A multiscale signalling network map of innate immune response in cancer reveals cell heterogeneity signatures

Development and validation of machine learning models for diagnosis and prognosis of lung adenocarcinoma, and immune infiltration analysis

The aim of our study was to develop robust diagnostic and prognostic models for lung adenocarcinoma (LUAD) using machine learning (ML) techniques, focusing on early immune infiltration. Feature selection was performed on The Cancer Genome Atlas (TCGA) data using least absolute shrinkage and selection Operator (LASSO), random forest (RF), and support vector machine (SVM) algorithms. Six ML algorithms were employed to construct the diagnostic models, which were evaluated through receiver operating characteristic (ROC) curves, precision-recall curves (PRC), and classification error (CE), and validated on the GSE7670 dataset. Additionally, a lasso cox prognostic model was built on the TCGA-LUAD dataset and externally validated using independent Gene Expression Omnibus datasets (GSE30219, GSE31210, GSE50081, and GSE37745). Single-sample gene set enrichment analysis (ssGSEA) was performed to assess immune cell infiltration in stage I LUAD samples, revealing significant differences in immune cell types. These findings demonstrate a positive correlation between immune infiltration in stage I LUAD and Th2 cells, Tcm cells, and T helper cells, while a negative correlation was observed with Macrophages, Eosinophils, and Tem cells. These insights provide novel perspectives for clinical diagnosis and treatment of LUAD.

Molecular Foundations of Inflammatory Diseases: Insights into Inflammation and Inflammasomes

Inflammatory diseases are a global health problem affecting millions of people with a wide range of conditions. These diseases, including inflammatory bowel disease (IBD), rheumatoid arthritis (RA), osteoarthritis (OA), gout, and diabetes, impose a significant burden on patients and healthcare systems. A complicated interaction between genetic variables, environmental stimuli, and dysregulated immune responses shows the complex biological foundation of various diseases. This review focuses on the molecular mechanisms underlying inflammatory diseases, including the function of inflammasomes and inflammation. We investigate the impact of environmental and genetic factors on the progression of inflammatory diseases, explore the connection between inflammation and inflammasome activation, and examine the incidence of various inflammatory diseases in relation to inflammasomes.

The Expression Characteristics and Function of the RECQ Family in Pan-Cancer

BACKGROUND

The genes of the RECQ DNA helicase family play a part in preserving the stability of the genome and controlling different disease mechanisms. However, the expression features of RECQs in relation to pan-cancer, their correlation with the immune microenvironment of tumors, and the landscape of prognostic power are still undisclosed.

METHODS

Various sequence and clinical data extracted from 33 cancers were utilized to generate a comprehensive overview of RECQs in the landscape. Afterward, we discovered variations in gene expression, potential enrichment of functions, genetic alterations, and analysis related to the immune response in tumors. Additionally, we explored the clinical characteristics and diagnostic significance of RECQs. And the important association of RECQL4 with liver hepatocellular carcinoma (LIHC) was investigated.

RESULTS

RECQs exhibited extensive mutations in different types of cancers. The expression of RECQ may be influenced by an oncogenic mutation in certain types of cancer, resulting in the observed genomic and epigenetic changes in diverse tumor formations. Furthermore, RECQs originating from tumors exhibited a significant association with the immune microenvironment of the tumor, indicating their potential as promising targets for therapy. Patient prognosis was significantly associated with the majority of genes in the RECQ family. In LIHC, RECQL4 eventually emerged as a separate prognostic determinant.

CONCLUSIONS

To summarize, RECQs are essential for the regulation of the immune system in tumors, and RECQL4 serves as a prognostic indicator in LIHC. The results of our study offer fresh perspectives on RECQs from a bioinformatics perspective and emphasize the importance of RECQs in the diagnosis and treatment of cancer.

Tumor-associated myeloid cells in cancer immunotherapy

Tumor-associated myeloid cells (TAMCs) are among the most important immune cell populations in the tumor microenvironment, and play a significant role on the efficacy of immune checkpoint blockade. Understanding the origin of TAMCs was found to be the essential to determining their functional heterogeneity and, developing cancer immunotherapy strategies. While myeloid-biased differentiation in the bone marrow has been traditionally considered as the primary source of TAMCs, the abnormal differentiation of splenic hematopoietic stem and progenitor cells, erythroid progenitor cells, and B precursor cells in the spleen, as well as embryo-derived TAMCs, have been depicted as important origins of TAMCs. This review article provides an overview of the literature with a focus on the recent research progress evaluating the heterogeneity of TAMCs origins. Moreover, this review summarizes the major therapeutic strategies targeting TAMCs with heterogeneous sources, shedding light on their implications for cancer antitumor immunotherapies.

Innate Immunity in Cardiovascular Diseases-Identification of Novel Molecular Players and Targets

During the past few years, unexpected developments have driven studies in the field of clinical immunology. One driver of immense impact was the outbreak of a pandemic caused by the novel virus SARS-CoV-2. Excellent recent reviews address diverse aspects of immunological re-search into cardiovascular diseases. Here, we specifically focus on selected studies taking advantage of advanced state-of-the-art molecular genetic methods ranging from genome-wide epi/transcriptome mapping and variant scanning to optogenetics and chemogenetics. First, we discuss the emerging clinical relevance of advanced diagnostics for cardiovascular diseases, including those associated with COVID-19-with a focus on the role of inflammation in cardiomyopathies and arrhythmias. Second, we consider newly identified immunological interactions at organ and system levels which affect cardiovascular pathogenesis. Thus, studies into immune influences arising from the intestinal system are moving towards therapeutic exploitation. Further, powerful new research tools have enabled novel insight into brain-immune system interactions at unprecedented resolution. This latter line of investigation emphasizes the strength of influence of emotional stress-acting through defined brain regions-upon viral and cardiovascular disorders. Several challenges need to be overcome before the full impact of these far-reaching new findings will hit the clinical arena.

Development and experimental verification of a prognosis model for cuproptosis-related subtypes in HCC

BACKGROUND

Cuproptosis is a recently discovered mechanism of programmed cell death caused by intracellular aggregation of mitochondrial lipoylated proteins and destabilization of iron-sulfur proteins triggered by copper. Hepatocellular carcinoma (HCC) is a common malignant tumor with a poor prognosis. We aimed to predict the survival of patients with HCC using the cuproptosis-related gene (CRG) expression.

