Quantitative Analysis of the Cellular Microenvironment of Glioblastoma to Develop Predictive Statistical Models of Overall Survival.
Glioblastomas, the most common primary malignant brain tumors, have a distinct tissue microenvironment. Although non-neoplastic cells contribute to glioblastoma progression, very few quantitative studies have shown the effect of tumor microenvironmental influences on patient survival. We examined relationships of the cellular microenvironment, including astrocytes, microglia, oligodendrocytes, and blood vessels, to survival in glioblastoma patients. Using histological staining and quantitative image analyses, we examined the tumor-associated parenchyma of 33 patients and developed statistical models to predict patient outcomes based on the cellular picture of the tumor parenchyma. We found that blood vessel density correlated with poorer prognosis. To examine the role of adjacent parenchymal versus higher tumor cell density bulk parenchymal tissue, we examined the glial components in these highly variable regions. Comparison of bulk and adjacent astrocytes and microglia in tissue yielded the strongest prediction of survival, with high levels of adjacent astrocytes predicted poor prognosis and high levels of microglia correlated with a better prognosis. These results indicate that parenchymal components predict survival in glioblastoma patients and in particular that the balance between reactive glial populations is important for patient prognosis.
From the University of Virginia School of Medicine (JXY, FFB, JWM, BJH, JMM), Department of Biomedical Engineering, University (JXY, JMM), Department of Pathology (FFB, JWM), and Department of Public Health Sciences (BJH), Division of Translational Research and Applied Statistics, University of Virginia, Charlottesville, Virginia.
Jessica X
JX
Yuan
From the University of Virginia School of Medicine (JXY, FFB, JWM, BJH, JMM), Department of Biomedical Engineering, University (JXY, JMM), Department of Pathology (FFB, JWM), and Department of Public Health Sciences (BJH), Division of Translational Research and Applied Statistics, University of Virginia, Charlottesville, Virginia.
Fahad F
FF
Bafakih
From the University of Virginia School of Medicine (JXY, FFB, JWM, BJH, JMM), Department of Biomedical Engineering, University (JXY, JMM), Department of Pathology (FFB, JWM), and Department of Public Health Sciences (BJH), Division of Translational Research and Applied Statistics, University of Virginia, Charlottesville, Virginia.
James W
JW
Mandell
From the University of Virginia School of Medicine (JXY, FFB, JWM, BJH, JMM), Department of Biomedical Engineering, University (JXY, JMM), Department of Pathology (FFB, JWM), and Department of Public Health Sciences (BJH), Division of Translational Research and Applied Statistics, University of Virginia, Charlottesville, Virginia.
Bethany J
BJ
Horton
From the University of Virginia School of Medicine (JXY, FFB, JWM, BJH, JMM), Department of Biomedical Engineering, University (JXY, JMM), Department of Pathology (FFB, JWM), and Department of Public Health Sciences (BJH), Division of Translational Research and Applied Statistics, University of Virginia, Charlottesville, Virginia.
Jennifer M
JM
Munson
D001932:Brain Neoplasms / Q000175:diagnosis* / Q000401:mortality*; D005260:Female; D005909:Glioblastoma / Q000175:diagnosis* / Q000401:mortality*; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011237:Predictive Value of Tests; D015996:Survival Rate / Q000639:trends; D059016:Tumor Microenvironment
Astrocytes; Glia; Glioblastoma; Microglia; Overall survival; Proportional hazards model; Tumor microenvironment.