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 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".