Next generation RNA-sequencing in prognostic subsets of chronic lymphocytic leukemia
2012 (English)In: American Journal of Hematology, ISSN 0361-8609, E-ISSN 1096-8652, Vol. 87, no 7, 737-740 p.Article in journal (Refereed) Published
Advances in next-generation RNA-sequencing have revealed the complexity of transcriptomes by allowing both coding and noncoding (nc) RNAs to be analyzed. However, limited data exist regarding the whole transcriptional landscape of chronic lymphocytic leukemia (CLL). In this pilot-study, we evaluated RNA-sequencing in CLL by comparing two subsets which carry almost identical or `` stereotyped'' B-cell receptors with distinct clinical outcome, that is the poor-prognostic subset # 1 (n = 4) and the more favorable-prognostic subset # 4 (n = 4). Our analysis revealed that 156 genes (e.g. LPL, WNT9A) and 76 ncRNAs, (e. g. SNORD48, SNORD115) were differentially expressed between the subsets. This technology also enabled us to identify numerous subset-specific splice variants (n = 406), which were predominantly expressed in subset # 1, including a splice-isoform of MSI2 with a novel start exon. A further important application of RNA-sequencing was for mutation detection and revealed 16-30 missense mutations per sample; notably many of these changes were found in genes with a strong potential for involvement in CLL pathogenesis, e. g., ATM and NOTCH2. This study not only demonstrates the effectiveness of RNA-sequencing for identifying mutations, quantifying gene expression and detecting splicing events, but also highlights the potential such global approaches have to significantly advance our understanding of the molecular mechanisms behind CLL development.
Place, publisher, year, edition, pages
2012. Vol. 87, no 7, 737-740 p.
LPL expression, LPL catalytical activity, IGHV mutational status, IGHV3 21 usage, Chronic lymphocytic leukemia, Prognosis
Medical and Health Sciences
IdentifiersURN: urn:nbn:se:uu:diva-176418DOI: 10.1002/ajh.23227ISI: 000305209700025PubMedID: 22674506OAI: oai:DiVA.org:uu-176418DiVA: diva2:535185