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免疫治疗中非小细胞肺癌免疫分子亚型的预后特征。

Prognostic characterization of immune molecular subtypes in non-small cell lung cancer to immunotherapy.

机构信息

Department of Gastroenterology, Affiliated Yueqing Hospital, Wenzhou Medical University, Wenzhou, 325600, Zhejiang, People's Republic of China.

Department of Laboratory Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, People's Republic of China.

出版信息

BMC Pulm Med. 2021 Nov 29;21(1):389. doi: 10.1186/s12890-021-01765-3.

Abstract

BACKGROUND

Non-small cell lung cancer (NSCLC) was usually associated with poor prognosis and invalid therapeutical response to immunotherapy due to biological heterogeneity. It is urgent to screen reliable biomarkers, especially immunotherapy-associated biomarkers, that can predict outcomes of these patients.

METHODS

Gene expression profiles of 1026 NSCLC patients were collected from The Cancer Genome Atlas (TCGA) datasets with their corresponding clinical and somatic mutation data. Based on immune infiltration scores, molecular clustering classification was performed to identify immune subtypes in NSCLC. After the functional enrichment analysis of subtypes, hub genes were further screened using univariate Cox, Lasso, and multivariate Cox regression analysis, and the risk score was defined to construct the prognostic model. Other microarray data and corresponding clinical information of 603 NSCLC patients from the GEO datasets were applied to conduct random forest models for the prognosis of NSCLC with 100 runs of cross-validation. Finally, external datasets with immunotherapy and chemotherapy were further applied to explore the significance of risk-scores in clinical immunotherapy response for NSCLC patients.

RESULTS

Compared with Subtype-B, the Subtype-A, associated with better outcomes, was characterized by significantly higher stromal and immune scores, T lymphocytes infiltration scores and up-regulation of immunotherapy markers. In addition, we found and validated an eleven -gene signatures for better application of distinguishing high- and low-risk NSCLC patients and predict patients' prognosis and therapeutical response to immunotherapy. Furthermore, combined with other clinical characteristics based on multivariate Cox regression analysis, we successfully constructed and validated a nomogram to effectively predict the survival rate of NSCLC patients. External immunotherapy and chemotherapy cohorts validated the patients with higher risk-scores exhibited significant therapeutic response and clinical benefits.

CONCLUSION

These results demonstrated the immunological and prognostic heterogeneity within NSCLC and provided a new clinical application in predicting the prognosis and benefits of immunotherapy for the disease.

摘要

背景

非小细胞肺癌(NSCLC)由于生物学异质性,通常与预后不良和免疫治疗无效相关。因此,迫切需要筛选可靠的生物标志物,特别是免疫治疗相关的生物标志物,以预测这些患者的预后。

方法

从癌症基因组图谱(TCGA)数据库中收集了 1026 例 NSCLC 患者的基因表达谱及其相应的临床和体细胞突变数据。基于免疫浸润评分,对 NSCLC 进行分子聚类分类,以鉴定免疫亚型。在对亚型进行功能富集分析后,使用单变量 Cox、Lasso 和多变量 Cox 回归分析进一步筛选枢纽基因,并定义风险评分构建预后模型。从 GEO 数据库中选择另外 603 例 NSCLC 患者的微阵列数据和相应的临床信息,通过 100 次交叉验证的随机森林模型进行 NSCLC 预后预测。最后,进一步应用免疫治疗和化疗的外部数据集,探索风险评分在 NSCLC 患者临床免疫治疗反应中的意义。

结果

与 Subtype-B 相比,Subtype-A 与更好的预后相关,其特征为基质和免疫评分、T 淋巴细胞浸润评分更高,以及免疫治疗标志物上调。此外,我们发现并验证了一个由 11 个基因组成的特征,可更好地区分 NSCLC 患者的高风险和低风险,并预测患者的预后和对免疫治疗的反应。此外,通过多变量 Cox 回归分析结合其他临床特征,我们成功构建并验证了一个列线图,以有效预测 NSCLC 患者的生存率。外部免疫治疗和化疗队列验证了风险评分较高的患者表现出显著的治疗反应和临床获益。

结论

这些结果表明 NSCLC 存在免疫和预后异质性,并为预测该疾病的预后和免疫治疗获益提供了新的临床应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35f8/8628446/a7c6f25a2067/12890_2021_1765_Fig1_HTML.jpg

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