Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidates for targeted treatment
Hua Sun, Song Cao, R. Jay Mashl, Chia-Kuei Mo, Simone Zaccaria, Michael C. Wendl, Sherri R. Davies, Matthew H. Bailey, Tina Primeau, Jeremy Hoog, Jacqueline L. Mudd, Dennis A. Dean, Rajesh Patidar, Li Chen, Matthew A. Wyczalkowski, Reyka G. Jayasinghe, Fernanda Martins Rodrigues, Nadezhda V. Terekhanova, Yize Li, Kian‐Huat Lim, Andrea Wang‐Gillam, Brian A. Van Tine, X. Cynthia, Rebecca Aft, Katherine C. Fuh, Julie K. Schwarz, José P. Zevallos, Sidharth V. Puram, John F. DiPersio, Julie Belmar, Jason M. Held, Jingqin Luo, Brian A. Van Tine, Rose Tipton, Yige Wu, Lijun Yao, Daniel Cui Zhou, Andrew Butterfield, Zhengtao Chu, Maihi Fujita, Chieh‐Hsiang Yang, Emilio Cortes-Sanchez, Sandra D. Scherer, Ling Zhao, Tijana Borovski, Vicki Chin, John J. DiGiovanna, Christian Frech, Jeffrey Grover, Ryan Jeon, Soner Koc, Jelena Randjelović, Sara Seepo, Tamara Stanković, Lacey E. Dobrolecki, Michael Ittmann, Susan G. Hilsenbeck, Bert W. O’Malley, Nicholas Mitsiades, Salma Kaochar, Argun Akçakanat, Jithesh J. Augustine, Huiqin Chen, Bingbing Dai, Kurt W. Evans, Kelly Gale, Don L. Gibbons, Min Jin Ha, V. Behrana Jensen, Michael P. Kim, Bryce P. Kirby, Scott Kopetz, Christopher D. Lanier, Dali Li, Mourad Majidi, David G. Menter, Ismail M. Meraz, Turçin Saridogan, Stephen Scott, Alexey V. Sorokin, Coya Tapia, Jing Wang, Shannon N. Westin, Yuanxin Xi, Yi Xu, Fei Yang, Timothy A. Yap, Vashisht G. Yennu-Nanda, Erkan Yuca, Jianhua Zhang, Ran Zhang, Xiaoshan Zhang, Xiaofeng Zheng, Dylan Fingerman, Haiyin Lin, Qin Liu, Andrew V. Kossenkov, Vito W. Rebecca, Rajasekharan Somasundaram, Michae T. Tetzlaff
Abstract
Development of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications and subclone events, and the potential PDX models for NCI-MATCH trials. Lastly, we provide a web portal having comprehensive pan-cancer PDX genomic profiles and source code to facilitate identification of more druggable events and further insights into PDXs' recapitulation of human tumors.