Comparative genomic analysis of oral versus laryngeal and pharyngeal cancer

Vossen, David M.; Verhagen, Caroline V.M.; Verheij, Marcel; Wessels, Lodewyk F.A.; Vens, Conchita; Brekel, Michiel W. M. van den

doi:10.1016/j.oraloncology.2018.04.006

Cited by 28 publications

(40 citation statements)

References 47 publications

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“…Hence, the growth, migration and invasion of head and neck cancer cells are accelerated [29]. A comparative genomic analysis of oral versus laryngeal and pharyngeal cancer also found that LAMA2 (TCGA: 5% vs 19%) mutations are enriched in laryngeal and pharyngeal squamous cell carcinoma (L/P-SCC) [30], while other factors are enriched in pathways such as "DNA REPLICATION" and "PYRIMIDINE METABOLISM". Overexpression of the key metabolite cytidine related gene ENTPD8, which is enriched in the "PYRIMIDINE METABOLISM" pathway, was reported to promote cell apoptosis and inhibit proliferation by promoting CTP metabolization into cytidine in pancreatic cancer tissue (PCT) [31].…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Prognostic Nomogram for Gastric Cancer Based on DNA Methylation-Driven Differentially Expressed Genes

et al. 2020

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Background/Aims: The incidence of gastric cancer (GC) ranks fifth among common tumors and GC is the third leading cause of cancer-related death worldwide. The aim of this study was to develop and validate a nomogram for predicting the overall survival (OS) of patients with GC. Methods: DNA methylation (DNAm)-driven genes were identified by integrating DNAm and gene expression profiling analyses from The Cancer Genome Atlas (TCGA) GC cohort. Then, a risk score model was built based on Kaplan-Meier (K-M), least absolute shrinkage and selector operation (LASSO), and multivariate Cox regression analyses. After analyzing the clinical parameters, a nomogram was constructed and assessed. Another cohort (GSE62254) was used for external validation. Results: Thirteen differentially expressed DNAm-driven genes were narrowed down to a six-gene signature (PODN, NPY, MICU3, TUBB6 and RHOJ were hypermethylated, and MYO1A was hypomethylated), which was associated with OS (P < 0.05) after survival and LASSO regression analyses. These differentially expressed genes (DEGs) with altered DNAm statuses were included in the prognostic risk score model. The univariate Cox regression analysis indicated that risk score, age, and number of positive lymph nodes were significantly associated with survival time in GC patients. The multivariate Cox regression analysis also indicated that these variables were significant prognostic factors for GC. A nomogram including these variables was constructed, and its performance in predicting the 1-, 3-and 5-year survival outcomes of GC patients was estimated through time-dependent receiver operating characteristic (ROC) curves. In addition, the clinical benefit of this model was revealed by decision curve analysis (DCA). Pathway enrichment analysis suggested that these DNAm-driven genes might impact tumor progression by affecting signaling pathways such as the "ECM RECEPTOR INTERACTION" and "DNA REPLICATION" pathways. Conclusions: The altered status of the DNAm-driven gene signature (PODN, MYO1A, NPY, MICU3, TUBB6 and RHOJ) was significantly associated with the OS of GC patients. A nomogram incorporating risk score, age and number of positive lymph nodes can be conveniently used to facilitate the individualized prediction of OS in patients with GC.

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Section: Discussionmentioning

confidence: 99%

Development and Validation of a Prognostic Nomogram for Gastric Cancer Based on DNA Methylation-Driven Differentially Expressed Genes

et al. 2020

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“…A lack of tumor volume data for example, even though clearly linked to LRC (81,(84)(85)(86), impedes the assessment of a role for or a bias from tumor volumes in such analyses. To minimize such treatment or tumor site related bias due to possible interactions; we deliberately excluded the biologically distinct oral cavity and HPV-positive oropharyngeal HNSCC (14,16,(87)(88)(89)(90) in our study. Our cohort also solely comprises definitive chemo-radiotherapytreated advanced HNSCC.…”

Section: Discussionmentioning

confidence: 99%

“…Since we focused on known determinants of radiation response, other biomarkers were not included despite their relevance or prognostic value in HNSCC (5,6,16,(100)(101)(102)(103)(104)(105)(106). Some, such as tumor mutational burden (TMB) are prevalent in laryngeal and HPV-negative pharyngeal HNSCC (14) but require DNA sequencing data. TMB was found to be associated with poor prognosis in HPV-negative chemo-radiotherapy treated patients in our previous study (36) and more strongly so in a cohort of patients that also included oral cavity cancers and HPV-positive oropharyngeal (107).…”

