A Pan-Cancer Analysis of Predictive Methylation Signatures of Response to Cancer Immunotherapy.
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ABSTRACT:
SUBMITTER: Xu B
PROVIDER: S-EPMC8695566 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundTransporter associated with antigen processing 1 (TAP1) is a molecule involved in processing and presentation of major histocompatibility complex class I restricted antigens, including tumor-associated antigens. TAP1 participates in tumor immunity, and is aberrantly expressed in multiple cancer types; METHODS: Transcriptome profiles were obtained from The Cancer Genome Atlas and Genotype-Tissue Expression databases. Genetic alterations, protein distribution, and interaction information for TAP1 were downloaded from cBioPortal, Human Protein Atlas and Compartmentalized Protein-Protein Interaction, respectively. Single-cell analyses of TAP1 across cancers were conducted via the Tumor Immune Single-cell Hub website. Gene set enrichment analysis was employed to investigate TAP1-associated functional mechanisms and processes. Immune cell infiltration was explored using Tumor Immune Estimation Resource 2.0. Pan-cancer correlations between TAP1 expression and immunotherapy biomarkers were explored using the Spearman's correlation test. Associations with immunotherapy responses were also investigated using clinicopathological and prognostic information from cohorts of patients with cancer receiving immune checkpoint inhibitors.ResultsTAP1 expression was elevated in most cancer types and exhibited distinct prognostic value. Immune cells expressed more TAP1 than malignant cells within most tumors. TAP1 expression was significantly correlated with immune-related pathways, T-lymphocyte infiltration, and immunotherapeutic biomarkers. Clinical cohort validation revealed a significant correlation with immune therapeutic effects and verified the prognostic role of TAP1 in immunotherapy. Western blot assay indicated that TAP1 is upregulated in glioblastoma compared with adjacent normal brain tissues.ConclusionTAP1 is a robust tumor prognostic biomarker and a novel predictor of clinical prognosis and immunotherapeutic responses in various cancer types.
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Project description:PANoptosis, a programmed cell death, shares key characteristics of apoptosis, pyroptosis, and necroptosis. Accumulating evidence suggests that PANoptosis plays a crucial role in tumorigenesis. However, the respective regulation mechanisms in cancer are so far unclear. Using various bioinformatic approaches, we comprehensively analyzed the expression patterns, genetic alterations, prognostic value, and immunological role of PANoptosis genes in pan-cancer. Expression of the PANoptosis gene, PYCARD, was validated based on the Human Protein Atlas database and real-time quantitative reverse transcription polymerase chain reaction (RT-PCR). We found that PANoptosis genes were aberrantly expressed in most cancer types, which was consistent with the validation of PYCARD expression. Concurrently, PANoptosis genes and PANoptosis scores were significantly associated with patient survival in 21 and 14 cancer types, respectively. Pathway analysis showed that PANoptosis score was positively correlated with pathways linked to immune and inflammatory responses in pan-cancer, such as IL6-JAK-STAT3 signaling, the interferon-gamma response, and IL2-STAT5 signaling. In addition, the PANoptosis score was significantly correlated with the tumor microenvironment, the infiltration levels of most immune cells (i.e.NK cells, CD8+ T cells, CD4+ T cells, DC cells), and immune-related genes. Furthermore, it was a predictive indicator of immunotherapy response in patients with tumors. These insights substantially improve our understanding of PANoptosis components in cancers and may inspire the discovery of novel prognostic and immunotherapy response biomarkers.
