No abstract

BackgroundTesting for tumor specific mutations on routine formalin-fixed paraffin-embedded (FFPE) tissues may predict response to treatment in Medical Oncology and has already entered diagnostics, with KRAS mutation assessment as a paradigm. The highly sensitive real time PCR (Q-PCR) methods developed for this purpose are usually standardized under optimal template conditions. In routine diagnostics, however, suboptimal templates pose the challenge. Herein, we addressed the applicability of sequencing and two Q-PCR methods on prospectively assessed diagnostic cases for KRAS mutations.Methodology/Principal FindingsTumor FFPE-DNA from 135 diagnostic and 75 low-quality control samples was obtained upon macrodissection, tested for fragmentation and assessed for KRAS mutations with dideoxy-sequencing and with two Q-PCR methods (Taqman-minor-groove-binder [TMGB] probes and DxS-KRAS-IVD). Samples with relatively well preserved DNA could be accurately analyzed with sequencing, while Q-PCR methods yielded informative results even in cases with very fragmented DNA (p<0.0001) with 100% sensitivity and specificity vs each other. However, Q-PCR efficiency (Ct values) also depended on DNA-fragmentation (p<0.0001). Q-PCR methods were sensitive to detect ≤1% mutant cells, provided that samples yielded cycle thresholds (Ct) <29, but this condition was met in only 38.5% of diagnostic samples. In comparison, FFPE samples (>99%) could accurately be analyzed at a sensitivity level of 10% (external validation of TMGB results). DNA quality and tumor cell content were the main reasons for discrepant sequencing/Q-PCR results (1.5%).Conclusions/SignificanceDiagnostic targeted mutation assessment on FFPE-DNA is very efficient with Q-PCR methods in comparison to dideoxy-sequencing. However, DNA fragmentation/amplification capacity and tumor DNA content must be considered for the interpretation of Q-PCR results in order to provide accurate information for clinical decision making.

The role of gene body methylation, which represents a major part of methylation in DNA, remains mostly unknown. Evidence based on the CpG distribution associates its presence with nucleosome positioning and alternative splicing. Recently, it was also shown that cytosine methylation influences splicing. However, to date, there is no methylation-based data on the association of methylation with alternative splicing and the distribution in exonic splicing enhancers (ESEs). We presently report that, based on the computational analysis of the Human Epigenome Project data, CpG hypermethylation (>80%) is frequent in alternatively spliced sites (particularly in noncanonical) but not in alternate promoters. The methylation frequency increases in sequences containing multiple putative ESEs. However, significant differences in the extent of methylation are observed among different ESEs. Specifically, moderate levels of methylation, ranging from 20% to 80%, are frequent in SRp55-binding elements, which are associated with response to extracellular conditions, but not in SF2/ASF, primarily responsible for alternative splicing, or in CpG islands. Finally, methylation is more frequent in the presence of AT repeats and CpGs separated by 10 nucleotides and lower in adjacent CpGs, probably indicating its dependence on helical formations and on the presence of nucleosome positioning-related sequences. In conclusion, our results show the regulation of methylation in ESEs and support its involvement in alternative splicing.

SummaryThe precisionFDA Truth Challenge V2 aimed to assess the state-of-the-art of variant calling in difficult-to-map regions and the Major Histocompatibility Complex (MHC). Starting with FASTQ files, 20 challenge participants applied their variant calling pipelines and submitted 64 variant callsets for one or more sequencing technologies (~35X Illumina, ~35X PacBio HiFi, and ~50X Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with the new GIAB benchmark sets and genome stratifications. Challenge submissions included a number of innovative methods for all three technologies, with graph-based and machine-learning methods scoring best for short-read and long-read datasets, respectively. New methods out-performed the 2016 Truth Challenge winners, and new machine-learning approaches combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants.