Performance of Afirma Gene Sequencing Classifier versus Gene Expression Classifier in thyroid nodules with indeterminate cytology

Yang, Zhongbo; Zhang, Tao; Layfield, Lester J.; Esebua, Magda

doi:10.1016/j.jasc.2021.07.002

Cited by 16 publications

(11 citation statements)

References 12 publications

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“…There have yet to be any false negatives or false positives. [24][25][26][27][28][29][30][31][32][33][34][35][36] In summary, the article by Randolph et al 11 provides insight into the development and validation of the Afirma RNA sequencing-based classifier for distinguishing MTC from non-MTC samples on thyroid FNAC. It presents a fascinating look into the complex process of machine learning and statistical models used to create such a test.…”

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confidence: 99%

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Development and validation of an RNA sequencing–based classifier for medullary thyroid carcinoma on thyroid FNA

Lubin

Sadow

2022

Cancer Cytopathology

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confidence: 99%

“…The Afirma RNA sequencing–based MTC classifier described in this text has also been performed on thyroid FNAC specimens from various independent institutions, with more than 2100 thyroid nodules reported in the literature. There have yet to be any false negatives or false positives 24–36 …”

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confidence: 99%

Development and validation of an RNA sequencing–based classifier for medullary thyroid carcinoma on thyroid FNA

Lubin

Sadow

2022

Cancer Cytopathology

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“…2,[17][18][19][20] A meta-analysis of these aforementioned studies was published during 2019-2020 11 as well as the several studies in 2021 have demonstrated similar benefits of GSC, such as elevated BCR and improved diagnostic performance, particularly with regards to specificity and PPV. 10,21,22 To the best of our knowledge, studies focused on the performance of GSC in individual AUS subcategories are lacking. The current study accordingly investigated the performance of GSC and histopathological outcome in individual subcategories of AUS including AUS-A, AUS-C, AUS-AC, and AUS-HC.…”

Section: Discussionmentioning

confidence: 99%

“…Since the validation study of GSC for the preoperative evaluation of cytologically indeterminate thyroid nodules, 16 there have been several published studies reporting real‐world comparison of the performance between GSC versus GEC in thyroid nodules categorized into Bethesda category III or IV 2,17–20 . A meta‐analysis of these aforementioned studies was published during 2019–2020 11 as well as the several studies in 2021 have demonstrated similar benefits of GSC, such as elevated BCR and improved diagnostic performance, particularly with regards to specificity and PPV 10,21,22 . To the best of our knowledge, studies focused on the performance of GSC in individual AUS subcategories are lacking.…”

Section: Discussionmentioning

confidence: 99%

Performance of Afirma genomic sequencing classifier and histopathological outcome are associated with patterns of atypia in Bethesda category III thyroid nodules

Jin

Lew

Pantanowitz

et al. 2022

Cancer Cytopathology

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Background:Data on Afirma’s genomic sequencing classifier (GSC) performance in atypia of undetermined significance (AUS) subcategories is limited. This study investigated GSC performance in AUS nodules with architectural atypia (AUS‐A), cytological atypia (AUS‐C), architectural and cytological atypia (AUS‐AC), and predominantly Hürthle cells (AUS‐HC).Methods:This study retrieved consecutive thyroid nodules having a recurrent cytologic diagnosis of AUS with qualifiers and a concurrent GSC diagnostic result. All nodules were followed by either surgical intervention or clinical and/or ultrasound monitoring (≥6 months). GSC benign call rate (BCR), rate of histology‐proven malignancy, and diagnostic parameters of GSC were calculated for individual AUS subcategories. Statistical analysis was performed using the Fisher exact test.Results:A total of 135 AUS nodules fulfilled inclusion criteria, including 79 AUS‐A, 9 AUS‐C, 29 AUS‐AC, and 18 AUS‐HC. BCR was 72.2%, 66.7%, 44.8%, and 77.8% in AUS‐A, AUS‐C, AUS‐AC, and AUS‐HC, respectively. AUS‐A showed a greater BCR than AUS‐AC (p < .05). All GSC‐benign nodules were considered benign on clinical or surgical follow‐up. Among GSC‐suspicious nodules, histology‐proven malignancies represented 4.5% of AUS‐A, 0% of AUS‐C, 56.3% of AUS‐AC, and 25.0% of AUS‐HC cases. AUS‐AC demonstrated a higher malignant rate compared with AUS‐A (p < .05). GSC offers 100% NPV and a wide range (5%–56%) of PPV across all AUS subcategories. AUS‐AC demonstrated a greater PPV compared with AUS‐A (p < .05).Conclusion:BCR of GSC and malignant rates associated with suspicious GSC may differ in various AUS subcategories. GSC‐suspicious nodules with both architectural and cytologic atypia are more likely to be malignant. These findings may improve clinical triage and/or management of patients with AUS thyroid nodules.

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“…Currently, commercially available genomic assays for thyroid nodule aspirates (such as ThyroSeq 43 and Thyrospec 44 ) focus on diagnosis and estimating the risk of malignancy, particularly in indeterminate cell samples (Bethesda 4). Another such test, Afirma 45 , described as a genomic sequencing classifier, also detects gene variants (including BRAF V600E mutations) and fusions in nodules that are clearly malignant (Bethesda 6) or are suspicious for malignancy (Bethesda 5). Our data demonstrate the limited utility of BRAF V600E mutations and other genomic features for prognostication.…”

Section: Discussionmentioning

confidence: 99%

A Clinically Useful and Biologically Informative Genomic Classifier for Papillary Thyroid Cancer

Craig

Stretch

Farshidfar

et al. 2022

Preprint

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Clinical management of papillary thyroid cancer depends on estimations of prognosis. Standard care, which relies on prognostication based on clinicopathologic features, is inaccurate. We applied a machine learning algorithm (HighLifeR) to 502 cases annotated by The Cancer Genome Atlas Project to derive an accurate molecular prognostic classifier. Unsupervised analysis of the 82 genes that were most closely associated with recurrence after surgery enabled identification of three unique molecular subtypes. One subtype had a high recurrence rate, an immunosuppressed microenvironment, and enrichment of the EZH2-HOTAIR pathway. Two other unique molecular subtypes with a lower rate of recurrence were identified, including one subtype with a paucity of BRAFV600E mutations and a high rate of RAS mutations. The genomic risk classifier, in addition to tumor size and lymph node status, enabled effective prognostication that outperformed the American Thyroid Association clinical risk stratification. The genomic classifier we derived can potentially be applied preoperatively to direct clinical decision-making. Distinct biological features of molecular subtypes also have implications regarding sensitivity to radioactive iodine, EZH2 inhibitors, and immune checkpoint inhibitors.

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Performance of Afirma Gene Sequencing Classifier versus Gene Expression Classifier in thyroid nodules with indeterminate cytology

Cited by 16 publications

References 12 publications

Development and validation of an RNA sequencing–based classifier for medullary thyroid carcinoma on thyroid FNA

Development and validation of an RNA sequencing–based classifier for medullary thyroid carcinoma on thyroid FNA

Performance of Afirma genomic sequencing classifier and histopathological outcome are associated with patterns of atypia in Bethesda category III thyroid nodules

A Clinically Useful and Biologically Informative Genomic Classifier for Papillary Thyroid Cancer

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