Single-cell RNA transcriptome reveals the intra-tumoral heterogeneity and regulators underlying tumor progression in metastatic pancreatic ductal adenocarcinoma

Injectable hybrid hydrogels enable enhanced combination chemotherapy and roused anti-tumor immunity in the synergistic treatment of pancreatic ductal adenocarcinoma

Chemotherapy and immunotherapy have shown no significant outcome for unresectable pancreatic ductal adenocarcinoma (PDAC). Multi-drug combination therapy has become a consensus in clinical trials to explore how to arouse anti-tumor immunity and meanwhile overcome the poorly tumoricidal effect and the stroma barrier that greatly hinders drug penetration. To address this challenge, a comprehensive strategy is proposed to fully utilize both the ferroptotic vulnerability of PDAC to potently irritate anti-tumor immunity and the desmoplasia-associated focal adhesion kinase (FAK) to wholly improve the immunosuppressive microenvironment via sustained release of drugs in an injectable hydrogel for increasing drug penetration in tumor location and averting systematic toxicity. The injectable hydrogel ED-M@CS/MC is hybridized with micelles loaded with erastin that exclusively induces ferroptosis and a FAK inhibitor defactinib for inhibiting stroma formation, and achieves sustained release of the drugs for up to 12 days. With only a single intratumoral injection, the combination treatment with erastin and defactinib produces further anti-tumor performance both in xenograft and KrasG12D-engineered primary PDAC mice and synergistically promotes the infiltration of CD8+ cytotoxic T cells and the reduction of type II macrophages. The findings may provide a novel promising strategy for the clinical treatment of PDAC.

Single-cell transcriptome reveals the heterogeneity of malignant ductal cells and the prognostic value of REG4 and SPINK1 in primary pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related deaths, with very limited therapeutic options available. This study aims to comprehensively depict the heterogeneity and identify prognostic targets for PDAC with single-cell RNA sequencing (scRNA-seq) analysis.

Methods

ScRNA-seq analysis was performed on 16 primary PDAC and three adjacent lesions. A series of analytical methods were applied for analysis in cell clustering, gene profiling, lineage trajectory analysis and cell-to-cell interactions. In vitro experiments including colony formation, wound healing and sphere formation assay were performed to assess the role of makers.

Results

A total of 32,480 cells were clustered into six major populations, among which the ductal cell cluster expressing high copy number variants (CNVs) was defined as malignant cells. Malignant cells were further subtyped into five subgroups which exhibited specific features in immunologic and metabolic activities. Pseudotime trajectory analysis indicated that components of various oncogenic pathways were differentially expressed along tumor progression. Furthermore, intensive substantial crosstalk between ductal cells and stromal cells was identified. Finally, genes (REG4 and SPINK1) screened out of differentially expressed genes (DEGs) were upregulated in PDAC cell lines. Silencing either of them significantly impaired proliferation, invasion, migration and stemness of PDAC cells.

Conclusions

Our findings offer a valuable resource for deciphering the heterogeneity of malignant ductal cells in PDAC. REG4 and SPINK1 are expected to be promising targets for PDAC therapy.

Boosting Clear Cell Renal Carcinoma-Specific Drug Discovery Using a Deep Learning Algorithm and Single-Cell Analysis

Clear cell renal carcinoma (ccRCC), the most common subtype of renal cell carcinoma, has the high heterogeneity of a highly complex tumor microenvironment. Existing clinical intervention strategies, such as target therapy and immunotherapy, have failed to achieve good therapeutic effects. In this article, single-cell transcriptome sequencing (scRNA-seq) data from six patients downloaded from the GEO database were adopted to describe the tumor microenvironment (TME) of ccRCC, including its T cells, tumor-associated macrophages (TAMs), endothelial cells (ECs), and cancer-associated fibroblasts (CAFs). Based on the differential typing of the TME, we identified tumor cell-specific regulatory programs that are mediated by three key transcription factors (TFs), whilst the TF EPAS1/HIF-2α was identified via drug virtual screening through our analysis of ccRCC's protein structure. Then, a combined deep graph neural network and machine learning algorithm were used to select anti-ccRCC compounds from bioactive compound libraries, including the FDA-approved drug library, natural product library, and human endogenous metabolite compound library. Finally, five compounds were obtained, including two FDA-approved drugs (flufenamic acid and fludarabine), one endogenous metabolite, one immunology/inflammation-related compound, and one inhibitor of DNA methyltransferase (N4-methylcytidine, a cytosine nucleoside analogue that, like zebularine, has the mechanism of inhibiting DNA methyltransferase). Based on the tumor microenvironment characteristics of ccRCC, five ccRCC-specific compounds were identified, which would give direction of the clinical treatment for ccRCC patients.

