Multi-Omics Analysis of Novel Signature for Immunotherapy Response and Tumor Microenvironment Regulation Patterns in Urothelial Cancer.
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ABSTRACT:
SUBMITTER: Chu G
PROVIDER: S-EPMC8678486 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Ferroptosis is a newly recognized mechanism of regulated cell death. It was reported to be highly associated with immune therapy and chemotherapy. However, its mechanism of regulation in the tumor microenvironment (TME) and influence on oral squamous cell carcinoma (OSCC) therapy are unknown. We identified a ferroptosis-specific gene-expression signature, an FPscore, developed by a principal component analysis (PCA) algorithm to evaluate the ferroptosis regulation patterns of individual tumor. Multi-omics analysis of ferroptosis regulation patterns was conducted. Three distinct ferroptosis regulation subtypes, which linked to outcomes and the clinical relevance of each patient, were established. A high FPscore of patients with OSCC was associated with a favorable prognosis, a ferroptosis-related immune-activation phenotype, potential sensitivities to the chemotherapy and immunotherapy. Importantly, a high FPscore correlated with a low gene copy number burden and high immune checkpoint expressions. We validated the prognostic value of the FPscore using independent immunotherapy and pan-cancer cohorts. Comprehensive evaluation of individual tumors with distinct ferroptosis regulation patterns provides new mechanistic insights, which may be clinically relevant for the application of combination therapies in OSCC.
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Project description:BackgroundLung adenocarcinoma (LUAD) contains a variety of genomic and epigenomic abnormalities; the effective tumor markers related to these abnormalities need to be further explored.MethodsClustering analysis was performed based on DNA methylation (MET), DNA copy number variation (CNV), and mRNA expression data, and the differences in survival and tumor immune microenvironment (TIME) between subtypes were compared. Further, we evaluated the signatures in terms of both prognostic value and immunological characteristics.ResultsThere was a positive correlation between MET and CNV in LUAD. Integrative analysis of multi-omics data from 443 samples determined molecular subtypes, iC1 and iC2. The fractions of CD8+ T cells and activated CD4+ T cells were higher, the fraction of Tregs was lower, and the expression level of programmed death-ligand 1 (PD-L1) was higher in iC2 with a poor prognosis showing a higher TIDE score. We selected PTTG1, SLC2A1, and FAM83A as signatures of molecular subtypes to build a prognostic risk model and divided patients into high-risk group and low-risk group representing poor prognosis and good prognosis, respectively, which were validated in 180 patients with LUAD. Further, the low-risk group with lower TIDE score had more infiltrating immune cells. In 100 patients with LUAD, the high-risk group with an immunosuppressive state had a higher expression of PD-L1 and lower counts of CD8+ T cells and dendritic cells.ConclusionsThese findings demonstrated that combined multi-omics data could determine molecular subtypes with significant differences of prognosis and TIME in LUAD and suggested potent utility of the signatures to guide immunotherapy.
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Project description:BackgroundImmune cell infiltration in the tumor microenvironment (TME) affects tumor initiation, patients' prognosis and immunotherapy strategies. However, their roles and interactions with genomics and molecular processes in hepatocellular carcinoma (HCC) still have not been systematically evaluated.MethodsWe performed unsupervised clustering of total 1000 HCC samples including discovery and validation group from available public datasets. Immune heterogeneity of each subtype was explored by multi-dimension analysis. And a support vector machine (SVM) model based on multi-omics signatures was trained and tested. Finally, we performed immunohistochemistry to verify the immune role of signatures.ResultsWe defined three immune subtypes in HCC, with diverse clinical, molecular, and genomic characteristics. Cluster1 had worse prognosis, better anti-tumor characteristics and highest immune scores, but also accompanied by immunosuppression and T cell dysfunction. Meanwhile, a better anti-PD1/CTLA4 immunotherapeutic response was predicted in cluster1. Cluster2 was enriched in TAM-M2 and stromal cells, indicating immunosuppression. Cluster3, with better prognosis, had lowest CD8 T cell but highest immune resting cells. Further, based on genomic signatures, we developed an SVM classifier to identify the patient's immunological status, which was divided into Type A and Type B, in which Type A had poorer prognosis, higher T cell dysfunction despite higher T cell infiltration, and had better immunotherapeutic response. At the same time, MMP9 may be a potential predictor of the immune characteristics and immunotherapeutic response in HCC.ConclusionsOur work demonstrated 3 immune clusters with different features. More importantly, multi-omics signatures, such as MMP9 was identified based on three clusters to help us recognize patients with different prognosis and responses to immunotherapy in HCC. This study could further reveal the immune status of HCC and provide potential predictors for immune checkpoint treatment response.
