Targeted therapies that inhibit oncogenic signalling pathways are the key for precision medicine in cancer treatment. Research over the past decades has revealed that deregulated or increased signalling of the epidermal growth factor receptor (EGFR) plays an integral role in the development of various cancer types, including colorectal cancer (CRC) and head and neck squamous cell carcinoma (HNSCC). After initially promising results of EGFR-targeted therapies, it became clear that therapeutic resistance is a major clinical problem. Moreover, as an increasing number of patients are currently considered as candidates for treatment with EGFR-targeted therapy, identification of predictive biomarkers is extremely important. The objective of this PhD project was to unravel and overcome resistance to the EGFR-targeting agent cetuximab in CRC and HNSCC. Hereby, we focused on the identification of drug resistance mechanisms, novel drug targets and therapeutic strategies as well as predictive biomarkers.
The present study demonstrated that afatinib, a second-generation irreversible inhibitor of EGFR, HER2 and HER4, has the potential to overcome cetuximab resistance in CRC and HNSCC cell lines. Therefore, these data support the hypothesis that afatinib may be a promising therapeutic agent to treat CRC and HNSCC patients experiencing intrinsic or acquired cetuximab resistance. Furthermore, we found that increased phosphorylation of Akt seems to be characteristic for acquired cetuximab resistance in HNSCC. Although further confirmation in tumour samples of HNSCC patients is imperative, Akt appears a novel drug target to improve outcome after cetuximab treatment as well as a potential predictive biomarker for EGFR-targeted therapies in HNSCC patients. In this view, we encourage further studies that focus on targeting Akt in combination with cetuximab, as this may be a promising strategy to overcome drug resistance in HNSCC patients. These findings can form a solid basis for further experiments with advanced in vitro and in vivo models.
(BELG J MED ONCOL 2020;14(4):155–8)