RESUMO
Oral squamous cell carcinoma (OSCC) is a highly unpredictable disease with devastating mortality rates that have not changed over the past decades, in the face of advancements in treatments and biomarkers, which have improved survival for other cancers. Delays in diagnosis are frequent, leading to more disfiguring treatments and poor outcomes in patients. The clinical challenge lies in identifying those patients at highest risk for developing OSCC. Oral epithelial dysplasia (OED) is a precursor of OSCC with highly variable behavior across patients. There is no reliable clinical, pathologic, histologic or molecular biomarker to determine individual risk in OED patients. Similarly, there are no robust biomarkers to predict treatment outcomes or mortality of OSCC patients. This review aims to highlight advancements in artificial intelligence (AI)-based methods to develop predictive biomarkers of OED transformation to OSCC or predictive biomarkers of OSCC mortality and treatment response. Machine-learning based biomarkers, such as S100A7, demonstrate promising appraisal for the risk of malignant transformation of OED. Machine learning-enhanced multiplex immunohistochemistry (mIHC) workflows examine immune cell patterns and organization within the tumor immune microenvironment to generate outcome predictions in immunotherapy. Deep learning (DL) is an AI-based method using an extended neural network or related architecture with multiple "hidden" layers of simulated neurons to combine simple visual features into complex patterns. DL-based digital pathology is currently being developed to assess OED and OSCC outcomes. The integration of machine learning in epigenomics aims to examine the epigenetic modification of diseases and improve our ability to detect, classify, and predict outcomes associated with epigenetic marks. Collectively, these tools showcase promising advancements in discovery and technology, which may provide a potential solution to addressing the current limitations in predicting OED transformation and OSCC behavior, both of which are clinical challenges that must be addressed in order to improve OSCC survival.
RESUMO
Bone cancer metastasis is extremely painful and decreases the quality of life of the affected patients. Available pharmacological treatments are not able to sufficiently ameliorate the pain, and as patients with cancer are living longer, new treatments for pain management are needed. Decitabine (5-aza-2'-deoxycytidine), a DNA methyltransferases inhibitor, has analgesic properties in preclinical models of postsurgical and soft-tissue oral cancer pain by inducing an upregulation of endogenous opioids. In this study, we report that daily treatment with decitabine (2 µg/g, intraperitoneally) attenuated nociceptive behavior in the 4T1-luc2 mouse model of bone cancer pain. We hypothesized that the analgesic mechanism of decitabine involved activation of the endogenous opioid system through demethylation and reexpression of the transcriptionally silenced endothelin B receptor gene, Ednrb. Indeed, Ednrb was hypermethylated and transcriptionally silenced in the mouse model of bone cancer pain. We demonstrated that expression of Ednrb in the cancer cells lead to release of ß-endorphin in the cell supernatant, which reduced the number of responsive dorsal root ganglia neurons in an opioid-dependent manner. Our study supports a role of demethylating drugs, such as decitabine, as unique pharmacological agents targeting the pain in the cancer microenvironment.