METHODS

We analyzed the expression, methylation, and mutation status of CRGs in 538 HCC patients and correlated the date with clinical prognosis. HCC patients were divided into two clusters based on their CRG expression. The relationship between CRGs, risk genes, and the immune microenvironment was analyzed using the CIBERSORT algorithm and the single-cell data analysis method. A cuproptosis risk model was constructed according to the five risk genes using the LASSO COX method. To facilitate the clinical applicability of the proposed risk model, we constructed a nomogram and conducted an antineoplastic drug sensitivity analysis.

RESULTS

Our results suggest that the expression levels of CRGs in HCC are regulated by methylation. The prognoses were significantly different between the patients of the two clusters. The prognostic risk score positively correlated with memory T cell activation and negatively correlated with natural killer (NK) and regulatory T cell activation.

CONCLUSION

Our findings indicate the involvement of CRG regulation in HCC and provide new insights into prognosis assessment. Drug sensitivity analysis predicted drug candidates for the treatment of patients with different HCC subtypes.

Graph representation learning in biomedicine and healthcare

Networks-or graphs-are universal descriptors of systems of interacting elements. In biomedicine and healthcare, they can represent, for example, molecular interactions, signalling pathways, disease co-morbidities or healthcare systems. In this Perspective, we posit that representation learning can realize principles of network medicine, discuss successes and current limitations of the use of representation learning on graphs in biomedicine and healthcare, and outline algorithmic strategies that leverage the topology of graphs to embed them into compact vectorial spaces. We argue that graph representation learning will keep pushing forward machine learning for biomedicine and healthcare applications, including the identification of genetic variants underlying complex traits, the disentanglement of single-cell behaviours and their effects on health, the assistance of patients in diagnosis and treatment, and the development of safe and effective medicines.

Gemcitabine-mediated depletion of immunosuppressive dendritic cells enhances the efficacy of therapeutic vaccination

Vaccination using optimized strategies may increase response rates to immune checkpoint inhibitors (ICI) in some tumors. To enhance vaccine potency and improve thus responses to ICI, we analyzed the gene expression profile of an immunosuppressive dendritic cell (DC) population induced during vaccination, with the goal of identifying druggable inhibitory mechanisms. RNAseq studies revealed targetable genes, but their inhibition did not result in improved vaccines. However, we proved that immunosuppressive DC had a monocytic origin. Thus, monocyte depletion by gemcitabine administration reduced the generation of these DC and increased vaccine-induced immunity, which rejected about 20% of LLC-OVA and B16-OVA tumors, which are non-responders to anti-PD-1. This improved efficacy was associated with higher tumor T-cell infiltration and overexpression of PD-1/PD-L1. Therefore, the combination of vaccine + gemcitabine with anti-PD-1 was superior to anti-PD-1 monotherapy in both models. B16-OVA tumors benefited from a synergistic effect, reaching 75% of tumor rejection, but higher levels of exhausted T-cells in LLC-OVA tumors co-expressing PD-1, LAG3 and TIM3 precluded similar levels of efficacy. Our results indicate that gemcitabine is a suitable combination therapy with vaccines aimed at enhancing PD-1 therapies by targeting vaccine-induced immunosuppressive DC.

Epithelial-mesenchymal transition in cancer stemness and heterogeneity: updated

Epithelial-mesenchymal transition (EMT) as a trans-differentiation program and a key process in tumor progression is linked positively with increased expansion of cancer stem cells and cells with stem-like properties. This is mediated through modulation of critical tumorigenic events and is positively correlated with hypoxic conditions in tumor microenvironment. The presence of cells eliciting diverse phenotypical states inside tumor is representative of heterogeneity and higher tumor resistance to therapy. In this review, we aimed to discuss about the current understanding toward EMT, stemness, and heterogeneity in tumors of solid organs, their contribution to the key tumorigenic events along with major signaling pathway involved, and, finally, to suggest some strategies to target these critical events.

Expression characteristic, immune signature, and prognosis value of EFNA family identified by multi-omics integrative analysis in pan-cancer

Background

EphrinA (EFNA) are Eph receptor ligands that regulate various disease processes. Nonetheless, the expression characteristics of EFNAs in pan-cancer, their relationship with tumor immune microenvironment, and prognostic value landscape remain unknown.

Methods

A comprehensive landscape of EFNAs was created using various statistical data extracted from 33 cancers. Subsequently, we identified differential expression, genetic variations, potential function enrichment, tumor immune-related analysis, and drug sensitivity. Further, we investigated the clinical features and diagnostic prognostic value of EFNAs. RT-qPCR, western blot and immunohistochemistry (IHC) were used to validate the expression level and significant clinical value of EFNA5 in lung adenocarcinoma cell lines and tissues.

Results

EFNAs were highly mutated in various cancers. Genomic and epigenetic alterations of EFNAs were observed in various tumors, where an oncogenic mutation in specific cancer types potentially affected EFNA expression. Moreover, tumor-derived EFNAs were significantly related to the tumor immune microenvironment, suggesting that they are promising therapeutic targets. The majority of EFNA family genes were significantly linked to patient prognosis. Eventually, EFNA5 was an independent prognostic factor in lung adenocarcinoma.

Conclusion

In summary, EFNAs are crucial in tumor immune regulation, and EFNA5 is a prognostic marker in lung adenocarcinoma. Our findings provide new insights into EFNAs from a bioinformatics standpoint and highlight the significance of EFNAs in cancer diagnosis and treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09951-0.

