Profiling networks of distinct immune-cells in tumors

Identification of driving genes of familial adenomatous polyposis by differential gene expression analysis and weighted gene co-expression network analysis

BACKGROUND

Despite the advancement of new screening strategies and the advances in pharmacological therapies, the cancerization rates of familial adenomatous polyposis (FAP) are stable and even increased in the last years. Therefore, it necessitates additional research to characterize and understand the underlying mechanisms of FAP.

OBJECTIVE

To determine the genes that drive the pathogenesis of familial adenomatous polyposis (FAP).

METHODS

We performed on a cohort (GSE111156) gene profile, which consist of four group of gene expressions (the gene expressions of cancer, adenoma and normal tissue of duodenal cancer from patients with FAP were defined as Case N, Case A and Case C respectively, while that of adenoma tissue from patients with FAP who did not have duodenal cancer was Ctrl A). Tracking Tumor Immunophenotype (TIP) website was applied to reveal immune infiltration profile and signature genes of FAP. We merged the genes of key module (pink and midnight module) with signature genes to obtained the biomarkers related with FAP pathogenesis. The expression of these five biomarkers in FAP intratumoral region (IT) and tumor rim (TR) was detected with Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR).

RESULTS

In total, 220, 23 and 63 DEGs were determined in Cases C, A and N, in comparison to Ctrl A. In total, 196 and 10 DEGs were determined in Cases C and A, separately, as compared to Case N. A total of four biomarkers including CCL5, CD3G, CD2 and TLR3 were finally identified associated with pink module, while only one biomarker (KLF2) associated with midnight module was identified. All biomarkers were evidently raised in FAP IT tissues utilizing qRT-PCR.

CONCLUSION

We identified five potential biomarkers for pathogenesis of FAP to understand the fundamental mechanisms of FAP progression and revealed some probable targets for the diagnosis or treatment of FAP.

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.

A large-scale screening and functional sorting of tumour microenvironment prognostic genes for breast cancer patients

PURPOSE

The aim of this study was to systematically establish a comprehensive tumour microenvironment (TME)-relevant prognostic gene and target miRNA network for breast cancer patients.

METHODS

Based on a large-scale screening of TME-relevant prognostic genes (760 genes) for breast cancer patients, the prognostic model was established. The primary TME prognostic genes were selected from the constructing database and verified in the testing database. The internal relationships between the potential TME prognostic genes and the prognosis of breast cancer patients were explored in depth. The associated miRNAs for the TME prognostic genes were generated, and the functions of each primary TME member were investigated in the breast cancer cell line.

RESULTS

Compared with sibling controls, breast cancer patients showed 55 differentially expressed TME prognostic genes, of which 31 were considered as protective genes, while the remaining 24 genes were considered as risk genes. According to the lambda values of the LASSO Cox analysis, the 15 potential TME prognostic genes were as follows: ENPEP, CCDC102B, FEZ1, NOS2, SCG2, RPLP2, RELB, RGS3, EMP1, PDLIM4, EPHA3, PCDH9, VIM, GFI1, and IRF1. Among these, there was a remarkable linear internal relationship for CCDC102B but non-linear relationships for others with breast cancer patient prognosis. Using the siRNA technique, we silenced the expression of each TME prognostic gene. Seven of the 15 TME prognostic genes (NOS2, SCG2, RGS3, EMP1, PDLIM4, PCDH9, and GFI1) were involved in enhancing cell proliferation, destroying cell apoptosis, promoting cell invasion, or migration in breast cancer. Six of them (CCDC102B, RPLP2, RELB, EPHA3, VIM, and IRF1) were favourable for maintaining cell invasion or migration. Only two of them (ENPEP and FEZ1) were favourable for the processes of cell proliferation and apoptosis.

CONCLUSIONS

This integrated study hypothesised an innovative TME-associated genetic functional network for breast cancer patients. The external relationships between these TME prognostic genes and the disease were measured. Meanwhile, the internal molecular mechanisms were also investigated.

