Ataxia–Telangiectasia Gene (ATM) Mutation Heterozygosity in Breast Cancer: A Narrative Review
Bibliographic record
Abstract
Background: Despite the fact that heterozygosity for a pathogenic ATM variant is present in 1%–2% of the adult population, clinical guidelines to inform physicians and genetic counsellors about optimal management in that population are lacking. Methods: In this narrative review, we describe the challenges and controversies in the management of women who are heterozygous for a pathogenic ATM variant with respect to screening for breast and other malignancies, to choices for systemic therapy, and to decisions about radiation therapy. Results: Given that the lifetime risk for breast cancer in women who are heterozygous for a pathogenic ATM variant is likely greater than 25%, those women should undergo annual mammographic screening starting at least by 40 years of age. For women in this group who have a strong family history of breast cancer, earlier screening with both magnetic resonance imaging and mammography should be considered. High-quality data to inform the management of established breast cancer in carriers of pathogenic ATM variants are lacking. Although deficiency in the ATM gene product might confer sensitivity to DNA-damaging pharmaceuticals such as inhibitors of poly (ADP–ribose) polymerase or platinum agents, prospective clinical trials have not been conducted in the relevant patient population. Furthermore, the evidence with respect to radiation therapy is mixed; some data suggest increased toxicity, and other data suggest improved clinical benefit from radiation in women who are carriers of a pathogenic ATM variant. Conclusions: As in the 2017 U.S. National Comprehensive Cancer Network guidelines, we recommend high-risk imaging for women in Ontario who are heterozygous for a pathogenic ATM variant. Currently, ATM carrier status should not influence decisions about systemic or radiation therapy in the setting of an established breast cancer diagnosis.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".