Genetic characterization of ABT-199 sensitivity in human AML.

Acute myeloid leukemias (AML) with mutations in the NPM1 gene (NPM1c+) represent a large AML subgroup with varying response to conventional treatment, highlighting the need to develop targeted therapeutic strategies for this disease. We screened a library of clinical drugs on a cohort of primary human AML specimens and identified the BCL2 inhibitor ABT-199 as a selective agent against NPM1c+ AML. Mutational analysis of ABT-199-sensitive and -resistant specimens identified mutations in NPM1, RAD21, and IDH1/IDH2 as predictors of ABT-199 sensitivity. Comparative transcriptome analysis further uncovered BCL2A1 as a potential mediator of ABT-199 resistance in AML. In line with our observation that RAD21 mutation confers sensitivity to ABT-199, we provide functional evidence that reducing RAD21 levels can sensitize AML cells to BCL2 inhibition. Moreover, we demonstrate that ABT-199 is able to produce selective anti-AML activity in vivo toward AML with mutations associated with compound sensitivity in PDX models. Overall, this study delineates the contribution of several genetic events to the response to ABT-199 and provides a rationale for the development of targeted therapies for NPM1c+ AML.

Enhancing the drug discovery process: Bayesian inference for the analysis and comparison of dose-response experiments.

MOTIVATION: The efficacy of a chemical compound is often tested through dose-response experiments from which efficacy metrics, such as the IC50, can be derived. The Marquardt-Levenberg algorithm (non-linear regression) is commonly used to compute estimations for these metrics. The analysis are however limited and can lead to biased conclusions. The approach does not evaluate the certainty (or uncertainty) of the estimates nor does it allow for the statistical comparison of two datasets. To compensate for these shortcomings, intuition plays an important role in the interpretation of results and the formulations of conclusions. We here propose a Bayesian inference methodology for the analysis and comparison of dose-response experiments. RESULTS: Our results well demonstrate the informativeness gain of our Bayesian approach in comparison to the commonly used Marquardt-Levenberg algorithm. It is capable to characterize the noise of dataset while inferring probable values distributions for the efficacy metrics. It can also evaluate the difference between the metrics of two datasets and compute the probability that one value is greater than the other. The conclusions that can be drawn from such analyzes are more precise. AVAILABILITY AND IMPLEMENTATION: We implemented a simple web interface that allows the users to analyze a single dose-response dataset, as well as to statistically compare the metrics of two datasets.