A Robust Prognostic Signature of Tumor Microenvironment in Colorectal Cancer

Background: Colorectal cancer (CRC) has been a major public health problem. Tumor microenvironment (TME) greatly contributes to the heterogeneity of CRC and is crucial for the regulation of CRC progression. The authors' study aimed to develop a robust prognostic signature for CRC patients based on TME-related genes. Materials and Methods: Gene expression data and clinicopathologic information of CRC patients were collected from Gene Expression Omnibus and The Cancer Genome Atlas databases. TME-related genes with prognostic value were identified by Cox regression and bootstrap method. The authors used the prognostic genes to construct a robust prognostic model using the least absolute shrinkage and selection operator (LASSO) regression method. The immune and stromal cell abundance of CRC samples were estimated by a microenvironment cell populations-counter method. Results: Based on a training set that comprised 893 CRC samples and 4775 TME-related genes, they established a prognostic model consisting of 25 TME-related genes. With specific risk score formulae, the prognostic model divided CRC patients into high-risk and low-risk subgroups with significantly different survival, which were further confirmed in validation cohorts consisting of other 473 CRC cases or subpopulation of specific stages. The result of time-dependent receiver operating characteristic analysis demonstrated strong predictive accuracy of the prognostic model both in training and validation cohorts. Multivariate Cox regression analysis showed that the 25-gene signature was an independent prognostic factor for overall survival, which was validated through clinical subgroups analysis. Further analysis revealed that CRC samples of high-risk group was abundant of stromal-relevant processes and had a significantly higher proportion of fibroblasts and endothelial cells infiltration. Conclusion: The authors established a robust prognostic signature of 25 TME-related genes which may be an effective tool for prognostic prediction and CRC patient stratification to assist in making treatment decisions.

Tumor microenvironment: barrier or opportunity towards effective cancer therapy

Tumor microenvironment (TME) is a specialized ecosystem of host components, designed by tumor cells for successful development and metastasis of tumor. With the advent of 3D culture and advanced bioinformatic methodologies, it is now possible to study TME’s individual components and their interplay at higher resolution. Deeper understanding of the immune cell’s diversity, stromal constituents, repertoire profiling, neoantigen prediction of TMEs has provided the opportunity to explore the spatial and temporal regulation of immune therapeutic interventions. The variation of TME composition among patients plays an important role in determining responders and non-responders towards cancer immunotherapy. Therefore, there could be a possibility of reprogramming of TME components to overcome the widely prevailing issue of immunotherapeutic resistance. The focus of the present review is to understand the complexity of TME and comprehending future perspective of its components as potential therapeutic targets. The later part of the review describes the sophisticated 3D models emerging as valuable means to study TME components and an extensive account of advanced bioinformatic tools to profile TME components and predict neoantigens. Overall, this review provides a comprehensive account of the current knowledge available to target TME.

Novel insights into the pathogenesis and treatment of NRAS mutant melanoma

INTRODUCTION

NRAS was the first mutated oncogene identified in melanoma and is currently the second most common driver mutation in this malignancy. For patients with NRAS^mutant advanced stage melanoma refractory to immunotherapy or with contraindications to immune-based regimens, there are few therapeutic options including low-efficacy chemotherapy regimens and binimetinib monotherapy. Here, we review recent advances in preclinical studies of molecular targets for NRAS mutant melanoma as well as the failures and successes of early-phase clinical trials. While there are no targeted therapies for NRAS-driven melanoma, there is great promise in approaches combining MEK inhibition with inhibitors of the focal adhesion kinase (FAK), inhibitors of autophagy pathways, and pan-RAF inhibitors.

AREAS COVERED

This review surveys new developments in all aspects of disease pathogenesis and potential treatment - including those that have failed, stalled, or progressed through various phases of preclinical and clinical development.

EXPERT OPINION

There are no currently approved targeted therapies for BRAF wild-type melanoma patients harboring NRAS driver mutations though an array of agents are in early phase clinical trials. The diverse strategies taken exploit combined MAP kinase signaling blockade with inhibition of cell cycle mediators, inhibition of the autophagy pathway, and alteration of kinases involved in actin cytoskeleton signaling. Future advances of developmental therapeutics into late stage trials may yield new options beyond immunotherapy for patients with advanced stage disease and NRAS mutation status.

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.

Identifying M1 Macrophage-Related Genes Through a Co-expression Network to Construct a Four-Gene Risk-Scoring Model for Predicting Thyroid Cancer Prognosis

