Integrated transcriptional analysis reveals macrophage heterogeneity and macrophage-tumor cell interactions in the progression of pancreatic ductal adenocarcinoma

TGFB2 mRNA Levels Prognostically Interact with Interferon-Alpha Receptor Activation of IRF9 and IFI27, and an Immune Checkpoint LGALS9 to Impact Overall Survival in Pancreatic Ductal Adenocarcinoma

The treatment of pancreatic ductal adenocarcinoma (PDAC) is an unmet challenge, with the median overall survival rate remaining less than a year, even with the use of FOLFIRINOX-based therapies. This study analyzed archived macrophage-associated mRNA expression using datasets deposited in the UCSC Xena web platform to compare normal pancreatic tissue and PDAC tumor samples. The TGFB2 gene exhibited low mRNA expression levels in normal tissue, with less than one TPM. In contrast, in tumor tissue, TGFB2 expression levels exhibited a 7.9-fold increase in mRNA expression relative to normal tissue (p < 0.0001). Additionally, components of the type-I interferon signaling pathway exhibited significant upregulation of mRNA levels in tumor tissue, including Interferon alpha/beta receptor 1 (IFNAR1; 3.4-fold increase, p < 0.0001), Interferon regulatory factor 9 (IRF9; 4.2-fold increase, p < 0.0001), Signal transducer and activator of transcription 1 (STAT1; 7.1-fold increase, p < 0.0001), and Interferon Alpha Inducible Protein 27 (IFI27; 66.3-fold increase, p < 0.0001). We also utilized TCGA datasets deposited in cBioportal and KMplotter to relate mRNA expression levels to overall survival outcomes. These increased levels of mRNA expression were found to be prognostically significant, whereby patients with high expression levels of either TGFB2, IRF9, or IFI27 showed median OS times ranging from 16 to 20 months (p < 0.01 compared to 72 months for patients with low levels of expression for both TGFB2 and either IRF9 or IFI27). Examination of the KMplotter database determined the prognostic impact of TGFB2 mRNA expression levels by comparing patients expressing high versus low levels of TGFB2 (50th percentile cut-off) in low macrophage TME. In TME with low macrophage levels, patients with high levels of TGFB2 mRNA exhibited significantly shorter OS outcomes than patients with low TGFB2 mRNA levels (Median OS of 15.3 versus 72.7 months, p < 0.0001). Furthermore, multivariate Cox regression models were applied to control for age at diagnosis. Nine genes exhibited significant increases in hazard ratios for TGFB2 mRNA expression, marker gene mRNA expression, and a significant interaction term between TGFB2 and marker gene expression (mRNA for markers: C1QA, CD74, HLA-DQB1, HLA-DRB1, HLA-F, IFI27, IRF9, LGALS9, MARCO). The results of our study suggest that a combination of pharmacological tools can be used in treating PDAC patients, targeting both TGFB2 and the components of the type-I interferon signaling pathway. The significant statistical interaction between TGFB2 and the nine marker genes suggests that TGFB2 is a negative prognostic indicator at low levels of the IFN-I activated genes and TAM marker expression, including the immune checkpoint LGALS9 (upregulated 16.5-fold in tumor tissue; p < 0.0001).

Unveiling the contribution of tumor-associated macrophages in driving epithelial-mesenchymal transition: a review of mechanisms and therapeutic Strategies

Tumor-associated macrophages (TAMs), fundamental constituents of the tumor microenvironment (TME), significantly influence cancer development, primarily by promoting epithelial-mesenchymal transition (EMT). EMT endows cancer cells with increased motility, invasiveness, and resistance to therapies, marking a pivotal juncture in cancer progression. The review begins with a detailed exposition on the origins of TAMs and their functional heterogeneity, providing a foundational understanding of TAM characteristics. Next, it delves into the specific molecular mechanisms through which TAMs induce EMT, including cytokines, chemokines and stromal cross-talking. Following this, the review explores TAM-induced EMT features in select cancer types with notable EMT characteristics, highlighting recent insights and the impact of TAMs on cancer progression. Finally, the review concludes with a discussion of potential therapeutic targets and strategies aimed at mitigating TAM infiltration and disrupting the EMT signaling network, thereby underscoring the potential of emerging treatments to combat TAM-mediated EMT in cancer. This comprehensive analysis reaffirms the necessity for continued exploration into TAMs' regulatory roles within cancer biology to refine therapeutic approaches and improve patient outcomes.

