Discovery and validation of a prognostic proteomic signature for tuberculosis progression: A prospective cohort study

Background A nonsputum blood test capable of predicting progression of healthy individuals to active tuberculosis (TB) before clinical symptoms manifest would allow targeted treatment to curb transmission. We aimed to develop a proteomic biomarker of risk of TB progression for ultimate translation into a point-of-care diagnostic. Methods and findings Proteomic TB risk signatures were discovered in a longitudinal cohort of 6,363 Mycobacterium tuberculosis-infected, HIV-negative South African adolescents aged 12–18 years (68% female) who participated in the Adolescent Cohort Study (ACS) between July 6, 2005 and April 23, 2007, through either active (every 6 months) or passive follow-up over 2 years. Forty-six individuals developed microbiologically confirmed TB disease within 2 years of follow-up and were selected as progressors; 106 nonprogressors, who remained healthy, were matched to progressors. Over 3,000 human proteins were quantified in plasma with a highly multiplexed proteomic assay (SOMAscan). Three hundred sixty-one proteins of differential abundance between progressors and nonprogressors were identified. A 5-protein signature, TB Risk Model 5 (TRM5), was discovered in the ACS training set and verified by blind prediction in the ACS test set. Poor performance on samples 13–24 months before TB diagnosis motivated discovery of a second 3-protein signature, 3-protein pair-ratio (3PR) developed using an orthogonal strategy on the full ACS subcohort. Prognostic performance of both signatures was validated in an independent cohort of 1,948 HIV-negative household TB contacts from The Gambia (aged 15–60 years, 66% female), longitudinally followed up for 2 years between March 5, 2007 and October 21, 2010, sampled at baseline, month 6, and month 18. Amongst these contacts, 34 individuals progressed to microbiologically confirmed TB disease and were included as progressors, and 115 nonprogressors were included as controls. Prognostic performance of the TRM5 signature in the ACS training set was excellent within 6 months of TB diagnosis (area under the receiver operating characteristic curve [AUC] 0.96 [95% confidence interval, 0.93–0.99]) and 6–12 months (AUC 0.76 [0.65–0.87]) before TB diagnosis. TRM5 validated with an AUC of 0.66 (0.56–0.75) within 1 year of TB diagnosis in the Gambian validation cohort. The 3PR signature yielded an AUC of 0.89 (0.84–0.95) within 6 months of TB diagnosis and 0.72 (0.64–0.81) 7–12 months before TB diagnosis in the entire South African discovery cohort and validated with an AUC of 0.65 (0.55–0.75) within 1 year of TB diagnosis in the Gambian validation cohort. Signature validation may have been limited by a systematic shift in signal magnitudes generated by differences between the validation assay when compared to the discovery assay. Further validation, especially in cohorts from non-African countries, is necessary to determine how generalizable signature performance is. Conclusions Both proteomic TB risk signatures predicted progression to incident TB within a year of diagnosis. To our knowledge, these are the first validated prognostic proteomic signatures. Neither meet the minimum criteria as defined in the WHO Target Product Profile for a progression test. More work is required to develop such a test for practical identification of individuals for investigation of incipient, subclinical, or active TB disease for appropriate treatment and care.

[1]  M. Behr,et al.  Revisiting the timetable of tuberculosis , 2018, British Medical Journal.

[2]  H. Randeva,et al.  Metformin increases the novel adipokine cartonectin/CTRP3 in women with polycystic ovary syndrome. , 2013, The Journal of clinical endocrinology and metabolism.

[3]  Purvesh Khatri,et al.  Genome-wide expression for diagnosis of pulmonary tuberculosis: a multicohort analysis. , 2016, The Lancet. Respiratory medicine.

[4]  R. Diel,et al.  Predictive value of interferon-γ release assays and tuberculin skin testing for progression from latent TB infection to disease state: a meta-analysis. , 2012, Chest.

[5]  G. A. Mack,et al.  K-Sample Rank Tests for Umbrella Alternatives , 1981 .

[6]  Daniel E. Zak,et al.  A prospective blood RNA signature for tuberculosis disease risk , 2016, The Lancet.

[7]  John L. Johnson,et al.  Elucidating Novel Serum Biomarkers Associated with Pulmonary Tuberculosis Treatment , 2013, PloS one.

[8]  Daniel E. Zak,et al.  Four‐Gene Pan‐African Blood Signature Predicts Progression to Tuberculosis , 2018, American journal of respiratory and critical care medicine.

[9]  Mark R Segal,et al.  Development and Validation of a Protein-Based Risk Score for Cardiovascular Outcomes Among Patients With Stable Coronary Heart Disease. , 2016, JAMA.

[10]  M. Katoh Multi-layered prevention and treatment of chronic inflammation, organ fibrosis and cancer associated with canonical WNT/β-catenin signaling activation (Review) , 2018, International journal of molecular medicine.

