A Review of Gene Expression Profiling in Early-Stage ER+/HER2- Breast Cancer With A Focus on The PAM50 Risk of Recurrence Assay
Bibliographic record
Abstract
In patients with breast cancer, the expression of oestrogen receptor, progesterone receptor, and human epidermal growth factor 2 (HER2) is used as a molecular marker to determine prognosis and direct treatment decisions; however, this does not fully reflect the molecular complexity of the disease. Patients with early-stage hormone receptor-positive (ER+), HER2-negative (HER2-) breast cancer are typically treated with surgery, followed by adjuvant systemic endocrine therapy with or without adjuvant radiation therapy. Gene expression profiling assays complement clinicopathological parameters, such as tumour size, grade, and nodal status, and can be used to classify risk of recurrence, thereby informing adjuvant therapy decision-making in early-stage breast cancer to prevent unnecessary treatment with chemotherapy in low risk patients. In this review, the authors evaluate the evidence to date supporting the use of one of the tests, the Prosigna PAM50 risk of recurrence assay (Nanostring, Seattle, Washington, USA), as a prognostic tool in ER+/HER2- early-stage breast cancer, and summarise findings from a clinical and cost-effectiveness analysis performed by the National Institute for Health and Care Excellence (NICE) in the UK. The authors also focus on recommendations from regulatory bodies and key ongoing research efforts to address the remaining uncertainty regarding the application of available genomic signatures in ER+/HER2- early-stage breast cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".