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

ABSTRACT: 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.

SUBMITTER: Huang J

PROVIDER: S-EPMC7293093 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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

Huang Jianbing J Li Jiagen J Zheng Sufei S Lu Zhiliang Z Che Yun Y Mao Shuangshuang S Lei Yuanyuan Y Zang Ruochuan R Liu Chengming C Wang Xinfeng X Fang Lingling L Sun Nan N He Jie J

Cancer science 20200417 6

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 ...[more]

PMID: 32187778

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Project description:Objective: Because of the modest immunotherapeutic response among ovarian carcinoma (OC) patients, it is significant to evaluate antitumor immune response and develop more effective precision immunotherapeutic regimens. Here, this study aimed to determine diverse immune subtypes of OC. Methods: This study curated the expression profiles of prognostic immunologically relevant genes and conducted consensus clustering analyses for determining immune subtypes among OC patients in TCGA cohort. With Boruta algorithm, characteristic genes were screened for conducting an immune scoring system through principal component analysis algorithm. The single-sample gene set enrichment analysis and ESTIAMTE methods were adopted for quantifying the immune infiltrates and responses to chemotherapeutic agents were estimated with pRRophetic algorithm. Two immunotherapeutic cohorts were used for investigating the efficacy of immune score in predicting therapeutic benefits. Results: Two immune subtypes were conducted among 377 OC patients. Immune subtype 2 was characterized by worse clinical prognosis, more frequent genetic variations and mutations, enhanced immune infiltrates, and increased expression of MHC molecules and programmed cell death protein 1/programmed death ligand 1 (PD-1/PD-L1). In total, 30 prognosis-relevant characteristic immune subtype-derived genes were identified for constructing the immune score of OC patients. High immune score was linked with more dismal prognosis, decreased immune infiltrations, and expression of MHC molecules. High immune score presented favorable sensitivity to doxorubicin and vinorelbine and reduced sensitivity to cisplatin. In addition, immune score possessed the potential in predicting benefits from anti-PD-1/PD-L1 therapy. Conclusion: Collectively, our findings propose two complex and diverse immune subtypes of OC. Quantitative assessment of immune subtypes in individual patients strengthens the understanding of tumor microenvironment features and promotes more effective immunotherapeutic regimens.

Project description:BackgroundThe essential roles of the tumor microenvironment (TME) have been recognized during the initiation and progression of primary lung adenocarcinoma (LUAD). The aim of the present study was to delineate the immune landscape in both primary cancer and matched lymph node metastasis from a cohort of locally advanced stage LUAD patients with distinct outcomes.MethodsFormalin-fixed, paraffin-embedded samples were collected from 36 locally advanced LUAD patients. Transcriptome data of the tumor immune microenvironment were resolved using an immune oncology panel RNA sequencing platform. Bioinformatics approaches were used to determine the differentially expressed genes (DEGs), dysregulated pathways, and immune cell fraction between patients with early recurrence (ER) and late recurrence (LR).ResultsHere, we showed that in primary cancer tissues, 23 DEGs were obtained between patients with ER and LR. Functional analysis revealed that the LR in LUAD patients may be associated with enriched gene sets belonging to the antigen presentation and MHC protein complex, innate immune response, and IFN-γ signaling pathways. Next, the transcriptome data were adopted to quantify immune cell fractions, indicating that high infiltration of mast cells and neutrophils was correlated with ER. Interestingly, similar findings were observed in metastatic lymph nodes from patients suffering from ER or LR. By analyzing the shared immune features of primary cancers and lymphatic metastases, we unraveled the prognostic value and joint utility of two DEGs, CORO1A and S100A8.ConclusionsIn LUAD, the enrichment in antigen presentation, MHC protein complex, and IFN-γ signaling, and low infiltration of neutrophils in primary or metastatic nodules may be indications for a favorable prognosis. Integrated with bioinformatics approaches, transcriptome data of immune-related genes from formalin-fixed, paraffin-embedded (FFPE) samples can effectively profile the landscape of the tumor immune microenvironment and help predict clinical outcomes.

Project description:BackgroundLung adenocarcinoma (LUAD) is a major subtype of lung cancer and closely associated with poor prognosis. N6-methyladenosine (m6A), one of the most predominant modifications in mRNAs, is found to participate in tumorigenesis. However, the potential function of m6A RNA methylation in the tumor immune microenvironment is still murky.MethodsThe gene expression profile cohort and its corresponding clinical data of LUAD patients were downloaded from TCGA database and GEO database. Based on the expression of 21 m6A regulators, we identified two distinct subgroups by consensus clustering. The single-sample gene-set enrichment analysis (ssGSEA) algorithm was conducted to quantify the relative abundance of the fraction of 28 immune cell types. The prognostic model was constructed by Lasso Cox regression. Survival analysis and receiver operating characteristic (ROC) curves were used to evaluate the prognostic model.ResultConsensus classification separated the patients into two clusters (clusters 1 and 2). Those patients in cluster 1 showed a better prognosis and were related to higher immune scores and more immune cell infiltration. Subsequently, 457 differentially expressed genes (DEGs) between the two clusters were identified, and then a seven-gene prognostic model was constricted. The survival analysis showed poor prognosis in patients with high-risk score. The ROC curve confirmed the predictive accuracy of this prognostic risk signature. Besides, further analysis indicated that there were significant differences between the high-risk and low-risk groups in stages, status, clustering subtypes, and immunoscore. Low-risk group was related to higher immune score, more immune cell infiltration, and lower clinical stages. Moreover, multivariate analysis revealed that this prognostic model might be a powerful prognostic predictor for LUAD. Ultimately, the efficacy of this prognostic model was successfully validated in several external cohorts (GSE30219, GSE50081 and GSE72094).ConclusionOur study provides a robust signature for predicting patients' prognosis, which might be helpful for therapeutic strategies discovery of LUAD.

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

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

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