Personalized cancer medicine, the matching of therapies to a given patient's somatic alterations, depends on highly accurate and complete identification of patients’ somatic alterations, or their mutome. Advances in sequencing technologies (exome sequencing, RNAseq, and whole genome sequencing) have provided a means to examine large portions of the genetic content of patients’ cancers. Computational tools have arisen that make somatic mutation predictions utilizing particular sequencing assays; however, each sequencing assay has limitations and existing mutation detection tools exhibit less than ideal agreement when analyzing the same data. The task of identifying all somatic mutations in one patient's cancer remains a challenge to personalized cancer medicine. Typically, somatic mutation detection is performed utilizing DNA sequencing. Because RNA sequencing is often a component of genome characterization projects along with DNA sequencing, we sought to evaluate the possible added value of RNA sequencing in somatic mutation detection. We have developed an original computational method, UNCeqR, that makes patient-specific somatic mutation predictions utilizing RNA sequencing combined with DNA sequencing. DNA mutations and RNA mutations are statistically modeled separately and results are combined in a meta-analytic fashion, resulting in up to three predictions for a locus: DNA-only, RNA-only, and DNA+RNA. In addition to de novo genomewide mutation predictions, UNCeqR can query specific a priori mutations. UNCeqR was applied to The Cancer Genome Atlas (TCGA) lung squamous cell carcinoma sequencing data, consisting of Ilumina RNAseq and Illumina exome sequencing. Of annotated exons, 20% had very low to zero coverage in RNA and 5% had very low to zero coverage in DNA, indicating that both sequencing assays add new genomic territory for mutation detection. Limiting to regions with both DNA and RNA coverage, 56% of mutations detected from DNA were also predicted by RNA, providing an independent validation of these mutations. To evaluate if mutation detection using DNA+RNA is superior to detection using DNA-only, cancer specimen DNA and RNA reads were randomly split into subsamples. UNCeqR was executed on each of the subsamples and mutation agreement was compared among pairs of subsamples within regions of DNA and RNA coverage. Compared with the DNA-only method, DNA+RNA mutation detection exhibited a 42% relative increase in percent agreement across subsamples and a 230% relative increase in the number of mutations detected. Therefore, RNA sequencing adds positive value to somatic mutation detection via UNCeqR.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3975. doi:1538-7445.AM2012-3975