Combinatorial immunoprofiling in latent tuberculosis infection

Toward better risk stratification

Patricio Escalante, Tobias D Peikert, Virginia P. Van Keulen, Courtney L. Erskine, Cathy L. Bornhorst, Boleyn R. Andrist, Kevin McCoy, Larry R Pease, Roshini S. Abraham, Keith L Knutson, Hirohito Kita, Adam G. Schrum, Andrew Harold Limper

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

Rationale: Most immunocompetent patients diagnosed with latent tuberculosis infection (LTBI) will not progress to tuberculosis (TB) reactivation. However, current diagnostic tools cannot reliably distinguish nonprogressing from progressing patients a priori, and thus LTBI therapy must be prescribed with suboptimal patient specificity. We hypothesized that LTBI diagnostics could be improved by generating immunomarker profiles capable of categorizing distinct patient subsets by a combinatorial immunoassay approach. Objectives: A combinatorial immunoassay analysis was applied to identify potential immunomarker combinations that distinguish among unexposed subjects, untreated patients with LTBI, and treated patients with LTBI and to differentiate risk of reactivation. Methods: IFN-g release assay (IGRA) was combined with a flow cytometric assay that detects induction of CD25<sup>+</sup>CD134<sup>+</sup> coexpression on TB antigen-stimulated T cells from peripheral blood. The combinatorial immunoassay analysis was based on receiver operating characteristic curves, technical cut-offs, 95% bivariate normal density ellipse prediction, and statistical analysis. Risk of reactivation was estimated with a prediction formula. Measurements and Main Results: Sixty-five out of 150 subjects were included. The combinatorial immunoassay approach identified at least four different T-cell subsets. The representation of these immune phenotypes was more heterogeneous in untreated patients with LTBI than in treated patients with LTBI or unexposed groups. Patients with IGRA(+) CD4<sup>+</sup>CD25<sup>+</sup>CD134<sup>+</sup> T-cell phenotypes had the highest estimated reactivation risk (4.11 ± 2.11%). Conclusions: These findings suggest that immune phenotypes defined by combinatorial assays may potentially have a role in identifying those at risk of developing TB; this potential role is supported by risk of reactivation modeling. Prospective studies will be needed to test this novel approach.

Original languageEnglish (US)
Pages (from-to)605-617
Number of pages13
JournalAmerican Journal of Respiratory and Critical Care Medicine
Volume192
Issue number5
DOIs
StatePublished - Sep 1 2015

Fingerprint

Latent Tuberculosis
Immunoassay
Tuberculosis
Phenotype
T-Lymphocytes
T-Lymphocyte Subsets
ROC Curve
Prospective Studies
Antigens

Keywords

  • Biomarker
  • Flow cytometry
  • Immunoassay
  • Latent tuberculosis infection
  • Tuberculosis

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine
  • Critical Care and Intensive Care Medicine

Cite this

Combinatorial immunoprofiling in latent tuberculosis infection : Toward better risk stratification. / Escalante, Patricio; Peikert, Tobias D; Van Keulen, Virginia P.; Erskine, Courtney L.; Bornhorst, Cathy L.; Andrist, Boleyn R.; McCoy, Kevin; Pease, Larry R; Abraham, Roshini S.; Knutson, Keith L; Kita, Hirohito; Schrum, Adam G.; Limper, Andrew Harold.

In: American Journal of Respiratory and Critical Care Medicine, Vol. 192, No. 5, 01.09.2015, p. 605-617.

Research output: Contribution to journalArticle

Escalante, Patricio ; Peikert, Tobias D ; Van Keulen, Virginia P. ; Erskine, Courtney L. ; Bornhorst, Cathy L. ; Andrist, Boleyn R. ; McCoy, Kevin ; Pease, Larry R ; Abraham, Roshini S. ; Knutson, Keith L ; Kita, Hirohito ; Schrum, Adam G. ; Limper, Andrew Harold. / Combinatorial immunoprofiling in latent tuberculosis infection : Toward better risk stratification. In: American Journal of Respiratory and Critical Care Medicine. 2015 ; Vol. 192, No. 5. pp. 605-617.
@article{8ac5b14c216d491ea89efdef1da3486a,
title = "Combinatorial immunoprofiling in latent tuberculosis infection: Toward better risk stratification",
abstract = "Rationale: Most immunocompetent patients diagnosed with latent tuberculosis infection (LTBI) will not progress to tuberculosis (TB) reactivation. However, current diagnostic tools cannot reliably distinguish nonprogressing from progressing patients a priori, and thus LTBI therapy must be prescribed with suboptimal patient specificity. We hypothesized that LTBI diagnostics could be improved by generating immunomarker profiles capable of categorizing distinct patient subsets by a combinatorial immunoassay approach. Objectives: A combinatorial immunoassay analysis was applied to identify potential immunomarker combinations that distinguish among unexposed subjects, untreated patients with LTBI, and treated patients with LTBI and to differentiate risk of reactivation. Methods: IFN-g release assay (IGRA) was combined with a flow cytometric assay that detects induction of CD25+CD134+ coexpression on TB antigen-stimulated T cells from peripheral blood. The combinatorial immunoassay analysis was based on receiver operating characteristic curves, technical cut-offs, 95{\%} bivariate normal density ellipse prediction, and statistical analysis. Risk of reactivation was estimated with a prediction formula. Measurements and Main Results: Sixty-five out of 150 subjects were included. The combinatorial immunoassay approach identified at least four different T-cell subsets. The representation of these immune phenotypes was more heterogeneous in untreated patients with LTBI than in treated patients with LTBI or unexposed groups. Patients with IGRA(+) CD4+CD25+CD134+ T-cell phenotypes had the highest estimated reactivation risk (4.11 ± 2.11{\%}). Conclusions: These findings suggest that immune phenotypes defined by combinatorial assays may potentially have a role in identifying those at risk of developing TB; this potential role is supported by risk of reactivation modeling. Prospective studies will be needed to test this novel approach.",
keywords = "Biomarker, Flow cytometry, Immunoassay, Latent tuberculosis infection, Tuberculosis",
author = "Patricio Escalante and Peikert, {Tobias D} and {Van Keulen}, {Virginia P.} and Erskine, {Courtney L.} and Bornhorst, {Cathy L.} and Andrist, {Boleyn R.} and Kevin McCoy and Pease, {Larry R} and Abraham, {Roshini S.} and Knutson, {Keith L} and Hirohito Kita and Schrum, {Adam G.} and Limper, {Andrew Harold}",
year = "2015",
month = "9",
day = "1",
doi = "10.1164/rccm.201412-2141OC",
language = "English (US)",
volume = "192",
pages = "605--617",
journal = "American Journal of Respiratory and Critical Care Medicine",
issn = "1073-449X",
publisher = "American Thoracic Society",
number = "5",

