Mutation Rates in Cancer Susceptibility Genes in Patients With Breast Cancer With Multiple Primary Cancers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 it