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Project description:IRAK1 is an active kinase which plays a critical role in IL-1/TLR signaling pathway involved in inflammation and innate immune response. Recently, increasing evidence supports a potential role of IRAK1 in cancer progression. However, no immunological pan-cancer analysis of IRAK1 is available. We aimed to explore the prognostic value and the immunological functions of IRAK1. A series of datasets including The Cancer Genome Atlas, GEPIA2, cBioPortal, HPA, TIMER2.0 were performed to explore the oncogenic and immunological roles of IRAK1, including the relationship between IRAK1 and prognosis, genetic mutation, GO and KEGG enrichment pathway analysis, immune state of different tumors, The results showed that IRAK1 levels were upregulated in more than 20 types of cancers compared to the normal tissues. IRAK1 expression was associated with poorer prognosis in different cancer types. For the most frequent DNA alteration of IRAK1 is amplification. And the result of the enrichment analysis suggested that IRAK1 related to immune checkpoint pathway in cancer. IRAK1 inhibitor pacritinib inhibit proliferation and upregulate PD-L1 expression in different cancer cell lines. Moreover, the patients who receiving anti-PD-L1 therapy with low IRAK1 expression had a better prognosis, and the objective response rate to anti-PD-L1 therapy was higher in the low IRAK1 group than in the high IRAK1 group in IMvigor210 cohort. Our study reveals that IRAK1 can function as a prognostic marker in various malignant tumors. And pacritinib upregulated PD-L1 expression in several cancer cell lines, which indicating that IRAK1 can be used as a reliable marker to predict the efficacy of immunotherapy.
| S-EPMC9163706 | biostudies-literature
Project description:There is conflicting data regarding the role of PBAF complex mutations and response to immune checkpoint blockade (ICB) therapy in clear cell renal cell carcinoma (ccRCC) and other solid tumors. We assess the prevalence of PBAF complex mutations from two large cohorts including the pan-cancer TCGA project (n?=?10,359) and the MSK-IMPACT pan-cancer immunotherapy cohort (n?=?3700). Across both cohorts, PBAF complex mutations, predominantly PBRM1 mutations, are most common in ccRCC. In multivariate models of ccRCC patients treated with ICB (n?=?189), loss-of-function (LOF) mutations in PBRM1 are not associated with overall survival (OS) (HR?=?1.24, p?=?0.47) or time to treatment failure (HR?=?0.85, p?=?0.44). In a series of 11 solid tumors (n?=?2936), LOF mutations are not associated with improved OS in a stratified multivariate model (HR?=?0.9, p?=?0.7). In a current series of solid tumors treated with ICB, we are unable to demonstrate favorable response to ICB in patients with PBAF complex mutations.
| S-EPMC7441387 | biostudies-literature
Project description:BackgroundCancer cells become immortalized through telomere maintenance mechanisms, such as telomerase reverse transcriptase (TERT) activation. In addition to maintaining telomere length, TERT activates manifold cell survival signaling pathways. However, telomerase-associated gene signatures in cancer remain elusive.MethodsWe performed a systematic analysis of TERT high (TERThigh) and low (TERTlow) cancers using multidimensional data from The Cancer Genome Atlas (TCGA). Multidimensional data were analyzed by propensity score matching weight algorithm. Coexpression networks were constructed by weight gene coexpression network analysis (WGCNA). Random forest classifiers were generated to identify cancer subtypes.ResultsThe TERThigh-specific mRNA expression signature is associated with cell cycle-related coexpression modules across cancer types. Experimental screening of hub genes in the cell cycle module suggested TPX2 and EXO1 as potential regulators of telomerase activity and cell survival. MiRNA analysis revealed that the TERThigh-specific miR-17-92 cluster can target biological processes enriched in TERTlow cancer and that its expression is negatively correlated with the tumor/normal telomere length ratio. Intriguingly, TERThigh cancers tend to have mutations in extracellular matrix organization genes and amplify MAPK signaling. By mining the clinical actionable gene database, we uncovered a number of TERThigh-specific somatic mutations, amplifications and high expression genes containing therapeutic targets. Finally, a random forest classifier integrating telomerase-associated multi-omics signatures identifies two cancer subtypes showed profound differences in telomerase activity and patient survival.ConclusionsIn summary, our results depict a telomerase-associated molecular landscape in cancers and provide therapeutic opportunities for cancer treatment.
