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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 cytological 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 malignant nodules and normal adjacent thyroid tissue and assume that normal thyroid tissues are the same as benign nodules. However, in contrast to normal thyroid tissues, benign thyroid nodules can contain genetic alterations that can be found in cancerous nodules. PATIENTS AND METHODS: 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 (Reduced Representation Bisulfite Sequencing). The test was validated in the retrospective cohort containing 64 thyroid nodules. RESULTS: By conducting Reduced Representation Bisulfite Sequencing in 109 thyroid specimens, we found significant differences between normal tissue, benign nodules, and cancer. Based on tissue-specific epigenetic signatures for benign and malignant nodules, we developed a new epigenetic approach for thyroid diagnostics. According to the validation cohort, our test has an estimated specificity of 97% (95% CI, 80 to 100), sensitivity of 100% (95% CI, 86 to 100), PPV of 97% (95% CI, 82-100), NPV of 100% (95%, 85 to 100). CONCLUSION: These data show that epigenetic testing can provide outstanding diagnostic accuracy for thyroid nodules by evaluating tissue specific DNA methylation.

ORGANISM(S): Homo sapiens

PROVIDER: GSE107738 | GEO | 2018/11/30

REPOSITORIES: GEO

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Project description:BACKGROUND: Novel biomarkers are urgently needed to distinguish between benign and malignant thyroid nodules and detect thyroid cancer in the early stage. The associations between serum IgG N-glycosylation and thyroid cancer risk have been revealed. We aimed to explore the potential of IgG N-glycan traits as biomarkers in the differential diagnosis of thyroid cancer.METHODS: Plasma IgG N-glycome analysis was applied to a discovery cohort followed by independent validation. IgG N-glycan profiles were obtained using a robust quantitative strategy based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. IgG N-glycans were relatively quantified, and classification performance was evaluated based on directly detected and derived glycan traits.RESULTS: Four directly detected glycans were significantly changed in thyroid cancer patients compared to that in non-cancer controls. Derived glycan traits and a classification glycol-panel were generated based on the directly detected glycan traits. In the discovery cohort, derived trait BN (bisecting type neutral N-glycans) and the glyco-panel showed potential in distinguishing between thyroid cancer and non-cancer controls with AUCs of 0.920 and 0.917, respectively. The diagnostic potential was further validated. Derived trait BN and the glycol-panel displayed “accurate” performance (AUC>0.8) in discriminating thyroid cancer from benign thyroid nodules and healthy controls in the validation cohort. Meanwhile, derived trait BN and the glycol-panel also showed diagnostic potential in detecting early-stage thyroid cancer.CONCLUSIONS: IgG N-glycome analysis revealed N-glycomic differences that allow classification of thyroid cancer from non-cancer controls. Our results suggested that derived trait BN and the classification glyco-panel rather than single N-glycans may serve as candidate biomarkers for further validation.

Project description:Background. Although gene expression arrays have been used to generate molecular predictors of relapse and drug sensitivity in breast cancer, no large study describes genes and exons differentially expressed between breast cancer and benign lesions. Methods. One hundred and sixty-five tumour samples were obtained by fine needle aspiration (FNA). cDNA were hybridized on Splice ArrayTM. A nearest centroid prediction rule was developed to classify lesions as malignant or benign on a training set, and its performance evaluated on an independent validation set. A two-way ANOVA model was used to identify probesets that present a differential expression between cancer and benign lesions while adjusting for scan dates. P-values were adjusted for False Discovery Rate. Findings. Overall 120 breast cancers and 45 benign lesions were included in the study. A 1228-probeset molecular classifier for breast cancer diagnosis was generated from the training set (n=94). This signature accurately classified all samples (100% accuracy, 95% exact CI: 96-100%). In the validation set (n=71), the molecular predictor accurately classified 68 out of 71 tumours (96%, 95% CI: 88-99%). When the 165 samples were taken into account, 37 858 exon-probesets (5.4%) and 3733 genes (20%) were found to be differentially expressed between malignant and benign conditions (adjusted p-value<0.05). Pathway analyses showed that genes involved in spliceosome assembly were significantly enriched in malignant condition (permutation p=0.002). In the same population of 165 samples, 956 exon-probesets presented both a higher intensity and higher splice index in breast cancer, although located on unchanged genes. Interpretation. The present study provides a thorough description of differentially expressed exons between breast cancer and benign lesions, and emphasizes the contribution of spliceosome and alternative transcripts to the molecular portrait of breast malignancy. This allowed the development of a molecular classifier for breast cancer diagnosis using FNA.

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Identification of Tissue Specific DNA Methylation Signatures for Thyroid Nodule Diagnostics

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