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.