Combination of HLA-DR on Mycobacterium tuberculosis-Specific Cells and Tuberculosis Antigen/Phytohemagglutinin Ratio  for Discriminating Active Tuberculosis From Latent Tuberculosis Infection

Luo, Ying; Xue, Ying; Tang, Guoqing; Lin, Qun; Song, Huijuan; Liu, Wei; Yin, Botao; Huang, Jin; Wei, Wei; L, Mao; Wang, Feng; Sun, Ziyong

doi:10.3389/fimmu.2021.761209

Cited by 6 publications

(3 citation statements)

References 35 publications

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“…It is a growing notion that single biomarker is insufficient for differentiating Mtb infection status, while the powerful combination of multiple indicators would be trend for enhancing the utility [ 24 , 25 ]. Nonetheless, the loss of diagnostic performance attributed to the unreasonable combination of data is usually easily neglected.…”

Section: Discussionmentioning

confidence: 99%

Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection

et al. 2022

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Background The discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging. The present study aims to investigate the value of diagnostic models established by machine learning based on multiple laboratory data for distinguishing Mycobacterium tuberculosis (Mtb) infection status. Methods T-SPOT, lymphocyte characteristic detection, and routine laboratory tests were performed on participants. Diagnostic models were built according to various algorithms. Results A total of 892 participants (468 ATB and 424 LTBI) and another 263 participants (125 ATB and 138 LTBI), were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Receiver operating characteristic (ROC) curve analysis showed that the value of individual indicator for differentiating ATB from LTBI was limited (area under the ROC curve (AUC) < 0.8). A total of 28 models were successfully established using machine learning. Among them, the AUCs of 25 models were more than 0.9 in test set. It was found that conditional random forests (cforest) model, based on the implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners, presented best discriminative power in segregating ATB from LTBI. Specially, cforest model presented an AUC of 0.978, with the sensitivity of 93.39% and the specificity of 91.18%. Mtb-specific response represented by early secreted antigenic target 6 (ESAT-6) and culture filtrate protein 10 (CFP-10) spot-forming cell (SFC) in T-SPOT assay, as well as global adaptive immunity assessed by CD4 cell IFN-γ secretion, CD8 cell IFN-γ secretion, and CD4 cell number, were found to contribute greatly to the cforest model. Superior performance obtained in the discovery cohort was further confirmed in the validation cohort. The sensitivity and specificity of cforest model in validation set were 92.80% and 89.86%, respectively. Conclusions Cforest model developed upon machine learning could serve as a valuable and prospective tool for identifying Mtb infection status. The present study provided a novel and viable idea for realizing the clinical diagnostic application of the combination of machine learning and laboratory findings.

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Section: Discussionmentioning

confidence: 99%

Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection

et al. 2022

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“…While no specific associations were found in our study, other HLA‐dependent genetic or immunological mechanisms may be at play. For example, the expression of HLA‐DR on Mtb ‐specific IFN‐

\gamma

+ TNF‐

\alpha

+ cells is reportedly higher in individuals with active disease compared with those with latent Mtb infection (Luo et al, 2021). Future work examining activated cellular immune responses to Mtb or cell‐type‐specific expression may gain additional mechanistic insight into the role of specific HLA genotypes.…”

Section: Discussionmentioning

confidence: 99%

Novel HLA associations with outcomes of Mycobacterium tuberculosis exposure and sarcoidosis in individuals of African ancestry using nearest‐neighbor feature selection

