A Digital Diagnostic Test for Oral Cancer

Objectives: With current techniques it can be notoriously difficult to identify precancerous oral lesions that will transform into cancer. The quantitative Malignancy Index Diagnostic System (qMIDS) is the first FOXM1 oncogene-based diagnostic test developed for quantifying oral squamous cell carcinoma (OSCC). This highly sensitive technique converts gene expression into a digital index to quantify cancer risk. It will reassure those patients with low cancer risk and reduce their need for intensive surveillance, whilst identifying those at high risk and ensuring earlier cancer detection and treatment. The current method takes 4-5 hours to obtain diagnostic results. This project aimed to validate a faster digital diagnostic method (less than 2 hours) for OSCC detection.
Methods: qMIDS compares total expression of 16 genes with median normal gene expression levels in a panel of healthy control samples of oral tissue. Real-time absolute quantitative (RT-PCR) is used for mRNA transcript quantitation. By measuring levels of the 16 genes, and conversion via the diagnostic qMIDS algorithm into a metric ‘malignancy index’ scoring system, the risk of a given oral biopsy sample becoming cancerous can be quantified.
Results: Testing OSCC patient biopsies from normal margin and tumour cores, results demonstrate a high detection rate (>90%) and low false positive rate (<3%), indicating good test performance at a qMIDS cut-off of 4.0. This project shows that the new version of the qMIDS digital diagnostic method is capable of segregating the malignancy status of OSCC clinical tissue biopsies with a high degree of confidence (p<0.001). Molecular tissue topology can also be reconstructed using qMIDS on surgical samples for margin assessment and determination of tumour heterogeneity.
Conclusions: Results reiterate that the qMIDS assay robustly measures a FOXM1-driven oncogenic program in OSCC to quantify malignancy. Benefits of qMIDS is its objectivity as a diagnostic method to quantitatively measure the malignancy of a biopsy tissue sample based on digital molecular profile to avoid misdiagnosis. It is a reproducible, high-throughput and cost-effective test amenable to automation in the clinical workflow.