Vaishnavi Kaipilyawar1, Yue Zhao2, Xutao Wang2, Noyal M Joseph3, Selby Knudsen4, Senbagavalli Prakash Babu5, Muthuraj Muthaiah6, Natasha S Hochberg4,7,8, Sonali Sarkar5, Charles R Horsburgh7, Jerrold J Ellner1, W Evan Johnson2,9, Padmini Salgame1. 1. Department of Medicine, Center for Emerging Pathogens, Rutgers-New Jersey Medical School, Newark, New Jersey, USA. 2. Department of Medicine, Division of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts, USA. 3. Department of Microbiology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India. 4. Boston Medical Center, Boston, Massachusetts, USA. 5. Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India. 6. Department of Microbiology, State TB Training and Demonstration Center, Government Hospital for Chest Disease, Gorimedu, Puducherry, India. 7. Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA. 8. Department of Medicine, Section of Infectious Diseases, Boston University School of Medicine, Boston, Massachusetts, USA. 9. Bioinformatics Program, Boston University, Boston, Massachusetts, USA.
Abstract
BACKGROUND: Blood-based biomarkers for diagnosing active tuberculosis (TB), monitoring treatment response, and predicting risk of progression to TB disease have been reported. However, validation of the biomarkers across multiple independent cohorts is scarce. A robust platform to validate TB biomarkers in different populations with clinical end points is essential to the development of a point-of-care clinical test. NanoString nCounter technology is an amplification-free digital detection platform that directly measures mRNA transcripts with high specificity. Here, we determined whether NanoString could serve as a platform for extensive validation of candidate TB biomarkers. METHODS: The NanoString platform was used for performance evaluation of existing TB gene signatures in a cohort in which signatures were previously evaluated on an RNA-seq dataset. A NanoString codeset that probes 107 genes comprising 12 TB signatures and 6 housekeeping genes (NS-TB107) was developed and applied to total RNA derived from whole blood samples of TB patients and individuals with latent TB infection (LTBI) from South India. The TBSignatureProfiler tool was used to score samples for each signature. An ensemble of machine learning algorithms was used to derive a parsimonious biomarker. RESULTS: Gene signatures present in NS-TB107 had statistically significant discriminative power for segregating TB from LTBI. Further analysis of the data yielded a NanoString 6-gene set (NANO6) that when tested on 10 published datasets was highly diagnostic for active TB. CONCLUSIONS: The NanoString nCounter system provides a robust platform for validating existing TB biomarkers and deriving a parsimonious gene signature with enhanced diagnostic performance.
BACKGROUND: Blood-based biomarkers for diagnosing active tuberculosis (TB), monitoring treatment response, and predicting risk of progression to TB disease have been reported. However, validation of the biomarkers across multiple independent cohorts is scarce. A robust platform to validate TB biomarkers in different populations with clinical end points is essential to the development of a point-of-care clinical test. NanoString nCounter technology is an amplification-free digital detection platform that directly measures mRNA transcripts with high specificity. Here, we determined whether NanoString could serve as a platform for extensive validation of candidate TB biomarkers. METHODS: The NanoString platform was used for performance evaluation of existing TB gene signatures in a cohort in which signatures were previously evaluated on an RNA-seq dataset. A NanoString codeset that probes 107 genes comprising 12 TB signatures and 6 housekeeping genes (NS-TB107) was developed and applied to total RNA derived from whole blood samples of TB patients and individuals with latent TB infection (LTBI) from South India. The TBSignatureProfiler tool was used to score samples for each signature. An ensemble of machine learning algorithms was used to derive a parsimonious biomarker. RESULTS: Gene signatures present in NS-TB107 had statistically significant discriminative power for segregating TB from LTBI. Further analysis of the data yielded a NanoString 6-gene set (NANO6) that when tested on 10 published datasets was highly diagnostic for active TB. CONCLUSIONS: The NanoString nCounter system provides a robust platform for validating existing TB biomarkers and deriving a parsimonious gene signature with enhanced diagnostic performance.