Dissecting the Mechanisms of Intestinal Immune Homeostasis by Analyzing T-Cell Immune Response in Crohn's Disease and Colorectal Cancer

INTRODUCTION

Crohn's disease (CD) and colorectal cancer (CRC) represent a group of intestinal disorders characterized by intricate pathogenic mechanisms linked to the disruption of intestinal immune homeostasis. Therefore, comprehending the immune response mechanisms in both categories of intestinal disorders is of paramount significance in the prevention and treatment of these debilitating intestinal ailments.

METHOD

IIn this study, we conducted single-cell analysis on paired samples obtained from primary colorectal tumors and individuals with Crohn's disease, which was aimed at deciphering the factors influencing the composition of the intestinal immune microenvironment. By aligning T cells across different tissues, we identified various T cell subtypes, such as γδ T cell, NK T cell, and regulatory T (Treg) cell, which maintained immune system homeostasis and were confirmed in enrichment analyses. Subsequently, we generated pseudo-time trajectories for subclusters of T cells in both syndromes to delineate their differentiation patterns and identify key driver genes Result: Furthermore, cellular communication and transcription factor regulatory networks are all essential components of the intricate web of mechanisms that regulate intestinal immune homeostasis. The identified complex cellular interaction suggested potential T-lineage immunotherapeutic targets against epithelial cells with high copy number variation (CNV) levels in CD and CRC.

CONCLUSION

Finally, the analysis of regulon networks revealed several promising candidates for cell-specific transcription factors (TFs). This study focused on the immune molecular mechanism under intestinal diseases. It contributed to the novel insight of depicting a detailed immune landscape and revealing T-cell responding mechanisms in CD and CRC.

Copy Number Variations in Pancreatic Cancer: From Biological Significance to Clinical Utility

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, characterized by high tumor heterogeneity and a poor prognosis. Inter- and intra-tumoral heterogeneity in PDAC is a major obstacle to effective PDAC treatment; therefore, it is highly desirable to explore the tumor heterogeneity and underlying mechanisms for the improvement of PDAC prognosis. Gene copy number variations (CNVs) are increasingly recognized as a common and heritable source of inter-individual variation in genomic sequence. In this review, we outline the origin, main characteristics, and pathological aspects of CNVs. We then describe the occurrence of CNVs in PDAC, including those that have been clearly shown to have a pathogenic role, and further highlight some key examples of their involvement in tumor development and progression. The ability to efficiently identify and analyze CNVs in tumor samples is important to support translational research and foster precision oncology, as copy number variants can be utilized to guide clinical decisions. We provide insights into understanding the CNV landscapes and the role of both somatic and germline CNVs in PDAC, which could lead to significant advances in diagnosis, prognosis, and treatment. Although there has been significant progress in this field, understanding the full contribution of CNVs to the genetic basis of PDAC will require further research, with more accurate CNV assays such as single-cell techniques and larger cohorts than have been performed to date.

Machine-learning based integrating bulk and single-cell RNA sequencing reveals the SLC38A5-CCL5 signaling as a promising target for clear cell renal cell carcinoma treatment

Cancer-associated fibroblasts paly critical roles in regulating cancer cell biological properties by intricate and dynamic communication networks. But the mechanism of CAFs in clear cell renal cell carcinoma (ccRCC) is not clear. In our study, we identified CAFs and malignant cells from the integrated scRNA-seq datasets and establish a CAF-derived communication signature based on the highly activated regulons ETS1 and MEF2C. We stratified the ccRCC TME into two molecular subtypes with distinct prognoses, immune cell infiltration landscapes, and immune-related characteristics. The model derived from signature demonstrated high accuracy and robustness in predicting prognosis and ICIs therapy responses. Subsequently, the SLC38A5 of the model was found upregulated in CAFs and was related to decreased survival probabilities, inflamed TME, and upregulated inhibitory checkpoints. SLC38A5 inhibition could attenuate the pro-tumoral abilities of CAFs in terms of proliferation, migration, and invasion. Mechanically, CCL5 could restore these properties induced by SLC38A5 inhibition. In conclusion, our communication signature and its derived model enabled a more precise selection of ccRCC patients who were potential beneficiaries of ICIs. Besides, the SLC38A5-CCL5 axis may serve as a promising target for ccRCC treatment.

