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

Eddie Cano-Gamez; Katie L. Burnham; Cyndi Goh; Alice Allcock; Zunaira H. Malick; Lauren Overend; Andrew Kwok; David A. Smith; Hessel Peters-Sengers; David Antclife; Stuart McKechnie; Brendon P. Scicluna; Tom van der Poll; Anthony C. Gordon; Charles J. Hinds; Emma E. Davenport; Julian C. Knight; Nigel Webster; Helen Galley; Jane Taylor; Sally Hall; Jenni Addison; Sian Roughton; Heather Tennant; Achyut Guleri; Natalia Waddington; Dilshan Arawwawala; John Durcan; Alasdair Short; Karen Swan; Sarah Williams; Susan Smolen; Christine Mitchell-Inwang; Tony Gordon; Emily Errington; Maie Templeton; Pyda Venatesh; Geraldine Ward; Marie McCauley; Simon Baudouin; Charley Higham; Jasmeet Soar; Sally Grier; Elaine Hall; Stephen Brett; David Kitson; Robert Wilson; Laura Mountford; Juan Moreno; Peter Hall; GAinS Investigators

doi:10.1126/scitranslmed.abq4433

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

Eddie Cano-Gamez, Katie L. Burnham, Cyndi Goh, Alice Allcock, Zunaira H. Malick, Lauren Overend, Andrew Kwok, David A. Smith, Hessel Peters-Sengers, David Antclife, Stuart McKechnie, Brendon P. Scicluna, Tom van der Poll, Anthony C. Gordon, Charles J. Hinds, Emma E. Davenport^*, Julian C. Knight^* (Corresponding Author), Nigel Webster, Helen Galley, Jane TaylorSally Hall, Jenni Addison, Sian Roughton, Heather Tennant, Achyut Guleri, Natalia Waddington, Dilshan Arawwawala, John Durcan, Alasdair Short, Karen Swan, Sarah Williams, Susan Smolen, Christine Mitchell-Inwang, Tony Gordon, Emily Errington, Maie Templeton, Pyda Venatesh, Geraldine Ward, Marie McCauley, Simon Baudouin, Charley Higham, Jasmeet Soar, Sally Grier, Elaine Hall, Stephen Brett, David Kitson, Robert Wilson, Laura Mountford, Juan Moreno, Peter Hall, GAinS Investigators

^*Corresponding author for this work

Applied Medicine

Research output: Contribution to journal › Article › peer-review

31 Citations (Scopus)

Abstract

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 3149 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 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.

Original language	English
Article number	eabq4433
Number of pages	36
Journal	Science translational medicine
Volume	14
Issue number	669
Early online date	2 Nov 2022
DOIs	https://doi.org/10.1126/scitranslmed.abq4433
Publication status	Published - 2 Nov 2022

Bibliographical note

Funding Information:
This work was funded in whole, or in part, by the Medical Research Council (MR/V002503/1) (J.C.K. and E.E.D.); Wellcome Trust Investigator Award (204969/Z/16/Z) (J.C.K.); Wellcome Trust core funding to the Wellcome Sanger Institute (grant numbers 206194 and 108413/A/15/D); Wellcome Trust grants (090532/Z/09/Z and 203141/Z/16/Z) to core facilities; Wellcome Centre for Human Genetics; Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Science (CIFMS), China (grant number: 2018-I2M-2-002); and National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre (J.C.K.). A.C.G. is supported by an NIHR Research Professor award (RP-2015-06-018) and the NIHR Imperial Biomedical Research Center.

We thank all the patients, patient families, nurses, and clinicians who participated in the GAinS and MARS studies; and the COMBAT Consortium, MOSAIC Consortium, and DeCOI.

Data Availability Statement

Codes are available at https://doi.org/10.5281/zenodo.7079357. The SepstratifieR package can be installed directly from GitHub and is available at Zenodo (https://doi.org/10.5281/zenodo.7079384). Gene expression data for GAinS study samples are publicly available in ArrayExpress (E-MTAB-4421, E-MTAB-4451, E-MTAB-5273, and E-MTAB-5274). Accession numbers for all public datasets used are listed in Table S1. This research was funded in whole or in part by The Wellcome Trust [Grant numbers 204969/Z/16/Z, 206194, 108413/A/15/D, 090532/Z/09/Z and 203141/Z/16/Z], a cOAlition S organization. The author will make the Author Accepted Manuscript (AAM) version available under a CC BY public copyright license.

