Abstract 5051
Background
A rapid increase in incidence of thyroid cancer has been reported in recent years. However, ambiguities between benign and malignant nodules in cytological features have led to unnecessary thyroidectomy or over-treatment. We aimed to identify DNA methylation markers to accurately classify thyroid nodules, which may be developed into non-invasive screening diagnostics for thyroid cancer to reduce unnecessary thyroid removals.
Methods
Genome-wide DNA methylation profiles of papillary thyroid cancer nodules (tissue, N = 37; plasma, N = 55), benign nodules (tissue, N = 37; plasma, N = 55) and healthy samples (buffy coat, N = 20; plasma, N = 123) were generated by a modified RRBS method. An independent RRBS dataset including 89 malignant and 72 benign nodules was derived from Yim et al. 2019. We adopted Singlera’s MONOD+ assay to interrogate the methylation state of over 4,000,000 CpG sites across more than 200,000 methylation haplotype blocks (MHBs). We identified discriminatory DNA methylation MHBs by Wilcox rank sum test. Using our dataset as a training set, we built several diagnostic models to classify thyroid nodules using machine learning methods. We validated the model by classifying public methylation datasets of thyroid tissues.
Results
We generated an initial list of top 1000 DNA methylation markers and built a classification model by the random forest method. We classified DNA methylation profiles of thyroid nodules published by Yim et al. 2019. Results show that this model achieved a sensitivity of 82%, a specificity of 94.4% and AUC of 0.94 (95% CI, 0.91-0.98). We randomly selected the 2/3 plasma samples as training data to build a prediction model. The model yielded an accuracy of 72% in the validation cohort. We also identified 2392 deferentially methylated regions by comparing benign tissues with malignant tissues. DMR associated genes are highly enriched in many cancer related pathways and MSigDB immunologic signatures, indicating the development of thyroid cancer is closely associated with immune dysfunction.
Conclusions
Our study demonstrates that DNA methylation markers can robustly differentiate thyroid nodules. They are thus promising candidates to develop non-invasive diagnostics for thyroid cancer screening.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The First Affiliated Hospital of Sun Yat-sen University.
Funding
National Natural Science Foundation of China (81772850).
Disclosure
All authors have declared no conflicts of interest.
Resources from the same session
1058 - Assessment of CPS+EG, Neo-Bioscore and modified Neo-Bioscore in breast cancer patients treated with preoperative systemic therapy: a multicenter cohort study
Presenter: LING XU
Session: Poster Display session 2
Resources:
Abstract
1156 - The concordance of treatment decision guided by Oncotype and the PREDICT tool in early stage breast cancer
Presenter: Hadar Goldvaser
Session: Poster Display session 2
Resources:
Abstract
3447 - Influence of first treatment delay on survival among breast cancer subtypes
Presenter: Irene Zarcos Pedrinaci
Session: Poster Display session 2
Resources:
Abstract
3505 - Clinical features of early-stage (I-III) triple-negative breast cancer (TNBC) patients with tumors exhibiting low-overall change in molecular profile after neoadjuvant therapy.
Presenter: Nour Abuhadra
Session: Poster Display session 2
Resources:
Abstract
5442 - Meta-analysis in HER2+ early breast cancer therapies and cost-effectiveness in a Brazilian perspective
Presenter: Marcos Magalhaes
Session: Poster Display session 2
Resources:
Abstract
1570 - Anti-mullerian hormone (AMH) levels and antral follicle counts (AFC) may predict ovarian reserves before systemic chemotherapy (SC) in women with breast cancer(BC); a prospective clinical study
Presenter: Cetin Ordu
Session: Poster Display session 2
Resources:
Abstract
2698 - Prognosis of selected triple negative apocrine breast cancer patients who did not receive adjuvant chemotherapy.
Presenter: Giuseppe Cancello
Session: Poster Display session 2
Resources:
Abstract
3104 - Novel Blood Based Circulating Tumor Cell Biomarker For Breast Cancer Detection
Presenter: Chun-Yu Liu
Session: Poster Display session 2
Resources:
Abstract
4631 - Multi-Gene Prognostic Signatures and Prediction of Pathological Complete Response of ER-Positive HER2-Negative Breast Cancer Patients to Neo-Adjuvant Chemotherapy
Presenter: Claudia Mazo
Session: Poster Display session 2
Resources:
Abstract
4632 - Impact of menopause status on breast cancer outcomes and amenorrhea incidence during adjuvant tailored dose dense chemotherapy
Presenter: Andri Papakonstantinou
Session: Poster Display session 2
Resources:
Abstract