scholarly journals Identification of the Nerve-Cancer Cross-Talk-Related Prognostic Gene Model in Head and Neck Squamous Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Jun Li ◽  
Yunhong Xu ◽  
Gang Peng ◽  
Kuikui Zhu ◽  
Zilong Wu ◽  
...  
Keyword(s):  
Head And Neck ◽  
Tumor Cells ◽  
Cross Talk ◽  
Prognostic Model ◽  
Clinical Stage ◽  
Sequencing Data ◽  
Gene Model ◽  
Tumor Mutation Burden ◽  
Single Cell Sequencing ◽  
Mutation Burden

The incidence of head and neck squamous cell carcinoma (HNSC) is increasing year by year. The nerve is an important component of the tumor microenvironment, which has a wide range of cross-talk with tumor cells and immune cells, especially in highly innervated organs, such as head and neck cancer and pancreatic cancer. However, the role of cancer-nerve cross-talk-related genes (NCCGs) in HNSC is unclear. In our study, we constructed a prognostic model based on genes with prognostic value in NCCGs. We used Pearson’s correlation to analyze the relationship between NCCGs and immune infiltration, microsatellite instability, tumor mutation burden, drug sensitivity, and clinical stage. We used single-cell sequencing data to analyze the expression of genes associated with stage in different cells and explored the possible pathways affected by these genes via gene set enrichment analysis. In the TCGA-HNSC cohort, a total of 23 genes were up- or downregulated compared with normal tissues. GO and KEGG pathway analysis suggested that NCCGs are mainly concentrated in membrane potential regulation, chemical synapse, axon formation, and neuroreceptor-ligand interaction. Ten genes were identified as prognosis genes by Kaplan-Meier plotter and used as candidate genes for LASSO regression. We constructed a seven-gene prognostic model (NTRK1, L1CAM, GRIN3A, CHRNA5, CHRNA6, CHRNB4, CHRND). The model could effectively predict the 1-, 3-, and 5-year survival rates in the TCGA-HNSC cohort, and the effectiveness of the model was verified by external test data. The genes included in the model were significantly correlated with immune infiltration, microsatellite instability, tumor mutation burden, drug sensitivity, and clinical stage. Single-cell sequencing data of HNSC showed that CHRNB4 was mainly expressed in tumor cells, and multiple metabolic pathways were enriched in high CHRNB4 expression tumor cells. In summary, we used comprehensive bioinformatics analysis to construct a prognostic gene model and revealed the potential of NCCGs as therapeutic targets and prognostic biomarkers in HNSC.

Association of driver genes with high-tumor mutation burden and outcome in patients with head and neck cancer: Implications for immunotherapy.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e18533-e18533
Author(s):  
Yu Chen ◽  
Chuan-ben Chen ◽  
Xiao-bin Zheng ◽  
Xuan Gao ◽  
Liu Jun ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Peripheral Blood Lymphocytes ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Driver Genes ◽  
Tumor Mutation Burden ◽  
Mutation Burden ◽  
Chinese Cohort

e18533 Background: While the immune checkpoint blockades had demonstrated promising benefits in head and neck cancer (HNSC), its clinical efficacy is limited to a selected subset of less than 20% HNSC patients. Tumor mutation burden (TMB) has been reported as a predictor for ICBs in multiple tumors, including HNSC. However, the association of driver genes with TMB and outcomes in patients with HNSC has not yet been established. Methods: Somatic mutation landscape was characterized by interactively analyzed the sequencing data of 495 HNSC samples obtained from The Cancer Genome Atlas (TCGA) database and 185 samples from a Chinese cohort (Geneplus-database). Hybrid capture of a 1021 gene panel with potential clinical relevance was performed on tumor and paired Peripheral blood lymphocytes (PBLs) from 185 HNSC samples in a Chinese cohort. Results: In the Chinese cohort, patients harboring ≥5 muts/Mb (the top quartile of tTMB distribution) were classified as the TMB-H group; while ≥4.7 muts/Mb were classified as the TMB-M group. TMB -H was associated with better OS in the TCGA cohort. The rest were classified as TMB-L patients. Thirteen aberrant genes were significant correlation with TMB-H, including TSC2, POLE, CDK4, TSC1, MLH1, PTCH1, NF1, MSH3, RAD50, MSH2, CDH1, TNFRSF14, TERC. Among them,10 were further verified in the TCGA Head and Neck Cancer cohort, including TNFRSF14, MSH3, NF1, TSC2, RAD50, MSH2, PTCH1, POLE, MLH1, TSC1. Moreover, aberrant genes such as CDH1, MSH2 and RAD50 implicated better DFS (DFS:HR = .296, p = .034; HR = 0.128, p = .016; HR = .0422, p = .043) in HNSC of the TCGA cohort. Transcriptomic analysis in the TCGA Head and Neck Cancer cohort showed various degrees of immune upregulation in the tumor microenvironment (TME) in CDH1, MSH2, and RAD50 mutated population. Conclusions: These findings indicated that CDH1, MSH2, and RAD50 mutations may be associated with TMB-H, immuno-upregulation in TME and better survival outcomes. Combined with TMB, genes mentioned above may give us some insights into how ICB therapeutic strategies assist natural host immune responses against HNSC in Chinese population.

scholarly journals Assessment of tumor mutation burden calculation from gene panel sequencing data

2019 ◽  
Vol Volume 12 ◽  
pp. 3401-3409 ◽  
Author(s):  
Zhenwu Xu ◽  
Jiawei Dai ◽  
Dandan Wang ◽  
Hui Lu ◽  
Heng Dai ◽  
...  
Keyword(s):  
Gene Panel ◽  
Sequencing Data ◽  
Tumor Mutation Burden ◽  
Mutation Burden ◽  
Gene Panel Sequencing ◽  
Panel Sequencing