Analysis of the OX40/OX40L immunoregulatory axis combined with alternative immune checkpoint molecules in pancreatic ductal adenocarcinoma

Immune checkpoint modulation has been a vital therapeutic option in many malignancies, and targeting of novel immune checkpoints, including OX40/OX40L costimulatory signaling, is being assessed in clinical trials. However, little is known about the role of OX40 and OX40L in pancreatic ductal adenocarcinoma (PDAC). Thus, we investigated the clinical significance of OX-40 and OX40L and their associations with alternative immune checkpoints, immune infiltrates, clinicopathological features, and clinical outcomes. We performed multiplexed immunofluorescence staining for OX40, OX40L, CD8, and CD68 using tissue microarrays from 255 patients. Immunohistochemistry data for PD-L1, B7-H3, B7-H4, CD3, and Foxp3 were analyzed. And the RNA sequencing data of OX40/OX40L in The Cancer Genome Atlas and International Cancer Genome Consortium databases were also evaluated. The positive rates for OX40 on tumor cells (TCs) and immune cells (ICs) were 8.6% and 10.2%, respectively, and the positive rates for OX40L on TCs, ICs, and macrophages were 20%, 40.4%, and 12.9%, respectively. OX40 was associated with favorable clinicopathological features. OX40+ on ICs, OX40L+ on TCs, or OX40L+ on macrophages, rather than the total gene and protein levels of OX40/OX40L, were associated with improved survival. OX40+ on ICs and OX40L+ on macrophages were independent factors of clinical outcomes. Moreover, we could more accurately stratify patients through the combination of OX40 on ICs and OX40L on TCs, and patients with OX40+ ICs and OX40L+CK+ showed the best outcome. And we demonstrated that patients with OX40-ICs and low CD8+ T cells infiltration had unfavorable survival. Intriguingly, OX40+ ICs or OX40L+ macrophages demonstrated superior survival in patients with PD-L1 negativity than in those with PD-L1 positivity. Furthermore, OX40+ ICs were correlated with negative B7-H4 on TCs, high densities of CD3 T cells, and high densities of Foxp3 T cells; OX40+ TCs and OX40L+ TCs were associated with low densities of Foxp3 T cells. We identified OX40 and OX40L as promising predictors for prognosis in PDAC.

The role of glycolysis and lactate in the induction of tumor-associated macrophages immunosuppressive phenotype

Growing evidence highlights that glycolysis and tumor-derived lactate could skew tumor-associated macrophages (TAMs) toward an immunosuppressive phenotype. However, the updated research has not been systematically summarized yet. TAMs are educated by the tumor microenvironment (TME) and exert immunosuppressive functions and tumorigenic effects via multiple biological processes. It is well known that lactate generated by aerobic glycolysis is significantly accumulated in TME and promotes tumor progression in solid tumors. Moreover, some recent research demonstrated that glycolysis is activated in TAMs to support M2-like polarization, which is absolutely in contrast with the metabolic profile of M2 macrophages in inflammation. Notably, lactate produced by high levels of glycolysis is not only a metabolic by-product but also an oncometabolite. TAMs could access the biological information delivered by lactate and further enhance protumor functions such as immunosuppression and angiogenesis. Here, we outline the connection between glycolysis and TAM phenotype to elucidate the metabolic characteristics of TAMs. Further, insights into the specific molecular mechanisms of lactate-induced TAM polarization and potential therapeutic targets are summarized. We sought to discuss the reciprocal interaction between tumor cells and TAMs mediated by lactate, which will lay a foundation for the research aiming to elucidate the complex functions of TAMs.

BIODICA: a computational environment for Independent Component Analysis of omics data

SUMMARY

We developed BIODICA, an integrated computational environment for application of independent component analysis (ICA) to bulk and single-cell molecular profiles, interpretation of the results in terms of biological functions and correlation with metadata. The computational core is the novel Python package stabilized-ica which provides interface to several ICA algorithms, a stabilization procedure, meta-analysis and component interpretation tools. BIODICA is equipped with a user-friendly graphical user interface, allowing non-experienced users to perform the ICA-based omics data analysis. The results are provided in interactive ways, thus facilitating communication with biology experts.

AVAILABILITY AND IMPLEMENTATION

BIODICA is implemented in Java, Python and JavaScript. The source code is freely available on GitHub under the MIT and the GNU LGPL licenses. BIODICA is supported on all major operating systems. URL: https://sysbio-curie.github.io/biodica-environment/.

Analysis of a novel immune checkpoint, Siglec-15, in pancreatic ductal adenocarcinoma

Siglec‐15, a novel immune checkpoint, is an emerging target for next‐generation cancer immunotherapy. However, the role of Siglec‐15 in pancreatic ductal adenocarcinoma (PDAC) remains poorly understood. We investigated the expression of Siglec‐15 and its association with clinicopathological characteristics, programmed cell death‐ligand 1 (PD‐L1), immune cells, and DNA damage repair (DDR) molecules in a cohort of 291 patients with PDAC. Positive tumour cell expression of Siglec‐15 and PD‐L1 was observed in 18.6 and 30.3% of the samples, respectively. We also detected Siglec‐15 positivity in macrophages in 3.4% of patients. Co‐expression of Siglec‐15 with PD‐L1 was observed in 6.1% of the patients. A total of 33 PD‐L1‐negative samples (18.0%) were Siglec‐15‐positive. Siglec‐15 was observed more frequently in moderate‐to‐well‐differentiated tumours. Siglec‐15 was associated with a low density of Tregs and CD45RO T cells, high BRCA1 expression, and improved survival. Both Siglec‐15 and PD‐L1 are independent factors of patient outcomes. The prognostic significance of Siglec‐15 for survival was more discriminative in lymph node‐negative, high BRCA1 expression, or low BRCA2 expression tumours than in lymph node‐positive, low BRCA1 expression, or high BRCA2 expression tumours. In conclusion, we identified Siglec‐15 as a promising predictor for prognosis combined with different DDR molecular statuses and complex tumour‐infiltrating cells in PDAC. Targeting Siglec‐15 may be a novel therapeutic option for patients who are unresponsive to anti‐PD‐1 therapy. Future studies are needed to validate the prognostic significance of Siglec‐15 and to investigate its regulatory mechanisms in this disease.

Revealing the transcriptional heterogeneity of organ-specific metastasis in human gastric cancer using single-cell RNA Sequencing

BACKGROUND

Deciphering intra- and inter-tumoural heterogeneity is essential for understanding the biology of gastric cancer (GC) and its metastasis and identifying effective therapeutic targets. However, the characteristics of different organ-tropism metastases of GC are largely unknown.

METHODS

Ten fresh human tissue samples from six patients, including primary tumour and adjacent non-tumoural samples and six metastases from different organs or tissues (liver, peritoneum, ovary, lymph node) were evaluated using single-cell RNA sequencing. Validation experiments were performed using histological assays and bulk transcriptomic datasets.

