All-Exon TP53 Sequencing and Protein Phenotype Analysis Accurately Predict Clinical Outcome after Surgical Treatment of Head and Neck Squamous Cell Carcinoma

Kobayashi, Kenya; Yoshimoto, Seiichi; Matsumoto, Fumihiko; Ando, Mizuo; Murakami, Naoya; Omura, Go; Fukasawa, Masahiko; Matsumoto, Yoshihisa; Matsumura, Satoko; Akamatsu, Maki; Hiraoka, Nobuyoshi; Ryo, Eigitsu; Mori, Taisuke

doi:10.1245/s10434-019-07287-x

Cited by 16 publications

(20 citation statements)

References 30 publications

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“…What’s more, the author discovered association between number of lymph node burden and the efficacy of postoperative adjuvant chemoradiation have an approximate positive linear trend. Similarly, margin status and mutation count were frequently reported risk factors of recurrence for patients with HNSCC, including oral cavity, oropharyngeal cancers, laryngeal carcinoma and so on 33–35 . In addition to these clinicopathologic factors, as expected, the lncRNAs classifier was an effective independent risk variables for the recurrence of patients with HNSCC.…”

Section: Discussionmentioning

confidence: 96%

An integrated nomogram combining lncRNAs classifier and clinicopathologic factors to predict the recurrence of head and neck squamous cell carcinoma

Cui

Wen

Tan

et al. 2019

Sci Rep

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Long non-coding RNAs (lncRNAs) which have little or no protein-coding capacity, due to their potential roles in the cancer disease, caught a particular interest. Our study aims to develop an lncRNAs-based classifier and a nomogram incorporating the lncRNAs classifier and clinicopathologic factors to help to improve the accuracy of recurrence prediction for head and neck squamous cell carcinoma (HNSCC) patients. The HNSCC lncRNAs profiling data and the corresponding clinicopathologic information were downloaded from TANRIC database and cBioPortal. Using univariable Cox regression and Least absolute shrinkage and selection operator (LASSO) analysis, we developed 15-lncRNAs-based classifier related to recurrence. On the basis of multivariable Cox regression analysis results, a nomogram integrating the genomic and clinicopathologic predictors was built. The predictive accuracy and discriminative ability of the inclusive nomogram were confirmed by calibration curve and a concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was conducted to evaluate clinical value of our nomogram. Consequently, fifteen recurrence-free survival (RFS) -related lncRNAs were identified, and the classifier consisting of the established 15 lncRNAs could effectively divide patients into high-risk and low-risk subgroup. The prediction ability of the 15-lncRNAs-based classifier for predicting 3- year and 5-year RFS were 0.833 and 0.771. Independent factors derived from multivariable analysis to predict recurrence were number of positive LNs, margin status, mutation count and lncRNAs classifier, which were all embedded into the nomogram. The calibration curve for the recurrence probability showed that the predictions based on the nomogram were in good coincide with practical observations. The C-index of the nomogram was 0.76 (0.72–0.79), and the area under curve (AUC) of nomogram in predicting RFS was 0.809, which were significantly higher than traditional TNM stage and 15-lncRNAs-based classifier. Decision curve analysis further demonstrated that our nomogram had larger net benefit than TNM stage and 15-lncRNAs-based classifier. The results were confirmed externally. In summary, a visually inclusive nomogram for patients with HNSCC, comprising genomic and clinicopathologic variables, generates more accurate prediction of the recurrence probability when compared TNM stage alone, but more additional data remains needed before being used in clinical practice.

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Section: Discussionmentioning

confidence: 96%

An integrated nomogram combining lncRNAs classifier and clinicopathologic factors to predict the recurrence of head and neck squamous cell carcinoma

Cui

Wen

Tan

et al. 2019

Sci Rep

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“…Nine studies included HPV-positive tumours in their analysis. [22, 24, 26, 33-36, 38, 40, 43, 44] Two studies fully excluded HPV-positive tumours, [32,45] and one paper only speci cally investigated HPV-positive cancers.…”

Section: Study Outcome Measuresmentioning

confidence: 99%

“…Russo et al, [30] in a cohort of 81 stage III and IV laryngeal SCC, found that mutations in exon 5 were an independent prognostic factor for both disease-free survival and overall survival, and that exon 8 mutations were independently associated with overall survival but importantly not relapse. TP53 mutation status and survival -meta-analysis 15 studies provided data from multivariate cox proportional hazards models amenable to inclusion in a meta-analysis; 11 on overall survival,[26, 29-32, 34-36, 42, 43] 8 on disease-free survival [26,27,29,30,32,42,43,45] and 3 on disease-speci c survival. [22,33,39] See Fig.…”

