Integrated clustering of multiple immune marker trajectories reveals different immunotypes in severely injured patients.
Critical illness
Immune markers
Immune response
Immunosuppression
Longitudinal study
Patient stratification
Sepsis
Trajectory
Transcriptomic
Journal
Critical care (London, England)
ISSN: 1466-609X
Titre abrégé: Crit Care
Pays: England
ID NLM: 9801902
Informations de publication
Date de publication:
15 Jul 2024
15 Jul 2024
Historique:
received:
21
03
2024
accepted:
14
06
2024
medline:
16
7
2024
pubmed:
16
7
2024
entrez:
15
7
2024
Statut:
epublish
Résumé
The immune response of critically ill patients, such as those with sepsis, severe trauma, or major surgery, is heterogeneous and dynamic, but its characterization and impact on outcomes are poorly understood. Until now, the primary challenge in advancing our understanding of the disease has been to concurrently address both multiparametric and temporal aspects. We used a clustering method to identify distinct groups of patients, based on various immune marker trajectories during the first week after admission to ICU. In 339 severely injured patients, we initially longitudinally clustered common biomarkers (both soluble and cellular parameters), whose variations are well-established during the immunosuppressive phase of sepsis. We then applied this multi-trajectory clustering using markers composed of whole blood immune-related mRNA. We found that both sets of markers revealed two immunotypes, one of which was associated with worse outcomes, such as increased risk of hospital-acquired infection and mortality, and prolonged hospital stays. This immunotype showed signs of both hyperinflammation and immunosuppression, which persisted over time. Our study suggest that the immune system of critically ill patients can be characterized by two distinct longitudinal immunotypes, one of which included patients with a persistently dysregulated and impaired immune response. This work confirms the relevance of such methodology to stratify patients and pave the way for further studies using markers indicative of potential immunomodulatory drug targets.
Sections du résumé
BACKGROUND
BACKGROUND
The immune response of critically ill patients, such as those with sepsis, severe trauma, or major surgery, is heterogeneous and dynamic, but its characterization and impact on outcomes are poorly understood. Until now, the primary challenge in advancing our understanding of the disease has been to concurrently address both multiparametric and temporal aspects.
METHODS
METHODS
We used a clustering method to identify distinct groups of patients, based on various immune marker trajectories during the first week after admission to ICU. In 339 severely injured patients, we initially longitudinally clustered common biomarkers (both soluble and cellular parameters), whose variations are well-established during the immunosuppressive phase of sepsis. We then applied this multi-trajectory clustering using markers composed of whole blood immune-related mRNA.
RESULTS
RESULTS
We found that both sets of markers revealed two immunotypes, one of which was associated with worse outcomes, such as increased risk of hospital-acquired infection and mortality, and prolonged hospital stays. This immunotype showed signs of both hyperinflammation and immunosuppression, which persisted over time.
CONCLUSION
CONCLUSIONS
Our study suggest that the immune system of critically ill patients can be characterized by two distinct longitudinal immunotypes, one of which included patients with a persistently dysregulated and impaired immune response. This work confirms the relevance of such methodology to stratify patients and pave the way for further studies using markers indicative of potential immunomodulatory drug targets.