RESULTS

Malignant epithelial subclusters associated with invasion features, intraperitoneal metastasis propensity, epithelial-mesenchymal transition-induced tumour stem cell phenotypes, or dormancy-like characteristics were discovered. High expression of the first three subcluster-associated genes displayed worse overall survival than those with low expression in a GC cohort containing 407 samples. Immune and stromal cells exhibited cellular heterogeneity and created a pro-tumoural and immunosuppressive microenvironment. Furthermore, a 20-gene signature of lymph node-derived exhausted CD8+ T cells was acquired to forecast lymph node metastasis and validated in GC cohorts. Additionally, although anti-NKG2A (KLRC1) antibody have not been used to treat GC patients even in clinical trials, we uncovered not only malignant tumour cells but one endothelial subcluster, mucosal-associated invariant T cells, T cell-like B cells, plasmacytoid dendritic cells, macrophages, monocytes, and neutrophils may contribute to HLA-E-KLRC1/KLRC2 interaction with cytotoxic/exhausted CD8+ T cells and/or natural killer (NK) cells, suggesting novel clinical therapeutic opportunities in GC. Additionally, our findings suggested that PD-1 expression in CD8+ T cells might predict clinical responses to PD-1 blockade therapy in GC.

CONCLUSIONS

This study provided insights into heterogeneous microenvironment of GC primary tumours and organ-specific metastases and provide support for precise diagnosis and treatment.

Network organization of the plant immune system: from pathogen perception to robust defense induction

The plant immune system has been explored essentially through the study of qualitative resistance, a simple form of immunity, and from a reductionist point of view. The recent identification of genes conferring quantitative disease resistance revealed a large array of functions, suggesting more complex mechanisms. In addition, thanks to the advent of high-throughput analyses and system approaches, our view of the immune system has become more integrative, revealing that plant immunity should rather be seen as a distributed and highly connected molecular network including diverse functions to optimize expression of plant defenses to pathogens. Here, we review the recent progress made to understand the network complexity of regulatory pathways leading to plant immunity, from pathogen perception, through signaling pathways and finally to immune responses. We also analyze the topological organization of these networks and their emergent properties, crucial to predict novel immune functions and test them experimentally. Finally, we report how these networks might be regulated by environmental clues. Although system approaches remain extremely scarce in this area of research, a growing body of evidence indicates that the plant response to combined biotic and abiotic stresses cannot be inferred from responses to individual stresses. A view of possible research avenues in this nascent biology domain is finally proposed.

Recent Developments in the Study of the Microenvironment of Cancer and Drug Delivery

Cancer is characterized by disrupted molecular variables caused by cells that deviate from regular signal transduction. The uncontrolled segment of such cancerous cells annihilates most of the tissues that contact them. Gene therapy, immunotherapy, and nanotechnology advancements have resulted in novel strategies for anticancer drug delivery. Furthermore, diverse dispersion of nanoparticles in normal stroma cells adversely affects the healthy cells and disrupts the crosstalk of tumour stroma. It can contribute to cancer cell progression inhibition and, conversely, to acquired resistance, enabling cancer cell metastasis and proliferation. The tumour's microenvironment is critical in controlling the dispersion and physiological activities of nano-chemotherapeutics which is one of the targeted drug therapy. As it is one of the methods of treating cancer that involves the use of medications or other substances to specifically target and kill off certain subsets of malignant cells. A targeted therapy may be administered alone or in addition to more conventional methods of care like surgery, chemotherapy, or radiation treatment. The tumour microenvironment, stromatogenesis, barriers and advancement in the drug delivery system across tumour tissue are summarised in this review.

Nanofactory for metabolic and chemodynamic therapy: pro-tumor lactate trapping and anti-tumor ROS transition

Lactate plays a critical role in tumorigenesis, invasion and metastasis. Exhausting lactate in tumors holds great promise for the reversal of the immunosuppressive tumor microenvironment (TME). Herein, we report on a “lactate treatment plant” (i.e., nanofactory) that can dynamically trap pro-tumor lactate and in situ transformation into anti-tumor cytotoxic reactive oxygen species (ROS) for a synergistic chemodynamic and metabolic therapy. To this end, lactate oxidase (LOX) was nano-packaged by cationic polyethyleneimine (PEI), assisted by a necessary amount of copper ions (PLNP^Cu). As a reservoir of LOX, the tailored system can actively trap lactate through the cationic PEI component to promote lactate degradation by two-fold efficiency. More importantly, the byproducts of lactate degradation, hydrogen peroxide (H₂O₂), can be transformed into anti-tumor ROS catalyzing by copper ions, mediating an immunogenic cell death (ICD). With the remission of immunosuppressive TME, ICD process effectively initiated the positive immune response in 4T1 tumor model (88% tumor inhibition). This work provides a novel strategy that rationally integrates metabolic therapy and chemodynamic therapy (CDT) for combating tumors.

Metabolic Signature-Based Subtypes May Pave Novel Ways for Low-Grade Glioma Prognosis and Therapy

Metabolic signatures are frequently observed in cancer and are starting to be recognized as important regulators for tumor progression and therapy. Because metabolism genes are involved in tumor initiation and progression, little is known about the metabolic genomic profiles in low-grade glioma (LGG). Here, we applied bioinformatics analysis to determine the metabolic characteristics of patients with LGG from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). We also performed the ConsensusClusterPlus, the CIBERSORT algorithm, the Estimate software, the R package "GSVA," and TIDE to comprehensively describe and compare the characteristic difference between three metabolic subtypes. The R package WGCNA helped us to identify co-expression modules with associated metabolic subtypes. We found that LGG patients were classified into three subtypes based on 113 metabolic characteristics. MC1 patients had poor prognoses and MC3 patients obtained longer survival times. The different metabolic subtypes had different metabolic and immune characteristics, and may have different response patterns to immunotherapy. Based on the metabolic subtype, different patterns were exhibited that reflected the characteristics of each subtype. We also identified eight potential genetic markers associated with the characteristic index of metabolic subtypes. In conclusion, a comprehensive understanding of metabolism associated characteristics and classifications may improve clinical outcomes for LGG.

