Till Örebro universitet

Sudden cardiac death (SCD) is a tragic and traumatic event. SCD is often associated with hereditary genetic disease and in such cases, sequencing of stored formalin fixed paraffin embedded (FFPE) tissue is often crucial in trying to find a causal genetic variant. This study was designed to compare two massive parallel sequencing assays for differences in sensitivity and precision regarding variants related to SCD in FFPE material. From eight cases of SCD where DNA from blood had been sequenced using HaloPlex, corresponding FFPE samples were collected six years later. DNA from FFPE samples were amplified using HaloPlex HS, sequenced on MiSeq, representing the first method, as well as amplified using modified Twist and sequenced on NextSeq, representing the second method. Molecular barcodes were included to distinguish artefacts from true variants. In both approaches, read coverage, uniformity and variant detection were compared using genomic DNA isolated from blood and corresponding FFPE tissue, respectively. In terms of coverage uniformity, Twist performed better than HaloPlex HS for FFPE samples. Despite higher overall coverage, amplicon-based HaloPlex technologies, both for blood and FFPE tissue, suffered from design and/or performance issues resulting in genes lacking complete coverage. Although Twist had considerably lower overall mean coverage, high uniformity resulted in equal or higher fraction of genes covered at ≥ 20X. By comparing variants found in the matched samples in a pre-defined cardiodiagnostic gene panel, HaloPlex HS for FFPE material resulted in high sensitivity, 98.0% (range 96.6-100%), and high precision, 99.9% (range 99.5-100%) for moderately fragmented samples, but suffered from reduced sensitivity (range 74.2-91.1%) in more severely fragmented samples due to lack of coverage. Twist had high sensitivity, 97.8% (range 96.8-98.7%) and high precision, 99.9% (range 99.3-100%) in all analyzed samples, including the severely fragmented samples.

Forensic molecular autopsies have emerged as a tool for medical examiners to establish the cause of death. It is particularly useful in sudden unexplained deaths where the cause of death cannot be determined with a regular medical autopsy. We provide the first study of exome data from formalin-fixed paraffin-embedded samples (FFPE) paired with data from high-quality blood samples in forensic applications. The approach allows exploration of the potential to use FFPE samples for molecular autopsies and identify variants in extensive exome data. We leverage the high uniformity of the hybridization capture approach provided by Twist Bioscience to target the complete exome and sequence the libraries on a NextSeq 550. Our findings suggest that exome sequencing is feasible for 24 out of a total of 35 included FFPE samples. When successful, the coverage across the exome is comparatively high (> 90% covered to 20X) and uniform (fold80 below 1.5). Detailed variant comparisons for matched FFPE and blood samples show high concordance with few false variants (positive predictive value of 0.98 and a sensitivity of 0.97) with no distinct FFPE artefacts. Ultimately, we apply carefully constructed forensic gene panels in a stepwise manner to find genetic variants associated with the clinical phenotype and with relevance to the sudden unexplained death.

Sudden unexpected death (SUD) is an unexpected event that in many cases are caused by diseases with an underlying genetic background. Forensic molecular autopsy is an approach that has gained wide-spread attention, in part explained by the rapid progress of DNA sequencing techniques. The approach leverages genetic data in combination with medical autopsy findings in post-mortem samples to explore a potential underlying genetic cause of death. Traditional forensic approaches to molecular autopsy focus on a small panel of genes, say <200 genes, with strong association to heart conditions whereas clinical genetics tend to capture entire exomes while subsequently selecting targeted panels bioinformatically. The drop in price and the increased throughput has promoted wider exome sequencing as a viable method to discover genetic variants. We explore a targeted gene panel consisting of 2422 genes, selected based on their broad association to sudden unexplained death. A hybridization capture approach from Twist Bioscience based on double stranded DNA probes was used to target exons of the included genes. We selected and sequenced a total of 98 post-mortem samples from historical forensic autopsy cases where the cause of death could not be unambiguously determined based on medical findings and that had a previous negative molecular autopsy. In the current study, we focus on the performance of the hybridization capture technology on a 2422 gene panel and explore metrics related to sequencing success using a mid-end NextSeq 550 as well as a MiSeq FGx platform. With the latter we demonstrate that our sequence data benefits from 2×300 bp sequencing increasing coverage, in particular, for difficult regions where shadow coverage, i.e. regions outside the probes, are utilized. The results further illustrate a highly uniform capture across the panel of genes (mean fold80=1.5), in turn minimizing excessive sequencing costs to reach sufficient coverage, i.e. 20X. We outline a stepwise procedure to select genes associated with SUD through virtual bioinformatical panels extracting tier of genes with increasing strength of association to SUD. We propose some prioritization strategies to filter variants with highest potential and show that the number of high priority genetic variant requiring manual inspections is few (0-3 for all tiers of genes) when all filters are applied.

We investigated differences in circulating cell-free DNA (cfDNA) methylation between patients with cancer and those presenting with severe, nonspecific symptoms. Plasma cfDNA from 229 patients was analyzed, of whom 37 were diagnosed with a wide spectrum of cancer types within 12 months. Samples underwent enzymatic conversion, library preparation, and enrichment using the NEBNext workflow and Twist pan-cancer methylation panel, followed by sequencing. Methylation analysis was performed with nf-core/methylseq. Differentially methylated regions (DMRs) were identified with DMRichR. Machine learning with cross-validation was used to classify cancer and controls. The classifier was applied to an external validation set of 144 controls previously unseen by the model. Cancer samples showed higher overall CpG methylation than controls (1.82% vs. 1.34%, p < 0.001). A total of 162 DMRs were detected, 95.7% being hypermethylated in cancer. Machine learning identified 20 key DMRs for classification between cancer and controls. The final model achieved an AUC of 0.88 (83.8% sensitivity, 83.8% specificity), while mean cross-validation performance reached an AUC of 0.73 (57.1% sensitivity, 77.5% specificity). The specificity of the classifier on unseen control samples was 79.2%. Distinct methylation differences and DMR-based classification support cfDNA methylation as a robust biomarker for cancer detection in patients with confounding conditions.