Characterization of the Immune Cell Infiltration Landscape of Thyroid Cancer for Improved Immunotherapy
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ABSTRACT:
SUBMITTER: Gong J
PROVIDER: S-EPMC8591054 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background: Gastric cancer (GC) was usually associated with poor prognosis and invalid therapeutical response to immunotherapy due to biological heterogeneity. It is urgent to screen reliable indices especially immunotherapy-associated parameters that can predict the therapeutic responses to immunotherapy of GC patients. Methods: Gene expression profile of 854 GC patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets (GSE84433) with their corresponding clinical and somatic mutation data. Based on immune cell infiltration (ICI) levels, molecular clustering classification was performed to identify subtypes and ICI scores in GC patients. After functional enrichment analysis of subtypes, we further explored the correlation between ICI scores and Tumor Mutation Burden (TMB) and the significance in clinical immunotherapy response. Results: Three subtypes were identified based on ICI scores with distinct immunological and prognostic characteristics. The ICI-cluster C, associated with better outcomes, was characterized by significantly higher stromal and immune scores, T lymphocytes infiltration and up-regulation of PD-L1. ICI scores were identified through using principal component analysis (PCA) and the low ICI scores were consistent with the increased TMB and the immune-activating signaling pathways. Contrarily, the high-ICI score cluster was involved in the immunosuppressive pathways, such as TGF-beta, MAPK and WNT signaling pathways, which might be responsible for poor prognosis of GC. External immunotherapy and chemotherapy cohorts validated the patients with lower ICI scores exhibited significant therapeutic responses and clinical benefits. Conclusion: This study elucidated that ICI score could sever as an effective prognostic and predictive indicator for immunotherapy in GC. These findings indicated that the systematic assessment of tumor ICI landscapes and identification of ICI scores have crucial clinical implications and facilitate tailoring optimal immunotherapeutic strategies.
| S-EPMC8770548 | biostudies-literature
Project description:The tumor microenvironment (TME) chiefly consists of tumor cells and tumor-infiltrating immune cells admixed with the stromal component. A recent clinical trial has shown that the tumor immune cell infiltration (ICI) is correlated with the sensitivity to immunotherapy and the head and neck squamous cell carcinoma (HNSC) prognosis. However, to date, the immune infiltrative landscape of HNSC has not yet been elucidated. Herein, we proposed two computational algorithms to unravel the ICI landscape of 1,029 HNSC patients. Three ICI patterns were defined, and the ICI scores were determined by using principal-component analysis. A high ICI score was characterized by an increased tumor mutation burden (TMB) and the immune-activating signaling pathways. Activation of transforming growth factor-β (TGF-β) and WNT signaling pathways were observed in low ICI score subtypes, indicating T cell suppression, and may be responsible for poor prognosis. Two immunotherapy cohorts confirmed patients with higher ICI scores demonstrated significant therapeutic advantages and clinical benefits. This study demonstrated that the ICI scores serve as an effective prognostic biomarker and predictive indicator for immunotherapy. Evaluating the ICI patterns of a larger cohort of samples will extend our understanding of TME, and it may provide directions to the current research investigations on immunotherapeutic strategies for HNSC.
| S-EPMC7522342 | biostudies-literature
Project description:Ovarian cancer (OC) is the second leading cause of death in gynecological cancer. Multiple study have shown that the efficacy of tumor immunotherapy is related to tumor immune cell infiltration (ICI). However, so far, the Immune infiltration landscape of tumor microenvironment (TME) in OC has not been elucidated. In this study, We organized the transcriptome data of OC in the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, evaluated the patient's TME information, and constructed the ICI scores to predict the clinical benefits of patients undergoing immunotherapy. Immune-related genes were further used to construct the prognostic model. After clustering analysis of ICI genes, we found that patients in ICI gene cluster C had the best prognosis, and their tumor microenvironment had the highest proportion of macrophage M1 and T cell follicular helper cells. This result was consistent with that of multivariate cox (multi-cox) analysis. The prognostic model constructed by immune-related genes had good predictive performance. By estimating Tumor mutation burden (TMB), we also found that there were multiple genes with statistically different mutation frequencies in the high and low ICI score groups. The model based on the ICI score may help to screen out patients who would benefit from immunotherapy. The immune-related genes screened may be used as biomarkers and therapeutic targets.
