MedicalResearch.com Author Interview:
Dr. Steven A. Branstetter, PhD
The Pennsylvania State University, 315 E. HHD, University Park, PA 16810.
MedicalResearch.com: What are the main findings of the study?
Dr. Branstetter: This study demonstrated that the time to the first cigarette of the day after waking is associated with increased levels of a NNAL, a metabolite of a powerful tobacco-specific carcinogen, NNK -- even after controlling for the total number of cigarettes smoked per day.
For years, the time to the first cigarette of the day after waking was one of several questions assessing nicotine dependence on the Fagerstrom Test for Nicotine Dependence (FTND), the gold standard questionnaire int he field. Over time, it was found that much of the predictive validity of the FTND was due to the time to first cigarette item. Researchers have found that single time to first cigarette item was highly correlated with other measures of nicotine dependence, and was predictive of more difficulty quitting smoking and increased intake of nicotine. Our current study demonstrates that this behavioral measure, is predictive of exposure to the cancer-causing components of cigarettes, regardless of the total number of cigarettes smoked per day. The results suggest that researchers, clinicians and smokers can assess the level of nicotine dependence and potential cancer risk by looking at the time to the first cigarette of the day after waking.
Department of Physics of Complex Systems and Department of Biological Regulation, Weizmann Institute of Science, Rehovot, 76100, Israel
MedicalResearch.com: What are the main findings of the study?
Prof. Domany: The findings are two-fold: methodological and clinical. A novel method was introduced for personalized analysis of cancer, and was applied on large colon cancer and glioblastoma datasets.
The method uses high throughput (gene expression) data to infer a pathway deregulation score (PDS) for individual tumors, for hundreds of pathways and biological processes. The method is knowledge-based in that it uses well known information about the assignment of genes to biologically relevant pathways. No detailed knowledge of the underlying networks of interactions and activations is necessary. Each tumor is represented by a few hundred of these PDSs, and further analysis uses this representation.
