A precision medicine test predicts clinical response after idarubicin and cytarabine induction therapy in AML patients

Complete remission (CR) after induction therapy is the first treatment goal in acute myeloid leukemia (AML) patients and has prognostic impact. Our purpose is to determine the correlation between the observed CR/CRi rate after idarubicin (IDA) and cytarabine (CYT) 3 + 7 induction and the leukemic chemosensitivity measured by an ex vivo test of drug activity. Bone marrow samples from adult patients with newly diagnosed AML were included in this study. Whole bone marrow samples were incubated for 48 h in well plates containing IDA, CYT, or their combination. Pharmacological response parameters were estimated using population pharmacodynamic models. Patients attaining a CR/CRi with up to two induction cycles of 3 + 7 were classified as responders and the remaining as resistant. A total of 123 patients fulfilled the inclusion criteria and were evaluable for correlation analyses. The strongest clinical predictors were the area under the curve of the concentration response curves of CYT and IDA. The overall accuracy achieved using MaxSpSe criteria to define positivity was 81%, predicting better responder (93%) than non-responder patients (60%). The ex vivo test provides better yet similar information than cytogenetics, but can be provided before treatment representing a valuable in-time addition. After validation in an external cohort, this novel ex vivo test could be useful to select AML patients for 3 + 7 regimen vs. alternative schedules

Time to Switch to Second-line Antiretroviral Therapy in Children With Human Immunodeficiency Virus in Europe and Thailand.

Background: Data on durability of first-line antiretroviral therapy (ART) in children with human immunodeficiency virus (HIV) are limited. We assessed time to switch to second-line therapy in 16 European countries and Thailand. Methods: Children aged <18 years initiating combination ART (≥2 nucleoside reverse transcriptase inhibitors [NRTIs] plus nonnucleoside reverse transcriptase inhibitor [NNRTI] or boosted protease inhibitor [PI]) were included. Switch to second-line was defined as (i) change across drug class (PI to NNRTI or vice versa) or within PI class plus change of ≥1 NRTI; (ii) change from single to dual PI; or (iii) addition of a new drug class. Cumulative incidence of switch was calculated with death and loss to follow-up as competing risks. Results: Of 3668 children included, median age at ART initiation was 6.1 (interquartile range (IQR), 1.7-10.5) years. Initial regimens were 32% PI based, 34% nevirapine (NVP) based, and 33% efavirenz based. Median duration of follow-up was 5.4 (IQR, 2.9-8.3) years. Cumulative incidence of switch at 5 years was 21% (95% confidence interval, 20%-23%), with significant regional variations. Median time to switch was 30 (IQR, 16-58) months; two-thirds of switches were related to treatment failure. In multivariable analysis, older age, severe immunosuppression and higher viral load (VL) at ART start, and NVP-based initial regimens were associated with increased risk of switch. Conclusions: One in 5 children switched to a second-line regimen by 5 years of ART, with two-thirds failure related. Advanced HIV, older age, and NVP-based regimens were associated with increased risk of switch

A Bayesian Poisson-lognormal Model for Count Data for Multiple-Trait Multiple-Environment Genomic-Enabled Prediction

When a plant scientist wishes to make genomic-enabled predictions of multiple traits measured in multiple individuals in multiple environments, the most common strategy for performing the analysis is to use a single trait at a time taking into account genotype × environment interaction (G × E), because there is a lack of comprehensive models that simultaneously take into account the correlated counting traits and G × E. For this reason, in this study we propose a multiple-trait and multiple-environment model for count data. The proposed model was developed under the Bayesian paradigm for which we developed a Markov Chain Monte Carlo (MCMC) with noninformative priors. This allows obtaining all required full conditional distributions of the parameters leading to an exact Gibbs sampler for the posterior distribution. Our model was tested with simulated data and a real data set. Results show that the proposed multi-trait, multi-environment model is an attractive alternative for modeling multiple count traits measured in multiple environments

Prediction of Multiple-Trait and Multiple-Environment Genomic Data Using Recommender Systems

