Exploiting FOXM1-orchestrated molecular network for early squamous cell carcinoma diagnosis and prognosis

摘要

Histopathological discordance with molecular phenotype of many human cancers poses clinically challenging tasks for accurate cancer diagnosis, which impacts on treatment strategy and patient outcome. Hence, an objective, accurate and quantitative method is needed. A quantitative Malignancy Index Diagnostic System (qMIDS) was developed based on 14 FOXM1 (isoform B)-associated genes implicated in the regulation of the cell cycle, differentiation, ageing, genomic stability, epigenetic and stem cell renewal, and two reference genes. Their mRNA expression levels were translated via a prospectively designed algorithm, into a metric scoring system. Subjects from UK and Norway (n = 299) provided 359 head and neck tissue specimens. Diagnostic test performance was assessed using detection rate (DR) and false-positive rate (FPR). The median qMIDS scores were 1.3, 2.9 and 6.7 in healthy tissue, dysplasia and head and neck squamous cell carcinomas (HNSCC), respectively (UK prospective dataset, p<0.001); 1.4, 2.3 and 7.6 in unaffected, oral lichen planus, or HNSCC, respectively (Norwegian retrospective dataset with up to 19 years survival data, p<0.001). At a qMIDS cut-off of 4.0, DR was 94% and FPR was 3.2% (Norwegian dataset); and DR was 91% and FPR was 1.3% (UK dataset). We further demonstrated the transferability of qMIDS for diagnosing premalignant human vulva (n = 58) and skin (n = 21) SCCs, illustrating its potential clinical use for other cancer types. This study provided evidence that qMIDS was able to quantitatively diagnose and objectively stratify cancer aggressiveness. With further validation, qMIDS could enable early HNSCC detection and guide appropriate treatment. Early treatment intervention can lead to long-term reduction in healthcare costs and improve patient outcome. What's new? Early detection of pre-cancer lesions would greatly improve outcomes in head and neck squamous cell carcinoma (HSNCC), if it were possible to predict which lesions were likely to progress to cancer. Conventional histopathology is not up to the task, and this paper demonstrates a new molecular method for HSNCC detection. Using 14 genes associated with the FOXM1 oncogene, the authors created a diagnostic index capable of objectively stratifying cancer aggressiveness. They demonstrated a high detection rate and low false positive rate in classifying clinical tissue samples, making this system a practical diagnostic tool to improve patient outcomes.