| S-EPMC8595115 | biostudies-literature
Project description:Although the vast majority of patients with papillary thyroid cancer (PTC) have a favorable prognosis when conventional treatments are implemented, local recurrence and distant metastasis of advanced PTCs still hamper the survival and clinical management in certain patients. As immune checkpoint blockade (ICB) therapy achieves a great success in some advanced cancers, we aimed to investigate the immune landscape in PTC and its potential implications for prognosis and immunotherapy. In this study, different algorithms were conducted to estimate immune infiltration in PTC samples. A series of bioinformatic and machine learning approaches were performed to identify PTC-specific immune-related genes (IRGs) and distinct immune clusters. Differences in intrinsic tumor immunogenicity and potential immunotherapy response were observed between distinct immune clusters. A prognostic immune-related signature (IRS) was established to predict progression-free survival (PFS). IRS exhibited more powerful prognostic capacity and accurate survival prediction compared to conventional clinicopathological features. Furthermore, an integrated survival decision tree and a scoring nomogram were constructed to improve prognostic stratification and predictive accuracy for individual patients. In addition, altered pathways, mutational patterns, and potential applicable drugs were analyzed in different immune-related risk groups. Our study gained some insight into the immune landscape of PTC, and provided some useful clues for introducing immune-based molecular classification into risk stratification and guiding ICB decision-making.
| S-EPMC8425706 | biostudies-literature
Project description:The tumor microenvironment, comprised of tumor cells and tumor-infiltrating immune cells, is closely associated with the clinical outcome of clear cell renal cell carcinoma (ccRCC) patients. However, the landscape of immune infiltration in ccRCC has not been fully elucidated. Herein, we applied multiple computational methods and various datasets to reveal the immune infiltrative landscape of ccRCC patients. The tumor immune infiltration (TII) levels of 525 ccRCC patients using a single-sample gene were examined and further categorized into immune infiltration subgroups. The TII score was characterized by distinct clinical traits and showed a significant divergence based on gender, grade, and stage. A high TII score was associated with the ERBB signaling pathway, the TGF-β signaling pathway, and the MTOR signaling pathway, as well as a better prognosis. Furthermore, patients with high TII scores exhibited greater sensitivity to pazopanib. The low TII score was characterized by a high immune infiltration level of CD8+ T cells, T follicular helper cells, and regulatory T cells (Tregs). Moreover, the immune check point genes, including CTLA-4, LAG3, PD-1, and IDO1, presented a high expression level in the low TII score group. Patients in the high TII score group demonstrated significant therapeutic advantages and clinical benefits. The findings in this study have the potential to assist in the strategic design of immunotherapeutic treatments for ccRCC.
| S-EPMC8177771 | biostudies-literature
Project description:BackgroundImmunotherapy has achieved remarkable efficacy in treating oesophageal squamous cell carcinoma (ESCC). However, this treatment has limited efficacy in some patients. An increasing number of evidence suggested that immune cells within the tumour microenvironment (TME) are strongly related to immunotherapy response and patient prognosis. Thus, the landscape of immune cell infiltration (ICI) in ESCC needs to be mapped.MethodsIn the study, the ICI pattern in 206 cases of ESCC was characterised by two algorithms, namely, CIBERSORT and single-sample gene set enrichment analysis (ssGSEA). The ICI score of each specimen was calculated by principal component analysis (PCA) according to ICI signature genes A (ICISGA) and B (ICISGB). The prognostic difference was evaluated by using the Kaplan-Meier method. The related pathways of ICI score were investigated by applying gene set enrichment analysis (GSEA). The R packages of 'regplot', 'timeROC' and 'rms' were applied for the construction of nomogram model.ResultThree TME subtypes were identified with no prognostic implication. A total of 333 differentially expressed genes (DEGs) among immune subtypes were determined, among which ICISGA and ICISGB were identified. Finally, ICI scores were constructed, and the patients were grouped into high or low ICI score group. Compared with the low ICI score group, the high ICI score group had better prognosis. GSEA revealed that the high ICI score group referred to multiple signalling pathways, including B cell receptor, Fc gamma R-mediated phagocytosis, NOD-like receptor and TGF-β signalling pathways. In addition, the nomogram model was constructed to evaluate 1-, 3- and 5-year probability of death in an ESCC patient. The ROC and calibration curves indicated that the model has a good discrimination ability.ConclusionWe depicted a comprehensive ICI landscape in ESCC. ICI score may be used as a predictor of survival rate, which may be helpful for guiding immunotherapy in the future.
