Mutation Rates in Cancer Susceptibility Genes in Patients With Breast Cancer With Multiple Primary Cancers
Bibliographic record
Abstract
PURPOSE Women with breast cancer have a 4%-16% lifetime risk of a second primary cancer. Whether mutations in genes other than BRCA1/2 are enriched in patients with breast and another primary cancer over those with a single breast cancer (S-BC) is unknown. PATIENTS AND METHODS We identified pathogenic germline mutations in 17 cancer susceptibility genes in patients with BRCA1/2-negative breast cancer in 2 different cohorts: cohort 1, high-risk breast cancer program (multiple primary breast cancer [MP-BC], n = 551; S-BC, n = 449) and cohort 2, familial breast cancer research study (MP-BC, n = 340; S-BC, n = 1,464). Mutation rates in these 2 cohorts were compared with a control data set (Exome Aggregation Consortium [ExAC]). RESULTS Overall, pathogenic mutation rates for autosomal, dominantly inherited genes were higher in patients with MP-BC versus S-BC in both cohorts (8.5% v 4.9% [ P = .02] and 7.1% v 4.2% [ P = .03]). There were differences in individual gene mutation rates between cohorts. In both cohorts, younger age at first breast cancer was associated with higher mutation rates; the age of non–breast cancers was unrelated to mutation rate. TP53 and MSH6 mutations were significantly enriched in patients with MP-BC but not S-BC, whereas ATM and PALB2 mutations were significantly enriched in both groups compared with ExAC. CONCLUSION Mutation rates are at least 7% in all patients with BRCA1/2 mutation–negative MP-BC, regardless of age at diagnosis of breast cancer, with mutation rates up to 25% in patients with a first breast cancer diagnosed at age < 30 years. Our results suggest that all patients with breast cancer with a second primary cancer, regardless of age of onset, should undergo multigene panel testing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".