Spatial proteo-transcriptomic profiling of pancreatic adenocarcinoma unveils distinct malignant subtype-associated immune and stromal functional states

Pancreatic adenocarcinoma (PDAC) remains one of the leading causes of cancer mortality. It has become increasingly clear that the spatial architecture of tumors can drastically influence treatment response and prognosis. Advances in large-scale spatial profiling have enabled detailed in situ mapping of cell types and states, which has unveiled multicellular neighborhoods and interactions associated with distinct clinicopathologic features. For example, we previously applied whole-transcriptome spatial molecular imaging (WT-SMI; ~19,000 protein-coding genes) to a prospective cohort of matched pre- and post-chemoradiation PDAC specimens (DF/HCC 18-469) and identified consistent treatment-induced transcriptional shifts in the tumor microenvironment (TME) that were linked to specific ligand-receptor interactions within distinct multicellular neighborhoods. However, many immune cell populations are poorly characterized at the transcriptional level and are better captured using proteomics. To address this gap, we leveraged a recently commercialized multi-omic SMI platform developed by Bruker Spatial/NanoString Technologies that enables concurrent WT and 64-plex protein analysis of a single tissue section to profile a human PDAC tissue microarray (TMA). Transcriptome-level data was used to assign broad cell type labels, and protein stains guided nested subtyping of immune cells by applying HieraType. This approach enabled the annotation of >450,000 cells at a coverage of >1000 transcripts and >750 unique genes per cell, in addition to quantitative staining of 64 protein targets per cell. Using spatial non-negative matrix factorization, we established cellular signatures for distinct neighborhoods composed of malignant cells, stromal cells and immune cells. Preliminary analyses suggest that different transcriptional neighborhoods correlate with distinct survival outcomes. We then used spatial proteomics data to guide functional annotation of immune cells within cellular neighborhoods. This enabled detailed differentiation of immune subtype populations and their interactions with other cells in the TME, which may uncover new mechanisms of immune evasion and therapeutic resistance. This study highlights a novel spatial multi-omics approach that enables more accurate characterization of key multicellular neighborhoods associated with treatment response and clinical prognosis.