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Identification of a prognostic immune signature for cervical cancer to predict survival and response to immune checkpoint inhibitors.

ABSTRACT: Cervical cancer (CC) is a leading cause of cancer-related death in women. Limited studies have investigated whether immune-related genes (IRGs) or tumor immune microenvironment (TIME) could be indicators for CC prognoses. The aim of this study was to develop an improved prognostic signature for CC based on IRGs or TIME to predict survival and response to immune checkpoint inhibitors (ICIs). A prognostic signature was constructed using bioinformatics method and its predictive capability was validated. The mechanisms underlying the signature's predictive capability were explored with CIBERSORT algorithm and mutation analysis. Immunophenoscore (IPS) is validated for ICIs response, and was therefore explored in relation to the signature. A prognostic signature based on 11 IRGs was developed. A multivariate analysis revealed that the 11-IRG signature was an independent prognostic factor for overall survival (OS) and progression-free interval in CC patients. In the 11-IRG signature high-risk group, CD8 T cells and resting mast cells, which are found to associate with better OS in our study, were lower; activated mast cells, associated with poorer OS, were higher, compared with the low-risk group. An IPS analysis suggested that the 11-IRG signature low-risk group, which possessed a higher IPS, represented a more immunogenic phenotype that was more inclined to respond to ICIs. In short, an 11-IRG prognostic signature for predicting CC patients' survival and response to ICIs was firmly established. The predictive capability of this model in CC requires further testing with the goal of better prognostic stratification and treatment management.

SUBMITTER: Yang S

PROVIDER: S-EPMC6844304 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Identification of a prognostic immune signature for cervical cancer to predict survival and response to immune checkpoint inhibitors.

Yang Si S Wu Ying Y Deng Yujiao Y Zhou Linghui L Yang Pengtao P Zheng Yi Y Zhang Dai D Zhai Zhen Z Li Na N Hao Qian Q Song Dingli D Kang Huafeng H Dai Zhijun Z

Oncoimmunology 20191003 12

Cervical cancer (CC) is a leading cause of cancer-related death in women. Limited studies have investigated whether immune-related genes (IRGs) or tumor immune microenvironment (TIME) could be indicators for CC prognoses. The aim of this study was to develop an improved prognostic signature for CC based on IRGs or TIME to predict survival and response to immune checkpoint inhibitors (ICIs). A prognostic signature was constructed using bioinformatics method and its predictive capability was valid ...[more]

PMID: 31741756

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Project description:Low response rates to immunotherapy have been reported in soft tissue sarcoma (STS). There are few predictive biomarkers of response, and the tumor immune microenvironment associated with progression and prognosis remains unclear in STS. Gene expression data from the Cancer Genome Atlas were used to identify the immune-related prognostic genes (IRPGs) and construct the immune gene-related prognostic model (IGRPM). The tumor immune microenvironment was characterized to reveal differences between patients with different prognoses. Furthermore, somatic mutation data and DNA methylation data were analyzed to understand the underlying mechanism leading to different prognoses. The IGRPM was constructed using five IRPGs (IFIH1, CTSG, STC2, SECTM1, and BIRC5). Two groups (high- and low-risk patients) were identified based on the risk score. Low-risk patients with higher overall survival time had higher immune scores, more immune cell infiltration (e.g., CD8 T cell and activated natural killer cells), higher expression of immune-stimulating molecules, higher stimulating cytokines and corresponding receptors, higher innate immunity molecules, and stronger antigen-presenting capacity. However, inhibition of immunity was observed in low-risk patients owing to the higher expression of immune checkpoint molecules and inhibiting cytokines. High-risk patients had high tumor mutation burden, which did not significantly influence survival. Gene set enrichment analysis further revealed that pathways of cell cycle and cancers were activated in high-risk patients. DNA methylation analysis indicated that relative high methylation was associated with better overall survival. Finally, the age, mitotic counts, and risk scores were independent prognostic factors for STS. Five IRPGs performed well in risk stratification of patients and are candidate biomarkers for predicting response to immunotherapy. Differences observed through the multi-omic study of patients with different prognoses may reveal the underlying mechanism of the development and progression of STS, and thereby improve treatment.

