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

Sun, Hua; Cao, Song; Mashl, R. Jay; Mo, Chia-Kuei; Zaccaria, Simone; Wendl, Michael C.; Davies, Sherri R.; Bailey, Matthew H.; Primeau, Tina; Hoog, Jeremy; Mudd, Jacqueline; 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; Tine, Brian A. Van; Ma, Cynthia; Aft, Rebecca; Fuh, Katherine C.; Schwarz, Julie K.; Zevallos, José P.; Puram, Sidharth V.; DiPersio, John F.; Belmar, Julie; Held, Jason M.; Luo, Jingqin; Tipton, Rose; Wu, Yige; Yao, Lijun; Zhou, Daniel Cui; Butterfield, Andrew; Chu, Zhengtao; Fujita, Maihi; Yang, Chieh-Hsiang; Cortes-Sanchez, Emilio; Scherer, Sandra D.; Zhao, Liang; Borovski, Tijana; Chin, Vicki; DiGiovanna, John J.; Frech, Christian; Grover, Jeffrey; Jeon, Ryan; Koc, Soner; Randjelović, Jelena; Seepo, Sara; Stankovic, Tamara; Dobrolecki, Lacey E.; Ittmann, Michael; Hilsenbeck, Susan G.; O’Malley, Bert W.; Mitsiades, Nicholas; Kaochar, Salma; Akçakanat, Argun; Augustine, Jithesh J.; Chen, Huiqin; Dai, Bingbing; Evans, Kurt W.; Gale, Kelly; Gibbons, Don L.; Ha, Min Jin; Jensen, Vanessa; Kim, Michael; Kirby, Bryce P.; Kopetz, Scott; Lanier, Christopher D.; Li, Dali; Majidi, Mourad; Menter, David G.; Meraz, Ismail M.; Saridogan, Turçin; Scott, Stephen; Sorokin, Alexey V.; Tapia, Coya; Wang, Jing; Westin, Shannon N.; Xi, Yuanxin; Xu, Yi; Yang, Fei; Yap, Timothy A.; Yennu-Nanda, Vashisht G.; Yuca, Erkan; Zhang, Jianhua; Zhang, Ran; Xiao-Shan, Zhang; Zheng, Xiaofeng; Fingerman, Dylan; Lin, Hui; Liu, Qin; Kossenkov, Andrew V.; Rebecca, Vito W.; Somasundaram, Rajasekharan; Tetzlaff, Michae T.; Wickramasinghe, Jayamanna; Xiao, Min; Xu, Xiaowei; Bult, Carol J.; Robinson, Peter N.; Srivastava, Anuj; Lloyd, Michael W.; Neuhauser, Steven B.; Rubinstein, Jill C.; Sanderson, Brian J.; White, Brian S.; Woo, Xing Yi; Wallace, Tiffany A.; Minna, John D.; Gao, Boning; Girard, Luc; Park, Hyunsil; Timmons, Brenda C.; Nathanson, Katherine L.; Xu, George; Pan, Chong-Xian; Chen, Moon S.; Carvajal‐Carmona, Luis G.; Cho, May; Coggins, Nicole B.; White, Ralph W. deVere; Polanco-Echeverry, Guadalupe; Estrada, Ana; Gandara, David R.; Kirane, Amanda; Le, Tiffany; Lott, Paul; Morales-Arana, Alexa; Reiss, Jonathan W.; Rocha, Sienna; Tepper, Clifford G.; Toal, Ted; Zhang, Hongyong; Ma, Ai-Hong; Davis‐Dusenbery, Brandi N.; 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.; Li, Ding

doi:10.1038/s41467-021-27678-7

Cited by 3 publications

(1 citation statement)

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“…For hundreds of cancer cell lines, there are extensive molecular and perturbation data available in the public domain from major endeavors, including the Cancer Cell Line Encyclopedia (CCLE) [30,31] and the Genomics of Drug Sensitivity in Cancer (GDSC) [32,33]. For PDX tumors, concerted efforts involving the NIH-NCI PDX Development and Trial Centers Research Network (PDXNet) and the NIH-NCI Patient-Derived Models Repository (PDMR) repositories have recently generated multi-omic data on over 1500 PDX tumors to date representing over 500 patients [2,34]. However, proteomics data on these tumors is currently limited, and perturbation data (e.g., gene knockdowns or drug treatments) remain to be systematically generated [35].…”

Section: Cancer Cell Line Datamentioning

confidence: 99%

Using cancer proteomics data to identify gene candidates for therapeutic targeting

et al. 2023

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Gene-level associations obtained from mass-spectrometry-based cancer proteomics datasets represent a resource for identifying gene candidates for functional studies. When recently surveying proteomic correlates of tumor grade across multiple cancer types, we identified specific protein kinases having a functional impact on uterine endometrial cancer cells. This previously published study provides just one template for utilizing public molecular datasets to discover potential novel therapeutic targets and approaches for cancer patients. Proteomic profiling data combined with corresponding multi-omics data on human tumors and cell lines can be analyzed in various ways to prioritize genes of interest for interrogating biology. Across hundreds of cancer cell lines, CRISPR loss of function and drug sensitivity scoring can be readily integrated with protein data to predict any gene's functional impact before bench experiments are carried out. Public data portals make cancer proteomics data more accessible to the research community. Drug discovery platforms can screen hundreds of millions of small molecule inhibitors for those that target a gene or pathway of interest. Here, we discuss some of the available public genomic and proteomic resources while considering approaches to how these could be leveraged for molecular biology insights or drug discovery. We also demonstrate the inhibitory effect of BAY1217389, a TTK inhibitor recently tested in a Phase I clinical trial for the treatment of solid tumors, on uterine cancer cell line viability.

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