MAGE-A expression clusters and antineoplastic treatment in head and neck cancer

The nonsurgical treatment of head and neck squamous cell carcinoma (HNSCC) usually consists of radiation and chemotherapy. In general, the treatment efficacy of chemotherapy in head and neck cancer is limited. Apart from the placenta, testis and fetal keratinocytes, melanoma-associated antigens-A (MAGE-A) are only found in malignancies. Even though their molecular role remains unclear, several subgroups have been found to contribute to resistance to different chemotherapeutic agents. In the present study, established human squamous cell carcinoma cell lines were incubated with various concentrations of cisplatin, 5-fluorouracil, paclitaxel, docetaxel, cetuximab and panitumumab for 5, 10, 20 and 40 h. The treatment efficacy was measured dynamically by real-time cell analysis (RTCA). In addition, we determined the expression of all known MAGE-A subgroups (MAGE-A1 to MAGE-A12, excluding pseudogene MAGE-A7) by reverse transcription quantitative polymerase chain reaction. Of note, one cell line showed only a marginal expression of MAGE-A antigens, whereas another cell line showed a distinct expression of almost all the MAGE-A subgroups. The expression pattern varied in the other cell lines. MAGE-A4 was the most highly expressed of all the subgroups, and MAGE-A8 could not be detected. With the exception of MAGE-A6, -A8, -A9 and -A10, the expression levels differed significantly between the cell lines. Factor analysis suggested simplifying the MAGE-A expression level into two groups. Spearman's rank correlation revealed a significant association between MAGE-A expression and treatment efficacy for 20.8% (25/120) of the experiments. In 100% of these cases (25/25), Spearman's Rho revealed a positive correlation between clustered MAGE-A expression and poor treatment efficacy. Our data highlight the fact that higher a MAGE-A expression correlates with a poorer outcome of antineoplastic treatment. Clustered MAGE-A expression analysis may help to identify patients who are at a higher risk of antineoplastic treatment failure.