Application Of Kinase Activity Profiles To Predict Upcoming Tki Resistance In Cml-Patients

Abstract Abstract 3425 Background: Major breakthroughs in CML research have revolutionized therapy by the introduction of a specific inhibitor of BCR-ABL activity; first Imatinib and now even more potent TKIs like Nilotinib and Dasatinib are available. Imatinib can induce complete hematologic responses in virtually all patients and complete cytogenetic responses in almost 90% of patients and progression free survival in 84% of patients. Patients that reach a more than 1000-fold reduction of the BCR-ABL transcript level (major molecular response) have durable responses with not a single event of progression. Unfortunately, not all patients attain such a favourable response. Primary and secondary resistance develops in around 20% of cases.Predicting the development this resistance would be of great clinical value. Aim: To establish a classifier based on peptide phosphorylation patterns as fingerprints using samples of peripheral blood cells or bone marrow of CML patients undergoing Imatinib treatment in order to enable prediction of emerging resistance against Imatinib. Methods: Stored (“snapfrozen”) mononuclear cell fractions, isolated by Ficoll-Paque centrifugation of bone marrow or peripheral blood of CML patients under continuous Imatinib treatment and in various stages of their disease were lysed in M-PER buffer supplemented with phosphatase and protease inhibitors. Kinase activity profiles of these lysates were generated with standard Tyrosine Kinase PamChip® Arrays that contain 144 peptides as kinase substrates on their porous surface. Peptide phosphorylation was followed through binding of a fluorescently labelled anti-phosphotyrosine antibody during flow-through cycling of the lysates. Activity profiles were generated with PamGene's BioNavigator software. The resulting data were analysed using the R-based package CMA (“Classification for MicroArrays”) that enables the survey and evaluation of most usual classification methods with double cross-validation procedures. Results: A distinct classifier for Imatinib response prediction could be derived for the bone marrow samples, collected at various intervals after diagnosis (18 Imatinib sensitive versus 19 Imatinib resistant patients, of which the samples were collected 3 months to 1.5 year before resistance emergence). Of the twenty-one classification methods that were studied the SupportVectorMachine (SVM) algorithm resulted in the smallest error rate: this was 16% with a standard error of 0.062 and a sensitivity of 84% and specificity of 82% respectively, meaning 3 misclassified sensitive patients and 3 misclassified resistant patients. This classifier has to be validated in a blinded, independent test set. Conclusions: We demonstrated that with this method it is possible to predict Imatinib resistance. Differences in phosphorylation patterns as detected using PamGene's peptide microarray technology with the aid of multivariate statistical analysis suggest the presence of an ongoing process in CML-patients destined in due time to a relapse in spite of continuous Imatinib treatment. We regard these initial class prediction results to be a basis for further development of this kinase activity based test for Imatinib response prediction in CML patients already at the time of diagnosis. Perspectives: Our approach of using kinase activity profiling for the prediction of Imatinib resistance, could equally well be applied to predict response to various other kinase inhibitors. These response predictions could be combined in the same test by adding these inhibitors in vitro and might direct the optimal drug selection at the time point of diagnosis. Disclosures: Boender: PamGene International BV: Employment. Ruijtenbeek:PamGene International BV: Employment. van den Berg:PamGene Inetrnational BV: Employment. de Wijn:PamGene International BV: Employment.