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Objective: To identify novel therapeutic targets in follicular thyroid cancer (FTC) using Whole-exome sequencing and bioinformatics analysis.
Design: Whole exome sequencing (WES) was performed on six established FTC cell lines. Stringent false-proof filtering and exclusion of synonymous and known polymorphisms yielded novel missense, nonsense and splice-site single nucleotide variants (SNV). Gene variants were analyzed for structural, functional and evolutionary properties using GO (Gene Ontology), Pfam (Protein Families), and KEGG (Kyoto Encyclopedia of Genes and Genomes) searches by STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) and GORILLA (Gene Ontology enRIchment anaLysis and visuaLizAtion tool) analyses. A false discovery rate of <0.5 was used to denote significantly enriched signaling pathways.
Setting: Academic medical center.
Patients: Six patient-derived FTC cell lines.
Main Outcome Measures: Identification of Druggable signaling pathways in FTC.
Results: An average of 657 (range 366-1158) SNVs including 31 (range 12-53) known cancer driver genes were identified in FTC cell line exomes. The SNV burden, distribution, frequency and signature followed the known thyroid mutation profiles, without any chromosome bias. Recurrently mutated cancer driver genes included FRG1 (6/6), CDC27, NCOR1, PRSS1 (5/6), AHCTF1, MUC20, PABPC1 and PABPC3 (4/6). Pathway analysis using bioinformatics tools STRING and GORILLA segregated FTC cell lines into two druggable signaling groups showing; (1) dominant RAS/ERK1-2/AKT and (2) CDK1/CyclinB signaling pathways.
Conclusions: Next-generation sequencing tools can be used to identify druggable signaling targets for precision treatment of FTCs.