Dataset Information

Clinicopathological and molecular characteristics of patients with hypermutant lung cancer: A retrospective cohort study.

ABSTRACT: Tumor mutation burden (TMB) is an independent indicator used to select patients sensitive to immunotherapy. The present study aimed to investigate the clinicopathological and molecular characteristics of patients with hypermutant lung cancer to identify an economical, simple and complementary method for predicting TMB and immunotherapy responses. In total, 1,000 patients with lung cancer were randomly selected, and their samples were submitted to next-generation sequencing, with their TMB status reviewed. The threshold of hypermutation was set to 17.24 mutations (muts)/Mb. The proportion of smokers was higher in the hypermutant cohort (n=67) compared with in the non-hypermutant cohort (n=933; 85.1 vs. 46.6%; P<0.0001). Compared with in the non-hypermutant cohort, the proportion of squamous cell carcinoma cases and small cell lung cancer cases was higher in the hypermutant cohort (22.4 vs. 13.1% and 6.0 vs. 2.6%, respectively). In addition, compared with in the non-hypermutant cohort, mutations in the low-density lipoprotein receptor-related protein 1B were more frequently observed in the hypermutant cohort (67.2 vs. 14.3%; P<0.0001). A similar trend was obtained for all genes tested, except for the EGFR gene. Furthermore, in the hypermutant cohort, the prevalence of microsatellite instability was extremely high (9.0%). The mutation frequency in DNA damage response (DDR) genes was notably higher in the hypermutant cohort, where several DDR-associated genes were enriched, compared with in the non-hypermutant cohort. The enrichment analysis revealed a strong association between mutations in Notch signaling and high TMB. To the best of our knowledge, the present study is the first to comprehensively investigate the clinical and genetic characteristics of patients with hypermutant lung cancer in a Chinese population. The results of the current study suggested that hypermutant lung cancer exerted distinctive clinical and genetic features, which may be used as complementary indicators for screening patients sensitive to immunotherapy.

SUBMITTER: Zhang H

PROVIDER: S-EPMC7933761 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Clinicopathological and molecular characteristics of patients with hypermutant lung cancer: A retrospective cohort study.

Zhang Hongbin H Wang Yuan Y Ji Qiaoxia Q Cai Hongmei H Liang Xiangcun X Xie Jiong J Li Hua H Wang Jun J Zhu Guiyun G Tian Erpeng E Zhu Lingling L Yuan Mingming M Chen Rongrong R Zhao Min M

Oncology letters 20210225 4

Tumor mutation burden (TMB) is an independent indicator used to select patients sensitive to immunotherapy. The present study aimed to investigate the clinicopathological and molecular characteristics of patients with hypermutant lung cancer to identify an economical, simple and complementary method for predicting TMB and immunotherapy responses. In total, 1,000 patients with lung cancer were randomly selected, and their samples were submitted to next-generation sequencing, with their TMB status ...[more]

PMID: 33692861

Similar Datasets

Project description:BackgroundAlthough the prognosis of non-small cell lung cancer (NSCLC) can be assessed based on pathological type, disease stage and inflammatory indicators, the prognostic scoring model of NSCLC still needs to improve. HDAC11 is associated with poor prognosis of partial tumors, but its prognostic relationship with NSCLC is poorly understood. In this study, the role of HDAC11 in NSCLC was studied to evaluate relationship with disease prognosis and potential therapeutic target.MethodsThe clinicopathological and paracancerous tissues of patients with NSCLC primarily diagnosed in Tangdu Hospital from 2009 to 2013 were collected. Follow-up of patients were made every three months and the last follow-up period was December 2018. The expression of HDAC11 was assessed by immunohistochemistry (IHC). Then, weighted gene co-expression network analysis (WGCNA) was used to analyze the relationship between HDAC11 expression and the prognosis of lung adenocarcinoma (LUAD) patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Kaplan-Meier plotter database was used to verify the connection between hub genes and tumor stage and prognosis. We accessed the relationship between HDAC11 expression and clinicopathological features, and impact on the prognosis.ResultsThe study assessed 326 patients with NSCLC. Compared with adjacent tissues, HDAC11 expression was upregulated (HR =1.503, 95% CI: 1.172 to 1.927, P=0.001). Kaplan-Meier survival analyses showed that HDAC11 expression was closely related to OS of NSCLC patients (P=0.0011). Univariate and multivariate analyses showed that the independent risk factors of OS were clinical stage, HDAC11 expression, and HDAC11 differentiation (all P≤0.001). HDAC11 was significantly associated with prognosis in LUAD. A total of 1,174 differential genes and WGCNA were obtained to construct a co-expression network in LUAD. The GO and KEGG pathway enrichment analyses showed the relevance with staphylococcus aureus infection, NOD-like receptor signaling pathway, and others. The results of LUAD survival analysis showed that HDAC11-related genes NKX2-5 and FABP7 were significantly associated with LUAD prognosis.ConclusionsThe high expression of HDAC11 is related to the poor prognosis of LUAD, and it is expected to become a therapeutic target and prognostic evaluation therapy for LUAD in the future. However, the relevant results need to be further studied and verified.

