Abstract
BackgroundExtensive genome rearrangements, known as chromothripsis, have been recently identified in several cancer types. Chromothripsis leads to complex structural variants (cSVs) causing aberrant gene expression and the formation of de novo fusion genes, which can trigger cancer development, or worsen its clinical course. The functional impact of cSVs can be studied at the RNA level using whole transcriptome sequencing (total RNA-Seq). It represents a powerful tool for discovering, profiling, and quantifying changes of gene expression in the overall genomic context. However, bioinformatic analysis of transcriptomic data, especially in cases with cSVs, is a complex and challenging task, and the development of proper bioinformatic tools for transcriptome studies is necessary.MethodsWe designed a bioinformatic workflow for the analysis of total RNA-Seq data consisting of two separate parts (pipelines): The first pipeline incorporates a statistical solution for differential gene expression analysis in a biologically heterogeneous sample set. We utilized results from transcriptomic arrays which were carried out in parallel to increase the precision of the analysis. The second pipeline is used for the identification of de novo fusion genes. Special attention was given to the filtering of false positives (FPs), which was achieved through consensus fusion calling with several fusion gene callers. We applied the workflow to the data obtained from ten patients with chronic lymphocytic leukemia (CLL) to describe the consequences of their cSVs in detail. The fusion genes identified by our pipeline were correlated with genomic break-points detected by genomic arrays.ResultsWe set up a novel solution for differential gene expression analysis of individual samples and de novo fusion gene detection from total RNA-Seq data. The results of the differential gene expression analysis were concordant with results obtained by transcriptomic arrays, which demonstrates the analytical capabilities of our method. We also showed that the consensus fusion gene detection approach was able to identify true positives (TPs) efficiently. Detected coordinates of fusion gene junctions were in concordance with genomic breakpoints assessed using genomic arrays.DiscussionByapplying our methods to real clinical samples, we proved that our approach for total RNA-Seq data analysis generates results consistent with other genomic analytical techniques. The data obtained by our analyses provided clues for the study of the biological consequences of cSVs with far-reaching implications for clinical outcome and management of cancer patients. The bioinformatic workflow is also widely applicable for addressing other research questions in different contexts, for which transcriptomic data are generated.
Highlights
Whole-genome sequencing of cancer samples has enabled the identification and detailed description of complex structural variants with chromothripsis being their prime example (Stephens et al, 2011; Rausch et al, 2012)
Obtained log2 scale normalized expressions were subjected to further computational steps, for which we developed a novel statistical pairwise comparison (PComp) approach that allows obtaining a gene expression profile of individual samples
CSVs reminiscent of chromothripsis likely share a common mechanism of origin across complex structural variants (cSVs) cases, they appear in various genomic loci and, they lead to diverse expression profiles which makes it very difficult to select an appropriate biological
Summary
Whole-genome sequencing of cancer samples has enabled the identification and detailed description of complex structural variants (cSVs) with chromothripsis being their prime example (Stephens et al, 2011; Rausch et al, 2012). The most widely accepted explanation of chromothripsis origin is based on aberrant mitosis, which is accompanied by physical separation of certain chromosomes in nuclear structures called micronuclei (Zhang et al, 2015; Ly & Cleveland, 2017) Another possible mechanism involved in chromothripsis formation revolves around the generation of so-called breakage-fusion-bridge cycles (Lo et al, 2002) leading to the occurrence of dicentric chromosomes that are disrupted during cell division. This is related to telomere shortening and, to the absence of telomeres at chromosome ends which enables chromosome fusion (Maciejowski et al, 2015; Ernst et al, 2016). We set up a novel solution for differential gene expression analysis of individual samples and de novo fusion gene detection from total RNA-Seq data. Detected coordinates of fusion gene junctions were in concordance with genomic breakpoints assessed using genomic arrays
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