[11]  G. Kaplan,et al.  Metformin as adjunct antituberculosis therapy , 2014, Science Translational Medicine.

[12]  M. Pai,et al.  Predictive value of interferon-γ release assays for incident active tuberculosis: a systematic review and meta-analysis. , 2012, The Lancet. Infectious diseases.

[13]  P. Drain,et al.  Diagnostic accuracy of C-reactive protein for active pulmonary tuberculosis: a meta-analysis. , 2017, The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease.

[14]  Peter J Dodd,et al.  The Global Burden of Latent Tuberculosis Infection: A Re-estimation Using Mathematical Modelling , 2016, PLoS medicine.

[15]  J. Schölmerich,et al.  The adiponectin paralog CORS‐26 has anti‐inflammatory properties and is produced by human monocytic cells , 2005, FEBS letters.

[16]  S. Kaufmann,et al.  Diagnostic performance of a seven-marker serum protein biosignature for the diagnosis of active TB disease in African primary healthcare clinic attendees with signs and symptoms suggestive of TB , 2016, Thorax.

[17]  A. Schäffler,et al.  C1q/TNF-related protein-3 (CTRP-3) attenuates lipopolysaccharide (LPS)-induced systemic inflammation and adipose tissue Erk-1/-2 phosphorylation in mice in vivo. , 2014, Biochemical and biophysical research communications.

[18]  D. Sherman,et al.  Incipient and Subclinical Tuberculosis: a Clinical Review of Early Stages and Progression of Infection , 2018, Clinical Microbiology Reviews.

[19]  S. Kaufmann,et al.  Ten challenges for TB biomarkers. , 2012, Tuberculosis.

[20]  P. Lønning,et al.  The insulin-like growth factor system in human immunodeficiency virus infection: relations to immunological parameters, disease progression, and antiretroviral therapy. , 2001, The Journal of clinical endocrinology and metabolism.

[21]  G. Leung,et al.  Lower risk of tuberculosis in obesity. , 2007, Archives of internal medicine.

[22]  J. Schölmerich,et al.  C1q/TNF-related protein-3 represents a novel and endogenous lipopolysaccharide antagonist of the adipose tissue. , 2010, Endocrinology.

[23]  John L. Johnson,et al.  Aptamer-based proteomic signature of intensive phase treatment response in pulmonary tuberculosis. , 2014, Tuberculosis.

[24]  John L. Johnson,et al.  Sequential inflammatory processes define human progression from M. tuberculosis infection to tuberculosis disease , 2017, PLoS pathogens.

[25]  M. Hattori,et al.  CTRP3 plays an important role in the development of collagen-induced arthritis in mice. , 2014, Biochemical and biophysical research communications.

[26]  Jason A. Skinner,et al.  Prospective Discrimination of Controllers From Progressors Early After Low-Dose Mycobacterium tuberculosis Infection of Cynomolgus Macaques using Blood RNA Signatures , 2018, The Journal of infectious diseases.

[27]  M. Hatherill,et al.  Predictive factors for latent tuberculosis infection among adolescents in a high-burden area in South Africa. , 2011, The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease.

[28]  David G. Sterling,et al.  Discovery and Validation of a Six-Marker Serum Protein Signature for the Diagnosis of Active Pulmonary Tuberculosis , 2017, Journal of Clinical Microbiology.

[29]  Gerard Tromp,et al.  Host blood RNA signatures predict the outcome of tuberculosis treatment , 2017, Tuberculosis.

[30]  Virginia Pascual,et al.  An Interferon-Inducible Neutrophil-Driven Blood Transcriptional Signature in Human Tuberculosis , 2010, Nature.

[31]  Fergal J. Duffy,et al.  A Serum Circulating miRNA Signature for Short-Term Risk of Progression to Active Tuberculosis Among Household Contacts , 2018, Front. Immunol..

[32]  M. Tameris,et al.  TB Incidence in an Adolescent Cohort in South Africa , 2013, PloS one.

[33]  S. Horswell,et al.  Complement pathway gene activation and rising circulating immune complexes characterize early disease in HIV-associated tuberculosis , 2018, Proceedings of the National Academy of Sciences.

[34]  Hsien-Ho Lin,et al.  Association of Obesity, Diabetes, and Risk of Tuberculosis: Two Population-Based Cohorts , 2017, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[35]  T. Magnuson,et al.  Lower Circulating C1q/TNF-Related Protein-3 (CTRP3) Levels Are Associated with Obesity: A Cross-Sectional Study , 2015, PloS one.

[36]  J. Schreiber,et al.  Regulation of Adhesion by Flexible Ectodomains of IgCAMs , 2013, Neurochemical Research.

[37]  D. Dowdy,et al.  Point-of-care C-reactive protein-based tuberculosis screening for people living with HIV: a diagnostic accuracy study , 2017, The Lancet. Infectious diseases.