Prognostic and predictive biomarkers in metastatic colorectal cancer anti-EGFR therapy.

ABSTRACT

AIM: To reviewing genetic and epigenetic make-up of metastatic colorectal cancers (mCRCs) addicted to epidermal growth factor receptor (EGFR) signalling. METHODS: The present study summarizes the potential value of prognostic and predictive biomarkers in selecting mCRC patients treated with anti-EGFR therapy. A meta-analysis was performed using a systematic search of PubMed, Medline and Web of Science to identify eligible papers until March 21(st), 2016 using these following terms ''colorectal cancer'', "predictive biomarkers'', "anti-EGFR therapy", "KRAS", "NRAS'', "PIK3CA", "TP53", "PTEN", ''EGFR", "MET", "HER2", "epiregulin", "amphiregulin", "prognostic biomarkers", "BRAF", "miRNA" and "antibody-dependent cell-mediated cytotoxicity (ADCC) activity". Two investigators independently evaluated and extracted data from each identified studies based on selected criteria of inclusion and exclusion. RESULTS: The introduction of agents targeting EGFR such as cetuximab and panitumumab increased overall survival of mCRCs. Nevertheless, it has firstly became evident that response rates to cetuximab regimens in unselected patient populations were typically lower than 30%. Clinical data confirmed the predictive value of RAS mutations for resistance to cetuximab and panitumumab leading to the license of these monoclonal antibodies exclusively for the management of patients with RAS-wild type colorectal cancers. So far the identification of predictive biomarkers have generated interesting, though preliminary and, at times, conflicting data on the importance of tumour mRNA levels of EGFR ligands, of activating mutations in other genes such as NRAS and PIK3CA. The prognostic value of selected microRNAs level and ADCC activity is under investigation, while the prognostic impact of BRAF status remains controversial. CONCLUSION: This review focuses on the personalized treatment of mCRC and discusses the potential of new prognostic and predictive biomarkers in selecting patients treated with anti-EGFR therapy.