High-Fructose Diet Alters Intestinal Microbial Profile and Correlates with Early Tumorigenesis in a Mouse Model of Barrett’s Esophagus

Esophageal adenocarcinoma (EAC) is mostly prevalent in industrialized countries and has been associated with obesity, commonly linked with a diet rich in fat and refined sugars containing high fructose concentrations. In meta-organisms, dietary components are digested and metabolized by the host and its gut microbiota. Fructose has been shown to induce proliferation and cell growth in pancreas and colon cancer cell lines and also alter the gut microbiota. In a previous study with the L2-IL-1B mouse model, we showed that a high-fat diet (HFD) accelerated EAC progression from its precursor lesion Barrett’s esophagus (BE) through changes in the gut microbiota. Aiming to investigate whether a high-fructose diet (HFrD) also alters the gut microbiota and favors EAC carcinogenesis, we assessed the effects of HFrD on the phenotype and intestinal microbial communities of L2-IL1B mice. Results showed a moderate acceleration in histologic disease progression, a mild effect on the systemic inflammatory response, metabolic changes in the host, and a shift in the composition, metabolism, and functionality of intestinal microbial communities. We conclude that HFrD alters the overall balance of the gut microbiota and induces an acceleration in EAC progression in a less pronounced manner than HFD.

ASF1B Promotes Oncogenesis in Lung Adenocarcinoma and Other Cancer Types

Anti-silencing function 1B histone chaperone (ASF1B) is known to be an important modulator of oncogenic processes, yet its role in lung adenocarcinoma (LUAD) remains to be defined. In this study, an integrated assessment of The Cancer Genome Atlas (TCGA) and genotype-tissue expression (GTEx) datasets revealed the overexpression of ASF1B in all analyzed cancer types other than LAML. Genetic, epigenetic, microsatellite instability (MSI), and tumor mutational burden (TMB) analysis showed that ASF1B was regulated by single or multiple factors. Kaplan-Meier survival curves suggested that elevated ASF1B expression was associated with better or worse survival in a cancer type-dependent manner. The CIBERSORT algorithm was used to evaluate immune microenvironment composition, and distinct correlations between ASF1B expression and immune cell infiltration were evident when comparing tumor and normal tissue samples. Gene set enrichment analysis (GSEA) indicated that ASF1B was associated with proliferation- and immunity-related pathways. Knocking down ASF1B impaired the proliferation, affected cell cycle distribution, and induced cell apoptosis in LUAD cell lines. In contrast, ASF1B overexpression had no impact on the malignant characteristics of LUAD cells. At the mechanistic level, ASF1B served as an indirect regulator of DNA Polymerase Epsilon 3, Accessory Subunit (POLE3), CDC28 protein kinase regulatory subunit 1(CKS1B), Dihydrofolate reductase (DHFR), as established through proteomic profiling and Immunoprecipitation-Mass Spectrometry (IP-MS) analyses. Overall, these data suggested that ASF1B serves as a tumor promoter and potential target for cancer therapy and provided us with clues to better understand the importance of ASF1B in many types of cancer.

Multiscale modeling in disease

Multiscale computational modeling aims to connect the complex networks of effects at different length and/or time scales. For example, these networks often include intracellular molecular signaling, crosstalk, and other interactions between neighboring cell populations, and higher levels of emergent phenomena across different regions of tissues and among collections of tissues or organs interacting with each other in the whole body. Recent applications of multiscale modeling across intracellular, cellular, and/or tissue levels are highlighted here. These models incorporated the roles of biochemical and biomechanical modulation in processes that are implicated in the mechanisms of several diseases including fibrosis, joint and bone diseases, respiratory infectious diseases, and cancers.

Mechanisms Underlying the Role of Myeloid-Derived Suppressor Cells in Clinical Diseases: Good or Bad

Myeloid-derived suppressor cells (MDSCs) have strong immunosuppressive activity and are morphologically similar to conventional monocytes and granulocytes. The development and classification of these cells have, however, been controversial. The activation network of MDSCs is relatively complex, and their mechanism of action is poorly understood, creating an avenue for further research. In recent years, MDSCs have been found to play an important role in immune regulation and in effectively inhibiting the activity of effector lymphocytes. Under certain conditions, particularly in the case of tissue damage or inflammation, MDSCs play a leading role in the immune response of the central nervous system. In cancer, however, this can lead to tumor immune evasion and the development of related diseases. Under cancerous conditions, tumors often alter bone marrow formation, thus affecting progenitor cell differentiation, and ultimately, MDSC accumulation. MDSCs are important contributors to tumor progression and play a key role in promoting tumor growth and metastasis, and even reduce the efficacy of immunotherapy. Currently, a number of studies have demonstrated that MDSCs play a key regulatory role in many clinical diseases. In light of these studies, this review discusses the origin of MDSCs, the mechanisms underlying their activation, their role in a variety of clinical diseases, and their function in immune response regulation.

Integrated Analysis Reveals Prognostic Value and Immune Correlates of CD86 Expression in Lower Grade Glioma

Background

CD86 has great potential to be a new target of immunotherapy by regulating cancer immune response. However, it remains unclear whether CD86 is a friend or foe in lower-grade glioma (LGG).

Methods

The prognostic value of CD86 expression in pan-cancer was analyzed using Cox regression and Kaplan-Meier analysis with data from the cancer genome atlas (TCGA). Cancer types where CD86 showed prognostic value in overall survival and disease-specific survival were identified for further analyses. The Chinese Glioma Genome Atlas (CGGA) dataset were utilized for external validation. Quantitative real-time PCR (qRT-PCR), Western blot (WB), and Immunohistochemistry (IHC) were conducted for further validation using surgical samples from Jiangsu Province hospital. The correlations between CD86 expression and tumor immunity were analyzed using the Estimation of Stromal and Immune cells in Malignant Tumours using Expression data (ESTIMATE) algorithm, Tumor IMmune Estimation Resource (TIMER) database, and expressions of immune checkpoint molecules. Gene Set Enrichment Analysis (GSEA) was performed using clusterprofiler r package to reveal potential pathways.

