scholarly journals Afirma Genomic Sequencing Classifier & Xpression Atlas Molecular Findings in Consecutive Bethesda III-VI Thyroid Nodules

Author(s):  
Mimi I Hu ◽  
Steven G Waguespack ◽  
Chrysoula Dosiou ◽  
Paul W Ladenson ◽  
Masha J Livhits ◽  
...  

Abstract Context Broad genomic analyses among thyroid histologies have been described from relatively small cohorts. Objective Investigate the molecular findings across a large, real-world cohort of thyroid fine needle aspiration (FNA) samples. Design Retrospective analysis of RNA sequencing data files. Setting CLIA laboratory performing Afirma Genomic Sequencing Classifier (GSC) and Xpression Atlas (XA) testing. Participants 50,644 consecutive Bethesda III-VI nodules. Intervention none. Main Outcome Measures Molecular test results. Results Of 48,952 Bethesda III/IV FNAs studied, 66% were benign by Afirma GSC. The prevalence of BRAF V600E was 2% among all Bethesda III/IV FNAs and 76% among Bethesda VI FNAs. Fusions involving NTRK, RET, BRAF, and ALK were most prevalent in Bethesda V (10%), and 130 different gene partners were identified. Among small consecutive Bethesda III/IV sample cohorts with one of these fusions and available surgical pathology excision data, the positive predictive value of an NTRK or RET fusion for carcinoma or non-invasive follicular thyroid neoplasm with papillary-like nuclear features was >95%, while for BRAF and ALK fusions it was 81% and 67%, respectively. At least one genomic alteration was identified by the expanded Afirma XA panel in 70% of Medullary Thyroid Carcinoma Classifier positive FNAs, 44% of Bethesda III or IV Afirma GSC suspicious FNAs, 64% of Bethesda V FNAs, and 87% of Bethesda VI FNAs. Conclusions This large study demonstrates that almost half of Bethesda III/IV Afirma GSC suspicious and most Bethesda V/VI nodules had at least 1 genomic variant or fusion identified, which may optimize personalized treatment decisions.

2015 ◽  
Vol 144 (suppl 2) ◽  
pp. A090-A090
Author(s):  
Christina Dean ◽  
Rachel Geller ◽  
Cynthia Cohen ◽  
Melinda Lewis ◽  
Krisztina Hanley

2021 ◽  
Author(s):  
Francis J J. Ambrosio

Uploading data to Terra.bio is an essential step in the protocol for analyzing locally stored genomic sequencing data. The Terra.bio uploads page allows users to easily organize their data files using an associated metadata file via a browser-based graphical user interface. This protocol explains the process to prepare the data files and the associated metadata file for upload, and provides the link to the Terra.bio uploads page.


2019 ◽  
Vol 30 (4) ◽  
pp. 312-317
Author(s):  
Lei Yin ◽  
Yi Tang ◽  
Shanshan Yu ◽  
Chenglong Wang ◽  
Ming Xiao ◽  
...  

Author(s):  
Shilpa Nadimpalli Kobren ◽  
◽  
Dustin Baldridge ◽  
Matt Velinder ◽  
Joel B. Krier ◽  
...  

Abstract Purpose Genomic sequencing has become an increasingly powerful and relevant tool to be leveraged for the discovery of genetic aberrations underlying rare, Mendelian conditions. Although the computational tools incorporated into diagnostic workflows for this task are continually evolving and improving, we nevertheless sought to investigate commonalities across sequencing processing workflows to reveal consensus and standard practice tools and highlight exploratory analyses where technical and theoretical method improvements would be most impactful. Methods We collected details regarding the computational approaches used by a genetic testing laboratory and 11 clinical research sites in the United States participating in the Undiagnosed Diseases Network via meetings with bioinformaticians, online survey forms, and analyses of internal protocols. Results We found that tools for processing genomic sequencing data can be grouped into four distinct categories. Whereas well-established practices exist for initial variant calling and quality control steps, there is substantial divergence across sites in later stages for variant prioritization and multimodal data integration, demonstrating a diversity of approaches for solving the most mysterious undiagnosed cases. Conclusion The largest differences across diagnostic workflows suggest that advances in structural variant detection, noncoding variant interpretation, and integration of additional biomedical data may be especially promising for solving chronically undiagnosed cases.


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