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: Nov-2013
Conference: EMBL Conference on Cancer Genomics edition:2 location:Heidelberg, Germany date:3-5 November 2013
Abstract: Sequencing cancer transcriptomes with RNA-seq is a powerful approach for finding expression perturbations and several tiers genomic aberrations such as mutations, indels, and genomic translocations. Here we provide a systematic exploration of the T cell acute lymphoblastic leukemia (T-ALL) transcriptome by sequencing 31 primary samples and 18 cell lines by high-coverage paired-end RNA-seq. T-ALL is characterized by chromosomal translocations causing over expression of transcription factors and additional mutations affecting the tumor suppressors, oncogenes and signaling molecules. Although the genomic landscape of T-ALL is investigated by targeted sequencing approaches, as well exome and whole genome sequencing, the transcriptomic landscape is yet to be described. We used this dataset for finding novel driver events in T-ALL. First, by using our previously published exome dataset, we have optimized and validated our mutation detection approach on RNA-seq data, and identified driver mutations not only in the established T-ALL driver genes such as NOTCH1, FBXW7, and BCL11B, but also in promising candidates such as H3F3A, PTK2B, and STAT5B. Next, we obtained gene expression levels from RNA-seq data after rigorous normalization and batch effect removal procedures, and classified the samples to distinct molecular subtypes using the expression levels of marker genes. Finally, we have obtained gene fusions from the RNA-seq data and identified several fusion events resulting with over expression of a known T-ALL driver gene such as TLX1, PLAG1, LMO1, or NKX2-1, or resulting in chimeric fusion genes such as SSBP2-FER and TPM3-JAK2. In conclusion, we demonstrated the power of transcriptome sequencing for identification of novel driver aberrations with the use of optimized analysis pipelines.
Publication status: accepted
KU Leuven publication type: IMa
Appears in Collections:Laboratory of Computational Biology
Laboratory of Molecular Biology of Leukemia
Department of Human Genetics - miscellaneous
Laboratory for Genetics of Malignant Disorders
× corresponding author
# (joint) last author

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