A 20-Gene Set Predictive of Progression to Severe Dengue
Makeda Robinson,
Timothy E. Sweeney,
Rina Barouch-Bentov,
Malaya Kumar Sahoo,
Larry Kalesinskas,
Francesco Vallania,
Ana Maria Sanz,
Eliana Ortiz-Lasso,
Ludwig Luis Albornoz,
Fernando Rosso,
Jose G. Montoya,
Benjamin A. Pinsky,
Purvesh Khatri,
Shirit Einav
Affiliations
Makeda Robinson
Department of Medicine, Division of Infectious Diseases and Geographic Medicine, Stanford University, Stanford, CA, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
Timothy E. Sweeney
Institute for Immunity, Transplantation, and Infection, Department of Medicine, Stanford University, Stanford, CA, USA; Department of Medicine, Division of Biomedical Informatics Research, Stanford University, Stanford, CA, USA
Rina Barouch-Bentov
Department of Medicine, Division of Infectious Diseases and Geographic Medicine, Stanford University, Stanford, CA, USA
Malaya Kumar Sahoo
Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
Larry Kalesinskas
Institute for Immunity, Transplantation, and Infection, Department of Medicine, Stanford University, Stanford, CA, USA; Department of Medicine, Division of Biomedical Informatics Research, Stanford University, Stanford, CA, USA
Francesco Vallania
Institute for Immunity, Transplantation, and Infection, Department of Medicine, Stanford University, Stanford, CA, USA; Department of Medicine, Division of Biomedical Informatics Research, Stanford University, Stanford, CA, USA
Ana Maria Sanz
Clinical Research Center, Fundación Valle del Lili, Cali, Colombia
Eliana Ortiz-Lasso
Pathology and Laboratory Department, Fundación Valle del Lili, Cali, Colombia
Ludwig Luis Albornoz
Pathology and Laboratory Department, Fundación Valle del Lili, Cali, Colombia
Fernando Rosso
Clinical Research Center, Fundación Valle del Lili, Cali, Colombia; Department of Internal Medicine, Division of Infectious Diseases, Fundación Valle del Lili, Cali, Colombia
Jose G. Montoya
Department of Medicine, Division of Infectious Diseases and Geographic Medicine, Stanford University, Stanford, CA, USA
Benjamin A. Pinsky
Department of Medicine, Division of Infectious Diseases and Geographic Medicine, Stanford University, Stanford, CA, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
Purvesh Khatri
Institute for Immunity, Transplantation, and Infection, Department of Medicine, Stanford University, Stanford, CA, USA; Department of Medicine, Division of Biomedical Informatics Research, Stanford University, Stanford, CA, USA; Corresponding author
Shirit Einav
Department of Medicine, Division of Infectious Diseases and Geographic Medicine, Stanford University, Stanford, CA, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA; Corresponding author
Summary: There is a need to identify biomarkers predictive of severe dengue. Single-cohort transcriptomics has not yielded generalizable results or parsimonious, predictive gene sets. We analyzed blood samples of dengue patients from seven gene expression datasets (446 samples, five countries) using an integrated multi-cohort analysis framework and identified a 20-gene set that predicts progression to severe dengue. We validated the predictive power of this 20-gene set in three retrospective dengue datasets (84 samples, three countries) and a prospective Colombia cohort (34 patients), with an area under the receiver operating characteristic curve of 0.89, 100% sensitivity, and 76% specificity. The 20-gene dengue severity scores declined during the disease course, suggesting an infection-triggered host response. This 20-gene set is strongly associated with the progression to severe dengue and represents a predictive signature, generalizable across ages, host genetic factors, and virus strains, with potential implications for the development of a host response-based dengue prognostic assay. : Biomarkers predictive of severe dengue are needed. Robinson et al. revealed a 20-gene set that predicts progression to severe dengue in existing and prospective cohorts via a multi-cohort analysis framework. This 20-gene set is generalizable across ages and host genetic factors and has potential implications for prognostic assay development. Keywords: transcriptomics, multi-coherent analysis, prognostics, biomarkers, severe dengue
