A framework to mine laser microdissection-based omics data and uncover regulators of pancreatic cancer heterogeneity.

Background: Pancreatic ductal adenocarcinoma (PDAC), the most common and aggressive form of pancreatic cancer, exhibits profound intratumor morphological heterogeneity, complicating the elucidation of the underlying molecular mechanisms driving its progression.

Results: We present and validate an optimized framework for RNA sequencing (RNA-seq) of multiple spatially resolved laser micro-dissected tumor areas (LMD-seq), along with methodological and analytical details to maximize reproducibility and data mining. This approach enhances sensitivity in detecting lowly expressed genes, outperforming single-cell RNA-seq methods, particularly in identifying rare tumor cell populations and transcriptional programs with low expression. We also present a detailed map of predicted regulatory networks underlying distinct PDAC morpho-biotypes, revealing novel mechanisms and key regulators associated with each subtype.

Conclusions: This study provides fully reproducible workflows, including processed data objects, documented code, and computational predictions of the regulatory activities, enabling robust exploration of intratumor heterogeneity of PDAC. The proposed methodology, datasets, and catalog of the molecular and regulatory mechanisms offer a framework for future studies and applications in PDAC and other cancers.