Development of a Molecular Assay for Detection and Quantification of the<i>BRAF</i>Variation in Residual Tissue From Thyroid Nodule Fine-Needle Aspiration Biopsy Specimens

Fu, Guodong; Chazen, Ronald S.; MacMillan, Christina; Witterick, Ian J.

doi:10.1001/jamanetworkopen.2021.27243

Cited by 13 publications

(20 citation statements)

References 59 publications

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“…Real-time polymerase chain reaction (PCR) or next-generation sequencing were traditionally used for BRAF variant detection to select who will respond to the BRAF inhibitors. Recently immunohistochemistry and digital polymerase chain reaction are used for detecting BRAF V600E variant ( 6 , 7 ). BRAF inhibitors have been approved for the treatment of melanoma ( 8 - 10 ), non-small cell lung cancer ( 11 ), and colon cancer ( 12 ).…”

Section: Introductionmentioning

confidence: 99%

Prediction of BRAF V600E variant from cancer gene expression data

Kang¹,

Lee²,

Lee³

et al. 2022

Transl Cancer Res TCR

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Background: BRAF inhibitors have been approved for the treatment of melanoma, non-small cell lung cancer, and colon cancer. Real-time polymerase chain reaction or next-generation sequencing were clinically used for BRAF variant detection to select who responds to BRAF inhibitors. The prediction of BRAF variants using gene expression data might be an alternative test when the direct variant sequencing test is not feasible.In this study, we built a prediction model to detect BRAF V600 variants with mRNA gene expression data in various cancer types.Methods: We adopted a penalized logistic regression for the BRAF V600E variants prediction model.Ten times bootstrap resampling was done with a combined target variable and cancer type stratification.Data preprocessing included knnimputation for missing value imputation, YeoJohnson transformation for skewness correction, center, and scale for standardization, synthetic minority over-sampling technique for class imbalance. Hyperparameter optimization with a grid search was undertaken for model selection in terms of area under the precision-recall. Results:The area under the curve of the receiver operating characteristic curve on the test set was 0.98 in thyroid carcinoma, 0.90 in colon adenocarcinoma, and 0.85 in cutaneous melanoma. The area under the precision-recall of the test set was 0.98 in thyroid carcinoma, 0.71 in colon adenocarcinoma, and 0.65 in cutaneous melanoma. Conclusions:Our penalized logistic regression model can predict BRAF V600E variants with good performance in thyroid carcinoma, cutaneous melanoma, and colon adenocarcinoma.

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Section: Introductionmentioning

confidence: 99%

Prediction of BRAF V600E variant from cancer gene expression data

Kang¹,

Lee²,

Lee³

et al. 2022

Transl Cancer Res TCR

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“…Several molecular tests are commercially available for risk stratification of nodules initially classified as AUS/ FLUS (16)(17)(18), but most of them also require another thyroid tissue sample from a full FNA pass via a repeat FNA procedure. Recently, we re-defined the clinical value of the residual tissues from FNA biopsies, which are considered discards after cytological examination, in assisting in diagnosis of AUS/FLUS biopsies by molecular assays (19). A sensitive digital polymerase chain reaction based molecular assay was established for accurate detection of BRAF V600E variation using residual specimens of FNA biopsies post cytology evaluation as an auxiliary approach for malignancy diagnosis of nodules falling into the AUS/FLUS or ND categories (19).…”

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confidence: 99%

“…Recently, we re-defined the clinical value of the residual tissues from FNA biopsies, which are considered discards after cytological examination, in assisting in diagnosis of AUS/FLUS biopsies by molecular assays (19). A sensitive digital polymerase chain reaction based molecular assay was established for accurate detection of BRAF V600E variation using residual specimens of FNA biopsies post cytology evaluation as an auxiliary approach for malignancy diagnosis of nodules falling into the AUS/FLUS or ND categories (19). Variant assay may be informative in AUS/ FLUS and, in particular, ND samples in which there is quantitative or qualitative inadequacy for cytological examination due to insufficient number of cells or poor sample preparation.…”

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confidence: 99%

Management of thyroid nodules with indeterminate fine-needle aspiration cytology: histogram analysis of greyscale sonograms and molecular assay of residual tissue from fine-needle aspiration biopsies

