Hereditary diffuse gastric cancer spectrum associated with germline <i>CTNNA1</i> loss of function revealed by clinical and molecular data from 351 carrier families and over 37 000 non-carrier controls
Bibliographic record
Abstract
Background Diffuse gastric cancer (DGC) is the most common manifestation in germline CTNNA1 variant carriers, with one study estimating a 49–57% lifetime risk by age 80. Knowledge on CTNNA1 -associated hereditary diffuse gastric cancer (HDGC), loss-of-function mechanisms, variant-type causality, disease spectrum and cancer risks remains scarce. Objective Explore CTNNA1 genotype–phenotype associations to improve genetic testing criteria, surveillance and risk-reduction recommendations for carriers. Design Using molecular, clinical and population data from 1308 individuals from 351 CTNNA1- variant carrier families and 37 428 non-carriers from European and American ancestries, we analysed genotype–phenotype associations with multivariable logistic regression. With CRISPR/Cas9 CTNNA1 -knockout gastric cancer (GC) cells and CTNNA1 -humanised Drosophila , we assessed CTNNA1 -associated loss-of-function mechanisms. Results CTNNA1 -truncating transcripts are degraded by nonsense-mediated mRNA decay (NMD), and DGCs from germline CTNNA1 -truncating carriers lose αE-catenin. These transcripts are non-functional in Drosophila , in contrast to non-truncating transcripts. DGC risk is eightfold higher in truncating, compared with non-truncating carriers. The risk of GC and lobular breast cancer (LBC) development in CTNNA1 -truncating variant carriers is fivefold and eightfold lower than in CDH1 pathogenic/likely pathogenic variant carriers, respectively. Compared with wild-type individuals, GC risk is 7-fold higher in CTNNA1 -truncating and 38-fold higher in CDH1 -truncating variant carriers. LBC is recurrent among CTNNA1 -truncating carriers, some lacking HDGC criteria. Simplification of previous criteria for CTNNA1 genetic testing produced the ‘Porto’ criteria, which increased CTNNA1 -carrier families’ pick-up rate by 9%, without performance loss compared with the HDGC 2020 clinical guidelines. Macular dystrophy patterned-2 was positively associated with non-truncating variants, specifically in the αE-catenin M-fragment. Conclusion We provide compelling evidence supporting that CTNNA1 -truncating variants positively associate with DGC and LBC, and NMD as the pathophysiological mechanism leading to CTNNA1 downregulation. We demonstrate that compared with CDH1 , CTNNA1 is a moderate penetrance HDGC gene. This new knowledge is essential to define surveillance and/or prophylactic measures for CTNNA1 -carrier individuals and families.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".