The ssGSEA29 (link) was introduced to quantify the relative infiltration of 28 immune cell types in the tumor microenvironment. Feature gene panels for each immune cell type were obtained from a recent publication17 (link). The relative abundance of each immune cell type was represented by an enrichment score in ssGSEA analysis. The ssGSEA score was normalized to unity distribution, for which zero is the minimal and one is the maximal score for each immune cell type. The bio-similarity of the immune cell filtration was estimated by multidimensional scaling (MDS) and a Gaussian fitting model.
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Tumor Microenvironment
Tumor Microenvironment
The Tumor Microenvironment refers to the complex and dynamic cellular and non-cellular components that surround and interact with tumor cells.
This microenvironment includes immune cells, blood vessels, extracellular matrix, and signaling molecules that can profoundly influence tumor growth, progression, and response to therapy.
Understanding the intricate dynamics of the tumor microenvironment is crucial for developing more effective cancer treatments and improving patient outcomes.
Explore this critical area of cancer research with PubCompare.ai's revolutionary protocols and AI-driven analysis tools to unlock the secrets of the tumor microenvironment and advance your work.
This microenvironment includes immune cells, blood vessels, extracellular matrix, and signaling molecules that can profoundly influence tumor growth, progression, and response to therapy.
Understanding the intricate dynamics of the tumor microenvironment is crucial for developing more effective cancer treatments and improving patient outcomes.
Explore this critical area of cancer research with PubCompare.ai's revolutionary protocols and AI-driven analysis tools to unlock the secrets of the tumor microenvironment and advance your work.
Most cited protocols related to «Tumor Microenvironment»
We used GSEA [57 (link)] to identify immune cell types that are over-represented in the tumor microenvironment. The expression levels of each gene were z-score normalized across all patients. For each patient (or group of patients) genes were then ranked in descending order according to their z-scores (mean of z-scores). The association was represented by a normalized enrichment score (NES). An immune cell type was considered enriched in a patient or group of patients when the false discovery rate (q-value) was ≤10%. The tumors were grouped into seven classes of molecular phenotypes based on their mutation rates, and CIMP and MSI status. Each phenotype class of patients was compared with the remaining patients to test if there is a nonrandom association between the phenotype classes and enrichment of an immune cell type (Fisher exact test). The resulting log-transformed odds ratio was used to cluster the phenotype classes and the immune cell types (two-dimensional hierarchical clustering, Euclidean distance, average linkage). Clustering and visualization were done with the software Genesis [58 (link)]. The relative number of immune cells, Ic, which is proportional to the absolute number of cells of a specific immune cell type c in a heterogeneous tumor sample, was estimated by calculating in each cell type:
where nc is the number of immune metagenes per cell type c, are the normalized counts per gene i (TPM), and is the weight defined as median log2-intensity of gene i in cell type c from microarray expression data, which was used to identify immune related genes.
where nc is the number of immune metagenes per cell type c, are the normalized counts per gene i (TPM), and is the weight defined as median log2-intensity of gene i in cell type c from microarray expression data, which was used to identify immune related genes.
Cells
Gene Expression
Genes
Genetic Heterogeneity
Microarray Analysis
Neoplasms
Patients
Phenotype
Tumor Microenvironment
ESTIMATE was designed to count scores for reflecting the infiltration levels of immune cells and stromal cells within the tumor microenvironment on the foundation of the specific genes expression level of immune and stromal cells using the R package “ESTIMATE” (10 (link)). First, we used ESTIMATE algorithm based on the expression level of RNA-seq to count the Tumor Purity, ESTIMATE Score, Immune Score, and Stromal Score of 101 osteosarcoma samples in three clusters of TARGET database using the R package “estimate” to validate the effectuality of ssGSEA grouping and to picture clustering heatmap. The vioplots of Tumor Purity, ESTIMATE Score, Immune Score, and Stromal Score in three clusters were presented employing the R package “ggpubr”. Next, to investigate the difference of immune cell subtypes, the R package “CIBERSORT” was applied to count the proportion of 22 immune cells of all osteosarcoma samples on the foundation of expression file (11 (link)), and the difference of three clusters was validated again. Besides, we also used K-M analysis and the expression of HLA family and PD-L1 to validate the difference between three clusters applying the R package “survival” and “ggpubr” respectively.
