Genomic and transcriptomic characterization of papillary microcarcinomas with lateral neck lymph node metastases.

CONTEXT Most papillary microcarcinomas (PMC) are indolent and subclinical, however as many as 10% can present with clinically significant nodal metastases. OBJECTIVE/DESIGN Characterization of the genomic and transcriptomic landscape of PMC presenting with or without clinically significant lymph node metastases. SUBJECTS/SAMPLES Formalin-fixed paraffin-embedded PMC samples from 40 patients with lateral neck nodal metastases (pN1b) and 71 PMC patients with documented absence of nodal disease (pN0). OUTCOME MEASURE(S) To interrogate DNA alterations in 410 genes commonly mutated in cancer and test for differential gene expression using a custom NanoString panel of 248 genes selected primarily based on their association with tumor size and nodal disease in the papillary thyroid cancer TCGA project. RESULTS The genomic landscapes of PMC with or without pN1b were similar. Mutations in TERT promoter (3%) and TP53 (1%) were exclusive to N1b cases. Transcriptomic analysis revealed differential expression of 43 genes in PMCs with pN1b compared to pN0. A random forest machine learning-based molecular classifier developed to predict regional lymph node metastasis demonstrated a negative predictive value of 0.98 and a positive predictive value of 0.72 at a prevalence of 10% pN1b disease. CONCLUSIONS The genomic landscape of tumors with pN1b and pN0 disease was similar, whereas 43 genes selected primarily by mining the TCGA RNAseq data were differentially expressed. This bioinformatics-driven approach to the development of a custom transcriptomic assay provides a basis for a molecular classifier for pN1b risk stratification in PMC.