In a Chinese cohort of 402 T1-2, N0-1, ER+/HER2- breast cancer patients, low-risk patients identified by Low Risk Prediction (LRP) model derived from a breast cancer subtyping assay MammaTyper® demonstrated a favourable long-term prognosis. These patients could potentially waive chemotherapy without compromising prognosis. Background Although Oncotype DX recurrence score (ODX-RS) has been proved its predictive and prognostic utility in ER+/HER2− breast cancer (BC), cost and accessibility limit its use. The MammaTyper® breast cancer subtyping assay derived Low Risk Prediction (LRP) model has demonstrated to highly predict an ODX-RS≤25. This study aimed to evaluate the prognostic value of the LRP model and its potential to identify patients who might forego chemotherapy. Methods In this retrospective study, women diagnosed with T1-2, N0-1, ER+/HER2- BC between 2014 and 2018 at Peking University First Hospital were included. Patients were stratified with the MammaTyper® LRP model. Distant disease-free survival (DDFS) was compared using Kaplan-Meier curves and Cox proportional hazards models. Results Among 402 eligible patients, 221 (55%) were classified as low-risk by LRP. The 9-year DDFS rate was significantly higher in this group than in the high-risk group (96.2% vs. 87.0%, p=0.0021). LRP low risk status was an independent favourable prognostic factor for DDFS (adjusted HR=0.37, p=0.04). No significant DDFS differences were observed between ET and ET+CT groups among LRP low-risk patients (HR=0.38, p>0.05). Similarly, clinical risk-high/LRP-low patients did not benefit from chemotherapy in DDFS (HR=0.33, p>0.05). Conclusions The MammaTyper® LRP model effectively identified a subset of low-risk patients with excellent long-term prognosis in 9-year DDFS from early BC. No significant DDFS benefits were observed from adjuvant chemotherapy in LRP low-risk patients, even in those with high risk clinical features. This results support the LRP model identified low-risk patients might undergo chemotherapy de-escalation, warranting prospective validation.
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Ding et al. (2026) studied this question.
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