No influence of the polymorphisms CYP2C19 and CYP2D6 on the efficacy of cyclophosphamide, thalidomide, and bortezomib in patients with Multiple Myeloma

BACKGROUND: The response to treatment varies among patients with multiple myeloma and markers for prediction of treatment outcome are highly needed. Bioactivation of cyclophosphamide and thalidomide, and biodegradation of bortezomib, is dependent on cytochrome P450 metabolism. We explored the potential influence of different polymorphisms in the CYP enzymes on the outcome of treatment. METHODS: Data was analyzed from 348 patients undergoing high-dose treatment and stem cell support in Denmark in 1994 to 2004. Clinical information on relapse treatment in 243 individual patients was collected. The patients were genotyped for the non-functional alleles CYP2C19*2 and CYP2D6*3, *4, *5 (gene deletion), *6, and CYP2D6 gene duplication. RESULTS: In patients who were treated with bortezomib and were carriers of one or two defective CYP2D6 alleles there was a trend towards a better time-to-next treatment. We found no association between the number of functional CYP2C19 and CYP2D6 alleles and outcome of treatment with cyclophosphamide or thalidomide. Neither was the number of functional CYP2C19 and CYP2D6 alleles associated with neurological adverse reactions to thalidomide and bortezomib. CONCLUSION: There was no association between functional CYP2C19 and CYP2D6 alleles and treatment outcome in multiple myeloma patients treated with cyclophosphamide, thalidomide or bortezomib. A larger number of patients treated with bortezomib are needed to determine the role of CYP2D6 alleles in treatment outcome

Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts

Electronic patient records remain a rather unexplored, but potentially rich data source for discovering correlations between diseases. We describe a general approach for gathering phenotypic descriptions of patients from medical records in a systematic and non-cohort dependent manner. By extracting phenotype information from the free-text in such records we demonstrate that we can extend the information contained in the structured record data, and use it for producing fine-grained patient stratification and disease co-occurrence statistics. The approach uses a dictionary based on the International Classification of Disease ontology and is therefore in principle language independent. As a use case we show how records from a Danish psychiatric hospital lead to the identification of disease correlations, which subsequently can be mapped to systems biology frameworks