Identifiants
pubmed: 39010113
doi: 10.1186/s13054-024-04990-4
pii: 10.1186/s13054-024-04990-4
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
240Subventions
Organisme : Association Nationale de la Recherche et de la Technologie
ID : 2020/0371
Investigateurs
Sophie Arnal
(S)
Caroline Augris-Mathieu
(C)
Frédérique Bayle
(F)
Liana Caruso
(L)
Charles-Eric Ber
(CE)
Asma Ben-Amor
(A)
Anne-Sophie Bellocq
(AS)
Farida Benatir
(F)
Anne Bertin-Maghit
(A)
Marc Bertin-Maghit
(M)
André Boibieux
(A)
Yves Bouffard
(Y)
Jean-Christophe Cejka
(JC)
Valérie Cerro
(V)
Jullien Crozon-Clauzel
(J)
Julien Davidson
(J)
Sophie Debord-Peguet
(S)
Benjamin Delwarde
(B)
Robert Deleat-Besson
(R)
Claire Delsuc
(C)
Bertrand Devigne
(B)
Laure Fayolle-Pivot
(L)
Alexandre Faure
(A)
Bernard Floccard
(B)
Julie Gatel
(J)
Charline Genin
(C)
Thibaut Girardot
(T)
Arnaud Gregoire
(A)
Baptiste Hengy
(B)
Laetitia Huriaux
(L)
Catherine Jadaud
(C)
Alain Lepape
(A)
Véronique Leray
(V)
Anne-Claire Lukaszewicz
(AC)
Guillaume Marcotte
(G)
Olivier Martin
(O)
Marie Matray
(M)
Delphine Maucort-Boulch
(D)
Pascal Meuret
(P)
Céline Monard
(C)
Florent Moriceau
(F)
Guillaume Monneret
(G)
Nathalie Panel
(N)
Najia Rahali
(N)
Thomas Rimmele
(T)
Cyrille Truc
(C)
Thomas Uberti
(T)
Hélène Vallin
(H)
Fabienne Venet
(F)
Sylvie Tissot
(S)
Abbès Zadam
(A)
Sophie Blein
(S)
Karen Brengel-Pesce
(K)
Elisabeth Cerrato
(E)
Valérie Cheynet
(V)
Emmanuelle Gallet-Gorius
(E)
Audrey Guichard
(A)
Camille Jourdan
(C)
Natacha Koenig
(N)
François Mallet
(F)
Boris Meunier
(B)
Virginie Moucadel
(V)
Marine Mommert
(M)
Guy Oriol
(G)
Alexandre Pachot
(A)
Estelle Peronnet
(E)
Claire Schrevel
(C)
Olivier Tabone
(O)
Julien Textoris
(J)
Javier Yugueros Marcos
(JY)
Jérémie Becker
(J)
Frédéric Bequet
(F)
Yacine Bounab
(Y)
Florian Brajon
(F)
Bertrand Canard
(B)
Muriel Collus
(M)
Nathalie Garcon
(N)
Irène Gorse
(I)
Cyril Guyard
(C)
Fabien Lavocat
(F)
Philippe Leissner
(P)
Karen Louis
(K)
Maxime Mistretta
(M)
Jeanne Moriniere
(J)
Yoann Mouscaz
(Y)
Laura Noailles
(L)
Magali Perret
(M)
Frédéric Reynier
(F)
Cindy Riffaud
(C)
Mary-Luz Rol
(ML)
Nicolas Sapay
(N)
Trang Tran
(T)
Christophe Vedrine
(C)
Christophe Carre
(C)
Pierre Cortez
(P)
Aymeric de Monfort
(A)
Karine Florin
(K)
Laurent Fraisse
(L)
Isabelle Fugier
(I)
Sandrine Payrard
(S)
Annick Peleraux
(A)
Laurence Quemeneur
(L)
Andrew Griffiths
(A)
Stephanie Toetsch
(S)
Teri Ashton
(T)
Peter J Gough
(PJ)
Scott B Berger
(SB)
David Gardiner
(D)
Iain Gillespie
(I)
Aidan Macnamara
(A)
Aparna Raychaudhuri
(A)
Rob Smylie
(R)
Lionel Tan
(L)
Craig Tipple
(C)
Informations de copyright
© 2024. The Author(s).
Références
Ikuta KS, et al. Global mortality associated with 33 bacterial pathogens in 2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2022;400(10369):2221–48.
doi: 10.1016/S0140-6736(22)02185-7
Singer M, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315(8):801–10.
doi: 10.1001/jama.2016.0287
pubmed: 26903338
pmcid: 4968574
Mira JC, et al. Sepsis pathophysiology, chronic critical illness, and persistent inflammation-immunosuppression and catabolism syndrome. Crit Care Med. 2017;45(2):253–62.
doi: 10.1097/CCM.0000000000002074
pubmed: 27632674
pmcid: 5243156
Marshall JC. Why have clinical trials in sepsis failed? Trends Mol Med. 2014;20(4):195–203.
doi: 10.1016/j.molmed.2014.01.007
pubmed: 24581450
Antcliffe DB, et al. Transcriptomic signatures in sepsis and a differential response to steroids. From the VANISH randomized trial. Am J Respir Crit Care Med. 2019;199(8):980–6.
