M1. Clinical studies of fungal infections
Oral Abstract Submission
Ephraim L. Tsalik, MD, MHS, PhD
Disclosure: BioFire: Grant/Research Support
Biomerieux: Grant/Research Support
Predigen, Inc.: Officer or Board Member, Research Grant
Qvella: Other Financial or Material Support, Research Collaboration
Barbara D. Alexander, MD, MHS
Professor of Medicine and Pathology
Disclosure: Astellas Pharma: Advisory Board, Consultant, Grant/Research Support, Other Financial or Material Support, Research Grant
Cidara: grant to institution, Research Grant
F2G: grant to institution, Research Grant
Lediant: grant to institution, Grant/Research Support, Other Financial or Material Support, Research Grant
Scynexis: Consultant, grant to institution, Independent Contractor, Research Grant
Candidemia is one of the most common nosocomial bloodstream infections in the United States and causes significant morbidity and mortality in hospitalized patients. Improved rapid diagnostics capable of differentiating candidemia from other causes of febrile illness in the hospitalized patient are of paramount importance. Pathogen class-specific biomarker-based diagnostics such as those focusing on host gene expression patterns in circulating leukocytes may offer a promising alternative.
RNA sequencing was performed on peripheral blood samples from twenty-seven hospitalized patients with blood culture positive invasive candidiasis. Samples from healthy controls as well as at-risk subjects with acute febrile illness and similar clinical backgrounds but other infectious or non-infectious etiologies were used as comparator phenotypes (35 subjects with culture-proven bacterial infection, 49 with confirmed viral infection, 17 with acute non-infectious illness). Bayesian techniques were utilized to develop infection-specific classifiers and leave one out cross-validation was used to estimate predictive probability of each pathogen class.
Candidemia triggers a unique, robust and conserved transcriptomic response in human hosts with 1170 genes differentially upregulated compared to healthy controls. Based on this strength of signal, we developed a transcriptomic classifier that was capable of identifying candidemia, viral, or bacterial infection with a high degree of accuracy (auROC for Candida 0.93, Bacterial 0.98, Viral 0.99). The Candida component of this classifier (29-genes) was able to diagnose candidemia with a sensitivity of 88% and specificity of 100%.
The host transcriptomic response during candidemia in hospitalized adults is highly conserved and unique from the genomic responses to acute viral and bacterial infection. This approach shows promise for the development of host response-based classifiers capable of differentiating multiple types of clinical illnesses at once in at-risk febrile patients.