A war on many fronts: cross disciplinary approaches for novel cancer treatment strategies

Cancer is a disease characterized by uncontrolled cellular growth where cancer cells take advantage of surrounding cellular populations to obtain resources and promote invasion. Carcinomas are the most common type of cancer accounting for almost 90% of cancer cases. One of the major subtypes of carcinomas are adenocarcinomas, which originate from glandular cells that line certain internal organs. Cancers such as breast, prostate, lung, pancreas, colon, esophageal, kidney are often adenocarcinomas. Current treatment strategies include surgery, chemotherapy, radiation, targeted therapy, and more recently immunotherapy. However, patients with adenocarcinomas often develop resistance or recur after the first line of treatment. Understanding how networks of tumor cells interact with each other and the tumor microenvironment is crucial to avoid recurrence, resistance, and high-dose therapy toxicities. In this review, we explore how mathematical modeling tools from different disciplines can aid in the development of effective and personalized cancer treatment strategies. Here, we describe how concepts from the disciplines of ecology and evolution, economics, and control engineering have been applied to mathematically model cancer dynamics and enhance treatment strategies.

Single-Cell Sequencing Reveals MYOF-Enriched Monocyte/Macrophage Subcluster as a Favorable Prognostic Factor in Sepsis

This research utilized single-cell RNA sequencing to map the immune cell landscape in sepsis, revealing 28 distinct cell clusters and categorizing them into nine major types. Delving into the monocyte/macrophage subclusters, 12 unique subclusters are identified and pathway enrichment analyses are conducted using KEGG and GO, discovering enriched pathways such as oxidative phosphorylation and antigen processing. Further GSVA and AUCell assessments show varied activation of interferon pathways, especially in subclusters 4 and 11. The clinical correlation analysis reveals genes significantly linked to survival outcomes. Additionally, cellular differentiation in these subclusters is explored. Building on these insights, the differential gene expression within these subclusters is specifically scrutinized, which reveal MYOF as a key gene with elevated expression levels in the survivor group. This finding is further supported by in-depth pathway enrichment analysis and the examination of cellular differentiation trajectories, where MYOF's role became evident in the context of immune response regulation and sepsis progression. Validating the role of the MYOF gene in sepsis, a dose-dependent response to LPS in THP-1 cells and C57 mice is observed. Finally, inter-cellular communications are analyzed, particularly focusing on the MYOF+Mono/Macro subcluster, which indicates a pivotal role in immune regulation and potential therapeutic targeting.

Integration of scRNA-seq data by disentangled representation learning with condition domain adaptation

BACKGROUND

The integration of single-cell RNA sequencing data from multiple experimental batches and diverse biological conditions holds significant importance in the study of cellular heterogeneity.

RESULTS

To expedite the exploration of systematic disparities under various biological contexts, we propose a scRNA-seq integration method called scDisco, which involves a domain-adaptive decoupling representation learning strategy for the integration of dissimilar single-cell RNA data. It constructs a condition-specific domain-adaptive network founded on variational autoencoders. scDisco not only effectively reduces batch effects but also successfully disentangles biological effects and condition-specific effects, and further augmenting condition-specific representations through the utilization of condition-specific Domain-Specific Batch Normalization layers. This enhancement enables the identification of genes specific to particular conditions. The effectiveness and robustness of scDisco as an integration method were analyzed using both simulated and real datasets, and the results demonstrate that scDisco can yield high-quality visualizations and quantitative outcomes. Furthermore, scDisco has been validated using real datasets, affirming its proficiency in cell clustering quality, retaining batch-specific cell types and identifying condition-specific genes.

CONCLUSION

scDisco is an effective integration method based on variational autoencoders, which improves analytical tasks of reducing batch effects, cell clustering, retaining batch-specific cell types and identifying condition-specific genes.

