Abstract
BACKGROUND: Treatment protocols for nasopharyngeal carcinoma (NPC) developed in the past decade have significantly improved patient survival. In most NPC patients, however, the disease is diagnosed at late stages, and for some patients treatment response is less than optimal. This investigation has two aims: to identify a blood-based gene-expression signature that differentiates NPC from other medical conditions and from controls and to identify a biomarker signature that correlates with NPC treatment response. METHODS: RNA was isolated from peripheral whole blood samples (2 x 10 ml) collected from NPC patients/controls (EDTA vacutainer). Gene expression patterns from 99 samples (66 NPC; 33 controls) were assessed using the Affymetrix array. We also collected expression data from 447 patients with other cancers (201 patients) and non-cancer conditions (246 patients). Multivariate logistic regression analysis was used to obtain biomarker signatures differentiating NPC samples from controls and other diseases. Differences were also analysed within a subset (n=28) of a pre-intervention case cohort of patients whom we followed post-treatment. RESULTS: A blood-based gene expression signature composed of three genes - LDLRAP1, PHF20, and LUC7L3 - is able to differentiate NPC from various other diseases and from unaffected controls with significant accuracy (area under the receiver operating characteristic curve of over 0.90). By subdividing our NPC cohort according to the degree of patient response to treatment we have been able to identify a blood gene signature that may be able to guide the selection of treatment. CONCLUSION: We have identified a blood-based gene signature that accurately distinguished NPC patients from controls and from patients with other diseases. The genes in the signature, LDLRAP1, PHF20, and LUC7L3, are known to be involved in carcinoma of the head and neck, tumour-associated antigens, and/or cellular signalling. We have also identified blood-based biomarkers that are (potentially) able to predict those patients who are more likely to respond to treatment for NPC. These findings have significant clinical implications for optimizing NPC therapy.