Luspatercept stimulates erythropoiesis, increases iron utilization, and redistributes body iron in transfusion-dependent thalassemia.

Luspatercept, a ligand-trapping fusion protein, binds select TGF-ß superfamily ligands implicated in thalassemic erythropoiesis, promoting late-stage erythroid maturation. Luspatercept reduced transfusion burden in the BELIEVE trial (NCT02604433) of 336 adults with transfusion-dependent thalassemia (TDT). Analysis of biomarkers in BELIEVE offers novel physiological and clinical insights into benefits offered by luspatercept. Transfusion iron loading rates decreased 20% by 1.4 g (~7 blood units; median iron loading rate difference: -0.05 ± 0.07 mg Fe/kg/day, p< .0001) and serum ferritin (s-ferritin) decreased 19.2% by 269.3 ± 963.7 µg/L (p < .0001), indicating reduced macrophage iron. However, liver iron content (LIC) did not decrease but showed statistically nonsignificant increases from 5.3 to 6.7 mg/g dw. Erythropoietin, growth differentiation factor 15, soluble transferrin receptor 1 (sTfR1), and reticulocytes rose by 93%, 59%, 66%, and 112%, respectively; accordingly, erythroferrone increased by 51% and hepcidin decreased by 53% (all p < .0001). Decreased transfusion with luspatercept in patients with TDT was associated with increased erythropoietic markers and decreasing hepcidin. Furthermore, s-ferritin reduction associated with increased erythroid iron incorporation (marked by sTfR1) allowed increased erythrocyte marrow output, consequently reducing transfusion needs and enhancing rerouting of hemolysis (heme) iron and non-transferrin-bound iron to the liver. LIC increased in patients with intact spleens, consistent with iron redistribution given the hepcidin reduction. Thus, erythropoietic and hepcidin changes with luspatercept in TDT lower transfusion dependency and may redistribute iron from macrophages to hepatocytes, necessitating the use of concomitant chelator cover for effective iron management.

Prognostic impact of NPM1 and FLT3 mutations in patients with AML in first remission treated with oral azacitidine.

The randomized, placebo-controlled, phase 3 QUAZAR AML-001 trial (ClinicalTrials.gov identifier: NCT01757535) evaluated oral azacitidine (Oral-AZA) in patients with acute myeloid leukemia (AML) in first remission after intensive chemotherapy (IC) who were not candidates for hematopoietic stem cell transplantation. Eligible patients were randomized 1:1 to Oral-AZA 300 mg or placebo for 14 days per 28-day cycle. We evaluated relapse-free survival (RFS) and overall survival (OS) in patient subgroups defined by NPM1 and FLT3 mutational status at AML diagnosis and whether survival outcomes in these subgroups were influenced by presence of post-IC measurable residual disease (MRD). Gene mutations at diagnosis were collected from patient case report forms; MRD was determined centrally by multiparameter flow cytometry. Overall, 469 of 472 randomized patients (99.4%) had available mutational data; 137 patients (29.2%) had NPM1 mutations (NPM1mut), 66 patients (14.1%) had FLT3 mutations (FLT3mut; with internal tandem duplications [ITD], tyrosine kinase domain mutations [TKDmut], or both), and 30 patients (6.4%) had NPM1mut and FLT3-ITD at diagnosis. Among patients with NPM1mut, OS and RFS were improved with Oral-AZA by 37% (hazard ratio [HR], 0.63; 95% confidence interval [CI], 0.41-0.98) and 45% (HR, 0.55; 95% CI, 0.35-0.84), respectively, vs placebo. Median OS was improved numerically with Oral-AZA among patients with NPM1mut whether without MRD (48.6 months vs 31.4 months with placebo) or with MRD (46.1 months vs 10.0 months with placebo) post-IC. Among patients with FLT3mut, Oral-AZA improved OS and RFS by 37% (HR, 0.63; 95% CI, 0.35-1.12) and 49% (HR, 0.51; 95% CI, 0.27-0.95), respectively, vs placebo. Median OS with Oral-AZA vs placebo was 28.2 months vs 16.2 months, respectively, for patients with FLT3mut and without MRD and 24.0 months vs 8.0 months for patients with FLT3mut and MRD. In multivariate analyses, Oral-AZA significantly improved survival independent of NPM1 or FLT3 mutational status, cytogenetic risk, or post-IC MRD status.

MultiBaC: an R package to remove batch effects in multi-omic experiments.

MOTIVATION: Batch effects in omics datasets are usually a source of technical noise that masks the biological signal and hampers data analysis. Batch effect removal has been widely addressed for individual omics technologies. However, multi-omic datasets may combine data obtained in different batches where omics type and batch are often confounded. Moreover, systematic biases may be introduced without notice during data acquisition, which creates a hidden batch effect. Current methods fail to address batch effect correction in these cases. RESULTS: In this article, we introduce the MultiBaC R package, a tool for batch effect removal in multi-omics and hidden batch effect scenarios. The package includes a diversity of graphical outputs for model validation and assessment of the batch effect correction. AVAILABILITY AND IMPLEMENTATION: MultiBaC package is available on Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/MultiBaC.html) and GitHub (https://github.com/ConesaLab/MultiBaC.git). The data underlying this article are available in Gene Expression Omnibus repository (accession numbers GSE11521, GSE1002, GSE56622 and GSE43747). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MultiBaC: A strategy to remove batch effects between different omic data types.

Diversity of omic technologies has expanded in the last years together with the number of omic data integration strategies. However, multiomic data generation is costly, and many research groups cannot afford research projects where many different omic techniques are generated, at least at the same time. As most researchers share their data in public repositories, different omic datasets of the same biological system obtained at different labs can be combined to construct a multiomic study. However, data obtained at different labs or moments in time are typically subjected to batch effects that need to be removed for successful data integration. While there are methods to correct batch effects on the same data types obtained in different studies, they cannot be applied to correct lab or batch effects across omics. This impairs multiomic meta-analysis. Fortunately, in many cases, at least one omics platform-i.e. gene expression- is repeatedly measured across labs, together with the additional omic modalities that are specific to each study. This creates an opportunity for batch analysis. We have developed MultiBaC (multiomic Multiomics Batch-effect Correction correction), a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. Our strategy is based on the existence of at least one shared data type which allows data prediction across omics. We validate this approach both on simulated data and on a case where the multiomic design is fully shared by two labs, hence batch effect correction within the same omic modality using traditional methods can be compared with the MultiBaC correction across data types. Finally, we apply MultiBaC to a true multiomic data integration problem to show that we are able to improve the detection of meaningful biological effects.

A multi-omics dataset of heat-shock response in the yeast RNA binding protein Mip6.

Gene expression is a biological process regulated at different molecular levels, including chromatin accessibility, transcription, and RNA maturation and transport. In addition, these regulatory mechanisms have strong links with cellular metabolism. Here we present a multi-omics dataset that captures different aspects of this multi-layered process in yeast. We obtained RNA-seq, metabolomics, and H4K12ac ChIP-seq data for wild-type and mip6Δ strains during a heat-shock time course. Mip6 is an RNA-binding protein that contributes to RNA export during environmental stress and is informative of the contribution of post-transcriptional regulation to control cellular adaptations to environmental changes. The experiment was performed in quadruplicate, and the different omics measurements were obtained from the same biological samples, which facilitates the integration and analysis of data using covariance-based methods. We validate our dataset by showing that ChIP-seq, RNA-seq and metabolomics signals recapitulate existing knowledge about the response of ribosomal genes and the contribution of trehalose metabolism to heat stress. Raw data, processed data and preprocessing scripts are made available.

Elucidating the Role of Chromatin State and Transcription Factors on the Regulation of the Yeast Metabolic Cycle: A Multi-Omic Integrative Approach.

The Yeast Metabolic Cycle (YMC) is a model system in which levels of around 60% of the yeast transcripts cycle over time. The spatial and temporal resolution provided by the YMC has revealed that changes in the yeast metabolic landscape and chromatin status can be related to cycling gene expression. However, the interplay between histone modifications and transcription factor activity during the YMC is still poorly understood. Here we apply an innovative statistical approach to integrate chromatin state (ChIP-seq) and gene expression (RNA-seq) data to investigate the transcriptional control during the YMC. By using the multivariate regression models N-PLS (Partial Least Squares) and MORE (Multi-Omics REgulation) methodologies, we assessed the contribution of histone marks and transcription factors to the regulation of gene expression in the YMC. We found that H3K18ac and H3K9ac were the most important histone modifications, whereas Sfp1, Hfi1, Pip2, Mig2, and Yhp1 emerged as the most relevant transcription factors. A significant association in the co-regulation of gene expression was found between H3K18ac and the transcription factors Pip2 (involved in fatty acids metabolism), Xbp1 (cyclin implicated in the regulation of carbohydrate and amino acid metabolism), and Hfi1 (involved in the formation of the SAGA complex). These results evidence the crucial role of histone lysine acetylation levels in the regulation of gene expression in the YMC through the coordinated action of transcription factors and lysine acetyltransferases.