Jayant V Rajan1, Fred C Semitala2, Tejas Mehta3, Mark Seielstad4, Lani Montalvo5, Alfred Andama6, Lucy Asege6, Martha Nakaye6, Jane Katende6, Sandra Mwebe6, Moses R Kamya2, Christina Yoon3, Adithya Cattamanchi3. 1. Division of Experimental Medicine, Department of Medicine, Zuckerberg San Francisco General Hospital, University of California, San Francisco. 2. Department of Medicine, Makerere University School of Medicine, Kampala, Uganda. 3. Division of Pulmonary and Critical Care Medicine, Department of Medicine, Zuckerberg San Francisco General Hospital, San Francisco. 4. Institute for Human Genetics, Department of Laboratory Medicine, Department of Epidemiology and Biostatistics, University of California, San Francisco. 5. Blood Systems Research Institute, San Francisco, California. 6. Infectious Diseases Research Collaboration, Kampala, Uganda.
Abstract
BACKGROUND: Gene-expression profiles have been reported to distinguish between patients with and without active tuberculosis (TB), but no prior study has been conducted in the context of TB screening. METHODS: We included all the patients (n = 40) with culture-confirmed TB and time-matched controls (n = 80) enrolled between July 2013 and April 2015 in a TB screening study among people living with human immunodeficiency virus (PLHIV) in Kampala, Uganda. We randomly split the patients into training (n = 80) and test (n = 40) datasets. We used the training dataset to derive candidate signatures that consisted of 1 to 5 differentially-expressed transcripts (P ≤ .10) and compared the performance of our candidate signatures with 4 published TB gene-expression signatures, both on the independent test dataset and in 2 external datasets. RESULTS: We identified a novel, 5-transcript signature that met the accuracy thresholds recommended for a TB screening test. On the independent test dataset, our signature had an area under the curve (AUC) of 0.87 (95% confidence interval [CI] 0.72-0.98), with sensitivity of 94% and specificity of 75%. None of the 4 published TB signatures achieved desired accuracy thresholds. Our novel signature performed well in external datasets from both high (AUC 0.81, 95% CI 0.74-0.88) and low (0.81, 95% CI 0.77-0.85) TB burden settings. CONCLUSIONS: We identified the first gene-expression signature for TB screening. Our signature has the potential to be translated into a point-of-care test to facilitate systematic TB screening among PLHIV and other high-risk populations.
BACKGROUND: Gene-expression profiles have been reported to distinguish between patients with and without active tuberculosis (TB), but no prior study has been conducted in the context of TB screening. METHODS: We included all the patients (n = 40) with culture-confirmed TB and time-matched controls (n = 80) enrolled between July 2013 and April 2015 in a TB screening study among people living with human immunodeficiency virus (PLHIV) in Kampala, Uganda. We randomly split the patients into training (n = 80) and test (n = 40) datasets. We used the training dataset to derive candidate signatures that consisted of 1 to 5 differentially-expressed transcripts (P ≤ .10) and compared the performance of our candidate signatures with 4 published TB gene-expression signatures, both on the independent test dataset and in 2 external datasets. RESULTS: We identified a novel, 5-transcript signature that met the accuracy thresholds recommended for a TB screening test. On the independent test dataset, our signature had an area under the curve (AUC) of 0.87 (95% confidence interval [CI] 0.72-0.98), with sensitivity of 94% and specificity of 75%. None of the 4 published TB signatures achieved desired accuracy thresholds. Our novel signature performed well in external datasets from both high (AUC 0.81, 95% CI 0.74-0.88) and low (0.81, 95% CI 0.77-0.85) TB burden settings. CONCLUSIONS: We identified the first gene-expression signature for TB screening. Our signature has the potential to be translated into a point-of-care test to facilitate systematic TB screening among PLHIV and other high-risk populations.
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