Immune Landscape Of Tumor Microenvironment Research Articles

Implications of single-cell immune landscape of tumor microenvironment for the colorectal cancer diagnostics and therapy.

Colorectal cancer (CRC) originates from the polyps lining the colon and is among the most common types of cancer. With the increasing popularity of single-cell sequencing technologies, researchers have been able to better understand the immune landscape of colorectal cancer, by analyzing their expression and interactions in detail with the tumor microenvironment (TME) at single-cell level. Since the tumor-immune cell interactions play a critical part in the advancement as well as treatment response in colorectal cancer, the release of inhibitory factors such as T cells are important for recognizing and destroying cancer cells. Such information is vital to identify immunotherapeutic targets for cure and monitoring response to treatments. Therefore, a comprehensive single-cell studies-based overview of key immunogenic agents regulating the TME of CRC is provided in this review. Tumor-associated macrophages can promote tumor growth and resistance to treatment by releasing factors that inhibit the function of other immune cells. Additionally, colorectal cancer cells can express programmed cell death protein 1 and its ligand, which can also inhibit T-cell function. Researchers have found that certain types of immune cells, prominently T cells, natural killer, and dendritic cells, can have a positive impact on the prognosis of colorectal cancer patients. Treatments like immune checkpoint inhibitors and CAR-T therapies that help to release the inhibitory signals from the cancer cells allow the immune cells to function more effectively.

Aggrephagy-related LncRNAs index: A predictor for HCC prognosis, immunotherapy efficacy, and chemosensitivity.

Due to the complexity and heterogeneity of hepatocellular carcinoma, the existing clinical staging criterias are insufficient to accurately reflect the tumor microenvironment and predict the prognosis of HCC patients. Aggrephagy, as a type of selective autophagy, is associated with various phenotypes of malignant tumors. This study aimed to identify and validate a prognostic model based on aggrephagy-related LncRNAs to assess the prognosis and immunotherapeutic response of HCC patients. Based on the TCGA-LIHC cohort, aggrephagy-related LncRNAs were identified. Univariate Cox regression analysis and lasso and multivariate Cox regression were used to construct a risk-scoring system based on eight ARLs. CIBERSORT, ssGSEA, and other algorithms were used to evaluate and present the immune landscape of tumor microenvironment. The high-risk group had a worse overall survival (OS) than the low-risk group. Patients in the high-risk group are more likely to benefit from immunotherapy because of their high infiltration level and high immune checkpoint expression. The ARLs signature is a powerful predictor of prognosis for HCC patients, and the nomogram based on this model can help clinicians accurately determine the prognosis of HCC patients and screen for specific subgroups of patients who are more sensitive to immunotherapy and chemotherapy.

Examination of antagonistic metabolic reactions and nicotinamide/methylnicotinamide as a biomarker in gastric cancer.

442 Background: Immune checkpoint inhibitors (ICIs) have revolutionized the therapy pattern of advanced gastric cancer (GC), but the efficacy is still unsatisfactory. The role of metabolism in the immune landscape of tumor microenvironment (TME) has been emphasized for the past few years. As the mediators of cellular interactions in TME, more metabolic biomarkers and their predictive value remain to be revealed. Methods: Metabolite activity scoring gene sets were constructed based on metabolite-protein interaction network. Integrated metabolite profile was collected from metabolic reactions in KEGG database. Transcriptome of ACRG (n=299) and anti-PD-1 cohort (PRJEB25780, n=45) were utilized to generate metabolite activity scores via GSVA algorithm. Cox regression was performed to calculate the hazard ratio (HR) to overall survival of metabolites in each metabolic reaction. Plasma samples (n=55) were obtained to measure nicotinamide metabolites from GC patients in Nanfang Hospital before and after ICIs treatment. Results: 78 metabolic reactions (78/6429) with opposite prognostic value among metabolic substrates and products were identified via the antagonistic metabolism screening workflow. Notably, nicotinamide (NAM)-methylnicotinamide (MNAM) (KEGG REACTION: R01269) showed significant antagonistic relationship (NAM, HR=0.76, 95% CI: 0.64-0.89, P<0.001; MNAM, HR=1.28, 95% CI: 1.09-1.49, P=0.002). NNMT, the rate-limiting enzyme catalyzing the formation of MNAM from NAM, was also correlated with poor prognosis (log-rank P=0.013). In terms of therapeutic prediction, NAM/MNAM ratio in plasma of GC patients was significantly correlated with anti-PD-1 response (PR vs SD/PD, median NAM/MNAM ratio (ng·mL-1/ng·mL-1) = 26.76 vs 10.40, P<0.0001). Compared with baseline, decreased plasma NAM/MNAM ratio was found in all (7/7) PR patients ( P=0.0011), while 73.33% (11/15) of non-responders showed a higher NAM/MNAM ratio after ICIs treatment ( P=0.0283). Correlation analysis of key metabolites with TME cells revealed that MNAM activity score was positively correlated with fibroblasts, MDSCs and M2 macrophages infiltration (Pearson correlation: R=0.60, P<0.001; R=0.42, P<0.001; R=0.34, P<0.001), suggesting that MNAM has a potential impact on immune-suppressive cells recruitment and functions. Conclusions: Nicotinamide metabolism was an indicator of prognosis in GC patients. Circulating NAM/MNAM ratio can be a novel metabolic biomarker in dynamic monitoring for ICIs therapy.

