Dataset Information

Prognostic Classifier Based on Genome-Wide DNA Methylation Profiling in Well-Differentiated Thyroid Tumors.

ABSTRACT: Even though the majority of well-differentiated thyroid carcinoma (WDTC) is indolent, a number of cases display an aggressive behavior. Cumulative evidence suggests that the deregulation of DNA methylation has the potential to point out molecular markers associated with worse prognosis.To identify a prognostic epigenetic signature in thyroid cancer.Genome-wide DNA methylation assays (450k platform, Illumina) were performed in a cohort of 50 nonneoplastic thyroid tissues (NTs), 17 benign thyroid lesions (BTLs), and 74 thyroid carcinomas (60 papillary, 8 follicular, 2 Hürthle cell, 1 poorly differentiated, and 3 anaplastic). A prognostic classifier for WDTC was developed via diagonal linear discriminant analysis. The results were compared with The Cancer Genome Atlas (TCGA) database.A specific epigenetic profile was detected according to each histological subtype. BTLs and follicular carcinomas showed a greater number of methylated CpG in comparison with NTs, whereas hypomethylation was predominant in papillary and undifferentiated carcinomas. A prognostic classifier based on 21 DNA methylation probes was able to predict poor outcome in patients with WDTC (sensitivity 63%, specificity 92% for internal data; sensitivity 64%, specificity 88% for TCGA data). High-risk score based on the classifier was considered an independent factor of poor outcome (Cox regression, P < 0.001).The methylation profile of thyroid lesions exhibited a specific signature according to the histological subtype. A meaningful algorithm composed of 21 probes was capable of predicting the recurrence in WDTC.

SUBMITTER: Bisarro Dos Reis M

PROVIDER: S-EPMC5673278 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Prognostic Classifier Based on Genome-Wide DNA Methylation Profiling in Well-Differentiated Thyroid Tumors.

Bisarro Dos Reis Mariana M Barros-Filho Mateus Camargo MC Marchi Fábio Albuquerque FA Beltrami Caroline Moraes CM Kuasne Hellen H Pinto Clóvis Antônio Lopes CAL Ambatipudi Srikant S Herceg Zdenko Z Kowalski Luiz Paulo LP Rogatto Silvia Regina SR

The Journal of clinical endocrinology and metabolism 20171101 11

<h4>Context</h4>Even though the majority of well-differentiated thyroid carcinoma (WDTC) is indolent, a number of cases display an aggressive behavior. Cumulative evidence suggests that the deregulation of DNA methylation has the potential to point out molecular markers associated with worse prognosis.<h4>Objective</h4>To identify a prognostic epigenetic signature in thyroid cancer.<h4>Design</h4>Genome-wide DNA methylation assays (450k platform, Illumina) were performed in a cohort of 50 nonneo ...[more]

PMID: 28938489

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Project description:Background: The current edition of the World Health Organization (WHO) classification of endocrine tumors introduced grading for follicular cell-derived thyroid cancer. Tumors with necrosis and/or high mitotic count but not fulfilling the Turin criteria for poorly differentiated carcinoma will be reclassified as differentiated high-grade thyroid carcinoma (DHGTC). However, the impact of this reclassification has not been evaluated. In this study, we performed a systematic review and meta-analysis to estimate the prevalence of this new entry across thyroid tumor subtypes. Methods: In this systematic review and meta-analysis, studies reporting data on necrosis and/or mitoses in well-differentiated thyroid carcinoma (WDTC) were used to estimate the prevalence of DHGTC. Heterogeneity and potential publication bias were also evaluated. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed, and quality assessment was performed using a modification of the Newcastle-Ottawa scale. The study has been registered in the International Prospective Register of Systematic Reviews (PROSPERO, ID: CRD42022378716). Results: In clinically unselected patients, the prevalence of DHGTC in WDTC was 0.072 [95% confidence interval, CI, = 0.045-0.113]. The proportion of high-grade tumors greatly varied across growth patterns and subtypes. Overall, the prevalence of DHGTC was higher in follicular thyroid carcinoma (FTC; 0.146 [CI = 0.101-0.205]) than in papillary thyroid carcinoma (PTC; 0.059 [CI = 0.036-0.097]). Diffuse sclerosing, follicular, and classic subtype PTC had the lowest rates of high-grade features (i.e., 0.018 [CI = 0.004-0.084]; 0.036 [CI = 0.010-0.124]; and 0.042 [CI = 0.027-0.066], respectively), while a greater proportion of solid trabecular and histologically aggressive PTC could be reclassified as DHGTC (i.e., 0.154 [CI = 0.067-0.314] and 0.168 [CI = 0.108-0.252], respectively). Similar proportions were obtained for minimally and widely invasive FTC (i.e., 0.136 [CI = 0.058-0.287] and 0.152 [CI = 0.086-0.254], respectively). Finally, in a cohort of patients with poor prognosis (i.e., fatal cases, metastatic and radioiodine resistant tumors, cases with biochemical recurrence), the proportion of DHGTC was 0.287 [CI = 0.155-0.469]. Conclusions: Following the current WHO indications, some tumors will be reclassified as DHGTC. The proportion of tumors with high-grade features is relevant in FTC, solid trabecular, and histologically aggressive PTC subtypes. A remarkable enrichment in DHGTC among patients with poor prognosis confirms the negative impact of high-grade features on outcome.

Project description:BackgroundMethylation profiling of central nervous system (CNS) tumors, pioneered by the German Cancer Research Center, has significantly improved diagnostic accuracy. This study aimed to further enhance the performance of methylation classifiers by leveraging publicly available data and innovative machine-learning techniques.ResultsSeoul National University Hospital Methylation Classifier (SNUH-MC) addressed data imbalance using the Synthetic Minority Over-sampling Technique (SMOTE) algorithm and incorporated OpenMax within a Multi-Layer Perceptron to prevent labeling errors in low-confidence diagnoses. Compared to two published CNS tumor methylation classification models (DKFZ-MC: Deutsches Krebsforschungszentrum Methylation Classifier v11b4: RandomForest, 767-MC: Multi-Layer Perceptron), our SNUH-MC showed improved performance in F1-score. For 'Filtered Test Data Set 1,' the SNUH-MC achieved higher F1-micro (0.932) and F1-macro (0.919) scores compared to DKFZ-MC v11b4 (F1-micro: 0.907, F1-macro: 0.627). We evaluated the performance of three classifiers; SNUH-MC, DKFZ-MC v11b4, and DKFZ-MC v12.5, using specific criteria. We set established 'Decisions' categories based on histopathology, clinical information, and next-generation sequencing to assess the classification results. When applied to 193 unknown SNUH methylation data samples, SNUH-MC notably improved diagnosis compared to DKFZ-MC v11b4. Specifically, 17 cases were reclassified as 'Match' and 34 cases as 'Likely Match' when transitioning from DKFZ-MC v11b4 to SNUH-MC. Additionally, SNUH-MC demonstrated similar results to DKFZ-MC v12.5 for 23 cases that were unclassified by v11b4.ConclusionsThis study presents SNUH-MC, an innovative methylation-based classification tool that significantly advances the field of neuropathology and bioinformatics. Our classifier incorporates cutting-edge techniques such as the SMOTE and OpenMax resulting in improved diagnostic accuracy and robustness, particularly when dealing with unknown or noisy data.

Dataset Information

Prognostic Classifier Based on Genome-Wide DNA Methylation Profiling in Well-Differentiated Thyroid Tumors.

Publications

Prognostic Classifier Based on Genome-Wide DNA Methylation Profiling in Well-Differentiated Thyroid Tumors.

Similar Datasets

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