Results

Pan-cancer survival analysis established CD86 expression as an unfavorable prognostic factor in tumor progression and survival for LGG. CD86 expression between Grade-II and Grade-III LGG was validated using qRT-PCR and WB. Additionally, CD86 expression in LGG with unmethylated O(6)-methylguanine-DNA-methyltransferase (MGMT) promoter was significantly higher than those with methylated MGMT (P<0.05), while in LGG with codeletion of 1p/19q it was significantly downregulated as opposed to those with non-codeletion (P<2.2*10-16). IHC staining validated that CD86 expression was correlated with MGMT status and X1p/19q subtypes, which was independent of tumor grade. Multivariate regression validated that CD86 expression acts as an unfavorable prognostic factor independent of clinicopathological factors in overall survival of LGG patients. Analysis of tumor immunity and GSEA revealed pivotal role of CD86 in immune response for LGG.

Conclusions

Integrated analysis shows that CD86 is an unfavorable prognostic biomarker in LGG patients. Targeting CD86 may become a novel approach for immunotherapy of LGG.

The Sarcoma Immune Landscape: Emerging Challenges, Prognostic Significance and Prospective Impact for Immunotherapy Approaches

Simple Summary

Sarcomas are a rare disease with high rates of recurrence and poor prognosis. Important discoveries about the biology of sarcomas have been done during the last decades, without a substantial improvement of systemic treatments. With the agnostic effectivity of immuno-oncological agents in different cancer indications, it is expected that sarcomas can also benefit from these treatments. This article gathers the available data on the specific immune tumor microenvironment of sarcoma and the immunotherapeutic strategies currently under investigation.

Abstract

Despite significant advances in multidisciplinary treatment strategies including surgery, radiotherapy, targeted therapy and chemotherapy there are yet no substantial improvements in the clinical benefit of patients with sarcomas. Current understanding of the underlying cellular and molecular pathways which govern the dynamic interactions between the tumor stroma, tumor cells and immune infiltrates in sarcoma tissues, led to the clinical development of new therapeutic options based on immunotherapies. Moreover, progress of the treatment of sarcomas also depends on the identification of biomarkers with prognostic and predictive values for selecting patients most likely to benefit from these new therapeutic treatments and also serving as potent therapeutic targets. Novel combinations with radiotherapy, chemotherapy, targeted therapy, vaccines, CAR-T cells and treatments targeting other immune components of the tumor microenvironment are underway aiming to bypass known resistance mechanisms. This review focuses on the role of tumor microenvironment in sarcoma, prognosis and response to novel immunotherapies.

Immune-stimulating antibody conjugates elicit robust myeloid activation and durable antitumor immunity

Innate pattern recognition receptor agonists, including Toll-like receptors (TLRs), alter the tumor microenvironment and prime adaptive antitumor immunity. However, TLR agonists present toxicities associated with widespread immune activation after systemic administration. To design a TLR-based therapeutic suitable for systemic delivery and capable of safely eliciting tumor-targeted responses, we developed immune-stimulating antibody conjugates (ISACs) comprising a TLR7/8 dual agonist conjugated to tumor-targeting antibodies. Systemically administered human epidermal growth factor receptor 2 (HER2)-targeted ISACs were well tolerated and triggered a localized immune response in the tumor microenvironment that resulted in tumor clearance and immunological memory. Mechanistically, ISACs required tumor antigen recognition, Fcγ-receptor-dependent phagocytosis and TLR-mediated activation to drive tumor killing by myeloid cells and subsequent T-cell-mediated antitumor immunity. ISAC-mediated immunological memory was not limited to the HER2 ISAC target antigen since ISAC-treated mice were protected from rechallenge with the HER2- parental tumor. These results provide a strong rationale for the clinical development of ISACs.

An overview of the tumor microenvironment, from cells to complex networks (Review)

For a long period, cancer has been believed to be a gene disease, in which oncogenic and suppressor mutations accumulate gradually, finally leading to the malignant transformation of cells. This vision has changed in the last few years, the involvement of the tumor microenvironment, the non-malignant part of the tumors, as an important contributor to the malignant growth being now largely recognized. There is a consensus according to which the understanding of the tumor microenvironment is important as a means to develop new approaches in the therapy of cancer. In this context, the present study is a review of the different types of non-malignant cells that can be found in tumors, with their pro or antitumoral actions, presence in tumors and therapeutic targeting. These cells establish complex relations between them, through cytokines, exosomes, cell adhesion, co-stimulation and co-inhibition; these relations will also be examined in the present work.

Insights on TAM Formation from a Boolean Model of Macrophage Polarization Based on In Vitro Studies

Simple Summary

The recent success of immunotherapy treatments against cancer relies on helping our own body’s defenses in the fight against tumours, namely reinvigorating the cancer killing action of T cells. Unfortunately, in a large proportion of patients these therapies are ineffective, in part due to the presence of other immune cells, macrophages, which are mis-educated by the cancer cells into promoting tumour growth. Here we start from an existing model of macrophage polarization and extend it to the specific conditions encountered inside a tumour by adding signals, receptors, transcription factors and cytokines that are known to be the key components in establishing the cancer cell-macrophage interaction. Then we use a mathematical Boolean model applied to a gene regulatory network of this biological process to simulate its temporal behaviour and explore scenarios that have not been experimentally tested so far. Additionally, the KO and overexpression simulations successfully reproduce the known experimental results while predicting the potential role of regulators (such as STAT1 and EGF) in preventing the formation of pro-tumoural macrophages, which can be tested experimentally.

Abstract

The tumour microenvironment is the surrounding of a tumour, including blood vessels, fibroblasts, signaling molecules, the extracellular matrix and immune cells, especially neutrophils and monocyte-derived macrophages. In a tumour setting, macrophages encompass a spectrum between a tumour-suppressive (M1) or tumour-promoting (M2) state. The biology of macrophages found in tumours (Tumour Associated Macrophages) remains unclear, but understanding their impact on tumour progression is highly important. In this paper, we perform a comprehensive analysis of a macrophage polarization network, following two lines of enquiry: (i) we reconstruct the macrophage polarization network based on literature, extending it to include important stimuli in a tumour setting, and (ii) we build a dynamical model able to reproduce macrophage polarization in the presence of different stimuli, including the contact with cancer cells. Our simulations recapitulate the documented macrophage phenotypes and their dependencies on specific receptors and transcription factors, while also unravelling the formation of a special type of tumour associated macrophages in an in vitro model of chronic lymphocytic leukaemia. This model constitutes the first step towards elucidating the cross-talk between immune and cancer cells inside tumours, with the ultimate goal of identifying new therapeutic targets that could control the formation of tumour associated macrophages in patients.

