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Tumor microenvironment characterization in gastric cancer id

時間:2019-04-23 11:52來源:生信自學網 作者:樂偉 點擊:
Tumor microenvironment characterization in gastric cancer identifies prognostic and immunotherapeutically relevant gene signatures 胃癌腫瘤微環境特征鑒定預后和免疫治療相關基因特征
腫瘤免疫一下子成了2019年的分析熱點,下面生信自學網給大家介紹一篇高分腫瘤微環境的文獻:
Tumor microenvironment characterization in gastric cancer identifies prognostic and immunotherapeutically relevant gene signatures
胃癌腫瘤微環境特征鑒定預后和免疫治療相關基因特征

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GC: Gastric cancer 胃癌
TME: Tumor microenvironment 腫瘤微環境
PCA: Principal component analysis 主成分分析
STAD: Stomach adenocarcinoma 胃腺癌
TPM: Transcripts Per Kilobase Million 每千堿基百萬的轉錄本
FPKM: Fragments per kilobase million 每千克碎片百萬
CIBERSORT: Cell type identification by estimating relative subset of known RNA transcripts
通過估計已知RNA轉錄物的相對子集來識別細胞類型
TMEscore: TME signature score 腫瘤微環境打分
GO: Gene ontology 基因本體論
GSEA: Gene set enrichment analysis 基因集富集分析
KEGG: Kyoto encyclopedia of genes and genomes 京都基因和基因組百科全書
FDR: False discovery rate 錯誤發現率
CYT: Cytolytic activity 細胞溶解活性
DEG: Differentially expressed gene 差異表達基因
MCP-counter: Microenvironment Cell Populations-counter 微環境細胞數量計數器
MSI: Microsatellite instability 微衛星不穩定性
EBV: Epstein-Barr virus 愛潑斯坦巴爾病毒
EMT: Epithelial-mesenchymal-transition 上皮間充質轉變
GS: Genomically stable 基因穩定
CIN: Chromosomal instability 染色體不穩定
CR: Complete response 完全響應
PR: Partial response 部分響應
SD: Stable disease 穩定疾病
PD: Progressive disease 進行性疾病
TMB: Tumor mutational burden 腫瘤突變負荷
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ABSTRACT
Tumor microenvironment (TME) cells constitute a vital element of tumor tissue. Increasing evidence has elucidated their clinicopathological significance in predicting outcomes and therapeutic efficacy. Nonetheless, no studies have reported a systematic analysis of cellular interactions in the tumor microenvironment. In this study, we comprehensively estimated the TME infiltration patterns of 1,524 gastric cancer patients and systematically correlated the TME phenotypes with genomic characteristics and clinicopathological features of gastric cancer using two proposed computational algorithms. Three TME phenotypes were defined, and the TMEscore was constructed using principal component analysis algorithms. The high TMEscore subtype was characterized by immune activation and response to virus and interferon-gamma. Additionally, activation of transforming growth factor β, epithelial mesenchymal transition, and angiogenesis pathways were observed in the low TMEscore subtype, which are considered T-cell suppressive and may be responsible for significantly worse prognosis in gastric cancer (hazard ratio [HR], 0.42; 95% confidence interval [CI], 0.330.54; P < 0.001). Furthermore, multivariate analysis revealed that TMEscore was an independent prognostic biomarker, and its value in predicting immunotherapeutic outcomes was also confirmed (IMvigor210 cohort: HR, 0.63; 95% CI, 0.460.89; P = 0.008; GSE78220 cohort: HR, 0.25; 95% CI, 0.070.89; P = 0.021). Depicting a comprehensive landscape of the TME characteristics of gastric cancer may therefore help to interpret the responses of gastric tumors to immunotherapies and provide new strategies for the treatment of cancers.


