Abstract 163P
Background
HER2-positive accounts for 15-20% of breast cancers. Today the decision to escalate or de-escalate systemic therapy for early HER2-positive breast cancer is based primarily on traditional clinical factors, which are insufficient to reflect the molecular complexity and heterogeneity of HER2-positive breast cancer. Here we describe the development and validation of a new HER2RI assay, a model that combines expression data of 16 genes and clinical features to obtain risk values to predict the prognosis of early HER2-positive breast cancer.
Methods
261 pT1-2N0-1M0 patients with HER2-positive breast cancer treated surgically at Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS), were collected retrospectively from June 2010 to September 2016. Multivariate analysis was carried out using logistic regression with logit function to analyze the factors affecting patient recurrence. The model was validated internally using the leave-out-cross-validation (LOOCV) strategy, and the model’s predictive capability was estimated using the receiver operating characteristic (ROC) curve. The survival endpoint was recurrence-free survival (RFS).
Results
The HER2RI model achieved an high AUC of 0.833 (AUC > 0.8), with sensitivity, specificity, PPV, and NPV of 81.2%, 73.2%, 29.9%, and 96.5%, respectively. Based on the cut-off of 0.115 of the risk score in the HER2RI model, 173 patients were classified as low risk, and 87 patients were high risk. Compared to the high-risk patients, the low-risk patients had a better 7-year RFS rate (96.42% vs 63.26%, HR: 0.1004, 95% CI: 0.0473-0.2134, P < 0.0001). Cox proportional hazards results showed that the predictive index derived from the HER2RI model is an independent prognostic factor (HR: 9.01, 95%CI: 3.53-23.0, P < 0.001).
Conclusions
We created a novel tool combining gene expression data and clinical characteristics to predict prognosis in patients who are early HER2-positive and have not received neoadjuvant therapy. Next, we will further validate the model’s performance using an external validation set.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
National Natural Science Foundation of China (81672634, 82172650), Beijing Medical Award Foundation (YXJL-2020-0941-0763).
Disclosure
All authors have declared no conflicts of interest.