PALB2 mutations in high-risk women with breast or ovarian cancer.
Bibliographic record
Abstract
1527 Background: In Canada, genetic testing for BRCA1 and BRCA2 is available free of charge to women who meet eligibility criteria, based on personal and family history of cancer. Less than 10% of women are identified with a BRCA mutation, despite features of hereditary cancer. PALB2 has been identified as a moderate penetrance gene in various populations. In the current study, we examined the frequency of PALB2 mutations in women with breast or ovarian cancer who met criteria for genetic testing for BRCA1 and BRCA2and tested negative. Methods: DNA samples from women with breast or ovarian cancer, who met criteria for provincial BRCA1 and BRCA2 genetic testing and tested negative between the years of 2007 and 2014 were included in this study. All 13 coding exons of PALB2 plus 20 base pairs from the exon boundaries were amplified using Wafergen SmartChip technology. The amplified DNA were paired-end sequenced at 2x250 cycles using an Illumina MiSeq sequencer. Results: 2,225 women with breast cancer and 429 women with ovarian cancer were tested for PALB2 mutations. No PALB2 mutations were found in women with ovarian cancer. Seventeen deleterious PALB2 mutations were detected in women with breast cancer (0.8%). The frequency of PALB2 mutations was significantly higher in women with bilateral breast cancer (2.4%) compared to women with unilateral breast cancer (0.6%) (p = 0.01). There was no significant difference in age at diagnosis between those with and without a PALB2mutation (50.9 years vs 53.8 years; p = 0.34). Conclusions: Genetic testing for PALB2 should be considered for high-risk women with breast cancer, especially those who present with bilateral breast cancer. However, PALB2 does not appear to contribute to ovarian cancer which has implications for counselling women who are identified with a PALB2 mutation.
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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 | 0.001 |
| 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 | 0.000 | 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".