Tumour morphology predicts PALB2 germline mutation status
Bibliographic record
Abstract
BACKGROUND: Population-based studies of breast cancer have estimated that at least some PALB2 mutations are associated with high breast cancer risk. For women carrying PALB2 mutations, knowing their carrier status could be useful in directing them towards effective cancer risk management and therapeutic strategies. We sought to determine whether morphological features of breast tumours can predict PALB2 germline mutation status. METHODS: Systematic pathology review was conducted on breast tumours from 28 female carriers of PALB2 mutations (non-carriers of other known high-risk mutations, recruited through various resources with varying ascertainment) and on breast tumours from a population-based sample of 828 Australian women diagnosed before the age of 60 years (which included 40 BRCA1 and 18 BRCA2 mutation carriers). Tumour morphological features of the 28 PALB2 mutation carriers were compared with those of 770 women without high-risk mutations. RESULTS: Tumours arising in PALB2 mutation carriers were associated with minimal sclerosis (odds ratio (OR)=19.7; 95% confidence interval (CI)=6.0-64.6; P=5 × 10(-7)). Minimal sclerosis was also a feature that distinguished PALB2 mutation carriers from BRCA1 (P=0.05) and BRCA2 (P=0.04) mutation carriers. CONCLUSION: This study identified minimal sclerosis to be a predictor of germline PALB2 mutation status. Morphological review can therefore facilitate the identification of women most likely to carry mutations in PALB2.
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.
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 | 0.001 | 0.005 |
| 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".