Abstract B03: Methods for integrated analysis of RNA and DNA sequencing in pediatric cancers

Pediatric cancers have a very different genomic profile from adult cancers. For example, single-nucleotide variants (SNVs) are common drivers in many adult cancers but are not as prevalent in many pediatric cancers. In particular, a large subset of solid tumors is driven by copy number alterations and structural variations (SV), including translocation-induced gene fusions. These SVs can be difficult to profile using commercial sequencing panels or DNA-only sequencing. However, by integrating the results from RNAseq and whole-genome sequencing (WGS), we can start to better understand the mechanisms behind these rare malignancies. The primary results of WGS analysis are SNVs, SVs, or CNAs. Each of these somatic classes of variation can be further refined using genome annotation tools and databases to prioritize variants and identify likely drivers. However, if one looks at DNA data alone, it is impossible to validate these predictions. We may identify a known oncogenic SNV, but due to a complex rearrangement, that particular SNV may not be expressed. By including RNA in the analysis, we now have the ability to assess how functional these variants truly are. With SNVs, we examine the expression of a variant in RNA, including a comparison of the allele frequencies. For both somatic and germline variants, we use RNAseq to identify allele-specific expression patterns. We also use RNAseq to confirm the expression of predicted gene fusions, and the functional significance of copy number gains or losses, even at modest levels. In tumor profiling, RNAseq is primarily used for the identification of gene fusions and gene expression outliers. At the present, both of these techniques produce a high degree of false positives. However, due to the potential for complex rearrangements, RNAseq can be used to identify gene fusions that may be missed by DNA specific methods. For example, RNAseq can effectively “rescue” the results of WGS that may have identified individual (non-viable) SVs but missed the overall combination of rearrangements that would result in a viable fusion. In a single-patient analysis, outlier expression is quite difficult. Each gene can have a wide range of “normal” expression, which is tissue specific. However, gene expression outliers can be validated with WGS analysis (CNA, SV, promoter hijacking, or SNVs in transcription factor binding sites) to prioritize outlier genes based upon those that can be mechanistically explained with a somatic (DNA) variant. Here we will describe the techniques and analysis pipelines used for the integrated analysis of RNA and DNA in a cohort of rare and high-risk pediatric cancer patients. RNAseq can provide a functional output whereas WGS can be used to provide a potential mechanism. Importantly, using both techniques lets us capture signal that may be otherwise missed with only one method. Together, we believe that the integration of RNA and DNA produces a more comprehensive analysis to better understand the mechanisms of each individual cancer. Citation Format: Marcus R. Breese, Alex G. Lee, Avanthi T. Shah, Henry J. Martell, Alejandro Sweet-Cordero. Methods for integrated analysis of RNA and DNA sequencing in pediatric cancers [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr B03.