Next-generation sequencing based deep learning model for prediction of HER2 status and response to HER2-targeted
Jia Wang1, Ge Gao2, Cong Tian1
1Department of Breast Disease, Henan Breast Cancer Center, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, 127 Dongming Road, Zhengzhou, 450008, China.
Introduction:
For patients with breast cancer, the amplification of Human Epidermal Growth Factor 2 (HER2) is closely related to their prognosis and treatment decisions. This study aimed to further improve the accuracy and efficiency of HER2 amplification status detection with a deep learning model, and apply the model to predict the efficacy of neoadjuvant therapy.
Methods:
We combined Next-Generation Sequencing (NGS) data and IHC staining images of 606 breast cancer patients and developed a Vision Transformer (ViT) deep learning model to identify the amplification of HER2 through these IHC staining images. This model was then applied to predict the efficacy of neoadjuvant therapy in 399 HER2-positive breast cancer patients.
Results:
The NGS data of 606 patients were split into training (N = 404), validation (N = 101), and testing (N = 101) sets. The top 3 genes with highest mutation frequency were TP53, ERBB2 and PIK3CA. With the NGS results as deep learning model labels, the accuracy of our ViT model was 93.1% for HER2 amplification recognition. The misidentifications was likely due to the heterogeneity of HER2 expression in cancer tissues. For predicting the efficacy of neoadjuvant therapy, receiver operating characteristic (ROC) curves were plotted, and the combination of image recognition result and clinical pathological features yielded an area under the curve (AUC) value of 0.855 in the training set and 0.841 in the testing set.
Conclusions:
Our study provided a method of HER2 status recognition based on IHC images, improving the efficiency and accuracy of HER2 status assessment, and can be used for predicting the efficacy of anti-HER2 targeted neoadjuvant therapy. We intend our deep learning model to assist pathologists in HER2 amplification recognition.