In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. In current genomic selection, although researchers have a large amount of information and appropriate statistical models to process it, there is still limited computing efficiency to do so. Although some statistical models are usually mathematically elegant, many of them are also computationally inefficient, and they are impractical for many traits, lines, environments, and years because they need to sample from huge normal multivariate distributions. For these reasons, this study explores two recommender systems: item-based collaborative filtering (IBCF) and the matrix factorization algorithm (MF) in the context of multiple traits and multiple environments. The IBCF and MF methods were compared with two conventional methods on simulated and real data. Results of the simulated and real data sets show that the IBCF technique was slightly better in terms of prediction accuracy than the two conventional methods and the MF method when the correlation was moderately high. The IBCF technique is very attractive because it produces good predictions when there is high correlation between items (environment–trait combinations) and its implementation is computationally feasible, which can be useful for plant breeders who deal with very large data sets.Peer reviewe

A Comparison of Three Machine Learning Methods for Multivariate Genomic Prediction Using the Sparse Kernels Method (SKM) Library

Genomic selection (GS) changed the way plant breeders select genotypes. GS takes advantage of phenotypic and genotypic information to training a statistical machine learning model, which is used to predict phenotypic (or breeding) values of new lines for which only genotypic information is available. Therefore, many statistical machine learning methods have been proposed for this task. Multi-trait (MT) genomic prediction models take advantage of correlated traits to improve prediction accuracy. Therefore, some multivariate statistical machine learning methods are popular for GS. In this paper, we compare the prediction performance of three MT methods: the MT genomic best linear unbiased predictor (GBLUP), the MT partial least squares (PLS) and the multi-trait random forest (RF) methods. Benchmarking was performed with six real datasets. We found that the three investigated methods produce similar results, but under predictors with genotype (G) and environment (E), that is, E + G, the MT GBLUP achieved superior performance, whereas under predictors E + G + genotype &times; environment (GE) and G + GE, random forest achieved the best results. We also found that the best predictions were achieved under the predictors E + G and E + G + GE. Here, we also provide the R code for the implementation of these three statistical machine learning methods in the sparse kernel method (SKM) library, which offers not only options for single-trait prediction with various statistical machine learning methods but also some options for MT predictions that can help to capture improved complex patterns in datasets that are common in genomic selection

Encuesta de opinión: Formación-información de los alumnos de la Universidad de Murcia sobre el proceso donación-trasplante de órganos

Estudio de investigación que analiza la formación-información de un colectivo universitario sobre el proceso de donación-trasplante de órganos. Segunda fase del estudio mediante el pase de una encuesta de opinión que pone de manifiesto aspectos de aceptación, indiferencia o rechazo ante el proceso

Clinical characteristics of patients with central nervous system relapse in BCR-ABL1-positive acute lymphoblastic leukemia : the importance of characterizing ABL1 mutations in cerebrospinal fluid

We investigated the frequency, predictors, and evolution of acute lymphoblastic leukemia (ALL) in patients with CNS relapse and introduced a novel method for studying BCR-ABL1 protein variants in cDNA from bone marrow (BM) and cerebrospinal fluid (CSF) blast cells. A total of 128 patients were analyzed in two PETHEMA clinical trials. All achieved complete remission after imatinib treatment. Of these, 30 (23%) experienced a relapse after achieving complete remission, and 13 (10%) had an isolated CNS relapse or combined CNS and BM relapses. We compared the characteristics of patients with and without CNS relapse and further analyzed CSF and BM samples from two of the 13 patients with CNS relapse. In both patients, classical sequencing analysis of the kinase domain of BCR-ABL1 from the cDNA of CSF blasts revealed the pathogenic variant p.L387M. We also performed ultra-deep next-generation sequencing (NGS) in three samples from one of the relapsed patients. We did not find the mutation in the BM sample, but we did find it in CSF blasts with 45% of reads at the time of relapse. These data demonstrate the feasibility of detecting BCR-ABL1 mutations in CSF blasts by NGS and highlight the importance of monitoring clonal evolution over time