Failure to rapidly diagnose tuberculosis disease (TB) and initiate treatment is a driving factor of TB as a leading cause of death in children. Current TB diagnostic assays have poor performance in children, and identifying novel non-sputum-based TB biomarkers to improve pediatric TB diagnosis is a global priority. We sought to develop a plasma biosignature for TB by probing the plasma proteome of 511 children stratified by TB diagnostic classification and HIV status from sites in four low- and middle-income countries, using high-throughput data-independent acquisition mass-spectrometry (DIA-PASEF-MS). We identified 47 proteins differentially regulated between children with microbiologically confirmed TB from children with non-TB lower respiratory tract infection (Unlikely TB). We further employed machine learning to derive three parsimonious biosignatures encompassing 4, 5, or 6 proteins that achieved AUCs of 0.86-0.88 all of which exceeded the minimum WHO target product profile accuracy thresholds (70% specificity at 90% sensitivity, PPV 0.65-0.74, NPV 0.92-0.95) for a TB screening test.
[doi:10.25345/C5F18SS6N]
[dataset license: CC0 1.0 Universal (CC0 1.0)]
Keywords: Plasma ; Mycobacterium tuberculosis ; Proteomics ; HIV ; Biomarkers ; DatasetType:Proteomics
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Principal Investigators: (in alphabetical order) |
Danielle Swaney, UCSF, United States |
| Submitting User: | JustinMcKetney |
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