24 Cross-talk of tumor cells with mesenchymal stromal cells enhances tumor progression in head and neck cancer

Oral Oncology ◽  
2015 ◽  
Vol 51 (5) ◽  
pp. e35
Author(s):  
P. Dissmann ◽  
B. Kansy ◽  
K. Bruderek ◽  
C. Dumitru ◽  
S. Lang ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Tumor Progression ◽  
Head And Neck ◽  
Tumor Cells ◽  
Mesenchymal Stromal Cells ◽  
Neck Cancer ◽  
Cross Talk ◽  
Stromal Cells

scholarly journals Identification of Prognostic Biomarkers of Cutaneous Melanoma Based on Analysis of Tumor Mutation Burden

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jiaqiong Lin ◽  
Yan Lin ◽  
Zena Huang ◽  
Xiaoyong Li
Keyword(s):  
Cutaneous Melanoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Predictive Biomarkers ◽  
Tumor Mutation Burden ◽  
Skin Development ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Mutation Burden

Background. Immunotherapy offers a novel approach for the treatment of cutaneous melanoma, but the clinical efficiency varies for individual patients. In consideration of the high cost and adverse effects of immunotherapy, it is essential to explore the predictive biomarkers of outcomes. Recently, the tumor mutation burden (TMB) has been proposed as a predictive prognosticator of the immune response. Method. RNA-seq and somatic mutation datasets of 472 cutaneous melanoma patients were downloaded from The Cancer Genome Atlas (TCGA) database to analyze mutation type and TMB. Differently expressed genes (DEGs) were identified for functional analysis. TMB-related signatures were identified via LASSO and multivariate Cox regression analysis. The association between mutants of signatures and immune cells was evaluated from the TIMER database. Furthermore, the Wilcox test was applied to assess the difference in immune infiltration calculated by the CIBERSORT algorithm in risk groupings. Results. C>T substitutions and TTN were the most common SNV and mutated gene, respectively. Patients with low TMB presented poor prognosis. DEGs were mainly implicated in skin development, cell cycle, DNA replication, and immune-associated crosstalk pathways. Furthermore, a prognostic model consisting of eight TMB-related genes was developed, which was found to be an independent risk factor for treatment outcome. The mutational status of eight TMB-related genes was associated with a low level of immune infiltration. In addition, the immune infiltrates of CD4+ and CD8+ T cells, NK cells, and M1 macrophages were higher in the low-risk group, while those of M0 and M2 macrophages were higher in the high-risk group. Conclusion. Our study demonstrated that a higher TMB was associated with favorable survival outcome in cutaneous melanoma. Moreover, a close association between prognostic model and immune infiltration was identified, providing a new potential target for immunotherapy.

Genomic landscape and tumor mutation burden analysis of Chinese patients with sarcomatoid carcinoma of the head and neck

Oral Oncology ◽  
2021 ◽  
Vol 121 ◽  
pp. 105436
Author(s):  
Hai-bing Chen ◽  
Xiao-yang Gong ◽  
Wang Li ◽  
Dong-sheng Chen ◽  
Le-le Zhao ◽  
...  
Keyword(s):  
Head And Neck ◽  
Sarcomatoid Carcinoma ◽  
Chinese Patients ◽  
Tumor Mutation Burden ◽  
Genomic Landscape ◽  
Mutation Burden

scholarly journals RNA Sequencing Data for FFPE Tumor Blocks Can Be Used for Robust Estimation of Tumor Mutation Burden in Individual Biosamples

2021 ◽  
Vol 11 ◽  
Author(s):  
Maxim Sorokin ◽  
Alexander Gorelyshev ◽  
Victor Efimov ◽  
Evgenia Zotova ◽  
Marianna Zolotovskaia ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Clinical Decision Making ◽  
Area Under The Curve ◽  
Single Sample ◽  
Spearman Correlation ◽  
Sequencing Data ◽  
High Quality ◽  
Tissue Samples ◽  
Tumor Mutation Burden ◽  
Mutation Burden

Tumor mutation burden (TMB) is a well-known efficacy predictor for checkpoint inhibitor immunotherapies. Currently, TMB assessment relies on DNA sequencing data. Gene expression profiling by RNA sequencing (RNAseq) is another type of analysis that can inform clinical decision-making and including TMB estimation may strongly benefit this approach, especially for the formalin-fixed, paraffin-embedded (FFPE) tissue samples. Here, we for the first time compared TMB levels deduced from whole exome sequencing (WES) and RNAseq profiles of the same FFPE biosamples in single-sample mode. We took TCGA project data with mean sequencing depth 23 million gene-mapped reads (MGMRs) and found 0.46 (Pearson)–0.59 (Spearman) correlation with standard mutation calling pipelines. This was converted into low (<10) and high (>10) TMB per megabase classifier with area under the curve (AUC) 0.757, and application of machine learning increased AUC till 0.854. We then compared 73 experimental pairs of WES and RNAseq profiles with lower (mean 11 MGMRs) and higher (mean 68 MGMRs) RNA sequencing depths. For higher depth, we observed ~1 AUC for the high/low TMB classifier and 0.85 (Pearson)–0.95 (Spearman) correlation with standard mutation calling pipelines. For the lower depth, the AUC was below the high-quality threshold of 0.7. Thus, we conclude that using RNA sequencing of tumor materials from FFPE blocks with enough coverage can afford for high-quality discrimination of tumors with high and low TMB levels in a single-sample mode.

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