Pan-cancer clinical and molecular landscape of MTAP deletion in nationwide and international comprehensive genomic data
Bibliographic record
Abstract
BACKGROUND: Early-phase clinical trials of protein arginine methyltransferase 5 (PRMT5) inhibitors as synthetic lethal strategies have shown promising efficacy in methylthioadenosine phosphorylase (MTAP)-deleted tumors. To refine and expand this promising therapeutic approach within the framework of precision oncology, it is critical to comprehensively characterize the clinical and molecular profiles of MTAP-deleted tumors. MATERIALS AND METHODS: This pan-cancer retrospective cohort study analyzed clinico-genomic data from the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database, which includes 99.7% of patients who underwent comprehensive genomic profiling (CGP) in Japan between June 2019 and November 2023. Machine learning and explainable artificial intelligence methods were applied to identify clinical predictors of MTAP deficiency. Findings were validated and compared using The Cancer Genome Atlas (TCGA) and American Association for Cancer Research (AACR) Genomics Evidence Neoplasia Information Exchange (GENIE) datasets. RESULTS: Among 51 828 pan-cancer patients in the C-CAT cohort, MTAP deletion was observed in 4964 cases (9.6%), with a high prevalence in pancreatic (18.4%), biliary tract (15.6%), and lung (14.3%) cancers. MTAP deletion was associated with distinct clinical features, including male sex (56.0% versus 47.8%), older age (mean 62.4 versus 59.8 years), and shorter interval from diagnosis to CGP (median 380.0 versus 567.0 days). In pancreatic cancer, MTAP deletion was more common in KRAS-mutant tumors (19.8%) compared with KRAS wild-type tumors (8.9%). Across cancer types, MTAP deletion was less frequent in RB1-mutant tumors (pan-cancer: 3.2%, pancreatic: 7.6%, lung: 2.5%, biliary tract: 5.4%) than in RB1 wild-type tumors (9.9%, 18.7%, 16.1%, 16.0%). These findings were validated using the TCGA (n = 9896) and GENIE (n = 178 034) datasets. In lung adenocarcinoma, MTAP deletion was found in 22.8% of EGFR-mutated tumors, 25.0% of ALK-translocated tumors, and 20.8% of ROS1-translocated tumors. CONCLUSIONS: MTAP deletion is associated with unique clinical and molecular features. These findings define the characteristics of MTAP-deleted cancers and provide a basis for synthetic lethal strategies in precision oncology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".