Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidates for targeted treatment

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 cance...

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Detalles Bibliográficos
Autores principales: Sun, Hua, Cao, Song, Mashl, R. Jay, Mo, Chia-Kuei, Zaccaria, Simone, Wendl, Michael C., Davies, Sherri R., Bailey, Matthew H., Primeau, Tina M., Hoog, Jeremy, Mudd, Jacqueline L., Dean, Dennis A., Patidar, Rajesh, Chen, Li, Wyczalkowski, Matthew A., Jayasinghe, Reyka G., Rodrigues, Fernanda Martins, Terekhanova, Nadezhda V., Li, Yize, Lim, Kian-Huat, Wang-Gillam, Andrea, Van Tine, Brian A., Ma, Cynthia X., Aft, Rebecca, Fuh, Katherine C., Schwarz, Julie K., Zevallos, Jose P., Puram, Sidharth V., Dipersio, John F., Davis-Dusenbery, Brandi, Ellis, Matthew J., Lewis, Michael T., Davies, Michael A., Herlyn, Meenhard, Fang, Bingliang, Roth, Jack A., Welm, Alana L., Welm, Bryan E., Meric-Bernstam, Funda, Chen, Feng, Fields, Ryan C., Li, Shunqiang, Govindan, Ramaswamy, Doroshow, James H., Moscow, Jeffrey A., Evrard, Yvonne A., Chuang, Jeffrey H., Raphael, Benjamin J., Ding, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8384880/
https://www.ncbi.nlm.nih.gov/pubmed/34429404
http://dx.doi.org/10.1038/s41467-021-25177-3
Descripción
Sumario: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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