Oncogenic dependency plays a dominant role in the immune response to cancer

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest human malignancies. Advanced PDAC is considered incurable. Nearly 90% of pancreatic cancers are caused by oncogenic KRAS mutations. The mechanisms of primary or acquired resistance to KRAS inhibition are currently unknown. Here, we propose that oncogenic dependency, rather than KRAS mutation per se, plays a dominant role in the immune response to cancer, including late-stage PDAC. Classifying tumor samples according to KRAS activity scores allows accurate prediction of tumor immune composition and therapy response. Dual RAS/MAPK pathway blockade combining KRAS and MEK inhibitors is more effective than the selective KRAS inhibitor alone in attenuating MAPK activation and unblocking the influx of T cells into the tumor. Lowering KRAS activity in established tumors promotes immune infiltration, but with a limited antitumor effect, whereas combining KRAS/MEK inhibition with immune checkpoint blockade achieves durable regression in preclinical models. The results are directly applicable to stratifying human PDAC based on KRAS dependency values and immune cell composition to improve therapeutic design.

Single-cell RNA sequencing analysis revealed malignant ductal cell heterogeneity and prognosis signatures in pancreatic cancer

Pancreatic cancer (PAC) remains one of the most lethal malignant neoplasms, which is diagnosed at an advanced stage and thus lose the chance for curative resection. Here, we further probed PAC with a comprehensive multi-omics approach. Using single-cell RNA sequencing, we provided an integrated analysis of ductal cell subpopulations over the Leiden algorithm to identify two mian subcluster: S100A6 + cells and FXYD2 + cells. The gene set enrichment analysis results show that the two subtypes focused on different pathways related to tumor development. Furthermore, we integrated bulk and single-cell RNA sequencing datasets to generate and validate the prognostic signatures of the overall survival (OS) in PAC patients and S100A6 + cells were significantly enriched in high-risk groups which had a poor prognosis. Collectively, this research expands our understanding of ductal cell and provides a new reliable prognosis signature in PAC.

Single-cell transcriptomic analysis uncovers the origin and intratumoral heterogeneity of parotid pleomorphic adenoma

Pleomorphic adenoma (PA) is the most common benign tumour in the salivary gland and has high morphological complexity. However, the origin and intratumoral heterogeneity of PA are largely unknown. Here, we constructed a comprehensive atlas of PA at single-cell resolution and showed that PA exhibited five tumour subpopulations, three recapitulating the epithelial states of the normal parotid gland, and two PA-specific epithelial cell (PASE) populations unique to tumours. Then, six subgroups of PASE cells were identified, which varied in epithelium, bone, immune, metabolism, stemness and cell cycle signatures. Moreover, we revealed that CD36+ myoepithelial cells were the tumour-initiating cells (TICs) in PA, and were dominated by the PI3K-AKT pathway. Targeting the PI3K-AKT pathway significantly inhibited CD36+ myoepithelial cell-derived tumour spheres and the growth of PA organoids. Our results provide new insights into the diversity and origin of PA, offering an important clinical implication for targeting the PI3K-AKT signalling pathway in PA treatment.

