Abstract
Pancreatic ductal adenocarcinoma (PDAC) is characterized by a relative paucity of cancer cells that are surrounded by an abundance of nontumor cells and extracellular matrix, known as stroma. The interaction between stroma and cancer cells contributes to poor outcome, but how proteins from these individual compartments drive aggressive tumor behavior is not known. Here, we report the proteomic analysis of laser-capture microdissected (LCM) PDAC samples. We isolated stroma, tumor, and bulk samples from a cohort with long- and short-term survivors. Compartment-specific proteins were measured by mass spectrometry, yielding what we believe to be the largest PDAC proteome landscape to date. These analyses revealed that, in bulk analysis, tumor-derived proteins were typically masked and that LCM was required to reveal biology and prognostic markers. We validated tumor CALB2 and stromal COL11A1 expression as compartment-specific prognostic markers. We identified and functionally addressed the contributions of the tumor cell receptor EPHA2 to tumor cell viability and motility, underscoring the value of compartment-specific protein analysis in PDAC.
Highlights
Large-scale omics efforts to identify key mediators of tumor biology have typically focused on the tumor compartment as an entity by itself, and tissue samples for gene expression and genomic analyses are commonly selected based on high tumor purity [1]
We report an extensive data set from laser-capture microdissected (LCM) Pancreatic ductal adenocarcinoma (PDAC) tissue and patient-derived xenografts (PDXs) and identify over 6000 proteins
This study reports an in-depth proteomic analysis of LCM PDAC
Summary
Large-scale omics efforts to identify key mediators of tumor biology have typically focused on the tumor compartment as an entity by itself, and tissue samples for gene expression and genomic analyses are commonly selected based on high tumor purity [1]. This bias toward the epithelial compartment understates the influence of the microenvironment and its heterocellular composition [2]. Bioinformatics efforts to delineate the compartment-specific expression have overcome this problem partially and increased our understanding of tumor biology [3]. In light of current molecular subtyping efforts to improve survival prediction and achieve a personalized treatment schedule, the characterization of the specific expression profiles of tumor cells and their microenvironment is key to identify patient groups more effectively and to discover therapeutic targets
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