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7,932 results for “leukemia”
KSR inhibitor APS-2-79 sensitivity test in JURKAT and ALL-SIL T-cell acute lymphoblastic leukemia cell lines measured by Cell Counting Kit 8
<p>APS-2-79 compound was purchased from MedChemExpress (Monmoutyh Junction, NJ, USA). The 20 mg/ml stock solution was prepared in DMSO. To calculate the IC50, JURKAT and ALL-SIL cells were cultured for 72h with a range of APS-2-79 concentrations (5-15 µM) added as equal volumes. Cells treated with 0.5% DMSO (vehicle) were used as negative control. Cells treated with 10% DMSO were used as positive control. The viability of cells was measured using Cell Counting Kit 8 (Sigma Aldrich) and GloMax Microplate Reader system (Promega) with 450 nm wavelength and 600 nm as reference wavelength. The relevant reads are made from following wells: 2A-2D (15 µM APS-2-79), 3A-3D (12.5 µM APS-2-79), 4A-4D (10 µM APS-2-79), 5A-5D (7.5 µM APS-2-79), 6A-6D (5 µM APS-2-79), 7A-7D (vehicle), 8A-8D (positive control).</p>
Cell-cell interactome of the hematopoietic niche and its changes in acute myeloid leukemia
<p>This repository contains data described in this study: Cell-cell interactome of the hematopoietic niche and its changes in acute myeloid leukemia. <em>Ennis S et. al., iScience, 2023. DOI: </em><a href="https://doi.org/10.1016/j.isci.2023.106943">10.1016/j.isci.2023.106943</a><em>.</em></p> <p> </p> <p><strong>Contents:</strong></p> <ul> <li>bone_marrow.h5ad - AnnData file with the integrated dataset</li> <li>ref_model_final.tar.gz - A zipped folder containing the scVI model for the integrated dataset</li> <li>Supplemental_material.tar.gz - A zipped folder containing the supplemental figures and tables from the publication</li> </ul>
Multi-study reanalysis of 2213 acute myeloid leukemia patients reveals age- and sex-dependent gene expression signatures
<p>Supplemental Information, Supplementary Tables, and Supplementary Files, as well as accompanying data for the manuscript "Stratified computational meta-analysis of 2213 acute myeloid leukemia patients reveals age- and sex-dependent gene expression signatures" by Raeuf Roushangar and George I. Mias. </p>
Identification of Genes Regulating Dexamethasone Resistance and Prognostic Model Development in Acute Lymphoblastic Leukemia
<p>This study investigates the mechanisms of dexamethasone resistance in acute lymphoblastic leukemia (ALL) and presents a prognostic model to predict patient outcomes and immunotherapy responses. By analyzing gene expression data, we identified autophagy-related genes associated with dexamethasone resistance, particularly focusing on STK38L’s role in modulating autophagy via ULK1. Our results reveal that high STK38L expression enhances dexamethasone resistance by promoting autophagy markers LC3II/LC3I and beclin-1. This study provides valuable insights into the molecular basis of dexamethasone resistance and highlights STK38L as a potential biomarker and therapeutic target for improving ALL treatment strategies.</p>
Metabolic shift of chronic myeloid leukemia patients under Imatinib-Pioglitazone regimen and discontinuation_dataset
<p>The EDI-PIO (<strong>E</strong>studo de <strong>D</strong>escontinuação de <strong>I</strong>matinibe após<strong> Pio</strong>glitazona) is a single-center, longitudinal, prospective, phase 2, non-randomized, open, clinical trial (NCT02852486, August 2, 2016 retrospectively registered) for the discontinuation of imatinib after concomitant use of pioglitazone, being the first of its kind in a Brazilian population with chronic myeloid leukemia. Due to remaining of leukemic quiescent cells that are not affected by tyrosine kinase inhibitors, it has been suggested the use of pioglitazone, a PPARγ agonist, together with imatinib as a strategy for the maintenance of deep molecular response. The clinical benefit to this association is still controversial, and the metabolic alteration along this process remains unclear. Therefore, we applied a metabolomic protocol using high-resolution mass spectrometry to profile plasmatic metabolic response of a prospective cohort of 10 individuals under discontinuation of imatinib and pioglitazone protocol. By comparing patients under pioglitazone and imatinib treatment with imatinib monotherapy and discontinuation phase, we were able to annotate 41 and 36 metabolites, respectively. The metabolic alterations observed during Imatinib-Pioglitazone combined therapy are associated with an extensive lipid remodelling, with activation of β-oxidation pathway, in addition to the presence of markers that suggest mitochondrial dysfunction.</p>
