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

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

Genomics in acute lymphoblastic leukaemia: insights and treatment implications

Acute lymphoblastic leukaemia (ALL) is the commonest childhood cancer and an important cause of morbidity from haematological malignancies in adults. In the past several years, we have witnessed major advances in the understanding of the genetic basis of ALL. Genome-wide profiling studies, including microarray analysis and genome sequencing, have helped identify multiple key cellular pathways that are frequently mutated in ALL such as lymphoid development, tumour suppression, cytokine receptors, kinase and Ras signalling, and chromatin remodeling. These studies have characterized new subtypes of ALL, notably Philadelphia chromosome-like ALL, which is a high-risk subtype characterized by a diverse range of alterations that activate cytokine receptors or tyrosine kinases amenable to inhibition with approved tyrosine kinase inhibitors. Genomic profiling has also enabled the identification of inherited genetic variants of ALL that influence the risk of leukaemia development, and characterization of the relationship between genetic variants, clonal heterogeneity and the risk of relapse. Many of these findings are of direct clinical relevance and ongoing studies implementing clinical sequencing in leukaemia diagnosis and management have great potential to improve the outcome of patients with high-risk ALL.Kathryn G. Roberts and Charles G. Mulligha