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Coupled immune stratification and identification of therapeutic candidates in patients with lung adenocarcinoma.

ABSTRACT: In recent years, personalized cancer immunotherapy, especially stratification-driven precision treatments have gained significant traction. However, due to the heterogeneity in clinical cohorts, the uncombined analysis of stratification/therapeutics may lead to confusion in determining ideal therapeutic options. We report that the coupled immune stratification and drug repurposing could facilitate identification of therapeutic candidates in patients with lung adenocarcinoma (LUAD). First, we categorized the patients into four groups based on immune gene profiling, associated with distinct molecular characteristics and clinical outcomes. Then, the weighted gene co-expression network analysis (WGCNA) algorithm was used to identify co-expression modules of each groups. We focused on C3 group which is characterized by low immune infiltration (cold tumor) and wild-type EGFR, posing a significant challenge for treatment of LUAD. Five drug candidates against the C3 status were identified which have potential dual functions to correct aberrant immune microenvironment and also halt tumorigenesis. Furthermore, their steady binding affinity against the targets was verified through molecular docking analysis. In sum, our findings suggest that such coupled analysis could be a promising methodology for identification and exploration of therapeutic candidates in the practice of personalized immunotherapy.

SUBMITTER: Hu W

PROVIDER: S-EPMC7485744 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Coupled immune stratification and identification of therapeutic candidates in patients with lung adenocarcinoma.

Hu Weilei W Wang Guosheng G Chen Yundi Y Yarmus Lonny B LB Liu Biao B Wan Yuan Y

Aging 20200827 16

In recent years, personalized cancer immunotherapy, especially stratification-driven precision treatments have gained significant traction. However, due to the heterogeneity in clinical cohorts, the uncombined analysis of stratification/therapeutics may lead to confusion in determining ideal therapeutic options. We report that the coupled immune stratification and drug repurposing could facilitate identification of therapeutic candidates in patients with lung adenocarcinoma (LUAD). First, we cat ...[more]

PMID: 32855362

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Project description:mRNA vaccines against cancer have advantages in safety, improved therapeutic efficacy, and large-scale production. Therefore, our purpose is to identify immune biomarkers and to analyze immune status for developing mRNA vaccines and selecting appropriate patients for vaccination. We downloaded clinical information and RNA-seq data of 494 LUAD patients from TCGA. LUAD mutational information was hierarchically clustered by NMF package (Version 0.23.0). DeconstructSigs package (Version 1.8.0) and NMF consistency clustering were used to identify mutation signatures. Maftools package (Version 2.6.05) was used to select LUAD-related immune biomarkers. TIMER was used to discuss the correlation between genetic mutations and cellular components. Unsupervised clustering Pam method was used to identify LUAD immune subtypes. Log-rank test and univariate/multivariate cox regression were used to predict the prognosis of immune subtypes. Dimensionality reduction analysis was dedicated to the description of LUAD immune landscape. LUAD patients are classified into four signatures: T >C, APOBEC mutation, age, and tobacco. Then, GPRIN1, MYRF, PLXNB2, SLC9A4, TRIM29, UBA6, and XDH are potential LUAD-related immune biomarker candidates to activate the immune response. Next, we clustered five LUAD-related immune subtypes (IS1-IS5) by prognostic prediction. IS3 showed prolonged survival. The reliability of our five immune subtypes was validated by Thorsson's results. IS2 and IS4 patients had high tumor mutation burden and large number of somatic mutations. Besides, we identified that immune subtypes of cold immunity (patients with IS2 and IS4) are ideal mRNA vaccination recipients. Finally, LUAD immune landscape revealed immune cells and prognostic conditions, which provides important information to select patients for vaccination. GPRIN1, MYRF, PLXNB2, SLC9A4, TRIM29, UBA6, and XDH are potential LUAD-related immune biomarker candidates to activate the immune response. Patients with IS2 and IS4 might potentially be immunization-sensitive patients for vaccination.

Project description:BackgroundLung adenocarcinoma (LUAD) is a common malignant tumor with the highest morbidity and mortality worldwide. The degree of tumor immune infiltration and clinical prognosis depend on immune-related genes, but their interaction with the tumor immune microenvironment, the specific mechanism driving immune infiltration and their prognostic value are still not very clear. Therefore, the aim of this work was focused on the elucidation of these unclear aspects.MethodsTCGA LUAD samples were divided into three immune infiltration subtypes according to the single sample gene set enrichment analysis (ssGSEA), in which the associated gene modules and hub genes were screened by weighted correlation network analysis (WGCNA). Four key genes related to immune infiltration were found and screened by differential expression analysis, univariate prognostic analysis, and Lasso-COX regression, and their PPI network was constructed. Finally, a Nomogram model based on the four genes and tumor stages was constructed and confirmed in two GEO data sets.ResultsAmong the three subtypes-high, medium, and low immune infiltration subtype-the survival rate of the patients in the high one was higher than the rate in the other two subtypes. The four key genes related to LUAD immune infiltration subtypes were CD69, KLRB1, PLCB2, and P2RY13. The PPI network revealed that the downstream genes of the G-protein coupled receptors (GPCRs) pathway were activated by these four genes through the S1PR1. The risk score signature based on these four genes could distinguish high and low-risk LUAD patients with different prognosis. The Nomogram constructed by risk score and clinical tumor stage showed a good ability to predict the survival rate of LUAD patients. The universality and robustness of the Nomogram was confirmed by two GEO datasets.ConclusionsThe prognosis of LUAD patients could be predicted by the constructed risk score signature based on the four genes, making this score a potential independent biomarker. The screening, identification, and analysis of these four genes could contribute to the understanding of GPCRs and LUAD immune infiltration, thus guiding the formulation of more effective immunotherapeutic strategies.

