Peripheral blood immune cell methylation profiles are associated with nonhematopoietic cancers

Abstract

Background: Blood leukocytes from patients with solid tumors exhibit complex and distinct cancer-associated patterns of DNA methylation. However, the biologic mechanisms underlying these patterns remain poorly understood. Because epigenetic biomarkers offer significant clinical potential for cancer detection, we sought to address a mechanistic gap in recently published works, hypothesizing that blood-based epigenetic variation may be due to shifts in leukocyte populations.

Methods: We identified differentially methylated regions (DMR) among leukocyte subtypes using epigenome-wide DNA methylation profiling of purified peripheral blood leukocyte subtypes from healthy donors. These leukocyte-tagging DMRs were then evaluated using epigenome-wide blood methylation data from three independent case-control studies of different cancers.

Results: A substantial proportion of the top 50 leukocyte DMRs were significantly differentially methylated among head and neck squamous cell carcinoma (HNSCC) cases and ovarian cancer cases compared with cancer-free controls (48 and 47 of 50, respectively). Methylation classes derived from leukocyte DMRs were significantly associated cancer case status (P < 0.001, P < 0.03, and P < 0.001) for all three cancer types: HNSCC, bladder cancer, and ovarian cancer, respectively and predicted cancer status with a high degree of accuracy (area under the curve [AUC] = 0.82, 0.83, and 0.67).

Conclusions: These results suggest that shifts in leukocyte subpopulations may account for a considerable proportion of variability in peripheral blood DNA methylation patterns of solid tumors.

Impact: This illustrates the potential use of DNA methylation profiles for identifying shifts in leukocyte populations representative of disease, and that such profiles may represent powerful new diagnostic tools, applicable to a range of solid tumors.

Figure 1. Results from the DMR subset analysis

(a) Hierarchy for the leukocyte subtypes. (b) Heat map of the methylation status for the top 50 leukocyte DMRs by leukocyte subtype. (c) Plot depicting the −log10(p-values) for the top 50 leukocyte DMRs across the three cancer data sets (blue - HNSCC; green - Ovarian; purple - Bladder;). P-values capture methylation differences between cancer cases and non-cancer controls and were obtained from individual unconditional logistic regression models fit to each of the 50 leukocyte DMRs. For the HNSCC data set, logistic regression models were adjusted for patient age, gender, smoking status (never, former, current), smoking pack years, weekly alcohol consumption, and HPV serology status. The bladder cancer data set was adjusted for patient age, gender, smoking status, smoking pack years, and family history of bladder cancer and the ovarian cancer data set was adjusted for patient age group (55–60, 60–65, 65–70, 70–75 and >75 years). The horizontal dashed line represents −log10(p = 0.05).

Figure 2. Figures depicting the results from the DMR profile analysis of the HNSCC data set

(a) Heat map of the HNSCC testing data set. Rows represent subjects, which are grouped by predicted methylation class membership. Columns represent the top 50 leukocyte DMRs that were used to generate the methylation classes for the HNSCC testing set. The right column of (a) represents a bar-plot depicting the percent cancer case/control across the predicted methylation classes in the HNSCC testing set. (b) ROC curves based on the predicted methylation classes only in the HNSCC testing set (blue) and methylation classes including patient age, gender, smoking status (never, former, current), smoking pack years, weekly alcohol consumption, and HPV serostatus (orange).

Figure 3. Figures depicting the results from the DMR profile analysis of the Ovarian data set

(a) Heat map of the ovarian testing data set. Rows represent subjects, which are grouped by predicted methylation class membership. Columns represent the top 10 leukocyte DMRs that were used to generate the methylation classes for the ovarian testing set. The right column of (a) represents a bar-plot depicting the percent cancer case/control across the predicted methylation classes in the ovarian testing set. (b) ROC curves based on the predicted methylation classes alone in the ovarian testing set (blue) and methylation classes plus patient age group (55–60, 60–65, 65–70, 70–75 and >75 years) (orange).

Figure 4. Figures depicting the results from the DMR profile analysis of the bladder data set

(a) Heat map of the bladder testing data set. Rows represent subjects, which are grouped by predicted methylation class membership. Columns represent the top 56 leukocyte DMRs that were used to generate the methylation classes for the bladder testing set. The right column of (a) represents a bar-plot depicting the percent cancer case/control across the predicted methylation classes in the bladder testing set. (b) ROC curves based on the predicted methylation classes alone in the bladder testing set (blue) and methylation classes plus patient age, gender, smoking status (never, former, current), smoking pack years, and family history of bladder cancer (orange).

Figure 5. Image plots representing the pairwise spearman correlation coefficients

(a) The 6 CpG loci identified by HNSCC analysis reported in (ref. 15) and the top 50 leukocyte DMRs used in the present analysis, (b) the 7 CpG loci identified by the alternative ovarian analysis (Supplementary Figure 5) and the top 10 leukocyte DMRs used in the present analysis, and (c) the 9 CpG loci identified by the bladder analysis reported in (ref. 13) and the top 56 leukocyte DMRs used in the present analysis.