| S-EPMC6556968 | biostudies-literature
Project description:The Fbw7 (F-box/WD repeat-containing protein 7) ubiquitin ligase targets multiple oncoproteins for degradation and is commonly mutated in cancers. Like other pleiotropic tumor suppressors, Fbw7's complex biology has impeded our understanding of how Fbw7 mutations promote tumorigenesis and hindered the development of targeted therapies. To address these needs, we employed a transfer learning approach to derive gene-expression signatures from The Cancer Gene Atlas datasets that predict Fbw7 mutational status across tumor types and identified the pathways enriched within these signatures. Genes involved in mitochondrial function were highly enriched in pan-cancer signatures that predict Fbw7 mutations. Studies in isogenic colorectal cancer cell lines that differed in Fbw7 mutational status confirmed that Fbw7 mutations increase mitochondrial gene expression. Surprisingly, Fbw7 mutations shifted cellular metabolism toward oxidative phosphorylation and caused context-specific metabolic vulnerabilities. Our approach revealed unexpected metabolic reprogramming and possible therapeutic targets in Fbw7-mutant cancers and provides a framework to study other complex, oncogenic mutations.
| S-EPMC6003503 | biostudies-literature
Project description:BackgroundDNA methylation is a key epigenetic regulator contributing to cancer development. To understand the role of DNA methylation in tumorigenesis, it is important to investigate and compare differential methylation (DM) patterns between normal and case samples across different cancer types. However, current pan-cancer analyses call DM separately for each cancer, which suffers from lower statistical power and fails to provide a comprehensive view for patterns across cancers.MethodsIn this work, we propose a rigorous statistical model, PanDM, to jointly characterize DM patterns across diverse cancer types. PanDM uses the hidden correlations in the combined dataset to improve statistical power through joint modeling. PanDM takes summary statistics from separate analyses as input and performs methylation site clustering, differential methylation detection, and pan-cancer pattern discovery. We demonstrate the favorable performance of PanDM using simulation data. We apply our model to 12 cancer methylome data collected from The Cancer Genome Atlas (TCGA) project. We further conduct ontology- and pathway-enrichment analyses to gain new biological insights into the pan-cancer DM patterns learned by PanDM.ResultsPanDM outperforms two types of separate analyses in the power of DM calling in the simulation study. Application of PanDM to TCGA data reveals 37 pan-cancer DM patterns in the 12 cancer methylomes, including both common and cancer-type-specific patterns. These 37 patterns are in turn used to group cancer types. Functional ontology and biological pathways enriched in the non-common patterns not only underpin the cancer-type-specific etiology and pathogenesis but also unveil the common environmental risk factors shared by multiple cancer types. Moreover, we also identify PanDM-specific DM CpG sites that the common strategy fails to detect.ConclusionsPanDM is a powerful tool that provides a systematic way to investigate aberrant methylation patterns across multiple cancer types. Results from real data analyses suggest a novel angle for us to understand the common and specific DM patterns in different cancers. Moreover, as PanDM works on the summary statistics for each cancer type, the same framework can in principle be applied to pan-cancer analyses of other functional genomic profiles. We implement PanDM as an R package, which is freely available at http://www.sta.cuhk.edu.hk/YWei/PanDM.html .
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Project description:Spindle component 25 (SPC25) is one of the four proteins that make up the nuclear division cycle 80 (NDC80) complex, the other three components being Ndc80p, Nuf2p, and spindle component 24. Deregulation of the components of this complex can lead to uncontrolled proliferation and reduced apoptosis. However, the prognostic and immunotherapeutic value of SPC25 in pan-cancer remains unclear. Data from the UCSC Xena, TIMER2.0, and TCGA were analyzed to investigate the overall differential expression of SPC25 across multiple cancer types. The survival prognosis, clinical features, and genetic changes of SPC25 were also evaluated. Finally, the relationship between SPC25 and immunotherapy response was further explored through Gene Set Enrichment Analysis, tumor microenvironment, and immune cell infiltration. The transcription and protein expression of SPC25 were significantly increased in most cancer types and had prognostic value for the survival of certain cancer patients such as ACC, CESC, KIRC, KIRP, LIHC, LUAD, MESO, STAD, THYM, and UCEC. In some cancer types, SPC25 expression was also markedly correlated with the TMB, MSI, and clinical characteristics. Gene Set Enrichment Analysis showed that SPC25 was significantly associated with immune-related pathways. In addition, it was also confirmed that the expression level of SPC25 was strongly correlated with immune cell infiltration, immune checkpoint genes, immune regulatory genes, Ferroptosis-related genes, Cuproptosis-related genes, and lactate metabolism-related genes. This study comprehensively explored the potential value of SPC25 as a prognostic and immunotherapeutic marker for pan-cancer, providing new direction and evidence for cancer therapy.