Modelling of Immune Checkpoint Network Explains Synergistic Effects of Combined Immune Checkpoint Inhibitor Therapy and the Impact of Cytokines in Patient Response

Simple Summary

The future of cancer immunotherapy relies on a combination of individually targeted therapies. However, a lot of experiments are needed to define the most effective combinations of drugs. A computational and modelling approach could help reduce the number of experiments and suggest optimal treatments to test. This article presents a logical model of T cell activation influenced by immune checkpoints, and explores the effect of these checkpoints, suggests mechanisms that would explain why some treatments might be better suited than others. The model includes not only programmed cell death protein 1 (PD1) and cytotoxic T-lymphocyte-associated protein 4 (CTL4) downstream pathways but also those of other immune checkpoints such as T cell immunoglobulin and ITIM (immunoreceptor tyrosine-based inhibition motif) domain (TIGIT), lymphocyte activation gene 3 (LAG3), T cell immunoglobulin and mucin domain-containing protein 3 (TIM3), cluster of differentiation 226 (CD226), inducible T-cell costimulator (ICOS), and tumour necrosis factor receptors (TNFRs).

Abstract

After the success of the new generation of immune therapies, immune checkpoint receptors have become one important center of attention of molecular oncologists. The initial success and hopes of anti-programmed cell death protein 1 (anti-PD1) and anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA4) therapies have shown some limitations since a majority of patients have continued to show resistance. Other immune checkpoints have raised some interest and are under investigation, such as T cell immunoglobulin and ITIM (immunoreceptor tyrosine-based inhibition motif) domain (TIGIT), inducible T-cell costimulator (ICOS), and T cell immunoglobulin and mucin domain-containing protein 3 (TIM3), which appear as promising targets for immunotherapy. To explore their role and study possible synergetic effects of these different checkpoints, we have built a model of T cell receptor (TCR) regulation including not only PD1 and CTLA4, but also other well studied checkpoints (TIGIT, TIM3, lymphocyte activation gene 3 (LAG3), cluster of differentiation 226 (CD226), ICOS, and tumour necrosis factor receptors (TNFRs)) and simulated different aspects of T cell biology. Our model shows good correspondence with observations from available experimental studies of anti-PD1 and anti-CTLA4 therapies and suggest efficient combinations of immune checkpoint inhibitors (ICI). Among the possible candidates, TIGIT appears to be the most promising drug target in our model. The model predicts that signal transducer and activator of transcription 1 (STAT1)/STAT4-dependent pathways, activated by cytokines such as interleukin 12 (IL12) and interferon gamma (IFNG), could improve the effect of ICI therapy via upregulation of Tbet, suggesting that the effect of the cytokines related to STAT3/STAT1 activity is dependent on the balance between STAT1 and STAT3 downstream signalling.

RNA-Seq Analysis of Colorectal Tumor-Infiltrating Myeloid-Derived Suppressor Cell Subsets Revealed Gene Signatures of Poor Prognosis

Elevated levels of myeloid-derived suppressor cells (MDSCs), including polymorphonuclear MDSCs (PMN-MDSCs) and immature MDSCs (I-MDSCs), are usually associated with disease progression in cancer patients, including colorectal cancer (CRC). However, biological mechanisms and molecular pathways regulated by MDSC subpopulations in the CRC tumor microenvironment (TME) have not been fully investigated. In this study, we performed transcriptomic analysis of tumor-infiltrating I-MDSCs and PMN-MDSCs isolated from tumor tissues of six CRC patients, compared to antigen-presenting cells (APCs). We also compared the transcriptomic profiles of tumor-infiltrating PMN-MDSCs to I-MDSCs. Our results showed different molecular pathways regulated by each MDSC subset, potentially reflecting their phenotypical/molecular/functional characteristics in the CRC TME. Moreover, we identified gene signatures in PMN-MDSC and I-MDSC of poor overall survival (OS) and disease-free survival (DFS) using the Cancer Genome Atlas (TCGA) dataset from patients with colon adenocarcinoma (COAD). However, functional studies are required to validate these findings.

Cancer-associated hypersialylated MUC1 drives the differentiation of human monocytes into macrophages with a pathogenic phenotype

The tumour microenvironment plays a crucial role in the growth and progression of cancer, and the presence of tumour-associated macrophages (TAMs) is associated with poor prognosis. Recent studies have demonstrated that TAMs display transcriptomic, phenotypic, functional and geographical diversity. Here we show that a sialylated tumour-associated glycoform of the mucin MUC1, MUC1-ST, through the engagement of Siglec-9 can specifically and independently induce the differentiation of monocytes into TAMs with a unique phenotype that to the best of our knowledge has not previously been described. These TAMs can recruit and prolong the lifespan of neutrophils, inhibit the function of T cells, degrade basement membrane allowing for invasion, are inefficient at phagocytosis, and can induce plasma clotting. This macrophage phenotype is enriched in the stroma at the edge of breast cancer nests and their presence is associated with poor prognosis in breast cancer patients.

Beatson et al. show that a sialylated tumour-associated glycoform of the mucin MUC1 induces the differentiation of monocytes into tumour-associated macrophages. These macrophages are found in breast cancer stroma and their presence is associated with poor prognosis.

Expression signature, prognosis value, and immune characteristics of Siglec-15 identified by pan-cancer analysis

Sialic acid-binding immunoglobulin-like lectin 15 (Siglec-15) is considered a novel anti-tumor target comparable to programmed cell death 1 ligand 1(PD-L1). However, little is known about Siglec-15. Our study aims to understand its expression signature, prognosis value, immune infiltration pattern, and biological function using multi-omic bioinformatics from public databases and verify them in lung cancer patients. Integrated analysis of The Cancer Genome Atlas and Genotype-Tissue Expression portals showed Siglec-15 was overexpressed across cancers. Genetic and epigenetic alteration analysis was performed using cBioportal and UALCAN, showed Siglec-15 was regulated at the genetic and epigenetic levels. Survival estimated using Kaplan-Meier plotter indicated high Siglec-15 expression correlated with favorable or unfavorable outcomes depending on the different type and subtype of cancer. Components of immune microenvironment were analyzed using CIBERSORT, and the correlation between immune cells and Siglec-15 was found to be distinct across cancer types. Based on Gene Set Enrichment Analysis, Siglec-15 was implicated in pathways involved in immunity, metabolism, cancer, and infectious diseases. Lung cancer patients with positive Siglec-15 expression showed significantly short survival rates in progression-free survival concomitant with reduced infiltration of CD20 + B, and dendritic cells by immunohistochemistry. Quantitative real-time PCR results indicated the overexpression of Siglec-15 was correlated with activation of the chemokine signaling pathway. In conclusion, Siglec-15 could serve as a vital prognostic biomarker and play an immune-regulatory role in tumors. These results provide us with clues to better understand Siglec-15 from the perspective of bioinformatics and highlight the importance of Siglec-15 in many types of cancer.

