Dataset Information

Computational measurement of tumor immune microenvironment in gastric adenocarcinomas.

ABSTRACT: The use of four groups of tumor immune microenvironments (TME) based on PD-L1 and tumor-infiltrating T lymphocytes (TIL) is a reliable biomarker for anti-PD-1/PD-L1 inhibitor therapy. We classified the TME in 241 gastric cancers which were subdivided according to 40 EBV+, 76 microsatellite instability-high (MSI-H), and 125 EBV-/microsatellite-stable (MSS) subtypes by quantitative image analysis (QIA) and correlated the results with mRNA expression levels. The mean PD-L1 ratio and CD8 ratio in EBV+, MSI-H, and EBV-/MSS GCs were significantly different (P < 0.006). The PD-L1 ratio and CD8 ratio obtained by QIA correlated well with the RNA levels of PD-L1 (r = 0.63) and CD8 (r = 0.67), respectively. The TME were type I (PD-L1^H/CD8^H) in 45, type II (PD-L1^L/CD8^L) in 106, type III (PD-L1^H/CD8^L) in 8, and type IV (PD-L1^L/CD8^H) in 82 cases. The type I TME was significantly associated with high TIL (P = 3.0E-11) and EBV+ status (P = 8.55E-08). In conclusion, QIA results correlated well with the mRNA expression levels and classified TME of gastric cancers.

SUBMITTER: Chang YH

PROVIDER: S-EPMC6141531 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Computational measurement of tumor immune microenvironment in gastric adenocarcinomas.

Chang Young Hwan YH Heo You Jeong YJ Cho Junhun J Song Sang Yong SY Lee Jeeyun J Kim Kyoung-Mee KM

Scientific reports 20180917 1

The use of four groups of tumor immune microenvironments (TME) based on PD-L1 and tumor-infiltrating T lymphocytes (TIL) is a reliable biomarker for anti-PD-1/PD-L1 inhibitor therapy. We classified the TME in 241 gastric cancers which were subdivided according to 40 EBV+, 76 microsatellite instability-high (MSI-H), and 125 EBV-/microsatellite-stable (MSS) subtypes by quantitative image analysis (QIA) and correlated the results with mRNA expression levels. The mean PD-L1 ratio and CD8 ratio in EB ...[more]

PMID: 30224753

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Project description:BackgroundGastroesophageal adenocarcinomas (GEAs) are heterogeneous cancers where immune checkpoint inhibitors have robust efficacy in heavily inflamed microsatellite instability (MSI) or Epstein-Barr virus (EBV)-positive subtypes. Immune checkpoint inhibitor responses are markedly lower in diffuse/genome stable (GS) and chromosomal instable (CIN) GEAs. In contrast to EBV and MSI subtypes, the tumor microenvironment of CIN and GS GEAs have not been fully characterized to date, which limits our ability to improve immunotherapeutic strategies.Patients and methodsHere we aimed to identify tumor-immune cell association across GEA subclasses using data from The Cancer Genome Atlas (N = 453 GEAs) and archival GEA resection specimen (N = 71). The Cancer Genome Atlas RNAseq data were used for computational inferences of immune cell subsets, which were correlated to tumor characteristics within and between subtypes. Archival tissues were used for more spatial immune characterization spanning immunohistochemistry and mRNA expression analyses.ResultsOur results confirmed substantial heterogeneity in the tumor microenvironment between distinct subtypes. While MSI-high and EBV+ GEAs harbored most intense T cell infiltrates, the GS group showed enrichment of CD4+ T cells, macrophages and B cells and, in ∼50% of cases, evidence for tertiary lymphoid structures. In contrast, CIN cancers possessed CD8+ T cells predominantly at the invasive margin while tumor-associated macrophages showed tumor infiltrating capacity. Relatively T cell-rich 'hot' CIN GEAs were often from Western patients, while immunological 'cold' CIN GEAs showed enrichment of MYC and cell cycle pathways, including amplification of CCNE1.ConclusionsThese results reveal the diversity of immune phenotypes of GEA. Half of GS gastric cancers have tertiary lymphoid structures and are therefore promising candidates for immunotherapy. The majority of CIN GEAs, however, exhibit T cell exclusion and infiltrating macrophages. Associations of immune-poor CIN GEAs with MYC activity and CCNE1 amplification may enable new studies to determine precise mechanisms of immune evasion, ultimately inspiring new therapeutic modalities.

