Open AccessComprehensive Analysis of Transcriptome Variation Uncovers Known and Novel Driver Events in T-Cell Acute Lymphoblastic Leukemia
Author(s) -
Zeynep Kalender Atak,
Valentina Gianfelici,
Gert Hulselmans,
Kim De Keersmaecker,
Arun George Devasia,
Ellen Geerdens,
Nele Mentens,
Sabina Chiaretti,
Kaat Durinck,
Anne Uyttebroeck,
Peter Vandenberghe,
Iwona Włodarska,
Jacqueline Cloos,
Robin Foà,
Franki Speleman,
Jan Cools,
Stein Aerts
Publication year - 2013
Publication title -
plos genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.587
H-Index - 233
eISSN - 1553-7404
pISSN - 1553-7390
DOI - 10.1371/journal.pgen.1003997
Subject(s) - biology , gene , transcriptome , fusion gene , genetics , computational biology , exon , rna , rna seq , gene expression , indel , exome sequencing , gene expression profiling , exome , mutation , single nucleotide polymorphism , genotype
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.