Abstract
Echinocandins are the recommended antifungal therapy for Candida auris infections in many countries. While echinocandin resistance remains uncommon, recent reports demonstrate an increase in such cases, with the potential for echinocandin-resistant C. auris transmission between persons. The expansion of C. auris 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 C. auris isolates categorized as echinocandin-susceptible (n = 53) and resistant (n = 47) by antifungal susceptibility testing. We implemented the fungal bioinformatics pipeline, MycoSNP-nf, to perform whole-genome sequencing analysis, including C. auris clade typing and the detection of FKS1 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 FKS1 and to help standardize comparisons across other bioinformatics tools. IMPORTANCE: Echinocandins are the recommended first-line treatment for invasive infections caused by Candida auris, 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 C. auris echinocandin-resistant and -susceptible isolates to demonstrate the utility of the bioinformatics tool, MycoSNP-nf, for detecting echinocandin resistance-related FKS1 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.