Whole genome sequencing (WGS) using tissue and matched blood samples from cancer patients is becoming in reach as the most complete genetic tumor diagnostic test. With a trend towards the availability of only small biopsies, and at the same time the need to screen for an increasing number of (complex) biomarkers, the use of a single all-inclusive test is preferred over multiple consecutive assays. To meet the high-quality diagnostics standards, we have optimized and validated the performance of a clinical grade WGS workflow, resulting in a technical success rate of 95.6% for samples with sufficient (≥20%) tumor cell percentage. Independent validation of identified biomarkers against commonly used diagnostic assays showed a high sensitivity (98.5%) and specificity (98.4%) for detection of somatic SNV and indels, and high concordance (93.3%) for gene amplification detection. Gene fusion analysis showed a concordance of 91.3% between DNA-based WGS and an orthogonal RNA-based gene fusion assay. Microsatellite (in)stability assessment showed a sensitivity of 100% with a specificity of 97%, and high-risk human papillomavirus detection showed an accuracy of 95.8% compared to standard pathological tests. In conclusion, whole genome sequencing has a >95% sensitivity and specificity compared to routinely used DNA techniques in diagnostics and all relevant mutation types can be detected reliably in a single assay.
Spliced fusion-transcripts are typically identified by RNA-seq without elucidating the causal genomic breakpoints. However, non poly(A)-enriched RNA-seq contains large proportions of intronic reads spanning also genomic breakpoints. Using 1.274 RNA-seq samples, we investigated what additional information is embedded in non poly(A)-enriched RNA-seq data. Here, we present our novel, graph-based, Dr. Disco algorithm that makes use of both intronic and exonic RNA-seq reads to identify not only fusion transcripts but also genomic breakpoints in gene but also in intergenic regions. Dr. Disco identified TMPRSS2-ERG fusions with genomic breakpoints and other transcribed rearrangements from multiple RNA-sequencing cohorts. In breast cancer and glioma samples Dr. Disco identified rearrangement hotspots near CCND1 and MDM2 and could directly associate this with increased expression. A comparison with matched DNA-sequencing revealed that most genomic breakpoints are not, or minimally, transcribed while also revealing highly expressed translocations missed by DNA-seq. By using the full potential of non poly(A)-enriched RNA-seq data, Dr. Disco can reliably identify expressed genomic breakpoints and their transcriptional effects.
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