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Title: Comprehensive analysis of transcriptome variation uncovers known and novel driver events in T-cell acute lymphoblastic leukemia
Authors: Kalender Atak, Zeynep *
Gianfelici, Valentina *
Hulselmans, Gert *
De Keersmaecker, Kim *
Devasia, Arun George
Geerdens, Ellen
Mentens, Nicole
Chiaretti, Sabina
Durinck, Kaat
Uyttebroeck, Anne
Vandenberghe, Peter
Wlodarska, Iwona
Cloos, Jacqueline
Foà, Robin
Speleman, Frank
Cools, Jan #
Aerts, Stein # ×
Issue Date: Dec-2013
Publisher: Public Library of Science
Series Title: PLoS Genetics vol:9 issue:12 pages:e1003997
Article number: 10.1371/journal.pgen.1003997
Abstract: RNA-seq is a promising technology to re-sequence protein coding genes for the identification of single nucleotide variants (SNV), while simultaneously obtaining information on structural variations and gene expression perturbations. We asked whether RNA-seq is suitable for the detection of driver mutations in T-cell acute lymphoblastic leukemia (T-ALL). These leukemias are caused by a combination of gene fusions, over-expression of transcription factors and cooperative point mutations in oncogenes and tumor suppressor genes. We analyzed 31 T-ALL patient samples and 18 T-ALL cell lines by high-coverage paired-end RNA-seq. First, we optimized the detection of SNVs in RNA-seq data by comparing the results with exome re-sequencing data. We identified known driver genes with recurrent protein altering variations, as well as several new candidates including H3F3A, PTK2B, and STAT5B. Next, we determined accurate gene expression levels from the RNA-seq data through normalizations and batch effect removal, and used these to classify patients into T-ALL subtypes. Finally, we detected gene fusions, of which several can explain the over-expression of key driver genes such as TLX1, PLAG1, LMO1, or NKX2-1; and others result in novel fusion transcripts encoding activated kinases (SSBP2-FER and TPM3-JAK2) or involving MLLT10. In conclusion, we present novel analysis pipelines for variant calling, variant filtering, and expression normalization on RNA-seq data, and successfully applied these for the detection of translocations, point mutations, INDELs, exon-skipping events, and expression perturbations in T-ALL.
URI: 
ISSN: 1553-7390
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Laboratory for Genetics of Malignant Disorders
Laboratory of Computational Biology
Laboratory of Molecular Biology of Leukemia
Department of Human Genetics - miscellaneous
* (joint) first author
× corresponding author
# (joint) last author

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