Dataset for "Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia"
<p><strong>Article: Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia</strong></p> <p><em>Cancer Discovery</em>, <strong>DOI:</strong> 10.1158/2159-8290.CD-21-0410</p> <p> </p> <p>Data Types:</p> <p>1. Clinical summary</p> <p>2. Drug response data</p> <p>3. Exome-sequencing data</p> <p>4. RNA-sequencing data</p> <p> </p> <p><strong>Updates:</strong></p> <p>- <strong>FILE</strong>: File_3.2. <strong>DATE</strong>: 28.11.2022.</p> <p> </p> <p><strong>1. Clinical summary</strong></p> <p><strong>File_0: </strong>Common sample annotation including patient and sample IDs, stage of the disease, tissue type and availability of different data types.</p> <p><strong>File_1.1: </strong>Clinical data for 186 AML patients including clinical diagnosis, disease classification, gender, age at diagnosis, treatments, cytogenetic and molecular details. The description of the variables/column titles is given below the clinical data.</p> <p><strong>File_1.2</strong>: Description of the clinical variables in File_1.1.</p> <p> </p> <p><strong>2. Drug response data for 164 AML patient samples and 17 healthy samples</strong></p> <p><strong>File_2: </strong>Drug library details for 515 chemical compounds. The compound collection includes drugs names, drug class defined by molecular targets or mode of action, concentration range used for drug testing, supplier information, solvent information and vendor information.</p> <p><strong>File_3.1.: </strong>Drug response data including selective drug sensitivity scores (sDSS) for 515 compounds across 181 samples (164 AML patient samples and 17 healthy control samples). The DSS is modified area under the curve values and are calculated as shown in Yadav et al publication (1). The selective drug sensitivity scores (sDSS) is healthy control normalized DSS that gives estimated cancer-selective drug responses. The higher the sDSS values indicate drug sensitivities and negative sDSS values represent drug resistance.</p> <p><strong>File_3.2.: </strong>Drug response data including drug sensitivity scores (DSS) and selective drug sensitivity scores (sDSS) for 515 compounds across 181 samples (164 AML patient samples and 17 healthy control samples). The data is identical to the Supplementary Table 7 in the manuscript.</p> <p><em>Note: We recommend using selective DSS values instead of raw values (% inhibition, IC50, DSS). </em></p> <p><em>Note: If the value is missing, </em><em>the drug was not tested for </em><em>that</em><em> given sample</em><em>.</em></p> <p><strong>File_4: </strong>Drug sensitivity and resistance testing (DSRT) assay details for 181 samples (164 AML patient samples and 17 healthy control samples). The information includes medium (MCM or CM) used for the drug testing, % cell viability after 72 h without drug testing and blast cell percentage of each sample.</p> <p><em>Note: Column E is </em><em>the ratio of luminescence values at 72 h and 0 h. The fold change in the cell viability without drug treatment was calculated as % cell viability. That is why the value could be more than 100% e.g. 70% cell viability meaning that 30% cells died during 72 h and 300% cell viability meaning that cells grew 3 times in 72 h incubation period.</em></p> <p> </p> <p><strong>3. Exome-sequencing data for 225 AML patient samples</strong></p> <p><em>Note: The number of samples in the manuscript is 226. The correct number used in the analyses is 225.</em></p> <p>Mutation data. The cancer specific gene list was prepared by combining AML related genes from TCGA(2) (n=23), InToGen(3) (n=32), Papaemmanuil et al.(4) (n=111) and Census database(5) (n=616). Out of these genes, we found 340 genes as mutated across 225 AML patient samples. The mutation was called with P-values less than 0.05.</p> <p><strong>File_5: </strong>VAF (variant allele frequency) of 340 cancer-specific genes across 225 AML patient samples. The VAF was calculated using paired skin samples as a control from the same AML patient.</p> <p><strong>File_6:</strong> Binary data for 57 cancer specific genes frequently mutated (a given mutation detected in 5 or more samples) across 225 AML patient samples.</p> <p> </p> <p><strong>4. RNA-sequencing data for 163 AML patient samples and 4 healthy</strong></p> <p>CPM (count per million) data: The CPM values are batch corrected values used for direct comparison of gene expression.