CD274 protein, human
Gene Expression
Neoplasms
Osteosarcoma
RNA-Seq
Stromal Cells
Tumor Microenvironment
PD-L1 expression in TC was assessed as the proportion of TC showing membrane staining of any intensity; expression in IC was assessed as the proportion of tumor area occupied by PD-L1-positive IC of any intensity. Only IC staining in the tumor microenvironment was evaluated, including different patterns of staining (aggregates and single cells dispersed among TC) and staining in different immune cell types (lymphocytes, macrophages, dendritic cells, and granulocytes).
Tumor area was defined as the area containing viable TC, their associated intratumoral stroma and contiguous peritumoral stroma (Supplemental Fig. 2A, Supplemental Digital Content 2,http://links.lww.com/AIMM/A187 showing NSCLC example). The staining protocol for IC is shown in Supplemental Figure 2B (Supplemental Digital Content 3, http://links.lww.com/AIMM/A188 ) (NSCLC only). An IHC-scoring convention was developed to categorize PD-L1 expression in TC and IC (Table 2 ).
Tumor area was defined as the area containing viable TC, their associated intratumoral stroma and contiguous peritumoral stroma (Supplemental Fig. 2A, Supplemental Digital Content 2,
CD274 protein, human
Cells
Conferences
Dendritic Cells
Granulocyte
Lymphocyte
Macrophage
Neoplasms
Non-Small Cell Lung Carcinoma
Tissue, Membrane
Tumor Microenvironment
The selected H&E slide of each whole tissue section was placed into a scanning cartridge and loaded into the slide scanner (Pannoramic P250, 3DHistech). All 60 slides could be processed in a single run. Digital images were uploaded onto the digital slide management platform Case Center (http://ngtma.path.unibe.ch/casecenter ). Each slide could then be opened and viewed using Pannoramic Viewer software (3DHistech).
Using the 1.0 mm annotation tool, annotations of different colors corresponding to various histological areas were placed onto each digital slide. Although other sizes were available (0.6, 1.5 and 2.0 mm diameter annotation tools), we opted for 1.0 mm annotations for several reasons. Firstly, we wanted to capture large enough regions containing both tumor and stroma. Secondly, because of the potential heterogeneity of the tumor sample, several different tissue spots would need to be included. Thirdly, at least 1 normal tissue core per case would be necessary to provide a “control” for comparing staining patterns in tumors for different proteins or genes via immunostaining and fourthly, using 1 mm cores still leads to a minimum number of ngTMAs to be constructed. One green annotation was placed on normal tissue, two yellow annotations on the tumor center, two red annotations for the tumor periphery or invasion front and two blue annotations were used to capture areas of tumor budding and surrounding stroma (i.e., tumor microenvironment at the invasion front) (Figure1 ). In total, 7 tissue spots were annotated per case, totaling 420 spots for subsequent punching and tissue microarraying.
Using the 1.0 mm annotation tool, annotations of different colors corresponding to various histological areas were placed onto each digital slide. Although other sizes were available (0.6, 1.5 and 2.0 mm diameter annotation tools), we opted for 1.0 mm annotations for several reasons. Firstly, we wanted to capture large enough regions containing both tumor and stroma. Secondly, because of the potential heterogeneity of the tumor sample, several different tissue spots would need to be included. Thirdly, at least 1 normal tissue core per case would be necessary to provide a “control” for comparing staining patterns in tumors for different proteins or genes via immunostaining and fourthly, using 1 mm cores still leads to a minimum number of ngTMAs to be constructed. One green annotation was placed on normal tissue, two yellow annotations on the tumor center, two red annotations for the tumor periphery or invasion front and two blue annotations were used to capture areas of tumor budding and surrounding stroma (i.e., tumor microenvironment at the invasion front) (Figure
Case Management
Exanthema
Genes
Genetic Heterogeneity
Neoplasms
Proteins
TAF1 protein, human
Tissues
Tumor Microenvironment
Most recents protocols related to «Tumor Microenvironment»
To further understand the composition of the tumor immune microenvironment (TIME) between the high- and low-risk groups in the TCGA database, we used a microenvironment cell population counter (MCP-counter) (17 (link)) to quantify the number of immune cells, fibroblasts and epithelial cells per COAD sample according to marker genes. Then single-sample gene set enrichment analysis (ssGSEA) was performed on tumor tissue infiltrating immune cells, and 28 immune cell types were obtained (16 (link), 18 (link), 19 (link)). The significant differences in immune cell numbers were identified by the Wilcoxon test. Furthermore, the correlation between prognostic genes and immune cells was analyzed by the Spearman method. The significance threshold was set at |r| > 0.5 and p < 0.05.
