Characterization of Tumor Microenvironment in Lung Adenocarcinoma Identifies Immune Signatures to Predict Clinical Outcomes and Therapeutic Responses

Frontiers in Oncology

Xi Zhang¹  Chang Chen²  Donglai Chen²  Yiming Mao³  Yuhang Xue⁴  Wei Wang⁴  Yifei Wang⁴  Qifeng Ding⁴  Yongbing Chen⁴  Xiaofan Wang⁴ 

[1] Department of Neurology, Tongji Hospital, Tongji University School of Medicine, Shanghai, China;Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, School of Medicine, Shanghai, China;Department of Thoracic Surgery, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medicine, Suzhou, China;Department of Thoracic Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, China;Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, China;

关键词: lung adenocarcinoma;    tumor microenvironment;    signature;    survival;    therapeutic response;   

DOI  :  10.3389/fonc.2021.581030

来源: DOAJ

【 摘 要 】

Background and ObjectiveIncreasing evidence has elucidated the clinicopathological significance of individual TME component in predicting outcomes and immunotherapeutic efficacy in lung adenocarcinoma (LUAD). Therefore, we aimed to investigate whether comprehensive TME-based signatures could predict patient survival and therapeutic responses in LUAD, and to assess the associations among TME signatures, single nucleotide variations and clinicopathological characteristics.MethodsIn this study, we comprehensively estimated the TME infiltration patterns of 493 LUAD patients and systematically correlated the TME phenotypes with genomic characteristics and clinicopathological features of LUADs using two proposed computational algorithms. A TMEscore was then developed based on the TME signature genes, and its prognostic value was validated in different datasets. Bioinformatics analysis was used to evaluate the efficacy of the TMEscore in predicting responses to immunotherapy and chemotherapy.ResultsThree TME subtypes were identified with no prognostic significance exhibited. Among them, naïve B cells accounted for the majority in TMEcluster1, while M2 TAMs and M0 TAMs took the largest proportion in TMEcluster2 and TMEcluster3, respectively. A total of 3395 DEGs among the three TME clusters were determined, among which 217 TME signature genes were identified. Interestingly, these signature genes were mainly involved in T cell activation, lymphocyte proliferation and mononuclear cell proliferation. With somatic variations and tumor mutation burden (TMB) of the LUAD samples characterized, a genomic landscape of the LUADs was thereby established to visualize the relationships among the TMEscore, mutation spectra and clinicopathological profiles. In addition, the TMEscore was identified as not only a prognosticator for long-term survival in different datasets, but also a predictive biomarker for the responses to immune checkpoint blockade (ICB) and chemotherapeutic agents. Furthermore, the TMEscore exhibited greater accuracy than other conventional biomarkers including TMB and microsatellite instability in predicting immunotherapeutic response (p < 0.001).ConclusionIn conclusion, our present study depicted a comprehensive landscape of the TME signatures in LUADs. Meanwhile, the TMEscore was proved to be a promising predictor of patient survival and therapeutic responses in LUADs, which might be helpful to the future administration of personalized adjuvant therapy.

【 授权许可】

Unknown