doi: 10.1164/rccm.201807-1419OC
pubmed: 30365341
pmcid: 6467319
Scicluna BP, et al. Classification of patients with sepsis according to blood genomic endotype: a prospective cohort study. Lancet Respir Med. 2017;5(10):816–26.
doi: 10.1016/S2213-2600(17)30294-1
pubmed: 28864056
Burnham KL, et al. Shared and distinct aspects of the sepsis transcriptomic response to fecal peritonitis and pneumonia. Am J Respir Crit Care Med. 2017;196(3):328–39.
doi: 10.1164/rccm.201608-1685OC
pubmed: 28036233
pmcid: 5549866
Sweeney TE, et al. Unsupervised analysis of transcriptomics in bacterial sepsis across multiple datasets reveals three robust clusters. Crit Care Med. 2018;46(6):915–25.
doi: 10.1097/CCM.0000000000003084
pubmed: 29537985
pmcid: 5953807
Monneret G, et al. How clinical flow cytometry rebooted sepsis immunology. Cytometry A. 2019;95(4):431–41.
doi: 10.1002/cyto.a.23749
pubmed: 30887636
pmcid: 6461502
Xiao W, et al. A genomic storm in critically injured humans. J Exp Med. 2011;208(13):2581–90.
doi: 10.1084/jem.20111354
pubmed: 22110166
pmcid: 3244029
Bodinier M, et al. Monocyte trajectories endotypes are associated with worsening in septic patients. Front Immunol. 2021;12:795052.
doi: 10.3389/fimmu.2021.795052
pubmed: 34912347
pmcid: 8667763
Yende S, et al. Long-term host immune response trajectories among hospitalized patients with sepsis. JAMA Netw Open. 2019;2(8):e198686.
doi: 10.1001/jamanetworkopen.2019.8686
pubmed: 31390038
pmcid: 6686981
Leijte GP, et al. Monocytic HLA-DR expression kinetics in septic shock patients with different pathogens, sites of infection and adverse outcomes. Crit Care. 2020;24(1):110.
doi: 10.1186/s13054-020-2830-x
pubmed: 32192532
pmcid: 7082984
Venet F, et al. Immune profiling demonstrates a common immune signature of delayed acquired immunodeficiency in patients with various etiologies of severe injury. Crit Care Med. 2022;50(4):565–75.
doi: 10.1097/CCM.0000000000005270
pubmed: 34534131
Sinha P, Meyer NJ, Calfee CS. Biological phenotyping in sepsis and acute respiratory distress syndrome. Annu Rev Med. 2023;74:457–71.
doi: 10.1146/annurev-med-043021-014005
pubmed: 36469902
Stanski NL, Wong HR. Prognostic and predictive enrichment in sepsis. Nat Rev Nephrol. 2020;16(1):20–31.
doi: 10.1038/s41581-019-0199-3
pubmed: 31511662
Maslove DM, et al. Redefining critical illness. Nat Med. 2022;28(6):1141–8.
doi: 10.1038/s41591-022-01843-x
pubmed: 35715504
Cajander S, et al. Profiling the dysregulated immune response in sepsis: overcoming challenges to achieve the goal of precision medicine. Lancet Respir Med. 2023;12:305–22.
doi: 10.1016/S2213-2600(23)00330-2
pubmed: 38142698
Rol ML, et al. The REAnimation Low Immune Status Markers (REALISM) project: a protocol for broad characterisation and follow-up of injury-induced immunosuppression in intensive care unit (ICU) critically ill patients. BMJ Open. 2017;7(6):e015734.
doi: 10.1136/bmjopen-2016-015734
pubmed: 28637738
pmcid: 5726091
Tremblay JA, et al. A stratification strategy to predict secondary infection in critical illness-induced immune dysfunction: the REALIST score. Ann Intensive Care. 2022;12(1):76.
doi: 10.1186/s13613-022-01051-3
pubmed: 35976460
pmcid: 9382015
Tawfik DM, et al. Immune profiling panel: a proof-of-concept study of a new multiplex molecular tool to assess the immune status of critically ill patients. J Infect Dis. 2020;222(Suppl 2):S84–95.