A Four-Cell-Senescence-Regulator-Gene Prognostic Index Verified by Genome-Wide CRISPR Can Depict the Tumor Microenvironment and Guide Clinical Treatment of Bladder Cancer.

Bladder cancer (BCa) is the 10th most commonly diagnosed cancer worldwide, and cellular senescence is defined as a state of permanent cell cycle arrest and considered to play important roles in the development and progression of tumor. However, the comprehensive effect of senescence in BCa has not ever been systematically evaluated. Using the genome-wide CRISPR screening data acquired from DepMap (Cancer Dependency Map), senescence genes from the CellAge database, and gene expression data from The Cancer Genome Atlas (TCGA), we screened out 12 senescence genes which might play critical roles in BCa. A four-cell-senescence-regulator-gene prognostic index was constructed using the least absolute shrinkage and selection operator (LASSO) and multivariate COX regression model. The transcriptomic data and clinical information of BCa patients were downloaded from TCGA and Gene Expression Omnibus (GEO). We randomly divided the patients in TCGA cohort into training and testing cohorts and calculated the risk score according to the expression of the four senescence genes. The validity of this risk score was validated in the testing cohort (TCGA) and validation cohort (GSE13507). The Kaplan–Meier curves revealed a significant difference in the survival outcome between the high- and low-risk score groups. A nomogram including the risk score and other clinical factors (age, gender, stage, and grade) was established with better predictive capacity of OS in 1, 3, and 5 years. Besides, we found that patients in the high-risk group had higher tumor mutation burden (TMB); lower immune, stroma, and ESTIMATE scores; higher tumor purity; aberrant immune functions; and lower expression of immune checkpoints. We also performed gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) to investigate the interaction between risk score and hallmark pathways and found that a high risk score was connected with activation of senescence-related pathways. Furthermore, we found that a high risk score was related to better response to immunotherapy and chemotherapy. In conclusion, we identified a four-cell-senescence-regulator-gene prognostic index in BCa and investigated its relationship with TMB, the immune landscape of tumor microenvironment (TME), and response to immunotherapy and chemotherapy, and we also established a nomogram to predict the prognosis of patients with BCa.

Am6Avalue predictive of prostate cancer stemness, tumor immune landscape and immunotherapy response.

The molecular mechanisms underpinning prostate cancer (PCa) progression are incompletely understood, and precise stratification of aggressive primary PCa (pri-PCa) from indolent ones poses a major clinical challenge. Here, we comprehensively dissect, genomically and transcriptomically, the m6A (N6-methyladenosine) pathway as a whole in PCa. Expression, but not the genomic alteration, repertoire of the full set of 24 m6A regulators at the population level successfully stratifies pri-PCa into three m6A clusters with distinct molecular and clinical features. These three m6A modification patterns closely correlate with androgen receptor signaling, stemness, proliferation and tumor immunogenicity of cancer cells, and stroma activity and immune landscape of tumor microenvironment (TME). We observe a discrepancy between a potentially higher neoantigen production and a deficiency in antigen presentation processes in aggressive PCa, offering insights into the failure of immunotherapy. Identification of PCa-specific m6A phenotype-associated genes provides a basis for construction of m6Avalue to measure m6A methylation patterns in individual patients. Tumors with lower m6Avalue are relatively indolent with abundant immune cell infiltration and stroma activity. Interestingly, m6Avalue separates PCa TME into fibrotic and nonfibrotic phenotypes (instead of previously reported immune-proficient or -desert phenotypes in other cancer types). Significantly, m6Avalue can be used to predict drug response and clinical immunotherapy efficacy in both castration-resistant PCa and other cancer types. Therefore, our study establishes m6A methylation modification pattern as a determinant in PCa progression via impacting cancer cell aggressiveness and TME remodeling.

