Publication details
Enhancing oral squamous cell carcinoma prediction: the prognostic power of the worst pattern of invasion and the limited impact of molecular resection margins
| Authors | |
|---|---|
| Year of publication | 2023 |
| Type | Article in Periodical |
| Magazine / Source | Frontiers in Oncology |
| MU Faculty or unit | |
| Citation | |
| web | https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1287650/full |
| Doi | http://dx.doi.org/10.3389/fonc.2023.1287650 |
| Keywords | orofacial oncology; squamous cell carcinoma; mutation; surgical margins; biomarkers |
| Description | Objective Oral squamous cell carcinoma (OSCC) originates from the mucosal lining of the oral cavity. Almost half of newly diagnosed cases are classified as advanced stage IV disease, which makes resection difficult. In this study, we investigated the pathological features and mutation profiles of tumor margins in OSCC. Methods We performed hierarchical clustering of principal components to identify distinct patterns of tumor growth and their association with patient prognosis. We also used next-generation sequencing to analyze somatic mutations in tumor and marginal tissue samples. Results Our analyses uncovered that the grade of worst pattern of invasion (WPOI) is strongly associated with depth of invasion and patient survival in multivariable analysis. Mutations were primarily detected in the DNA isolated from tumors, but several mutations were also identified in marginal tissue. In total, we uncovered 29 mutated genes, mainly tumor suppressor genes involved in DNA repair including BRCA genes; however none of these mutations significantly correlated with a higher chance of relapse in our medium-size cohort. Some resection margins that appeared histologically normal harbored tumorigenic mutations in TP53 and CDKN2A genes. Conclusion Even histologically normal margins may contain molecular alterations that are not detectable by conventional histopathological methods, but NCCN classification system still outperforms other methods in the prediction of the probability of disease relapse. |