Dataset Information

Novel mutational landscapes and expression signatures of lung squamous cell carcinoma.

ABSTRACT: Lung squamous cell carcinoma (LUSC) is a major subtype of Non-Small Cell Lung Cancer. To increase our understanding of the LUSC pathobiology, we performed exome sequencing and RNA-seq in 16 murine carcinogen-induced LUSC tumors and 8 normal murine lung tissue samples. Additionally, we conducted single-cell RNA-seq on two independent tumors from the same murine model. We identified a list of 59 cancer genes recurrently mutated in the mice LUSC tumors, 47 (80%) of which were also mutated in human LUSCs. At the single cell level, we detected unique clonal mutation patterns for each of the two LUSC tumors, being initiated from clones carrying the mutant Igfbp7 and Trp53 genes, respectively. We also identified an expression signature serving as an effective classifier for LUSC tumors and a strong predictor of survival outcomes of lung cancer patients. Lastly, we found that some of the mutant LUSC genes were associated with the significantly altered tumoral expression of inhibitory immune checkpoint genes such as PD-L1, VISTA, TIM3 and LAG3 in human LUSCs. The novel findings of clonal evolution, mutational landscapes and expression signatures of LUSC suggested new targets for the overall LUSC therapy and the immunotherapy of LUSC.

SUBMITTER: Xiong D

PROVIDER: S-EPMC5800913 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Novel mutational landscapes and expression signatures of lung squamous cell carcinoma.

Xiong Donghai D Pan Jing J Yin Yuxin Y Jiang Hui H Szabo Eva E Lubet Ronald A RA Wang Yian Y You Ming M

Oncotarget 20171227 7

Lung squamous cell carcinoma (LUSC) is a major subtype of Non-Small Cell Lung Cancer. To increase our understanding of the LUSC pathobiology, we performed exome sequencing and RNA-seq in 16 murine carcinogen-induced LUSC tumors and 8 normal murine lung tissue samples. Additionally, we conducted single-cell RNA-seq on two independent tumors from the same murine model. We identified a list of 59 cancer genes recurrently mutated in the mice LUSC tumors, 47 (80%) of which were also mutated in human ...[more]

PMID: 29484121

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Project description:BackgroundTumor mutational burden (TMB) has been identified as one of the predictors for the response to anti-programmed cell death-1 (anti-PD-1) antibody therapy and reported to correlate with smoking history in lung adenocarcinoma. However, in squamous cell carcinoma of the lung, the association between TMB and clinicopathological background factors, such as smoking history, has not been reported, including in our previous study. The mutational signature is a tool to identify the mutagens that are contributing to the mutational spectrum of a tumor by investigating the pattern of DNA changes. Here, we analyzed the mutational signature in lung squamous cell carcinoma to identify mutagens affecting the TMB.MethodsSeven representative mutational signatures including signature 7 (SI7) [ultraviolet (UV)-related], SI4 (smoking), SI6/15 [mismatch repair (MMR)], SI2/13 [apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like (APOBEC)], and SI5 (clock-like) were analyzed in Japanese patients with lung squamous cell carcinoma (n=67) using data generated by next-generation sequencing consisting of a 415-gene panel. The relationships between signatures and clinico-pathological data including TMB and programmed death-ligand 1 (PD-L1) expression were analyzed.ResultsAlthough the reconstructed mutational counts were small with targeted sequencing (median: 30.1, range: 13.3-98.7), the distributions of signatures were comparable among samples, with 56 cases containing more than four signatures. The smoking-related SI4 was found in 45 cases and was significantly related with pack-year index (PYI) (P=0.026). The reconstructed mutation counts were highly correlated with SI4 (r=0.51, P<0.0001), whereas the correlation was weak with SI6/15 (MMR-related) and SI2/13 (APOBEC-related). There was no mutational signature related with PD-L1 expression. Some patients exhibited unique signatures; the patient with the highest mutational counts had a MMR signature, and another patient with a prominent UV signature had occupational exposure to UV, as he was employed as a neon sign engineer.ConclusionsMutational signatures can predict the cause of lung squamous cell carcinoma. Tobacco smoking is the mutagen most related with TMB.

