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.
DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis / Y. Yang, T.M. Walker, A.S. Walker, D.J. Wilson, T.E.A. Peto, D.W. Crook, F. Shamout, T. Zhu, D.A. Clifton, I. Arandjelovic, I. Comas, M.R. Farhat, Q. Gao, V. Sintchenko, D. Soolingen, S. Hoosdally, A.L.G. Cruz, J. Carter, C. Grazian, S.G. Earle, S. Kouchaki, P.W. Fowler, Z. Iqbal, M. Hunt, E.G. Smith, P. Rathod, L. Jarrett, D. Matias, D.M. Cirillo, E. Borroni, S. Battaglia, A. Ghodousi, A. Spitaleri, A. Cabibbe, S. Tahseen, K. Nilgiriwala, S. Shah, C. Rodrigues, P. Kambli, U. Surve, R. Khot, S. Niemann, T. Kohl, M. Merker, H. Hoffmann, N. Molodtsov, S. Plesnik, N. Ismail, S.V. Omar, G. Thwaites, T.N.T. Thuong, N.H. Ngoc, V. Srinivasan, D. Moore, J. Coronel, W. Solano, G.F. Gao, G. He, Y. Zhao, A. Ma, C. Liu, B. Zhu, I. Laurenson, P. Claxton, A. Koch, R. Wilkinson, A. Lalvani, J. Posey, J. Gardy, J. Werngren, N. Paton, R. Jou, M.-. Wu, W.-. Lin, L. Ferrazoli, R.S. de Oliveira. - In: BIOINFORMATICS. - ISSN 1367-4803. - 35:18(2019), pp. 3240-3249. [10.1093/bioinformatics/btz067]
DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis
A. Spitaleri;
2019
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.
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