doi: 10.1093/infdis/jiaa248
pubmed: 32691839
pmcid: 7372218
Bidar F, et al. Concomitant assessment of monocyte HLA-DR expression and ex vivo TNF-alpha release as markers of adverse outcome after various injuries-insights from the REALISM Study. J Clin Med. 2021;11(1):96.
doi: 10.3390/jcm11010096
pubmed: 35011836
pmcid: 8745266
Haem Rahimi M, et al. Interferon-Gamma-Release assay and absolute CD8 lymphocyte count for acquired immunosuppression monitoring in critically ill patients. Cytokine. 2024;174:156474.
doi: 10.1016/j.cyto.2023.156474
pubmed: 38101166
Genolini C, Alacoque X, Sentenac M, Arnaud C. kml and kml3d: R packages to cluster longitudinal data. J Stat Softw. 2015;65(4):1–34.
doi: 10.18637/jss.v065.i04
Șenbabaoğlu Y, Michailidis G, Li JZ. Critical limitations of consensus clustering in class discovery. Sci Rep. 2014;4(1):6207.
doi: 10.1038/srep06207
pubmed: 25158761
pmcid: 4145288
R Core Team. R: a language and environment for statistical computing. 2022.
Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer; 2016.
doi: 10.1007/978-3-319-24277-4
Giamarellos-Bourboulis EJ, et al. The pathophysiology of sepsis and precision-medicine-based immunotherapy. Nat Immunol. 2024;25(1):19–28.
doi: 10.1038/s41590-023-01660-5
pubmed: 38168953
Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics. 2010;26(12):1572–3.
doi: 10.1093/bioinformatics/btq170
pubmed: 20427518
pmcid: 2881355
Monti S. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach Learn. 2003;52(1/2):91–118.
doi: 10.1023/A:1023949509487
Davenport EE, et al. Genomic landscape of the individual host response and outcomes in sepsis: a prospective cohort study. Lancet Respir Med. 2016;4(4):259–71.
doi: 10.1016/S2213-2600(16)00046-1
pubmed: 26917434
pmcid: 4820667
Wong HR, et al. Identification of pediatric septic shock subclasses based on genome-wide expression profiling. BMC Med. 2009;7:34.
doi: 10.1186/1741-7015-7-34
pubmed: 19624809
pmcid: 2720987
Calfee CS, et al. Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials. Lancet Respir Med. 2014;2(8):611–20.
doi: 10.1016/S2213-2600(14)70097-9
pubmed: 24853585
pmcid: 4154544
Papin G, et al. Clinical and biological clusters of sepsis patients using hierarchical clustering. PLoS ONE. 2021;16(8):e0252793.
doi: 10.1371/journal.pone.0252793
pubmed: 34347776
pmcid: 8336799
Hotchkiss RS, Monneret G, Payen D. Sepsis-induced immunosuppression: from cellular dysfunctions to immunotherapy. Nat Rev Immunol. 2013;13(12):862–74.
doi: 10.1038/nri3552
pubmed: 24232462
pmcid: 4077177
Hawkins RB, et al. Chronic critical illness and the persistent inflammation, immunosuppression, and catabolism syndrome. Front Immunol. 2018;9:1511.
doi: 10.3389/fimmu.2018.01511
pubmed: 30013565
pmcid: 6036179
Gentile LF, et al. Persistent inflammation and immunosuppression: a common syndrome and new horizon for surgical intensive care. J Trauma Acute Care Surg. 2012;72(6):1491–501.
doi: 10.1097/TA.0b013e318256e000
pubmed: 22695412
pmcid: 3705923
Cavaillon J-M, Adib-conquy M, Cloëz-Tayarani I, Fitting C. Review: Immunodepression in sepsis and SIRS assessed by ex vivo cytokine production is not a generalized phenomenon: a review. J Endotoxin Res. 2001;7:85–93.
pubmed: 11521088
Darden DB, et al. Dysregulated Immunity and Immunotherapy after Sepsis. J Clin Med. 2021;10(8):1742.
doi: 10.3390/jcm10081742
pubmed: 33920518
pmcid: 8073536