The replication error phenotype is associated with the development of distant metastases in hormonally treated patients with breast carcinoma
Bibliographic record
Abstract
BACKGROUND: The positive replication error (RER+) phenotype defines a distinct subgroup of tumors with specific clinical, pathologic, and molecular features that have been documented well in hereditary nonpolyposis colon carcinoma (HNPCC). More recently, this phenotype also has been described in breast carcinoma. METHODS: To determine the effect of RER phenotype on prognosis in patients with breast carcinoma, the authors examined matched archival tumor and normal tissue from 100 women with Stage I and Stage II breast carcinoma, all of whom were treated with hormonal therapy. Patients had been followed for a minimum of 5 years or until death. Seven microsatellite loci were examined, including hMLH1 (3p22, D3S1611), hMSH2 (2p16, D2S123), NM23-H1 (17q21), TP53-Dint (17p13), TP53-Penta (17p13), APC (5q21, D5S346), and HPC1 (1q24, D1S2883). The RER+ phenotype was defined as the presence of allelic shifts at three of the seven loci examined. RESULTS: Twenty-five percent of patients were classified with the RER+ phenotype based on these criteria. The two groups, women with positive RER status and women with negative RER status, were comparable in terms of other factors that may influence prognosis: age, tumor size, lymph node status, disease stage, and estrogen receptor status. The development of distant metastases to the lung, liver, or brain was correlated significantly with the positive RER phenotype, with a relative risk of 2.625 (95% confidence interval, 1.059-6.057). CONCLUSIONS: The presence of high-frequency RER+ may predict for the development of distant metastatic disease in patients with early-stage breast carcinoma who are treated with hormonal therapy.
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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 | 0.002 |
| 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".