A BIOINFORMATIC APPROACH FOR NGS DATA TO ANALIZE AND VISUALIZE CHROMOSOMAL FUSION EVENTS IN HUMAN BREAST CANCER.

Cancer is a multi-stage process often driven by progressive accumulation of genomic rearrangements that can result in cells acquiring cancer properties such as tumor invasive and metastatic behavior. Many genes associated with cancer are the result of complex somatically and inherited chromosomal rearrangements, resulting in aberrant transcripts or defects in transcription [1-5]. The classical approach for the identification of genome rearrangements such as G-banded cytogenetics, spectral karyotyping and FISH, are poor in sensitivity, while copy number array can identify just imbalanced breakpoints and do not describe the resulted genome structure produced by the events, which may cause the breakpoints. The aim of this project is to obtain, by the paired-end mapping (PEM) approach applied to the massive parallel sequencing, an high resolution virtual karyotype of the genome of a breast-cancer-patient of which we obtained previously the transcriptomic portrait [6]. The introduction of massively parallel high throughput sequencing (HTS) techniques have created a broad range of new and exciting research applications by increasing the output sequencing data dramatically. In recent years, the continuous technical improvements of next-generation sequencing technology have made RNA sequencing (RNA-seq) particularly effective for the detection of gene fusions, which are involved in several diseases. Gene fusions are found in many cancer types, and they have proved to be prognostic biomarkers in several studies [7-9]. In addition, gene fusions have often a direct functional impact on the molecular processes in the cell [10]. Several analysis steps are needed to process the data provided by the sequencer and to use them for robust gene fusion detection. We propose a workflow to analyze NGS paired-end sequences in order to identify possible candidates to be the results of a fusion between different genes, looking for fusion events occurring on the same chromosome (intra-chromosomal rearrangement). The basic idea is to map the reads onto the reference genome and to study the insert size length distribution of the paired-end, looking at its peak and select all the mapping pairs having an insert size value quite far from the observed peak. In this way we are sure to select paired-end sequences mapping on different regions of the genome far from each other connecting different genes.