Integrated Single-cell and Plasma Proteomic Modeling to Predict Surgical Site Complications: A Prospective Cohort Study.
Adult
Anastomotic Leak
/ epidemiology
Blood Proteins
/ analysis
Cohort Studies
Dietary Proteins
/ blood
Digestive System Surgical Procedures
Female
Humans
Male
Middle Aged
Models, Theoretical
Prognosis
Prospective Studies
Proteome
Single-Cell Analysis
Surgical Wound Dehiscence
/ epidemiology
Surgical Wound Infection
/ epidemiology
Journal
Annals of surgery
ISSN: 1528-1140
Titre abrégé: Ann Surg
Pays: United States
ID NLM: 0372354
Informations de publication
Date de publication:
01 03 2022
01 03 2022
Historique:
pubmed:
27
12
2021
medline:
19
2
2022
entrez:
26
12
2021
Statut:
ppublish
Résumé
The aim of this study was to determine whether single-cell and plasma proteomic elements of the host's immune response to surgery accurately identify patients who develop a surgical site complication (SSC) after major abdominal surgery. SSCs may occur in up to 25% of patients undergoing bowel resection, resulting in significant morbidity and economic burden. However, the accurate prediction of SSCs remains clinically challenging. Leveraging high-content proteomic technologies to comprehensively profile patients' immune response to surgery is a promising approach to identify predictive biological factors of SSCs. Forty-one patients undergoing non-cancer bowel resection were prospectively enrolled. Blood samples collected before surgery and on postoperative day one (POD1) were analyzed using a combination of single-cell mass cytometry and plasma proteomics. The primary outcome was the occurrence of an SSC, including surgical site infection, anastomotic leak, or wound dehiscence within 30 days of surgery. A multiomic model integrating the single-cell and plasma proteomic data collected on POD1 accurately differentiated patients with (n = 11) and without (n = 30) an SSC [area under the curve (AUC) = 0.86]. Model features included coregulated proinflammatory (eg, IL-6- and MyD88- signaling responses in myeloid cells) and immunosuppressive (eg, JAK/STAT signaling responses in M-MDSCs and Tregs) events preceding an SSC. Importantly, analysis of the immunological data obtained before surgery also yielded a model accurately predicting SSCs (AUC = 0.82). The multiomic analysis of patients' immune response after surgery and immune state before surgery revealed systemic immune signatures preceding the development of SSCs. Our results suggest that integrating immunological data in perioperative risk assessment paradigms is a plausible strategy to guide individualized clinical care.
Sections du résumé
OBJECTIVE
The aim of this study was to determine whether single-cell and plasma proteomic elements of the host's immune response to surgery accurately identify patients who develop a surgical site complication (SSC) after major abdominal surgery.
SUMMARY BACKGROUND DATA
SSCs may occur in up to 25% of patients undergoing bowel resection, resulting in significant morbidity and economic burden. However, the accurate prediction of SSCs remains clinically challenging. Leveraging high-content proteomic technologies to comprehensively profile patients' immune response to surgery is a promising approach to identify predictive biological factors of SSCs.
METHODS
Forty-one patients undergoing non-cancer bowel resection were prospectively enrolled. Blood samples collected before surgery and on postoperative day one (POD1) were analyzed using a combination of single-cell mass cytometry and plasma proteomics. The primary outcome was the occurrence of an SSC, including surgical site infection, anastomotic leak, or wound dehiscence within 30 days of surgery.
RESULTS
A multiomic model integrating the single-cell and plasma proteomic data collected on POD1 accurately differentiated patients with (n = 11) and without (n = 30) an SSC [area under the curve (AUC) = 0.86]. Model features included coregulated proinflammatory (eg, IL-6- and MyD88- signaling responses in myeloid cells) and immunosuppressive (eg, JAK/STAT signaling responses in M-MDSCs and Tregs) events preceding an SSC. Importantly, analysis of the immunological data obtained before surgery also yielded a model accurately predicting SSCs (AUC = 0.82).
CONCLUSIONS
The multiomic analysis of patients' immune response after surgery and immune state before surgery revealed systemic immune signatures preceding the development of SSCs. Our results suggest that integrating immunological data in perioperative risk assessment paradigms is a plausible strategy to guide individualized clinical care.
Identifiants
pubmed: 34954754
doi: 10.1097/SLA.0000000000005348
pii: 00000658-202203000-00027
pmc: PMC8816871
mid: NIHMS1765944
doi:
Substances chimiques
Blood Proteins
0
Dietary Proteins
0
Proteome
0
single cell proteins
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
582-590Subventions
Organisme : NIGMS NIH HHS
ID : R35 GM137936
Pays : United States
Organisme : NIA NIH HHS
ID : R33 AG065744
Pays : United States
Organisme : NINDS NIH HHS
ID : R61 NS114926
Pays : United States
Organisme : NIGMS NIH HHS
ID : R35 GM138353
Pays : United States
Organisme : NIA NIH HHS
ID : R21 AG065744
Pays : United States
Informations de copyright
Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.