Project description:The proportion of patients with stage I lung adenocarcinoma (LUAD) has dramatically increased with the prevalence of low-dose computed tomography use for screening. Up to 30% of patients with stage I LUAD experience recurrence within 5 years after curative surgery. A robust risk stratification tool is urgently needed to identify patients who might benefit from adjuvant treatment. In this first investigation of the relationship between metabolic reprogramming and recurrence in stage I LUAD, we developed a recurrence-associated metabolic signature (RAMS). This RAMS was based on metabolism-associated genes to predict cancer relapse and overall prognoses of patients with stage I LUAD. The clinical significance and immune landscapes of the signature were comprehensively analyzed. Based on a gene expression profile from the GSE31210 database, functional enrichment analysis revealed a significant difference in metabolic reprogramming that distinguished patients with stage I LUAD with relapse from those without relapse. We then identified a metabolic signature (i.e., RAMS) represented by 2 genes (ACADM and RPS8) significantly related to recurrence-free survival and overall survival times of patients with stage I LUAD using transcriptome data analysis of a training set. The training set was well validated in a test set. The discriminatory power of the 2 gene metabolic signature was further validated using protein values in an additional independent cohort. The results indicated a clear association between a high risk score and a very poor patient prognosis. Stratification analysis and multivariate Cox regression analysis showed that the RAMS was an independent prognostic factor. We also found that the risk score was positively correlated with inflammatory response, the antigen-presenting process, and the expression levels of many immunosuppressive checkpoint molecules (e.g., PD-L1, PD-L2, B7-H3, galectin-9, and FGL-1). These results suggested that high risk patients had immune response suppression. Further analysis revealed that anti-PD-1/PD-L1 immunotherapy did not have significant benefits for high risk patients. However, the patients could respond better to chemotherapy. This study is the first to highlight the relationship between metabolic reprogramming and recurrence in stage I LUAD, and is the first to also develop a clinically feasible signature. This signature may be a powerful prognostic tool and help further optimize the cancer therapy paradigm.

Project description:Accumulating evidence indicates that immunotherapy helped to improve the survival and quality-of-life of patients with lung adenocarcinoma (LUAD) or lung squamous cell carcinoma (LUSC) besides chemotherapy and gene targeting treatment. This study aimed to develop immune-related gene signatures in LUAD and LUSC subtypes, respectively. LUAD and LUSC samples were divided into high- and low-abundance groups of immune cell infiltration (Immunity_H and Immunity_L) based on the abundance of immune cell infiltrations. The distribution of immune cells was significantly different between the high- and low-immunity subtypes in LUAD and LUSC samples. The differentially expressed genes (DEGs) between those two groups in LUAD and LUSC contain some key immune-related genes, such as PDL1, PD1, CTLA-4, and HLA families. The DEGs were enriched in multiple immune-related pathways. Furthermore, the seven-immune-related-gene-signature (CD1B, CHRNA6, CLEC12B, CLEC17A, CLNK, INHA, and SLC14A2) prognostic model-based high- and low-risk groups were significantly associated with LUAD overall survival and clinical characteristics. The eight-immune-related-gene-signature (C4BPB, FCAMR, GRAPL, MAP1LC3C, MGC2889, TRIM55, UGT1A1, and VIPR2) prognostic model-based high- and low-risk groups were significantly associated with LUSC overall survival and clinical characteristics. The prognostic models were tested as good ones by receiver operating characteristic, principal component analysis, univariate and multivariate analysis, and nomogram. The verifications of these two immune-related-gene-signature prognostic models showed consistency in the train and test cohorts of LUAD and LUSC. In addition, patients with LUAD in the low-risk group responded better to immunotherapy than those in the high-risk group. This study revealed two reliable immune-related-gene-signature models that were significantly associated with prognosis and tumor microenvironment cell infiltration in LUAD and LUSC, respectively. Evaluation of the integrated characterization of multiple immune-related genes and pathways could help to predict the response to immunotherapy and monitor immunotherapy strategies.

Dataset Information

Coupled immune stratification and identification of therapeutic candidates in patients with lung adenocarcinoma.

Publications

Coupled immune stratification and identification of therapeutic candidates in patients with lung adenocarcinoma.

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