Project description:BackgroundLung cancer is the leading cause of cancer deaths globally, with lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) being major subtypes. Immunotherapy has emerged as a promising approach for the treatment of lung cancer, but understanding the underlying mechanisms of immune dysregulation is crucial for the development of effective therapies. This study aimed to investigate the distinctive cellular features of LUAD and LUSC and identify potential biomarkers associated with the pathogenesis and clinical outcomes of each subtype.MethodsWe used digital cytometry techniques to analyze the RNA-Seq data of 1128 lung cancer patients from The Cancer Genome Atlas (TCGA) database. The abundance of cell subtypes and ecotypes in LUAD and LUSC patients was quantified. Univariate survival analysis was used to investigate their associations with patient overall survival (OS). Differential gene expression analysis and gene co-expression network construction were carried out to explore the gene expression patterns of LUSC patients with distinct survival outcomes. Scratch wound-healing assay, colony formation assay, and transwell assay were used to validate the candidate drugs for LUSC treatment.ResultsWe found differential expression of cell subtypes between LUAD and LUSC, with certain cell subtypes being prognostic for survival in both subtypes. We also identified differential gene expression and gene co-expression modules associated with macrophages.3/PCs.2 ratio in LUSC patients with distinct survival outcomes. Furthermore, ecotype ratios were found to be prognostic in both subtypes and machine learning models showed that certain cell subtypes, such as epithelial.cells.1, epithelial.cells.5, and endothelial.cells.2 are important for predicting LUSC. Ginkgolide B and triamterene can inhibit the proliferation, invasion, and migration of LUSC cell lines.ConclusionWe provide insight into the distinctive cellular features of LUAD and LUSC, and identify potential biomarkers associated with the pathogenesis and clinical outcomes of each subtype. Ginkgolide B and triamterene could be promising drugs for LUSC treatment.

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.

Project description:Purpose: Alternative splicing (AS) is a post-transcriptional process that plays a significant role in enhancing the diversity of transcription and protein. Accumulating evidences have demonstrated that dysregulation of AS is associated with oncogenic processes. However, AS signature specifically in lung squamous cell carcinoma (LUSC) remains unknown. This study aimed to evaluate the prognostic values of AS events in LUSC patients. Methods: The RNA-seq data, AS events data and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression analysis was performed to identify survival-related AS events and survival-related parent genes were subjected to Gene Ontology enrichment analysis and gene network analysis. The least absolute shrinkage and selection operator (LASSO) method and multivariate Cox regression analysis were used to construct prognostic prediction models, and their predictive values were assessed by Kaplan-Meier analysis and receiver operating characteristic (ROC) curves. Then a nomogram was established to predict the survival of LUSC patients. And the interaction network of splicing factors (SFs) and survival-related AS events was constructed by Spearman correlation analysis and visualized by Cytoscape. Results: Totally, 467 LUSC patients were included in this study and 1,991 survival-related AS events within 1,433 genes were identified. SMAD4, FOS, POLR2L, and RNPS1 were the hub genes in the gene interaction network. Eight prognostic prediction models (seven types of AS and all AS) were constructed and all exhibited high efficiency in distinguishing good or poor survival of LUSC patients. The final integrated prediction model including all types of AS events exhibited the best prognostic power with the maximum AUC values of 0.778, 0.816, 0.814 in 1, 3, 5 years ROC curves, respectively. Meanwhile, the nomogram performed well in predicting the 1-, 3-, and 5-year survival of LUSC patients. In addition, the SF-AS regulatory network uncovered a significant correlation between SFs and survival-related AS events. Conclusion: This is the first comprehensive study to analyze the role of AS events in LUSC specifically, which improves our understanding of the prognostic value of survival-related AS events for LUSC. And these survival-related AS events might serve as novel prognostic biomarkers and drug therapeutic targets for LUSC.

Dataset Information

Novel mutational landscapes and expression signatures of lung squamous cell carcinoma.

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Novel mutational landscapes and expression signatures of lung squamous cell carcinoma.

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