Pancreatic draining lymph nodes (PLNs) serve as a pathogenic hub contributing to the development of type 1 diabetes

Type 1 diabetes (T1D) is a chronic, progressive autoinflammatory disorder resulting from the breakdown of self-tolerance and unrestrained β cell-reactive immune response. Activation of immune cells is initiated in islet and amplified in lymphoid tissues, especially those pancreatic draining lymph nodes (PLNs). The knowledge of PLNs as the hub of aberrant immune response is continuously being replenished and renewed. Here we provide a PLN-centered view of T1D pathogenesis and emphasize that PLNs integrate signal inputs from the pancreas, gut, viral infection or peripheral circulation, undergo immune remodeling within the local microenvironment and export effector cell components into pancreas to affect T1D progression. In accordance, we suggest that T1D intervention can be implemented by three major ways: cutting off the signal inputs into PLNs (reduce inflammatory β cell damage, enhance gut integrity and control pathogenic viral infections), modulating the immune activation status of PLNs and blocking the outputs of PLNs towards pancreatic islets. Given the dynamic and complex nature of T1D etiology, the corresponding intervention strategy is thus required to be comprehensive to ensure optimal therapeutic efficacy.

Single-cell RNA sequencing reveals enhanced antitumor immunity after combined application of PD-1 inhibitor and Shenmai injection in non-small cell lung cancer

BACKGROUND

Immune checkpoint inhibitors (ICIs) have altered the clinical management of non-small cell lung cancer (NSCLC). However, the low response rate, severe immune-related adverse events (irAEs), and hyperprogressive disease following ICIs monotherapy require attention. Combination therapy may overcome these limitations and traditional Chinese medicine with immunomodulatory effects provides a promising approach. Shenmai injection (SMI) is a clinically effective adjuvant treatment for cancer with chemotherapy and radiotherapy. Therefore, the combined effects and mechanisms of SMI and programmed death-1 (PD-1) inhibitor against NSCLC was focused on this study.

METHODS

A Lewis lung carcinoma mouse model and a lung squamous cell carcinoma humanized mouse model were used to investigate the combined efficacy and safety of SMI and PD-1 inhibitor. The synergistic mechanisms of the combination therapy against NSCLC were explored using single-cell RNA sequencing. Validation experiments were performed using immunofluorescence analysis, in vitro experiment, and bulk transcriptomic datasets.

RESULTS

In both models, combination therapy alleviated tumor growth and prolonged survival without increasing irAEs. The GZMA^high and XCL1^high natural killer (NK) cell subclusters with cytotoxic and chemokine signatures increased in the combination therapy, while malignant cells from combination therapy were mainly in the apoptotic state, suggesting that mediating tumor cell apoptosis through NK cells is the main synergistic mechanisms of combination therapy. In vitro experiment confirmed that combination therapy increased secretion of Granzyme A by NK cells. Moreover, we discovered that PD-1 inhibitor and SMI combination blocked inhibitory receptors on NK and T cells and restores their antitumoral activity in NSCLC better than PD-1 inhibitor monotherapy, and immune and stromal cells exhibited a decrease of angiogenic features and attenuated cancer metabolism reprogramming in microenvironment of combination therapy.

CONCLUSIONS

This study demonstrated that SMI reprograms tumor immune microenvironment mainly by inducing NK cells infiltration and synergizes with PD-1 inhibitor against NSCLC, suggested that targeting NK cells may be an important strategy for combining with ICIs. Video Abstract.

CD168+ macrophages promote hepatocellular carcinoma tumor stemness and progression through TOP2A/β-catenin/YAP1 axis

Liver cancer stem-like cells (LCSCs) are the main cause of heterogeneity and poor prognosis in hepatocellular carcinoma (HCC). In this study, we aimed to explore the origin of LCSCs and the role of the TOP2A/β-catenin/YAP1 axis in tumor stemness and progression. Using single-cell RNA-seq analysis, we identified TOP2A+CENPF+ LCSCs, which were mainly regulated by CD168+ M2-like macrophages. Furthermore, spatial location analysis and fluorescent staining confirmed that LCSCs were enriched at tumor margins, constituting the spatial heterogeneity of HCC. Mechanistically, TOP2A competitively binds to β-catenin, leading to disassociation of β-catenin from YAP1, promoting HCC stemness and overgrowth. Our study provides valuable insights into the spatial transcriptome heterogeneity of the HCC microenvironment and the critical role of TOP2A/β-catenin/YAP1 axis in HCC stemness and progression.

