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Peripheral gene signatures reveal distinct cancer patient immunotypes with therapeutic implications for autologous DC-based vaccines.

ABSTRACT: Dendritic cells (DCs) have received considerable attention as potential targets for the development of novel cancer immunotherapies. However, the clinical efficacy of DC-based vaccines remains suboptimal, largely reflecting local and systemic immunosuppression at baseline. An autologous DC-based vaccine (DCVAC) has recently been shown to improve progression-free survival and overall survival in randomized clinical trials enrolling patients with lung cancer (SLU01, NCT02470468) or ovarian carcinoma (SOV01, NCT02107937), but not metastatic castration-resistant prostate cancer (SP005, NCT02111577), despite a good safety profile across all cohorts. We performed biomolecular and cytofluorometric analyses on peripheral blood samples collected prior to immunotherapy from 1000 patients enrolled in these trials, with the objective of identifying immunological biomarkers that may improve the clinical management of DCVAC-treated patients. Gene signatures reflecting adaptive immunity and T cell activation were associated with favorable disease outcomes and responses to DCVAC in patients with prostate and lung cancer, but not ovarian carcinoma. By contrast, the clinical benefits of DCVAC were more pronounced among patients with ovarian carcinoma exhibiting reduced expression of T cell-associated genes, especially those linked to T_H2-like signature and immunosuppressive regulatory T (T_REG) cells. Clinical responses to DCVAC were accompanied by signs of antitumor immunity in the peripheral blood. Our findings suggest that circulating signatures of antitumor immunity may provide a useful tool for monitoring the potency of autologous DC-based immunotherapy.

SUBMITTER: Hensler M

PROVIDER: S-EPMC9311316 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Peripheral gene signatures reveal distinct cancer patient immunotypes with therapeutic implications for autologous DC-based vaccines.

Hensler Michal M Rakova Jana J Kasikova Lenka L Lanickova Tereza T Pasulka Josef J Holicek Peter P Hraska Marek M Hrnciarova Tereza T Kadlecova Pavla P Schoenenberger Andreu A Sochorova Klara K Rozkova Daniela D Sojka Ludek L Drozenova Jana J Laco Jan J Horvath Rudolf R Podrazil Michal M Hongyan Guo G Brtnicky Tomas T Halaska Michal J MJ Rob Lukas L Ryska Ales A Coosemans An A Vergote Ignace I Garg Abhishek D AD Cibula David D Bartunkova Jirina J Spisek Radek R Fucikova Jitka J

Oncoimmunology 20220722 1

Dendritic cells (DCs) have received considerable attention as potential targets for the development of novel cancer immunotherapies. However, the clinical efficacy of DC-based vaccines remains suboptimal, largely reflecting local and systemic immunosuppression at baseline. An autologous DC-based vaccine (DCVAC) has recently been shown to improve progression-free survival and overall survival in randomized clinical trials enrolling patients with lung cancer (SLU01, NCT02470468) or ovarian carcino ...[more]

PMID: 35898703

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Project description:BackgroundPeriodontitis is a chronic immuno-inflammatory disease characterized by inflammatory destruction of tooth-supporting tissues. Its pathogenesis involves a dysregulated local host immune response that is ineffective in combating microbial challenges. An integrated investigation of genes involved in mediating immune response suppression in periodontitis, based on multiple studies, can reveal genes pivotal to periodontitis pathogenesis. Here, we aimed to apply a deep learning (DL)-based autoencoder (AE) for predicting immunosuppression genes involved in periodontitis by integrating multiples omics datasets.MethodsTwo periodontitis-related GEO transcriptomic datasets (GSE16134 and GSE10334) and immunosuppression genes identified from DisGeNET and HisgAtlas were included. Immunosuppression genes related to periodontitis in GSE16134 were used as input to build an AE, to identify the top disease-representative immunosuppression gene features. Using K-means clustering and ANOVA, immune subtype labels were assigned to disease samples and a support vector machine (SVM) classifier was constructed. This classifier was applied to a validation set (Immunosuppression genes related to periodontitis in GSE10334) for predicting sample labels, evaluating the accuracy of the AE. In addition, differentially expressed genes (DEGs), signaling pathways, and transcription factors (TFs) involved in immunosuppression and periodontitis were determined with an array of bioinformatics analysis. Shared DEGs common to DEGs differentiating periodontitis from controls and those differentiating the immune subtypes were considered as the key immunosuppression genes in periodontitis.ResultsWe produced representative molecular features and identified two immune subtypes in periodontitis using an AE. Two subtypes were also predicted in the validation set with the SVM classifier. Three "master" immunosuppression genes, PECAM1, FCGR3A, and FOS were identified as candidates pivotal to immunosuppressive mechanisms in periodontitis. Six transcription factors, NFKB1, FOS, JUN, HIF1A, STAT5B, and STAT4, were identified as central to the TFs-DEGs interaction network. The two immune subtypes were distinct in terms of their regulating pathways.ConclusionThis study applied a DL-based AE for the first time to identify immune subtypes of periodontitis and pivotal immunosuppression genes that discriminated periodontitis from the healthy. Key signaling pathways and TF-target DEGs that putatively mediate immune suppression in periodontitis were identified. PECAM1, FCGR3A, and FOS emerged as high-value biomarkers and candidate therapeutic targets for periodontitis.

Project description:BackgroundBone metastasis from prostate cancer severely impacts patient outcomes and quality of life. Anoikis, a form of programmed cell death triggered by the loss of cell-matrix interactions, plays a critical role in cancer progression. However, its precise relationship with prostate cancer-induced bone metastasis remains unclear. This study aims to elucidate this relationship, focusing on anoikis-related gene signatures, molecular pathways, and therapeutic implications.MethodsWe used the TCGA-PRAD dataset for training, with MSKCC and GSE70769 as validation cohorts. To evaluate immunotherapy efficacy, we examined IMvigor 210 and GSE91016 datasets, and GSE137829 provided single-cell insights into prostate cancer. Specific anoikis-related genes (ARGs) were identified, and Random Survival Forest analysis and multivariate Cox regression were employed to develop anoikis-linked features. The 'clustanoikisProfilanoikis' and 'GSEA' packages were used to explore potential ARG-related pathways.ResultsAnalyzing 553 samples from TCGA, 231 from MSKCC, 94 from GSE70769, and single-cell data from 6 prostate cancer patients (GSE137829), we constructed a prognostic model based on 9 ARGs. GSVA revealed upregulation of carcinogenic pathways, including epithelial-mesenchymal transition, E2F targets, and angiogenesis, with downregulation of metabolic pathways. Significant differences in somatic mutations were observed between cohorts, with a positive correlation between anoikis scores and tumor mutational burden (TMB). Immune landscape analysis suggested high-risk patients might benefit more from chemotherapy than immunotherapy based on their risk score. Single-cell analysis indicated overactivation of carcinogenic pathways in the high anoikis score group.ConclusionThis study elucidates the complex interplay between anoikis and bone metastasis in prostate cancer. Our findings highlight the critical role of anoikis in metastatic progression, enhancing the understanding of key biomarkers and molecular dynamics. The identified anoikis-related gene signatures and disrupted pathways offer promising avenues for predictive and therapeutic strategies in prostate cancer management.

Dataset Information

Peripheral gene signatures reveal distinct cancer patient immunotypes with therapeutic implications for autologous DC-based vaccines.

Publications

Peripheral gene signatures reveal distinct cancer patient immunotypes with therapeutic implications for autologous DC-based vaccines.

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