TY - JOUR
T1 - Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidatesfor targeted treatment
AU - The NCI PDXNet Consortium
AU - Sun, Hua
AU - Cao, Song
AU - Mashl, R. Jay
AU - Mo, Chia Kuei
AU - Zaccaria, Simone
AU - Wendl, Michael C.
AU - Davies, Sherri R.
AU - Bailey, Matthew H.
AU - Primeau, Tina M.
AU - Hoog, Jeremy
AU - Mudd, Jacqueline L.
AU - Dean, Dennis A.
AU - Patidar, Rajesh
AU - Chen, Li
AU - Wyczalkowski, Matthew A.
AU - Jayasinghe, Reyka G.
AU - Rodrigues, Fernanda Martins
AU - Terekhanova, Nadezhda V.
AU - Li, Yize
AU - Lim, Kian Huat
AU - Wang-Gillam, Andrea
AU - Van Tine, Brian A.
AU - Ma, Cynthia X.
AU - Aft, Rebecca
AU - Fuh, Katherine C.
AU - Schwarz, Julie K.
AU - Zevallos, Jose P.
AU - Puram, Sidharth V.
AU - Akcakanat, Argun
AU - Chen, Huiqin
AU - Dai, Bingbing
AU - Gibbons, Don
AU - Ha, Min Jin
AU - Jensen, Vanessa
AU - Kim, Michael
AU - Kopetz, Scott
AU - Menter, David
AU - Meraz, Ismail
AU - Sorokin, Alexey
AU - Wang, Jing
AU - Westin, Shannon
AU - Xi, Yuanxin
AU - Yang, Fei
AU - Yap, Timothy A.
AU - Yennu-Nanda, Vashisht G.
AU - Zhang, Xiaoshan
AU - Zheng, Xiaofeng
AU - Davies, Michael A.
AU - Fang, Bingliang
AU - Meric-Bernstam, Funda
N1 - Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12/1
Y1 - 2021/12/1
N2 - 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.
AB - 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.
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U2 - 10.1038/s41467-021-25177-3
DO - 10.1038/s41467-021-25177-3
M3 - Article
C2 - 34429404
AN - SCOPUS:85114981330
SN - 2041-1723
VL - 12
JO - Nature communications
JF - Nature communications
IS - 1
M1 - 5086
ER -