Computational models for predicting anticancer drug efficacy: A multi linear regression analysis based on molecular, cellular and clinical data of oral squamous cell carcinoma cohort.

BACKGROUND AND OBJECTIVES: The computational prediction of drug responses based on the analysis of multiple clinical features of the tumor will be a novel strategy for accomplishing the long-term goal of precision medicine in oncology. The cancer patients will be benefitted if we computationally account all the tumor characteristics (data) for the selection of most effective and precise therapeutic drug. In this study, we developed and validated few computational models to predict anticancer drug efficacy based on molecular, cellular and clinical features of 31 oral squamous cell carcinoma (OSCC) cohort using computational methods. METHODS: We developed drug efficacy prediction models using multiple tumor features by employing the statistical methods like multi linear regression (MLR), modified MLR-weighted least square (MLR-WLS) and enhanced MLR-WLS. All the three developed drug efficacy prediction models were then validated using the data of actual OSCC samples (train-test ratio 31: 31) and actual Vs hypothetical samples (train-test ratio 31: 30). The selected best statistical model i.e. enhanced MLR-WLS has then been cross-validated (CV) using 341 theoretical tumor data. Finally, the performances of the models were assessed by the level of learning confidence, significance, accuracy and error terms. RESULTS: The train-test process for the real tumor samples of MLR-WLS method revealed the drug efficacy prediction enhancement and we observed that there was very less priming difference between actual and predicted. Furthermore, we found there was a less difference between actual apoptotic priming and predicted apoptotic priming for the tumors 6, 8, 21 and 30 whereas, for the remaining tumors there were no differences between predicted and actual priming data. The error terms (Actual Vs Predicted) also revealed the reliability of enhanced MLR-WLS model for drug efficacy prediction. CONCLUSION: We developed effective computational prediction models using MLR analysis for anticancer drug efficacy which will be useful in the field of precision medicine to choose the choice of drug in a personalized manner. We observed that the enhanced MLR-WLS model was the best fit to predict anticancer drug efficacy which may have translational applications.

Predicting Tumor Sensitivity to Chemotherapeutic Drugs in Oral Squamous Cell Carcinoma Patients.

Oral Squamous Cell Carcinoma (OSCC) patients respond poorly to chemotherapy. We analyzed the expression of 11 drug response-related genes in 31 OSCC biopsies, collected prior to any treatment, using custom-designed PCR array. Further, we investigated the drug response pattern of selected anticancer drugs by BH3 (Bcl2 Homology-3) profiling in the primary cells isolated from OSCC tissues. Then, we correlated the results of drug-response gene expression pattern with apoptotic priming to predict tumor response to chemotherapy. The best performing drug (BPD) and response differences (RD) between the drugs were identified using statistical methods to select the best choice of drug in a personalized manner. Based on the correlation, we classified OSCC tumors as sensitive (13 tumors), moderately responsive (16 tumors) or resistant (2 tumors) to chemotherapy. We found that up-regulation of genes linked with drug resistance facilitates survival of tumor samples, which was revealed by the percentage of apoptotic priming. Moreover, we found that paclitaxel-induced 40-45% apoptotic priming compared to other drugs. Average response difference (RD) analysis showed that 80% of tumors responded well to paclitaxel as compared to other drugs studied. Therefore, gene expression analysis with BH3 profiling reveals drug sensitivity that could be translated for drug selection before treatment.

Preventive effect of apigenin against isoproterenol-induced apoptosis in cardiomyoblasts.

We investigated the effect of apigenin, a dietary flavonoid, on isoproterenol hydrochloride (ISO)-induced apoptotic signaling in cardiomyoblast H9C2 cells. The results showed that apigenin treatment (10 µM) prevented ISO (31.25 µM)-induced lipid peroxidative levels and antioxidants status in H9C2 cells. Furthermore, apigenin inhibited expression of inflammatory markers in ISO-treated cells. In addition, apigenin prevented ISO-induced DNA damage and apoptotic signaling through modulating the expression of Bax, caspase-3, -8 and -9, cytochrome c, and Fas proteins in H9C2 cells. It is concluded that apigenin prevents ISO-induced antioxidants depletion, oxidative DNA damage, inflammatory, and apoptotic signaling in H9C2 cells. Thus, the present results demonstrated that apigenin has a cardioprotective effect on cardiomyoblasts cells.

Linalool prevents oxidative stress activated protein kinases in single UVB-exposed human skin cells.

Ultraviolet-B radiation (285-320 nm) elicits a number of cellular signaling elements. We investigated the preventive effect of linalool, a natural monoterpene, against UVB-induced oxidative imbalance, activation of mitogen-activated protein kinase (MAPK) and nuclear factor kappa-B (NF-κB) signaling in HDFa cells. We observed that linalool treatment (30 µM) prevented acute UVB-irradiation (20 mJ/cm2) mediated loss of activities of antioxidant enzymes in HDFa cells. The comet assay results illustrate that linalool significantly prevents UVB-mediated 8-deoxy guanosine formation (oxidative DNA damage) rather than UVB-induced cyclobutane pyrimidine (CPD) formation. This might be due to its ability to prevent UVB-induced ROS formation and to restore the oxidative imbalance of cells. This has been reflected in UVB-induced overexpression of MAPK and NF-κB signaling. We observed that linalool inhibited UVB-induced phosphorylation of ERK1, JNK and p38 proteins of MAPK family. Linalool inhibited UVB-induced activation of NF-κB/p65 by activating IκBa. We further observed that UVB-induced expression of TNF-α, IL6, IL-10, MMP-2 and MMP-9 was modulated by linalool treatment in HDFa cells. Thus, linalool protects the human skin cells from the oxidative damages of UVB radiation and modulates MAPK and NF-κB signaling in HDFa cells. The present findings substantiate that linalool may act as a photoprotective agent against UVB-induced skin damages.