Mapping AML heterogeneity - multi-cohort transcriptomic analysis identifies novel clusters and divergent ex-vivo drug responses.

Subtyping of acute myeloid leukaemia (AML) is predominantly based on recurrent genetic abnormalities, but recent literature indicates that transcriptomic phenotyping holds immense potential to further refine AML classification. Here we integrated five AML transcriptomic datasets with corresponding genetic information to provide an overview (n = 1224) of the transcriptomic AML landscape. Consensus clustering identified 17 robust patient clusters which improved identification of CEBPA-mutated patients with favourable outcomes, and uncovered transcriptomic subtypes for KMT2A rearrangements (2), NPM1 mutations (5), and AML with myelodysplasia-related changes (AML-MRC) (5). Transcriptomic subtypes of KMT2A, NPM1 and AML-MRC showed distinct mutational profiles, cell type differentiation arrests and immune properties, suggesting differences in underlying disease biology. Moreover, our transcriptomic clusters show differences in ex-vivo drug responses, even when corrected for differentiation arrest and superiorly capture differences in drug response compared to genetic classification. In conclusion, our findings underscore the importance of transcriptomics in AML subtyping and offer a basis for future research and personalised treatment strategies. Our transcriptomic compendium is publicly available and we supply an R package to project clusters to new transcriptomic studies.

Virulence and antimicrobial resistance of Shiga toxin-producing Escherichia coli from dairy goat and sheep farms in The Netherlands.

AIMS: The aim of our study was to investigate the virulence and resistance of STEC from small ruminants farms in The Netherlands. Moreover, the potential transmission of STEC between animals and humans on farms was evaluated. METHODS AND RESULTS: From 182 farms, in total, 287 unique STEC isolates were successfully recovered from animal samples. In addition, STEC was isolated from eight out of 144 human samples. The most detected serotype was O146:H21; however, among other serotypes also O26:H11, O157:H7, and O182:H25 isolates were present. Whole genome sequencing covering all human isolates and 50 of the animal isolates revealed a diversity of stx1, stx2, and eae sub-types and an additional 57 virulence factors. The assessed antimicrobial resistance phenotype, as determined by microdilution, was concordant with the genetic profiles identified by WGS. WGS also showed that three of the human isolates could be linked to an animal isolate from the same farm. CONCLUSIONS: The obtained STEC isolates showed great diversity in serotype, virulence, and resistance factors. Further analysis by WGS allowed for an in-depth assessment of the virulence and resistance factors present and to determine the relatedness of human and animal isolates.

From gene to dose: Long-read sequencing and *-allele tools to refine phenotype predictions of CYP2C19.

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.

Comprehensive diagnostics of acute myeloid leukemia by whole transcriptome RNA sequencing.

Acute myeloid leukemia (AML) is caused by genetic aberrations that also govern the prognosis of patients and guide risk-adapted 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.

Concordance of SNP- and allele-based typing workflows in the context of a large-scale international Salmonella Enteritidis outbreak investigation.

A large European multi-country Salmonella enterica serovar Enteritidis outbreak associated with Polish eggs was characterized by whole-genome sequencing (WGS)-based analysis, with various European institutes using different analysis workflows to identify isolates potentially related to the outbreak. The objective of our study was to compare the output of six of these different typing workflows (distance matrices of either SNP-based or allele-based workflows) in terms of cluster detection and concordance. To this end, we analysed a set of 180 isolates coming from confirmed and probable outbreak cases, which were representative of the genetic variation within the outbreak, supplemented with 22 unrelated contemporaneous S. enterica serovar Enteritidis isolates. Since the definition of a cluster cut-off based on genetic distance requires prior knowledge on the evolutionary processes that govern the bacterial populations in question, we used a variety of hierarchical clustering methods (single, average and complete) and selected the optimal number of clusters based on the consensus of the silhouette, Dunn2, and McClain-Rao internal validation indices. External validation was done by calculating the concordance with the WGS-based case definition (SNP-address) for this outbreak using the Fowlkes-Mallows index. Our analysis indicates that with complete-linkage hierarchical clustering combined with the optimal number of clusters, as defined by three internal validity indices, the six different allele- and SNP-based typing workflows generate clusters with similar compositions. Furthermore, we show that even in the absence of coordinated typing procedures, but by using an unsupervised machine learning methodology for cluster delineation, the various workflows that are currently in use by six European public-health authorities can identify concordant clusters of genetically related S. enterica serovar Enteritidis isolates; thus, providing public-health researchers with comparable tools for detection of infectious-disease outbreaks.

Characterization and whole genome sequencing of closely related multidrug-resistant Salmonella enterica serovar Heidelberg isolates from imported poultry meat in the Netherlands.

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 blaCMY-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.