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An immune dysfunction score for stratification of patients with acute infection based on whole blood gene expression

Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole blood transcriptomics for stratification...

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Autores principales: Cano-Gamez, Eddie, Burnham, Katie L, Goh, Cyndi, Allcock, Alice, Malick, Zunaira H., Overend, Lauren, Kwok, Andrew, Smith, David A., Peters-Sengers, Hessel, Antcliffe, David, McKechnie, Stuart, Scicluna, Brendon P., van der Poll, Tom, Gordon, Anthony C., Hinds, Charles J., Davenport, Emma E., Knight, Julian C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7613832/
https://www.ncbi.nlm.nih.gov/pubmed/36322631
http://dx.doi.org/10.1126/scitranslmed.abq4433
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author Cano-Gamez, Eddie
Burnham, Katie L
Goh, Cyndi
Allcock, Alice
Malick, Zunaira H.
Overend, Lauren
Kwok, Andrew
Smith, David A.
Peters-Sengers, Hessel
Antcliffe, David
McKechnie, Stuart
Scicluna, Brendon P.
van der Poll, Tom
Gordon, Anthony C.
Hinds, Charles J.
Davenport, Emma E.
Knight, Julian C.
author_facet Cano-Gamez, Eddie
Burnham, Katie L
Goh, Cyndi
Allcock, Alice
Malick, Zunaira H.
Overend, Lauren
Kwok, Andrew
Smith, David A.
Peters-Sengers, Hessel
Antcliffe, David
McKechnie, Stuart
Scicluna, Brendon P.
van der Poll, Tom
Gordon, Anthony C.
Hinds, Charles J.
Davenport, Emma E.
Knight, Julian C.
author_sort Cano-Gamez, Eddie
collection PubMed
description Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole blood transcriptomics for stratification of patients with severe infection by integrating data from 3,149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or a 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection.
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spelling pubmed-76138322022-11-16 An immune dysfunction score for stratification of patients with acute infection based on whole blood gene expression Cano-Gamez, Eddie Burnham, Katie L Goh, Cyndi Allcock, Alice Malick, Zunaira H. Overend, Lauren Kwok, Andrew Smith, David A. Peters-Sengers, Hessel Antcliffe, David McKechnie, Stuart Scicluna, Brendon P. van der Poll, Tom Gordon, Anthony C. Hinds, Charles J. Davenport, Emma E. Knight, Julian C. Sci Transl Med Article Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole blood transcriptomics for stratification of patients with severe infection by integrating data from 3,149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or a 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection. 2022-11-02 2022-11-02 /pmc/articles/PMC7613832/ /pubmed/36322631 http://dx.doi.org/10.1126/scitranslmed.abq4433 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) International license.
spellingShingle Article
Cano-Gamez, Eddie
Burnham, Katie L
Goh, Cyndi
Allcock, Alice
Malick, Zunaira H.
Overend, Lauren
Kwok, Andrew
Smith, David A.
Peters-Sengers, Hessel
Antcliffe, David
McKechnie, Stuart
Scicluna, Brendon P.
van der Poll, Tom
Gordon, Anthony C.
Hinds, Charles J.
Davenport, Emma E.
Knight, Julian C.
An immune dysfunction score for stratification of patients with acute infection based on whole blood gene expression
title An immune dysfunction score for stratification of patients with acute infection based on whole blood gene expression
title_full An immune dysfunction score for stratification of patients with acute infection based on whole blood gene expression
title_fullStr An immune dysfunction score for stratification of patients with acute infection based on whole blood gene expression
title_full_unstemmed An immune dysfunction score for stratification of patients with acute infection based on whole blood gene expression
title_short An immune dysfunction score for stratification of patients with acute infection based on whole blood gene expression
title_sort immune dysfunction score for stratification of patients with acute infection based on whole blood gene expression
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7613832/
https://www.ncbi.nlm.nih.gov/pubmed/36322631
http://dx.doi.org/10.1126/scitranslmed.abq4433
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