The application of single-cell sequencing in pancreatic neoplasm: analysis, diagnosis and treatment

Pancreatic neoplasms, including pancreatic ductal adenocarcinoma (PDAC), intraductal papillary mucinous neoplasm (IPMN) and pancreatic cystic neoplasms (PCNs), are the most puzzling diseases. Numerous studies have not brought significant improvements in prognosis and diagnosis, especially in PDAC. One important reason is that previous studies only focused on differences between patients and healthy individuals but ignored intratumoral heterogeneity. In recent years, single-cell sequencing techniques, represented by single-cell RNA sequencing (scRNA-seq), have emerged by which researchers can analyse each cell in tumours instead of their average levels. Herein, we summarise the new current knowledge of single-cell sequencing in pancreatic neoplasms with respect to techniques, tumour heterogeneities and treatments.

LAMC2 marks a tumor-initiating cell population with an aggressive signature in pancreatic cancer

BACKGROUND

Tumor-initiating cells (TIC), also known as cancer stem cells, are considered a specific subpopulation of cells necessary for cancer initiation and metastasis; however, the mechanisms by which they acquire metastatic traits are not well understood.

METHODS

LAMC2 transcriptional levels were evaluated using publicly available transcriptome data sets, and LAMC2 immunohistochemistry was performed using a tissue microarray composed of PDAC and normal pancreas tissues. Silencing and tracing of LAMC2 was performed using lentiviral shRNA constructs and CRISPR/Cas9-mediated homologous recombination, respectively. The contribution of LAMC2 to PDAC tumorigenicity was explored in vitro by tumor cell invasion, migration, sphere-forming and organoids assays, and in vivo by tumor growth and metastatic assays. mRNA sequencing was performed to identify key cellular pathways upregulated in LAMC2 expressing cells. Metastatic spreading induced by LAMC2- expressing cells was blocked by pharmacological inhibition of transforming growth factor beta (TGF-β) signaling.

RESULTS

We report a LAMC2-expressing cell population, which is endowed with enhanced self-renewal capacity, and is sufficient for tumor initiation and differentiation, and drives metastasis. mRNA profiling of these cells indicates a prominent squamous signature, and differentially activated pathways critical for tumor growth and metastasis, including deregulation of the TGF-β signaling pathway. Treatment with Vactosertib, a new small molecule inhibitor of the TGF-β type I receptor (activin receptor-like kinase-5, ALK5), completely abrogated lung metastasis, primarily originating from LAMC2-expressing cells.

CONCLUSIONS

We have identified a highly metastatic subpopulation of TICs marked by LAMC2. Strategies aimed at targeting the LAMC2 population may be effective in reducing tumor aggressiveness in PDAC patients. Our results prompt further study of this TIC population in pancreatic cancer and exploration as a potential therapeutic target and/or biomarker.

Single Cell RNA Sequencing: A New Frontier in Pancreatic Ductal Adenocarcinoma

Simple Summary

Pancreatic cancer has a very low survival rate for several reasons. One of those is primarily due to the difficulty in diagnosing it at an early stage. For this reason, it is important to refine our understanding of this disease to guide the development of new diagnostic and therapeutic modalities to combat this fatal illness. Here we attempt to provide a review of current progress in utilizing single-cell RNA sequencing (scRNA-seq) techniques in the molecular profiling of pancreatic ductal adenocarcinoma.

Abstract

Pancreatic ductal adenocarcinoma is a malignancy with a high mortality rate. It exhibits significant heterogeneity in metabolic pathways which are associated with its progression. In this review, we discuss the role of single cell RNA sequencing in unraveling the metabolic and clinical features of these highly malignant tumors.

Single-Cell RNA Sequencing Reveals the Role of Epithelial Cell Marker Genes in Predicting the Prognosis of Colorectal Cancer Patients

