Discovery Proteomics and Nonparametric Modeling Pipeline in the Development of a Candidate Biomarker Panel for Dengue Hemorrhagic Fever

Allan R. Brasier, Josefina Garcia, John E. Wiktorowicz, Heidi Spratt, Guillermo Comach, Hyunsu Ju, Adrian Recinos, Kizhake Soman, Brett M. Forshey, Eric S. Halsey, Patrick J. Blair, Claudio Rocha, Isabel Bazan, Sundar S. Victor, Zheng Wu, Susan Stafford, Douglas Watts, Amy C. Morrison, Thomas W. Scott, Tadeusz J. KochelGloria Sierra, Iris Villalobos, Carlos Espino

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

Secondary dengue viral infection can produce capillary leakage associated with increased mortality known as dengue hemorrhagic fever (DHF). Because the mortality of DHF can be reduced by early detection and intensive support, improved methods for its detection are needed. We applied multidimensional protein profiling to predict outcomes in a prospective dengue surveillance study in South America. Plasma samples taken from initial clinical presentation of acute dengue infection were subjected to proteomics analyses using ELISA and a recently developed biofluid analysis platform. Demographics, clinical laboratory measurements, nine cytokines, and 419 plasma proteins collected at the time of initial presentation were compared between the DF and DHF outcomes. Here, the subject's gender, clinical parameters, two cytokines, and 42 proteins discriminated between the outcomes. These factors were reduced by multivariate adaptive regression splines (MARS) that a highly accurate classification model based on eight discriminant features with an area under the receiver operator curve (AUC) of 0.999. Model analysis indicated that the feature-outcome relationship were nonlinear. Although this DHF risk model will need validation in a larger cohort, we conclude that approaches to develop predictive biomarker models for disease outcome will need to incorporate nonparametric modeling approaches.

Original languageEnglish (US)
Pages (from-to)8-20
Number of pages13
JournalClinical and Translational Science
Volume5
Issue number1
DOIs
StatePublished - Feb 2012

Fingerprint

Severe Dengue
Biomarkers
Proteomics
Dengue
Pipelines
Clinical laboratories
Cytokines
Mortality
South America
Virus Diseases
Splines
Area Under Curve
Blood Proteins
Proteins
Enzyme-Linked Immunosorbent Assay
Demography
Plasmas
Infection

Keywords

  • Hemorrhagic disorders and therapies
  • Host response
  • Infectious disease
  • Plasma
  • Proteins
  • Viral infection

ASJC Scopus subject areas

  • Medicine(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Neuroscience(all)
  • Pharmacology, Toxicology and Pharmaceutics(all)

Cite this

Discovery Proteomics and Nonparametric Modeling Pipeline in the Development of a Candidate Biomarker Panel for Dengue Hemorrhagic Fever. / Brasier, Allan R.; Garcia, Josefina; Wiktorowicz, John E.; Spratt, Heidi; Comach, Guillermo; Ju, Hyunsu; Recinos, Adrian; Soman, Kizhake; Forshey, Brett M.; Halsey, Eric S.; Blair, Patrick J.; Rocha, Claudio; Bazan, Isabel; Victor, Sundar S.; Wu, Zheng; Stafford, Susan; Watts, Douglas; Morrison, Amy C.; Scott, Thomas W.; Kochel, Tadeusz J.; Sierra, Gloria; Villalobos, Iris; Espino, Carlos.

In: Clinical and Translational Science, Vol. 5, No. 1, 02.2012, p. 8-20.

Research output: Contribution to journalArticle

Brasier, AR, Garcia, J, Wiktorowicz, JE, Spratt, H, Comach, G, Ju, H, Recinos, A, Soman, K, Forshey, BM, Halsey, ES, Blair, PJ, Rocha, C, Bazan, I, Victor, SS, Wu, Z, Stafford, S, Watts, D, Morrison, AC, Scott, TW, Kochel, TJ, Sierra, G, Villalobos, I & Espino, C 2012, 'Discovery Proteomics and Nonparametric Modeling Pipeline in the Development of a Candidate Biomarker Panel for Dengue Hemorrhagic Fever', Clinical and Translational Science, vol. 5, no. 1, pp. 8-20. https://doi.org/10.1111/j.1752-8062.2011.00377.x
Brasier, Allan R. ; Garcia, Josefina ; Wiktorowicz, John E. ; Spratt, Heidi ; Comach, Guillermo ; Ju, Hyunsu ; Recinos, Adrian ; Soman, Kizhake ; Forshey, Brett M. ; Halsey, Eric S. ; Blair, Patrick J. ; Rocha, Claudio ; Bazan, Isabel ; Victor, Sundar S. ; Wu, Zheng ; Stafford, Susan ; Watts, Douglas ; Morrison, Amy C. ; Scott, Thomas W. ; Kochel, Tadeusz J. ; Sierra, Gloria ; Villalobos, Iris ; Espino, Carlos. / Discovery Proteomics and Nonparametric Modeling Pipeline in the Development of a Candidate Biomarker Panel for Dengue Hemorrhagic Fever. In: Clinical and Translational Science. 2012 ; Vol. 5, No. 1. pp. 8-20.
@article{5c80825d017b47e2814727d830e4ef40,
title = "Discovery Proteomics and Nonparametric Modeling Pipeline in the Development of a Candidate Biomarker Panel for Dengue Hemorrhagic Fever",
abstract = "Secondary dengue viral infection can produce capillary leakage associated with increased mortality known as dengue hemorrhagic fever (DHF). Because the mortality of DHF can be reduced by early detection and intensive support, improved methods for its detection are needed. We applied multidimensional protein profiling to predict outcomes in a prospective dengue surveillance study in South America. Plasma samples taken from initial clinical presentation of acute dengue infection were subjected to proteomics analyses using ELISA and a recently developed biofluid analysis platform. Demographics, clinical laboratory measurements, nine cytokines, and 419 plasma proteins collected at the time of initial presentation were compared between the DF and DHF outcomes. Here, the subject's gender, clinical parameters, two cytokines, and 42 proteins discriminated between the outcomes. These factors were reduced by multivariate adaptive regression splines (MARS) that a highly accurate classification model based on eight discriminant features with an area under the receiver operator curve (AUC) of 0.999. Model analysis indicated that the feature-outcome relationship were nonlinear. Although this DHF risk model will need validation in a larger cohort, we conclude that approaches to develop predictive biomarker models for disease outcome will need to incorporate nonparametric modeling approaches.",
keywords = "Hemorrhagic disorders and therapies, Host response, Infectious disease, Plasma, Proteins, Viral infection",
author = "Brasier, {Allan R.} and Josefina Garcia and Wiktorowicz, {John E.} and Heidi Spratt and Guillermo Comach and Hyunsu Ju and Adrian Recinos and Kizhake Soman and Forshey, {Brett M.} and Halsey, {Eric S.} and Blair, {Patrick J.} and Claudio Rocha and Isabel Bazan and Victor, {Sundar S.} and Zheng Wu and Susan Stafford and Douglas Watts and Morrison, {Amy C.} and Scott, {Thomas W.} and Kochel, {Tadeusz J.} and Gloria Sierra and Iris Villalobos and Carlos Espino",
year = "2012",
month = "2",
doi = "10.1111/j.1752-8062.2011.00377.x",
language = "English (US)",
volume = "5",
pages = "8--20",
journal = "Clinical and Translational Science",
issn = "1752-8054",
publisher = "Wiley-Blackwell",
number = "1",

