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. 2023 Oct 2;32(10):1328-1337.
doi: 10.1158/1055-9965.EPI-23-0331.

Genome-Scale Methylation Analysis Identifies Immune Profiles and Age Acceleration Associations with Bladder Cancer Outcomes

Ji-Qing Chen  1 , Lucas A Salas  1 , John K Wiencke  2 , Devin C Koestler  3 , Annette M Molinaro  2 , Angeline S Andrew  4 , John D Seigne  5 , Margaret R Karagas  1 , Karl T Kelsey  6 , Brock C Christensen  1   7
Affiliations

Genome-Scale Methylation Analysis Identifies Immune Profiles and Age Acceleration Associations with Bladder Cancer Outcomes

Ji-Qing Chen et al. Cancer Epidemiol Biomarkers Prev. .

Abstract

Background: Immune profiles have been associated with bladder cancer outcomes and may have clinical applications for prognosis. However, associations of detailed immune cell subtypes with patient outcomes remain underexplored and may contribute crucial prognostic information for better managing bladder cancer recurrence and survival.

Methods: Bladder cancer case peripheral blood DNA methylation was measured using the Illumina HumanMethylationEPIC array. Extended cell-type deconvolution quantified 12 immune cell-type proportions, including memory, naïve T and B cells, and granulocyte subtypes. DNA methylation clocks determined biological age. Cox proportional hazards models tested associations of immune cell profiles and age acceleration with bladder cancer outcomes. The partDSA algorithm discriminated 10-year overall survival groups from clinical variables and immune cell profiles, and a semi-supervised recursively partitioned mixture model (SS-RPMM) with DNA methylation data was applied to identify a classifier for 10-year overall survival.

Results: Higher CD8T memory cell proportions were associated with better overall survival [HR = 0.95, 95% confidence interval (CI) = 0.93-0.98], while higher neutrophil-to-lymphocyte ratio (HR = 1.36, 95% CI = 1.23-1.50), CD8T naïve (HR = 1.21, 95% CI = 1.04-1.41), neutrophil (HR = 1.04, 95% CI = 1.03-1.06) proportions, and age acceleration (HR = 1.06, 95% CI = 1.03-1.08) were associated with worse overall survival in patient with bladder cancer. partDSA and SS-RPMM classified five groups of subjects with significant differences in overall survival.

Conclusions: We identified associations between immune cell subtypes and age acceleration with bladder cancer outcomes.

Impact: The findings of this study suggest that bladder cancer outcomes are associated with specific methylation-derived immune cell-type proportions and age acceleration, and these factors could be potential prognostic biomarkers.

