Frontiers in Oncology (Dec 2023)

Enhancing oral squamous cell carcinoma prediction: the prognostic power of the worst pattern of invasion and the limited impact of molecular resection margins

  • Pavel Hurník,
  • Pavel Hurník,
  • Pavel Hurník,
  • Jana Režnarová,
  • Jana Režnarová,
  • Zuzana Chyra,
  • Oldřich Motyka,
  • Barbora Moldovan Putnová,
  • Barbora Moldovan Putnová,
  • Zuzana Čermáková,
  • Tomáš Blažek,
  • Martin Fománek,
  • Daria Gaykalova,
  • Daria Gaykalova,
  • Daria Gaykalova,
  • Marcela Buchtová,
  • Marcela Buchtová,
  • Tereza Ševčíková,
  • Jan Štembírek,
  • Jan Štembírek,
  • Jan Štembírek

DOI
https://doi.org/10.3389/fonc.2023.1287650
Journal volume & issue
Vol. 13

Abstract

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ObjectiveOral 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.MethodsWe 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.ResultsOur 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.ConclusionEven 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.

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