Heterogeneity of Cancer Stem Cells in Tumorigenesis, Metastasis, and Resistance to Antineoplastic Treatment of Head and Neck Tumours

The discovery of a small subset of cancer cells with self-renewal properties that can give rise to phenotypically diverse tumour populations has shifted our understanding of cancer biology. Targeting cancer stem cells (CSCs) is becoming a promising therapeutic strategy in various malignancies, including head and neck squamous cell carcinoma (HNSCC). Diverse sub-populations of head and neck cancer stem cells (HNCSCs) have been identified previously using CSC specific markers, the most common being CD44, Aldehyde Dehydrogenase 1 (ALDH1), and CD133, or by side population assays. Interestingly, distinct HNCSC subsets play different roles in the generation and progression of tumours. This article aims to review the evidence for a role of specific CSCs in HNSCC tumorigenesis, invasion, and metastasis, together with resistance to treatment.

Radiotherapy for head and neck tumours using an oral fixation and parameter acquisition device and TOMO technology: a randomised controlled study

IntroductionRadiotherapy has become one of the main methods used for the treatment of malignant tumours of the head and neck. Spiral tomographic intensity-modulated radiotherapy has the many advantages of precision radiotherapy, which puts forward high requirements for postural reproducibility and accuracy. We will aim to ensure that the accurate positioning of the tumour will reduce the side effects of radiotherapy caused by positioning errors. We will design and implement this clinical trial using the patent of ‘a radiotherapy oral fixation and parameter acquisition device (patent number: ZL201921877986.5)’.Methods and analysisThis will be a randomised, controlled, prospective study with 120 patients with head and neck tumours. Using the random number table method, a random number sequence will be generated, and the patients will be enrolled in the experimental group (oral fixation device) and the control group (conventional fixation) in a 2:1 ratio. The primary outcome will be the progression-free survival time after the treatment. Secondary outcomes will include the oral mucosal reaction and the quality of life. Follow-ups will be carried out according to the plan. This is V.1.0 of protocol on 1 April 2021. The recruitment process for this clinical trial commenced on 1 May 2021, and will end on 1 October 2022.Ethics and disseminationThe trial received ethical approval from Medical Ethics Committee of Liaoning Provincial Cancer Hospital (number 20210131X). The final results will be presented at a scientific conference and published in a peer-reviewed journal in accordance with the journal’s guidelines.Trial registration numberChiCTR2100045096.

Qualitative and Quantitative Diagnosis in Head and Neck Cancer

The diagnosis is the art of determining the nature of a disease, and an accurate diagnosis is the true cornerstone on which rational treatment should be built. Within the workflow in the management of head and neck tumours, there are different types of diagnosis. The purpose of this work is to point out the differences and the aims of the different types of diagnoses and to highlight their importance in the management of patients with head and neck tumours. Qualitative diagnosis is performed by a pathologist and is essential in determining the management and can provide guidance on prognosis. The evolution of immunohistochemistry and molecular biology techniques has made it possible to obtain more precise diagnoses and to identify prognostic markers and precision factors. Quantitative diagnosis is made by the radiologist and consists of identifying a mass lesion and the estimation of the tumour volume and extent using imaging techniques, such as CT, MRI, and PET. The distinction between the two types of diagnosis is clear, as the methodology is different. The accurate establishment of both diagnoses plays an essential role in treatment planning. Getting the right diagnosis is a key aspect of health care, and it provides an explanation of a patient’s health problem and informs subsequent decision. Deep learning and radiomics approaches hold promise for improving diagnosis.