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PLoS Genet. 2013;9(12):e1003997. doi: 10.1371/journal.pgen.1003997. Epub 2013 Dec 19.

Comprehensive analysis of transcriptome variation uncovers known and novel driver events in T-cell acute lymphoblastic leukemia.

Author information

1
Laboratory of Computational Biology, Center for Human Genetics, KU Leuven, Leuven, Belgium.
2
Laboratory for the Molecular Biology of Leukemia, Center for Human Genetics, KU Leuven and Center for the Biology of Disease, VIB, Leuven, Belgium ; Division of Hematology, Department of Cellular Biotechnologies and Hematology, 'Sapienza' University of Rome, Rome, Italy.
3
Laboratory for the Molecular Biology of Leukemia, Center for Human Genetics, KU Leuven and Center for the Biology of Disease, VIB, Leuven, Belgium.
4
Laboratory of Computational Biology, Center for Human Genetics, KU Leuven, Leuven, Belgium ; Laboratory for the Molecular Biology of Leukemia, Center for Human Genetics, KU Leuven and Center for the Biology of Disease, VIB, Leuven, Belgium.
5
Division of Hematology, Department of Cellular Biotechnologies and Hematology, 'Sapienza' University of Rome, Rome, Italy.
6
Center for Medical Genetics, Ghent University, Ghent, Belgium.
7
Pediatric Hemato-Oncology, University Hospitals Leuven, Leuven, Belgium.
8
Pediatric Oncology/Hematology and Hematology, VU Medical Center, Amsterdam, The Netherlands.

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.

PMID:
24367274
PMCID:
PMC3868543
DOI:
10.1371/journal.pgen.1003997
[Indexed for MEDLINE]
Free PMC Article

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