}

TY - JOUR

T1 - Discovery Proteomics and Nonparametric Modeling Pipeline in the Development of a Candidate Biomarker Panel for Dengue Hemorrhagic Fever

AU - Brasier, Allan R.

AU - Garcia, Josefina

AU - Wiktorowicz, John E.

AU - Spratt, Heidi

AU - Comach, Guillermo

AU - Ju, Hyunsu

AU - Recinos, Adrian

AU - Soman, Kizhake

AU - Forshey, Brett M.

AU - Halsey, Eric S.

AU - Blair, Patrick J.

AU - Rocha, Claudio

AU - Bazan, Isabel

AU - Victor, Sundar S.

AU - Wu, Zheng

AU - Stafford, Susan

AU - Watts, Douglas

AU - Morrison, Amy C.

AU - Scott, Thomas W.

AU - Kochel, Tadeusz J.

AU - Sierra, Gloria

AU - Villalobos, Iris

AU - Espino, Carlos

PY - 2012/2

Y1 - 2012/2

N2 - Secondary dengue viral infection can produce capillary leakage associated with increased mortality known as dengue hemorrhagic fever (DHF). Because the mortality of DHF can be reduced by early detection and intensive support, improved methods for its detection are needed. We applied multidimensional protein profiling to predict outcomes in a prospective dengue surveillance study in South America. Plasma samples taken from initial clinical presentation of acute dengue infection were subjected to proteomics analyses using ELISA and a recently developed biofluid analysis platform. Demographics, clinical laboratory measurements, nine cytokines, and 419 plasma proteins collected at the time of initial presentation were compared between the DF and DHF outcomes. Here, the subject's gender, clinical parameters, two cytokines, and 42 proteins discriminated between the outcomes. These factors were reduced by multivariate adaptive regression splines (MARS) that a highly accurate classification model based on eight discriminant features with an area under the receiver operator curve (AUC) of 0.999. Model analysis indicated that the feature-outcome relationship were nonlinear. Although this DHF risk model will need validation in a larger cohort, we conclude that approaches to develop predictive biomarker models for disease outcome will need to incorporate nonparametric modeling approaches.

AB - Secondary dengue viral infection can produce capillary leakage associated with increased mortality known as dengue hemorrhagic fever (DHF). Because the mortality of DHF can be reduced by early detection and intensive support, improved methods for its detection are needed. We applied multidimensional protein profiling to predict outcomes in a prospective dengue surveillance study in South America. Plasma samples taken from initial clinical presentation of acute dengue infection were subjected to proteomics analyses using ELISA and a recently developed biofluid analysis platform. Demographics, clinical laboratory measurements, nine cytokines, and 419 plasma proteins collected at the time of initial presentation were compared between the DF and DHF outcomes. Here, the subject's gender, clinical parameters, two cytokines, and 42 proteins discriminated between the outcomes. These factors were reduced by multivariate adaptive regression splines (MARS) that a highly accurate classification model based on eight discriminant features with an area under the receiver operator curve (AUC) of 0.999. Model analysis indicated that the feature-outcome relationship were nonlinear. Although this DHF risk model will need validation in a larger cohort, we conclude that approaches to develop predictive biomarker models for disease outcome will need to incorporate nonparametric modeling approaches.

KW - Hemorrhagic disorders and therapies

KW - Host response

KW - Infectious disease

KW - Plasma

KW - Proteins

KW - Viral infection

UR - http://www.scopus.com/inward/record.url?scp=84863421886&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84863421886&partnerID=8YFLogxK

U2 - 10.1111/j.1752-8062.2011.00377.x

DO - 10.1111/j.1752-8062.2011.00377.x

M3 - Article

C2 - 22376251

AN - SCOPUS:84863421886

VL - 5

SP - 8

EP - 20

JO - Clinical and Translational Science

JF - Clinical and Translational Science

SN - 1752-8054

IS - 1

ER -