Transformative Technologies in Pancreatic Cancer: Multi-omic Insights and Organoid Innovation

In recognition of Pancreatic Cancer Awareness Month, join us to explore how cutting-edge technologies are transforming our understanding of pancreatic ductal adenocarcinoma (PDAC), one of the most lethal and therapy-resistant cancers. Dr. Hartland Jackson utilizes integrated spatial proteomics and laser-capture mass spectrometry to map tumor, stroma, and immune interactions, uncovering novel molecular co-dependencies and enhancing patient stratification. Dr. Max Reichert applies organoid models and spatial transcriptomics to reveal hierarchical cell states that drive PDAC’s heterogeneity, metastatic potential, and therapy response. Together, these presentations showcase how advanced spatial imaging and multi-omics technologies are revolutionizing PDAC research, offering new hope for overcoming resistance and improving outcomes in one of the most challenging cancers.

Learning Objectives: 

• Understand how spatial single-cell and multi-omics data outperform genomic and clinical features in predicting patient outcomes in PDAC
• Recognize that distinct tumor cell states in PDAC exhibit variable metastatic potential and therapy sensitivity, and that these states can be dynamically reshaped by treatment 
• Learn how organoid-based models can recapitulate cellular heterogeneity and hierarchical organization in PDAC

Featured Talks: 

From Organoids to Insight: Decoding Plasticity and Heterogeneity in Pancreatic Cancer

Presented by Dr. Max Reichert 

Pancreatic ductal adenocarcinoma (PDAC) exhibits extensive cellular heterogeneity, which underlies its aggressive clinical behavior and resistance to therapy. To decode the principles driving this diversity, we have developed a collagen-embedded, branching organoid model that mirrors the hierarchical organization and phenotypic complexity observed in human and murine PDAC.

This system reveals that tumor cell states in PDAC are not randomly distributed, but emerge through self-organizing programs governed by epithelial–mesenchymal plasticity. Single-cell and spatial transcriptomic analyses uncover distinct cell state hierarchies—such as tip–trunk architectures—that map to specific molecular profiles and functional behaviors. Notably, individual tumor cell states exhibit divergent metastatic capacities and therapy sensitivities, highlighting their clinical relevance.

We further demonstrate that treatment can rewire these hierarchies, inducing dynamic state transitions that open new therapeutic windows. Ongoing work leverages this platform to functionally interrogate intra-organoid lineage trajectories and to define how specific subpopulations contribute to metastatic colonization and drug resistance.

Together, our data position cell state hierarchies as a fundamental organizing principle of PDAC biology and a critical determinant of therapeutic response. Our organoid-based framework offers a tractable system for mechanistic dissection and rational design of state-targeted interventions.

Integrated spatial proteomics of human PDAC uncovers an expanded tumor–immune–stroma spectrum with genomic associations

Presented by Dr. Hartland Jackson

Distinctively, pancreatic ductal adenocarcinoma (PDAC) consists of sparse tumor lesions intertwined with extensive desmoplastic stroma. The complexity of tumor–microenvironment interactions within the desmoplasia poses a challenge for accurate tumor profiling and patient stratification, and contributes to all tumors becoming profoundly resistant to therapy. Here we mapped the spatial relationships between tumor, stroma, and immune cell compartments delineating tumor and microenvironment types that expand the classical to basal spectrum of human PDAC. We used imaging mass cytometry to profile the in situ multi-cellular organization of 81 cell types in resected cases with paired whole genome sequencing. Cell types, functions, and pathway activation were distributed as highly reproducible environments in discrete locations throughout these tumors, which we deep-profiled using laser-capture mass spectrometry. We show that the connections between tumor phenotype, vascularization, immune response, and stromal biophysical state are reinforced by genomic aberrations, altered by treatment, and associated with patient outcome. Predictive machine-learning models showed that spatial single cell data outperformed genomic or clinical features but integrated multi-omics models provide the best prediction of patient survival with compressed models requiring only 10 non-redundant robust molecular measures associated with the phenotypic spectrum of PDAC. Together, these findings define a phenotypic and molecular framework of PDAC that captures tumour–microenvironment co-dependencies and offers a refined basis for patient stratification and therapeutic targeting.