Network Approaches for Dissecting the Immune System

The immune system is a complex biological network composed of hierarchically organized genes, proteins, and cellular components that combat external pathogens and monitor the onset of internal disease. To meet and ultimately defeat these challenges, the immune system orchestrates an exquisitely complex interplay of numerous cells, often with highly specialized functions, in a tissue-specific manner. One of the major methodologies of systems immunology is to measure quantitatively the components and interaction levels in the immunologic networks to construct a computational network and predict the response of the components to perturbations. The recent advances in high-throughput sequencing techniques have provided us with a powerful approach to dissecting the complexity of the immune system. Here we summarize the latest progress in integrating omics data and network approaches to construct networks and to infer the underlying signaling and transcriptional landscape, as well as cell-cell communication, in the immune system, with a focus on hematopoiesis, adaptive immunity, and tumor immunology. Understanding the network regulation of immune cells has provided new insights into immune homeostasis and disease, with important therapeutic implications for inflammation, cancer, and other immune-mediated disorders.

Transcriptomic Profiling of Circulating HLA-DR- Myeloid Cells, Compared with HLA-DR+ Myeloid Antigen-presenting Cells

Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of cells with potent immunosuppressive functions, which can inhibit the activation of immune responses under a steady-state condition and pathological conditions. We performed transcriptomic profiling of circulating CD33⁺HLA-DR⁺ myeloid antigen-presenting cells (APCs) and CD33⁺HLA-DR^- myeloid cells (potentially MDSCs) in healthy individuals. We sorted both subpopulations from peripheral blood mononuclear cells (PBMCs) of 10 healthy donors and performed RNA sequencing (RNA-Seq). We found that several signaling pathways associated with the positive regulation of immune responses, such as antigen presentation/processing, FcγR-mediated phagocytosis and immune cell trafficking, phosphoinositide 3-kinase (PI3K)/Akt signaling, DC maturation, triggering receptor expressed on myeloid cells 1 (TREM1) signaling, nuclear factor of activated T cells (NFAT) and IL-8 signaling were downregulated in CD33⁺HLA-DR^- myeloid cells. In contrast, pathways implicated in tumor suppression and anti-inflammation, including peroxisome proliferator-activated receptor (PPAR) and phosphatase and tensin homolog (PTEN), were upregulated in CD33⁺HLA-DR^- myeloid cells. These data indicate that PPAR/PTEN axis could be upregulated in myeloid cells to keep the immune system in check in normal physiological conditions. Our data reveal some of the molecular and functional differences between CD33⁺HLA-DR⁺ APCs and CD33⁺HLA-DR^- myeloid cells in a steady-state condition, reflecting the potential suppressive function of CD33⁺HLA-DR^- myeloid cells to maintain immune tolerance. For future studies, the same methodological approach could be applied to perform transcriptomic profiling of myeloid subsets in pathological conditions.

Toward Systems Biomarkers of Response to Immune Checkpoint Blockers

Immunotherapy with checkpoint blockers (ICBs), aimed at unleashing the immune response toward tumor cells, has shown a great improvement in overall patient survival compared to standard therapy, but only in a subset of patients. While a number of recent studies have significantly improved our understanding of mechanisms playing an important role in the tumor microenvironment (TME), we still have an incomplete view of how the TME works as a whole. This hampers our ability to effectively predict the large heterogeneity of patients' response to ICBs. Systems approaches could overcome this limitation by adopting a holistic perspective to analyze the complexity of tumors. In this Mini Review, we focus on how an integrative view of the increasingly available multi-omics experimental data and computational approaches enables the definition of new systems-based predictive biomarkers. In particular, we will focus on three facets of the TME toward the definition of new systems biomarkers. First, we will review how different types of immune cells influence the efficacy of ICBs, not only in terms of their quantification, but also considering their localization and functional state. Second, we will focus on how different cells in the TME interact, analyzing how inter- and intra-cellular networks play an important role in shaping the immune response and are responsible for resistance to immunotherapy. Finally, we will describe the potential of looking at these networks as dynamic systems and how mathematical models can be used to study the rewiring of the complex interactions taking place in the TME.

Independent Component Analysis for Unraveling the Complexity of Cancer Omics Datasets

Independent component analysis (ICA) is a matrix factorization approach where the signals captured by each individual matrix factors are optimized to become as mutually independent as possible. Initially suggested for solving source blind separation problems in various fields, ICA was shown to be successful in analyzing functional magnetic resonance imaging (fMRI) and other types of biomedical data. In the last twenty years, ICA became a part of the standard machine learning toolbox, together with other matrix factorization methods such as principal component analysis (PCA) and non-negative matrix factorization (NMF). Here, we review a number of recent works where ICA was shown to be a useful tool for unraveling the complexity of cancer biology from the analysis of different types of omics data, mainly collected for tumoral samples. Such works highlight the use of ICA in dimensionality reduction, deconvolution, data pre-processing, meta-analysis, and others applied to different data types (transcriptome, methylome, proteome, single-cell data). We particularly focus on the technical aspects of ICA application in omics studies such as using different protocols, determining the optimal number of components, assessing and improving reproducibility of the ICA results, and comparison with other popular matrix factorization techniques. We discuss the emerging ICA applications to the integrative analysis of multi-level omics datasets and introduce a conceptual view on ICA as a tool for defining functional subsystems of a complex biological system and their interactions under various conditions. Our review is accompanied by a Jupyter notebook which illustrates the discussed concepts and provides a practical tool for applying ICA to the analysis of cancer omics datasets.