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

Abstract

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.

Exploration of the prognostic signature reflecting tumor microenvironment of lung adenocarcinoma based on immunologically relevant genes

“…Four immune-related prognostic biomarkers were screened by integrating five public microarray datasets, and the expression level of markers was positively correlated with different types of tumor-infiltrating immune cells [ 15 ]. Chen et al [ 16 ] have revealed TME-based signatures capable of predicting LUAD patient survival and therapeutic responses. However, large amounts of studies tended to mainly concentrate on a single critical gene/pathway or one aspect of tumor development.…”

Immunoprofiling: An Encouraging Method for Predictive Factors Examination in Lung Cancer Patients Treated with Immunotherapy

“…The better efficacy of immunotherapy in patients with "inflamed" tumors than in patients with "non-inflamed" tumors seems to be well documented in the literature [40,44,62,63]. The results obtained in melanoma patients treated with immunological checkpoint inhibitors indicated that tumor regression after PD-1 blockade requires tumor infiltration by CD8-positive cells.…”

“…It was clearly shown that the information regarding immune system status in tumor tissue may be of fundamental importance in predicting the prognosis of lung cancer patients [40][41][42]. However, all these considerations obviously indicate that the information about the absolute number of T cells is not enough to assess the patient's prognosis, but the information about the functioning of T lymphocytes and the presence of other immune system cells in the cancer tissue is necessary.…”

An Effective Hypoxia-Related Long Non-Coding RNA Assessment Model for Prognosis of Lung Adenocarcinoma

“…Currently, the diagnosis of LUAD is primarily based on symptoms, which included the size and location of the tumor, the location of the tumor in the lymph nodes, and where the cancer has spread ( Lemjabbar-Alaoui et al, 2015 ; Rami-Porta et al, 2017 ; Carter et al, 2018 ). Although many potential biomarkers for early detection of LUAD have been studied, such as autophagy-related survival model, immune-related survival model, and ferroptosis-related gene signature, there is still lack of clinically used biomarkers due to lack of sensitivity and validity of these biomarkers on the development of LUAD ( Hirsch et al, 2017 ; Chen et al, 2021 ; Shi et al, 2021 ; Wu et al, 2021 ; Jiang et al, 2021 ; Li et al, 2021 ).…”