Dataset Information

Gene Methylation and Cytological Atypia in Random Fine-Needle Aspirates for Assessment of Breast Cancer Risk.

ABSTRACT: Methods to determine individualized breast cancer risk lack sufficient sensitivity to select women most likely to benefit from preventive strategies. Alterations in DNA methylation occur early in breast cancer. We hypothesized that cancer-specific methylation markers could enhance breast cancer risk assessment. We evaluated 380 women without a history of breast cancer. We determined their menopausal status or menstrual cycle phase, risk of developing breast cancer (Gail model), and breast density and obtained random fine-needle aspiration (rFNA) samples for assessment of cytopathology and cumulative methylation index (CMI). Eight methylated gene markers were identified through whole-genome methylation analysis and included novel and previously established breast cancer detection genes. We performed correlative and multivariate linear regression analyses to evaluate DNA methylation of a gene panel as a function of clinical factors associated with breast cancer risk. CMI and individual gene methylation were independent of age, menopausal status or menstrual phase, lifetime Gail risk score, and breast density. CMI and individual gene methylation for the eight genes increased significantly (P < 0.001) with increasing cytological atypia. The findings were verified with multivariate analyses correcting for age, log (Gail), log (percent density), rFNA cell number, and body mass index. Our results demonstrate a significant association between cytological atypia and high CMI, which does not vary with menstrual phase or menopause and is independent of Gail risk and mammographic density. Thus, CMI is an excellent candidate breast cancer risk biomarker, warranting larger prospective studies to establish its utility for cancer risk assessment. Cancer Prev Res; 9(8); 673-82. ©2016 AACR.

SUBMITTER: Stearns V

PROVIDER: S-EPMC4970896 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Gene Methylation and Cytological Atypia in Random Fine-Needle Aspirates for Assessment of Breast Cancer Risk.

Stearns Vered V Fackler Mary Jo MJ Hafeez Sidra S Bujanda Zoila Lopez ZL Chatterton Robert T RT Jacobs Lisa K LK Khouri Nagi F NF Ivancic David D Kenney Kara K Shehata Christina C Jeter Stacie C SC Wolfman Judith A JA Zalles Carola M CM Huang Peng P Khan Seema A SA Sukumar Saraswati S

Cancer prevention research (Philadelphia, Pa.) 20160603 8

Methods to determine individualized breast cancer risk lack sufficient sensitivity to select women most likely to benefit from preventive strategies. Alterations in DNA methylation occur early in breast cancer. We hypothesized that cancer-specific methylation markers could enhance breast cancer risk assessment. We evaluated 380 women without a history of breast cancer. We determined their menopausal status or menstrual cycle phase, risk of developing breast cancer (Gail model), and breast densit ...[more]

PMID: 27261491

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Project description:BackgroundMicroRNA (miR) expression signatures are proposed to be able to differentiate thyroid cancer from benign thyroid lesions. We selected eight miRs (miR-146b, -221, -187, -197, -346, -30d, -138, and -302c) to examine the potential use of miRs to supplement diagnostic cytology in cases designated as "atypia of undetermined significance."MethodsmiR expression was measured in thyroid fine needle aspiration (FNA) specimens by quantitative polymerase chain reaction. Gene expression analyses and linear discriminant analysis (LDA) were performed in a training sample set (n=60) to obtain a classification rule to predict FNA cases as benign or malignant. The predictions were cross-validated by comparing with the corresponding histological diagnoses. A validation sample set (n=68) was further tested with the established four-miR LDA classification rule.ResultsA set of four miRs (miR-146b, -221, -187, and -30d) was identified that could differentiate malignant from benign lesions. A four-miR LDA classification rule was obtained and used to predict FNA cases as benign or malignant. For the training sample set, we obtained a diagnostic accuracy of 93.3%, sensitivity of 93.2%, specificity of 93.8%, positive predictive value (PPV) of 0.98, and negative predictive value (NPV) of 0.83. For the validation sample set, we obtained a diagnostic accuracy of 85.3%, sensitivity of 88.9%, specificity of 78.3%, PPV of 0.89, and NPV of 0.78. For the 30 atypia cases in the validation sample set, we obtained a diagnostic accuracy of 73.3%, sensitivity of 63.6%, specificity of 78.9%, PPV of 0.64, and NPV of 0.79. Based on the miR predictions, we classified the atypia cases predicted as "malignant" into "high risk" and those predicted as "benign" into "low risk" categories. While thyroid carcinomas, particularly papillary thyroid carcinomas (PTCs), were relatively enriched in the high-risk category, this particular miR panel is subject to inaccurate results in follicular neoplasias in atypia cases.ConclusionsWe demonstrate that miR amplification from FNA samples is feasible and that the particular four miR profile in this study can identify PTCs. However, further refinement is required for application to FNA cytology of "atypia of undetermined significance" cases due to low accuracy in classifying follicular neoplasias.

Dataset Information

Gene Methylation and Cytological Atypia in Random Fine-Needle Aspirates for Assessment of Breast Cancer Risk.

Publications

Gene Methylation and Cytological Atypia in Random Fine-Needle Aspirates for Assessment of Breast Cancer Risk.

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