DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis
Authors: Yang Yang , Walker Timothy M , Walker A Sarah , Wilson Daniel J , Peto Timothy E A , Crook Derrick W , Shamout Farah ,   CRyPTIC Consortium, Arandjelovic Irena , Comas Iñaki , Farhat Maha R , Gao Qian , Sintchenko Vitali , van Soolingen Dick , Hoosdally Sarah , Gibertoni Cruz Ana L , Carter Joshua , Grazian Clara , Earle Sarah G , Kouchaki Samaneh , Yang Yang , Walker Timothy M , Fowler Philip W , Clifton David A , Iqbal Zamin , Hunt Martin , Smith E Grace , Rathod Priti , Jarrett Lisa , Matias Daniela , Cirillo Daniela M , Borroni Emanuele , Battaglia Simone , Ghodousi Arash , Spitaleri Andrea , Cabibbe Andrea , Tahseen Sabira , Nilgiriwala Kayzad , Shah Sanchi , Rodrigues Camilla , Kambli Priti , Surve Utkarsha , Khot Rukhsar , Niemann Stefan , Kohl Thomas , Merker Matthias , Hoffmann Harald , Molodtsov Nikolay , Plesnik Sara , Ismail Nazir , Thwaites Guy , Thuy Thuong Thuong Nguyen , Ngoc Nhung Hoang , Srinivasan Vijay , Moore David , Coronel David Jorge , Solano Walter , Gao George F , He Guangxue , Zhao Yanlin , Ma Aijing , Liu Chunfa , Zhu Baoli , Laurenson Ian , Claxton Pauline , Koch Anastasia , Wilkinson Robert , Lalvani Ajit , Posey James , Gardy James Jennifer , Werngren Jim , Paton Nicholas , Jou Ruwen , Wu Mei-Hua , Lin Wan-Hsuan , Ferrazoli Lucilaine , de Oliveira Rosangela Siqueira , Paulo São , Zhu Tingting , Clifton David A 
Published: January 2019

Motivation

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.