Abstract 5407: A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis
Authors: White Brian S. , Woo Xing Yi , Koc Soner , Sheridan Todd , Neuhauser Steven B. , Wang Shidan , Evrard Yvonne A. , Landua John David , Mashl R Jay , Davies Sherri R. , Fang Bingliang , Raso Maria Gabriela , Evans Kurt W. , Bailey Matthew H. , Chen Yeqing , Xiao Min , Rubinstein Jill , Foroughi pour Ali , Dobrolecki Lacey Elizabeth , Fujita Maihi , Fujimoto Junya , Xiao Guanghua , Fields Ryan C. , Mudd Jacqueline L. , Xu Xiaowei , Hollingshead Melinda G. , Jiwani Shahanawaz , consortium PDXNet , Wallace Tiffany A. , Moscow Jeffrey A. , Doroshow James H. , Mitsiades Nicholas , Kaochar Salma , Pan Chong-xian , Chen Moon S. , Carvajal-Carmona Luis G. , Welm Alana L. , Welm Bryan E. , Govindan Ramaswamy , Li Shunqiang , Davies Michael A. , Roth Jack A. , Meric-Bernstam Funda , Xie Yang , Herlyn Meenhard , Ding Li , Lewis Michael T. , Bolt Carol J. , Dean Dennis A. , Chuang Jeffrey H. 
Published: April 2023

Abstract
Patient-derived xenografts (PDXs) model human intra-tumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histological imaging via hematoxylin and eosin (H&E) staining is performed on PDX samples for routine assessment and, in principle, captures the complex interplay between tumor and stromal cells. Deep learning (DL)-based analysis of large human H&E image repositories has extracted inter-cellular and morphological signals correlated with disease phenotype and therapeutic response. Here, we present an extensive, pan-cancer repository of nearly 1,000 PDX and paired human progenitor H&E images. These images, curated from the PDXNet consortium, are associated with genomic and transcriptomic data, clinical metadata, pathological assessment of cell composition, and, in several cases, detailed pathological annotation of tumor, stroma, and necrotic regions. We demonstrate that DL can be applied to these images to classify tumor regions with an accuracy of 0.87. Further, we show that DL can predict xenograft-transplant lymphoproliferative disorder, the unintended outgrowth of human lymphocytes at the transplantation site, with an accuracy of 0.97. This repository enables PDX-specific investigations of cancer biology through histopathological analysis and contributes important model system data that expand on existing human histology repositories. We expect the PDXNet Image Repository to be valuable for controlled digital pathology analysis, both for the evaluation of technical issues such as stain normalization and for development of novel computational methods based on spatial behaviors within cancer tissues.
Citation Format: Brian S. White, Xing Yi Woo, Soner Koc, Todd Sheridan, Steven B. Neuhauser, Shidan Wang, Yvonne A. Evrard, John David Landua, R Jay Mashl, Sherri R. Davies, Bingliang Fang, Maria Gabriela Raso, Kurt W. Evans, Matthew H. Bailey, Yeqing Chen, Min Xiao, Jill Rubinstein, Ali Foroughi pour, Lacey Elizabeth Dobrolecki, Maihi Fujita, Junya Fujimoto, Guanghua Xiao, Ryan C. Fields, Jacqueline L. Mudd, Xiaowei Xu, Melinda G. Hollingshead, Shahanawaz Jiwani, PDXNet consortium, Tiffany A. Wallace, Jeffrey A. Moscow, James H. Doroshow, Nicholas Mitsiades, Salma Kaochar, Chong-xian Pan, Moon S. Chen, Luis G. Carvajal-Carmona, Alana L. Welm, Bryan E. Welm, Ramaswamy Govindan, Shunqiang Li, Michael A. Davies, Jack A. Roth, Funda Meric-Bernstam, Yang Xie, Meenhard Herlyn, Li Ding, Michael T. Lewis, Carol J. Bolt, Dennis A. Dean, Jeffrey H. Chuang. A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5407.