Quantitative drug susceptibility testing for Mycobacterium tuberculosis using unassembled sequencing data and machine learning

Lachapelle A.S.; Barilar I.; Battaglia S.; Borroni E.; Brandao A.P.; Brankin A.; Cabibbe A.M.; Carter J.; Cirillo D.M.; Claxton P.; Clifton D.A.; Cohen T.; Coronel J.; Crook D.W.; Dreyer V.; Earle S.G.; Escuyer V.; Ferrazoli L.; Fowler P.W.; Gao G.F.; Gardy J.; Gharbia S.; Ghisi K.T.; Ghodousi A.; Cruz A.L.G.; Grandjean L.; Grazian C.; Groenheit R.; Guthrie J.L.; He W.; Hoffmann H.; Hoosdally S.J.; Hunt M.; Iqbal Z.; Ismail N.A.; Jarrett L.; Joseph L.; Jou R.; Kambli P.; Khot R.; Knaggs J.; Koch A.; Kohlerschmidt D.; Kouchaki S.; Lalvani A.; Lapierre S.G.; Laurenson I.F.; Letcher B.; Lin W.-H.; Liu C.; Liu D.; Malone K.M.; Mandal A.; Mansjö M.; Matias D.; Meintjes G.; Mendes F.F.; Merker M.; Mihalic M.; Millard J.; Miotto P.; Mistry N.; Moore D.A.J.; Musser K.A.; Ngcamu D.; Hoang N.N.; Niemann S.; Nilgiriwala K.S.; Nimmo C.; Okozi N.; Oliveira R.S.; Omar S.V.; Paton N.I.; Peto T.E.A.; Pinhata J.M.W.; Plesnik S.; Puyen Z.M.; Rabodoarivelo M.S.; Rakotosamimanana N.; Rancoita P.M.V.; Rathod P.; Robinson E.; Rodger G.; Rodrigues C.; Rodwell T.C.; Roohi A.; Santos-Lazaro D.; Shah S.; Kohl T.A.; Smith E.G.; Solano W.; Spitaleri A.; Supply P.; Surve U.; Tahseen S.; Thuong N.T.T.; Thwaites G.; Todt K.; Trovato A.; Utpatel C.; Van Rie A.; Vijay S.; Walker T.M.; Walker A.S.; Warren R.M.; Werngren J.; Wijkander M.; Wilkinson R.J.; Wilson D.J.; Wintringer P.; Xiao Y.-X.; Yang Y.; Yanlin Z.; Yao S.-Y.; Zhu B.; Arandjelovic I.; Barbova A.; Caws M.; Comas I.; Diel R.; Duncan C.; Dunstan S.; Farhat M.; Fitzgibbon M.M.; Furió V.; Ha D.T.M.; Holt K.; Inouye M.; Jamieson F.B.; Kamal S.M.M.; Kus J.V.; Mathys V.; Ong R.T.-H.; Qin Y.; Rogers T.R.; Rossolini G.M.; Roycroft E.; Sintchenko V.; Skrahina A.; Teo Y.Y.; Thai P.V.K.; van Soolingen D.; Wilcox M.; Zignol M. · PLoS Computational Biology (2024) · DOI: 10.1371/journal.pcbi.1012260

Resumen

There remains a clinical need for better approaches to rapid drug susceptibility testing in view of the increasing burden of multidrug resistant tuberculosis. Binary susceptibility phenotypes only capture changes in minimum inhibitory concentration when these cross the critical concentration, even though other changes may be clinically relevant. We developed a machine learning system to predict minimum inhibitory concentration from unassembled whole-genome sequencing data for 13 anti-tuberculosis drugs. We trained, validated and tested the system on 10,859 isolates from the CRyPTIC dataset. Essential agreement rates (predicted MIC within one doubling dilution of observed MIC) were above 92% for first-line drugs, 91% for fluoroquinolones and aminoglycosides, and 90% for new and repurposed drugs, albeit with a significant drop in performance for the very few phenotypically resistant isolates in the latter group. To further validate the model in the absence of external MIC datasets, we predicted MIC and converted values to binary for an external set of 15,239 isolates with binary phenotypes, and compare their performance against a previously validated mutation catalogue, the expected performance of existing molecular assays, and World Health Organization Target Product Profiles. The sensitivity of the model on the external dataset was greater than 90% for all drugs except ethionamide, clofazimine and linezolid. Specificity was greater than 95% for all drugs except ethambutol, ethionamide, bedaquiline, delamanid and clofazimine. The proposed system can provide quantitative susceptibility phenotyping to help guide antimicrobial therapy, although further data collection and validation are required before machine learning can be used clinically for all drugs.

Instituciones: Universidad Peruana Cayetano Heredia