Much of our current understanding of cancer has come from investigating how normal cells are transformed into malignant cancers through the stepwise acquisition of somatic genomic abnormalities. These abnormalities include single nucleotide variants (SNVs), insertions and deletions (INDELs), chromosomal rearrangements, and copy number aberrations. The detection of SNVs is a crucial component to the characterization of the cancer genome. They assist in identifying key genes as possible drug targets, diagnostic markers for early detection, and prognostic markers for monitoring a patient's response to therapy. Variant calling algorithms thus far have focused on comparing the normal and tumor genomes from the same individual. In recent years, it has become routine for projects like The Cancer Genome Atlas (TCGA) to also sequence the tumor RNA. A novel computational method called RADIA (RNA and DNA Integrated Analysis) that combines the patient-matched normal and tumor DNA with the tumor RNA to detect SNVs is presented here. RADIA has detected somatic mutations for nearly 4,500 patients across 22 different cancers, and including the RNA provided a 2-7% increase in sensitivity.
RNA editing is an additional epigenetic mechanism involved in cancer development and progression. RNA editing of the AZIN1 gene has been identified as a driver in the pathogenesis of hepatocellular carcinoma and may be a potential driver for other human cancers as well. An investigation of AZIN1 RNA editing in data collected from nearly 5,000 patients across 12 cancers has been performed. Higher editing frequencies significantly correlated with clinical data such as larger tumor sizes, greater lymph node involvement, the presence of metastases, and higher tumor grades. They were also associated with subtypes that often have the worst prognosis. Over-editing in many cancers is correlated with poor overall and recurrence free survival.
With projects like TCGA providing sequencing data for both DNA and RNA from the same patients across multiple cancers, it is now possible to characterize germline variants, somatic mutations, and RNA editing events on a genome-wide scale. The identification of SNVs that occur in specific genes across multiple cancers provides a powerful way to discover genes that are important to these diseases.