</p> <p><strong>File_7:</strong> Log2CPM values for 18,202 protein coding genes across 167 samples (163 AML patient samples and 4 healthy CD34+ samples).</p> <p><strong>File_8: </strong>Raw read count data RNA-seq library information for all 60,619 genes across 167 samples (163 AML patient samples and 4 healthy CD34+ samples). The raw read count data was used to calculate differential gene expression.</p> <p><strong>File_9: </strong>RNA-seq library information including RNA extraction method and sequencing library preparation information for 167 samples (163 AML patient samples and 4 healthy CD34+ samples).</p> <p> </p> <p><strong>References</strong></p> <p>1. Yadav B, Pemovska T, Szwajda A, Kulesskiy E, Kontro M, Karjalainen R<em>, et al.</em> Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Scientific Reports <strong>2014</strong>;4:5193.</p> <p>2. Ley TJ, Miller C, Ding L, Raphael BJ, Mungall AJ, Robertson A<em>, et al.</em> Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med <strong>2013</strong>;368(22):2059-74.</p> <p>3. Gonzalez-Perez A, Perez-Llamas C, Deu-Pons J, Tamborero D, Schroeder MP, Jene-Sanz A<em>, et al.</em> IntOGen-mutations identifies cancer drivers across tumor types. Nature Methods <strong>2013</strong>;10(11):1081-2.</p> <p>4. Papaemmanuil E, Gerstung M, Bullinger L, Gaidzik VI, Paschka P, Roberts ND<em>, et al.</em> Genomic classification and prognosis in acute myeloid leukemia. New England Journal of Medicine <strong>2016</strong>;374(23):2209-21.</p> <p>5. Tate JG, Bamford S, Jubb HC, Sondka Z, Beare DM, Bindal N<em>, et al.</em> COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Research <strong>2019</strong>;47(D1):D941-D7.</p> <p> </p>
Single-Cell Profiling of CD8+ T Cells in Acute Myeloid Leukemia Reveals a Continuous Spectrum of Differentiation and Clonal Hyperexpansion
<p>Data for the publication <strong>Single-Cell Profiling of CD8<sup>+</sup> T Cells in Acute Myeloid Leukemia Reveals a Continuous Spectrum of Differentiation and Clonal Hyperexpansion</strong></p>
Distinct Stromal Cell Populations Define the B-cell Acute Lymphoblastic Leukemia Microenvironment
<p>Processed single-cell RNA-seq from the study </p> <ul> <li>10X Genomics CellRanger output (barcodes.tsv, genes.tsv, matrix.mtx) for each each sample</li> <li>Metadata</li> <li>Seurat object of the integrated scRNAseq dataset</li> <li>Xenium object of the spatial transcriptomic data</li> </ul> <p>Distinct Stromal Cell Populations Define the B-cell Acute Lymphoblastic Leukemia Microenvironment</p> <p>Mauricio N. Ferrao Blanco<sup>1</sup>, Bexultan Kazybay<sup>1</sup>, Mirjam Belderbos<sup>1</sup>, Olaf Heidenreich<sup>1</sup>, Hermann Josef Vormoor<sup>1,2</sup></p> <p><sup>1 </sup>Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands</p> <p><sup>2 </sup>University Medical Center Utrecht, Utrecht, the Netherlands</p> <p><strong>Abstract</strong></p> <p>The bone marrow microenvironment plays a critical role in B-cell acute lymphoblastic leukemia (B-ALL) progression, yet its cellular heterogeneity remains poorly understood. Using single-cell RNA sequencing on patient-derived of bone marrow aspirates from pediatric B-ALL patients, we identified two distinct mesenchymal stromal cell (MSC) populations: early mesenchymal progenitors and adipogenic progenitors. Spatial transcriptomic analysis further revealed the localization of these cell types and identified a third stromal population, osteogenic-lineage cells, exclusively present in the bone biopsy. Functional <em>ex vivo</em> assays using sorted stromal populations derived from B-ALL patient bone marrow aspirates demonstrated that both early mesenchymal and adipogenic progenitors secrete key niche-supportive factors, including CXCL12 and Osteopontin, and support leukemic cell survival and chemoresistance. Transcriptomic profiling revealed that B-ALL cells interact differently with stromal subtypes. Notably, adipogenic progenitors, but not early mesenchymal progenitors, provide support to leukemic cells through interleukin-7 and VCAM1 signaling. Stromal cells from B-ALL patients exhibited an enhanced adipogenic differentiation capacity compared to healthy controls. Moreover, co-culture experiments showed that B-ALL cells induce adipogenic differentiation in healthy MSCs through a cell contact-dependent mechanism. Adipogenic progenitors were also enriched in relapse samples, implicating them in disease progression. These findings highlight the complexity of the B-ALL microenvironment and identify different specialized stromal niches with which the leukemic cells can engage.</p> <p> </p> <p> </p> <p> </p>
