An α‐E‐catenin (<i><scp>CTNNA1</scp></i>) mutation in hereditary diffuse gastric cancer
Bibliographic record
Abstract
Diffuse gastric cancers typically present as late-stage tumours and, as a result, the 5 year survival rate is poor. Some gastric cancers are hereditary and these tend to be of the diffuse type; 30-40% of hereditary diffuse gastric cancers (HDGCs) can be explained by defective germline alleles of E-cadherin (CDH1), but for the remaining families the factors driving susceptibility remain unknown. We had access to a large HDGC pedigree with no obvious mutation in CDH1, and applied exome sequencing to identify new genes involved in gastric cancer. We identified a germline truncating allele of α-E-catenin (CTNNA1) that was present in two family members with invasive diffuse gastric cancer and four in which intramucosal signet ring cells were detected as part of endoscopic surveillance. The remaining CTNNA1 allele was silenced in the two diffuse gastric cancers from the family that were available for screening, and this was also true for signet ring cells identified in endoscopic biopsies. Since α-E-catenin functions in the same complex as E-cadherin, our results call attention to the broader signalling network surrounding these proteins in HDGC. We also detected somatic mutations in one tumour and found substantial overlap with genes mutated in sporadic gastric cancer, including PIK3CA, ARID1A, MED12 and MED23.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".