| S-EPMC11009294 | biostudies-literature
Project description:Immunotherapy response score (IRS) integrates tumor mutation burden (TMB) and quantitative expression biomarkers to predict anti-PD-1/PD-L1 [PD-(L)1] monotherapy benefit. Here, we evaluated IRS in additional cohorts. Patients from an observational trial (NCT03061305) treated with anti-PD-(L)1 monotherapy were included and assigned to IRS-High (-H) versus -Low (-L) groups. Associations with real-world progression-free survival (rwPFS) and overall survival (OS) were determined by Cox proportional hazards (CPH) modeling. Those with available PD-L1 IHC treated with anti-PD-(L)1 with or without chemotherapy were separately assessed. Patients treated with PD-(L)1 and/or chemotherapy (five relevant tumor types) were assigned to three IRS groups [IRS-L divided into IRS-Ultra-Low (-UL) and Intermediate-Low (-IL), and similarly assessed]. In the 352 patient anti-PD-(L)1 monotherapy validation cohort (31 tumor types), IRS-H versus IRS-L patients had significantly longer rwPFS and OS. IRS significantly improved CPH associations with rwPFS and OS beyond microsatellite instability (MSI)/TMB alone. In a 189 patient (10 tumor types) PD-L1 IHC comparison cohort, IRS, but not PD-L1 IHC nor TMB, was significantly associated with anti-PD-L1 rwPFS. In a 1,103-patient cohort (from five relevant tumor types), rwPFS did not significantly differ in IRS-UL patients treated with chemotherapy versus chemotherapy plus anti-PD-(L)1, nor in IRS-H patients treated with anti-PD-(L)1 versus anti-PD-(L)1 + chemotherapy. IRS associations were consistent across subgroups, including both Europeans and non-Europeans. These results confirm the utility of IRS utility for predicting pan-solid tumor PD-(L)1 monotherapy benefit beyond available biomarkers and demonstrate utility for informing on anti-PD-(L)1 and/or chemotherapy treatment.SignificanceThis study confirms the utility of the integrative IRS biomarker for predicting anti-PD-L1/PD-1 benefit. IRS significantly improved upon currently available biomarkers, including PD-L1 IHC, TMB, and MSI status. Additional utility for informing on chemotherapy, anti-PD-L1/PD-1, and anti-PD-L1/PD-1 plus chemotherapy treatments decisions is shown.
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Project description:Senescence is a double-edged sword in tumorigenesis and affects the immunotherapy response through the modulation of the host's immune system. However, there is currently a lack of comprehensive analysis of the senescence-related genes (SRGs) in human cancers, and the predictive role of senescence in cancer immunotherapy response has not been explored. The multi-omics approaches were performed in this article to conduct a systematic pan-cancer genomic analysis of SRGs in cancer. In addition, we calculated the generic senescence score (SS) to quantify the senescence levels in cancers and explored the correlations of SS with cancer prognosis, biological processes, and tumor microenvironment (TME). The gene signatures were deregulated in multiple cancers and indicated a context-dependent correlation with prognosis, tumor-immune evasion, and response to therapy across various tumor types. Further analysis disclosed that SS was positively associated with the infiltration levels of immune suppressive cells, including induced Tregs (iTregs), central memory Ts (Tcms), and natural Tregs (nTregs), and negatively associated with immune killer cells, including natural killers (NKs) and mucosal-associated invariant Ts (MAITs). Moreover, the SS was significantly correlated with tumor-associated macrophages (TAMs), cancer-associated fibroblasts (CAFs), immune-related genes, and immune checkpoints and had a predictive value of immunotherapy response. Thus, the expression of SRGs was involved in resistance to several anticancer drugs. Our work illustrates the characterization of senescence across various malignancies and highlights the potential of senescence as a biomarker of the response to immunotherapy.
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