RESUMO
Although many models have been proposed to accurately predict the response of drugs in cell lines recent years, understanding the genome related to drug response is also the key for completing oncology precision medicine. In this paper, based on the cancer cell line gene expression and the drug response data, we established a reliable and accurate drug response prediction model and found predictor genes for some drugs of interest. To this end, we first performed pre-selection of genes based on the Pearson correlation coefficient and then used ElasticNet regression model for drug response prediction and fine gene selection. To find more reliable set of predictor genes, we performed regression twice for each drug, one with IC50 and the other with area under the curve (AUC) (or activity area). For the 12 drugs we tested, the predictive performance in terms of Pearson correlation coefficient exceeded 0.6 and the highest one was 17-AAG for which Pearson correlation coefficient was 0.811 for IC50 and 0.81 for AUC. We identify common predictor genes for IC50 and AUC, with which the performance was similar to those with genes separately found for IC50 and AUC, but with much smaller number of predictor genes. By using only common predictor genes, the highest performance was AZD6244 (0.8016 for IC50, 0.7945 for AUC) with 321 predictor genes.
RESUMO
Although many models have been proposed to accurately predict the response of drugs in cell lines recent years, understanding the genome related to drug response is also the key for completing oncology precision medicine. In this paper, based on the cancer cell line gene expression and the drug response data, we established a reliable and accurate drug response prediction model and found predictor genes for some drugs of interest. To this end, we first performed pre-selection of genes based on the Pearson correlation coefficient and then used ElasticNet regression model for drug response prediction and fine gene selection. To find more reliable set of predictor genes, we performed regression twice for each drug, one with IC50 and the other with area under the curve (AUC) (or activity area). For the 12 drugs we tested, the predictive performance in terms of Pearson correlation coefficient exceeded 0.6 and the highest one was 17-AAG for which Pearson correlation coefficient was 0.811 for IC50 and 0.81 for AUC. We identify common predictor genes for IC50 and AUC, with which the performance was similar to those with genes separately found for IC50 and AUC, but with much smaller number of predictor genes. By using only common predictor genes, the highest performance was AZD6244 (0.8016 for IC50, 0.7945 for AUC) with 321 predictor genes.
RESUMO
PURPOSE: Poisoning is an important cause of death in Korea. We aimed to investigate the epidemiological characteristics and outcomes for in-hospital cardiac arrest (IHCA) in poisoned patients in Korea. METHODS: This is a population-based study, analyzing 576 IHCA patients who were poisoned and registered in the Korean Health Insurance Review and Assessment Service in 2013. The cardiopulmonary resuscitation outcomes, including survival discharge and 30-day survival rate, were analyzed. The main diagnoses were categorized in accordance with the Korean Standard Classification of Diseases version 6. RESULTS: The overall survival discharge and 30-day survival rate were 31.6% and 15.3%, respectively. The most common etiologies of poisoning were pesticides (54.3%), drugs and medications (21.9%), carbon monoxide (8.9%), and unspecified substances (5.4%); the 30-day survival rate for each etiology was 16.6%, 15.2%, 9.8%, and 19.4%, respectively. A geographical analysis showed a high 30-day survival rate in Gwangju (32.0%), Daejeon (25.0%) and Ulsan (25.0%). CONCLUSION: Pesticides poisoning is the most common cause for IHCA patients. The survival rate after IHCA by poisoning was similar in pesticides poisoning than in other toxic etiologies. Therefore, it is crucial to reduce pesticide poisoning and to establish a poisoning information inquiry system.
