Abstract
Background: High-throughput next generation sequencing (NGS) technologies enable the detection of biomarkers used for tumor classification, disease monitoring and cancer therapy. Whole-transcriptome analysis using RNA-seq is important, not only as a means of understanding the mechanisms responsible for complex diseases but also to efficiently identify novel genes/exons, splice isoforms, RNA editing, allele-specific mutations, differential gene expression and fusion-transcripts or chimeric RNA (chRNA). Methods: We used Crac, a tool that uses genomic locations and local coverage to classify biological events and directly infer splice and chimeric junctions within a single read. Crac's algorithm extracts transcriptional chimeric events irrespective of annotation with a high sensitivity, and CracTools was used to aggregate, annotate and filter the chRNA reads. The selected chRNA candidates were validated by real time PCR and sequencing. In order to check the tumor specific expression of chRNA, we analyzed a publicly available dataset using a new tag search approach. Results: We present data related to acute myeloid leukemia (AML) RNA-seq analysis. We highlight novel biological cases of chRNA, in addition to previously well characterized leukemia chRNA. We have identified and validated 17 chRNAs among 3 AML patients: 10 from an AML patient with a translocation between chromosomes 15 and 17 (AML-t(15;17), 4 from patient with normal karyotype (AML-NK) 3 from a patient with chromosomal 16 inversion (AML-inv16). The new fusion transcripts can be classified into four groups according to the exon organization. Conclusions: All groups suggest complex but distinct synthesis mechanisms involving either collinear exons of different genes, non-collinear exons, or exons of different chromosomes. Finally, we check tumor-specific expression in a larger RNA-seq AML cohort and identify new AML biomarkers that could improve diagnosis and prognosis of AML.
Highlights
High-throughput sequencing technologies (NGS) enable the detection of new biomarkers used for tumor classification and disease monitoring, including patient response to therapies
In order to assess Crac’s potential to identify new biomarkers, we present data related to acute myeloid leukemia (AML) RNAseq analysis
The CBFB-MYH11 and PML-RARA fusion transcripts expressed in the AML-inv16 and AML-t(15;17) samples were identified using both RNA-seq and quantitative polymerase chain reaction (qPCR) analysis, confirming the reliability of RNA-seq and the Crac suite in this type of analysis
Summary
High-throughput sequencing technologies (NGS) enable the detection of new biomarkers used for tumor classification and disease monitoring, including patient response to therapies. Whole-transcriptome analysis with RNA-seq is increasingly acquiring a key role, to learn about mechanisms responsible for complex disease, and to identify novel genes/exons, splice isoforms, RNA editing, allele-specific mutation, differential gene expression, fusion-transcripts and chimeric RNA (chRNA). For chimeric RNA, a group of fusion transcripts is increasingly used by geneticists in oncology diagnosis3,4 These cancer biomarkers are generated at DNA level from gene fusions by mechanisms such as translocations, inversions, or more complex chromosomal rearrangements. Wholetranscriptome analysis using RNA-seq is important, as a means of understanding the mechanisms responsible for complex diseases and to efficiently identify novel genes/exons, splice isoforms, RNA editing, allele-specific mutations, differential gene expression and fusion-transcripts or chimeric RNA (chRNA). We highlight novel biological cases of chRNA, in version 2
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