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Presence of a 34-gene signature is a favorable prognostic marker in squamous non-small cell lung carcinoma.

ABSTRACT: BACKGROUND:The tumor immune microenvironment is a heterogeneous entity. Gene expression analysis allows us to perform comprehensive immunoprofiling and may assist in dissecting the different components of the immune infiltrate. As gene expression analysis also provides information regarding tumor cells, differences in interactions between the immune system and specific tumor characteristics can also be explored. This study aims to gain further insights in the composition of the tumor immune infiltrate and to correlate these components to histology and overall survival in non-small cell lung cancer (NSCLC). METHODS:Archival tissues from 530 early stage, resected NSCLC patients with annotated tumor and patient characteristics were analyzed using the NanoString nCounter Analysis system. RESULTS:Unsupervised clustering of the samples was mainly driven by the overall level of inflammation, which was not correlated with survival in this patient set. Adenocarcinoma (AD) showed a significantly higher degree of immune infiltration compared to squamous cell carcinoma (SCC). A 34-gene signature, which did not correlate with the overall level of immune infiltration, was identified and showed an OS benefit in SCC. Strikingly, this benefit was not observed in AD. This difference in OS in SCC specifically was confirmed in two independent NSCLC cohorts. The highest correlation between expression of the 34-gene signature and specific immune cell populations was observed for NK cells, but although a plausible mechanism for NK cell intervention in tumor growth could be established in SCC over AD, this could not be translated back to immunohistochemistry, which showed that NK cell infiltration is scarce irrespective of histology. CONCLUSIONS:These findings suggest that the ability of immune cell infiltration and the interaction between tumor and immune cells may be different between AD and SCC histology and that a subgroup of SCC tumors seems more susceptible to Natural Killer cell recognition and killing, whereas this may not occur in AD tumors. A highly sensitive technique like NanoString was able to detect this subgroup based on a 34-gene signature, but further research will be needed to assist in explaining the biological rationale of such low-level expression signatures.

SUBMITTER: Theelen WSME

PROVIDER: S-EPMC7333331 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Presence of a 34-gene signature is a favorable prognostic marker in squamous non-small cell lung carcinoma.

Theelen W S M E WSME Krijgsman O O Monkhorst K K Kuilman T T Peters D D G C DDGC Cornelissen S S Ligtenberg M A MA Willems S M SM Blaauwgeers J L G JLG van Noesel C J M CJM Peeper D S DS van den Heuvel M M MM Schulze K K

Journal of translational medicine 20200703 1

<h4>Background</h4>The tumor immune microenvironment is a heterogeneous entity. Gene expression analysis allows us to perform comprehensive immunoprofiling and may assist in dissecting the different components of the immune infiltrate. As gene expression analysis also provides information regarding tumor cells, differences in interactions between the immune system and specific tumor characteristics can also be explored. This study aims to gain further insights in the composition of the tumor imm ...[more]

PMID: 32620126

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Project description:BackgroundLung squamous cell carcinoma (LUSC), as the second frequent subtype of lung cancer, causes lots of mortalities primarily due to a lack of precise prognostic markers and timely treatment intervention. Previous studies have constructed several risk prognostic models based on DNA methylation sites in multiple tumors, whereas, DNA methylation signature of LUSC remains to be built, and its predictive value need to be evaluated.MethodsThe genome-wide DNA methylation data of LUSC samples was obtained from The Cancer Genome Atlas dataset. Univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) were implemented to identify DNA methylation sites related to overall survival of LUSC patients. Thus, we performed multivariate Cox regression to establish a DNA methylation signature. The Kaplan-Meier (K-M) survival curves and time-dependent receiver operating characteristic (ROC) curves were plotted to estimate the prognostic power of the signature. Comparison with other known prognostic biomarkers, our DNA methylation signature showed higher predictive specificity and sensitivity. In addition, multivariate Cox regression screened out independent prognostic factors and constructed a nomogram.ResultsSeveral statistical methods were performed to construct an 11-DNA methylation signature. LUSC patients were divided into low- and high-risk group based on risk score, and high-risk group had a shorter survival time. According to the results of K-M and ROC analyses, the 11-DNA methylation signature showed significant sensitivity and specificity in predicting the LUSC patients’ overall survival. Finally, we integrated some independent prognostic factors (risk score, metastasis stage, and tobacco smoking history) to construct a nomogram, which has excellent prognostic power and may provide guidance for the therapeutic strategies.ConclusionsWe constructed the first risk prognosis model based on DNA methylation site in LUSC, which showed better predictive ability. In addition, a nomogram integrating the DNA methylation signature, metastasis stage, and tobacco smoking history was developed.

