Dataset Information

Multiplexed Imaging Mass Cytometry Analysis in Preclinical Models of Pancreatic Cancer.

ABSTRACT: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers. PDAC is characterized by a complex tumor microenvironment (TME), that plays a pivotal role in disease progression and resistance to therapy. Investigating the spatial distribution and interaction of TME cells with the tumor is the basis for understanding the mechanisms underlying disease progression and represents a current challenge in PDAC research. Imaging mass cytometry (IMC) is the major multiplex imaging technology for the spatial analysis of tumor heterogeneity. However, there is a dearth of reports of multiplexed IMC panels for different preclinical mouse models, including pancreatic cancer. We addressed this gap by utilizing two preclinical models of PDAC: the genetically engineered, bearing KRAS-TP53 mutations in pancreatic cells, and the orthotopic, and developed a 28-marker panel for single-cell IMC analysis to assess the abundance, distribution and phenotypes of cells involved in PDAC progression and their reciprocal functional interactions. Herein, we provide an unprecedented definition of the distribution of TME cells in PDAC and compare the diversity between transplanted and genetic disease models. The results obtained represent an important and customizable tool for unraveling the complexities of PDAC and deciphering the mechanisms behind therapy resistance.

SUBMITTER: Erreni M

PROVIDER: S-EPMC10855072 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Multiplexed Imaging Mass Cytometry Analysis in Preclinical Models of Pancreatic Cancer.

Erreni Marco M Fumagalli Maria Rita MR Zanini Damiano D Candiello Ermes E Tiberi Giorgia G Parente Raffaella R D'Anna Raffaella R Magrini Elena E Marchesi Federica F Cappello Paola P Doni Andrea A

International journal of molecular sciences 20240123 3

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers. PDAC is characterized by a complex tumor microenvironment (TME), that plays a pivotal role in disease progression and resistance to therapy. Investigating the spatial distribution and interaction of TME cells with the tumor is the basis for understanding the mechanisms underlying disease progression and represents a current challenge in PDAC research. Imaging mass cytometry (IMC) is the major multiplex imaging technology ...[more]

PMID: 38338669

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Project description:Imaging Mass Cytometry (IMC) combines laser ablation and mass spectrometry to quantitate metal-conjugated primary antibodies incubated in intact tumor tissue slides. This strategy allows spatially-resolved multiplexing of dozens of simultaneous protein targets with 1μm resolution. Each slide is a spatial assay consisting of high-dimensional multivariate observations (m-dimensional feature space) collected at different spatial positions and capturing data from a single biological sample or even representative spots from multiple samples when using tissue microarrays. Often, each of these spatial assays could be characterized by several regions of interest (ROIs). To extract meaningful information from the multi-dimensional observations recorded at different ROIs across different assays, we propose to analyze such datasets using a two-step graph-based approach. We first construct for each ROI a graph representing the interactions between the m covariates and compute an m dimensional vector characterizing the steady state distribution among features. We then use all these m-dimensional vectors to construct a graph between the ROIs from all assays. This second graph is subjected to a nonlinear dimension reduction analysis, retrieving the intrinsic geometric representation of the ROIs. Such a representation provides the foundation for efficient and accurate organization of the different ROIs that correlates with their phenotypes. Theoretically, we show that when the ROIs have a particular bi-modal distribution, the new representation gives rise to a better distinction between the two modalities compared to the maximum a posteriori (MAP) estimator. We applied our method to predict the sensitivity to PD-1 axis blockers treatment of lung cancer subjects based on IMC data, achieving 97.3% average accuracy on two IMC datasets. This serves as empirical evidence that the graph of graphs approach enables us to integrate multiple ROIs and the intra-relationships between the features at each ROI, giving rise to an informative representation that is strongly associated with the phenotypic state of the entire image.

Project description:IntroductionPancreatic ductal adenocarcinoma (PDAC) represents the complexity of interaction between cancer and cells of the tumor microenvironment (TME). Immune cells affect tumor cell behavior, thus driving cancer progression. Cancer-associated fibroblasts (CAFs) are responsible of the desmoplastic and fibrotic reaction by regulating deposition and remodeling of extracellular matrix (ECM). As tumor-promoting cells abundant in PDAC ECM, CAFs represent promising targets for novel anticancer interventions. However, relevant clinical trials are hampered by the lack of specific markers and elusive differences among CAF subtypes. Indeed, while single-cell transcriptomic analyses have provided important information on the cellular constituents of PDACs and related molecular pathways, studies based on the identification of protein markers in tissues aimed at identifying CAF subtypes and new molecular targets result incomplete.MethodsHerein, we applied multiplexed Imaging Mass Cytometry (IMC) at single-cell resolution on 8 human PDAC tissues to depict the PDAC composing cells, and profiling immune cells, endothelial cells (ECs), as well as endocrine cells and tumor cells.ResultsWe focused on CAFs by characterizing up to 19 clusters distinguished by phenotype, spatiality, and interaction with immune and tumor cells. We report evidence that specific subtypes of CAFs (CAFs 10 and 11) predominantly are enriched at the tumor-stroma interface and closely associated with tumor cells. CAFs expressing different combinations of FAP, podoplanin and cadherin-11, were associated with a higher level of CA19-9. Moreover, we identified specific subsets of FAP+ and podoplanin+/cadherin-11+ CAFs enriched in patients with negative prognosis.DiscussionThe present study provides new general insights into the complexity of the PDAC microenvironment by defining phenotypic heterogeneities and spatial distributions of CAFs, thus suggesting different functions of their subtypes in the PDAC microenvironment.

Dataset Information

Multiplexed Imaging Mass Cytometry Analysis in Preclinical Models of Pancreatic Cancer.

Publications

Multiplexed Imaging Mass Cytometry Analysis in Preclinical Models of Pancreatic Cancer.

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