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes.

Metabolic regulation of tumor-associated macrophage heterogeneity: insights into the tumor microenvironment and immunotherapeutic opportunities

Tumor-associated macrophages (TAMs) are a heterogeneous population that play diverse functions in tumors. Their identity is determined not only by intrinsic factors, such as origins and transcription factors, but also by external signals from the tumor microenvironment (TME), such as inflammatory signals and metabolic reprogramming. Metabolic reprogramming has rendered TAM to exhibit a spectrum of activities ranging from pro-tumorigenic to anti-tumorigenic, closely associated with tumor progression and clinical prognosis. This review implicates the diversity of TAM phenotypes and functions, how this heterogeneity has been re-evaluated with the advent of single-cell technologies, and the impact of TME metabolic reprogramming on TAMs. We also review current therapies targeting TAM metabolism and offer new insights for TAM-dependent anti-tumor immunotherapy by focusing on the critical role of different metabolic programs in TAMs.

Single-cell sequencing in pancreatic cancer research: A deeper understanding of heterogeneity and therapy

Pancreatic cancer, including pancreatic ductal adenocarcinomas (PDACs), is a malignant tumor with characteristics of tumor-stroma interactions. Patients often have a poor prognosis and a poor long-term survival rate. In recent years, rapidly-developing single-cell sequencing techniques have been used to analyze cell populations at a single-cell resolution, so that it is now possible to have a more in-depth and clearer understanding of the genetic composition of pancreatic cancer. In this review, we provide an overview of the current single-cell sequencing techniques and their applications in the exploration of intratumoral heterogeneity, the tumor microenvironment, therapy resistance, and novel treatments. Our hope is to provide new insight into the potential of precision therapy, which will perhaps one day lead to significant advances in PDAC treatment.

Visualizing Macrophage Phenotypes and Polarization in Diseases: From Biomarkers to Molecular Probes

Macrophage is a kind of immune cell and performs multiple functions including pathogen phagocytosis, antigen presentation and tissue remodeling. To fulfill their functionally distinct roles, macrophages undergo polarization towards a spectrum of phenotypes, particularly the classically activated (M1) and alternatively activated (M2) subtypes. However, the binary M1/M2 phenotype fails to capture the complexity of macrophages subpopulations in vivo. Hence, it is crucial to employ spatiotemporal imaging techniques to visualize macrophage phenotypes and polarization, enabling the monitoring of disease progression and assessment of therapeutic responses to drug candidates. This review begins by discussing the origin, function and diversity of macrophage under physiological and pathological conditions. Subsequently, we summarize the identified macrophage phenotypes and their specific biomarkers. In addition, we present the imaging probes locating the lesions by visualizing macrophages with specific phenotype in vivo. Finally, we discuss the challenges and prospects associated with monitoring immune microenvironment and disease progression through imaging of macrophage phenotypes.

The cellular trajectories of tumor-associated macrophages decipher the heterogeneity of pancreatic cancer

Emerging evidence indicates that the interactions and dynamic changes among tumor-associated macrophages (TAMs) are pivotal in molding the tumor microenvironment (TME), thereby influencing diverse clinical outcomes. However, the potential clinical ramifications of these evolutionary shifts in tumor-associated macrophages within pancreatic adenocarcinoma (PAAD) remain largely unexamined. Single-cell RNA sequencing (scRNA-seq) data were retrieved from the Tumor Immune Single-cell Hub. The Seurat and Monocle algorithms were employed to elucidate the progression of TAMs, using non-negative matrix factorization (NMF) to determine molecular classifications. Subsequently, the prognosis, biological characteristics, genomic modifications, and immune landscape across various clusters were interpreted. Furthermore, the sensitivity of potential therapeutic drugs between subtypes was predicted. Cellular experiments were conducted to explore the function of the NR1H3 gene in pancreatic cancer. These experiments encompassed gene knockdown, proliferation assessment, clone formation evaluation, transwell examination, and apoptosis analysis. Trajectory gene expression analysis of tumor-associated macrophages identified three disparate clusters, each associated with different clinical outcomes Compared to clusters C1 and C2, cluster C3 is seemingly at a less advanced pathological stage and associates with a relatively favorable prognosis. Further investigation revealed pronounced genetic instability in cluster C2, whereas cluster C3 demonstrated notable genetic stability. Cluster C1, characterized as "immune-hot," exhibits an abundance of immune cells and elevated immune checkpoint expression, suggesting its suitability for immunotherapy. Furthermore, several potential therapeutic agents have been pinpointed, potentially facilitating the clinical application of these insights. Cell assays indicated that NR1H3 knockdown markedly induced apoptosis and suppressed clonogenesis, migration, and proliferation of pancreatic cancer cells in the PTAU-8988 and PANC-1 cell lines. Overall, our study discerned three clusters with unique characteristics, defined by the evolution of TAMs. We propose customized therapeutic strategies for patients within these specific clusters to improve clinical outcomes and optimize clinical management.

