A benchmark dataset for validating mutations in .

UNLABELLED

Echinocandins are the recommended antifungal therapy for infections in many countries. While echinocandin resistance remains uncommon, recent reports demonstrate an increase in such cases, with the potential for echinocandin-resistant transmission between persons. The expansion of whole-genome sequencing capacity in public health laboratories provides a great opportunity to leverage genomic data to detect echinocandin resistance-conferring mutations. However, curated datasets for validating genomic tools for these purposes are lacking. Therefore, we developed a benchmark dataset comprising 100 whole-genome sequenced isolates categorized as echinocandin-susceptible ( = 53) and resistant ( = 47) by antifungal susceptibility testing. We implemented the fungal bioinformatics pipeline, MycoSNP-nf, to perform whole-genome sequencing analysis, including clade typing and the detection of mutations in hotspot (HS) regions. Phylogenetic analysis classified isolates into four major clades (Clades I-IV). Of the 47 isolates considered resistant by AFST, 44 showed HS mutations identified by MycoSNP-nf-with 41 positioned in two well-described HS regions and 3 within a potential third hotspot that was recently reported. This benchmark dataset is designed to be a resource to build sequencing capacity to detect echinocandin resistance-conferring mutations in and to help standardize comparisons across other bioinformatics tools.

IMPORTANCE

Echinocandins are the recommended first-line treatment for invasive infections caused by s, a multi-drug-resistant yeast that has emerged in healthcare facilities globally. Increasing instances of echinocandin-resistant cases highlight the need for rapid detection and response. We developed a benchmark dataset comprising 100 echinocandin-resistant and -susceptible isolates to demonstrate the utility of the bioinformatics tool, MycoSNP-nf, for detecting echinocandin resistance-related mutations and to assess their concordance with antifungal susceptibility testing results. This benchmark may help validate MycoSNP-nf and other bioinformatics tools aimed at detecting these mechanisms using whole-genome sequencing data.