Single-cell RNA sequencing (scRNA-seq) is increasingly used in studies on gastrointestinal cancers. This study investigated the prognostic value of epithelial cell-associated biomarkers in colorectal cancer (CRC) using scRNA-seq data. We downloaded and analysed scRNA-seq data from four CRC samples from the Gene Expression Omnibus (GEO), and we identified marker genes of malignant epithelial cells (MECs) using CRC transcriptome and clinical data downloaded from The Cancer Genome Atlas (TCGA) and GEO as training and validation cohorts, respectively. In the TCGA training cohort, weighted gene correlation network analysis, univariate Cox proportional hazard model (Cox) analysis, and least absolute shrinkage and selection operator regression analysis were performed on the marker genes of MEC subsets to identify a signature of nine prognostic MEC-related genes (MECRGs) and calculate a risk score based on the signature. CRC patients were divided into high- and low-risk groups according to the median risk score. We found that the MECRG risk score was significantly correlated with the clinical features and overall survival of CRC patients, and that CRC patients in the high-risk group showed a significantly shorter survival time. The univariate and multivariate Cox regression analyses showed that the MECRG risk score can serve as an independent prognostic factor for CRC patients. Gene set enrichment analysis revealed that the MECRG signature genes are involved in fatty acid metabolism, p53 signalling, and other pathways. To increase the clinical application value, we constructed a MECRG nomogram by combining the MECRG risk score with other independent prognostic factors. The validity of the nomogram is based on receiver operating characteristics and calibration curves. The MECRG signature and nomogram models were well validated in the GEO dataset. In conclusion, we established an epithelial cell marker gene-based risk assessment model based on scRNA-seq analysis of CRC samples for predicting the prognosis of CRC patients.

Novel γδ T cell-based prognostic signature to estimate risk and aid therapy in hepatocellular carcinoma

BACKGROUND

Numerous studies have revealed that gamma delta (γδ) T cell infiltration plays a crucial regulatory role in hepatocellular carcinoma (HCC) development. Nonetheless, a comprehensive analysis of γδ T cell infiltration in prognosis evaluation and therapeutic prediction remains unclear.

METHODS

Multi-omic data on HCC patients were obtained from public databases. The CIBERSORT algorithm was applied to decipher the tumor immune microenvironment (TIME) of HCC. Weighted gene co-expression network analysis (WGCNA) was performed to determine significant modules with γδ T cell-specific genes. Kaplan-Meier survival curves and receiver operating characteristic analyses were used to validate prognostic capability. Additionally, the potential role of RFESD inhibition by si-RFESD in vitro was investigated using EdU and CCK-8 assays.

RESULTS

A total of 16,421 genes from 746 HCC samples (616 cancer and 130 normal) were identified based on three distinct cohorts. Using WGCNA, candidate modules (brown) with 1755 significant corresponding genes were extracted as γδ T cell-specific genes. Next, a novel risk signature consisting of 11 hub genes was constructed using multiple bioinformatic analyses, which presented great prognosis prediction reliability. The risk score exhibited a significant correlation with ICI and chemotherapeutic targets. HCC samples with different risks experienced diverse signalling pathway activities. The possible interaction of risk score with tumor mutation burden (TMB) was further analyzed. Subsequently, the potential functions of the RFESD gene were explored in HCC, and knockdown of RFESD inhibited cell proliferation in HCC cells. Finally, a robust prognostic risk-clinical nomogram was developed and validated to quantify clinical outcomes.

CONCLUSIONS

Collectively, comprehensive analyses focusing on γδ T cell patterns will provide insights into prognosis prediction, the mechanisms of immune infiltration, and advanced therapy strategies in HCC.

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.

Understanding Tricky Cellular and Molecular Interactions in Pancreatic Tumor Microenvironment: New Food for Thought

Pancreatic ductal adenocarcinoma (PDAC) represents 90% of all pancreatic cancer cases and shows a high mortality rate among all solid tumors. PDAC is often associated with poor prognosis, due to the late diagnosis that leads to metastasis development, and limited efficacy of available treatments. The tumor microenvironment (TME) represents a reliable source of novel targets for therapy, and even if many of the biological interactions among stromal, immune, and cancer cells that populate the TME have been studied, much more needs to be clarified. The great limitation in the efficacy of current standard chemoterapy is due to both the dense fibrotic inaccessible TME barrier surrounding cancer cells and the immunological evolution from a tumor-suppressor to an immunosuppressive environment. Nevertheless, combinatorial therapies may prove more effective at overcoming resistance mechanisms and achieving tumor cell killing. To achieve this result, a deeper understanding of the pathological mechanisms driving tumor progression and immune escape is required in order to design rationale-based therapeutic strategies. This review aims to summarize the present knowledge about cellular interactions in the TME, with much attention on immunosuppressive functioning and a specific focus on extracellular matrix (ECM) contribution.