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BioinformaticsHemaSphere
Peer-reviewed journal article

Integrating detection of copy neutral chromosomal losses in a clinical setting in leukemia and lymphoma by means of allelic imbalance and read depth ratio comparison

M. C. Hansen, Oriane CédileiD, M. Ludvigsen, E. Kjeldsen, Peter Loof MølleriD, Niels AbildgaardiD, Peter HoklandiD, Charlotte Guldborg NyvoldiD

Abstract

Chromosomal aberrations are common features of hematological malignancies, with several recurrent aberrations recognized as important diagnostic or prognostic molecular markers. While genome-wide genotyping and genomic hybridization microarray analyses have been implemented in clinical laboratories for several years the usage of next generation sequencing for detection of acquired copy number alterations has not yet reached full clinical integration. One evident problem is the identification of copy neutral loss of heterozygosity (CN-LoH), which is not detectable by sequencing read depth correlation or the analogous microarray CGH. We selected 23 paired samples of hematological disorders from 14 individuals, focusing on leukemia and lymphomas, and tested whether a low complexity approach, relying on Fishers exact test or {chi}2, is efficient for the analysis of variant allele frequencies with read-depth ratio correlations in order to resolve both copy-altering and neutral chromosomal aberrations. This combination helped to identify 69 altered chromosomes and offered mutual confirmation. Moreover, six CN-LoHs (>1% AF shifts, p<0.01) were found with one additional suspected low frequency deletion (~10-15% burden) in a case of CLL. We conclude that this simple method is directly clinically applicable for the detection of copy neutral chromosomal loss of single genes from WES with intermediate coverage.\n\nAvailabilityMentioned plot software is available at Harvard Dataverse http://doi.org/10.7910/DVN/KFMGNY\n\nContactMarcus.celik.hansen@rsyd.dk or marcus@clin.au.dk\n\nSupplementary informationSupplementary plots are available

PDFDOI

02 /Specification & Classification

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