Proteomics-based Predictive Model for the Early Detection of Metastasis and Recurrence in Head and Neck Cancer

Abstract

Background/aim: Head and neck squamous cell carcinoma (HNSCC) presents high morbidity, an overall poor prognosis and survival, and a compromised quality of life of the survivors. Early tumor detection, prediction of its behavior and prognosis as well as the development of novel therapeutic strategies are urgently needed for a more successful HNSCC management.

Materials and methods: In this study, a proteomics analysis of HNSCC tumor and non-tumor samples was performed and a model to predict the risk of recurrence and metastasis development was built.

Results: This predictive model presented good accuracy (>80%) and comprises as variables the tumor staging along with DHB12, HMGB3 and COBA1 proteins. Differences at the intensity levels of these proteins were correlated with the development of metastasis and recurrence as well as with patient's survival.

Conclusion: The translation of proteomic predictive models to routine clinical practice may contribute to a more precise and individualized clinical management of the HNSCC patients, reducing recurrences and improving patients' quality of life. The capability of generalization of this proteomic model to predict the recurrence and metastases development should be evaluated and validated in other HNSCC populations.

Keywords: Proteomic profiling; biomarkers; head and neck squamous cell carcinoma; predictive model; recurrence and metastasis.