Keywords

Adult
Humans
Child
Influenza A Virus, H1N1 Subtype
Gene Expression Profiling
COVID-19
Sepsis/genetics
Transcriptome/genetics

Access to Document

10.1126/scitranslmed.abq4433Licence: Unspecified

Cano-Gomez_etal_STM_An_Immune_dysfunction_AAM
This work is licensed under a CC BY 4.0 International license. https://creativecommons.org/licenses/by/4.0/ This is the accepted author manuscript. Published in final edited form as: Sci Transl Med. 2022 November 02; 14(669): eabq4433. doi:10.1126/scitranslmed.abq4433.
Accepted author manuscript, 3.5 MBLicence: CC BY

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7613832/Licence: CC BY

Cite this

Cano-Gamez, E., Burnham, K. L., Goh, C., Allcock, A., Malick, Z. H., Overend, L., Kwok, A., Smith, D. A., Peters-Sengers, H., Antclife, D., McKechnie, S., Scicluna, B. P., van der Poll, T., Gordon, A. C., Hinds, C. J., Davenport, E. E., Knight, J. C., Webster, N., Galley, H., ... GAinS Investigators (2022). An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression. Science translational medicine, 14(669), Article eabq4433. https://doi.org/10.1126/scitranslmed.abq4433

Cano-Gamez, E, Burnham, KL, Goh, C, Allcock, A, Malick, ZH, Overend, L, Kwok, A, Smith, DA, Peters-Sengers, H, Antclife, D, McKechnie, S, Scicluna, BP, van der Poll, T, Gordon, AC, Hinds, CJ, Davenport, EE, Knight, JC, Webster, N, Galley, H, Taylor, J, Hall, S, Addison, J, Roughton, S, Tennant, H, Guleri, A, Waddington, N, Arawwawala, D, Durcan, J, Short, A, Swan, K, Williams, S, Smolen, S, Mitchell-Inwang, C, Gordon, T, Errington, E, Templeton, M, Venatesh, P, Ward, G, McCauley, M, Baudouin, S, Higham, C, Soar, J, Grier, S, Hall, E, Brett, S, Kitson, D, Wilson, R, Mountford, L, Moreno, J, Hall, P & GAinS Investigators 2022, 'An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression', Science translational medicine, vol. 14, no. 669, eabq4433. https://doi.org/10.1126/scitranslmed.abq4433

@article{21fbe35ba3d44d54a0fc134539092ff1,

title = "An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression",

abstract = "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 3149 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 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.",

keywords = "Adult, Humans, Child, Influenza A Virus, H1N1 Subtype, Gene Expression Profiling, COVID-19, Sepsis/genetics, Transcriptome/genetics",

author = "Eddie Cano-Gamez and Burnham, {Katie L.} and Cyndi Goh and Alice Allcock and Malick, {Zunaira H.} and Lauren Overend and Andrew Kwok and Smith, {David A.} and Hessel Peters-Sengers and David Antclife and Stuart McKechnie and Scicluna, {Brendon P.} and {van der Poll}, Tom and Gordon, {Anthony C.} and Hinds, {Charles J.} and Davenport, {Emma E.} and Knight, {Julian C.} and Nigel Webster and Helen Galley and Jane Taylor and Sally Hall and Jenni Addison and Sian Roughton and Heather Tennant and Achyut Guleri and Natalia Waddington and Dilshan Arawwawala and John Durcan and Alasdair Short and Karen Swan and Sarah Williams and Susan Smolen and Christine Mitchell-Inwang and Tony Gordon and Emily Errington and Maie Templeton and Pyda Venatesh and Geraldine Ward and Marie McCauley and Simon Baudouin and Charley Higham and Jasmeet Soar and Sally Grier and Elaine Hall and Stephen Brett and David Kitson and Robert Wilson and Laura Mountford and Juan Moreno and Peter Hall and {GAinS Investigators}",

