The Crosstalk Between Malignant Cells and Tumor-Promoting Immune Cells Relevant to Immunotherapy in Pancreatic Ductal Adenocarcinoma

Upregulated Matrisomal Proteins and Extracellular Matrix Mechanosignaling Underlie Obesity-Associated Promotion of Pancreatic Ductal Adenocarcinoma

Diet-induced obesity (DIO) promotes pancreatic ductal adenocarcinoma (PDAC) in mice expressing KRasG12D in the pancreas (KC mice), but the precise mechanisms remain unclear. Here, we performed multiplex quantitative proteomic and phosphoproteomic analysis by liquid chromatography-tandem mass spectrometry and further bioinformatic and spatial analysis of pancreas tissues from control-fed versus DIO KC mice after 3, 6, and 9 months. Normal pancreatic parenchyma and associated proteins were steadily eliminated and the novel proteins, phosphoproteins, and signaling pathways associated with PDAC tumorigenesis increased until 6 months, when most males exhibited cancer, but females did not. Differentially expressed proteins and phosphoproteins induced by DIO revealed the crucial functional role of matrisomal proteins, which implies the roles of upstream regulation by TGFβ, extracellular matrix-receptor signaling to downstream PI3K-Akt-mTOR-, MAPK-, and Yap/Taz activation, and crucial effects in the tumor microenvironment such as metabolic alterations and signaling crosstalk between immune cells, cancer-associated fibroblasts (CAFs), and tumor cells. Staining tissues from KC mice localized the expression of several prognostic PDAC biomarkers and elucidated tumorigenic features, such as robust macrophage infiltration, acinar-ductal metaplasia, mucinous PanIN, distinct nonmucinous atypical flat lesions (AFLs) surrounded by smooth muscle actin-positive CAFs, invasive tumors with epithelial-mesenchymal transition arising close to AFLs, and expanding deserted areas by 9 months. We next used Nanostring GeoMX to characterize the early spatial distribution of specific immune cell subtypes in distinct normal, stromal, and PanIN areas. Taken together, these data richly contextualize DIO promotion of Kras-driven PDAC tumorigenesis and provide many novel insights into the signaling pathways and processes involved.

An integrated study to decipher immunosuppressive cellular communication in the PDAC environment

Pancreatic ductal adenocarcinoma (PDAC) is one the most aggressive cancers and characterized by a highly rigid and immunosuppressive tumor microenvironment (TME). The extensive cellular interactions are known to play key roles in the immune evasion, chemoresistance, and poor prognosis. Here, we used the spatial transcriptomics, scRNA-seq, and bulk RNA-seq datasets to enhance the insights obtained from each to decipher the cellular communication in the TME. The complex crosstalk in PDAC samples was revealed by the single-cell and spatial transcriptomics profiles of the samples. We show that tumor-associated macrophages (TAMs) are the central cell types in the regulation of microenvironment in PDAC. They colocalize with the cancer cells and tumor-suppressor immune cells and take roles to provide an immunosuppressive environment. LGALS9 gene which is upregulated in PDAC tumor samples in comparison to healthy samples was also found to be upregulated in TAMs compared to tumor-suppressor immune cells in cancer samples. Additionally, LGALS9 was found to be the primary component in the crosstalk between TAMs and the other cells. The widespread expression of P4HB gene and its interaction with LGALS9 was also notable. Our findings point to a profound role of TAMs via LGALS9 and its interaction with P4HB that should be considered for further elucidation as target in the combinatory immunotherapies for PDAC.

The combined prognostic model of copper-dependent to predict the prognosis of pancreatic cancer

Purpose: To assess the prognostic value of copper-dependent genes, copper-dependent-related genes (CDRG), and CDRG-associated immune-infiltrating cells (CIC) for pancreatic cancer.

Methods: CDRG were obtained by single-cell analysis of the GSE156405 dataset in the Gene Expression Omnibus (GEO) database. In a ratio of 7:3, we randomly divided the Cancer Genome Atlas (TCGA) cohort into a training cohort and a test cohort. Tumor samples from the GSE62452 dataset were used as the validation cohort. CIBERSORT was used to obtain the immune cell infiltration. We identified the prognostic CDRG and CIC by Cox regression and the least absolute selection operator (LASSO) method. The clinical significance of these prognostic models was assessed using survival analysis, immunological microenvironment analysis, and drug sensitivity analysis.

Results: 536 CDRG were obtained by single-cell sequencing analysis. We discovered that elevated LIPT1 expression was associated with a worse prognosis in pancreatic cancer patients. EPS8, CASC8, TATDN1, NT5E, and LDHA comprised the CDRG-based prognostic model. High infiltration of Macrophages.M2 in pancreatic cancer patients results in poor survival. The combined prognostic model showed great predictive performance, with the area under the curve (AUC) values being basically between 0.7 and 0.9 in all three cohorts.

Conclusion: We found a cohort of CDRG and CIC in patients with pancreatic cancer. The combined prognostic model provided new insights into the prognosis and treatment of pancreatic cancer.

Gene Coexpression Network Characterizing Microenvironmental Heterogeneity and Intercellular Communication in Pancreatic Ductal Adenocarcinoma: Implications of Prognostic Significance and Therapeutic Target

Background

Pancreatic ductal adenocarcinoma (PDAC) is characterized by intensive stromal involvement and heterogeneity. Pancreatic cancer cells interact with the surrounding tumor microenvironment (TME), leading to tumor development, unfavorable prognosis, and therapy resistance. Herein, we aim to clarify a gene network indicative of TME features and find a vulnerability for combating pancreatic cancer.

Methods

Single-cell RNA sequencing data processed by the Seurat package were used to retrieve cell component marker genes (CCMGs). The correlation networks/modules of CCMGs were determined by WGCNA. Neural network and risk score models were constructed for prognosis prediction. Cell–cell communication analysis was achieved by NATMI software. The effect of the ITGA2 inhibitor was evaluated in vivo by using a Kras^G12D-driven murine pancreatic cancer model.

Results

WGCNA categorized CCMGs into eight gene coexpression networks. TME genes derived from the significant networks were able to stratify PDAC samples into two main TME subclasses with diverse prognoses. Furthermore, we generated a neural network model and risk score model that robustly predicted the prognosis and therapeutic outcomes. A functional enrichment analysis of hub genes governing gene networks revealed a crucial role of cell junction molecule–mediated intercellular communication in PDAC malignancy. The pharmacological inhibition of ITGA2 counteracts the cancer-promoting microenvironment and ameliorates pancreatic lesions in vivo.

Conclusion

By utilizing single-cell data and WGCNA to deconvolute the bulk transcriptome, we exploited novel PDAC prognosis–predicting strategies. Targeting the hub gene ITGA2 attenuated tumor development in a PDAC mouse model. These findings may provide novel insights into PDAC therapy.