}

TY - JOUR

T1 - Combinatorial immunoprofiling in latent tuberculosis infection

T2 - Toward better risk stratification

AU - Escalante, Patricio

AU - Peikert, Tobias D

AU - Van Keulen, Virginia P.

AU - Erskine, Courtney L.

AU - Bornhorst, Cathy L.

AU - Andrist, Boleyn R.

AU - McCoy, Kevin

AU - Pease, Larry R

AU - Abraham, Roshini S.

AU - Knutson, Keith L

AU - Kita, Hirohito

AU - Schrum, Adam G.

AU - Limper, Andrew Harold

PY - 2015/9/1

Y1 - 2015/9/1

N2 - Rationale: Most immunocompetent patients diagnosed with latent tuberculosis infection (LTBI) will not progress to tuberculosis (TB) reactivation. However, current diagnostic tools cannot reliably distinguish nonprogressing from progressing patients a priori, and thus LTBI therapy must be prescribed with suboptimal patient specificity. We hypothesized that LTBI diagnostics could be improved by generating immunomarker profiles capable of categorizing distinct patient subsets by a combinatorial immunoassay approach. Objectives: A combinatorial immunoassay analysis was applied to identify potential immunomarker combinations that distinguish among unexposed subjects, untreated patients with LTBI, and treated patients with LTBI and to differentiate risk of reactivation. Methods: IFN-g release assay (IGRA) was combined with a flow cytometric assay that detects induction of CD25+CD134+ coexpression on TB antigen-stimulated T cells from peripheral blood. The combinatorial immunoassay analysis was based on receiver operating characteristic curves, technical cut-offs, 95% bivariate normal density ellipse prediction, and statistical analysis. Risk of reactivation was estimated with a prediction formula. Measurements and Main Results: Sixty-five out of 150 subjects were included. The combinatorial immunoassay approach identified at least four different T-cell subsets. The representation of these immune phenotypes was more heterogeneous in untreated patients with LTBI than in treated patients with LTBI or unexposed groups. Patients with IGRA(+) CD4+CD25+CD134+ T-cell phenotypes had the highest estimated reactivation risk (4.11 ± 2.11%). Conclusions: These findings suggest that immune phenotypes defined by combinatorial assays may potentially have a role in identifying those at risk of developing TB; this potential role is supported by risk of reactivation modeling. Prospective studies will be needed to test this novel approach.

AB - Rationale: Most immunocompetent patients diagnosed with latent tuberculosis infection (LTBI) will not progress to tuberculosis (TB) reactivation. However, current diagnostic tools cannot reliably distinguish nonprogressing from progressing patients a priori, and thus LTBI therapy must be prescribed with suboptimal patient specificity. We hypothesized that LTBI diagnostics could be improved by generating immunomarker profiles capable of categorizing distinct patient subsets by a combinatorial immunoassay approach. Objectives: A combinatorial immunoassay analysis was applied to identify potential immunomarker combinations that distinguish among unexposed subjects, untreated patients with LTBI, and treated patients with LTBI and to differentiate risk of reactivation. Methods: IFN-g release assay (IGRA) was combined with a flow cytometric assay that detects induction of CD25+CD134+ coexpression on TB antigen-stimulated T cells from peripheral blood. The combinatorial immunoassay analysis was based on receiver operating characteristic curves, technical cut-offs, 95% bivariate normal density ellipse prediction, and statistical analysis. Risk of reactivation was estimated with a prediction formula. Measurements and Main Results: Sixty-five out of 150 subjects were included. The combinatorial immunoassay approach identified at least four different T-cell subsets. The representation of these immune phenotypes was more heterogeneous in untreated patients with LTBI than in treated patients with LTBI or unexposed groups. Patients with IGRA(+) CD4+CD25+CD134+ T-cell phenotypes had the highest estimated reactivation risk (4.11 ± 2.11%). Conclusions: These findings suggest that immune phenotypes defined by combinatorial assays may potentially have a role in identifying those at risk of developing TB; this potential role is supported by risk of reactivation modeling. Prospective studies will be needed to test this novel approach.

KW - Biomarker

KW - Flow cytometry

KW - Immunoassay

KW - Latent tuberculosis infection

KW - Tuberculosis

UR - http://www.scopus.com/inward/record.url?scp=84942245265&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84942245265&partnerID=8YFLogxK

U2 - 10.1164/rccm.201412-2141OC

DO - 10.1164/rccm.201412-2141OC

M3 - Article

VL - 192

SP - 605

EP - 617

JO - American Journal of Respiratory and Critical Care Medicine

JF - American Journal of Respiratory and Critical Care Medicine

SN - 1073-449X

IS - 5

ER -