Authors: Margaret H Veldman-Jones; Roz Brant; Claire Rooney; Catherine Geh; Hollie Emery; Chris G Harbron; Mark Wappett; Alan Sharpe; Michael Dymond; J Carl Barrett; Elizabeth A Harrington; Gayle Marshall Journal: Cancer Res Date: 2015-06-11 Impact factor: 12.701
Authors: Jeroen Maertzdorf; January Weiner; Hans-Joachim Mollenkopf; Torsten Bauer; Antje Prasse; Joachim Müller-Quernheim; Stefan H E Kaufmann Journal: Proc Natl Acad Sci U S A Date: 2012-04-30 Impact factor: 11.205
Authors: Marc Jacobsen; Dirk Repsilber; Andrea Gutschmidt; Albert Neher; Knut Feldmann; Hans J Mollenkopf; Andreas Ziegler; Stefan H E Kaufmann Journal: J Mol Med (Berl) Date: 2007-02-23 Impact factor: 5.606
Authors: January Weiner; Jeroen Maertzdorf; Jayne S Sutherland; Fergal J Duffy; Ethan Thompson; Sara Suliman; Gayle McEwen; Bonnie Thiel; Shreemanta K Parida; Joanna Zyla; Willem A Hanekom; Robert P Mohney; W Henry Boom; Harriet Mayanja-Kizza; Rawleigh Howe; Hazel M Dockrell; Tom H M Ottenhoff; Thomas J Scriba; Daniel E Zak; Gerhard Walzl; Stefan H E Kaufmann Journal: Nat Commun Date: 2018-12-06 Impact factor: 14.919
Authors: Novel N Chegou; Jayne S Sutherland; Stephanus Malherbe; Amelia C Crampin; Paul L A M Corstjens; Annemieke Geluk; Harriet Mayanja-Kizza; Andre G Loxton; Gian van der Spuy; Kim Stanley; Leigh A Kotzé; Marieta van der Vyver; Ida Rosenkrands; Martin Kidd; Paul D van Helden; Hazel M Dockrell; Tom H M Ottenhoff; Stefan H E Kaufmann; Gerhard Walzl Journal: Thorax Date: 2016-05-04 Impact factor: 9.139
Authors: Chloe I Bloom; Christine M Graham; Matthew P R Berry; Fotini Rozakeas; Paul S Redford; Yuanyuan Wang; Zhaohui Xu; Katalin A Wilkinson; Robert J Wilkinson; Yvonne Kendrick; Gilles Devouassoux; Tristan Ferry; Makoto Miyara; Diane Bouvry; Dominique Valeyre; Valeyre Dominique; Guy Gorochov; Derek Blankenship; Mitra Saadatian; Phillip Vanhems; Huw Beynon; Rama Vancheeswaran; Melissa Wickremasinghe; Damien Chaussabel; Jacques Banchereau; Virginia Pascual; Ling-Pei Ho; Marc Lipman; Anne O'Garra Journal: PLoS One Date: 2013-08-05 Impact factor: 3.240
Authors: Daniel E Zak; Adam Penn-Nicholson; Thomas J Scriba; Ethan Thompson; Sara Suliman; Lynn M Amon; Hassan Mahomed; Mzwandile Erasmus; Wendy Whatney; Gregory D Hussey; Deborah Abrahams; Fazlin Kafaar; Tony Hawkridge; Suzanne Verver; E Jane Hughes; Martin Ota; Jayne Sutherland; Rawleigh Howe; Hazel M Dockrell; W Henry Boom; Bonnie Thiel; Tom H M Ottenhoff; Harriet Mayanja-Kizza; Amelia C Crampin; Katrina Downing; Mark Hatherill; Joe Valvo; Smitha Shankar; Shreemanta K Parida; Stefan H E Kaufmann; Gerhard Walzl; Alan Aderem; Willem A Hanekom Journal: Lancet Date: 2016-03-24 Impact factor: 79.321