Project description:BackgroundGastric cancer (GC) still represents the third leading cause of cancer-related death worldwide. Peritoneal relapse (PR) is the most frequent metastasis occurring among patients with advanced gastric cancer. Increasingly more evidence have clarified the tumor immune microenvironment (TIME) may predict survival and have clinical significance in GC. However, tumor-transcriptomics based immune signatures derived from immune profiling have not been established for predicting the peritoneal recurrence of the advanced GC.MethodsIn this study, we depict the immune landscape of GC by using transcriptome profiling and clinical characteristics retrieved from GSE62254 of Gene Expression Omnibus (GEO). Immune cell infiltration score was evaluated via single-sample gene set enrichment (ssGSEA) analysis algorithm. The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was used to select the valuable immune cells and construct the final model for the prediction of PR. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to check the accuracy of PRIs. Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to explore the molecular pathways associated with PRIs.ResultsA peritoneal recurrence related immune score (PRIs) with 10 immune cells was constructed. Compared to the low-PRIs group, the high-PRIs group had a greater risk. The upregulation of the focal adhesion signaling was observed in the high-PRIs subtype by GSEA and KEGG. Multivariate analysis found that both in the internal training cohort and the internal validation cohort, PRIs was a stable and independent predictor for PR. A nomogram that integrated clinicopathological features and PRIs to predict peritoneal relapse was constructed. Subgroup analysis indicated that the PRIs could obviously distinguish peritoneal recurrence in different molecular subtypes, pathological stages and Lauren subtypes, in which PRIs of Epithelial-Mesenchymal Transitions (EMT) subtype, III-IV stage and diffuse subtype are higher respectively.ConclusionOverall, we performed a comprehensive evaluation of the immune landscape of GC and constructed a predictive PR model based on the immune cell infiltration. The PRIs represents novel promising feature of predicting peritoneal recurrence of GC and sheds light on the improvement of the personalized management of GC patients after surgery.

Project description:BackgroundAdvanced gastric cancer (AGC) is a disease with poor prognosis due to the current lack of effective therapeutic strategies. Immune checkpoint blockade treatments have shown effective responses in patient subgroups but biomarkers remain challenging. Traditional classification of gastric cancer (GC) is based on genomic profiling and molecular features. Therefore, it is critical to identify the immune-related subtypes and predictive markers by immuno-genomic profiling.MethodsSingle-sample gene-set enrichment analysis (ssGSEA) and ESTIMATE algorithm were used to identify the immue-related subtypes of AGC in two independent GEO datasets. Weighted gene co-expression network analysis (WGCNA) and Molecular Complex Detection (MCODE) algorithm were applied to identify hub-network of immune-related subtypes. Hub genes were confirmed by prognostic data of KMplotter and GEO datasets. The value of hub-gene in predicting immunotherapeutic response was analyzed by IMvigor210 datasets. MTT assay, Transwell migration assay and Western blotting were performed to confirm the cellular function of hub gene in vitro.ResultsThree immune-related subtypes (Immunity_H, Immunity_M and Immunity_L) of AGC were identified in two independent GEO datasets. Compared to Immunity_L, the Immuntiy_H subtype showed higher immune cell infiltration and immune activities with favorable prognosis. A weighted gene co-expression network was constructed based on GSE62254 dataset and identified one gene module which was significantly correlated with the Immunity_H subtype. A Hub-network which represented high immune activities was extracted based on topological features and Molecular Complex Detection (MCODE) algorithm. Furthermore, ADAM like decysin 1 (ADAMDEC1) was identified as a seed gene among hub-network genes which is highly associated with favorable prognosis in both GSE62254 and external validation datasets. In addition, high expression of ADAMDEC1 correlated with immunotherapeutic response in IMvigor210 datasets. In vitro, ADAMDEC1 was confirmed as a potential protein in regulating proliferation and migration of gastric cancer cell. Deficiency of ADAMDEC1 of gastric cancer cell also associated with high expression of PD-L1 and Jurkat T cell apoptosis.ConclusionsWe identified immune-related subtypes and key tumor microenvironment marker in AGC which might facilitate the development of novel immune therapeutic targets.

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Computational measurement of tumor immune microenvironment in gastric adenocarcinomas.

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Computational measurement of tumor immune microenvironment in gastric adenocarcinomas.

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