note = "Funding Information: This work was funded in whole, or in part, by the Medical Research Council (MR/V002503/1) (J.C.K. and E.E.D.); Wellcome Trust Investigator Award (204969/Z/16/Z) (J.C.K.); Wellcome Trust core funding to the Wellcome Sanger Institute (grant numbers 206194 and 108413/A/15/D); Wellcome Trust grants (090532/Z/09/Z and 203141/Z/16/Z) to core facilities; Wellcome Centre for Human Genetics; Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Science (CIFMS), China (grant number: 2018-I2M-2-002); and National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre (J.C.K.). A.C.G. is supported by an NIHR Research Professor award (RP-2015-06-018) and the NIHR Imperial Biomedical Research Center. We thank all the patients, patient families, nurses, and clinicians who participated in the GAinS and MARS studies; and the COMBAT Consortium, MOSAIC Consortium, and DeCOI.",

year = "2022",

month = nov,

day = "2",

doi = "10.1126/scitranslmed.abq4433",

language = "English",

volume = "14",

journal = "Science translational medicine",

issn = "1946-6234",

publisher = "American Association for the Advancement of Science",

number = "669",

}

TY - JOUR

T1 - An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression

AU - Cano-Gamez, Eddie

AU - Burnham, Katie L.

AU - Goh, Cyndi

AU - Allcock, Alice

AU - Malick, Zunaira H.

AU - Overend, Lauren

AU - Kwok, Andrew

AU - Smith, David A.

AU - Peters-Sengers, Hessel

AU - Antclife, David

AU - McKechnie, Stuart

AU - Scicluna, Brendon P.

AU - van der Poll, Tom

AU - Gordon, Anthony C.

AU - Hinds, Charles J.

AU - Davenport, Emma E.

AU - Knight, Julian C.

AU - Webster, Nigel

AU - Galley, Helen

AU - Taylor, Jane

AU - Hall, Sally

AU - Addison, Jenni

AU - Roughton, Sian

AU - Tennant, Heather

AU - Guleri, Achyut

AU - Waddington, Natalia

AU - Arawwawala, Dilshan

AU - Durcan, John

AU - Short, Alasdair

AU - Swan, Karen

AU - Williams, Sarah

AU - Smolen, Susan

AU - Mitchell-Inwang, Christine

AU - Gordon, Tony

AU - Errington, Emily

AU - Templeton, Maie

AU - Venatesh, Pyda

AU - Ward, Geraldine

AU - McCauley, Marie

AU - Baudouin, Simon

AU - Higham, Charley

AU - Soar, Jasmeet

AU - Grier, Sally

AU - Hall, Elaine

AU - Brett, Stephen

AU - Kitson, David

AU - Wilson, Robert

AU - Mountford, Laura

AU - Moreno, Juan

AU - Hall, Peter

AU - GAinS Investigators

N1 - Funding Information: This work was funded in whole, or in part, by the Medical Research Council (MR/V002503/1) (J.C.K. and E.E.D.); Wellcome Trust Investigator Award (204969/Z/16/Z) (J.C.K.); Wellcome Trust core funding to the Wellcome Sanger Institute (grant numbers 206194 and 108413/A/15/D); Wellcome Trust grants (090532/Z/09/Z and 203141/Z/16/Z) to core facilities; Wellcome Centre for Human Genetics; Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Science (CIFMS), China (grant number: 2018-I2M-2-002); and National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre (J.C.K.). A.C.G. is supported by an NIHR Research Professor award (RP-2015-06-018) and the NIHR Imperial Biomedical Research Center. We thank all the patients, patient families, nurses, and clinicians who participated in the GAinS and MARS studies; and the COMBAT Consortium, MOSAIC Consortium, and DeCOI.

PY - 2022/11/2

Y1 - 2022/11/2

N2 - 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 3149 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 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.

AB - 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 3149 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 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.

KW - Adult

KW - Humans

KW - Child

KW - Influenza A Virus, H1N1 Subtype

KW - Gene Expression Profiling

KW - COVID-19

KW - Sepsis/genetics

KW - Transcriptome/genetics

UR - http://www.scopus.com/inward/record.url?scp=85142089291&partnerID=8YFLogxK

U2 - 10.1126/scitranslmed.abq4433

DO - 10.1126/scitranslmed.abq4433

M3 - Article

C2 - 36322631

AN - SCOPUS:85142089291

SN - 1946-6234

VL - 14

JO - Science translational medicine

JF - Science translational medicine

IS - 669

M1 - eabq4433

ER -

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

Abstract

Bibliographical note

Data Availability Statement

Keywords

Access to Document

Other files and links

Fingerprint

Cite this