Supplementary Data for Stabilizing the Proteomes of Acute Myeloid Leukemia Cells: Implications for Cancer Proteomics
<p>Supplementary data for: <br>Stabilizing the Proteomes of Acute Myeloid Leukemia Cells: Implications for Cancer Proteomics<br>Authors: Robert Sprung, Qiang Zhang, Michael H. Kramer, Matthew C. Christopher, Petra Erdmann-Gilmore, Yiling Mi, James P. Malone, Timothy J. Ley, and R. Reid Townsend.</p><p>Table S1 - AML Case descriptors and LC-MS data files<br>Table S2 - All Peptides by Case -LFQ<br>Table S3 - Identification of tryptic and non-tryptic peptides from five AML cases with high and low expression of ELANE<br>Table S4 - Number of proteins identified by LFQ proteomics with a minimum of 2 tryptic peptides<br>Table S5 - DFP Adduct Database Search Tryptic Peptides<br>Table S6 - Protein quantification from TMT 11-plex tryptic peptides with and without DFP<br>Table S7 - Tryptic peptides used for protein quantification from TMT 11-plex with and without DFP<br>Table S8 - Changes in TMT relative abund. with DFP treatment<br>Table S9 - Protein quantification from LFQ tryptic peptides with and without DFP<br>Table S10 - Proteins with significant change in abundance with DFP treatment using Label-Free Quantitation</p>
Electrophoresis Images of Acute Myeloid Leukemia Patients
<p>The database consists of a set of 22 2DGE images obtained from the peripheral blood samples of 11 patients with acute myeloid leukemia. Of these, 11 images correspond to samples taken at the time of diagnosis, and the other 11 correspond to samples taken from the same patients after induction therapy (approximately 21–28 days after starting treatment). Images named with the suffix BEFORE refer to 2DGE images of samples taken at the time of diagnosis (before treatment), while images named with the suffix AFTER correspond to 2DGE images of samples taken after treatment. These 22 images are also made available with the preprocessing stage applied, to which the prefix PREPROC has been applied. Each image in the database is in tagged image file format (TIFF) format with a resolution of 300 dots per inch (DPI). In total, the database, which can be found in the Supplementary Materials, contains 44 images (22 raw 2DGE images and 22 pre-processed 2DGE images).</p>
Detection of early seeding of Richter transformation in chronic lymphocytic leukemia: scRNA-seq data
<p>Richter transformation (RT) is a paradigmatic evolution of chronic lymphocytic leukemia (CLL) into a very aggressive large B cell lymphoma conferring a dismal prognosis. The mechanisms driving RT remain largely unknown. We characterized the whole genome, epigenome and transcriptome, combined with single-cell DNA/RNA-sequencing analyses and functional experiments, of 19 cases of CLL developing RT. Studying 54 longitudinal samples covering up to 19 years of disease course, we uncovered minute subclones carrying genomic, immunogenetic and transcriptomic features of RT cells already at CLL diagnosis, which were dormant for up to 19 years before transformation. We also identified new driver alterations, discovered a new mutational signature (SBS-RT), recognized an oxidative phosphorylation (OXPHOS)high–B cell receptor (BCR)low-signaling transcriptional axis in RT and showed that OXPHOS inhibition reduces the proliferation of RT cells. These findings demonstrate the early seed- ing of subclones driving advanced stages of cancer evolution and uncover potential therapeutic targets for RT.</p> <p>This repository contains the processed scRNA-seq data (expression matrices, Seurat objects, metadata) related with this publication.</p>
Intracellular Chiral Metabolome in Pediatric Leukemia
