RADIA: RNA and DNA integrated analysis for somatic mutation detection

• Published in: PloS one 2014-11, Vol.9 (11), p.e111516
• Main Authors: Radenbaugh, Amie J, Ma, Singer, Ewing, Adam, Stuart, Joshua M, Collisson, Eric A, Zhu, Jingchun, Haussler, David
• Format: Article
• Language: English
• Subjects: Adenocarcinoma; Adenocarcinoma - genetics; Algorithms; Analysis; Biology and Life Sciences; Cancer; Cancer genetics; Computer applications; Computer simulation; Datasets; Deoxyribonucleic acid; DNA; Endometrial cancer; Endometrial Neoplasms - genetics; Endometrium; Female; Gene sequencing; Genetic aspects; Genetic research; Genome, Human; Genomes; Genomics; Humans; Kinases; Lung cancer; Lung carcinoma; Lung Neoplasms - genetics; Medicine and Health Sciences; Mutation; Nucleotide sequence; Polymorphism, Single Nucleotide; Ribonucleic acid; RNA; Sensitivity; Sensitivity analysis; Sensitivity and Specificity; Sequence Analysis, DNA - methods; Sequence Analysis, RNA - methods; Simulation; Software; Tumors; Uterine cancer
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0111516

The detection of somatic single nucleotide variants is a crucial component to the characterization of the cancer genome. Mutation 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. Here we present RADIA (RNA and DNA Integrated Analysis), a novel computational method combining the patient-matched normal and tumor DNA with the tumor RNA to detect somatic mutations. The inclusion of the RNA increases the power to detect somatic mutations, especially at low DNA allelic frequencies. By integrating an individual's DNA and RNA, we are able to detect mutations that would otherwise be missed by traditional algorithms that examine only the DNA. We demonstrate high sensitivity (84%) and very high precision (98% and 99%) for RADIA in patient data from endometrial carcinoma and lung adenocarcinoma from TCGA. Mutations with both high DNA and RNA read support have the highest validation rate of over 99%. We also introduce a simulation package that spikes in artificial mutations to patient data, rather than simulating sequencing data from a reference genome. We evaluate sensitivity on the simulation data and demonstrate our ability to rescue back mutations at low DNA allelic frequencies by including the RNA. Finally, we highlight mutations in important cancer genes that were rescued due to the incorporation of the RNA.