Immune monitoring using the predictive power of immune profiles

Abstract

In an effort to identify and monitor the immune status of cancer patients, we have developed a novel approach to categorize immunity in patients. Our approach is based on determining the number (cells/ul) of the major leukocyte subsets in unfractionated, whole blood using quantitative flow cytometry. These measurements were repeated in 40 healthy volunteers and 120 patients with glioblastoma, renal cell carcinoma, non-Hodgkin lymphoma, ovarian cancer or a non-malignant condition (acute lung injury). After normalization, we used unsupervised hierarchical clustering and principal component analysis to sort individuals by similarity into discreet groups we call immune profiles. Five immune profiles were identified with four of the diseases tested having patients distributed across four of the profiles. Cancer patients found in immune profiles dominated by healthy volunteers showed improved survival (p<0.01). Clustering also objectively identified relationships between immune markers. For example, we found a positive correlation between the number of granulocytes and immunosuppressive CD14+HLA-DRlo/neg monocytes in contrast to the lack of correlation between CD14+HLA-DRlo/neg monocytes and myeloid derived suppressor cells. Clustering analysis identified a potential novel biomarker using a cell count ratio using CD4+ T cells and CD14+HLA-DRlo/neg monocytes predictive of survival for glioblastoma, renal cell carcinoma, and lymphoma patients. Comprehensive multi-factorial immune analysis resulting in immune profiles may uncover relationships among immune markers, streamline evaluation of immune modulating therapies, and allow immune based biomarker development.