MammaPrint predicts chemotherapy benefit in HR+HER2- early breast cancer: FLEX Registry real-world data
Bibliographic record
Abstract
BACKGROUND: Gene expression assays help personalize adjuvant chemotherapy decisions for hormone receptor-positive, HER2-negative (HR+HER2-) early breast cancer (EBC). The 70-gene risk of distant-recurrence signature, MammaPrint, demonstrated clinical utility in guiding chemotherapy de-escalation in genomically low risk patients in the MINDACT trial. This study evaluates MammaPrint as a continuous predictor of chemotherapy benefit in HR+HER2- EBC using real-world data (RWD) from the FLEX Registry. METHODS: The study evaluated 1002 patients treated with endocrine therapy (ET) only or ET with chemotherapy (ET+CT) enrolled in FLEX (NCT03053193) with 5-year median follow-up. Propensity-score matching balanced treatment groups by menopausal status, T-stage, and nodal status. The primary endpoint was distant recurrence-free interval (DRFI). Regression and Cox proportional hazards models assessed chemotherapy benefit across MammaPrint Index (MPI) risk. RESULTS: Most patients were postmenopausal (70.1%), node-negative (70.0%), and had grade 2 tumors (51.2%). The regression models showed that MPI strongly predicted 5-year DRFI in ET only (R2 = 0.99, P < .001) and ET + CT (R2 = 0.90, P < .001) groups, corresponding to an average absolute chemotherapy benefit of 5.6% in High 1 and 10.9% in High 2. Minimal improvement in DRFI with chemotherapy was observed for Low (1.7%) and UltraLow (<1.0%) risk groups. A multivariate Cox model with an MPI-by-treatment interaction term demonstrated that increasing MPI risk was associated with greater chemotherapy benefit on DRFI (HR = 0.15, P = .047). Chemotherapy benefit was significantly associated with premenopausal status, but not age, T-stage, nodal status, or grade. CONCLUSIONS: These RWD from the FLEX Registry demonstrate that MPI is predictive of both DRFI prognosis and chemotherapy benefit in HR+HER2- EBC. (NCT03053193).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".