Cells
Chronic Obstructive Airway Disease
Epithelial Cells
Fibroblasts
Genes
Neoplasms
Tissues
Tumor Microenvironment
The immune score and stromal cell score were calculated by the ESTIMATE package (16 (link)) in the R software, thereby quantifying the proportion of immune stromal components in the tumor microenvironment for each sample. The results were expressed in the form of three scores:ImmuneScore, StromalScore, and ESTIMATEScore, which were positively correlated with the proportion of immune, stromal, and the sum of both, respectively, which means that the higher the respective score, the greater the proportion of the corresponding component in the tumor microenvironment. The Wilcoxon test was used to assess the difference between the three scores of the high-risk and low-risk groups in the TCGA-COAD database.
Chronic Obstructive Airway Disease
Stromal Cells
Tumor Microenvironment
To further explore the difference of tumor microenvironment (TME) between subtypes, CIBERSORT algorithms (22 kinds of immune cells) [21 (link), 22 (link)] was used to evaluate the immune microenvironment state based on the gene expression in periodontitis samples. Then, the enrichment fraction of each immune cell in different subtypes was calculated to represent the relative abundance of each infiltrating cell. The Wilxcon's signed rank test was used to reveal difference (P value) of immune cells between subtypes. In addition, the ESTIMATE algorithm was used to estimate the stromal score, immune score and ESTIMATE Score of periodontitis samples according to the expression data. The P value between groups was calculated with Wilcox test. The result was visualized by violin plot.
Cells
Gene Expression
Periodontitis
Tumor Microenvironment
We applied the ESTIMATE tool [19 (link)] to gene expression data from the discovery cohort to determine the approximate relative populations of stromal, immune, and neoplastic cells within the microenvironment of each tumour. Briefly, this method uses bulk deconvolution to compare input gene expression data to previously established transcriptomic “signatures” of immune and stromal cell populations. These were established through differential gene expression analysis over a large pan-cancer analysis using multiple databases resulting in a stromal signature containing 141 genes and an immune signature containing 141 genes. Tumour purity (proportion of neoplastic cells) was validated against genomic data in the development of the tool as well. These characteristics were compared between subgroups using a t-test.
To validate differences in microenvironment, we sought methylation probes which best correlated with immune, stromal, and purity scores in the discovery cohort since these scores are generated from transcriptomic input. Mean beta values of the 10 most correlated probes to each parameter was selected to be a marker of these microenvironment parameters. The relative proportion of each cell type in the validation cohort was therefore estimated as the difference in these markers (between subgroups) in the validation cohort.
In additional to the transcriptomic-based ESTIMATE, we calculated the methylation-based LUMP (leukocytes unmethylation to infer tumour purity) scores on all tumours. This method uses a previously validated methylation signature to infer tumour purity [20 (link)]. Unfortunately, to our knowledge, a complementary methylation-based method for inferring immune and stromal populations using methylation data alone doesn’t exist, necessitating the indirect approach outlined above.
To validate differences in microenvironment, we sought methylation probes which best correlated with immune, stromal, and purity scores in the discovery cohort since these scores are generated from transcriptomic input. Mean beta values of the 10 most correlated probes to each parameter was selected to be a marker of these microenvironment parameters. The relative proportion of each cell type in the validation cohort was therefore estimated as the difference in these markers (between subgroups) in the validation cohort.
