The primary objective of cancer classification is to predict a patient’s
response to a panel of treatment modalities, thereby guiding the development of a
rational therapy and optimizing therapeutic success. However, traditional staging
methods used to classify oral squamous cell carcinoma (OSCC) are marginally effective
in predicting clinical outcomes. Emerging data and increasingly refined and costeffective
high-throughput technologies have the potential to advance oral cancer
classification and treatment beyond the empiric towards a more molecularly-defined,
individualized approach. Yet, despite an increasing appreciation of the diverse genomic,
proteomic and epigenetic aberrations associated with OSCC, the promise of basing
OSCC treatment decisions on molecular biomarkers has yet to be realized. Herein, we
highlight a few specific examples of well-studied or intriguing prognostic biomarkers,
and include a brief review of high-throughput technologies that have been used in the
study of OSCC, and that could emerge as critical tools in the prediction of OSCC
prognosis and in the design of novel oral cancer therapeutics. In the age of molecular
therapeutics and personalized cancer medicine, this is likely to take the form of drugs or
other compounds targeted to specific proteins or other cellular components, and with a
minimal side effect profile.
Keywords: Oral squamous cell carcinoma, patient-tailored therapy, prognosis,
cancer biomarkers, epigenetics, high-throughput technology, biomarker, hypoxia
inducible factor-1A (HIF1A), MYC, RNA, microRNA, epigenetics, p16, histone
modifications.