Déclaration de conflit d'intérêts
Conflicts of Interest and Source of Funding: This work was supported by the Stanford Department of Anesthesiology, Pain and Perioperative Medicine, the Stanford Department of Surgery, the national institute of health (NIH) R35GM137936 (BG), R35GM138353 (NA), NS114926 (MSA), AG065744 (MSA), the Fluegel Research Fund (KR), and the Center for Human Systems Immunology (BG). A provisional patent application that covers aspects of the subject matter of the article has been filed (S31-07151.PRO, title: systems and methods to generate a surgical risk score and uses thereof, co-inventors: B.G., J.H., K.R., N.A., M.S.A.). The authors report no conflicts of interest.
Références
Meara JG, Leather AJ, Hagander L, et al. Global Surgery 2030: evidence and solutions for achieving health, welfare, and economic development. Lancet 2015; 386:569–624.
Healy MA, Mullard AJ, Campbell DA Jr, et al. Hospital and payer costs associated with surgical complications. JAMA Surg 2016; 151:823–830.
Nikolian VC, Kamdar NS, Regenbogen SE, et al. Anastomotic leak after colorectal resection: a population-based study of risk factors and hospital variation. Surgery 2017; 161:1619–1627.
Gantz O, Zagadailov P, Merchant AM. The cost of surgical site infections after colorectal surgery in the United States from 2001 to 2012: a longitudinal analysis. Am Surg 2019; 85:142–149.
Eamer G, Al-Amoodi MJH, Holroyd-Leduc J, et al. Review of risk assessment tools to predict morbidity and mortality in elderly surgical patients. Am J Surg 2018; 216:585–594.
Cohen ME, Liu Y, Ko CY, et al. An examination of American College of Surgeons NSQIP Surgical Risk Calculator accuracy. J Am Coll Surg 2017; 224:787–795.e1.
Bilimoria KY, Liu Y, Paruch JL, et al. Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons. J Am Coll Surg 2013; 217:833–842. e1-3.
Bihorac A, Ozrazgat-Baslanti T, Ebadi A, et al. MySurgeryRisk: development and validation of a machine-learning risk algorithm for major complications and death after surgery. Ann Surg 2019; 269:652–662.
Vos EL, Russo AE, Hohmann A, et al. Performance of the American College of Surgeons NSQIP Surgical Risk Calculator for total gastrectomy. J Am Coll Surg 2020; 231:650–656.
Baek B, Lee H. Prediction of survival and recurrence in patients with pancreatic cancer by integrating multi-omics data. Sci Rep 2020; 10:18951.
Angele MK, Faist E. Clinical review: immunodepression in the surgical patient and increased susceptibility to infection. Crit Care 2002; 6:298–305.
Stoecklein VM, Osuka A, Lederer JA. Trauma equals danger—damage control by the immune system. J Leukoc Biol 2012; 92:539–551.
Gaudillière B, Fragiadakis GK, Bruggner RV, et al. Clinical recovery from surgery correlates with single-cell immune signatures. Sci Transl Med 2014; 6:255ra131.
Ganio EA, Stanley N, Lindberg-Larsen V, et al. Preferential inhibition of adaptive immune system dynamics by glucocorticoids in patients after acute surgical trauma. Nat Commun 2020; 11:3737.
Huber-Lang M, Lambris JD, Ward PA. Innate immune responses to trauma. Nat Immunol 2018; 19:327–341.
Sugimoto MA, Vago JP, Perretti M, et al. Mediators of the resolution of the inflammatory response. Trends Immunol 2019; 40:212–227.
Rettig TC, Verwijmeren L, Dijkstra IM, et al. Postoperative interleukin-6 level and early detection of complications after elective major abdominal surgery. Ann Surg 2016; 263:1207–1212.
Veenhof AA, Vlug MS, van der Pas MH, et al. Surgical stress response and postoperative immune function after laparoscopy or open surgery with fast track or standard perioperative care: a randomized trial. Ann Surg 2012; 255:216–221.
Brown JR, Jacobs JP, Alam SS, et al. Utility of biomarkers to improve prediction of readmission or mortality after cardiac surgery. Ann Thorac Surg 2018; 106:1294–1301.
Fong TG, Chan NY, Dillon ST, et al. Identification of plasma proteome signatures associated with surgery using SOMAscan. Ann Surg 2021; 273:732–742.
Tarnok A. Revisiting the crystal ball—high content single cells analysis as predictor of recovery. Cytometry A 2015; 87:97–98.
Seshadri A, Brat GA, Yorkgitis BK, et al. Phenotyping the immune response to trauma: a multiparametric systems immunology approach. Crit Care Med 2017; 45:1523–1530.