摘要
腫瘤微環境(TME)細胞是腫瘤組織的重要組成部分。越來越多的證據闡明了它們在預測預后和療效方面的臨床病理學意義。盡管如此,還沒有研究報告對腫瘤微環境中的細胞相互作用進行了系統的分析。在本研究中,我們綜合評估了1524例胃癌患者的TME浸潤模式,并使用兩種建議的計算算法,系統地將TME表型與胃癌的基因組特征和臨床病理特征相關聯。定義了三個TME表型,并利用主成分分析算法構建了TME譜。高TME score亞型的特點是免疫激活和對病毒和干擾素γ的反應。此外,在低TME score亞型中觀察到轉化生長因子β的激活、上皮間質轉化和血管生成途徑,這些亞型被認為是T細胞抑制,可能導致胃癌預后顯著惡化(危險比[HR],0.42;95%置信區間[CI],0.33–0.54;P< 0.001)。此外,多變量分析顯示,TME score是一個獨立的預后生物標志物,其在預測免疫治療結果方面的價值也得到了證實(IMvigor 210隊列:HR,0.63;95%CI,0.46–0.89;P=0.008;GSE78220隊列:HR,0.25;95%CI,0.07–0.89;P=0.021)。因此,全面描述胃癌的TME特征有助于解釋胃癌對免疫治療的反應,并為癌癥的治療提供新的策略。
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Introduction
Genomic analysis has been the primary methodology used in international efforts to discover novel biological targets in gastric cancer (1,2), although this method has not led to the successful discovery of distinct mechanisms. However, some studies have revealed the significance of tumor-related structures as well as upregulated signaling pathways in both cancer cells and the tumor microenvironment (TME) (3,4), suggesting that intercellular relationships are more important than genomic factors at the single-cell level (5,6). In addition, an increasing body of literature suggests a crucial role for the TME in cancer progression and therapeutic responses (7,8). For example, differences in the compositions of resident cell types within the TME, including cytotoxic T cells, helper T cells, dendritic cells (DCs), tumor-associated macrophages (TAMs), mesenchymal stem cells (MSCs), and associated inflammatory pathways have been reported in patients with cancer (5,6,9,10). The TME context determined at diagnosis reflects the immune response (11) and chemotherapy benefit (8), and changes in the numbers of CD8+ T cells, CD4+ T cells, macrophages, and cancer-associated fibroblasts infiltrating in the TME correlate with clinical outcomes in various malignancies, including gastric cancer, melanoma, urothelial cancer, lung cancer, and breast cancer (10,12-14).
Because gastric cancers are significantly associated with infectious agents, most notably Helicobacter pylori and Epstein-Barr virus (EBV), biomarkers that can predict responsiveness to immune-checkpoint blockade are being extensively investigated to further improve precision immunotherapy (15). Moreover, the abundance of immune cells and other cells in the TME can be estimated using computational methods (16-18). Although several studies using these methodologies have explored the clinical utility of TME infiltrates (7,19), and although several mechanisms associated with the role of TME in immunotherapy response and resistance have been experimentally identified for some tumor types (4,13,14,20,21), to date, the comprehensive landscape of cells infiltrating the TME has not yet been elucidated.
In the present study, two proposed computational algorithms (16,17) were employed to estimate the fractions of 22 immune cell types and cancer-associated fibroblasts based on clinically annotated gastric cancer gene expression profiles (1,22). We estimated the TME infiltration patterns of 1524 tumors from patients with gastric cancer and systematically correlated the TME phenotypes with genomic characteristics and clinical and pathological features of gastric cancer. As a result, we established a methodology to quantify the TME infiltration pattern (TMEscore). TMEscore was found to be a robust prognostic biomarker and predictive factor for response to immune-checkpoint inhibitors.




介紹

基因組分析已成為國際上探索胃癌新生物學靶點(1,2)的主要方法,盡管這種方法尚未成功發現不同的機制。然而,一些研究揭示了腫瘤相關結構的重要性以及在癌細胞和腫瘤微環境(TME)(3,4)中上調的信號通路,表明細胞間關系比單細胞水平的基因組因子更重要(5,6)。此外,越來越多的文獻表明,TME在癌癥進展和治療反應中起著至關重要的作用(7,8)。例如,在癌癥患者(5,6,9,10)中,報告了TME內常駐細胞類型的組成的差異,包括細胞毒性T細胞、輔助T細胞、樹突狀細胞(DCs)、腫瘤相關巨噬細胞(TAMs)、間充質干細胞(MSCs)和相關炎癥途徑。診斷時確定的TME背景反映了免疫反應(11)和化療益處(8),TME中CD8+T細胞、CD4+T細胞、巨噬細胞和腫瘤相關成纖維細胞浸潤數量的變化與各種惡性腫瘤的臨床結果相關,包括胃癌、黑色素瘤、尿路上皮癌、肺癌和乳腺癌。
由于胃癌與感染因子,尤其是幽門螺桿菌和愛潑斯坦-巴爾病毒(EBV)顯著相關,可以預測免疫檢查點阻斷反應的生物標志物正在被廣泛研究,以進一步提高免疫治療的精確度(15)。此外,TME中免疫細胞和其他細胞的豐度可用計算方法(16-18)估算。盡管使用這些方法進行的幾項研究已經探索了TME浸潤的臨床效用(7,19),盡管迄今為止,一些腫瘤類型(4,13,14,20,21)已經通過實驗確定了與TME在免疫治療反應和抵抗中的作用相關的幾種機制,滲透TME的細胞的綜合前景尚未闡明。
在本研究中,基于臨床注釋的胃癌基因表達譜(1,22),采用兩種建議的計算算法(16,17)來估計22種免疫細胞類型和腫瘤相關成纖維細胞的部分。我們估計了1524例胃癌患者的TME浸潤模式,并系統地將TME表型與胃癌的基因組特征、臨床和病理特征聯系起來。因此,我們建立了一個量化TME滲透模式(TMESCORE)的方法。TMESCORE被發現是一個強有力的預后生物標志物和免疫檢測點抑制劑反應的預測因子。


