Syeda Aiman Nadeem James J. Kelley, MS Andrey Grigoriev, PhD Rutgers University, Camden Biology Department
Poster # 36
Structural variants (SVs), large genomic alterations, are often responsible for tumor development. Most tumor sequencing studies focus on single-nucleotide mutations and leave SVs aside due to the difficulties in their analysis. We have analyzed the data obtained in a recent effort to sequence 1,143 patients with multiple myeloma to address this disparity in analysis by focusing on SVs. We used two computational tools developed in our lab to locate and visualize SVs after comparing two different genomes (normal and tumor). The first one was the Genome Rearrangement OmniMapper (GROM), a variant caller with superior speed, sensitivity, and precision used to identify the variants. The second tool was the Variant Navigator (VN) to visually examine the reads and compare SVs in tumor samples with corresponding normal samples. Such an examination is important for samples of lower sequencing quality. Our analysis pipeline enables us to validate the type and location of mutation, and genes overlapping or near the variant. We found many common SVs present in tumor (but not normal) samples affecting a range of genes including LRP5, IGFN1, GLB1L2, PLIN4, MUC5B, and CDC34. These genes are related to various functions that play a potential part in tumorigenesis, for example, affecting tumor incidence, tumor growth, innate immunity, and inflammation. Finding these genes disrupted by SVs helps us identify the proteins involved in tumorigenesis and this analysis may further allow us to find potential drug targets.
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