Integrated plasma proteomics identifies tuberculosis-specific diagnostic biomarkers
Hannah F. Schiff, Naomi F. Walker, Cesar Ugarte-Gil, Marc Tebruegge, Antigoni Manousopoulou, Spiros D. Garbis, Salah Mansour, Pak Ho (Michael) Wong, Gabrielle Rockett, Paolo Piazza, Mahesan Niranjan, Andres F. Vallejo, Christopher H. Woelk, Robert J. Wilkinson, Liku B. Tezera, Diana Garay-Baquero, Paul Elkington
Hannah F. Schiff, Naomi F. Walker, Cesar Ugarte-Gil, Marc Tebruegge, Antigoni Manousopoulou, Spiros D. Garbis, Salah Mansour, Pak Ho (Michael) Wong, Gabrielle Rockett, Paolo Piazza, Mahesan Niranjan, Andres F. Vallejo, Christopher H. Woelk, Robert J. Wilkinson, Liku B. Tezera, Diana Garay-Baquero, Paul Elkington
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Clinical Research and Public Health Infectious disease Pulmonology

Integrated plasma proteomics identifies tuberculosis-specific diagnostic biomarkers

Abstract

BACKGROUND Novel biomarkers to identify infectious patients transmitting Mycobacterium tuberculosis are urgently needed to control the global tuberculosis (TB) pandemic. We hypothesized that proteins released into the plasma in active pulmonary TB are clinically useful biomarkers to distinguish TB cases from healthy individuals and patients with other respiratory infections.METHODS We applied a highly sensitive non-depletion tandem mass spectrometry discovery approach to investigate plasma protein expression in pulmonary TB cases compared to healthy controls in South African and Peruvian cohorts. Bioinformatic analysis using linear modeling and network correlation analyses identified 118 differentially expressed proteins, significant through 3 complementary analytical pipelines. Candidate biomarkers were subsequently analyzed in 2 validation cohorts of differing ethnicity using antibody-based proximity extension assays.RESULTS TB-specific host biomarkers were confirmed. A 6-protein diagnostic panel, comprising FETUB, FCGR3B, LRG1, SELL, CD14, and ADA2, differentiated patients with pulmonary TB from healthy controls and patients with other respiratory infections with high sensitivity and specificity in both cohorts.CONCLUSION This biomarker panel exceeds the World Health Organization Target Product Profile specificity criteria for a triage test for TB. The new biomarkers have potential for further development as near-patient TB screening assays, thereby helping to close the case-detection gap that fuels the global pandemic.FUNDING Medical Research Council (MRC) (MR/R001065/1, MR/S024220/1, MR/P023754/1, and MR/W025728/1); the MRC and the UK Foreign Commonwealth and Development Office; the UK National Institute for Health Research (NIHR); the Wellcome Trust (094000, 203135, and CC2112); Starter Grant for Clinical Lecturers (Academy of Medical Sciences UK); the British Infection Association; the Program for Advanced Research Capacities for AIDS in Peru at Universidad Peruana Cayetano Heredia (D43TW00976301) from the Fogarty International Center at the US NIH; the UK Technology Strategy Board/Innovate UK (101556); the Francis Crick Institute, which receives funding from UKRI-MRC (CC2112); Cancer Research UK (CC2112); and the NIHR Biomedical Research Centre of Imperial College NHS.

Authors

Hannah F. Schiff, Naomi F. Walker, Cesar Ugarte-Gil, Marc Tebruegge, Antigoni Manousopoulou, Spiros D. Garbis, Salah Mansour, Pak Ho (Michael) Wong, Gabrielle Rockett, Paolo Piazza, Mahesan Niranjan, Andres F. Vallejo, Christopher H. Woelk, Robert J. Wilkinson, Liku B. Tezera, Diana Garay-Baquero, Paul Elkington

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Figure 12

The final 6-protein panel differentiates TB from both HC and ORI in a separate clinical cohort.

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The final 6-protein panel differentiates TB from both HC and ORI in a se...
(A–F) Box-and-whisker plots of the 6 proteins in the panel in pulmonary TB compared with HC and ORI by proximity extension assay. Boxes show median values and interquartile ranges and whiskers show minimum to maximum values. Statistical differences were calculated using 1-way ANOVA with Tukey’s multiple-comparison test for data with a Gaussian distribution and Kruskal-Willis test with Dunn’s multiple-comparison test for nonparametrically distributed data. (G) Receiver operating characteristic (ROC) curve of the 6-protein panel distinguishing pulmonary TB from HCs. The 6-protein combined panel AUC = 0.882 (95% CI: 0.796–0.968). Full coordinates in Supplemental Table 16. (H) ROC curve of the 6-protein panel distinguishing pulmonary TB from ORI, AUC = 0.876 (95% CI: 0.765–0.987). Full coordinates in Supplemental Table 17. (I) Classification grid illustrating diagnostic performance of the 6-protein panel distinguishing pulmonary TB from HCs, demonstrating a sensitivity of 75.0% (95% CI: 54.8%–88.6%), specificity of 83.3% (95% CI: 64.5%–93.7%), and correct classification in 79.3% of cases in this cohort. (J) Classification grid illustrating diagnostic performance of the 6-protein panel distinguishing pulmonary TB from other respiratory infection, demonstrating a sensitivity of 92.9% (95% CI: 75.0%–98.8%), specificity of 78.9% (95% CI: 53.9%–93.0%), and correct classification in 87.2% of cases in this cohort. All ROC curves and classification grids were generated using SPSS v28.0.1.0 after binary logistic regression for combined proteins. AUC was calculated under nonparametric assumption. TB was set as the positive test outcome and the test direction such that a larger test result indicates a more positive test. NPX, normalized protein expression (log2 scale); AUC, area under the curve; HC, healthy control (n = 30); TB, tuberculosis (n= 29); ORI, other respiratory infection (n = 19); ADA2, adenosine deaminase 2; CD14, monocyte differentiation antigen CD14; LRG1, leucine-rich α-2-glycoprotein; TNFSF13B, tumor necrosis factor ligand superfamily member 13B; vWF, von Willebrand factor. NS, P > 0.05; *P ≤ 0.05; ***P ≤ 0.001; ****P ≤ 0.0001.