Omics and Multiomics-Based Diagnostics for Invasive Candidiasis: Toward Precision Medicine

Invasive candidiasis (IC) is a serious, life-threatening, and costly fungal infection if not diagnosed early and treated appropriately. However, this healthcare-associated mycosis caused by Candida spp. is difficult to diagnose because of its nonspecific clinical signs and symptoms, and the lack of early and accurate detection methods. IC is also difficult to treat due to its late diagnosis, the limited antifungal arsenal, and the rapid emergence and spread of (multi)drug-resistant Candida strains. Therefore, early and accurate innovative methods for species and resistance identification in IC (candidemia and deep-seated candidiasis) are urgently needed to initiate timely and appropriate antifungal therapy, and reduce its high morbidity, mortality, and health care costs in hospitalized patients (especially, severely immunocompromised or critically ill patients). The availability of the complete genome sequences of the most clinically relevant Candida species coupled with recent advances in high-throughput omics technologies have spurred an unprecedented era in the discovery and development of IC diagnostics at different levels of molecular complexity. Here, we review the contribution of current and emerging omics technologies, including genomics, transcriptomics, proteomics, peptidomics, metabolomics, lipidomics, glycomics, immunomics (immunoproteomics, immunopeptidomics, and immunoglycomics), imiomics (imaging-omics), and microbiomics (metagenomics, metatranscriptomics, metaproteomics, and metabonomics), to the process of biomarker development for early diagnosis, antifungal susceptibility, prognosis, follow-up, and therapeutic monitoring in IC. We highlight the potential of integrating multiple omic data (through integromics, multiomics, or panomics, together with systems biology and artificial intelligence) for the discovery of multidimensional biomarker signatures and computational algorithms for IC diagnosis. Finally, we discuss future challenges and prospects for their clinical implementation. These next-generation IC diagnostics promise to revolutionize medical practice by unraveling the complexity of biological systems at multiple levels. Furthermore, these could help clinicians make more precise and personalized clinical decisions through multiomics or panomics-based precision medicine approaches, rather than traditional one-size-fits-all approaches.