| S-EPMC9300817 | biostudies-literature
Project description:BackgroundIncreasing evidence supports that immune cell infiltration (ICI) patterns play a key role in the tumor progression of lung squamous cell carcinoma (LUSC). However, to date, the immune infiltration picture of LUSC has not been elucidated.MethodTCGA was used to download multiomics data from LUSC samples. At the same time, we included two datasets on lung squamous cell carcinoma, GSE17710 and GSE157010. To reveal the landscape of tumor immune microenvironment (TIME), the ESTIMATE algorithm, ssGSEA approach, and CIBERSORT analysis are used. To quantify the ICI pattern in a single tumor, consistent clustering is used to determine the LUSC subtype based on the ICI pattern, and principal component analysis (PCA) is used to obtain the ICI score. The prognostic value of the Kaplan-Meier curves is confirmed. GSEA (Gene Set Enrichment Analysis) was used to perform functional annotation. To investigate the immunotherapeutic effects of the ICI score, the immunophenotyping score (IPS) is used. Finally, analyze the mutation data with the "maftools" R package.ResultsWe identified four different immune infiltration patterns with different prognosis and biological characteristics in 792 LUSC samples. The identification of ICI patterns in individual tumors developed under ICI-related characteristic genes based on the ICI score helps to analyze the biological process, clinical results, immune cell infiltration, immunotherapy effects, and genetic variation. Immune failure is indicated by a high ICI score subtype marked by immunosuppression. Patients with low ICI scores have an abundance of efficient immune cells, which corresponds to the immunological activation phenotype and may have therapeutic benefits. The immunophenotypic score was used as a surrogate indicator of immunotherapy results, and samples with low ICI scores obtained significantly higher immunophenotypic scores. Finally, the relationship between the ICI score and tumor mutation burden (TMB) was proven.ConclusionThis study fully clarified the indispensable role of the ICI model in the complexity and diversity of TIME. The quantitative identification of ICI patterns in a single tumor will help draw the picture of TIME and further optimize precision immunotherapy.
| S-EPMC9674995 | biostudies-literature
Project description:BackgroundIncreasing evdence supports the suggestion that the immune cell infiltration (ICI) patterns play a pivotal role in tumor progression in breast cancer (BRCA). Nonetheless, there has been no comprehensive analysis of the ICI patterns effects on the clinical outcomes and immunotherapy.MethodsMultiomic data for BRCA samples were downloaded from TCGA. ESTIMATE algorithm, ssGSEA method, and CIBERSORT analysis were used to uncover the landscape of the tumor immune microenvironment (TIME). BRCA subtypes based on the ICI pattern were identified by consensus clustering and principal-component analysis was performed to obtain the ICI scores to quantify the ICI patterns in individual tumors. Their prognostic value was validated by the Kaplan-Meier survival curves. Gene set enrichment analysis (GSEA) was applied for functional annotation. Immunophenoscore (IPS) was employed to explore the immunotherapeutic role of the ICI scores. Finally, the mutation data was analyzed by using the "maftools" R package.ResultsThree different immune infiltration patterns with a distinct prognosis and biological signature were recognized among 1,198 BRCA samples. The characteristics of TIME under these three patterns were highly consistent with three known immune profiles: immune- excluded, immune-desert, and immune-inflamed phenotypes, respectively. The identification of the ICI patterns within individual tumors based on the ICI score, developed under the ICI-related signature genes, contributed into dissecting biological processes, clinical outcome, immune cells infiltration, immunotherapeutic effect, and genetic variation. High ICI score subtype, characterized with a suppression of immunity, suggested an immune-exhausted phenotype. Abundant effective immune cells were discovered in the low ICI score patients, which corresponded to an immune-activated phenotype and might present an immunotherapeutic advantage. Immunophenoscore was implemented as a surrogate of immunotherapeutic outcome, low-ICI scores samples obtained a significantly higher immunophenoscore. Enrichment of the JAK/STAT and VEGF signal pathways were activated in the ICI low-score subgroup. Finally, the synergistic effect between the ICI score and the tumor mutation burden (TMB) was confirmed.ConclusionThis work comprehensively elucidated that the ICI patterns served as an indispensable player in complexity and diversity of TIME. Quantitative identification of the ICI patterns in individual tumor will contribute into mapping the landscape of TIME further optimizing precision immunotherapy.