Fu¹,

Witterick²

2022

Transl Cancer Res TCR

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Determinate cancer screening enhances accurate cancer diagnosis and timely decision-making for treatment. Neck ultrasonography (US) has become the first routine evaluation of all patients with a known or suspected thyroid nodule to stratify its risk of malignancy. The morphological features of thyroid nodules observed during the US are reported via scoring classification systems for malignancy risk stratification based on the Thyroid Imaging Reporting And Data System (TIRADS) or 2015 American Thyroid Association (ATA) guideline (1,2). Different versions of TIRADS have been developed in different countries in line with their national conditions and medical status, such as Chinese TIRADS (3), European TIRADS (4) and Korean TIRADS (5). The sonographic features/patterns of thyroid ultrasound are widely used to identify nodules for further biopsy or followup ultrasound. Nodules with the more suspicious features of US are recommended to undergo thyroid biopsy. The Chinese TIRADS guideline recommends ultrasound-guided fine-needle aspiration (FNA) biopsy for TIRADS 4A nodules >15 mm and TIRADS 4B or 4C or 5 nodules >10 mm, but not for TIRADS 2 and 3 nodules (3). Needle biopsy was first introduced for diagnosis of thyroid nodules using an 18-gauge-needle puncture and aspiration by Martin and Ellis in 1930 and then fine needle (22-27-gauge) in the 1960s (6,7). This technique came into widespread use in North America in the 1980s and has become a gold standard to distinguish malignancy from benign thyroid nodules (7-9). Advances in diagnostics, including increasing use of computed tomography and magnetic resonance imaging, allow the detection and biopsy of thyroid nodules as small as 2 mm (10). Thyroid FNA cytological findings are classified across six diagnostic categories by the Bethesda System for Reporting Thyroid Cytopathology (BSRTC) with risk of malignancy in each category, including (I) nondiagnostic or unsatisfactory (ND), (II) benign, (III) atypia of undetermined significance (AUS)/follicular lesion of undetermined significance (FLUS), (IV) follicular neoplasm/suspicious for a follicular neoplasm (FN/SFN), (V) suspicious for malignancy (SM), and (VI) malignant (11). A nodule may be considered for resection if its cytology is malignant or SM (1,11) or for active surveillance for select papillary thyroid carcinomas and microcarcinomas (12). FNA cytology is the most accurate and cost-effective method for evaluating thyroid nodules. However, discrepancies exist between the US and FNA results in real world clinical practice. Up to 30% of the FNA biopsies are reported as indeterminate cytology: ND, AUS/FLUS, FN/SFN, or SM. According to the 2015 ATA guidelines, a repeat ultrasound-guided FNA is recommended to re-do the cytology examination or perform molecular testing when a nodule was predicted highly SM by US but

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“…However, false-positive staining can be observed by IHC and false-negative results by Sanger sequencing can be obtained when tumor DNA volume is low (6). More sensitive methods such as allele specific quantitative PCR (ASQ-PCR) (6), digital PCR (dPCR) (9,10), high-resolution melting analysis (11) and IntelliPlex TM multiplex system (12) exist, but they are less clinically available. Finally, several NGS cancer genome panels exist, most able to detect BRAF alterations, although not universally accessible due to high cost, approval for analysis at relapse but not initial diagnosis, and the process of clinical laboratory improvement amendments (CLIA) certification (13).…”

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confidence: 99%

Predicting BRAF V600E variants: yet another new method

Natsumeda¹

2022

Transl Cancer Res

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BRAF V600 variants, especially BRAF V600E, are known to be important driver mutations in melanoma (1,2), nonsmall cell lung cancer (3), and colon cancer (4), as BRAF inhibitors ± MEK inhibitors have been approved for treatment in these cancers. Thus, finding BRAF V600 variants in these cancers is mandatory to provide optimal treatment. In the perfect world, next generation sequencing (NGS) of all cancers would be possible at diagnosis. However, this is not always the case, so robust, alternative methods of detection are welcomed.In the present issue of Translational Cancer Research, Kang et al. build a prediction model to detect BRAF V600 variants with mRNA gene expression data in various cancer types. The authors obtained mRNA gene expression data of BRAF V600-variant cancers from The Cancer Genome Atlas (TCGA) pan-cancer database, and constructed a training set from thyroid carcinoma, cutaneous melanoma and colon adenocarcinoma cases, which are known to have high prevalence of BRAF V600 alterations. The authors then adopted a penalized logistic regression for prediction of BRAF V600E variants. Area under the receiver operating characteristic (AUROC) and area under the precisionrecall (AUPR) for the test set was 0.98 and 0.98 in thyroid carcinoma, 0.90 and 0.71 in colon carcinoma, and 0.85 and 0.65 in cutaneous melanoma, respectively. However, AUROC and AUPR was low in the unseen test set for cancer types with low prevalence of BRAF V600 variants. These results suggested that this prediction model can reliably detect BRAF V600E-variant cases using mRNA expression data in cancer types with high incidence of BRAF V600E, but not those with low incidence.

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Development of a Molecular Assay for Detection and Quantification of theBRAFVariation in Residual Tissue From Thyroid Nodule Fine-Needle Aspiration Biopsy Specimens

Cited by 13 publications

References 59 publications

Prediction of BRAF V600E variant from cancer gene expression data

Prediction of BRAF V600E variant from cancer gene expression data

Management of thyroid nodules with indeterminate fine-needle aspiration cytology: histogram analysis of greyscale sonograms and molecular assay of residual tissue from fine-needle aspiration biopsies

Predicting BRAF V600E variants: yet another new method

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