Rare Mutations in <i>RINT1</i> Predispose Carriers to Breast and Lynch Syndrome–Spectrum Cancers
Bibliographic record
Abstract
UNLABELLED: Approximately half of the familial aggregation of breast cancer remains unexplained. A multiple-case breast cancer family exome-sequencing study identified three likely pathogenic mutations in RINT1 (NM_021930.4) not present in public sequencing databases: RINT1 c.343C>T (p.Q115X), c.1132_1134del (p.M378del), and c.1207G>T (p.D403Y). On the basis of this finding, a population-based case-control mutation-screening study was conducted that identified 29 carriers of rare (minor allele frequency < 0.5%), likely pathogenic variants: 23 in 1,313 early-onset breast cancer cases and six in 1,123 frequency-matched controls [OR, 3.24; 95% confidence interval (CI), 1.29-8.17; P = 0.013]. RINT1 mutation screening of probands from 798 multiple-case breast cancer families identified four additional carriers of rare genetic variants. Analysis of the incidence of first primary cancers in families of women carrying RINT1 mutations estimated that carriers were at increased risk of Lynch syndrome-spectrum cancers [standardized incidence ratio (SIR), 3.35; 95% CI, 1.7-6.0; P = 0.005], particularly for relatives diagnosed with cancer under the age of 60 years (SIR, 10.9; 95% CI, 4.7-21; P = 0.0003). SIGNIFICANCE: The work described in this study adds RINT1 to the growing list of genes in which rare sequence variants are associated with intermediate levels of breast cancer risk. Given that RINT1 is also associated with a spectrum of cancers with mismatch repair defects, these findings have clinical applications and raise interesting biological questions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".