Tumor Microenvironment Characterization in Gastric Cancer Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures

Zeng, Dongqiang; Li, Meiyi; Zhou, Rui; Zhang, Jingwen; Sun, Huiying; Shi, Min; Bin, Jianping; Liao, Yulin; Rao, Jinjun; Liao, Wangjun

doi:10.1158/2326-6066.cir-18-0436

Cited by 780 publications

(719 citation statements)

References 77 publications

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“…These discrepancies suggest that when considered alone, molecular subtypes or immune infiltration does not fully capture the varied clinical course of pGBMs. Our work established that both factors have independent prognostic value and combining them significantly improved our ability to predict survival, as also reported by others (Gruosso et al , 2019; Zeng et al , 2019), including recently in GBM (Klopfenstein et al , 2019).…”

Section: Discussionsupporting

confidence: 88%

Improved prognostication of glioblastoma beyond molecular subtyping by transcriptional profiling of the tumor microenvironment

et al. 2020

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Glioblastoma (GBM), the most aggressive form of brain cancer, is characterized by a high level of molecular heterogeneity, and infiltration by various immune and stromal cell populations. Important advances have been made in deciphering the microenvironment of GBMs, but its association with existing molecular subtypes and its potential prognostic role remain elusive. We have investigated the abundance of infiltrating immune and stromal cells in silico, from gene expression profiles. Two cohorts, including in-house normal brain and glioma samples (n = 70) and a large sample set from TCGA (n = 393), were combined into a single exploratory dataset. A third independent cohort (n = 124) was used for validation. Tumors were clustered based on their microenvironment infiltration profiles, and associations with known GBM molecular subtypes and patient outcome were tested a posteriori in a multivariable setting. We identified a subset of GBM samples with significantly higher abundances of most immune and stromal cell populations. This subset showed increased expression of both immune suppressor and immune effector genes compared to other GBMs and was enriched for the mesenchymal molecular subtype. Survival analyses suggested that tumor microenvironment infiltration pattern was an independent prognostic factor for GBM patients. Among all, patients with the mesenchymal subtype with low immune and stromal infiltration had the poorest survival. By combining molecular subtyping with gene expression measures of tumor infiltration, the present work contributes with improving prognostic models in GBM.

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Section: Discussionsupporting

confidence: 88%

Improved prognostication of glioblastoma beyond molecular subtyping by transcriptional profiling of the tumor microenvironment

et al. 2020

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“…Next, a consensus clustering algorithm was used to cluster the LUAD patients based on the signature genes. Then PCA was carried out for each LUAD patient based on the signature genes, and principal component 1 was extracted to serve as the signature gene score for each patient 21,22 . The obtained signature gene score was used to represent the signature of TME cluster and was defined as the TME score for each patient.…”

Section: Methodsmentioning

confidence: 99%

Tumor microenvironment characterization identifies two lung adenocarcinoma subtypes with specific immune and metabolic state

Huang

Zheng

et al. 2020

Cancer Science

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The tumor microenvironment (TME) is a vital component of tumor tissue. Increasing evidence suggests their significance in predicting outcomes and guiding therapies. However, no studies have reported a systematic analysis of the clinicopathologic significance of TME in lung adenocarcinoma (LUAD). Here, we inferred tumor stromal cells in 1184 LUAD patients using computational algorithms based on bulk tumor expression data, and evaluated the clinicopathologic significance of stromal cells. We found LUAD patients showed heterogeneous abundance in stromal cells. Infiltration of stromal cells was influenced by clinicopathologic features, such as age, gender, smoking, and TNM stage. By clustering stromal cells, we identified 2 clinically and molecularly distinct LUAD subtypes with immune active and immune repressed features. The immune active subtype is characterized by repressed metabolism and repressed proliferation of tumor cells, while the immune repressed subtype is characterized by active metabolism and active proliferation of tumor cells. Differentially expressed gene analysis of the two LUAD subtypes identified an immune activation signature. To diagnose TME subtypes practically, we constructed a TME score using principal component analysis based on the immune activation signature. The TME score predicted TME subtypes effectively in 3 independent datasets with areas under the receiver operating characteristic curves of 0.960, 0.812, and 0.819, respectively. In conclusion, we proposed 2 clinically and molecularly distinct LUAD subtypes based on tumor microenvironment that could be valuable in predicting clinical outcome and guiding immunotherapy.

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“…Increasing studies suggest the importance of the immune microenvironment on DSC development, which could provide potential biomarkers to improve the reliability and precision of diagnosis and prognosis. However, because of the complex and dynamic nature of the immune microenvironment, our understanding of its role remains incomplete.…”

Section: Introductionmentioning

confidence: 99%

Immune cell infiltration as a biomarker for the diagnosis and prognosis of digestive system cancer

et al. 2019

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The digestive system cancers are aggressive cancers with the highest mortality worldwide. In this study, we undertook a systematic investigation of the tumor immune microenvironment to identify diagnostic and prognostic biomarkers. The fraction of 22 immune cell types of patients were estimated using CIBERSORT. The least absolute shrinkage and selection operator (LASSO) analysis was carried out to identify important immune predictors. By comparing immune cell compositions in 801 tumor samples and 46 normal samples, we constructed the diagnostic immune score (DIS), showing high specificity and sensitivity in the training (area under the receiver operating characteristic curve [AUC] = 0.929), validation (AUC = 0.935), and different cancer type cohorts (AUC > 0.70 for all).We also established the prognostic immune score (PIS), which was an effective prognostic factor for relapse-free survival in training, validation, and entire cohorts (P < .05). In addition, PIS provided a higher net benefit than TNM stage. A composite nomogram was built based on PIS and patients' clinical information with well-fitted calibration curves (c-index = 0.84). We further used other cohorts from Gene Expression Omnibus databases and obtained similar results, confirming the reliability and validity of the DIS and PIS. In addition, the unsupervised clustering analysis using immune cell proportions revealed 6 immune subtypes, suggesting that the immune types defined as having relatively high levels of M0 or/and M1 macrophages were the high-risk subtypes of relapse. In conclusion, this study comprehensively analyzed the tumor immune microenvironment and identified DIS and PIS for digestive system cancers. K E Y W O R D Sdiagnosis, digestive system cancer, immune cell, prognosis, TCGA

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Tumor Microenvironment Characterization in Gastric Cancer Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures

Cited by 780 publications

References 77 publications

Improved prognostication of glioblastoma beyond molecular subtyping by transcriptional profiling of the tumor microenvironment

Improved prognostication of glioblastoma beyond molecular subtyping by transcriptional profiling of the tumor microenvironment

Tumor microenvironment characterization identifies two lung adenocarcinoma subtypes with specific immune and metabolic state

Immune cell infiltration as a biomarker for the diagnosis and prognosis of digestive system cancer

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