Compared with a control that fixes node coefficients to zero, the full model improves pancancer AP by 0.278 percentage points and cancer-specific macro-average AP by 1.663 percentage points. Each model repeats selection and fitting independently; mean AP improves in all 16 cancer types. Public patient-derived organoid data further support growth dependency on the prescreened candidate GRB2 in organoids from 34 of 49 patients with esophageal adenocarcinoma. Together, the score decomposition, linear analysis, and paired ablation explain the structural score and support its complementary predictive value in the evaluated tasks.