Dawkins

Garman

Cejda

et al. 2022

Genetic Epidemiology

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Tuberculosis and sarcoidosis are inflammatory diseases characterized by granulomas that may occur in any organ but are often found in the lung. The panoply of classical human leukocyte antigen (HLA) alleles associated with occurrence and/or severity of both diseases varies considerably across studies. This heterogeneity of results, due to variation in factors like ancestry and disease subphenotype, as well as the use of simple modeling strategies to elucidate likely complex relationships, has made conclusions about underlying commonalities difficult. Here we perform HLA association analyses in individuals of African ancestry, using a greater resolution to include subphenotypes of disease and employing more comprehensive analytical techniques. Using a novel application of nearest‐neighbor feature selection to score allelic importance, we investigated HLA allele association with Mycobacterium tuberculosis exposure outcomes in the first analysis of both latent Mycobacterium tuberculosis infection and active disease compared with those who, despite long‐term exposure to active index cases, have neither positive diagnostic tests nor display clinical symptoms. We also compared persistent to resolved sarcoidosis. This led to the identification of novel HLA associations and evidence of main effects and interaction effects. We found strikingly similar main effects and interaction effects at HLA‐DRB1, ‐DQB1, and ‐DPB1 in those resistant to tuberculosis (either latent or active) and persistent sarcoidosis.

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“…In contrast, the activation [ 14 , 15 ], differentiation [ 16 ], and cytokine coexpression profiles [ 17 ] of TB-reactive T cells have shown promising diagnostic applications in ATB diagnosis. Among them, the activation phenotype expressed in Mtb-specific T cells appears to be particularly outstanding [ 18 ]. For example, CD27 has been associated with active disease and tissue destruction in TB [ 19 ]; CD137 has been used to identify reactive T cells in response to TB antigens [ 20 ]; CD38, an early immune marker of T cell activation, was found to be significantly superior to CD27 in the accurate diagnosis of TB [ 21 ]; HLA-DR, usually highly expressed during the late stage of T cell activation [ 22 , 23 ], is believed to have the potential to distinguish patients with ATB from LTBI individuals [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Value of CD25, CD69, and CD134 on Tuberculosis-Specific Antigen-Stimulated CD4+ T Cells for Tuberculous Pleurisy

Shi,

Yang,

Zhang

et al. 2023

Journal of Immunology Research

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Rapid and accurate methods for the diagnosis of tuberculous pleurisy (TP) are urgently needed. Activation markers of tuberculosis (TB)–reactive T cells are considered promising for the diagnosis of active TB (ATB). Different activation indexes may play different roles in the progression of TB, but there are few reports on T cell activation indicators, except for HLA-DR. Hence, we evaluated the expression of early (CD25 and CD69) and late (CD134) activation markers on TB antigen-stimulated CD4+ T cells in populations with different TB infection status and investigated their diagnostic value for ATB, particularly, for TP. Moreover, we compared the differences in the diagnostic efficacy among the indexes from peripheral blood (PB) and pleural fluid (PF) for TP. The expression of each activation marker was significantly increased in TB-infected populations (patients with ATB and latent TB infection vs. healthy individuals; patients with TP vs. non-TP) and was significantly higher in the PF than in the PB of patients with TP. The diagnostic performance of the coexpressed activation markers was superior to that of single expression markers in the differential diagnosis of ATB and non-TB, with CD25+CD134+ showing the best diagnostic efficiency (AUC: 0.93, 95% CI, 0.87–0.99; sensitivity: 86.7%, 95% CI, 72.5%–94.5%; and specificity: 94.0%, 95% CI, 82.5%–98.4%). Except for TB-IGRA, the activation indexes were more accurate than conventional laboratory methods for ATB diagnosis. In addition, the expression of CD25+CD134+ in PB and PF was the best values for differential diagnosis of TP and NTP, with AUCs of 0.87 (95% CI, 0.77–0.96) and 0.95 (95% CI, 0.90–1.00), respectively. Our study provides information on the diagnostic value of different activation markers for TB and shows that the expression of CD25+CD134+ on CD4+ T cells in PF can serve as a potential marker for TP diagnosis.

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Combination of HLA-DR on Mycobacterium tuberculosis-Specific Cells and Tuberculosis Antigen/Phytohemagglutinin Ratio for Discriminating Active Tuberculosis From Latent Tuberculosis Infection

Cited by 6 publications

References 35 publications

Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection

Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection

Novel HLA associations with outcomes of Mycobacterium tuberculosis exposure and sarcoidosis in individuals of African ancestry using nearest‐neighbor feature selection

Diagnostic Value of CD25, CD69, and CD134 on Tuberculosis-Specific Antigen-Stimulated CD4+ T Cells for Tuberculous Pleurisy

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