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Your search keyword '"Ryan Jeon"' showing total 3 results
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3 results on '"Ryan Jeon"'

1. Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidatesfor targeted treatment

Author

Li Chen, Jacqueline Mudd, Michael Ittmann, Carol J. Bult, Amanda R. Kirane, Jelena Randjelovic, Stephen Scott, Yige Wu, Li Ding, Vashisht G. Yennu-Nanda, Jing Wang, Christopher D. Lanier, Maihi Fujita, Emilio Cortes-Sanchez, Sienna Rocha, Susan G. Hilsenbeck, Kian-Huat Lim, Fernanda Martins Rodrigues, Jill Rubinstein, Nicholas Mitsiades, Haiyin Lin, Jayamanna Wickramasinghe, Andrew Butterfield, Bryan E. Welm, Alana L. Welm, Jose P. Zevallos, Jason Held, Nicole B. Coggins, Song Cao, Yuanxin Xi, Brenda C. Timmons, Paul Lott, David Menter, Shunqiang Li, Tina Primeau, Fei Yang, Andrea Wang-Gillam, Ramaswamy Govindan, Dali Li, Brandi Davis-Dusenbery, Sara Seepo, Michael C. Wendl, Jeffrey Grover, Brian S. White, Clifford G. Tepper, Peter N. Robinson, Michael A. Davies, Zhengtao Chu, Michael W. Lloyd, Hua Sun, Xiaoshan Zhang, Tamara Stankovic, Dylan Fingerman, Anuj Srivastava, Luis G. Carvajal-Carmona, Don L. Gibbons, Lijun Yao, Rebecca Aft, Hongyong Zhang, Ismail Meraz, John DiGiovanna, Scott Kopetz, Ling Zhao, Guadalupe Polanco-Echeverry, Feng Chen, Jeremy Hoog, Matthew A. Wyczalkowski, George Xu, John D. Minna, Yi Xu, Julie Belmar, Xiaowei Xu, Luc Girard, Dennis A. Dean, Tijana Borovski, Chong-xian Pan, Cynthia X. Ma, Alexa Morales Arana, Yize Li, Turcin Saridogan, Steven B. Neuhauser, Sandra Scherer, Vicki Chin, Rose Tipton, David R. Gandara, Sherri R. Davies, Argun Akcakanat, Rajesh Patidar, Julie K. Schwarz, Soner Koc, Gao Boning, Michael Kim, Bryce P. Kirby, Yvonne A. Evrard, Hyunsil Park, Christian Frech, Chia-Kuei Mo, Ran Zhang, Brian A. Van Tine, Jonathan W. Reiss, Min Xiao, Xing Yi Woo, Tiffany Le, Ana Estrada, Xiaofeng Zheng, Jeffrey A. Moscow, Mourad Majidi, Nadezhda V. Terekhanova, Katherine Fuh, Erkan Yuca, Timothy A. Yap, Jianhua Zhang, Matthew J. Ellis, Shannon Westin, James H. Doroshow, Vito W. Rebecca, Moon S. Chen, Coya Tapia, Reyka G Jayasinghe, Jack A. Roth, Jithesh Augustine, Ryan C. Fields, Michae T. Tetzlaff, Michael T. Lewis, Kurt W. Evans, Ralph W. deVere White, Brian J. Sanderson, May Cho, Jeffrey H. Chuang, Tiffany Wallace, Ryan Jeon, Ted Toal, Matthew H. Bailey, Bert W. O'Malley, Katherine L. Nathanson, Qin Liu, Benjamin J. Raphael, Jingqin Luo, Salma Kaochar, Huiqin Chen, Rajasekharan Somasundaram, Daniel Cui Zhou, John F. DiPersio, Andrew V. Kossenkov, Bingliang Fang, Vanessa Jensen, Simone Zaccaria, Alexey Sorokin, Ai-Hong Ma, Sidharth V. Puram, Min Jin Ha, Meenhard Herlyn, R. Jay Mashl, Kelly Gale, Bingbing Dai, Lacey E. Dobrolecki, Chieh-Hsiang Yang, and Funda Meric-Bernstam

Subjects
endocrine system, Science, Druggability, General Physics and Astronomy, Genomics, Computational biology, Biology, Genome, digestive system, General Biochemistry, Genetics and Molecular Biology, Article, Research community, Multiple time, medicine, Cancer genomics, Cancer models, Tumor xenograft, Multidisciplinary, Cancer, General Chemistry, medicine.disease, Pharmacogenomics, Data integration, hormones, hormone substitutes, and hormone antagonists
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., Patient-derived xenograft models (PDX) have been extensively used to study the molecular and clinical features of cancers. Here the authors present a cohort of 536 PDX models from 25 cancers, as well as their genomic and evolutionary profiles and their suitability for clinical trials.

Published
2021

2. Systematic Establishment of Robustness and Standards in Patient-Derived Xenograft Experiments and Analysis

Author

Anuj Srivastava, Shunqiang Li, Brian A. Van Tine, Christian Frech, Carol J. Bult, Rajesh Patidar, Vito W. Rebecca, Jeffrey S. Morris, Michael Davies, Huiqin Chen, Sasi Arunachalam, Jeffrey A. Moscow, Ramaswamy Govindan, Jayamanna Wickramasinghe, Zi-Ming Zhao, James H. Doroshow, Adam Stanojevic, Dennis A. Dean, Funda Meric-Bernstam, Jacqueline Rosains, Michael T. Lewis, Bryan E. Welm, Min Xiao, Jelena Randjelovic, Sherri R. Davies, Lily Chen, Brandi N. Davis-Dusenbery, David A. Nix, Meenhard Herlyn, Li Ding, Jack DiGiovanna, Xing Yi Woo, Jack A. Roth, Min Jin Ha, Steven B. Neuhauser, Ryan Jeon, Peter N. Robinson, Bingliang Fang, Michael Lloyd, Tamara Stankovic, Jeffrey H. Chuang, Yvonne A. Evrard, Nevena Miletic, Alana L. Welm, Isheeta Seth, and Andrew V. Kossenkov

Subjects
endocrine system, 0303 health sciences, Computer science, Cancer, Computational biology, medicine.disease, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, Robustness (computer science), 030220 oncology & carcinogenesis, medicine, In patient, 030304 developmental biology
Abstract

Patient-Derived Xenografts (PDXs) are tumor-in-mouse models for cancer. PDX collections, such as those supported by the NCI PDXNet program, are powerful resources for preclinical therapeutic testing. However, variations in experimental design and analysis procedures have limited interpretability. To determine the robustness of PDX studies, the PDXNet tested temozolomide drug response for three pre-validated PDX models (sensitive, resistant, and intermediate) across four blinded PDX Development and Trial Centers (PDTCs) using independently selected SOPs. Each PDTC was able to correctly identify the sensitive, resistant, and intermediate models, and statistical evaluations were concordant across all groups. We also developed and benchmarked optimized PDX informatics pipelines, and these yielded robust assessments across xenograft biological replicates. These studies show that PDX drug responses and sequence results are reproducible across diverse experimental protocols. Here we share the range of experimental procedures that maintained robustness, as well as standardized cloud-based workflows for PDX exome-seq and RNA-Seq analysis and for evaluating growth.

Published
2019
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3. Author Correction: Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts

Author

Michael Lloyd, R. Jay Mashl, Yvonne A. Evrard, Jeffrey A. Moscow, Jong Il Kim, Alana L. Welm, Michael A. Davies, Carol J. Bult, Jayamanna Wickramasinghe, Rania El Botty, Dennis A. Dean, Jessica Giordano, Anuj Srivastava, Sandra D. Scherer, Jacqueline Rosains, Christian Frech, Elisabetta Marangoni, Jeffrey H. Chuang, Ryan Jeon, Shunqiang Li, Matthew H. Bailey, Yun-Suhk Suh, Elodie Modave, Yuanxin Xi, Enzo Medico, Li Ding, Livio Trusolino, Adam Lafferty, Vito W. Rebecca, Han-Kwang Yang, Jing Wang, Annette T. Byrne, Xing Yi Woo, Alice C. O’Farrell, Claudio Isella, Li Chen, Brandi N. Davis-Dusenbery, James H. Doroshow, Sherri R. Davies, Diether Lambrechts, Jos Jonkers, Bingliang Fang, Charles Lee, Roebi de Bruijn, Violeta Serra, Jack A. Roth, Rajesh Patidar, Funda Meric-Bernstam, Petra ter Brugge, Andrew V. Kossenkov, Hyun-Soo Kim, Andrea Bertotti, Emilio Cortes-Sanchez, Chieh-Hsiang Yang, Ramaswamy Govindan, Francesco Galimi, Jelena Randjelovic, Zi-Ming Zhao, Bryan E. Welm, Hua Sun, Meenhard Herlyn, and Michael T. Lewis

Subjects
Oncology, medicine.medical_specialty, DNA Copy Number Variations, MEDLINE, Biology, Polymorphism, Single Nucleotide, Mice, Text mining, Internal medicine, Databases, Genetic, Exome Sequencing, Genetics, medicine, Animals, Humans, Neoplasm Metastasis, Author Correction, Cancer, business.industry, Published Erratum, medicine.disease, Xenograft Model Antitumor Assays, Computational biology and bioinformatics, Gene Expression Regulation, Neoplastic, business
Abstract

Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution during PDX engraftment and propagation, affecting the accuracy of PDX modeling of human cancer. Here, we exhaustively analyze copy number alterations (CNAs) in 1,451 PDX and matched patient tumor (PT) samples from 509 PDX models. CNA inferences based on DNA sequencing and microarray data displayed substantially higher resolution and dynamic range than gene expression-based inferences, and they also showed strong CNA conservation from PTs through late-passage PDXs. CNA recurrence analysis of 130 colorectal and breast PT/PDX-early/PDX-late trios confirmed high-resolution CNA retention. We observed no significant enrichment of cancer-related genes in PDX-specific CNAs across models. Moreover, CNA differences between patient and PDX tumors were comparable to variations in multiregion samples within patients. Our study demonstrates the lack of systematic copy number evolution driven by the PDX mouse host.

Published
2021
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