Abstract 859: Deep learning predicts expression-based molecular subtypes and immune status of urothelial cancer using digital pathology slides
Authors: Matthew Bryan Brendel, Pegah Khosravi, Emily Tse, Lilly Gu, Kyrillus Shohdy, Rohan Bareja, Bhavneet Bhinder, Olivier Elemento, Bishoy Faltas, Iman Hajirasouliha
Journal: Cancer Research (2020)

Abstract RNA-sequencing (RNA-seq) has revealed intrinsic subtypes of urothelial carcinoma (UC). We have recently developed a molecular classifier to separate UC tumors to ‘hot' and ‘cold' based on the expression of immune-related genes. Both molecular subtype and immune-status correlate with response to different treatments, however, RNA-seq data is not readily available for all tumors thus limiting our ability to predict treatment response. We hypothesized that a deep learning approach can predict urothelial cancer molecular subtype and classify immune cell levels from pathology tissue slides. To test this hypothesis, we developed a deep learning model to classify urothelial carcinoma subtypes from hematoxylin and eosin (H&amp;E) stained tissue sections using BASE47 luminal-basal classification and our immune-status classifiers based on RNA-seq data from patients in the TCGA dataset as ground truth. Patches of 2048x2048 pixels were generated from 446 whole slide images (WSIs) resulting in a total of 79,053 images. 70% of the patients were used for training, and 15% are being used as a validation set. The remaining 15% of the data will be a blind test set. We used the Resnet-18 architecture, which performed best based on empirical testing (high accuracy while minimizing overfitting). We performed a color normalization step based on the Vahadane algorithm (IEEE Trans Med Imaging. 2016;35(8):1962 71) to analyze the predictive performance and generalizability of the model compared to non-normalized slides. We also used gradient-weighted class activation mapping to highlight the regions of the tissue that may be important for classification. After 20 epochs of training, we achieved a 74% patch prediction accuracy on the validation set with a ROC-AUC of 0.80 for non-normalized immune cell classification. For normalized images, we achieved a 70% patch prediction accuracy on the validation set with a ROC-AUC of 0.75. For the luminal-basal subtype classification task, we achieved a 68% patch wise accuracy on the validation set with a ROC-AUC of 0.76 for normalized data and 68% patch-wise accuracy with a ROC-AUC of 0.74 for non-normalized data. We are currently testing the model on an internal urothelial cancer cohort at Weill Cornell Medicine. Our data demonstrates the utility of deep learning methods in predicting UC molecular subtype and immune status from digital pathology slides. This data has important implications for predicting prognosis and treatment response in UC patients. Citation Format: Matthew Bryan Brendel, Pegah Khosravi, Emily Tse, Lilly Gu, Kyrillus Shohdy, Rohan Bareja, Bhavneet Bhinder, Olivier Elemento, Bishoy Faltas, Iman Hajirasouliha. Deep learning predicts expression-based molecular subtypes and immune status of urothelial cancer using digital pathology slides [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 859.