Variance Decomposition Accesses a Clinically Supported Discovery Space Systematically Missed by Mean-Based Transcriptomic Prioritization

Background Computational prioritization of therapeutic antigen candidates relies predominantly on mean-level differential expression (DEG) analysis. Whether this approach systematically excludes a clinically relevant tier of the transcriptome has not been formally tested. Methods We compared genome-wide variance decomposition (TANK) and mean-based ranking across 32 TCGA cancer types (60,656 genes), defining TANK-only, DEG-only, and shared gene sets. Clinical pipeline enrichment was assessed by Fisher's exact test against a curated database of approved and active therapeutic programs. Results TANK nominated 5,068 genes invisible to mean-based ranking (TANK-only), while mean-based methods exclusively nominated 2,009 genes (DEG-only). TANK-only genes were significantly enriched for active or approved clinical programs compared to DEG-only genes (9/5,068 [0.18%] vs. 0/2,009 [0.00%]; Fisher's exact p = 0.049), although the limited number of events warrants cautious interpretation. DEG-only genes exhibited substantially higher DepMap functional dependency than TANK-only genes (mean 0.348 vs. 0.098; p < 10–160), reflecting enrichment of core translational machinery rather than therapeutically tractable targets. Three independent prospective convergences were identified: CLDN6/BNT211 (Phase 1/2), SLC34A2/TUB-040 (Phase 1/2, NCT06303505), and MAGEA4/Tecelra (FDA-approved August 2024) — in each case with variance-based nomination preceding literature consultation. Conclusions Variance decomposition accesses a clinically supported tier of the cancer transcriptome distinct from and complementary to mean-based prioritization. These results establish variance decomposition as a quantitatively justified and empirically supported complement to differential expression analysis in computational oncology.