Interactions of Age and Blood Immune Factors and Noninvasive Prediction of Glioma Survival

Abstract

Background: Tumor-based classification of human glioma portends patient prognosis, but considerable unexplained survival variability remains. Host factors (eg, age) also strongly influence survival times, partly reflecting a compromised immune system. How blood epigenetic measures of immune characteristics and age augment molecular classifications in glioma survival has not been investigated. We assess the prognostic impact of immune cell fractions and epigenetic age in archived blood across glioma molecular subtypes for the first time.

Methods: We evaluated immune cell fractions and epigenetic age in archived blood from the University of California San Francisco Adult Glioma Study, which included a training set of 197 patients with IDH-wild type, 1p19q intact, TERT wild type (IDH/1p19q/TERT-WT) glioma, an evaluation set of 350 patients with other subtypes of glioma, and 454 patients without glioma.

Results: IDH/1p19q/TERT-WT patients had lower lymphocyte fractions (CD4+ T, CD8+ T, natural killer, and B cells) and higher neutrophil fractions than people without glioma. Recursive partitioning analysis delineated 4 statistically significantly different survival groups for patients with IDH/1p19q/TERT-WT based on an interaction between chronological age and 2 blood immune factors, CD4+ T cells, and neutrophils. Median overall survival ranged from 0.76 years (95% confidence interval = 0.55-0.99) for the worst survival group (n = 28) to 9.72 years (95% confidence interval = 6.18 to not available) for the best (n = 33). The recursive partitioning analysis also statistically significantly delineated 4 risk groups in patients with other glioma subtypes.

Conclusions: The delineation of different survival groups in the training and evaluation sets based on an interaction between chronological age and blood immune characteristics suggests that common host immune factors among different glioma types may affect survival. The ability of DNA methylation-based markers of immune status to capture diverse, clinically relevant information may facilitate noninvasive, personalized patient evaluation in the neuro-oncology clinic.

Figure 1.

Adult Glioma Study (AGS) glioma cases and participants without glioma. A) Data flow diagram for the AGS participants, with World Health Organization (WHO) 2016 glioma classification and molecular subtypes 2015², for IDH/1p19q/TERT-wild type (WT) gliomas (training set) vs other glioma groups (evaluation set). B) Survival curves for the AGS glioma cases by WHO 2016 glioma classification and molecular subtypes 2015² classification. Astro = astrocytoma; GBM = glioblastoma; Oligo = oligodendroglioma.

Figure 2.

Comparisons of immune cell fractions in blood from the training set of 197 IDH/1p19q/TERT-wild type (WT) patients, evaluation set of 350 other glioma groups, and 454 participants without glioma. ^aIndicates a statistically significant difference in the specified immune cell fractions via analysis of variance.

Figure 3.

Forest plot of hazard ratios for univariate survival models and recursive partitioning analysis (RPA) model inclusion. A) The hazard ratios (squares) and 95% confidence intervals (error bars) for the training set of 197 IDH/1p19q/TERT-wild type (WT) gliomas in light blue and the 350 other glioma groups in dark blue. For the IDH/1p19q/TERT-WT gliomas, violations of the proportional hazards assumption occurred in the univariate models for chronological age, monocytes, DNAmAge, DNAmPhenoAge, and HannumAge. For the other gliomas, violations of the proportional hazards assumption occurred in the univariate models for chronological age, World Health Organization (WHO) grouping, diagnosis grade, taking dexamethasone, monocytes, neutrophils, natural killer cells, CD4-positive T cells, CD8-positive T cells, DNAmAge, DNAmPhenoAge, and HannumAge. B) Variable groupings depict which variables are included in each of the 3 RPA models: clinical variables only; clinical variables + immune profiles (IPs); clinical variables, IPs, and epigenetic ages. Astro = astrocytoma; GBM = glioblastoma; Oligo = oligodendroglioma.

Figure 4.

Clinical + immune profiles (IPs) recursive partitioning analysis (RPA), and Kaplan-Meier curves in the training and evaluation sets. A) For the clinical + IPs RPA in the training set of 197 IDH/1p19q/TERT-wild type (WT) patients, chronological age was the RPA’s primary node, with neutrophil counts and CD4 T-cell counts as secondary nodes. Patients fell into 1 of 4 risk groups: Group 1 (gold) consisted of the 33 patients who were ≤58 years of age and had CD4 T-cell counts >14, with a median overall survival (OS) of 9.73 years (95% confidence interval [CI] = 6.18 to NA). Group 2 (tan) consisted of the 93 patients who were ≤58 years of age and had CD4-positive T-cell counts ≤14, with a median OS of 1.75 years (95% CI = 1.50 to 2.06). Group 3 (gray) consisted of the 43 patients who were >58 years of age and had a neutrophil count ≤77, with a median OS of 1.27 years (95% CI = 0.83 to 1.64). Group 4 (blue) consisted of the 28 patients >58 years of age with a neutrophil count >77 and a median OS of 0.76 years (95% CI = 0.55 to 0.99). World Health Organization (WHO) 2016 glioma classifications for 4 groups are shown in pie charts. B) Kaplan-Meier curves are shown for the training set based on the clinical + IPs RPA. Kaplan-Meier curves and risk table in the training set of 197 IDH/1p19q/TERT-WT patients for risk groups 1-4 are defined in Figure  4, A. C) The evaluation set of 350 patients with another glioma for risk groups 1-4 are defined by the training set RPA in Figure  4, A. Evaluation set patients fell into 1 of 4 risk groups defined by the training set. Group 1 (gold) consisted of the 123 patients who were ≤58 years of age and had CD4 T-cell count >14, with a median OS of 13.58 years (95% CI = 10.57 to 18.18). Group 2 (tan) consisted of the 156 patients who were ≤58 years of age and had a CD4 T-cell count ≤14, with a median OS of 8.26 years (95% CI = 6.21 to NA). Group 3 (gray) consisted of the 52 patients who were >58 years of age and had a neutrophil count ≤77, with a median OS of 2.496 years (95% CI = 1.58 to 5.72). Group 4 (blue) consisted of the 19 patients >58 years of age, with a neutrophil count >77 and a median OS of 0.70 years (95% CI = 0.36 to 1.44). WHO 2016 glioma classifications for the 4 groups are shown in pie charts. D) Kaplan-Meier curves are shown for the evaluation set based on the training set clinical + IPs RPA. Kaplan-Meier curves and risk table in the evaluation set of 350 patients with another glioma for risk groups 1-4 are defined by the training set in Figure  4, A. Astro = astrocytoma; GBM = glioblastoma; Oligo = oligodendroglioma.