Genome-wide expression for diagnosis of pulmonary tuberculosis: a multicohort analysis.

BACKGROUND Active pulmonary tuberculosis is difficult to diagnose and treatment response is difficult to effectively monitor. A WHO consensus statement has called for new non-sputum diagnostics. The aim of this study was to use an integrated multicohort analysis of samples from publically available datasets to derive a diagnostic gene set in the peripheral blood of patients with active tuberculosis. METHODS We searched two public gene expression microarray repositories and retained datasets that examined clinical cohorts of active pulmonary tuberculosis infection in whole blood. We compared gene expression in patients with either latent tuberculosis or other diseases versus patients with active tuberculosis using our validated multicohort analysis framework. Three datasets were used as discovery datasets and meta-analytical methods were used to assess gene effects in these cohorts. We then validated the diagnostic capacity of the three gene set in the remaining 11 datasets. FINDINGS A total of 14 datasets containing 2572 samples from 10 countries from both adult and paediatric patients were included in the analysis. Of these, three datasets (N=1023) were used to discover a set of three genes (GBP5, DUSP3, and KLF2) that are highly diagnostic for active tuberculosis. We validated the diagnostic power of the three gene set to separate active tuberculosis from healthy controls (global area under the ROC curve (AUC) 0·90 [95% CI 0·85-0·95]), latent tuberculosis (0·88 [0·84-0·92]), and other diseases (0·84 [0·80-0·95]) in eight independent datasets composed of both children and adults from ten countries. Expression of the three-gene set was not confounded by HIV infection status, bacterial drug resistance, or BCG vaccination. Furthermore, in four additional cohorts, we showed that the tuberculosis score declined during treatment of patients with active tuberculosis. INTERPRETATION Overall, our integrated multicohort analysis yielded a three-gene set in whole blood that is robustly diagnostic for active tuberculosis, that was validated in multiple independent cohorts, and that has potential clinical application for diagnosis and monitoring treatment response. Prospective laboratory validation will be required before it can be used in a clinical setting. FUNDING National Institute of Allergy and Infectious Diseases, National Library of Medicine, the Stanford Child Health Research Institute, the Society for University Surgeons, and the Bill and Melinda Gates Foundation.

[1]  W. Youden,et al.  Index for rating diagnostic tests , 1950, Cancer.

[2]  S. Kaufmann,et al.  Concise gene signature for point‐of‐care classification of tuberculosis , 2015, EMBO molecular medicine.

[3]  F. Buntinx,et al.  Meta-analysis of ROC Curves , 2000, Medical decision making : an international journal of the Society for Medical Decision Making.

[4]  V. Pascual,et al.  Transcriptional Blood Signatures Distinguish Pulmonary Tuberculosis, Pulmonary Sarcoidosis, Pneumonias and Lung Cancers , 2013, PloS one.

[5]  C. Khor,et al.  Genome-Wide Expression Profiling Identifies Type 1 Interferon Response Pathways in Active Tuberculosis , 2012, PloS one.

[6]  Xinchun Chen,et al.  Increased Complement C1q Level Marks Active Disease in Human Tuberculosis , 2014, PloS one.

[7]  Dirk Repsilber,et al.  Functional Correlations of Pathogenesis-Driven Gene Expression Signatures in Tuberculosis , 2011, PloS one.

[8]  J. Lingrel,et al.  Myeloid-Specific Krüppel-Like Factor 2 Inactivation Increases Macrophage and Neutrophil Adhesion and Promotes Atherosclerosis , 2012, Circulation research.

[9]  Alimuddin Zumla,et al.  Biomarkers and diagnostics for tuberculosis: progress, needs, and translation into practice , 2010, The Lancet.

[10]  Noor B. Dawany,et al.  Identification of a 251 Gene Expression Signature That Can Accurately Detect M. tuberculosis in Patients with and without HIV Co-Infection , 2014, PloS one.

[11]  P. Cresswell,et al.  GBP5 Promotes NLRP3 Inflammasome Assembly and Immunity in Mammals , 2012, Science.

[12]  M. Joseph,et al.  Kruppel-like factor 2 (KLF2) regulates monocyte differentiation and functions in mBSA and IL-1β-induced arthritis. , 2012, Current molecular medicine.

[13]  Gordon K. Smyth,et al.  limma: Linear Models for Microarray Data , 2005 .

[14]  L. Coin,et al.  Diagnosis of childhood tuberculosis and host RNA expression in Africa. , 2014, The New England journal of medicine.

[15]  B. Vickery,et al.  An Interferon-Inducible Neutrophil-Driven Blood Transcriptional Signature in Human Tuberculosis , 2011, Pediatrics.

