Whole-Blood 3-Gene Signature as a Decision Aid for Rifapentine-based Tuberculosis Preventive Therapy

Hung Ling Huang, Jung Yu Lee, Yu Shu Lo, I. Hsin Liu, Sing Han Huang, Yu Wei Huang, Meng Rui Lee, Chih Hsin Lee, Meng Hsuan Cheng, Po Liang Lu, Jann Yuan Wang, Jinn Moon Yang, Inn Wen Chong

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

BACKGROUND: Systemic drug reaction (SDR) is a major safety concern with weekly rifapentine plus isoniazid for 12 doses (3HP) for latent tuberculosis infection (LTBI). Identifying SDR predictors and at-risk participants before treatment can improve cost-effectiveness of the LTBI program. METHODS: We prospectively recruited 187 cases receiving 3HP (44 SDRs and 143 non-SDRs). A pilot cohort (8 SDRs and 12 non-SDRs) was selected for generating whole-blood transcriptomic data. By incorporating the hierarchical system biology model and therapy-biomarker pathway approach, candidate genes were selected and evaluated using reverse-transcription quantitative polymerase chain reaction (RT-qPCR). Then, interpretable machine learning models presenting as SHapley Additive exPlanations (SHAP) values were applied for SDR risk prediction. Finally, an independent cohort was used to evaluate the performance of these predictive models. RESULTS: Based on the whole-blood transcriptomic profile of the pilot cohort and the RT-qPCR results of 2 SDR and 3 non-SDR samples in the training cohort, 6 genes were selected. According to SHAP values for model construction and validation, a 3-gene model for SDR risk prediction achieved a sensitivity and specificity of 0.972 and 0.947, respectively, under a universal cutoff value for the joint of the training (28 SDRs and 104 non-SDRs) and testing (8 SDRs and 27 non-SDRs) cohorts. It also worked well across different subgroups. CONCLUSIONS: The prediction model for 3HP-related SDRs serves as a guide for establishing a safe and personalized regimen to foster the implementation of an LTBI program. Additionally, it provides a potential translational value for future studies on drug-related hypersensitivity.

Original languageEnglish
Pages (from-to)743-752
Number of pages10
JournalClinical Infectious Diseases
Volume75
Issue number5
DOIs
StatePublished - 14 Sep 2022

Keywords

  • interpretable machine learning
  • latent tuberculosis infection
  • rifapentine
  • systemic drug reaction
  • transcriptome

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