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Project description:Objectives: We carried out an integrated analysis based on multiple-dimensional types of data from cohorts of bladder cancer patients to identify multi-omics perspective (genomics and transcriptomics) on the tumor microenvironment on the bases of the programmed cell death 1 ligand (PD-L1) and CD8 T-cell infiltration in urothelial carcinoma. Methods: Multiple-dimensional types of data, including clinical, genomic and transcriptomic data of 408 bladder cancer patients were retrieved from the Cancer Genome Atlas database. Based on the median values of PD-L1 and CD8A, the tumor samples were grouped into four tumor microenvironment immune types (TMIT). The RNA sequencing profiles, somatic mutation and PD-L1 amplification data of bladder cancer were analyzed by different TMITs. Results: Our research demonstrated that 36.8% of the evaluated bladder cancer belonged to TMIT I (high PD-L1/high CD8A). TIMT subtypes were not significantly associated with overall survival or disease free survival in urothelial cancer. TMIT I facilitates CD8+ T-cell infiltration and activates T-effector and interferon gamma (IFN-?) associated gene signature. The number of somatic mutations, cytolytic activity, IFN-? mRNA expression and TIGIT mRNA expression in TMIT I was remarkably higher than those in other TMIT groups. Our results showed a high rate of C>T transversion and a high rate of transition/transversion (Ti/Tv) in TMIT I bladder tumors. The RB1 mutation was significantly associated with TMIT I bladder cancer and be significantly co-occurring with the TP53 mutation. However, FGFR3 mutation and TP53 mutation were mutually exclusive in TMIT II bladder tumors. More importantly, different amino acid changes by FGFR3/RB1 mutations were also found between TMIT I and TMIT II bladder cancer, such as amino acid changes in "Immunoglobulin I-set domain (260-356)"and "Protein tyrosine kinase (472-748)". We also detected 9 genes as significantly cancer-associated genes in TMIT I bladder cancer, of which, RAD51C has been reported to play an important role in DNA damage responses. Further analysis concentrated on the potential molecular mechanism found that TMIT I was significantly associated with anti-tumor immune-related signaling pathway, and kataegis was present on chromosome 21 in TMIT I bladder tumors. Conclusions: The classification of bladder cancer into four TMITs on the bases of the PD-L1 expression and the CD8+ CTLs statuses is an appropriate approach for bladder tumor immunotherapy. TMIT I (high PD-L1/high CD8A) is significantly correlated with more somatic mutation burden, and facilitates CD8+ T-cell infiltration and activates T-effector and IFN-? associated gene signature. Alteration landscape for somatic variants was different between TMIT I and TMIT II (low PD-L1/low CD8A).
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Project description:Pyroptosis plays a critical role in the occurrence and development of colon cancer (CC). However, the specific mechanisms of pyroptosis patterns on immune regulation and tumor microenvironment (TME) formation in CC remain unclear. Based on 30 pyroptosis-related genes (PRGs), we evaluated the pyroptosis patterns of 1689 CC samples from the Cancer Genome Atlas and the Gene Expression Omnibus databases. The signatures of pyroptosis patterns and PRGs were identified in CC. In addition to systematically associating these patterns with TME cell infiltration characteristics, we constructed a pyroptosis signature score (PPSscore) to quantify pyroptosis patterns in individual tumor patients with immune responses. We discovered three distinct pyroptosis patterns, each with a different survival probability and being biologically relevant. TME infiltrating characteristics of revealed these patterns, consistent with immune-inflamed, immune-desert and immune-excluded phenotypes. Furthermore, a low PPSscore was associated with better clinical benefits. A high PPSscore was associated with a lower chance of survival due to its association with stromal activation. Additionally, two immunotherapy cohorts revealed that patients with lower PPSscore had better immune responses and durable clinical benefits. Our findings indicate that pyroptosis patterns play a vital role in immunoregulation and the formation of TME in CC.
