Related Experiment Video
Updated: Apr 19, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Immune monitoring using the predictive power of immune profiles
Michael P Gustafson1, Yi Lin2, Betsy LaPlant3
1Human Cellular Therapy Laboratory, Division of Transfusion Medicine, Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First Street, Rochester, MN, USA.
Insights
A novel immune profiling method using flow cytometry and clustering identified five distinct immune profiles. Patients in profiles with healthy volunteers showed improved survival, and a CD4(+) T cell to monocyte ratio emerged as a potential survival biomarker.
Area of Science:
- Immunology
- Computational Biology
- Oncology
Background:
- Developed a novel approach to categorize patient immunity using whole blood flow cytometry and hierarchical clustering.
- Quantitative flow cytometry was used to determine leukocyte subset counts in healthy volunteers and patients with various cancers and acute lung injury.
Purpose of the Study:
- To categorize patient immunity using a novel multi-factorial approach.
- To identify relationships between immune markers and their prognostic value in cancer patients.
Main Methods:
- Quantitative flow cytometry to measure leukocyte subsets in whole blood.
- Unsupervised hierarchical clustering to group individuals into distinct immune profiles.
- Analysis of immune profiles in 40 healthy volunteers and 120 patients across five disease categories.
Main Results:
- Identified five distinct immune profiles, with patients from four diseases distributed across at least four profiles.
- Cancer patients in immune profiles resembling healthy volunteers demonstrated significantly improved survival (p < 0.01).
- Discovered a potential survival biomarker: the ratio of CD4(+) T cells/μl to CD14(+)HLA-DR(lo/neg) monocytes/μL, and identified correlations between granulocytes and immunosuppressive monocytes.
Conclusions:
- Immune profiles derived from multi-factorial analysis are prognostic and reveal immune marker relationships.
- Identified a potential biomarker for cancer prognosis, suggesting utility in evaluating immunomodulatory therapies.
- Immune profiles offer a streamlined method for immune-based biomarker discovery and therapeutic evaluation.
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.
