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. 2017 Mar;26(3):328-338.
doi: 10.1158/1055-9965.EPI-16-0461. Epub 2016 Dec 13.

DNA Methylation-Derived Neutrophil-to-Lymphocyte Ratio: An Epigenetic Tool to Explore Cancer Inflammation and Outcomes

Devin C Koestler  1 , Joseph Usset  2 , Brock C Christensen  3   4   5 , Carmen J Marsit  3   4   5 , Margaret R Karagas  3   5 , Karl T Kelsey  6 , John K Wiencke  7
Affiliations

DNA Methylation-Derived Neutrophil-to-Lymphocyte Ratio: An Epigenetic Tool to Explore Cancer Inflammation and Outcomes

Devin C Koestler et al. Cancer Epidemiol Biomarkers Prev. 2017 Mar.

Abstract

Background: The peripheral blood neutrophil-to-lymphocyte ratio (NLR) is a cytologic marker of both inflammation and poor outcomes in patients with cancer. DNA methylation is a key element of the epigenetic program defining different leukocyte subtypes and may provide an alternative to cytology in assessing leukocyte profiles. Our aim was to create a bioinformatic tool to estimate NLR using DNA methylation, and to assess its diagnostic and prognostic performance in human populations.Methods: We developed a DNA methylation-derived NLR (mdNLR) index based on normal isolated leukocyte methylation libraries and established cell-mixture deconvolution algorithms. The method was applied to cancer case-control studies of the bladder, head and neck, ovary, and breast, as well as publicly available data on cancer-free subjects.Results: Across cancer studies, mdNLR scores were either elevated in cases relative to controls, or associated with increased hazard of death. High mdNLR values (>5) were strong indicators of poor survival. In addition, mdNLR scores were elevated in males, in nonHispanic white versus Hispanic ethnicity, and increased with age. We also observed a significant interaction between cigarette smoking history and mdNLR on cancer survival.Conclusions: These results mean that our current understanding of mature leukocyte methylomes is sufficient to allow researchers and clinicians to apply epigenetically based analyses of NLR in clinical and epidemiologic studies of cancer risk and survival.Impact: As cytologic measurements of NLR are not always possible (i.e., archival blood), mdNLR, which is computed from DNA methylation signatures alone, has the potential to expand the scope of epigenome-wide association studies. Cancer Epidemiol Biomarkers Prev; 26(3); 328-38. ©2016 AACR.

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Conflict of interest statement

Conflict of Interest: The authors have no conflicts of interest to disclose.

Figures

Figure 1
Figure 1. Conceptual diagram illustrating the various steps involved in estimating the mdNLR
In Step 1, putative leukocyte differentially methylated regions (L-DMRs) are identified between monocytes, granulocytes, and lymphocytes. Step 2 involves computing the within cell type mean methylation beta-values for each of the putative L-DMRs identified in Step 1. In Step 3, the within cell type mean methylation beta-values are used in conjunction with Houseman et al., (23) to predict the proportion monocytes, granulocytes, and lymphocytes for a sample consisting of DNA methylation signatures profiled in whole blood. In Step 4, the mdNLR is calculated as the ratio of the predicted proportions of granulocytes and lymphocytes and finally, in Step 5 the estimated mdNLR is examined with respect to its association with cancer risk, outcomes, or other clinical variables of interest.
Figure 2
Figure 2. Results obtained from examining the association between mdNLR and HNSCC case-control status and survival
(A) Distribution of the predicted proportion of lymphocytes, monocytes, and granulocytes between HNSCC cases and age-matched cancer-free controls. (B) Distribution of the mdNLR between HNSCC cases and age-matched cancer-free controls. (C) ROC curves demonstrating the ability of mdNLR to correctly classify HNSCC cases from cancer-free controls. Each ROC curve was generated from a different classifier: red ROC curve was based on a classifier that used covariates only (i.e., age, gender, smoking history, and HPV16 status), black ROC curve was from a classifier based on mdNLR only, and green curve was based on a classifier using both mdNLR and the aforementioned covariates. (D) Scatter plot of the –log10 log-rank P-value as a function of the mdNLR breakpoint used to determine mdNLR low and high groups. Blue line represents the estimated lowess smoothed curve. (E) Kaplan-Meier survival curves for the HNSCC cases in the mdNLR high and low groups.
Figure 3
Figure 3. Results obtained from examining the association between mdNLR and bladder cancer case-control status and survival
(A) Distribution of the predicted proportion of lymphocytes, monocytes, and granulocytes between bladder cancer cases and cancer-free controls. (B) Distribution of the mdNLR between bladder cancer cases and cancer-free controls. (C) Distribution of the mdNLR among bladder cancer cases that died during the follow-up period compared to those that survived or were censored. (D) Scatter plot of the –log10 log-rank P-value as a function of the mdNLR breakpoint used to determine mdNLR low and high groups. Blue line represents the estimated lowess smoothed curve. (E) Kaplan-Meier survival curves for bladder cancer cases in the mdNLR high and low groups.
Figure 4
Figure 4. Results from the analysis of the mdNLR within the ovarian cancer, breast cancer, and aging data sets
(A) Distribution of the mdNLR between pre-treatment ovarian cancer cases, post-treatment ovarian cancer cases, and age-matched cancer-free controls. (B) ROC curves demonstrating the ability of mdNLR to correctly classify controls versus pre-treatment ovarian cancer cases (red line), controls versus post-treatment ovarian cancer cases (black line), and pre- versus post-treatment ovarian cancer cases (green line). (C) Scatter plot of the within twin-pair difference in the mdNLR between breast cancer cases and controls (y-axis) as a function of whole blood sample collection relative to cancer diagnosis (x-axis). Green quadrant indicates that samples were collected pre-diagnosis and that the mdNLR was higher among the twin-pair member that would eventually be diagnosed with breast cancer. Red quadrant indicates that samples were collected pre-diagnosis and that the mdNLR was lower among the twin-pair member that would eventually be diagnosed with breast cancer. Blue quadrant indicates that samples were collected post-diagnosis and that the mdNLR was higher among the twin-pair member diagnosed with breast cancer. Purple quadrant indicates that samples were collected post-diagnosis and that the mdNLR was lower among the twin-pair member diagnosed with breast cancer. Blue line represents the lowess-smoothed curve estimated using all 13 twin pairs (D) Distribution of mdNLR as a function of age group among the samples in the aging study. (E) Distribution of mdNLR as a function ethnic background among the samples in the aging study.
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