Dataset Information

Analytical and clinical validation of pairwise microRNA expression analysis to identify medullary thyroid cancer in thyroid fine-needle aspiration samples.

ABSTRACT:

Background

Medullary thyroid carcinoma (MTC) is an aggressive malignancy originating from the parafollicular C cells. Preoperatively, thyroid nodule fine-needle aspiration cytology (FNAC) and pathogenic gene mutations are definitive in approximately one-half of cases. MicroRNAs (miRNAs) are endogenous, noncoding, single-stranded RNAs that regulate gene expression, a characteristic that confers the potential for identifying malignancy. In the current study, the authors hypothesized that differential pairwise (diff-pair) analysis of miRNA expression levels would reliably identify MTC in FNA samples.

Methods

The relative abundance of 10 different miRNAs in total nucleic acids was obtained from ThyraMIR test results. Diff-pair analysis was performed by subtracting the critical threshold value of one miRNA from the critical threshold values of other miRNAs. Next-generation sequencing with the ThyGeNEXT panel identified oncogenic gene alterations. The discovery cohort consisted of 30 formalin-fixed, paraffin-embedded benign and malignant thyroid neoplasms, including 4 cases of MTC. After analytical validation, clinical validation was performed using 3 distinct cohorts (total of 7557 specimens).

Results

In the discovery cohort, 9 diff-pairs were identified as having significant power using the Kruskal-Wallis test (P < .0001) to distinguish MTC samples from non-MTC samples. The assay correctly classified all MTC and non-MTC samples in the analytical validation study and in the 3 clinical validation cohorts. The overall test accuracy was 100% (95% confidence interval, 99%-100%). In indeterminate FNAC samples, the sensitivity of the diff-pair analysis was greater than that of the MTC-specific mutation analysis (100% vs 25%; P = .03).

Conclusions

Pairwise miRNA expression analysis of ThyraMIR results were found to accurately predict MTC in thyroid FNA samples, including those with indeterminate FNAC findings.

SUBMITTER: Ciarletto AM

PROVIDER: S-EPMC7984450 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Analytical and clinical validation of pairwise microRNA expression analysis to identify medullary thyroid cancer in thyroid fine-needle aspiration samples.

Ciarletto Andrea M AM Narick Christina C Malchoff Carl D CD Massoll Nicole A NA Labourier Emmanuel E Haugh Keith K Mireskandari Alidad A Finkelstein Sydney D SD Kumar Gyanendra G

Cancer cytopathology 20201005 3

<h4>Background</h4>Medullary thyroid carcinoma (MTC) is an aggressive malignancy originating from the parafollicular C cells. Preoperatively, thyroid nodule fine-needle aspiration cytology (FNAC) and pathogenic gene mutations are definitive in approximately one-half of cases. MicroRNAs (miRNAs) are endogenous, noncoding, single-stranded RNAs that regulate gene expression, a characteristic that confers the potential for identifying malignancy. In the current study, the authors hypothesized that d ...[more]

PMID: 33017868

Similar Datasets

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

Analytical and clinical validation of pairwise microRNA expression analysis to identify medullary thyroid cancer in thyroid fine-needle aspiration samples.

Background

Methods

Results

Conclusions

Publications

Analytical and clinical validation of pairwise microRNA expression analysis to identify medullary thyroid cancer in thyroid fine-needle aspiration samples.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets