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Preprint WatchStrongSeptember 17th, 2026

Simulated Annealing Identifies Five Shared Drivers of T Cell Exhaustion Across Four Human Cancers

Ebadi, A.; Hashemi, M.

Simulated annealing over single-cell RNA-seq from four human cancers selects TIGIT as one of five conserved drivers of T cell exhaustion (with TOX, PDCD1, HAVCR2 and CXCL13), and the authors propose these shared drivers as pan-cancer therapeutic targets.

Strong contradiction

4 prior failures

Three or more documented clinical failures match this mechanism, or a Phase 3 efficacy failure is on record.

The preprint nominates TIGIT as a pan-cancer therapeutic target on the strength of its co-expression with other exhaustion markers in single-cell data from NSCLC, melanoma, colorectal carcinoma and hepatocellular carcinoma. The Claidex graph holds four TIGIT failures that bear directly on that inference, and two of them are randomized efficacy failures in the exact tumour types the preprint analyses. Ociperlimab failed on efficacy in first-line PD-L1 high NSCLC (ociperlimab-tigit-advantig-302-nsclc-class-failure) and tiragolumab failed in adjuvant PD-L1 positive NSCLC (tiragolumab-tigit-adjuvant-nsclc-phase3-class-efficacy-failure). Vibostolimab plus pembrolizumab was terminated in adjuvant melanoma on a safety signal (vibostolimab-pembrolizumab-tigit-adjuvant-melanoma-keyvibe-010-phase3-safety-termination), and the bladder combination was stopped as a strategic reprioritization (vibostolimab-favezelimab-tigit-bcg-unresponsive-nmibc-keynote-057-phase2-strategic-termination). Marker co-expression in exhausted T cells is what motivated the original TIGIT class, and it did not predict clinical benefit. A target nomination resting on the same class of evidence inherits that record and needs a causal perturbation readout rather than a co-expression one.

Abstract excerpt

T cell exhaustion is a major barrier to effective cancer immunotherapy. While individual exhaustion markers such as PDCD1 and TOX have been extensively studied, the shared drivers across different cancer types remain poorly defined. Identifying conserved exhaustion drivers could provide pan-cancer therapeutic targets. We analyzed single-cell RNA-seq data from four human cancers: hepatocellular carcinoma (HCC), colorectal cancer (CRC), melanoma, and non-small cell lung cancer (NSCLC). We applied three complementary computational approaches weighted gene co-expression network analysis (WGCNA), XGBoost-based feature importance, and simulated annealing (SA) for optimal gene subset selection. Pathway enrichment analysis was performed using KEGG, Reactome, and Gene Ontology (GO) databases. We developed a simulated annealing framework that outperformed WGCNA and XGBoost in identifying conserved exhaustion drivers. SA identified five shared drivers (TOX, PDDC1, HAVCR2, TIGIT, CXCL13) across all four cancers, and twenty novel candidate genes including ITM2A, TNFRSF1B, COTL1, SLA, and PTPN22 that have not been previously linked to exhaustion. Notably, NR4A1 a widely reported exhaustion driver was not selected in any cancer type, challenging its role as a shared driver. HCC showed a distinct exhaustion signature compared to other cancers. Our study provides a new computational framework (

Matching Claidex post-mortems

4 of 4 indexed

This is an automated contradiction flag, not an editorial judgment on the preprint's quality. Flags identify where the preclinical literature and the clinical failure record diverge.