DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis

Yang Yang, Timothy M. Walker, A. Sarah Walker, Daniel J. Wilson, Timothy E.A. Peto, Derrick W. Crook, Farah Shamout, Tingting Zhu, David A. Clifton, Irena Arandjelovic, Iñaki Comas, Maha R. Farhat, Qian Gao, Vitali Sintchenko, Dickvan Soolingen, Sarah Hoosdally, Ana L.Gibertoni Cruz, Joshua Carter, Clara Grazian, Sarah G. EarleSamaneh Kouchaki, Philip W. Fowler, Zamin Iqbal, Martin Hunt, E. Grace Smith, Priti Rathod, Lisa Jarrett, Daniela Matias, Daniela M. Cirillo, Emanuele Borroni, Simone Battaglia, Arash Ghodousi, Andrea Spitaleri, Andrea Cabibbe, Sabira Tahseen, Kayzad Nilgiriwala, Sanchi Shah, Camilla Rodrigues, Priti Kambli, Utkarsha Surve, Rukhsar Khot, Stefan Niemann, Thomas Kohl, Matthias Merker, Harald Hoffmann, Nikolay Molodtsov, Sara Plesnik, Nazir Ismail, Shaheed Vally Omar, Guy Thwaites, Thuong Nguyen Thuy Thuong, Nhung Hoang Ngoc, Vijay Srinivasan, David Moore, Jorge Coronel, Walter Solano, George F. Gao, Guangxue He, Yanlin Zhao, Aijing Ma, Chunfa Liu, Baoli Zhu, Ian Laurenson, Pauline Claxton, Anastasia Koch, Robert Wilkinson, Ajit Lalvani, James Posey, Jennifer Gardy, Jim Werngren, Nicholas Paton, Ruwen Jou, Mei Hua Wu, Wan Hsuan Lin, Lucilaine Ferrazoli, Rosangela Siqueira de Oliveira

Research output: Contribution to journalArticle

Abstract

Resistance co-occurrence within first-line anti-tuberculosis (TB) drugs is a common phenomenon. Existing methods based on genetic data analysis of Mycobacterium tuberculosis (MTB) have been able to predict resistance of MTB to individual drugs, but have not considered the resistance co-occurrence and cannot capture latent structure of genomic data that corresponds to lineages. Results: We used a large cohort of TB patients from 16 countries across six continents where whole-genome sequences for each isolate and associated phenotype to anti-TB drugs were obtained using drug susceptibility testing recommended by the World Health Organization. We then proposed an end-to-end multi-task model with deep denoising auto-encoder (DeepAMR) for multiple drug classification and developed DeepAMR-cluster, a clustering variant based on DeepAMR, for learning clusters in latent space of the data. The results showed that DeepAMR outperformed baseline model and four machine learning models with mean AUROC from 94.4% to 98.7% for predicting resistance to four first-line drugs [i.e. isoniazid (INH), ethambutol (EMB), rifampicin (RIF), pyrazinamide (PZA)], multi-drug resistant TB (MDR-TB) and pan-susceptible TB (PANS-TB: MTB that is susceptible to all four first-line anti-TB drugs). In the case of INH, EMB, PZA and MDR-TB, DeepAMR achieved its best mean sensitivity of 94.3%, 91.5%, 87.3% and 96.3%, respectively. While in the case of RIF and PANS-TB, it generated 94.2% and 92.2% sensitivity, which were lower than baseline model by 0.7% and 1.9%, respectively. t-SNE visualization shows that DeepAMR-cluster captures lineage-related clusters in the latent space. Availability and implementation: The details of source code are provided at http://www.robots.ox.ac.uk/∼davidc/code.php. Supplementary information: Supplementary data are available at Bioinformatics online.

Original languageEnglish (US)
Pages (from-to)3240-3249
Number of pages10
JournalBioinformatics
Volume35
Issue number18
DOIs
StatePublished - Sep 15 2019

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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  • Cite this

    Yang, Y., Walker, T. M., Walker, A. S., Wilson, D. J., Peto, T. E. A., Crook, D. W., Shamout, F., Zhu, T., Clifton, D. A., Arandjelovic, I., Comas, I., Farhat, M. R., Gao, Q., Sintchenko, V., Soolingen, D., Hoosdally, S., Cruz, A. L. G., Carter, J., Grazian, C., ... de Oliveira, R. S. (2019). DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis. Bioinformatics, 35(18), 3240-3249. https://doi.org/10.1093/bioinformatics/btz067