The Prosigna gene expression assay and responsiveness to adjuvant cyclophosphamide-based chemotherapy in premenopausal high-risk patients with breast cancer
Bibliographic record
Abstract
The PAM50-based (Prosigna) risk of recurrence (ROR) score and intrinsic subtypes are prognostic for women with high-risk breast cancer. We investigate the predictive ability of Prosigna regarding the effectiveness of cyclophosphamide-based adjuvant chemotherapy in premenopausal patients with high-risk breast cancer. Prosigna assays were performed on the NanoString platform in tumors from participants in Danish Breast Cancer Group (DBCG) 77B, a four-arm trial that randomized premenopausal women with high-risk early breast cancer to no systemic treatment, levamisole, oral cyclophosphamide (C) or cyclophosphamide, methotrexate and fluorouracil (CMF). In total, this retrospective analysis included 460 women (40% of the 1146 randomized patients). The continuous Prosigna ROR score was prognostic in the no systemic treatment group (unadjusted P < 0.001 for disease-free survival (DFS), P = 0.001 for overall survival (OS)). No statistically significant interaction of continuous ROR score and treatment on DFS and OS was found. A highly significant association was observed between intrinsic subtypes and C/CMF treatment for DFS ( P interaction = 0.003 unadjusted, P = 0.001 adjusted) and OS ( P interaction = 0.04). In the adjusted analysis treatment with C/CMF was associated with a reduced risk of DFS events in patients with basal-like (hazard ratio (HR) 0.14; 95% CI 0.06; 0.32) and luminal B (HR 0.48; 95% CI 0.27; 0.84) subtypes but not in patients with Human epidermal growth factor receptor-enriched (HR 1.05; 95% CI 0.56; 1.95) or luminal A (HR 0.61; 95% CI 0.32; 1.16) subtypes. The Prosigna ROR score and intrinsic subtypes were prognostic in high-risk premenopausal patients with breast cancer, and intrinsic subtypes identify high-risk patients with or without major benefit from adjuvant C/CMF treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".