Identification of Targetable Pathways in Oral Cancer Patients via Random Forest and Chemical Informatics

Treatment of head and neck cancer has been slow to change with epidermal growth factor receptor (EGFR) inhibitors, PD1 inhibitors, and taxane-/plant-alkaloid-derived chemotherapies being the only therapies approved by the U.S. Food and Drug Administration (FDA) in the last 10 years for the treatment of head and neck cancers. Head and neck cancer is a relatively rare cancer compared to breast or lung cancers. However, it is possible that existing therapies for more common solid tumors or for the treatment of other diseases could also prove effective against oral cancers. Many therapies have molecular targets that could be appropriate in oral cancer as well as the cancer in which the drug gained initial FDA approval. Also, there may be targets in oral cancer for which existing FDA-approved drugs could be applied. This study describes informatics methods that use machine learning to identify influential gene targets in patients receiving platinum-based chemotherapy, non-platinum-based chemotherapy, and genes influential in both groups of patients. This analysis yielded 6 small molecules that had a high Tanimoto similarity (>50%) to ligands binding genes shown to be highly influential in determining treatment response in oral cancer patients. In addition to influencing treatment response, these genes were also found to act as gene hubs connected to more than 100 other genes in pathways enriched with genes determined to be influential in treatment response by a random forest classifier with 20 000 trees trying 320 variables at each tree node. This analysis validates the use of multiple informatics methods to identify small molecules that have a greater likelihood of efficacy in a given cancer of interest.