Identification of Tissue-Specific DNA Methylation Signatures for Thyroid Nodule Diagnostics

Abstract

Purpose: Thyroid cancer is frequently difficult to diagnose due to an overlap of cytologic features between malignant and benign nodules. This overlap leads to unnecessary removal of the thyroid in patients without cancer. While providing some improvement over cytopathologic diagnostics, molecular methods frequently fail to provide a correct diagnosis for thyroid nodules. These approaches are based on the difference between cancer and adjacent thyroid tissue and assume that adjacent tissues are the same as benign nodules. However, in contrast to adjacent tissues, benign thyroid nodules can contain genetic alterations that can be found in cancer.Experimental Design: For the development of a new molecular diagnostic test for thyroid cancer, we evaluated DNA methylation in 109 thyroid tissues by using genome-wide single-base resolution DNA methylation analysis. The test was validated in a retrospective cohort containing 65 thyroid nodules.

Results: By conducting reduced representation bisulfite sequencing in 109 thyroid specimens, we found significant differences between adjacent tissue, benign nodules, and cancer. These tissue-specific signatures are strongly linked to active enhancers and cancer-associated genes. Based on these signatures, we developed a new epigenetic approach for thyroid diagnostics. According to the validation cohort, our test has an estimated specificity of 97% [95% confidence interval (CI), 81-100], sensitivity of 100% (95% CI, 87-100), positive predictive value of 97% (95% CI, 83-100), and negative predictive value of 100% (95% CI, 86-100).

Conclusions: These data show that epigenetic testing can provide outstanding diagnostic accuracy for thyroid nodules.See related commentary by Mitmaker et al., p. 457.

Figure 1

Differential DNA methylation in adjacent thyroid tissues, benign and malignant nodules. Centered clustering analysis of DNA methylation in normal thyroid tissues, benign and malignant nodules at 18,593 sites which demonstrated FDR<0.01 in comparisons: (i) malignant nodules versus adjacent thyroid tissues, (ii) malignant nodules versus benign nodules, (iii) benign nodules versus adjacent tissues and (iv) benign nodules versus malignant nodules and adjacent thyroid tissues. Each row represents DNA regions. Each column represents tissue specimen. Tissue status and lymphocytic thyroiditis are indicated. Red color indicates a high level of DNA methylation, green color reflects a low level DNA methylation while black color marks a medium level of DNA methylation. Different tissue clusters and cytosine groups are indicated.

Figure 2

DNA locations associated with thyroid tissue specific signatures (DDMS locations) are linked to cancer associated genes and active enhancers. A. Distribution in genome of DDMS locations. B. Genes co-localized with DDMS locations frequently have cancer associated functions according to Ingenuity® Pathway Analysis (Qiagen Inc.). Number of genes in each group is indicated. C. Distribution of DDMS locations relative to active enhancers in adjacent thyroid tissues. A number of DDMS locations without active enhancers, with active enhancers in one out two adjacent thyroid tissue and active enhancers in both tissues are shown. Enhancer shores were defined as 1 kb areas surrounding H3K27Ac peaks. D. Representative snap-shot of co-localization DDMS site with H3K27Ac enrichment at the ANGPTL4 promoter in two analyzed adjacent thyroid tissues (7N and 10N). The snap-shot indicates the normalized number of reads. The red dotted line indicates the position of the DDMS location. Transcriptional start site and transcript direction are shown. E. Representative snap-shot of co-localization DDMS site with H3K27Ac enrichment at the MUC1 promoter in two analyzed adjacent thyroid tissues (7N and 10N). The snap-shot indicate the normalized number of reads. The red dotted line indicates the position of the DDMS location. Transcriptional start site and transcript direction are shown.

Figure 3

Diagnostic DNA Methylation Signature approach (DDMS). A. Cancer risk scores for specimens with conclusive epigenetic state according to leave-one-out cross-validation technique in the developing cohort. The threshold of cancer risk score of 1.0 is indicated. The values of cancer risk score for epigenetically conclusive thyroid nodules are shown. Abbreviation “B” is for thyroid benign nodule and “T” is for thyroid cancer. B. Diagnostics of thyroid nodules based on DDMS epigenetic signatures. C. Receiver operating characteristic for DDMS based on leave-one-out cross-validation technique results in the developing cohort.

Figure 4

DDMS validation in the testing cohort. A. Cancer risk scores for specimens with conclusive epigenetic state in the developing cohort. The values of cancer risk score for epigenetically conclusive thyroid nodules are shown. Abbreviation “B” is for thyroid benign nodule and “T” is for thyroid cancer. The threshold of cancer risk score of 1.0 is indicated. B. Receiver operating characteristics for DDMS in the testing cohort.

Figure 5

Multiplex bisulfite PCR assay (MB-PCR) for DNA methylation analysis in thyroid nodules. A. Flowchart of the multiplex bisulfite PCR assay combined with NextGen sequencing for DNA methylation analysis. B. Correlation between DNA methylation patterns from two technical replicates, one with 100 ng starting material and another 500 ng starting material. C. Correlation between RRBS and multiplex bisulfite PCR for 32 thyroid nodules. “B” is used for benign thyroid nodule and “T” is used for PTC.