[16]  T. Clark,et al.  Distinct phases of blood gene expression pattern through tuberculosis treatment reflect modulation of the humoral immune response. , 2013, The Journal of infectious diseases.

[17]  S. Kaufmann,et al.  Perspectives on host adaptation in response to Mycobacterium tuberculosis: modulation of inflammation. , 2014, Seminars in immunology.

[18]  H. Mollenkopf,et al.  Differential transcriptomic and metabolic profiles of M. africanum- and M. tuberculosis-infected patients after, but not before drug treatment , 2015, Genes and Immunity.

[19]  Purvesh Khatri,et al.  Integrated multi-cohort transcriptional meta-analysis of neurodegenerative diseases , 2014, Acta neuropathologica communications.

[20]  Marco Schito,et al.  Defining the needs for next generation assays for tuberculosis. , 2015, The Journal of infectious diseases.

[21]  Michael Levin,et al.  Detection of Tuberculosis in HIV-Infected and -Uninfected African Adults Using Whole Blood RNA Expression Signatures: A Case-Control Study , 2013, PLoS medicine.

[22]  Alexander A. Morgan,et al.  A common rejection module (CRM) for acute rejection across multiple organs identifies novel therapeutics for organ transplantation , 2013, The Journal of experimental medicine.

[23]  V. Pascual,et al.  Detectable Changes in The Blood Transcriptome Are Present after Two Weeks of Antituberculosis Therapy , 2012, PloS one.

[24]  Purvesh Khatri,et al.  Integrated, Multi-cohort Analysis Identifies Conserved Transcriptional Signatures across Multiple Respiratory Viruses , 2015, Immunity.

[25]  D Repsilber,et al.  Human gene expression profiles of susceptibility and resistance in tuberculosis , 2011, Genes and Immunity.

[26]  Li Jiang,et al.  Solar thermal polymerase chain reaction for smartphone-assisted molecular diagnostics , 2014, Scientific Reports.

[27]  Julia Tzu-Ya Weng,et al.  Systematic Expression Profiling Analysis Identifies Specific MicroRNA-Gene Interactions that May Differentiate between Active and Latent Tuberculosis Infection , 2014, BioMed research international.

[28]  Masahiro Yamamoto,et al.  Guanylate-binding proteins promote AIM2 inflammasome activation during Francisella novicida infection by inducing cytosolic bacteriolysis and DNA release , 2015, Nature Immunology.

[29]  R. Temple,et al.  Enrichment of Clinical Study Populations , 2010, Clinical pharmacology and therapeutics.

[30]  J. Lingrel,et al.  A Myeloid Hypoxia-inducible Factor 1α-Krüppel-like Factor 2 Pathway Regulates Gram-positive Endotoxin-mediated Sepsis* , 2011, The Journal of Biological Chemistry.

[31]  Purvesh Khatri,et al.  A comprehensive time-course–based multicohort analysis of sepsis and sterile inflammation reveals a robust diagnostic gene set , 2015, Science Translational Medicine.

[32]  Rafael A. Irizarry,et al.  A Model-Based Background Adjustment for Oligonucleotide Expression Arrays , 2004 .

[33]  A. Diacon,et al.  Assessment of the sensitivity and specificity of Xpert MTB/RIF assay as an early sputum biomarker of response to tuberculosis treatment. , 2013, The Lancet. Respiratory medicine.

[34]  D. Dash,et al.  Expression profiling of lymph nodes in tuberculosis patients reveal inflammatory milieu at site of infection , 2015, Scientific Reports.

[35]  S. Aaronson,et al.  Expression cloning of a human dual-specificity phosphatase. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[36]  Aldert L. Zomer,et al.  A predictive signature gene set for discriminating active from latent tuberculosis in Warao Amerindian children , 2013, BMC Genomics.

[37]  N. Dendukuri,et al.  Xpert® MTB/RIF assay for pulmonary tuberculosis and rifampicin resistance in adults , 2014, The Cochrane database of systematic reviews.

[38]  T. Mustelin,et al.  Inhibitory Role for Dual Specificity Phosphatase VHR in T Cell Antigen Receptor and CD28-induced Erk and Jnk Activation* , 2001, The Journal of Biological Chemistry.

[39]  Stefan H. E. Kaufmann,et al.  Common patterns and disease-related signatures in tuberculosis and sarcoidosis , 2012, Proceedings of the National Academy of Sciences.

[40]  L. Riley,et al.  A real-time PCR signature to discriminate between tuberculosis and other pulmonary diseases. , 2015, Tuberculosis.