Immune monitoring using the predictive power of immune profiles

Journal for ImmunoTherapy of Cancer

Allan B Dietz⁴  Dennis A Gastineau²  Eugene D Kwon¹  Matthew K Tollefson¹  Roshini S Abraham⁶  Philippe R Bauer⁵  Stacy C League⁶  Mary L Maas⁴  Courtney J Liwski⁴  Betsy LaPlant³  Yi Lin²  Michael P Gustafson⁴ 

[1] Department of Urology, Mayo Clinic, 200 First Street, Rochester, MN, USA;Division of Hematology, Department of Medicine, Mayo Clinic, 200 First Street, Rochester, MN, USA;Biomedical Statistics and Informatics, Mayo Clinic, 200 First Street, Rochester, MN, USA;Human Cellular Therapy Laboratory, Division of Transfusion Medicine, Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First Street, Rochester, MN, USA;Pulmonary and Critical Care Medicine, Mayo Clinic, 200 First Street, Rochester, MN, USA;Cellular and Molecular Immunology, Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First Street, Rochester, MN, USA

关键词: Human;    Cancer;    Survival;    CD4;    Treg;    Myeloid suppressor;    Monocytes;    Biomarker;    CD14;    Immunity;   

Others  :  815053
DOI  :  10.1186/2051-1426-1-7

 received in 2012-12-19, accepted in 2013-04-15,  发布年份 2013

【 摘 要 】

Background

We have developed a novel approach to categorize immunity in patients that uses a combination of whole blood flow cytometry and hierarchical clustering.

Methods

Our approach was based on determining the number (cells/μl) of the major leukocyte subsets in unfractionated, whole blood using quantitative flow cytometry. These measurements were performed in 40 healthy volunteers and 120 patients with glioblastoma, renal cell carcinoma, non-Hodgkin lymphoma, ovarian cancer or acute lung injury. After normalization, we used unsupervised hierarchical clustering to sort individuals by similarity into discreet groups we call immune profiles.

Results

Five immune profiles were identified. Four of the diseases tested had patients distributed across at least four of the profiles. Cancer patients found in immune profiles dominated by healthy volunteers showed improved survival (p < 0.01). Clustering objectively identified relationships between immune markers. We found a positive correlation between the number of granulocytes and immunosuppressive CD14⁺HLA-DR^lo/neg monocytes and no correlation between CD14⁺HLA-DR^lo/neg monocytes and Lin^-CD33⁺HLA-DR^- myeloid derived suppressor cells. Clustering analysis identified a potential biomarker predictive of survival across cancer types consisting of the ratio of CD4⁺ T cells/μl to CD14⁺HLA-DR^lo/neg monocytes/μL of blood.

Conclusions

Comprehensive multi-factorial immune analysis resulting in immune profiles were prognostic, uncovered relationships among immune markers and identified a potential biomarker for the prognosis of cancer. Immune profiles may be useful to streamline evaluation of immune modulating therapies and continue to identify immune based biomarkers.

【 授权许可】

   
2013 Gustafson et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

【 参考文献 】

• [1]Davis MM: A prescription for human immunology. Immunity 2008, 29:835-838.
• [2]Maecker HT, McCoy JP, Nussenblatt R: Standardizing immunophenotyping for the human immunology project. Nat Rev Immunol 2012, 12:191-200.
• [3]Palmer C, Diehn M, Alizadeh AA, Brown PO: Cell-type specific gene expression profiles of leukocytes in human peripheral blood. BMC Genomics 2006, 16:115.
• [4]de Jager W, Bourcier K, Rijkers GT, Prakken BJ, Seyfert-Margolis V: Prerequisites for cytokine measurements in clinical trials with multiplex immunoassays. BMC Immunol 2009, 10:52. BioMed Central Full Text
• [5]Kohrt HE, Nouri N, Nowels K, Johnson D, Holmes S, Lee P: Profile of immune cells in axillary lymph nodes predicts disease-free survival in breast cancer. PLoS Med 2005, 2:e284.
• [6]Autissier P, Soulas C, Burdo TH, Williams KC: Evaluation of a 12-color flow cytometry panel to study lymphocyte, monocyte, and dendritic cell subsets in humans. Cytometry A 2010, 77:410-419.
• [7]Longo DM, Louie B, Putta S, et al.: Single-cell network profiling of peripheral blood mononuclear cells from healthy donors reveals age- and race-associated differences in immune signaling pathway activation. J Immunol 2012, 188:1717-1725.
• [8]Gustafson MP, Lin Y, New KC, Bulur PA, O'Neill BP, Dietz AB: Systemic immune suppression in glioblastoma: the interplay between CD14 + HLA-DRlo/neg monocytes, tumor factors, and dexamethasone. Neuro Oncol 2010, 12:631-644.
• [9]Lin Y, Gustafson MP, Bulur PA, Gastineau DA, Witzig TE, Dietz AB: Immunosuppressive CD14 + HLA-DR(low)/- monocytes in B-cell non-Hodgkin lymphoma. Blood 2011, 117:872-881.
• [10]Iscimen R, Cartin-Ceba R, Yilmaz M, et al.: Risk factors for the development of acute lung injury in patients with septic shock: an observational cohort study. Crit Care Med 2008, 36:1518-1522.
• [11]Bernard GR, Artigas A, Brigham KL, et al.: Report of the American-European consensus conference on ARDS: definitions, mechanisms, relevant outcomes and clinical trial coordination. The Consensus Committee. Am J Crit Care Med 1994, 20:225-232.
• [12]Bauer PR, Kashyap R, Abraham RS, Peikert T, Gajic O: Accuracy of waste blood measurement in critically ill patients. Intensive Care Med 2011, 37:721-722.
• [13]Appay V, Reynard S, Voelter V, Romero P, Speiser DE, Leyvraz S: Immuno-monitoring of CD8+ T cells in whole blood versus PBMC samples. J Immunol Methods 2006, 309:192-199.
• [14]Contal C, O'Quigley J: An application of changepoint methods in studying the effect of age on survival in breast cancer. Comput Stat Data Anal 1999, 30:253-270.
• [15]Banham AH: Cell-surface IL-7 receptor expression facilitates the purification of FOXP3(+) regulatory T cells. Trends Immunol 2006, 27:541-544.
• [16]Rittirsch D, Flierl MA, Ward PA: Harmful molecular mechanisms in sepsis. Nat Rev Immunol 2008, 8:776-787.
• [17]Moore FA, Sauaia A, Moore EE, Haenel JB, Burch JM, Lezotte DC: Postinjury multiple organ failure: a bimodal phenomenon. J Trauma 1996, 40(4):501-510.
• [18]Gustafson MP, Abraham RS, Lin Y, et al.: Association of an increased frequency of CD14(+) HLA-DR(lo/neg) monocytes with decreased time to progression in chronic lymphocytic leukaemia (CLL). Br J Haematol 2012, 156:674-676.
• [19]Filipazzi P, Valenti R, Huber V, et al.: Identification of a new subset of myeloid suppressor cells in pheripheral blood of melanoma patients with modulation by a granulocyte-macrophage colony-stimulation factor-based antitumor vaccine. J Clin Oncol 2007, 25:2546-2553.
• [20]Walter S, Weinschenk T, Stenzl A, et al.: Multipeptide immune response to cancer vaccine IMA901 after single-dose cyclophosphamide associates with longer patient survival. Nat Med 2012, 18:1254-1261.
• [21]Porrata LF, Inwards DJ, Ansell SM, et al.: Early lymphocyte recovery predicts superior survival after autologous stem cell transplantation in non-Hodgkin lymphoma: a prospective study. Biol Blood Marrow Transplant 2008, 14:807-816.
• [22]Ege H, Gertz MA, Markovic SN, et al.: Prediction of survival using absolute lymphocyte count for newly diagnosed patients with multiple myeloma: a retrospective study. Br J Haematol 2008, 141:792-798.
• [23]Coventry BJ, Ashdown ML, Quinn MA, Markovic SN, Yatomi-Clarke SL, Robinson AP: CRP identifies homeostatic immune oscillations in cancer patients: a potential treatment targeting tool? J Transl Med 2009, 7:102. BioMed Central Full Text
• [24]Gfeller D, De Los Rios P, Caflisch A, Rao F: Complex network analysis of free-energy landscapes. Proc Nat Acad Sci USA 2007, 104:1817-1822.
• [25]Xiao W, Mindrinos MN, Seok J, et al.: A genomic storm in critically injured humans. J Exp Med 2011, 208:2581-2590.