<p><span>Aberrations in the immunoglobulin heavy chain (IGH) locus are associated with poor prognosis in pediatric precursor B-cell acute lymphoblastic leukemia (BCP-ALL) patients. The primary objective of this pilot study is to enhance our understanding of the IGH phenotype by exploring the intracellular chiral metabolome. </span><span>Leukemia cells were isolated from the bone marrow of BCP-ALL pediatric patients at diagnosis and at the end of induction therapy (EIT). The samples’ metabolome and transcriptome were characterized using untargeted chiral metabolomic and next-generation sequencing transcriptomic analyses. </span><span>For the first time D- amino acids were identified in the leukemic cells' intracellular metabolome from the bone marrow niche. Chiral metabolic signatures at both diagnosis and EIT were indicative of a resistant phenotype. </span><span>Through integrated network analysis and Pearson correlation, confirmation was obtained regarding the association of the IGH phenotype with several genes linked to poor prognosis. </span><span>The findings of this study have contributed to the understanding that the chiral metabolome plays a role in the poor prognosis observed in an exceptionally rare patient cohort. </span><span>The findings include elevated D-amino acid incorporation in the IGH group, the emergence of several unknown, potentially enantiomeric, metabolites, and insights into metabolic pathways that all warrant further exploration. </span></p>
Single-cell proteo-transcriptomic profiling reveals altered characteristics of stem and progenitor cells in patients receiving cytoreductive hydroxyurea in early-phase chronic myeloid leukemia
<p>This repository contains CITE-seq data generated from CML stem and progenitor cells before and after hydroxyurea treatment using the BD Rhapsody Single-Cell Analysis System. </p> <p><strong><br>File descriptions:</strong></p> <p>1. RSEC-adjusted UMI count files generated using the BD Rhapsody Targeted Analysis Pipeline (v. 1.10.1):</p> <ul> <li>CartridgeS1_RSEC_MolsPerCell.csv</li> </ul> <p>2. RSEC-adjusted UMI counts for cells remaining after cell quality filtering using SeqGeq software (genes expressed vs library size):</p> <ul> <li>CartridgeS1_RSEC_MolsPerCell_postQC.csv</li> </ul> <p>3. Sample tag (sample of origin) calls for each putative cell, outputted by the BD Rhapsody Targeted Analysis Pipeline (v. 1.10.1). </p> <ul> <li>CartridgeS1_Sample_Tag_Calls.csv</li> </ul> <p> </p>
Supplemental Data from: Association Between Mutation Clearance After Induction Therapy and Outcomes in Acute Myeloid Leukemia
<p>Supplemental Data for:</p> <p>Association Between Mutation Clearance After Induction Therapy and Outcomes in Acute Myeloid Leukemia. JAMA. 2015<br> (Paper available at: <a href="https://www.ncbi.nlm.nih.gov/pubmed/26305651">PubMed</a> <a href="https://jamanetwork.com/journals/jama/fullarticle/2429715">JAMA</a>)<br> <br> Authors: Jeffery M. Klco, M.D., Ph.D.* Christopher A. Miller, Ph.D.*, Malachi Griffith, Ph.D., Allegra Petti, Ph.D., David H. Spencer, M.D., Ph.D., Shamika Ketkar-Kulkarni, M.S., Lukas D. Wartman, M.D., Matthew Christopher, M.D., Ph.D., Tamara L. Lamprecht, B.S., Nicole M. Helton, B.S., Eric J. Duncavage, M.D., Jacqueline E. Payton, M.D., Ph.D., Jack Baty, B.A., Sharon E. Heath, Obi L. Griffith, Ph.D., Dong Shen, Ph.D., Jasreet Hundal, M.S., Gue Su Chang, Ph.D., Robert Fulton, M.S., Michelle O'Laughlin, B.S., Catrina Fronick, B.S., Vincent Magrini, Ph.D., Ryan T. Demeter, B.E., David E. Larson, Ph.D., Shashikant Kulkarni, M.S., Ph.D., Bradley A. Ozenberger, Ph.D., John S. Welch, M.D., Ph.D., Matthew J. Walter, M.D., Timothy A. Graubert, M.D., Peter Westervelt, M.D., Ph.D., Jerald P. Radich, M.D., Daniel C. Link, M.D., Elaine R. Mardis, Ph.D., John F. DiPersio, M.D., Ph.D., Richard K. Wilson, Ph.D., and Timothy J. Ley</p>
Supplementary data for: Detection of expressed mutations in acute myeloid leukemia cells using single cell RNA-sequencing
<p>Supplemental data for the publication:<br> Detection of expressed mutations in acute myeloid leukemia cells using single cell RNA-sequencing </p> <p>Contents: <br> - expression_matrices.tar - Gene/Barcode expression matrices from `cellranger count`<br> - *.seurat.rds - R object files with Seurat analyses and data structures for each sample<br> - scrna_mutations.tar.gz - copy of a git repository containing additional scripts and data - also hosted at <a href="https://github.com/genome/scrna_mutations">https://github.com/genome/scrna_mutations</a> (snapshot as of May 20, 2019)</p>
Genome Sizes of Bacterial Species Detected in Cell-Free DNA of Patients with Acute Leukemia and Sepsis, Including Those Undergoing Bone Marrow Transplantation
