ABSTRACT
The mainstay of acute myeloid leukemia (AML) treatment still relies on traditional chemotherapy, with a survival rate of approximately 30% for patients under 65 years of age and as low as 5% for those beyond. This unfavorable prognosis primarily stems from frequent relapses, resistance to chemotherapy, and limited approved targeted therapies for specific AML subtypes. Around 70% of all AML cases show overexpression of the transcription factor HOXA9, which is associated with a poor prognosis, increased chemoresistance, and higher relapse rates. However, direct targeting of HOXA9 in a clinical setting has not been achieved yet. The dysregulation caused by the leukemic HOXA9 transcription factor primarily results from its binding activity to DNA, leading to differentiation blockade. Our previous investigations have identified two HOXA9/DNA binding competitors, namely DB1055 and DB818. We assessed their antileukemic effects in comparison to HOXA9 knockdown or cytarabine treatment. Using human AML cell models, DB1055 and DB818 induced in vitro cell growth reduction, death, differentiation, and common transcriptomic deregulation but did not impact human CD34+ bone marrow cells. Furthermore, DB1055 and DB818 exhibited potent antileukemic activities in a human THP-1 AML in vivo model, leading to the differentiation of monocytes into macrophages. In vitro assays also demonstrated the efficacy of DB1055 and DB818 against AML blasts from patients, with DB1055 successfully reducing leukemia burden in patient-derived xenografts in NSG immunodeficient mice. Our findings indicate that inhibiting HOXA9/DNA interaction using DNA ligands may offer a novel differentiation therapy for the future treatment of AML patients dependent on HOXA9.
ABSTRACT
Background: With the proliferation of available microarray and high-throughput sequencing experiments in the public domain, the use of meta-analysis methods increases. In these experiments, where the sample size is often limited, meta-analysis offers the possibility to considerably enhance the statistical power and give more accurate results. For those purposes, it combines either effect sizes or results of single studies in an appropriate manner. R packages metaMA and metaRNASeq perform meta-analysis on microarray and next generation sequencing (NGS) data, respectively. They are not interchangeable as they rely on statistical modeling specific to each technology. Results: SMAGEXP (Statistical Meta-Analysis for Gene EXPression) integrates metaMA and metaRNAseq packages into Galaxy. We aim to propose a unified way to carry out meta-analysis of gene expression data, while taking care of their specificities. We have developed this tool suite to analyze microarray data from the Gene Expression Omnibus database or custom data from Affymetrix© microarrays. These data are then combined to carry out meta-analysis using metaMA package. SMAGEXP also offers to combine raw read counts from NGS experiments using DESeq2 and metaRNASeq package. In both cases, key values, independent from the technology type, are reported to judge the quality of the meta-analysis. These tools are available on the Galaxy main tool shed. A dockerized instance of galaxy containing SMAGEXP and its dependencies is available on Docker hub. Source code, help, and installation instructions are available on GitHub. Conclusion: The use of Galaxy offers an easy-to-use gene expression meta-analysis tool suite based on the metaMA and metaRNASeq packages.
Subject(s)
Computational Biology/methods , Gene Expression Profiling/methods , Meta-Analysis as Topic , Software , Data Analysis , High-Throughput Nucleotide Sequencing/methods , Humans , Neoplasms/genetics , Oligonucleotide Array Sequence Analysis/methodsABSTRACT
BACKGROUND: Last generations of Single Nucleotide Polymorphism (SNP) arrays allow to study copy-number variations in addition to genotyping measures. RESULTS: MPAgenomics, standing for multi-patient analysis (MPA) of genomic markers, is an R-package devoted to: (i) efficient segmentation and (ii) selection of genomic markers from multi-patient copy number and SNP data profiles. It provides wrappers from commonly used packages to streamline their repeated (sometimes difficult) manipulation, offering an easy-to-use pipeline for beginners in R.The segmentation of successive multiple profiles (finding losses and gains) is performed with an automatic choice of parameters involved in the wrapped packages. Considering multiple profiles in the same time, MPAgenomics wraps efficient penalized regression methods to select relevant markers associated with a given outcome. CONCLUSIONS: MPAgenomics provides an easy tool to analyze data from SNP arrays in R. The R-package MPAgenomics is available on CRAN.
