Upfront Treatment of FLT3-Mutated AML: A Look Back at the RATIFY Trial and Beyond.

In April 2017, following the results of the RATIFY trial (1), midostaurin, a multikinase FLT3 inhibitor, became the first FDA approved targeted agent for the treatment of acute myeloid leukemia (AML) (2). The addition of midostaurin to standard induction therapy with anthracycline and cytarabine (7 + 3) rapidly became the new standard of care for treatment-naïve, fit patients with FLT3-mutated (FLTmut+) AML (3). More recently, gilteritinib, a selective FLT3 inhibitor, showed superiority to chemotherapy in the treatment of relapsed or refractory FLTmut+ AML (4). With two FLT3 inhibitors now approved by the FDA—that is, the more selective gilteritinib and the less selective midostaurin—the question of which FLT3 inhibitor to use in combination with chemotherapy in the upfront setting has become the subject of much debate (5–7). Leukemia physicians are faced with the choice of using a more selective agent in the front line vs. reserving that agent for the time of relapse. Here, we evaluate the rationale for both approaches

Assessment of regression methods for inference of regulatory networks involved in circadian regulation

We assess the accuracy of three established regression methods for reconstructing gene and protein regulatory networks in the context of circadian regulation. Data are simulated from a recently published regulatory network of the circadian clock in Arabidopsis thaliana, in which protein and gene interactions are described by a Markov jump process based on Michaelis-Menten kinetics. We closely follow recent experimental protocols, including the entrainment of seedlings to different light-dark cycles and the knock-out of various key regulatory genes. Our study provides relative assessment scores for the comparison of state-of-the art regression methods, investigates the influence of systematically missing values related to unknown protein concentrations and mRNA transcription rates, and quantifies the dependence of the performance on the degree of recurrency

Validation of an intrinsic groundwater pollution vulnerability methodology using a national nitrate database.

The importance of groundwater for potable supply, and the many sources of anthropogenic contamination, has led to the development of intrinsic groundwater vulnerability mapping. An Analysis of Co-Variance and Analysis of Variance are used to validate the extensively applied UK methodology, based upon nitrate concentrations from 1,108 boreholes throughout England and Wales. These largely confirm the current aquifer and soil leaching potential classifications and demonstrate the benefits of combining soil and low permeability drift information. European legislation such as the Water Framework Directive will require more dynamic assessments of pollutant risk to groundwater. These results demonstrate that a number of improvements are required to future intrinsic groundwater vulnerability methodologies. The vertical succession of geological units must be included, so that non-aquifers can be zoned in the same way as aquifers for water supply purposes, while at the same time recognising their role in influencing the quality of groundwater in deeper aquifers. Classifications within intrinsic vulnerability methodologies should be based upon defined diagnostic properties rather than expert judgement. Finally the incorporation into groundwater vulnerability methodologies of preferential flow in relation to geological deposits, soil type and land management practices represents a significant, but important, future challenge

Reconstructing ecological networks with hierarchical Bayesian regression and Mondrian processes

Ecological systems consist of complex sets of interactions among species and their environment, the understanding of which has implications for predicting environmental response to perturbations such as invading species and climate change. However, the revelation of these interactions is not straightforward, nor are the interactions necessarily stable across space. Machine learning can enable the recovery of such complex, spatially varying interactions from relatively easily obtained species abundance data. Here, we describe a novel Bayesian regression and Mondrian process model (BRAMP) for reconstructing species interaction networks from observed field data. BRAMP enables robust inference of species interactions considering autocorrelation in species abundances and allowing for variation in the interactions across space. We evaluate the model on spatially explicit simulated data, produced using a trophic niche model combined with stochastic population dynamics. We compare the model’s performance against L1-penalized sparse regression (LASSO) and non-linear Bayesian networks with the BDe scoring scheme. Finally, we apply BRAMP to real ecological data