Drug response prediction using deep neural network trained by adaptive resampling of histopathological images

Abstract

Recent advances in programmed death-1 (PD-1) and programmed death-ligand 1 (PD-L1) immune checkpoint inhibitor (ICI) revolutionized the clinical practice in lung cancer treatment. The PD-L1 immunohistochemistry (IHC) test is a widely used biomarker for ICI responder in lung cancer. However, the accuracy of PD-L1 IHC test for selecting ICI responder is unsatisfactory, especially due to its low specificity. Therefore, methods which could effectively predict the efficacy of ICI are crucial for patient selection. In this article, we apply a deep neural network (DNN) to predict responders for anti-PD1 blockade on the basis of histopathological images of hematoxylin and eosin (H&E) stained tissue. The main difficulty to train DNN for responder prediction is the inability to accurately label at the image patch level. We employed a semi-supervised multi-instance learning (MIL) framework with adaptive positive patch selection in each region-of-interest (ROI). Using a dataset of 250 whole slide images (WSIs) of non-small cell lung cancer (111 responders and 139 nonresponders), we train a DNN-based MIL classifier on a case-level partition of the dataset (150 WSIs) and obtain an area under curve (AUC) of 0.773 for the test dataset (50 WSIs), which outperforms the PD-L1 IHC test (AUC=0.636). These results suggest that a DNN model can be used for assisting clinicians to make decision for treatment plan. We also confirm that the locations of adaptively selected positive patches can give valuable insights into the histological features associated with drug response.