Integrative analysis of genomic and transcriptomic data informs precancer progression in the pancreas

Kathleen Noller, Jiaying Lai, Daniel Lesperance, Ricky S. Adkins, Ahmed Elhossiny, Paola A. Guerrero, Kimal I. Rajapakshe, Michelle Giglio, Anirban Maitra, Anup Mahurkar, Owen White, Marina Pasca Di Magliano, Michael F. Ochs, Luciane T. Kagohara, Laura D. Wood, Rachel Karchin, Elana J. Fertig

Pancreatic ductal adenocarcinoma (PDAC) arises from heterogeneous precursor lesions, including intraductal papillary mucinous neoplasms (IPMNs), but the features distinguishing indolent from progressive lesions remain unclear. We performed an integrative analysis of transcriptomic, genomic, and microenvironmental profiles of IPMNs to define multi-omic phenotypes. Using transfer learning, we projected IPMN-derived transcriptional programs onto spatial transcriptomic datasets from IPMNs and pancreatic intraepithelial neoplasias (PanINs). We identified two major phenotypes: one associated with cancer-associated fibroblasts and epithelial-to-mesenchymal transition, shared across IPMN, PanIN, and PDAC; and a second, glycolysis-enriched phenotype with a unique somatic variant profile specific to IPMN. Spatial mapping further revealed grade-specific enrichment of transcriptional programs and distinct interactions with stromal and immune subtypes, underscoring the role of the precancer microenvironment in progression. These findings establish multi-omic phenotypes that unify genetic, transcriptional, and microenvironmental heterogeneity, providing a framework for distinguishing progressive from indolent precancers and a web-based public atlas for future exploration of these data and transcriptional phenotypes.

Public dissemination and user-friendly analysis and visualization enabled by the cancer gEAR platform

• A: Visualization of spatial transcriptomic IPMN data in Cancer gEAR portal.
• B: Projection analysis tool using projectR in Cancer gEAR.
• C: Flow chart of steps to perform projection analysis in Cancer gEAR.
• D: Example visualizations of Semaan 2023 et al. Bulk RNA-Seq and Carpenter et al. 2023 Single Cell RNA-Seq analysis.
• E: Table displaying the number of datasets loaded into Cancer gEAR for each publication.

Semaan IPMN paper workflow

Workflow defining transcriptional patterns from bulk RNA-seq data of IPMN lesions from Semaan et al, 2023. CancerGear enables further projection of these signatures across pancreatic precancer datasets, including single-cell and spatial transcriptomics, to evaluate the impact of the microenvironment on these states and the occurrence in different subtypes of lesions. This software also allows for exploration of PanIN signatures defined in Bell et al, 2024, custom gene signatures, and gene expression analyses across collated cohorts of pancreatic precancer data.

1. Data collections (profiles) focused on pancreatic ductal adenocarcinoma in Cancer gEAR

2. Pattern signatures derived from CoGAPS non-negative matrix factorization (NMF) projected across pancreatic precancer data using ProjectR.

3. Projection links to explore pattern signatures across the PDAC data collections