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Project description:Due to the strong heterogeneity of bladder cancer (BC), there is often substantial variation in the prognosis and efficiency of immunotherapy among BC patients. For the precision treatment and assessment of prognosis, the subtyping of BC plays a critical role. Despite various subtyping methods proposed previously, most of them are based on a limited number of molecules, and none of them is developed on the basis of cell states. In this study, we construct a single-cell atlas by integrating single cell RNA-seq, RNA microarray, and bulk RNA-seq data to identify the absolute proportion of 22 different cell states in BC, including immune and nonimmune cell states derived from tumor tissues. To explore the heterogeneity of BC, BC was identified into four different subtypes in multiple cohorts using an improved consensus clustering algorithm based on cell states. Among the four subtypes, C1 had median prognosis and best overall response rate (ORR), which characterized an immunosuppressive tumor microenvironment. C2 was enriched in epithelial-mesenchymal transition/invasion, angiogenesis, immunosuppression, and immune exhaustion. Surely, C2 performed the worst in prognosis and ORR. C3 with worse ORR than C2 was enriched in angiogenesis and almost nonimmune exhaustion. Displaying an immune effective environment, C4 performed the best in prognosis and ORR. We found that patients with just an immunosuppressive environment are suitable for immunotherapy, but patients with an immunosuppressive environment accompanied by immune exhaustion or angiogenesis may resist immunotherapy. Furthermore, we conducted exploration into the heterogeneity of the transcriptome, mutational profiles, and somatic copy-number alterations in four subtypes, which could explain the significant differences related to cell states in prognosis and ORR. We also found that PD-1 in immune and tumor cells could both influence ORR in BC. The level of TGFβ in a cell state can be opposite to the overall level in the tissues, and the level in a specific cell state could predict ORR more accurately. Thus, our work furthers the understanding of heterogeneity and immunotherapy resistance in BC, which is expected to assist clinical practice and serve as a supplement to the current subtyping method from a novel perspective of cell states.
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Project description:Osteosarcoma (OS) is the most common primary malignancy of bone. Epigenetic regulation plays a pivotal role in cancer development in various aspects, including immune response. In this study, we studied the potential association of alterations in the DNA methylation and transcription of immune-related genes with changes in the tumor microenvironment (TME) and tumor prognosis of OS. We obtained multi-omics data for OS patients from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. By referring to curated immune signatures and using a consensus clustering method, we categorized patients based on immune-related DNA methylation patterns (IMPs), and evaluated prognosis and TME characteristics of the resulting patient subgroups. Subsequently, we used a machine-learning approach to construct an IMP-associated prognostic risk model incorporating the expression of a six-gene signature (MYC, COL13A1, UHRF2, MT1A, ACTB, and GBP1), which was then validated in an independent patient cohort. Furthermore, we evaluated TME patterns, transcriptional variation in biological pathways, somatic copy number alteration, anticancer drug sensitivity, and potential responsiveness to immune checkpoint inhibitor therapy with regard to our IMP-associated signature scoring model. By integrative IMP and transcriptomic analysis, we uncovered distinct prognosis and TME patterns in OS. Finally, we constructed a classifying model, which may aid in prognosis prediction and provide a potential rationale for targeted- and immune checkpoint inhibitor therapy in OS.
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Project description:BackgroundThe tumor microenvironment (TME) regulates the proliferation and metastasis of solid tumors and the effectiveness of immunotherapy against them. We investigated the prognostic role of TME-related genes based on transcriptomic data of bladder urothelial carcinoma (BLCA) and formulated a prediction model of TME-related signatures.MethodsMolecular subtypes were identified using the non-negative matrix factorization (NMF) algorithm based on TME-related genes from the TCGA database. TME-related genes with prognostic significance were screened with univariate Cox regression analysis and lasso regression. Nomogram was developed based on risk genes. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used for inner and outer validation of the model. Risk scores (RS) of patients were calculated and divided into high-risk group (HRG) and low-risk group (LRG) to compare the differences in clinical characteristics and PD-L1 treatment responsiveness between HRG and LRG.ResultsWe identified two molecular subtypes (C1 and C2) according to the NMF algorithm. There were significant differences in overall survival (OS) (p<0.05), progression-free survival (PFS) (p<0.05), and immune cell infiltration between the two subtypes. A total of eight TME-associated genes (CABP4, ZNF432, BLOC1S3, CXCL11, ANO9, OAS1, FBN2, CEMIP) with independent prognostic significance were screened to build prognostic risk models. Age (p<0.001), grade (p<0.001), and RS (p<0.001) were independent predictors of survival in BLCA patients. The developed RS nomogram was able to predict the prognosis of BLCA patients at 1, 3, and 5 years more potentially than the models of other investigators according to ROC and DCA. RS showed significantly higher values (p = 0.047) in patients with stable disease (SD)/progressive disease (PD) compared to patients with complete response (CR)/partial response (PR).ConclusionsWe successfully clustered and constructed predictive models for TME-associated genes and helped guide immunotherapy strategies.