| S-EPMC8429934 | biostudies-literature
Project description:Background?PD-1 ablation or PD-L1 specific monoclonal antibody against PD-1 can recruit the accumulation of functional T cells, leading to tumor rejection in the microenvironment and significantly improving the prognosis of various cancers. Despite these unprecedented clinical successes, intervention remission rates remain low after treatment, rarely exceeding 40%. The observation of PD-1/L1 blocking in patients is undoubtedly multifactorial, but the infiltrating degree of CD8+T cell may be an important factor for immunotherapeutic resistance. Methods:We proposed two computational algorithms to reveal the immune cell infiltration (ICI) landscape of 1646 lung adenocarcinoma patients. Three immune cell infiltration patterns were defined and the relative ICI scoring depended on principal-component analysis. Results:A high ICI score was associated with the increased tumor mutation burden and cell proliferation-related signaling pathways. Different cellular signaling pathways were observed in low ICI score subtypes, indicating active cell proliferation, and may be associated with poor prognosis. Conclusion:Our research identified that the ICI scores worked as an effective immunotherapy index, which may provide promising therapeutic strategies on immune therapeutics for lung adenocarcinoma.
| S-EPMC8964969 | biostudies-literature
Project description:Glioblastoma multiforme (GBM) has been identified as a frequently occurring adult primary brain cancer that is highly aggressive. Currently, the prognostic outcome for GBM patients is dismal, even with intensive treatment, and the median overall survival (OS) is 14.6 months. Immunotherapy, which is specific at the cellular level and can generate persistent immunosurveillance, is now becoming a promising tool to treat diverse cancers. However, the complicated nature of the tumor microenvironment (TME) makes it challenging to develop anti-GBM immunotherapy because several cell types, cytokines, and signaling pathways are involved in generating the immunosuppressive environment. Novel immunotherapies can illustrate novel tumor-induced immunosuppressive mechanisms. Here, we used unsupervised clustering analysis to identify different subtypes of immune cell infiltration that actuated different prognoses, biological actions, and immunotherapy responses. Gene cluster A, with a hot immune cell infiltration phenotype, had high levels of immune-related genes (IRGs), which were associated with immune pathways including the interferon-gamma response and interferon-alpha response, and had low IDH1 and ATRX mutation frequencies. Gene cluster B, a cold immune cell infiltration subtype, exhibited a high expression of the KCNIP2, SCRT1, CPLX2, JPH3, UNC13A, GABRB3, ARPP21, DLGAP1, NRXN1, DLL3, CA10, MAP2, SEZ6L, GRIA2, and GRIA4 genes and a low expression of immune-related genes, i.e., low levels of immune reactivity. Our study highlighted the complex interplay between immune cell infiltration and genetic mutation in the establishment of the tumor immune phenotype. Gene cluster A was identified as an important subtype with a better prognosis and improved immunotherapy response.
| S-EPMC9254719 | biostudies-literature