Somatic and Germline Genomic Alterations in Very Young Women with Breast Cancer
Bibliographic record
Abstract
PURPOSE: Young age at breast cancer diagnosis correlates with unfavorable clinicopathologic features and worse outcomes compared with older women. Understanding biological differences between breast tumors in young versus older women may lead to better therapeutic approaches for younger patients. EXPERIMENTAL DESIGN: We identified 100 patients ≤35 years old at nonmetastatic breast cancer diagnosis who participated in the prospective Young Women's Breast Cancer Study cohort. Tumors were assigned a surrogate intrinsic subtype based on receptor status and grade. Whole-exome sequencing of tumor and germline samples was performed. Genomic alterations were compared with older women (≥45 years old) in The Cancer Genome Atlas, according to intrinsic subtype. RESULTS: Ninety-three tumors from 92 patients were successfully sequenced. Median age was 32.5 years; 52.7% of tumors were hormone receptor-positive/HER2-negative, 28.0% HER2-positive, and 16.1% triple-negative. Comparison of young to older women (median age 61 years) with luminal A tumors (N = 28 young women) revealed three significant differences: PIK3CA alterations were more common in older patients, whereas GATA3 and ARID1A alterations were more common in young patients. No significant genomic differences were found comparing age groups in other intrinsic subtypes. Twenty-two patients (23.9%) in the Young Women's Study cohort carried a pathogenic germline variant, most commonly (13 patients, 14.1%) in BRCA1/2. CONCLUSIONS: Somatic alterations in three genes (PIK3CA, GATA3, and ARID1A) occur at different frequencies in young versus older women with luminal A breast cancer. Additional investigation of these genes and associated pathways could delineate biological susceptibilities and improve treatment options for young patients with breast cancer. See related commentary by Yehia and Eng, p. 2209.
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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 | 0.000 |
| 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) | 0.001 | 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".