Inflammation response of epithelial mucosa to chemo- radiotherapy cytotoxic effects leads to mucositis, a painful side effect of antineoplastic treatments. About 40% of the patients treated with chemotherapy develop mucositis; this percentage rises to about 90% for head and neck cancer patients (HNC) treated with both chemo- and radiotherapy. 19% of the latter will be hospitalized and will experience a delay in antineoplastic treatment for high-grade mucositis management, resulting in a reduction of the quality of life, a worse prognosis and an increase in patient management costs. Currently, several interventions and prevention guidelines are available, but their effectiveness is uncertain. This review comprehensively describes mucositis, debating the impact of standard chemo-radiotherapy and targeted therapy on mucositis development and pointing out the limits and the benefits of current mucositis treatment strategies and assessment guidelines. Moreover, the review critically examines the feasibility of the existing biomarkers to predict patient risk of developing oral mucositis and their role in early diagnosis. Despite the expression levels of some proteins involved in the inflammation response, such as TNF-α or IL-1β, partially correlate with mucositis process, their presence does not exclude others mucositis-independent inflammation events. This strongly suggests the need to discover biomarkers that specifically feature mucositis process development. Non-coding RNAs might hold this potential.

Significance: Cells can change their phenotype from epithelial to mesenchymal during development and in cancer progression, where this transition is often associated with metastasis and poor disease prognosis. Here we show this process involves the transit through a multitude of metastable hybrid phenotypes in a way that is similar to the driven dynamics of disordered materials. Our method shows that highly aggressive hybrid epithelial/mesenchymal cell phenotypes are located in metastable regions that can easily switch under external and internal perturbations. We propose a general mapping strategy that can be used for other pathways, providing a useful tool to visualize the ever increasing number of gene expression data obtained from single cells and tissues.