Advances in Immunotherapeutics in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) accounts for up to 95% of all pancreatic cancer cases and is the seventh-leading cause of cancer death. Poor prognosis is a result of late presentation, a lack of screening tests and the fact some patients develop resistance to chemotherapy and radiotherapy. Novel therapies like immunotherapeutics have been of recent interest in pancreatic cancer. However, this field remains in its infancy with much to unravel. Immunotherapy and other targeted therapies have yet to yield significant progress in treating PDAC, primarily due to our limited understanding of the disease immune mechanisms and its intricate interactions with the tumour microenvironment (TME). In this review we provide an overview of current novel immunotherapies which have been studied in the field of pancreatic cancer. We discuss their mechanisms, evidence available in pancreatic cancer as well as the limitations of such therapies. We showcase the potential role of combining novel therapies in PDAC, postulate their potential clinical implications and the hurdles associated with their use in PDAC. Therapies discussed with include programmed death checkpoint inhibitors, Cytotoxic T-lymphocyte-associated protein 4, Chimeric Antigen Receptor-T cell therapy, oncolytic viral therapy and vaccine therapies including KRAS vaccines, Telomerase vaccines, Gastrin Vaccines, Survivin-targeting vaccines, Heat-shock protein (HSP) peptide complex-based vaccines, MUC-1 targeting vaccines, Listeria based vaccines and Dendritic cell-based vaccines.

Uncoding the interdependency of tumor microenvironment and macrophage polarization: insights from a continuous network approach

The balance between pro- and anti-inflammatory immune system responses is crucial to preventing complex diseases like cancer. Macrophages are essential immune cells that contribute to this balance constrained by the local signaling profile of the tumor microenvironment. To understand how pro- and anti-inflammatory unbalance emerges in cancer, we developed a theoretical analysis of macrophage differentiation that is derived from activated monocytes circulating in the blood. Once recruited to the site of inflammation, monocytes can be polarized based on the specific interleukins and chemokines in the microenvironment. To quantify this process, we used a previous regulatory network reconstructed by our group and transformed Boolean Network attractors of macrophage polarization to an ODE scheme, it enables us to quantify the activation of their genes in a continuous fashion. The transformation was developed using the interaction rules with a fuzzy logic approach. By implementing this approach, we analyzed different aspects that cannot be visualized in the Boolean setting. For example, this approach allows us to explore the dynamic behavior at different concentrations of cytokines and transcription factors in the microenvironment. One important aspect to assess is the evaluation of the transitions between phenotypes, some of them characterized by an abrupt or a gradual transition depending on specific concentrations of exogenous cytokines in the tumor microenvironment. For instance, IL-10 can induce a hybrid state that transits between an M2c and an M2b macrophage. Interferon- γ can induce a hybrid between M1 and M1a macrophage. We further demonstrated the plasticity of macrophages based on a combination of cytokines and the existence of hybrid phenotypes or partial polarization. This mathematical model allows us to unravel the patterns of macrophage differentiation based on the competition of expression of transcriptional factors. Finally, we survey how macrophages may respond to a continuously changing immunological response in a tumor microenvironment.