Search

Background: Cytopathological evaluation of thyroid fine-needle aspiration biopsy (FNAB) specimens can fail to raise preoperative suspicion of medullary thyroid carcinoma (MTC). The Afirma RNA-sequencing MTC classifier identifies MTC among FNA samples that are cytologically indeterminate, suspicious, or malignant (Bethesda categories III–VI). In this study we report the development and clinical performance of this MTC classifier. Methods: Algorithm training was performed with a set of 483 FNAB specimens (21 MTC and 462 non-MTC). A support vector machine classifier was developed using 108 differentially expressed genes, which includes the 5 genes in the prior Afirma microarray-based MTC cassette. Results: The final MTC classifier was blindly tested on 211 preoperative FNAB specimens with subsequent surgical pathology, including 21 MTC and 190 non-MTC specimens from benign and malignant thyroid nodules independent from those used in training. The classifier had 100% sensitivity (21/21 MTC FNAB specimens correctly called positive; 95% confidence interval [CI] = 83.9–100%) and 100% specificity (190/190 non-MTC FNAs correctly called negative; CI = 98.1–100%). All positive samples had pathological confirmation of MTC, while all negative samples were negative for MTC on surgical pathology. Conclusions: The RNA-sequencing MTC classifier accurately identified MTC from preoperative thyroid nodule FNAB specimens in an independent validation cohort. This identification may facilitate an MTC-specific preoperative evaluation and resulting treatment. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to demonstrate the analytical validity of an RNA classifier for medullary thyroid carcinoma (MTC). Methods: Fresh-frozen tissue specimens were obtained from commercial sources, and MTC diagnoses were confirmed by histopathology review. De-identified patient fine-needle aspiration biopsies (FNABs) and whole blood from normal donors were obtained. Total RNA was extracted, amplified, and hybridized to custom microarrays for gene expression analysis. Gene expression data were normalized and classified via a machine learning algorithm. Positive control materials were produced from MTC tissues and tested across multiple experiments and laboratories. Twenty-seven MTC tissue specimens were used to evaluate the sensitivity of the MTC classifier. Gene expression data from tissues and FNABs were used to model classifier response to mixtures of MTC samples with normal thyroid tissue, a benign thyroid nodule, a Hurthle cell adenoma, and whole blood. Select mixture conditions were confirmed in vitro. Assay tolerance to RNA input variation (5-25 ng) and genomic DNA contamination (30% by mass) was evaluated. The intra- and inter-run reproducibility and inter-laboratory accuracy of MTC classifier results were characterized. Results: The MTC classifier sensitivity of 96.3% [confidence interval 81.0-99.9%] was determined retrospectively using 27 MTC confirmed tissue specimens. One false-negative result in a necrotic tissue implicated sample necrosis in reduced classifier sensitivity. Dilution modeling ofMTCsamples with normal or benign tissues showed consistent detection of MTC down to 20% sample proportions, with in vitro confirmation of 20% analytical sensitivity. Classifier tolerance to RNA input variation (5-25 ng), genomic DNA contamination (30% by mass), and an interfering substance (blood) was demonstrated with 100% accurate classifier results under all tested conditions. The maximum observed run-to-run score difference for a single FNAB sample was ~1 unit compared with the average score difference between 38MTCand non-MTC FNABs of*32 units.MTCclassifier results for 20 tissues processed from total RNA in two different laboratories showed 100% concordance. Conclusions: The MTC classifier, offered as part of the routine molecular testing of cytology-indeterminate thyroid nodules, demonstrates robust analytical sensitivity, specificity, accuracy, and reproducibility. [ABSTRACT FROM AUTHOR]

Background: The use of calcitonin screening for the rare medullary thyroid cancer (MTC) is controversial due to questions of efficacy, accuracy, and cost-effectiveness. This study reports the results of a large prospective validation using a machine-trained algorithm (MTC Classifier) to preoperatively identify MTC in fine-needle aspiration biopsies in lieu of calcitonin measurements. Methods: Cytology analysis on a prospective consecutive series of 50,430 thyroid nodule biopsies yielded a total of 7815 indeterminate (Bethesda categories III/IV) cases, which were tested with the MTC classifier. A prospective, consecutively submitted series of 2673 Bethesda III-VI cases with cytology determined locally was also evaluated. RNA was isolated and tested for the MTC Classifier using microarrays. Results: Forty-three cases were positive by the MTC Classifier among 10,488 tested nodules (0.4%), consistent with the low prevalence of MTC. Of these, all but one was histologically or biochemically confirmed as MTC, yielding a positive predictive value (PPV) of 98%. Of the positive cases, only 19 (44%) had been specifically suspected of MTC by cytology, highlighting the limitations of light microscopy to detect this disease. Three surgically confirmed MTC cases that were detected by the MTC Classifier had low basal serum calcitonin values, indicating these would have been missed by traditional calcitonin screening methods. A pooled analysis of three independent validation sets demonstrates high test sensitivity (97.9%), specificity (99.8%), PPV (97.9%), and negative predictive value (99.8%). Conclusions: A clinical paradigm is proposed, whereby cytologically indeterminate thyroid nodules being tested for common malignancies using gene expression can be simultaneously tested for MTC using the same genomic assay at no added cost. [ABSTRACT FROM AUTHOR]