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Open Access Highly Accessed Research article

Immune monitoring using the predictive power of immune profiles

Michael P Gustafson1, Yi Lin2, Betsy LaPlant3, Courtney J Liwski1, Mary L Maas1, Stacy C League4, Philippe R Bauer5, Roshini S Abraham4, Matthew K Tollefson6, Eugene D Kwon6, Dennis A Gastineau12 and Allan B Dietz1*

Author Affiliations

1 Human Cellular Therapy Laboratory, Division of Transfusion Medicine, Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First Street, Rochester, MN, USA

2 Division of Hematology, Department of Medicine, Mayo Clinic, 200 First Street, Rochester, MN, USA

3 Biomedical Statistics and Informatics, Mayo Clinic, 200 First Street, Rochester, MN, USA

4 Cellular and Molecular Immunology, Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First Street, Rochester, MN, USA

5 Pulmonary and Critical Care Medicine, Mayo Clinic, 200 First Street, Rochester, MN, USA

6 Department of Urology, Mayo Clinic, 200 First Street, Rochester, MN, USA

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Journal for ImmunoTherapy of Cancer 2013, 1:7  doi:10.1186/2051-1426-1-7

Published: 27 June 2013

Abstract

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-DRlo/neg monocytes and no correlation between CD14+HLA-DRlo/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-DRlo/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.

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