Macrophages are key innate immune cells in the tumor microenvironment that regulate primary tumor growth, vascularization, metastatic spread and response to therapies. Macrophages can polarize into two different states (M1 and M2) with distinct phenotypes and functions. To investigate the known tumoricidal effects of M1 macrophages, we obtained RNA expression profiles and clinical data from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA). The proportions of immune cells in tumor samples were assessed using CIBERSORT, and weighted gene co-expression network analysis (WGCNA) was used to identify M1 macrophage-related modules. Univariate Cox analysis and LASSO-Cox regression analysis were performed, and four genes (SPP1, DHRS3, SLC11A1, and CFB) with significant differential expression were selected through GEPIA. These four genes can be considered hub genes. The four-gene risk-scoring model may be an independent prognostic factor for THCA patients. The validation cohort and the entire cohort confirmed the results. Univariate and multivariate Cox analysis was performed to identify independent prognostic factors for THCA. Finally, a prognostic nomogram was built based on the entire cohort, and the nomogram combining the risk score and clinical prognostic factors was superior to the nomogram with individual clinical prognostic factors in predicting overall survival. Time-dependent ROC curves and DCA confirmed that the combined nomogram is useful. Gene set enrichment analysis (GSEA) was used to elucidate the potential molecular functions of the high-risk group. Our study identified four genes associated with M1 macrophages and established a prognostic nomogram that predicts overall survival for patients with THCA, which may help determine clinical treatment options for different patients.

Molecular characterization, biological function, tumor microenvironment association and clinical significance of m6A regulators in lung adenocarcinoma

N6-methyladenosine (m6A) modification can regulate a variety of biological processes. However, the implications of m6A modification in lung adenocarcinoma (LUAD) remain largely unknown. Here, we systematically evaluated the m6A modification features in more than 2400 LUAD samples by analyzing the multi-omics features of 23 m6A regulators. We depicted the genetic variation features of m6A regulators, and found mutations of FTO and YTHDF3 were linked to worse overall survival. Many m6A regulators were aberrantly expressed in tumors, among which FTO, IGF2BP3, YTHDF1 and RBM15 showed consistent alteration features across 11 independent cohorts. Besides, the regulator-pathway interaction network demonstrated that m6A modification was associated with various biological pathways, including immune-related pathways. The correlation between m6A regulators and tumor microenvironment was also assessed. We found that LRPPRC was negatively correlated with most tumor-infiltrating immune cells. On the other hand, we established a scoring tool named m6Sig, which was positively correlated with PD-L1 expression and could reflect both the tumor microenvironment characterization and prognosis of LUAD patients. Comparison of CNV between high and low m6Sig groups revealed differences on chromosome 7. Application of m6Sig on an anti-PD-L1 immunotherapy cohort confirmed that the high m6Sig group demonstrated therapeutic advantages and clinical benefits. Our study indicated that m6A modification is involved in many aspects of LUAD and contributes to tumor microenvironment formation. A better understanding of m6A modification will provide more insights into the molecular mechanisms of LUAD and facilitate developing more effective personalized treatment strategies. A web application was built along with this study (http://www.bioinfo-zs.com/luadexpress/).

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.

High-dimensional single-cell proteomics analysis reveals the landscape of immune cells and stem-like cells in renal tumors

Background

Renal tumors are highly heterogeneous, and identification of tumor heterogeneity is an urgent clinical need for effective treatment. Mass cytometry (MC) can be used to perform high‐dimensional single‐cell proteomics analysis of heterogeneous samples via cytometry by time‐of‐flight (CyTOF), in order to achieve more accurate observation and classification of phenotypes within a cell population. This study aimed to develop a high‐dimensional MC method for the detection and analysis of heterogeneity in renal tumors.

Materials and Methods

We collected tissue samples from 8 patients with different types of renal tumors. Single‐cell suspensions were prepared and stained using a panel of 28 immune cell‐centric antibodies and a panel of 21 stem‐like cell‐centric antibodies. The stained cells were detected using CyTOF.

Result

Renal tumors were divided into 25 immune cell subsets (4 CD4+ T cells, 7 CD8+ T cells, 1 B cells, 8 macrophages, 1 dendritic cells, 2 natural killer (NK) cells, 1 granulocyte, and 1 other subset) and 7 stem‐like cells subsets (based on positivity of vimentin, CD326, CD34, CD90, CD13, CD44, and CD47). Different types of renal tumors have different cell subsets with significantly different characteristics.

Conclusion

High‐dimensional single‐cell proteomics analysis using MC aids in the discovery and analysis of renal tumors heterogeneity. Additionally, it can be used to accurately classify the immune cell population and analyze the expression of stem cell‐related markers in renal tumors. Our findings provide a valuable resource for deciphering tumor heterogeneity and might improve the clinical management of patients with renal tumors.