Project description:BackgroundGlioma is the most common primary brain tumor with high mortality and poor outcomes. As a hallmark of cancers, inflammatory responses are crucial for their progression. The present study is aimed at exploring the prognostic value of inflammatory response-related genes (IRRGs) and constructing a prognostic IRRG signature for gliomas.Materials and methodsWe investigated the relationship between IRRGs and gliomas by integrating the transcriptomic data for gliomas from public databases. Differentially expressed IRRGs (DE-IRRGs) were identified in the GSE4290 cohort. Further, univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses were conducted to construct an IRRG signature using The Cancer Genome Atlas (TCGA) cohort. Gliomas from the Chinese Glioma Genome Atlas (CGGA) cohort were employed for independent validation. The performance of gene signature was assessed by survival and receiver operating characteristic curve analyses. The differences in clinical correlations, immune infiltrate types, immunotherapeutic response predictions, and pathway enrichment among subgroups were investigated via bioinformatic algorithms.ResultsIn total, 37 DE-IRRGs were determined, of which 31 were found to be associated with survival. Ultimately, eight genes were retained to construct an IRRG signature that further classified glioma patients into two groups; the high-risk group suffered a poorer outcome as compared to the low-risk group. Furthermore, the high-risk group was significantly correlated with several risk factors, including older age, higher tumor grade, IDH wild type, 1p19q noncodel, and MGMT unmethylation. The nomogram was constructed by integrating the risk scores and other independent clinical characteristics. Moreover, the high-risk group had a greater immune infiltration and was most likely to benefit from immunotherapy. Gene set enrichment analysis suggested that immune and oncogenic pathways were enriched in high-risk glioma patients.ConclusionWe constructed a signature composed of eight IRRGs for gliomas, which could effectively predict survival and guide decision-making for treatment.

Project description:BackgroundImmune checkpoint inhibitor (ICI) treatment has enhanced survival outcomes in clear cell renal cell carcinoma (ccRCC) patients. Nevertheless, the effectiveness of immunotherapy in ccRCC patients is restricted and we intended to develop and characterize an immune response prediction signature (IRPS) to forecast the efficacy of immunotherapy.MethodsRNA-seq expression profile and clinicopathologic characteristics of 539 kidney cancer and 72 patients with normal specimens, were downloaded from the Cancer Genome Atlas (TCGA) database, while the Gene Expression Omnibus (GEO) database was used as the validation set, which included 24 ccRCC samples. Utilization of the TCGA data and immune genes databases (ImmPort and the InnateDB), we explored through Weighted Gene Co-expression Network Analysis (WGCNA), along with Least Absolute Shrinkage and Selection Operator method (LASSO), and constructed an IRPS for kidney cancer patients. GSEA and CIBERSORT were performed to declare the molecular and immunologic mechanism underlying the predictive value of IRPS. The Human Protein Atlas (HPA) was deployed to verify the protein expressions of IRPS genes. Tumor immune dysfunction and exclusion (TIDE) score and immunophenoscore (IPS) were computed to determine the risk of immune escape and value the discrimination of IRPS. A ccRCC cohort with anti-PD-1 therapy was obtained as an external validation data set to verify the predictive value of IRPS.ResultsWe constructed a 10 gene signature related to the prognosis and immune response of ccRCC patients. Considering the IRPS risk score, patients were split into high and low risk groups. Patients with high risk in the TCGA cohort tended towards advanced tumor stage and grade with poor prognosis (p < 0.001), which was validated in GEO database (p = 0.004). High-risk group tumors were related with lower PD-L1 expression, higher TMB, higher MSIsensor score, lower IPS, higher TIDE score, and enriched Treg cells, which might be the potential mechanism of immune dysfunction and exclusion. Patients in the IRPS low risk group had better PFS (HR:0.73; 95% CI: 0.54-1.0; P = 0.047).ConclusionA novel biomarker of IRPS was constructed to predict the benefit of immunotherapy, which might lead to more individualized prognoses and tailored therapy for kidney cancer patients.

Dataset Information

Identification of a prognostic immune signature for cervical cancer to predict survival and response to immune checkpoint inhibitors.

Publications

Identification of a prognostic immune signature for cervical cancer to predict survival and response to immune checkpoint inhibitors.

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