Project description:BackgroundWith the emerging radiological techniques and the increasing incidence of adenocarcinoma, the composition and structure of cavitary lung cancer have been significantly changed. The aim of the study was to demonstrate clinicopathological characteristics of solitary cavitary lung cancer which was ≤3 cm.MethodsA case-control study was designed through retrospective data analysis of clinicopathological data of 946 cases with solitary lung cancer smaller than 3 cm. Univariable and multivariable analysis were used to identify the risk factors of cavitation.ResultsCavitary lung cancer occurred more frequently in patients who were elderly (P=0.044), male (P=0.004), who had a smoking history (P=0.018), higher carcinoembryonic antigen (CEA) level (P<0.001), peripheral lesions (P=0.017), solid nodules (P<0.001), spiculation (P=0.003), vascular convergence (P<0.001), air bronchogram (P=0.004), larger tumor size (P<0.001), advanced T stage (P<0.001), lymph node metastasis (P=0.028) and advanced pTNM stage (P=0.004). In addition, cavitary lung cancer was more common in papillary predominant tumors (P=0.017), while noncavitary lung cancer occurred more frequently in AIS/MIA (P=0.002) and lepidic predominant tumors (P<0.001). It was confirmed that cavitation was significantly associated with elderly (P=0.013), male (P=0.003), larger maximum tumor diameter (P<0.001), solid nodules (P<0.001), larger pT size (P=0.016) and advanced pN stage (P=0.036) in multivariable analysis. ROC curves showed that the AUV was greater in maximum tumor diameter than in pT size predicting cavitation (0.71 vs. 0.66). A cut off value of 20.9 mm showed a discriminatory power of cavitation with a sensitivity of 68.7% and a specificity of 71.2%.ConclusionsComparing with noncavitary lung cancer, cavitary lung cancer smaller than 3 cm may have worse prognostic clinical, radiological and pathological characteristics. Especially, cavitary lung cancer present as more solid nodules on CT images and present with more invasive on pathological findings.

Project description:IntroductionCommensal microbiota dysbiosis is associated with the development of lung cancer. The current studies about composition of respiratory microbiota in lung cancer patients yielded inconsistent results. This study aimed to examine the association between airway microbiota and lung cancer clinicopathological characteristics.MethodsSurgically removed lesion tissues from 75 non-small cell lung cancer patients and 7 patients with benign pulmonary diseases were analyzed by 16S rRNA sequencing. Taxonomy, relative abundance, and diversity of respiratory microbiota were compared among lung cancer of different pathology and TNM stages. The effects of antibiotic and cigarette exposure on respiratory microbiota in lung cancer patients were also evaluated.ResultsBacterial relative abundance and alpha- and beta-diversity analysis of lung microbiota showed significant differences among lung cancer of different pathology and benign pulmonary diseases. At the genus level, the abundance differences of 13 taxa between lung squamous cell carcinoma and lung adenocarcinoma, 63 taxa between lung squamous cell carcinoma and benign pulmonary diseases, and 4 taxa between lung adenocarcinoma and benign pulmonary diseases reached statistical significance. In contrast, diversity differences were not as significant across lung cancer of different stages. No significant differences were observed in tissue taxonomic abundances and diversity at all taxonomic levels between lung cancer patients with and without antibiotic exposure 3 months prior to surgery. For lung adenocarcinoma, respiratory bacterial abundance and diversity at all taxonomic levels did not show significant differences between smokers and non-smokers.ConclusionsOur results confirm significantly differential respiratory microbiome taxa, abundance, and diversity in lung cancer of different pathology and some stages. Short-term antibiotic application might play a minor role in molding airway microbiota in lung cancer patients. Composition and diversity of respiratory microbiota in lung adenocarcinoma are not affected by cigarette exposure.

Dataset Information

Clinicopathological and molecular characteristics of patients with hypermutant lung cancer: A retrospective cohort study.

Publications

Clinicopathological and molecular characteristics of patients with hypermutant lung cancer: A retrospective cohort study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets