Dataset Information

Hierarchical Clustering of Cutaneous Melanoma Based on Immunogenomic Profiling.

ABSTRACT: Cutaneous melanoma is an aggressive malignancy with high heterogeneity. Several studies have been performed to identify cutaneous melanoma subtypes based on genomic profiling. However, few classifications based on assessments of immune-associated genes have limited clinical implications for cutaneous melanoma. Using 470 cutaneous melanoma samples from The Cancer Genome Atlas (TCGA), we calculated the enrichment levels of 29 immune-associated gene sets in each sample and hierarchically clustered them into Immunity High (Immunity_H, n=323, 68.7%), Immunity Medium (Immunity_M, n=135, 28.7%), and Immunity Low (Immunity_L, n=12, 2.6%) based on the ssGSEA score. The ESTIMATE algorithm was used to calculate stromal scores (range: -1,800.51-1,901.99), immune scores (range: -1,476.28-3,780.33), estimate scores (range: -2,618.28-5,098.14) and tumor purity (range: 0.216-0.976) and they were significantly correlated with immune subtypes (Kruskal-Wallis test, P < 0.001). The Immunity_H group tended to have higher expression levels of HLA and immune checkpoint genes (Kruskal-Wallis test, P < 0.05). The Immunity_H group had the highest level of naïve B cells, resting dendritic cells, M1 macrophages, resting NK cells, plasma cells, CD4 memory activated T cells, CD8 T cells, follicular helper T cells and regulatory T cells, and the Immunity_L group had better overall survival. The GO terms identified in the Immunity_H group were mainly immune related. In conclusion, immune signature-associated cutaneous melanoma subtypes play a role in cutaneous melanoma prognosis stratification. The construction of immune signature-associated cutaneous melanoma subtypes predicted possible patient outcomes and provided possible immunotherapy candidates.

SUBMITTER: Yu J

PROVIDER: S-EPMC7735560 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Hierarchical Clustering of Cutaneous Melanoma Based on Immunogenomic Profiling.

Yu Jie J Xie Minyue M Ge Shengfang S Chai Peiwei P Zhou Yixiong Y Ruan Jing J

Frontiers in oncology 20201130

Cutaneous melanoma is an aggressive malignancy with high heterogeneity. Several studies have been performed to identify cutaneous melanoma subtypes based on genomic profiling. However, few classifications based on assessments of immune-associated genes have limited clinical implications for cutaneous melanoma. Using 470 cutaneous melanoma samples from The Cancer Genome Atlas (TCGA), we calculated the enrichment levels of 29 immune-associated gene sets in each sample and hierarchically clustered ...[more]

PMID: 33330057

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Project description:Diffuse large B cell lymphoma (DLBCL) is one of the most usual types of adult lymphoma with heterogeneousness in histological morphology, prognosis, and clinical indications. Prior to this, several studies were carried out to determine the DLBCL subtype based on the analysis of the genome profile. However, classification based on assessment of genes related to the immune system has limited clinical significance for DLBCL. We systematically explored the DLBCL gene expression dataset and provided publicly available clinical information on patients with GEO. In this research, 928 DLBCL samples were applied, and we calculated 29 immune-related genomes' enrichment levels in each sample and stratified them into high immunity (Immunity_H, n = 135, 28.7%), moderate immunity (Immunity_M, n = 135, 28.7%), and low immunity (Immunity_L, n = 12, 2.6%) that was based on ssGSEA score. The ESTIMATE algorithm was used to calculate stromal scores (range 586.88 to 1982.43), immune scores, estimated scores (range 2,618.2 to 8,098.14), and tumor purity (range 0.216 to 0.976). All of them were significantly correlated with immune subtypes (Kruskal-Wallis test, p < 0.001). At the same time, the correlation of related genes was analyzed by immunohistochemistry staining. In addition, DLBCL cells were cultured in transfected and in vitro with siRNA to verify correlation analysis and gene expression. Finally, human peripheral blood lymphocytes were incubated with DLBCL cells and stained. Flow cytometry was applied to analyze genes' influence on immune function. By analysis, immune checkpoint and HLA gene expression levels were higher in the Immunity_H group (Kruskal-Wallis test, p < 0.05). The levels of Tfhs (follicular helper T cells), monocytes, CD8+ T cells, M1 macrophages, M2 macrophages, and CD4+ memory-activated T cells were the most excellent in Immunity_H, and the total survival rate was higher in the Immunity_L. Through analysis, IRF4 (MUM1) was identified by us as immunotherapeutic target and a potential prognostic marker for DLBCL, which was made sure by using molecular biology experimentations. To conclude, immunosignature made a connection between DLBCL subtypes playing a position in DLBCL prognostic stratification. Immunocharacteristics-related DLBCL subtypes' construction predicts expected patient results and supplies conceivable immunotherapy candida.

Project description:BACKGROUND:Abundant evidence shows that triple-negative breast cancer (TNBC) is heterogeneous, and many efforts have been devoted to identifying TNBC subtypes on the basis of genomic profiling. However, few studies have explored the classification of TNBC specifically based on immune signatures that may facilitate the optimal stratification of TNBC patients responsive to immunotherapy. METHODS:Using four publicly available TNBC genomics datasets, we classified TNBC on the basis of the immunogenomic profiling of 29 immune signatures. Unsupervised and supervised machine learning methods were used to perform the classification. RESULTS:We identified three TNBC subtypes that we named Immunity High (Immunity_H), Immunity Medium (Immunity_M), and Immunity Low (Immunity_L) and demonstrated that this classification was reliable and predictable by analyzing multiple different datasets. Immunity_H was characterized by greater immune cell infiltration and anti-tumor immune activities, as well as better survival prognosis compared to the other subtypes. Besides the immune signatures, some cancer-associated pathways were hyperactivated in Immunity_H, including apoptosis, calcium signaling, MAPK signaling, PI3K-Akt signaling, and RAS signaling. In contrast, Immunity_L presented depressed immune signatures and increased activation of cell cycle, Hippo signaling, DNA replication, mismatch repair, cell adhesion molecule binding, spliceosome, adherens junction function, pyrimidine metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and RNA polymerase pathways. Furthermore, we identified a gene co-expression subnetwork centered around five transcription factor (TF) genes (CORO1A, STAT4, BCL11B, ZNF831, and EOMES) specifically significant in the Immunity_H subtype and a subnetwork centered around two TF genes (IRF8 and SPI1) characteristic of the Immunity_L subtype. CONCLUSIONS:The identification of TNBC subtypes based on immune signatures has potential clinical implications for TNBC treatment.

Dataset Information

Hierarchical Clustering of Cutaneous Melanoma Based on Immunogenomic Profiling.

Publications

Hierarchical Clustering of Cutaneous Melanoma Based on Immunogenomic Profiling.

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