Acute myeloid leukemia (AML) is caused by genetic aberrations that also govern the prognosis of patients and guide riskadapted and targeted therapy. Genetic aberrations in AML are structurally diverse and currently detected by different diagnostic assays. This study sought to establish whole transcriptome RNA sequencing as single, comprehensive, and flexible platform for AML diagnostics. We developed HAMLET (Human AML Expedited Transcriptomics) as bioinformatics pipeline for simultaneous detection of fusion genes, small variants, tandem duplications, and gene expression with all information assembled in an annotated, user-friendly output file. Whole transcriptome RNA sequencing was performed on 100 AML cases and HAMLET results were validated by reference assays and targeted resequencing. The data showed that HAMLET accurately detected all fusion genes and overexpression of EVI1 irrespective of 3q26 aberrations. In addition, small variants in 13 genes that are often mutated in AML were called with 99.2% sensitivity and 100% specificity, and tandem duplications in FLT3 and KMT2A were detected by a novel algorithm based on soft-clipped reads with 100% sensitivity and 97.1% specificity. In conclusion, HAMLET has the potential to provide accurate comprehensive diagnostic information relevant for AML classification, risk assessment and targeted therapy on a single technology platform. Supplementary informationThe online version of this article (https://

Multidrug-resistant Salmonella enterica serovar Heidelberg isolates are frequently recovered in the Netherlands from poultry meat imported from South America. Our aim was to retrospectively assess the characteristics of the antimicrobial determinants, gene content and the clonal relatedness of 122 unique S . Heidelberg isolates from chicken meat from Brazil (n = 119) and Argentina (n = 3) that were imported between 2010 and 2015. These isolates were subjected to antimicrobial susceptibility testing, PCR and Illumina HiSeq2500 whole genome sequencing. Draft genomes were assembled to assess the gene content, and the phylogenetic relationships between isolates were determined using single nucleotide polymorphisms. Ciprofloxacin-resistance was identified in 98.4% of the isolates and 83.7% isolates showed resistance to the extended-spectrum cephalosporins cefotaxime and ceftazidime (83.6% and 82.8% respectively). Of the latter, 97.1% exhibited an AmpC phenotype and contained bla CMY-2 , whereas the remaining three isolates contained an extended spectrum beta-lactamase. Of the 99 extended-spectrum cephalosporins-resistant isolates harboring CMY-2 plasmids, 56.6% contained the incompatibility group I1 replicon. Phylogenetic cluster analysis showed that all isolates from Brazil clustered together, with 49% occurring in clusters larger than 5 isolates that revealed intra-cluster similarities based on geographical location and/or resistance profiles. The remaining isolates were classified in smaller clusters or as singletons, highlighting the large diversity of S . Heidelberg in the poultry chain in Brazil that was revealed by this study. Considering the potential public health risk associated with multidrug-resistant S . Heidelberg in imported poultry, collaborative whole genome sequencing-based surveillance is needed to monitor the spread, pathogenic properties and epidemiological distribution of these isolates.

No abstract

Pharmacogenomics (PGx)-guided drug treatment is one of the cornerstones of personalized medicine. However, the genes involved in drug response are highly complex and known to carry many (rare) variants. Current technologies (short-read sequencing and SNP panels) are limited in their ability to resolve these genes and characterize all variants. Moreover, these technologies cannot always phase variants to their allele of origin. Recent advance in long-read sequencing technologies have shown promise in resolving these problems. Here we present a long-read sequencing panel-based approach for PGx using PacBio HiFi sequencing. A capture based approach was developed using a custom panel of clinically-relevant pharmacogenes including up- and downstream regions. A total of 27 samples were sequenced and panel accuracy was determined using benchmarking variant calls for 3 Genome in a Bottle samples and GeT-RM star(*)-allele calls for 21 samples.. The coverage was uniform for all samples with an average of 94% of bases covered at >30x. When compared to benchmarking results, accuracy was high with an average F1 score of 0.89 for INDELs and 0.98 for SNPs. Phasing was good with an average of 68% the target region phased (compared to ~20% for short-reads) and an average phased haploblock size of 6.6kbp. Using Aldy 4, we compared our variant calls to GeT-RM data for 8 genes (CYP2B6, CYP2C19, CYP2C9, CYP2D6, CYP3A4, CYP3A5, SLCO1B1, TPMT), and observed highly accurate star(*)-allele calling with 98.2% concordance (165/168 calls), with only one discordance in CYP2C9 leading to a different predicted phenotype. We have shown that our long-read panel-based approach results in high accuracy and target phasing for SNVs as well as for clinical star(*)-alleles.

Background: Inter-individual differences in drug response based on genetic variations can lead to drug toxicity and treatment inefficacy. A large part of this variability is caused by genetic variants in pharmacogenes. Unfortunately, the Single Nucleotide Variant arrays currently used in clinical pharmacogenomic (PGx) testing are unable to detect all genetic variability in these genes. Long-read sequencing, on the other hand, has been shown to be able to resolve complex (pharmaco) genes. In this study we aimed to assess the value of long-read sequencing for research and clinical PGx focusing on the important and highly polymorphic CYP2C19 gene.Methods and Results: With a capture-based long-read sequencing panel we were able to characterize the entire region and assign variants to their allele of origin (phasing), resulting in the identification of 813 unique variants in 37 samples. To assess the clinical utility of this data we have compared the performance of three different *-allele tools (Aldy, PharmCat and PharmaKU) which are specifically designed to assign haplotypes to pharmacogenes based on all input variants.Conclusion: We conclude that long-read sequencing can improve our ability to characterize the CYP2C19 locus, help to identify novel haplotypes and that *-allele tools are a useful asset in phenotype prediction. Ultimately, this approach could help to better predict an individual’s drug response and improve therapy outcomes. However, the added value in clinical PGx might currently be limited.