PURPOSE: Intraductal papillary mucinous neoplasms (IPMN) occur in 5-10% of the population, but only a small minority progress to pancreatic ductal adenocarcinoma (PDAC). The lack of accurate predictors of high-risk disease leads both to unnecessary operations for indolent neoplasms as well as missed diagnoses of PDAC. Digital spatial RNA profiling (DSP-RNA) provides an opportunity to define and associate transcriptomic states with cancer risk. EXPERIMENTAL DESIGN: We performed whole-transcriptome DSP-RNA profiling on 10 IPMN specimens encompassing the spectrum of dysplastic changes from normal duct to cancer. Epithelial regions within each tissue were annotated as normal duct (NL), low-grade dysplasia (LGD), high-grade dysplasia (HGD), or invasive carcinoma (INV). The resulting digital gene expression data were analyzed with R/Bioconductor. RESULTS: Our analysis uncovered three distinct epithelial transcriptomic states - "normal-like" (cNL), "low-risk" (cLR), and "high-risk" (cHR) - which were significantly associated with pathologic grade. Furthermore, the three states were significantly correlated with the exocrine, classical, and basal-like molecular subtypes described in PDAC. Specifically, exocrine function diminished in cHR, classical activation distinguished neoplasia (cLR and cHR) from cNL, and basal-like genes were specifically upregulated in cHR. Intriguingly, markers of cHR were detected in NL and LGD regions from specimens with PDAC but not low-grade IPMN. CONCLUSIONS: DSP-RNA of IPMN revealed low-risk (indolent) and high-risk (malignant) expression programs that correlated with the activity of exocrine and basal-like PDAC signatures, respectively, and distinguished pathologically low-grade from malignant specimens. These findings contextualize IPMN pathogenesis and have the potential to improve risk stratification.