LncRNA MIAT correlates with immune infiltrates and drug reactions in hepatocellular carcinoma

Long non-coding RNA (lncRNA) is a kind of important molecules involved in the formation of immune landscape in tumor microenvironment. However, there are few studies on the relationship between lncRNA and immunomodulatory regulation of hepatocellular carcinoma (HCC). In this study, we combined with single cell transcriptome sequencing and TCGA data to analyze the relationship between lncRNA MIAT and immune cells in HCC. TIMER database analysis indicated that the expression of MIAT in HCC was negatively correlated with tumor purity, positively correlated with the number of immune cells such as B cells, T lymphocytes and macrophages, and positively correlated with the expression of immune checkpoint molecules such as PD-1, PD-L1 and CTLA4. Analysis of single cell sequencing data of immune cells in HCC showed that MIAT was mainly distributed in tumor, and enriched in FOXP3+CD4+T cells and PDCD1+CD8+, GZMK+CD8+T cells, indicating that MIAT may be involved in the immune escape process of HCC. Besides, through the construction of transcription factor (TF) regulatory network, MIAT-TF-mRNA, we found that the interaction of MIAT and TFs may affect the immune microenvironment of LIHC by regulating the expression of target genes JAK2, SLC6A6, KCND1, MEIS3 or RIN1; LncMAP and CARE analysis showed that MIAT was highly related to the sensitivity of many anticancer drugs, especially sorafenib. In addition, the effect of MIAT on PD-L1 and its relationship with sorafinib were verified in clinical specimens and cells. This study made a meaningful attempt to reveal the immune escape mechanism and to find the effectiveness of targeted drugs in patients with HCC.

Immune Landscape in Tumor Microenvironment: Implications for Biomarker Development and Immunotherapy.

Integration of the tumor microenvironment as a fundamental part of the tumorigenic process has undoubtedly revolutionized our understanding of cancer biology. Increasing evidence indicates that neoplastic cells establish a dependency relationship with normal resident cells in the affected tissue and, furthermore, develop the ability to recruit new accessory cells that aid tumor development. In addition to normal stromal and tumor cells, this tumor ecosystem includes an infiltrated immune component that establishes complex interactions that have a critical effect during the natural history of the tumor. The process by which immune cells modulate tumor progression is known as immunoediting, a dynamic process that creates a selective pressure that finally leads to the generation of immune-resistant cells and the inability of the immune system to eradicate the tumor. In this context, the cellular and functional characterization of the immune compartment within the tumor microenvironment will help to understand tumor progression and, ultimately, will serve to create novel prognostic tools and improve patient stratification for cancer treatment. Here we review the impact of the immune system on tumor development, focusing particularly on its clinical implications and the current technologies used to analyze immune cell diversity within the tumor.

The Multifaceted Roles of CXCL9 Within the Tumor Microenvironment.

Chemokines are soluble proteins that orchestrate cell migration in a regulated concentration gradient. During early stages of tumor development, chemokines shape the immune landscape of tumor microenvironment. CXCL9, also known as monokine induced by gamma-interferon (MIG), can be produced during inflammatory conditions by myeloid cells within the tumor microenvironment. It attracts cells expressing the CXCR3 receptor including activated T and NK cells and has been shown to play a role in responses to immune checkpoint therapy. Overexpression of CXCL9 has also shown to reduce tumor progression and metastasis via the inhibition of angiogenesis. Conversely, CXCL9 can act directly on tumor cells expressing the CXCR3 receptor to promote cell migration and epithelial mesenchymal transition. In this chapter we discuss the anti- and pro-tumoral features of CXCL9 within the tumor microenvironment.

Immune landscape of the tumor microenvironment to predict prognosis and DNA mutations in patients with lung adenocarcinoma.

8544 Background: The tumor microenvironment (TME) influences prognosis and response to therapy. The correlation between immune profiles in the TME and cancer DNA mutations is not well established. Methods: Clinical outcomes data, mRNA-seq, and DNA mutation of 480 patients (pts) with lung adenocarcinoma (LAD) were obtained from TCGA. Pts were clustered into 4 groups using unsupervised machine learning, based on mRNA expression of genes related to antigen presentation (AP) and cytolytic activity (CA): group (G) 1 with high AP and CA (52 pts); G2, high AP, low CA (82); G3, low AP, high CA (66); G4, low AP and CA (280). Analysis of the immune landscape was performed using mRNA-seq of 191 genes enriched in cellular and structural elements of TME. DNA mutations were analyzed using the R package ggpubr and correlated in G1-G4. Results: Pts in G1 have high expression of genes related to immune activation (IA) and decreased expression of immune suppression (IS) and have the best prognosis. Pts in G2 have intermediate prognosis with decreased IA genes and intermediate expression of genes related to IS and immune checkpoints. Pts in G3 have the worst prognosis with very high expression of genes related to immune checkpoints, desmoplasia, T cell co-inhibition, and IS. They also have low CD39 expression implying low cancer antigen-driven T cells. Pts in G4 have intermediate prognosis with highly depressed IA genes. Out of 70,199 non-synonymous mutations, the top 50 mutated genes in each pt group were identified: 36, 26, 31, and 17 DNA mutations were only found in G1, G2, G3, and G4 (refer to presentation). EGFR mutation was only found in G2; KRAS in G2/4; TP53 in G2/3/4. Conclusions: Our correlation analysis of mRNA-seq and DNA mutation shows that the immune landscape of TME can predict DNA mutations and prognosis. It further demonstrates a close connection between DNA mutations and changes in TME mRNA expressivity which appear to have valuable prognostication potential in the clinical setting with now widely available genomic testing.