Abstract
The treatment of patients with ERBB2 (HER2)-positive breast cancer with anti-ERBB2 therapy is based on the detection of ERBB2 gene amplification or protein overexpression. Machine learning (ML) algorithms can predict the amplification of ERBB2 based on tumor morphological features, but it is not known whether ML-derived features can predict survival and efficacy of anti-ERBB2 treatment. In this study, we trained a deep learning model with digital images of hematoxylin–eosin (H&E)-stained formalin-fixed primary breast tumor tissue sections, weakly supervised by ERBB2 gene amplification status. The gene amplification was determined by chromogenic in situ hybridization (CISH). The training data comprised digitized tissue microarray (TMA) samples from 1,047 patients. The correlation between the deep learning–predicted ERBB2 status, which we call H&E-ERBB2 score, and distant disease-free survival (DDFS) was investigated on a fully independent test set, which included whole-slide tumor images from 712 patients with trastuzumab treatment status available. The area under the receiver operating characteristic curve (AUC) in predicting gene amplification in the test sets was 0.70 (95% CI, 0.63–0.77) on 354 TMA samples and 0.67 (95% CI, 0.62–0.71) on 712 whole-slide images. Among patients with ERBB2-positive cancer treated with trastuzumab, those with a higher than the median morphology–based H&E-ERBB2 score derived from machine learning had more favorable DDFS than those with a lower score (hazard ratio [HR] 0.37; 95% CI, 0.15–0.93; P = 0.034). A high H&E-ERBB2 score was associated with unfavorable survival in patients with ERBB2-negative cancer as determined by CISH. ERBB2-associated morphology correlated with the efficacy of adjuvant anti-ERBB2 treatment and can contribute to treatment-predictive information in breast cancer.
Highlights
The treatment of patients with ERBB2 (HER2)-positive breast cancer with anti-ERBB2 therapy is based on the detection of ERBB2 gene amplification or protein overexpression
We explored whether a Convolutional neural networks (CNN) weakly supervised by tumor ERBB2 gene amplification status as determined by chromogenic in situ hybridization (CISH) and trained with standard hematoxylin and eosin (H&E) stained tissues samples, can predict breast cancer ERBB2 status
We have shown that a CNN trained on a primary breast tumor tissue morphology is able to learn patterns predictive of breast cancer ERBB2 gene amplification status as assessed by chromogenic in situ hybridization
Summary
The treatment of patients with ERBB2 (HER2)-positive breast cancer with anti-ERBB2 therapy is based on the detection of ERBB2 gene amplification or protein overexpression. Machine learning (ML) algorithms can predict the amplification of ERBB2 based on tumor morphological features, but it is not known whether ML-derived features can predict survival and efficacy of anti-ERBB2 treatment. Of molecular events associated with cancer include the prediction of up to six different gene mutations in lung cancer[9] and microsatellite instability in colorectal cancer[10] It is, not known whether the CNN-derived morphological features that predict the molecular status of a tumor could be used to guide the choice of molecularly targeted therapies. A specific question related to breast cancer is whether a CNN trained to predict the ERBB2 status of a tumor could predict the efficacy of anti-ERBB2 adjuvant treatment. There is a need for more accurate approaches to predict the efficacy of anti-ERBB2 treatment
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