Whole-Exome Sequencing Identifies Two Discrete Druggable Signaling Pathways in Follicular Thyroid Cancer

Background: Thyroid cancer is the most common endocrine malignancy, with continuously increasing incidence. Follicular thyroid cancer (FTC) accounts for approximately 10% to 15% of these cases and is known to be associated with several gene mutations. The purpose of this study was to identify novel therapeutic targets in FTC using whole-exome sequencing (WES) and bioinformatics analysis.

Study design: Whole-exome sequencing was performed on 6 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.

Results: An average of 657 (range 366 to 1,158) SNVs including 31 (range 12 to 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 chromosomal 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 2 druggable signaling groups showing dominant RAS/ERK1-2/AKT and CDK1/CyclinB signaling pathway targets.

Conclusions: Next-generation sequencing tools can be used to identify druggable signaling targets for precision treatment of FTCs.