Pharmacogenomics of drug efficacy in the interferon treatment of chronic hepatitis C using classification algorithms

Chronic hepatitis C (CHC) patients often stop pursuing interferon-alfa and ribavirin (IFN-alfa/RBV) treatment because of the high cost and associated adverse effects. It is highly desirable, both clinically and economically, to establish tools to distinguish responders from nonresponders and to predict possible outcomes of the IFN-alfa/RBV treatments. Single nucleotide polymorphisms (SNPs) can be used to understand the relationship between genetic inheritance and IFN-alfa/RBV therapeutic response. The aim in this study was to establish a predictive model based on a pharmacogenomic approach. Our study population comprised Taiwanese patients with CHC who were recruited from multiple sites in Taiwan. The genotyping data was generated in the high-throughput genomics lab of Vita Genomics, Inc. With the wrapper-based feature selection approach, we employed multilayer feedforward neural network (MFNN) and logistic regression as a basis for comparisons. Our data revealed that the MFNN models were superior to the logistic regression model. The MFNN approach provides an efficient way to develop a tool for distinguishing responders from nonresponders prior to treatments. Our preliminary results demonstrated that the MFNN algorithm is effective for deriving models for pharmacogenomics studies and for providing the link from clinical factors such as SNPs to the responsiveness of IFN-alfa/RBV in clinical association studies in pharmacogenomics

Mutation and Lineage Analysis of DNMT3A in BCR-ABL1-negative Chronic Myeloproliferative Neoplasms

SummaryIn addition to the JAK2 V617F mutation, somatic mutation in DNMT3A has been described in BCL-ABL1-negative myeloproliferative neoplasms (MPNs). We have screened for DNMT3A exon 23 mutations in 130 adult Taiwanese patients with chronic phase myeloproliferative neoplasms. Only one somatic DNMT3A R882H mutation was identified in one JAK2 V617F mutation-positive essential thrombocythemia patient (1/91, 1%). Both mutations were detected in the CD34+-, CD19+-, peripheral blood mononuclear cell- and granulocyte-enriched fractions, but were not detected in the CD3+-enriched fraction by lineage analysis. Our findings suggest that DNMT3A mutation is not prevalent in MPNs, and further study is needed to clarify its role in the molecular pathogenesis of myeloproliferative neoplasms