RT Journal Article
SR Electronic
T1 An Integrated Framework for Genome Analysis Reveals Numerous Previously Unrecognizable Structural Variants in Leukemia Patients’ Samples
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 563270
DO 10.1101/563270
A1 Jie Xu
A1 Fan Song
A1 Emily Schleicher
A1 Christopher Pool
A1 Darrin Bann
A1 Max Hennessy
A1 Kathryn Sheldon
A1 Emma Batchelder
A1 Charyguly Annageldiyev
A1 Arati Sharma
A1 Yuanyuan Chang
A1 Alex Hastie
A1 Barbara Miller
A1 David Goldenberg
A1 Shin Mineishi
A1 David Claxton
A1 George-Lucian Moldovan
A1 Feng Yue
A1 James R. Broach
YR 2019
UL http://biorxiv.org/content/early/2019/02/28/563270.abstract
AB While genomic analysis of tumors has stimulated major advances in cancer diagnosis, prognosis and treatment, current methods fail to identify a large fraction of somatic structural variants in tumors. We have applied a combination of whole genome sequencing and optical genome mapping to a number of adult and pediatric leukemia samples, which revealed in each of these samples a large number of structural variants not recognizable by current tools of genomic analyses. We developed computational methods to determine which of those variants likely arose as somatic mutations. The method identified 97% of the structural variants previously reported by karyotype analysis of these samples and revealed an additional fivefold more such somatic rearrangements. The method identified on average tens of previously unrecognizable inversions and duplications and hundreds of previously unrecognizable insertions and deletions. These structural variants recurrently affected a number of leukemia associated genes as well as cancer driver genes not previously associated with leukemia and genes not previously associated with cancer. A number of variants only affected intergenic regions but caused cis-acting alterations in expression of neighboring genes. Analysis of TCGA data indicates that the status of several of the recurrently mutated genes identified in this study significantly affect survival of AML patients. Our results suggest that current genomic analysis methods fail to identify a majority of structural variants in leukemia samples and this lacunae may hamper diagnostic and prognostic efforts.