The clinical significance of family history, young age at diagnosis and polymorphic variation in breast cancer
Bibliographic record
Abstract
The overall objective of this study is to investigate the association of first-degree family history of breast and ovarian cancer, diagnosis at age 35 or younger, and polymorphisms in candidate genes with tumor characteristics, treatment and/or survival in breast cancer. Candidate polymorphisms included non-synonymous amino acid substitutions in key genes implicated in pathways involved in proteolysis, cell adhesion, motility, transcription, inflammation, and growth control including the transforming growth factors-beta family. This cohort study consisted of 967 incident female pathologically-confirmed cases of primary invasive breast cancer from the Ontario Familial Breast Cancer Registry (OFBCR). All cases were identified from 1996 to 1998 and followed prospectively for the occurrence of clinical outcomes until 2005. Patient characteristics, clinical features and tumor pathological characteristics were obtained from self-completed questionnaires, review of clinical records and a full pathological review. To assess the association of genetic factors with the primary endpoint of interest, time to distant recurrence, the Cox proportional hazards model and survival trees, were used. First-degree family history was associated with increased detection by mammography, low T stage, and a non-significant improved survival. Individuals diagnosed under age 35 were more likely to self-detect tumors and to present with inflammatory breast cancer. Tumours in young women were more likely to be ductal carcinoma of no special type, less likely to be T1 stage, and more likely to be grade III and estrogen receptor-negative, and to have lymphovascular invasion. Younger women were more likely to receive chemotherapy, particularly anthracyclines, and less likely to receive hormonal therapy even if receptor positive. Diagnosis at 35 or younger was associated with significantly reduced DRFS. Poor outcomes were restricted to younger women with hormone responsive breast cancer. In multivariable analyses, age did not exert an adverse prognostic effect. The nsSNP in the ETS transcription factor, ELF1-N58S, was associated with poor survival in adjusted models. In summary, deciphering the clinical significance of genetic factors may have important implications for the practice of medicine if validated and shown to have a meaningful significance above current measures of prognosis and treatment response.
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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 | 0.004 |
| 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".