Project description:BackgroundCancers of the oral cavity are primarily oral squamous cell carcinomas (OSCCs). Many of the OSCCs present at late stages with an exceptionally poor prognosis. A probable limitation in management of patients with OSCC lies in the insufficient knowledge pertaining to the linkage between copy number alterations in OSCC and oral tumourigenesis thereby resulting in an inability to deliver targeted therapy.ObjectivesThe current study aimed to identify copy number alterations (CNAs) in OSCC using array comparative genomic hybridization (array CGH) and to correlate the CNAs with clinico-pathologic parameters and clinical outcomes.Materials and methodsUsing array CGH, genome-wide profiling was performed on 75 OSCCs. Selected genes that were harboured in the frequently amplified and deleted regions were validated using quantitative polymerase chain reaction (qPCR). Thereafter, pathway and network functional analysis were carried out using Ingenuity Pathway Analysis (IPA) software.ResultsMultiple chromosomal regions including 3q, 5p, 7p, 8q, 9p, 10p, 11q were frequently amplified, while 3p and 8p chromosomal regions were frequently deleted. These findings were in confirmation with our previous study using ultra-dense array CGH. In addition, amplification of 8q, 11q, 7p and 9p and deletion of 8p chromosomal regions showed a significant correlation with clinico-pathologic parameters such as the size of the tumour, metastatic lymph nodes and pathological staging. Co-amplification of 7p, 8q, 9p and 11q regions that harbored amplified genes namely CCND1, EGFR, TPM2 and LRP12 respectively, when combined, continues to be an independent prognostic factor in OSCC.ConclusionAmplification of 3q, 5p, 7p, 8q, 9p, 10p, 11q and deletion of 3p and 8p chromosomal regions were recurrent among OSCC patients. Co-alteration of 7p, 8q, 9p and 11q was found to be associated with clinico-pathologic parameters and poor survival. These regions contain genes that play critical roles in tumourigenesis pathways.

Project description:BackgroundEarly-stage lung squamous cell carcinoma (LUSC) progression is accompanied by changes in immune microenvironments and the expression of immune-related genes (IRGs). Identifying innate IRGs associated with prognosis may improve treatment and reveal new immunotherapeutic targets.MethodsGene expression profiles and clinical data of early-stage LUSC patients were obtained from the Gene Expression Omnibus and The Cancer Genome Atlas databases and IRGs from the InnateDB database. Univariate and multivariate Cox regression and LASSO regression analyses were performed to identify an innate IRG signature model prognostic in patients with early-stage LUSC. The predictive ability of this model was assessed by time-dependent receiver operator characteristic curve analysis, with the independence of the model-determined risk score assessed by univariate and multivariate Cox regression analyses. Overall survival (OS) in early-stage LUSC patients was assessed using a nomogram and decision curve analysis (DCA). Functional and biological pathways were determined by gene set enrichment analysis, and differences in biological functions and immune microenvironments between the high- and low-risk groups were assessed by ESTIMATE and the CIBERSORT algorithm.ResultsA signature involving six IRGs (SREBF2, GP2, BMX, NR1H4, DDX41, and GOPC) was prognostic of OS. Samples were divided into high- and low-risk groups based on median risk scores. OS was significantly shorter in the high-risk than in the low-risk group in the training (P < 0.001), GEO validation (P = 0.00021) and TCGA validation (P = 0.034) cohorts. Multivariate Cox regression analysis showed that risk score was an independent risk factor for OS, with the combination of risk score and T stage being optimally predictive of clinical benefit. GSEA, ESTIMATE, and the CIBERSORT algorithm showed that immune cell infiltration was higher and immune-related pathways were more strongly expressed in the low-risk group.ConclusionA signature that includes these six innate IRGs may predict prognosis in patients with early-stage LUSC.

Dataset Information

Presence of a 34-gene signature is a favorable prognostic marker in squamous non-small cell lung carcinoma.

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Presence of a 34-gene signature is a favorable prognostic marker in squamous non-small cell lung carcinoma.

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