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Comprehensive characterization of patient-derived xenograft models of pediatric leukemia

Authors :
Anna Rogojina
Laura J. Klesse
Erin Butler
Jiwoong Kim
He Zhang
Xue Xiao
Lei Guo
Qinbo Zhou
Taylor Hartshorne
Dawn Garcia
Korri Weldon
Trevor Holland
Abhik Bandyopadhyay
Luz Perez Prado
Shidan Wang
Donghan M. Yang
Anne-Marie Langevan
Yi Zou
Allison C. Grimes
Chatchawin Assanasen
Vinod Gidvani-Diaz
Siyuan Zheng
Zhao Lai
Yidong Chen
Yang Xie
Gail E. Tomlinson
Stephen X. Skapek
Raushan T. Kurmasheva
Peter J. Houghton
Lin Xu
Source :
iScience, Vol 26, Iss 11, Pp 108171- (2023)
Publication Year :
2023
Publisher :
Elsevier, 2023.

Abstract

Summary: Patient-derived xenografts (PDX) remain valuable models for understanding the biology and for developing novel therapeutics. To expand current PDX models of childhood leukemia, we have developed new PDX models from Hispanic patients, a subgroup with a poorer overall outcome. Of 117 primary leukemia samples obtained, successful engraftment and serial passage in mice were achieved in 82 samples (70%). Hispanic patient samples engrafted at a rate (51/73, 70%) that was similar to non-Hispanic patient samples (31/45, 70%). With a new algorithm to remove mouse contamination in multi-omics datasets including methylation data, we found PDX models faithfully reflected somatic mutations, copy-number alterations, RNA expression, gene fusions, whole-genome methylation patterns, and immunophenotypes found in primary tumor (PT) samples in the first 50 reported here. This cohort of characterized PDX childhood leukemias represents a valuable resource in that germline DNA sequencing has allowed the unambiguous determination of somatic mutations in both PT and PDX.

Subjects

Subjects :
Cancer
Omics
Transcriptomics
Science

Details

Language :
English
ISSN :
25890042
Volume :
26
Issue :
11
Database :
Directory of Open Access Journals
Journal :
iScience
Publication Type :
Academic Journal
Accession number :
edsdoj.22cacf03f3b04f17bc8080bce501067f
Document Type :
article
Full Text :
https://doi.org/10.1016/j.isci.2023.108171