Section: Discussionmentioning

confidence: 99%

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Biological Determinants of Chemo-Radiotherapy Response in HPV-Negative Head and Neck Cancer: A Multicentric External Validation

et al. 2020

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Purpose: Tumor markers that are related to hypoxia, proliferation, DNA damage repair and stem cell-ness, have a prognostic value in advanced stage HNSCC patients when assessed individually. Here we aimed to evaluate and validate this in a multifactorial context and assess interrelation and the combined role of these biological factors in determining chemo-radiotherapy response in HPV-negative advanced HNSCC. Methods: RNA sequencing data of pre-treatment biopsy material from 197 HPV-negative advanced stage HNSCC patients treated with definitive chemoradiotherapy was analyzed. Biological parameter scores were assigned to patient samples using previously generated and described gene expression signatures. Locoregional control rates were used to assess the role of these biological parameters in radiation response and compared to distant metastasis data. Biological factors were ranked according to their clinical impact using bootstrapping methods and multivariate Cox regression analyses that included clinical variables. Multivariate Cox regression analyses comprising all biological variables were used to define their relative role among all factors when combined. Results: Only few biomarker scores correlate with each other, underscoring their independence. The different biological factors do not correlate or cluster, except for the two stem cell markers CD44 and SLC3A2 (r = 0.4, p < 0.001) and acute hypoxia prediction scores which correlated with T-cell infiltration score, CD8 + T cell abundance and proliferation scores (r = 0.52, 0.56, and 0.6, respectively with p < 0.001). van der Heijden et al. HNSCC Biology Impacting Chemo-Radiotherapy Outcomes Locoregional control association analyses revealed that chronic (Hazard Ratio (HR) = 3.9) and acute hypoxia (HR = 1.9), followed by stem cell-ness (CD44/SLC3A2; HR = 2.2/2.3), were the strongest and most robust determinants of radiation response. Furthermore, multivariable analysis, considering other biological and clinical factors, reveal a significant role for EGFR expression (HR = 2.9, p < 0.05) and T-cell infiltration (CD8 + T-cells: HR = 2.2, p < 0.05; CD8 + T-cells/Treg: HR = 2.6, p < 0.01) signatures in locoregional control of chemoradiotherapy-treated HNSCC. Conclusion: Tumor acute and chronic hypoxia, stem cell-ness, and CD8 + Tcell parameters are relevant and largely independent biological factors that together contribute to locoregional control. The combined analyses illustrate the additive value of multifactorial analyses and support a role for EGFR expression analysis and immune cell markers in addition to previously validated biomarkers. This external validation underscores the relevance of biological factors in determining chemoradiotherapy outcome in HNSCC.

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“…11 Genomic analyses revealed the presence of several mutational signatures in HNSCC. 12,13 However, it is worth noting that most individual cancer genomes exhibit more than one mutational signature and many different combinations of signatures were observed. 14 In addition, global gene expression and DNA methylome profiling analysis elucidated distinct HNSCC subgroups with characteristic features concerning clinical and pathological traits as well as patient prognosis.…”

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Unraveling most abundant mutational signatures in head and neck cancer

Plath

Gass

Hlevnjak

et al. 2020

Intl Journal of Cancer

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Genomic alterations are a driving force in the multistep process of head and neck cancer (HNC) and result from the interaction of exogenous environmental exposures and endogenous cellular processes. Each of these processes leaves a characteristic pattern of mutations on the tumor genome providing the unique opportunity to decipher specific signatures of mutational processes operative during HNC pathogenesis and to address their prognostic value. Computational analysis of whole exome sequencing data of the HIPO-HNC (Heidelberg Center for Personalized Oncologyhead and neck cancer) (n = 83) and TCGA-HNSC (The Cancer Genome Atlas-Head and Neck Squamous Cell Carcinoma) (n = 506) cohorts revealed five common mutational signatures (Catalogue of Somatic Mutations in Cancer [COSMIC] Signatures 1, 2, 3, 13 and 16) and demonstrated their significant association with etiological risk factors (tobacco, alcohol and HPV16). Unsupervised hierarchical clustering identified four clusters (A, B, C1 and C2) of which Subcluster C2 was enriched for cases with a higher frequency of signature 16 mutations. Tumors of Subcluster C2 had significantly lower p16 INK4A expression accompanied by homozygous CDKN2A deletion in almost one half of cases. Survival analysis revealed an unfavorable prognosis for patients with tumors characterized by a higher mutation burden attributed to signature 16 as well as cases in Subcluster C2. Finally, a LASSO-Cox regression model was applied to prioritize clinically relevant signatures and to establish a prognostic risk score for head and neck squamous cell carcinoma patients. In conclusion, our study provides a proof of concept that computational analysis of somatic mutational signatures is not only a powerful tool to decipher environmental and intrinsic processes in the pathogenesis of HNC, but could also pave the way to establish reliable prognostic patterns. Abbreviations: FFPE, formalin fixed paraffin embedded; HIPO, Heidelberg Center for Personalized Oncology; HNC, head and neck cancer; HNSCC, head and neck squamous cell carcinoma; HPV, human papillomavirus; TCGA, The Cancer Genome Atlas; TSS, transcription start sites.

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Comparative genomic analysis of oral versus laryngeal and pharyngeal cancer

Cited by 28 publications

References 47 publications

Development and Validation of a Prognostic Nomogram for Gastric Cancer Based on DNA Methylation-Driven Differentially Expressed Genes

Development and Validation of a Prognostic Nomogram for Gastric Cancer Based on DNA Methylation-Driven Differentially Expressed Genes

Biological Determinants of Chemo-Radiotherapy Response in HPV-Negative Head and Neck Cancer: A Multicentric External Validation

Unraveling most abundant mutational signatures in head and neck cancer

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