ABSTRACT
Technical advances in genome sequencing, in particular whole-genome sequencing (WGS), provide adequate tools to understanding cancer at the molecular level while specifically focusing on genetic variants that contribute to the causation and progression of pathogenic cancers. Multiple myeloma (MM), a malignant disease of plasma cells that is marked as rare yet incurable, may be diagnosed by WGS tools, as this cancer is associated with chromosomal translocations and mutations in specific protein-coding genes. Among these protein-coding genes, many are known to be responsible for cell cycle regulation in MM. The initial significant protein-coding mutations were found in NRAS, KRAS and TP53 and later reported in FAM46C, DIS3, CCND1, PNRC1, ALOX12B, HLA-A and MAGED1. Here, we report gene network associations of MM using Qiagen's Ingenuity Pathway Analysis (IPA) software and compared biomarker information reported in IPA for these protein-coding genes (NRAS, TP53 and KRAS). Using Qiagen's Ingenuity Variant Analysis (IVA), we characterized cancer driver variants in MT-ND1 as likely pathogenic or variants of uncertain significance.