Bendall SC, Simonds EF, Qiu P, et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 2011; 332:687–696.
Fragiadakis GK, Gaudillière B, Ganio EA, et al. Patient-specific immune states before surgery are strong correlates of surgical recovery. Anesthesiology 2015; 123:1241–1255.
Culos A, Tsai AS, Stanley N, et al. Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions. Nat Mach Intell 2020; 2:619–628.
Stanley N, Stelzer IA, Tsai AS, et al. VoPo leverages cellular heterogeneity for predictive modeling of single-cell data. Nat Commun 2020; 11:3738.
Assarsson E, Lundberg M, Holmquist G, et al. Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS One 2014; 9:e95192.
Hastie T, Tibshirani R, Friedman jh. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed.New York, NY: Springer; 2009.
Ghaemi MS, DiGiulio DB, Contrepois K, et al. Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy. Bioinformatics 2019; 35:95–103.
Chatterjee A, Lahiri SN. Bootstrapping Lasso estimators. J Am Stat Assoc 2011; 106:608–625.
van der Maaten L, Hinton G. Visualizing Data using t-SNE. J Mach Learn Res 2008; 9:2579–2605.
Matthiessen P, Strand I, Jansson K, et al. Is early detection of anastomotic leakage possible by intraperitoneal microdialysis and intraperitoneal cytokines after anterior resection of the rectum for cancer? Dis Colon Rectum 2007; 50:1918–1927.
Gomez-Pina V, Soares-Schanoski A, Rodriguez-Rojas A, et al. Metalloproteinases shed TREM-1 ectodomain from lipopolysaccharide-stimulated human monocytes. J Immunol 2007; 179:4065–4073.
Nguyen HN, Noss EH, Mizoguchi F, et al. Autocrine loop involving IL-6 family member LIF, LIF receptor, and STAT4 drives sustained fibroblast production of inflammatory mediators. Immunity 2017; 46:220–232.
Bevec D, Klier H, Holter W, et al. Induced gene expression of the hypusine-containing protein eukaryotic initiation factor 5A in activated human T lymphocytes. Proc Natl Acad Sci U S A 1994; 91:10829–10833.
Custodero C, Wu Q, Ghita GL, et al. Prognostic value of NT-proBNP levels in the acute phase of sepsis on lower long-term physical function and muscle strength in sepsis survivors. Crit Care 2019; 23:230.
Majumdar D, Tiernan JP, Lobo AJ, et al. Keratins in colorectal epithelial function and disease. Int J Exp Pathol 2012; 93:305–318.
Bagchi A, Herrup EA, Warren HS, et al. MyD88-dependent and MyD88-independent pathways in synergy, priming, and tolerance between TLR agonists. J Immunol 2007; 178:1164–1171.
Fallahzadeh R, Verdonk F, Ganio E, et al. Objective activity parameters track patient-specific physical recovery trajectories after surgery and link with individual preoperative immune states. Ann Surg Published online October 8, 2021; doi: 10.1097/SLA.0000000000005250.
doi: 10.1097/SLA.0000000000005250
Gabrilovich DI, Nagaraj S. Myeloid-derived suppressor cells as regulators of the immune system. Nat Rev Immunol 2009; 9:162–174.
Mathias B, Delmas AL, Ozrazgat-Baslanti T, et al. Human myeloid-derived suppressor cells are associated with chronic immune suppression after severe sepsis/septic shock. Ann Surg 2017; 265:827–834.
Chinen T, Kannan AK, Levine AG, et al. An essential role for the IL-2 receptor in Treg cell function. Nat Immunol 2016; 17:1322–1333.
Metcalfe SM. LIF in the regulation of T-cell fate and as a potential therapeutic. Genes Immun 2011; 12:157–168.
Gibot S, Cravoisy A, Levy B, et al. Soluble triggering receptor expressed on myeloid cells and the diagnosis of pneumonia. N Engl J Med 2004; 350:451–458.
Gibot S, Kolopp-Sarda MN, Bene MC, et al. Plasma level of a triggering receptor expressed on myeloid cells-1: its diagnostic accuracy in patients with suspected sepsis. Ann Intern Med 2004; 141:9–15.
Klesney-Tait J, Turnbull IR, Colonna M. The TREM receptor family and signal integration. Nat Immunol 2006; 7:1266–1273.
Namkoong S, Lee KI, Lee JI, et al. The integral membrane protein ITM2A, a transcriptional target of PKA-CREB, regulates autophagic flux via interaction with the vacuolar ATPase. Autophagy 2015; 11:756–768.
Stelzer IA, Ghaemi MS, Han X, et al. Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset. Sci Transl Med 2021; 13 (592.):
Zunder ER, Finck R, Behbehani GK, et al. Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm. Nat Protoc 2015; 10:316–333.
Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143:29–36.