The prognostic and predictive role of 21‐gene recurrence scores in hormone receptor‐positive early‐stage breast cancer
Bibliographic record
Abstract
Over the past two decades, gene expression profiling of breast cancer has emerged as an important tool in early-stage breast cancer management. The approach provides important information on underlying biological mechanisms, breast cancer classification, future risk potential of developing recurrent metastatic disease, and provides beneficial clues for adjuvant chemotherapy in hormone receptor (HR) positive breast cancer. Of the commercially available genomic tests for breast cancer, the prognostic and predictive value of 21-gene recurrence score tests have been validated using both retrospective data and prospective clinical trials. In this paper, we reviewed the current evidence on 21-gene expression profiles for HR-positive HER2-negative early-stage breast cancer management. We show that current evidence supports endocrine therapy alone as an appropriate adjuvant systemic therapy for approximately 70% of women with HR-positive, HER2-negative, node-negative breast cancer. Evolving evidence also suggests that 21-gene recurrence scores have predictive values for node-positive breast cancer and that chemotherapy can be avoided in more than half of women with nodes 1 to 3 positive HR-positive breast cancer. Furthermore, retrospective data also supports the predictive role of 21-gene recurrence scores for adjuvant radiation therapy. A prospective trial in this area is ongoing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".