Bioinformatic strategies for the analysis of genomic aberrations detected by targeted NGS panels with clinical application

Molecular profiling of tumor samples has acquired importance in cancer research, but currently also plays an important role in the clinical management of cancer patients. Rapid identification of genomic aberrations improves diagnosis, prognosis and effective therapy selection. This can be attributed mainly to the development of next-generation sequencing (NGS) methods, especially targeted DNA panels. Such panels enable a relatively inexpensive and rapid analysis of various aberrations with clinical impact specific to particular diagnoses. In this review, we discuss the experimental approaches and bioinformatic strategies available for the development of an NGS panel for a reliable analysis of selected biomarkers. Compliance with defined analytical steps is crucial to ensure accurate and reproducible results. In addition, a careful validation procedure has to be performed before the application of NGS targeted assays in routine clinical practice. With more focus on bioinformatics, we emphasize the need for thorough pipeline validation and management in relation to the particular experimental setting as an integral part of the NGS method establishment. A robust and reproducible bioinformatic analysis running on powerful machines is essential for proper detection of genomic variants in clinical settings since distinguishing between experimental noise and real biological variants is fundamental. This review summarizes state-of-the-art bioinformatic solutions for careful detection of the SNV/Indels and CNVs for targeted sequencing resulting in translation of sequencing data into clinically relevant information. Finally, we share our experience with the development of a custom targeted NGS panel for an integrated analysis of biomarkers in lymphoproliferative disorders

Bioinformatic pipelines for whole transcriptome sequencing data exploitation in leukemia patients with complex structural variants

Background Extensive 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. Methods We 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. Results 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. 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. Discussion Byapplying 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

Distinct p53 phosphorylation patterns in chronic lymphocytic leukemia patients are reflected in the activation of circumjacent pathways upon DNA damage

TP53 gene abnormalities represent the most important biomarker in chronic lymphocytic leukemia (CLL). Altered protein modifications could also influence p53 function, even in the wild‐type protein. We assessed the impact of p53 protein phosphorylations on p53 functions as an alternative inactivation mechanism. We studied p53 phospho‐profiles induced by DNA‐damaging agents (fludarabine, doxorubicin) in 71 TP53‐intact primary CLL samples. Doxorubicin induced two distinct phospho‐profiles: profile I (heavily phosphorylated) and profile II (hypophosphorylated). Profile II samples were less capable of activating p53 target genes upon doxorubicin exposure, resembling TP53‐mutant samples at the transcriptomic level, whereas standard p53 signaling was triggered in profile I. ATM locus defects were more common in profile II. The samples also differed in the basal activity of the hypoxia pathway: the highest level was detected in TP53‐mutant samples, followed by profile II and profile I. Our study suggests that wild‐type TP53 CLL cells with less phosphorylated p53 show TP53‐mutant‐like behavior after DNA damage. p53 hypophosphorylation and the related lower ability to respond to DNA damage are linked to ATM locus defects and the higher basal activity of the hypoxia pathway