| S-EPMC8921452 | biostudies-literature
Project description:Background: The tumor suppressor gene TP53 is frequently mutated or inactivated in bladder cancer (BLCA), which is implicated in the pathogenesis of tumor. However, the cellular mechanisms of TP53 mutations are complicated, yet well-defined, but their clinical prognostic value in the management of BLCA remains controversial. This study aimed to explore the role of TP53 mutation in regulating the tumor microenvironment (TME), elucidate the effects of TP53 activity on BLCA prognosis and immunotherapy response. Methods: A TP53-related signature based on TP53-induced and TP53-repressed genes was used to construct a TP53 activity-related score and classifier. The abundance of different immune cell types was determined using CIBERSORT to estimate immune cell infiltration. Moreover, the heterogeneity of the tumor immune microenvironment between the high and low TP53 score groups was further evaluated using single-cell mass cytometry (CyTOF) and imaging mass cytometry (IMC). Moreover, pathway enrichment analysis was performed to explore the differential biological functions between tumor epithelial cells with high and low TP53 activity scores. Finally, the receptor-ligand interactions between immune cells and tumor epithelial cells harboring distinct TP53 activity were analyzed by single-cell RNA-sequencing. Results: The TP53 activity-related gene signature differentiated well between TP53 functional retention and inactivation in BLCA. BLCA patients with low TP53 scores had worse survival prognosis, more TP53 mutations, higher grade, and stronger lymph node metastasis than those with high TP53 scores. Additionally, CyTOF and IMC analyses revealed that BLCA patients with low TP53 scores exhibited a potent immunosuppressive TME. Consistently, single-cell sequencing results showed that tumor epithelial cells with low TP53 scores were significantly associated with high cell proliferation and stemness abilities and strongly interacted with immunosuppressive receptor-ligand pairs. Conclusion: BLCA patients with low TP53 scores have a worse prognosis and a more immunosuppressive TME. This TP53 activity-related signature can serve as a potential prognostic signature for predicting the immune response, which may facilitate the development of new strategies for immunotherapy in BLCA.
| S-EPMC9780475 | biostudies-literature
Project description:BackgroundAlthough prognostic models based on pyroptosis-related genes (PRGs) have been constructed in bladder cancer (BLCA), the comprehensive impact of these genes on tumor microenvironment (TME) and immunotherapeutic response has yet to be investigated.MethodsBased on expression profiles of 52 PRGs, we utilized the unsupervised clustering algorithm to identify PRGs subtypes and ssGSEA to quantify immune cells and hallmark pathways. Moreover, we screened feature genes of distinct PRGs subtypes and validated the associations with immune infiltrations in tissue using the multiplex immunofluorescence. Univariate, LASSO, and multivariate Cox regression analyses were employed to construct the scoring scheme.ResultsFour PRGs clusters were identified, samples in cluster C1 were infiltrated with more immune cells than those in others, implying a favorable response to immunotherapy. While the cluster C2, which shows an extremely low level of most immune cells, do not respond to immunotherapy. CXCL9/CXCL10 and SPINK1/DHSR2 were identified as feature genes of cluster C1 and C2, and the specimen with high CXCL9/CXCL10 was characterized by more CD8 + T cells, macrophages and less Tregs. Based on differentially expressed genes (DEGs) among PRGs subtypes, a predictive model (termed as PRGs score) including five genes (CACNA1D, PTK2B, APOL6, CDK6, ANXA2) was built. Survival probability of patients with low-PRGs score was significantly higher than those with high-PRGs score. Moreover, patients with low-PRGs score were more likely to benefit from anti-PD1/PD-L1 regimens.ConclusionPRGs are closely associated with TME and oncogenic pathways. PRGs score is a promising indicator for predicting clinical outcome and immunotherapy response.
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