Background: Patients with head and neck cancer are at high risk of developing additional primary tumours in the aerodigestive tract as a result of field cancerization phenomena. In this context, the appearance of a new neoplasm often poses a problem of differential diagnosis between recurrence and new primary tumour. The differentiation between the two entities in essentially clinical and conventionally based on the histological and spatio-temporal relations between the two lesions; however, the validity of these criteria has still to be assessed.
Design: To evaluate whether field cancerization phenomena may affect the clinical diagnosis of relapse/metastasis in the head and neck region, p53 mutation pattern was analysed in a series of primary tumours and corresponding recurrences/metastases. The rationale was that, since p53 mutations are a very early and polymorphic phenomenon, a recurrence/metastasis must retain the same mutation as the the primary tumour, whereas independent tumours are likely to display a different p53 gene status.
Results: Molecular analysis provided conclusive results in 9 of 12 cases analysed. The clinical diagnosis of recurrence was confirmed by molecular analysis in 4 of these cases. In contrast, a differential p53 mutation pattern supported an independent origin for 3 presumed local recurrences and 2 lung lesions.
Conclusions: The use of p53 mutation analysis as a clonality marker allowed us to ascertain the inadequacy of the current diagnostic criteria for the differentiation between a new independent tumour and recurrence/metastasis. These findings substantiate the relevance of field cancerization phenomena in the head and neck region and prompt the use of p53 mutation analysis as a fundamental tool to improve the diagnosis and management of head and neck cancers.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1093/oxfordjournals.annonc.a059362 | DOI Listing |
Mol Cancer
January 2025
Foshan Maternity and Child Healthcare Hospital; School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, 515150, China.
Background: Intratumor-resident bacteria represent an integral component of the tumor microenvironment (TME). Microbial dysbiosis, which refers to an imbalance in the bacterial composition and bacterial metabolic activities, plays an important role in regulating breast cancer development and progression. However, the impact of specific intratumor-resident bacteria on tumor progression and their underlying mechanisms remain elusive.
View Article and Find Full Text PDFBMC Cancer
January 2025
Department of Tumor Biology and Genetics, Medical University of Warsaw, Warsaw, Poland.
Aim: The study was designed to evaluate molecular alterations, relevant to the prognosis and personalized therapy of salivary gland cancers (SGCs).
Materials And Methods: DNA was extracted from archival tissue of 40 patients with various SGCs subtypes. A targeted next-generation sequencing (NGS) panel was used for the identification of small-scale mutations, focal and chromosomal arm-level copy number changes.
Nat Med
January 2025
Department of Neurosurgery, NYU Langone Health, New York, NY, USA.
The adoption of large language models (LLMs) in healthcare demands a careful analysis of their potential to spread false medical knowledge. Because LLMs ingest massive volumes of data from the open Internet during training, they are potentially exposed to unverified medical knowledge that may include deliberately planted misinformation. Here, we perform a threat assessment that simulates a data-poisoning attack against The Pile, a popular dataset used for LLM development.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA.
This study addresses the limited noninvasive tools for Head and Neck Squamous Cell Carcinoma (HNSCC) progression-free survival (PFS) prediction by identifying Computed Tomography (CT)-based biomarkers for predicting prognosis. A retrospective analysis was conducted on data from 203 HNSCC patients. An ensemble feature selection involving correlation analysis, univariate survival analysis, best-subset selection, and the LASSO-Cox algorithm was used to select functional features, which were then used to build final Cox Proportional Hazards models (CPH).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!