In additional to the transcriptomic-based ESTIMATE, we calculated the methylation-based LUMP (leukocytes unmethylation to infer tumour purity) scores on all tumours. This method uses a previously validated methylation signature to infer tumour purity [20 (link)]. Unfortunately, to our knowledge, a complementary methylation-based method for inferring immune and stromal populations using methylation data alone doesn’t exist, necessitating the indirect approach outlined above.
Cells
Dietary Fiber
Gene Expression
Gene Expression Profiling
Genes
Genome
Leukocytes
Malignant Neoplasms
Methylation
Neoplasms
Population Group
Stromal Cells
Tumor Microenvironment
The CIBERSORT R package [23 (link)] was applied to calculate the infiltration proportion of the 22 immune cell subtypes. Normalized TCGA-COAD and TCGA-READ expression data were included for immune infiltration proportion analysis. The relative expression of 22 tumor microenvironment infiltrating cells was calculated in each sample. Cancer stemness was evaluated based on the one-class logistic regression according to [24 (link)] research (https://bioinformaticsfmrp.github.io/PanCanStem_Web/ ).
Chronic Obstructive Airway Disease
Malignant Neoplasms
Tumor Microenvironment
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More about "Tumor Microenvironment"
The Tumor Microenvironment (TME) refers to the complex and dynamic ecosystem that surrounds and interacts with cancer cells.
This intricate network includes immune cells, blood vessels, extracellular matrix, and signaling molecules that can profoundly influence tumor growth, progression, and response to therapy.
Understanding the TME is crucial for developing more effective cancer treatments and improving patient outcomes.
Researchers can explore this critical area of cancer research using cutting-edge tools and protocols, such as those offered by PubCompare.ai.
The TME is composed of various cellular and non-cellular components, including: - Immune cells: Such as T cells, B cells, natural killer cells, and myeloid-derived suppressor cells, which can either promote or suppress tumor growth. - Blood vessels: The vascular system that supplies oxygen and nutrients to the tumor, as well as facilitates metastasis. - Extracellular matrix (ECM): The structural and signaling components of the TME, including collagen, fibronectin, and proteoglycans. - Signaling molecules: Growth factors, cytokines, and chemokines that regulate cell-cell communication and influence tumor behavior.
Researchers can leverage advanced techniques and materials, such as FBS, Prism 8, RNeasy Mini Kit, Matrigel, RNeasy FFPE Kit, DMEM, Prism 6, NSolver version 4.0, and Caspase ELISA kit, to study the complex dynamics of the TME and uncover new insights.
By exploring the TME with PubCompare.ai's revolutionary protocols and AI-driven analysis tools, scientists can unlock the secrets of this critical cancer research area and advance their work towards more effective therapies.
Remember, a typo can sometimes add a natural feel to the content: 'experince' the future of protocol discovery and selection today with PubCompare.ai.
This intricate network includes immune cells, blood vessels, extracellular matrix, and signaling molecules that can profoundly influence tumor growth, progression, and response to therapy.
Understanding the TME is crucial for developing more effective cancer treatments and improving patient outcomes.
Researchers can explore this critical area of cancer research using cutting-edge tools and protocols, such as those offered by PubCompare.ai.
The TME is composed of various cellular and non-cellular components, including: - Immune cells: Such as T cells, B cells, natural killer cells, and myeloid-derived suppressor cells, which can either promote or suppress tumor growth. - Blood vessels: The vascular system that supplies oxygen and nutrients to the tumor, as well as facilitates metastasis. - Extracellular matrix (ECM): The structural and signaling components of the TME, including collagen, fibronectin, and proteoglycans. - Signaling molecules: Growth factors, cytokines, and chemokines that regulate cell-cell communication and influence tumor behavior.
Researchers can leverage advanced techniques and materials, such as FBS, Prism 8, RNeasy Mini Kit, Matrigel, RNeasy FFPE Kit, DMEM, Prism 6, NSolver version 4.0, and Caspase ELISA kit, to study the complex dynamics of the TME and uncover new insights.
By exploring the TME with PubCompare.ai's revolutionary protocols and AI-driven analysis tools, scientists can unlock the secrets of this critical cancer research area and advance their work towards more effective therapies.
Remember, a typo can sometimes add a natural feel to the content: 'experince' the future of protocol discovery and selection today with PubCompare.ai.