A novel assessing system for predicting the prognosis of gastric cancer

Aim: To develop novel diagnostic tools that can predict the prognosis of gastric cancer. Material & methods: Using RNA expression data from The Cancer Genome Atlas and Gene Expression Omnibus, we established protein-coding RNAs-noncoding RNAs-tumor microenvironment type (PNM) scores, which contain signatures of tumor protein coding genes (P), tumor noncoding genes (N) and immune/stroma cells in tumor microenvironment (M) to predict the prognosis of gastric cancer. Results & conclusion: Based on PNM scores, gastric cancer patients were divided into three subgroups and Kaplan-Meier survival curves revealed significant differences among the subgroups (p < 0.001). Our study showed that the PNM scores could be used as a robust predicting tool for the prognosis of gastric cancer.

A Gene Signature Predicting Natural Killer Cell Infiltration and Improved Survival in Melanoma Patients

Natural killer (NK) cell activity is essential for initiating antitumor responses and may be linked to immunotherapy success. NK cells and other innate immune components could be exploitable for cancer treatment, which drives the need for tools and methods that identify therapeutic avenues. Here, we extend our gene-set scoring method singscore to investigate NK cell infiltration by applying RNA-seq analysis to samples from bulk tumors. Computational methods have been developed for the deconvolution of immune cell types within solid tumors. We have taken the NK cell gene signatures from several such tools, then curated the gene list using a comparative analysis of tumors and immune cell types. Using a gene-set scoring method to investigate RNA-seq data from The Cancer Genome Atlas (TCGA), we show that patients with metastatic cutaneous melanoma have an improved survival rate if their tumor shows evidence of NK cell infiltration. Furthermore, these survival effects are enhanced in tumors that show higher expression of genes that encode NK cell stimuli such as the cytokine IL15 Using this signature, we then examine transcriptomic data to identify tumor and stromal components that may influence the penetrance of NK cells into solid tumors. Our results provide evidence that NK cells play a role in the regulation of human tumors and highlight potential survival effects associated with increased NK cell activity. Our computational analysis identifies putative gene targets that may be of therapeutic value for boosting NK cell antitumor immunity.

A robust panel based on tumour microenvironment genes for prognostic prediction and tailoring therapies in stage I-III colon cancer

Background

Tumour microenvironment (TME) is critical for the regulation of cancer development as well as therapy. The objective of the current study was the development of a robust prognostic model based on TME-relevant genes.

Methods

Five public microarray datasets providing clinical information were obtained. The least absolute shrinkage and selection operator regression method was used to reduce the dimensionality of robust prognostic genes identified via the bootstrap method.

Findings

We established a prognostic panel, designated as tumour microenvironment risk score (TMRS), consisting of 100 genes. With specific risk score formulae, the TMRS panel possesses a strong ability to predict relapse-free survival and overall survival through both univariate and multivariate analyses. Compared with the TNM stage, the TMRS panel showed much higher predictive accuracy. Further analysis revealed that patients with higher TMRS scores exhibited no therapeutic benefits from adjuvant chemotherapy, probably due to the activation of stromal relevant pathways and infiltration of stromal cells. Besides colon cancer, the TMRS panel was also revealed to be a reliable tool for prognostic prediction and chemotherapeutic decision-making in gastric cancer. Its value in predicting immunotherapy outcomes was also confirmed in two other cohorts consisting of metastatic urothelial carcinoma patients and melanoma patients.

Interpretation

Our TMRS panel may be an effective tool for survival prediction and treatment guidance in patients with stage I–III colon cancer.

Fund

This work was supported by the National Natural Science Foundation of China (No. 81772580) and Guangzhou Planed Project of Science and Technology (No. 201803010070).

Tumor infiltrating lymphocytes: The regulator of melanoma evolution

Melanoma is the most severe type of skin cancer and its incidence has increased in the last decades. In the United States, it is the 6th most common cancer in both men and women. Prognosis for patients with melanoma depends on the stage of the disease at the time of diagnosis and it can be influenced by the immunologic response. Melanoma has been historically considered an immunogenic malignancy. It often contains great amount of immune cells (different subsets of T-cells, dendritic cells, macrophages, neutrophils, mast cells, B lymphocytes), which may reflect a continuous intercommunication between host and tumor. It is not established if tumor-infiltrating lymphocytes (TILs) are induced by tumor cells or by other components of the microenvironment or when they are a host direct immunologic reaction. It has been observed that in many cases, the presence of a dense TIL is associated with good prognosis. The pattern and activation state of the cells which constitute TIL is variable and modulates the clinical outcome. An important step in the understanding of tumor immunobiology is the analysis of the populations and subsets of immune cells that form TIL. Besides its prognostic significance, after approval of cytotoxic T lymphocyte antigen 4, programmed cell death-1 and programmed death-1 ligand antibodies for the treatment of melanoma, the assessment of immune infiltrate composition has become even more captivating, as it could provide new target molecules and new biomarkers for predicting the effect of the treatment and disease outcome in patients treated with immunotherapy. In this review we discuss current state of knowledge in the field of immune cells that infiltrate melanoma, resuming the potential of TIL components to become prognostic markers for natural evolution, for response to drugs or valuable targets for new medication.