Section: Study Outcome Measuresmentioning

confidence: 99%

Value of p53 sequencing in the prognostication of head and neck cancer: a systematic review and meta-analysis

Basyuni

Nugent

Ferro

et al. 2022

Preprint

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This review aimed to examine the relationship between TP53 mutational status, as determined by genomic sequencing, and survival in squamous cell carcinoma of the head and neck. The databases Medline, Embase, Web of Science (core collection), Scopus and Cochrane Library were searched from inception to April 2021 for studies assessing P53 status and survival. Qualitative analysis was carried out using the REMARK criteria. A meta-analyses was performed and statistical analysis was carried out to test the stability and reliability of results. Twenty-five studies met the inclusion criteria, of which fifteen provided enough data for quantitative evaluation. TP53 mutation was associated with worse overall survival (HR 1.75 [95%CI 1.45–2.10], p < 0.001), disease-specific survival (HR 4.23 [95%CI 1.19–15.06], p = 0.03), and disease-free survival (HR 1.80 [95%CI 1.28-; 2.53], p < 0.001). Qualitative assessment identified room for improvement and the pooled analysis of all anatomical subsites leads to heterogeneity that may erode the validity of the observed overall effect and its subsequent extrapolation and application to individual patients. Our systematic review and meta-analysis supports the utility of TP53 mutational as a prognostic factor for survival in head and neck squamous cell carcinoma. A well designed prospective, multi-centre trial is needed to definitively answer this question Value of p53 sequencing in the prognostication of head and neck cancer: a systematic review and meta-analysis

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“…Recently, the identification of prognostic markers would allow personalized treatment approaches to be possible, but a standard unified prognostic model has not yet established in HNSCC (Mirghani et al., 2015; O'Sullivan et al., 2013). Recent studies have reported that many clinical biomarkers can be used to predict the outcome of patients with HNSCC, such as TNM staging system, P53 gene mutation, and HPV infection status (Amit et al., 2020; Gulati et al., 2014; Hess, 2017; Kobayashi et al., 2019). However, due to the heterogeneity of tumors, the TNM staging system and other signatures have insufficient value to predict HNSCC patients' prognosis (Ljungberg et al., 2015).…”

Section: Introductionmentioning

confidence: 99%

Identification of autophagy‐related gene and lncRNA signatures in the prognosis of HNSCC

Wang

Meng

et al. 2021

Oral Diseases

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Objective The aim of this study was to identify prognostic autophagy‐related genes and lncRNAs to predict clinical outcomes in head and neck squamous cell carcinoma (HNSCC). Subjects and methods Differentially expressed autophagy‐related genes and autophagy‐related lncRNAs were identified by comparing pare‐carcinoma and carcinoma samples of HNSCC. And then, we constructed an ARG and an AR‐lncRNA signature risk score. Receiver operating characteristic (ROC) curve analyses were performed to assess the prognostic prediction capacity. Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) functional annotation were used to analysis the functions of ARGs and AR‐lncRNAs. Results Six ARGs and thirteen AR‐lncRNAs were identified in the ARG and AR‐lncRNA signatures, and overall survival (OS) in the high‐risk group was significantly shorter than the low‐risk group. ROC analysis showed the ARG and AR‐lncRNA signatures have excellent ability of predicting the total OS of patients with HNSCC. What's more, GSEA and GO functional annotation proved that autophagy‐related pathways are mainly enriched in the high‐risk group. Conclusions These findings indicated that our ARG signature and AR‐lncRNA signature could be considered to predict the prognosis of patients with HNSCC and provide a deep understanding of the biological mechanisms of autophagy in HNSCC.

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All-Exon TP53 Sequencing and Protein Phenotype Analysis Accurately Predict Clinical Outcome after Surgical Treatment of Head and Neck Squamous Cell Carcinoma

Cited by 16 publications

References 30 publications

An integrated nomogram combining lncRNAs classifier and clinicopathologic factors to predict the recurrence of head and neck squamous cell carcinoma

An integrated nomogram combining lncRNAs classifier and clinicopathologic factors to predict the recurrence of head and neck squamous cell carcinoma

Value of p53 sequencing in the prognostication of head and neck cancer: a systematic review and meta-analysis

Identification of autophagy‐related gene and lncRNA signatures in the prognosis of HNSCC

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