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EBioMedicine. 2019 May;43:356-369. doi: 10.1016/j.ebiom.2019.04.016. Epub 2019 Apr 29.

Beyond multidrug resistance: Leveraging rare variants with machine and statistical learning models in Mycobacterium tuberculosis resistance prediction.

Author information

1
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States of America.
2
University of Virginia School of Medicine, Charlottesville, VA, United States of America.
3
Analysis Group Inc., United States of America.
4
Critical Path Institute, 1730 E River Rd., Tucson, AZ, United States of America.
5
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States of America; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America.
6
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States of America; Division of Pulmonary & Critical Care, Massachusetts General Hospital, Boston, MA, United States of America. Electronic address: Maha_Farhat@hms.harvard.edu.

Abstract

BACKGROUND:

The diagnosis of multidrug resistant and extensively drug resistant tuberculosis is a global health priority. Whole genome sequencing of clinical Mycobacterium tuberculosis isolates promises to circumvent the long wait times and limited scope of conventional phenotypic antimicrobial susceptibility, but gaps remain for predicting phenotype accurately from genotypic data especially for certain drugs. Our primary aim was to perform an exploration of statistical learning algorithms and genetic predictor sets using a rich dataset to build a high performing and fast predicting model to detect anti-tuberculosis drug resistance.

METHODS:

We collected targeted or whole genome sequencing and conventional drug resistance phenotyping data from 3601 Mycobacterium tuberculosis strains enriched for resistance to first- and second-line drugs, with 1228 multidrug resistant strains. We investigated the utility of (1) rare variants and variants known to be determinants of resistance for at least one drug and (2) machine and statistical learning architectures in predicting phenotypic drug resistance to 10 anti-tuberculosis drugs. Specifically, we investigated multitask and single task wide and deep neural networks, a multilayer perceptron, regularized logistic regression, and random forest classifiers.

FINDINGS:

The highest performing machine and statistical learning methods included both rare variants and those known to be causal of resistance for at least one drug. Both simpler L2 penalized regression and complex machine learning models had high predictive performance. The average AUCs for our highest performing model was 0.979 for first-line drugs and 0.936 for second-line drugs during repeated cross-validation. On an independent validation set, the highest performing model showed average AUCs, sensitivities, and specificities, respectively, of 0.937, 87.9%, and 92.7% for first-line drugs and 0.891, 82.0% and 90.1% for second-line drugs. Our method outperforms existing approaches based on direct association, with increased sum of sensitivity and specificity of 11.7% on first line drugs and 3.2% on second line drugs. Our method has higher predictive performance compared to previously reported machine learning models during cross-validation, with higher AUCs for 8 of 10 drugs.

INTERPRETATION:

Statistical models, especially those that are trained using both frequent and less frequent variants, significantly improve the accuracy of resistance prediction and hold promise in bringing sequencing technologies closer to the bedside.

KEYWORDS:

Extensively drug-resistant tuberculosis; Genome sequencing; Machine learning; Multidrug-resistance; Mycobacterium tuberculosis

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