IADR Abstract Archives
Personalized Therapeutics and Target Identification for Oral Diseases
Objectives: Cancer networks were used to create predictive computational simulation models of head and neck squamous cell carcinoma (HNSCC) cell lines SCC4, SCC15, and SCC25. For a proof-of-concept, these models were used to predict programmed death-ligand 1 (PD-L1) expression and predictions were validated against PD-L1 detected on the same cells grown in culture. The objective here was to extend this application and use models to predict biomarker expression of cell lines and thereafter to accurately identify and predict unique drug targets for individuals with HNSCC.
Methods: Established cancer networks were used to create HNSCC cell line-specific models. Cell line genomic aberration profiles, from next generation sequencing (NGS) information for SCC4, SCC15, and SCC25 cell lines were annotated into the workflow to create cell line-specific models. Model predictions were validated against the production of 14 immunosuppressive biomarkers and used to identify PD-L1 drug responder status. Descriptive statistics were computed, and Fisher’s exact test was used to detect early differences in responder status under different conditions (alpha=0.05). HNSCC models are being used to predict responses to unique drug targets.
Results: There was an overall significant difference in match status among predicted expression of biomarkers and observed concentrations of biomarkers (p=0.0499; Fisher’s exact test). Using PD-L1, chemokine, and immunosuppressive biomarker expression in a decision tree, SCC4 was predicted to be a PD-L1 drug nonresponder and SCC15 and SCC25 were predicted to be PD-L1 drug responders. Drug target predictions and experimental validations for greater than 50 drugs and targets are currently under investigation and waiting to be validated across the different models.
Conclusions: SCC4, SCC15, and SCC25-specific models and future patient HNSCC cell-specific models will be used to predict unique drug targets on HNSCC cells, information that can form the basis of future therapeutic strategies for treatment of HNSCC.
Methods: Established cancer networks were used to create HNSCC cell line-specific models. Cell line genomic aberration profiles, from next generation sequencing (NGS) information for SCC4, SCC15, and SCC25 cell lines were annotated into the workflow to create cell line-specific models. Model predictions were validated against the production of 14 immunosuppressive biomarkers and used to identify PD-L1 drug responder status. Descriptive statistics were computed, and Fisher’s exact test was used to detect early differences in responder status under different conditions (alpha=0.05). HNSCC models are being used to predict responses to unique drug targets.
Results: There was an overall significant difference in match status among predicted expression of biomarkers and observed concentrations of biomarkers (p=0.0499; Fisher’s exact test). Using PD-L1, chemokine, and immunosuppressive biomarker expression in a decision tree, SCC4 was predicted to be a PD-L1 drug nonresponder and SCC15 and SCC25 were predicted to be PD-L1 drug responders. Drug target predictions and experimental validations for greater than 50 drugs and targets are currently under investigation and waiting to be validated across the different models.
Conclusions: SCC4, SCC15, and SCC25-specific models and future patient HNSCC cell-specific models will be used to predict unique drug targets on HNSCC cells, information that can form the basis of future therapeutic strategies for treatment of HNSCC.