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Figures

Figure 1. Clinical and immune profiles recursive partitioning analysis, and 10-year OS Kaplan–Meier curves stratified by the grouping result from partDSA in patients with NMIBC: A, partDSA model setting and analysis results. For 601 patients with NMIBC, the neutrophil cell proportion in peripheral blood was the primary node, with the CD8 naïve cell proportion as the secondary node. Patients with NMIBC fell into one of three risk groups. Group 1 consisted of the 454 patients who had neutrophil cell proportions ≤76.46 and CD8 naïve cell proportions ≤1.76. Group 2 consisted of the 17 patients who had neutrophil cell proportions >76.46. Group 3 consisted of the 130 patients who had neutrophil cell proportions ≤76.46 and CD8 naïve cell proportions >1.76. CD4T memory, CD8T naïve, CD8T memory, NK cells, and neutrophils cell proportions were employed in the model using Pheno age acceleration; B memory, CD4T memory, CD8T naïve, CD8T memory, regulatory T, NK cells, neutrophils, and basophils cell proportions were employed in the model using Hannum age acceleration. Both models generated the same partitioning results. B, Kaplan–Meier curves are shown based on clinical and immune profiles recursive partitioning analysis. C, 5-year OS Kaplan–Meier curves in patients with NMIBC in Groups 1 and 3. D, Five- to 10-year OS Kaplan–Meier curves in patients with NMIBC in Groups 1 and 3 who were deceased or censored after 60 months. P values for log-rank tests are shown. All Kaplan–Meier curves are univariate analyses without adjusting for other variables. CI, confidence intervals; HR, hazard ratio; Neu, neutrophil.
Figure 1.
Clinical and immune profiles recursive partitioning analysis, and 10-year OS Kaplan–Meier curves stratified by the grouping result from partDSA in patients with NMIBC: A,partDSA model setting and analysis results. For 601 patients with NMIBC, the neutrophil cell proportion in peripheral blood was the primary node, with the CD8 naïve cell proportion as the secondary node. Patients with NMIBC fell into one of three risk groups. Group 1 consisted of the 454 patients who had neutrophil cell proportions ≤76.46 and CD8 naïve cell proportions ≤1.76. Group 2 consisted of the 17 patients who had neutrophil cell proportions >76.46. Group 3 consisted of the 130 patients who had neutrophil cell proportions ≤76.46 and CD8 naïve cell proportions >1.76. CD4T memory, CD8T naïve, CD8T memory, NK cells, and neutrophils cell proportions were employed in the model using Pheno age acceleration; B memory, CD4T memory, CD8T naïve, CD8T memory, regulatory T, NK cells, neutrophils, and basophils cell proportions were employed in the model using Hannum age acceleration. Both models generated the same partitioning results. B, Kaplan–Meier curves are shown based on clinical and immune profiles recursive partitioning analysis. C, 5-year OS Kaplan–Meier curves in patients with NMIBC in Groups 1 and 3. D, Five- to 10-year OS Kaplan–Meier curves in patients with NMIBC in Groups 1 and 3 who were deceased or censored after 60 months. P values for log-rank tests are shown. All Kaplan–Meier curves are univariate analyses without adjusting for other variables. CI, confidence intervals; HR, hazard ratio; Neu, neutrophil.
Figure 2. SS-RPMM for 10-year OS in patients with NMIBC (for Pheno age acceleration): A, Data analysis schematic of SS-RPMM used for identification of blood DNA methylation profiles associated with NMIBC. B, Heat map of predicted class memberships for the observations in all patients with NMIBC using the average beta values of the 15 CpG loci with the largest absolute Cox scores. C, Kaplan–Meier curves of 10-year OS stratified by the SS-RPMM classification of 202 patients with NMIBC in the testing set by the 15 CpG loci. D, Kaplan–Meier analysis of 10-year OS. Ten-year OS curves stratified by the grouping result from SS-RPMM in all patients with NMIBC. P values for log-rank tests are shown. All Kaplan–Meier curves are univariate analyses without adjusting for other variables; CI, confidence intervals; HR, hazard ratio; SS-RPMM, semi-supervised recursively partitioned mixture model.
Figure 2.
SS-RPMM for 10-year OS in patients with NMIBC (for Pheno age acceleration): A, Data analysis schematic of SS-RPMM used for identification of blood DNA methylation profiles associated with NMIBC. B, Heat map of predicted class memberships for the observations in all patients with NMIBC using the average beta values of the 15 CpG loci with the largest absolute Cox scores. C, Kaplan–Meier curves of 10-year OS stratified by the SS-RPMM classification of 202 patients with NMIBC in the testing set by the 15 CpG loci. D, Kaplan–Meier analysis of 10-year OS. Ten-year OS curves stratified by the grouping result from SS-RPMM in all patients with NMIBC. P values for log-rank tests are shown. All Kaplan–Meier curves are univariate analyses without adjusting for other variables; CI, confidence intervals; HR, hazard ratio; SS-RPMM, semi-supervised recursively partitioned mixture model.
Figure 3. Kaplan–Meier analysis of 10-year OS based on the grouping results from both partDSA and SS-RPMM in all patients with NMIBC (for Pheno age acceleration): A, contingency table based on the grouping results from both partDSA and SS-RPMM in all patients with NMIBC. B, Ten-year OS curves of all five groups. P values for log-rank tests are shown. All Kaplan–Meier curves are univariate analyses without adjusting for other variables. CI, confidence intervals; HR, hazard ratio; NMIBC, non–muscle-invasive bladder cancer; partDSA, partitioning deletion/substitution/addition algorithm; SS-RPMM, semi-supervised recursively partitioned mixture model.
Figure 3.
Kaplan–Meier analysis of 10-year OS based on the grouping results from both partDSA and SS-RPMM in all patients with NMIBC (for Pheno age acceleration): A, contingency table based on the grouping results from both partDSA and SS-RPMM in all patients with NMIBC. B, Ten-year OS curves of all five groups. P values for log-rank tests are shown. All Kaplan–Meier curves are univariate analyses without adjusting for other variables. CI, confidence intervals; HR, hazard ratio; NMIBC, non–muscle-invasive bladder cancer; partDSA, partitioning deletion/substitution/addition algorithm; SS-RPMM, semi-supervised recursively partitioned mixture model.
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