Global immune fingerprinting in glioblastoma patient peripheral blood reveals immune-suppression signatures associated with prognosis

Glioblastoma (GBM) remains uniformly lethal, and despite a large accumulation of immune cells in the microenvironment, there is limited antitumor immune response. To overcome these challenges, a comprehensive understanding of GBM systemic immune response during disease progression is required. Here, we integrated multiparameter flow cytometry and mass cytometry TOF (CyTOF) analysis of patient blood to determine changes in the immune system among tumor types and over disease progression. Utilizing flow cytometry analysis in a cohort of 259 patients ranging from benign to malignant primary and metastatic brain tumors, we found that GBM patients had a significant elevation in myeloid-derived suppressor cells (MDSCs) in peripheral blood but not immunosuppressive Tregs. In GBM patient tissue, we found that increased MDSC levels in recurrent GBM portended poor prognosis. CyTOF analysis of peripheral blood from newly diagnosed GBM patients revealed that reduced MDSCs over time were accompanied by a concomitant increase in DCs. GBM patients with extended survival also had reduced MDSCs, similar to the levels of low-grade glioma (LGG) patients. Our findings provide a rationale for developing strategies to target MDSCs, which are elevated in GBM patients and predict poor prognosis.

Potential effect of tumor-specific Treg-targeted antibodies in the treatment of human cancers: A bioinformatics analysis

One of the mechanisms of tumor rejection in immune-modulatory treatments is antibody-dependent cell-mediated cytotoxicity (ADCC) of regulatory T cells (Tregs) that infiltrate tumors in which cells expressing activating Fcγ receptors (FcγRs) are present. Our objective was to identify, through a bioinformatics analysis, Treg marker(s) expressed at the highest levels in nine types of human cancers, in order to determine the best targets for ADCC-inducing antitumor antibodies. We analyzed the mRNA levels of 24 surface Treg markers evaluated by the Affymetrix Human Genome U133 Plus 2.0 Array in 5728 cancer samples obtained via the Genevestigator v3 suite. Our analysis was based on overexpression of markers in tumors as compared to healthy tissues (HTs) and correlation between overexpression of the markers and the tumor suppressive microenvironment. Moreover, we evaluated tumoral infiltration of activating FcγR-expressing cells and calculated the ADCC index for each overexpressed marker, as an indicator of whether the marker was a good target for ADCC induction in tumor-infiltrating Tregs. The results demonstrated that the ADCC strategy is unlikely to succeed in colorectal, liver, prostate and ovarian cancers. Moreover, we identified nine Treg markers that could be targeted in the other tumors: 4-1BB, CD39, galectin-9, GITR, IL-21R, LAP, neuropilin-1, TIGIT and TNFR2. GITR and TIGIT were the only markers that could be potentially useful as targets for the treatment of three cancers: non-squamous and squamous NSCLC and breast infiltrating ductal carcinoma. LAP, neuropilin-1 and CD39 presented as good targets in the treatment of renal cell carcinoma. Our findings may have value for the development of new anti-tumor antibodies.

Treatment of NRAS-mutated advanced or metastatic melanoma: rationale, current trials and evidence to date

The disease course of BRAF (v-raf murine sarcoma viral oncogene homolog B1)-mutant melanoma has been drastically improved by the arrival of targeted therapies. NRAS (neuroblastoma RAS viral oncogene homolog)-mutated melanoma represents 15–25% of all metastatic melanoma patients. It currently does not have an approved targeted therapy. Metastatic patients receive immune-based therapies as first-line treatments, then cytotoxic chemotherapy like carboplatin/paclitaxel (C/P), dacarbazine (DTIC) or temozolomide (TMZ) as a second-line treatment. We will review current preclinical and clinical developments in NRAS-mutated melanoma, and analyze ongoing clinical trials that are evaluating the benefit of different targeted and immune-based therapies, either tested as single agents or in combination, in NRAS-mutant melanoma.