Materials and Methods Gastric cancer datasets and preprocessing We systematically searched for gastric cancer gene expression datasets that were publicly available and reported full clinical annotations. Patients without survival information were removed from further evaluation. In total, we gathered six treatment-naive cohorts of samples from patients with gastric cancer for this study: ACRG/GSE62254, GSE57303, GSE84437, GSE15459, GSE26253, GSE29272, and TCGA-STAD. The raw data from the microarray datasets generated by Affymetrix and Illumina were downloaded from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/). The raw data for the dataset from Affymetrix were processed using the RMA algorithm for background adjustment in the Affy software package (23). RMA was used to perform background adjustment, quantile normalization, and final summarization of oligonucleotides per transcript using the median polish algorithm. The raw data from Illumina were processed using the lumi software package. Data from The Cancer Genome Atlas (TCGA) were downloaded from the UCSC Xena browser (GDC hub), as detailed in the supplementary methods. For TCGA dataset, RNA-sequencing data (FPKM values) were transformed into transcripts per kilobase million (TPM) values, which are more similar to those resulting from microarrays and more comparable between samples (24). The criteria
used for dataset selection, platform and source of each dataset, numbers of samples, and clinical end points are summarized in the Supplementary Methods and Supplementary Table S1. Data were analyzed with the R (version 3.4.0) and R Bioconductor packages.



材料和方法

胃癌數據集和預處理
我們系統地搜索了胃癌基因表達數據集,這些數據集是公開的,并報告了完整的臨床注釋。無生存信息的患者從進一步評估中剔除。本研究共收集了6組胃癌患者的治療樣本:ACRG/GSE62254, GSE57303, GSE84437, GSE15459, GSE26253, GSE29272, and TCGA-STAD。Affymetrix和Illumina生成的微陣列數據集的原始數據從Gene Expression Omnibus(https://www.ncbi.nlm.nih.gov/geo/)下載。在Affy軟件包(23)中,使用用于背景調整的RMA算法處理來自Affymetrix的數據集的原始數據。使用RMA進行背景調整、分位數歸一化以及使用中位數波蘭算法對每個轉錄物的寡核苷酸進行最終總結。使用lumi軟件包處理來自Illumina的原始數據。癌癥基因組圖集(TCGA)的數據從UCSC Xena瀏覽器(GDC hub)下載,詳情見補充方法。對于TCGA數據集,RNA測序數據(FPKM值)被轉換成每千堿基百萬(TPM)值的轉錄物,這與微陣列產生的結果更相似,并且在樣本之間更具可比性(24)。數據集選擇的標準、每個數據集的平臺和來源、樣本數量和臨床終點總結在補充方法和補充表S1中。使用R(3.4.0版)和R生物導體包分析數據。


Collection of clinical data
The corresponding clinical data from these datasets were retrieved and manually organized when available. For some series, clinical data not attached to gene expression profiles were obtained through one of the following three methods: i) directly downloaded from the corresponding item page in the GEO dataset website, ii) from the supplementary materials in the relative literature, and iii) using the GEOquery package in R. Corresponding authors were contacted for further information where necessary. Updated clinical data and sample information for TCGA-STAD samples were obtained from the Genomic Data Commons (https://portal.gdc.cancer.gov/) using the R package TCGAbiolinks (25). Overall survival information of all TCGA datasets was obtained from the supplementary data of recently published research (26).



臨床資料收集

從這些數據集中檢索相應的臨床數據,并在可用時手動組織。對于一些系列,未附在基因表達譜上的臨床數據是通過以下三種方法之一獲得的:i)直接從GEO數據集網站的相應項目頁面下載,i i)從相關文獻的補充材料下載,以及i i i)使用R中的GEOquery包。必要時聯系了相應的作者以獲取進一步的信息。更新的TCGA-STAD樣本的臨床數據和樣本信息來自基因組數據共享(https://portal.gdc.cancer.gov/)使用R包TCGAbiolinks(25)。所有TCGA數據集的總體存活信息均來自最近發表的研究補充數據(26)。
文獻原文下載:

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責任編輯:樂偉
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