<p>Next Generation Sequencing (NGS) analysis of Cell-Free DNA provides valuable insights into a spectrum of pathogenic species (particularly bacterial) in blood. Patients with Sepsis often face problems like delays in treatment regimens (combination or cocktail of antibiotics) due to the long turnaround time (TAT) of classical and standard blood culture procedures. NGS gives results with lower TAT along with high-depth coverage. The use of NGS may be a possible solution to deciding treatment regimens for patients without losing precious time and more accurately possibly saving lives.</p> <p>Our curated dataset is of bacterial species or strains detected along with their genome size in 107 AML patients diagnosed with Sepsis clinically. Cell-free DNA profiles of patients were built and sequencing was done in Illumina (NovaSeq and NextSeq). Bioinformatic analysis was performed using two classification algorithms namely kraken2 and kaiju. For kraken2 based classification reference bacterial index developed by Carlo Ferravante et al (Zenodo 2020) (link: https://zenodo.org/records/4055180) was used, while for kaiju-based classification reference database named "nr_euk" dated "2023-05-10" (link: https://bioinformatics-centre.github.io/kaiju/downloads.html) was used.</p> <p>Genome size annotation is important in metagenomics since for the use of depth of coverage (abundance), genome size is required. In metagenomic classification algorithms like kraken/kraken2 and kaiju output computes reads assigned only and not abundance. In kaiju, the problem is more complicated since the reference database does not have a fasta file but only an index file from which alignment is done. </p> <p>To address the above challenges to compute "depth of coverage" or simply abundance, we build a Genome size annotator tool (https://github.com/patkarlab/Genome-Size-Annotation) which provides genome size for each species detected given its taxid is available. In this tool, the NCBI Datasets tool, NCBI Genome API check tool, and Data Mining from AI search engines like perplexity.ai are used. </p> <p>We have curated two datasets</p> <p>Kraken2 dataset named "FINAL METAGENOMIC DATA MASTERSHEET - kraken_genome_annotation"<br>Kaiju dataset named "FINAL METAGENOMIC DATA MASTERSHEET - kaiju_genome_annotation"</p> <p>*Please note that for kraken2 curated dataset, we used data mining from the AI search engine perplexity.ai while for kaiju we did not use perplexity, ai, and any species whose genome size was not found was labeled "NA"</p>
Mimicking Clinical Trials with Synthetic Acute Myeloid Leukemia Patients Using Generative Artificial Intelligence
<p>We used two different methodologies of generative artificial intelligence, CTAB-GAN+ and normalizing flows (NFlow), to synthesize patient data based on 1606 patients with acute myeloid leukemia that were treated within four multicenter clinical trials. The resulting data set consists of 1606 synthetic patients for each of the models.</p> <p>This dataset is associated with our publication "Mimicking clinical trials with synthetic acute myeloid leukemia patients using generative artificial intelligence" by Eckardt et al., npj Digital Medicine, 2024 (<a href="https://doi.org/10.1038/s41746-024-01076-x" target="_new">https://doi.org/10.1038/s41746-024-01076-x</a>). If you use this dataset, please cite our paper.</p> <p> </p> <p><strong>Data Dictionary</strong></p> <table> <tbody><tr> <th>NAME</th> <th>LABEL</th> <th>TYPE</th> <th>CODELIST</th> </tr> </tbody><tbody> <tr> <td>AGE</td> <td>age</td> <td>num</td> <td>in years</td> </tr> <tr> <td>AMLSTAT</td> <td>AML status</td> <td>char</td> <td>de novo, sAML, tAML</td> </tr> <tr> <td>ASXL1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>ATRX</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>BCOR</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>BCORL1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>BRAF</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CALR</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CBL</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CBLB</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CDKN2A</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CEBPA</td> <td>CEBPA mutation</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CGCX</td> <td>complex cytogenetic karyotype</td> <td>char</td> <td>0 'No', 1 'Yes'</td> </tr> <tr> <td>CGNK</td> <td>cytogenetic normal karyotype</td> <td>char</td> <td>0 'No', 1 'Yes'</td> </tr> <tr> <td>CR1</td> <td>first complete remission</td> <td>char</td> <td>0 = 'not achieved', 1 = 'achieved'</td> </tr> <tr> <td>CSF3R</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CUX1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>DNMT3A</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>EFSSTAT</td> <td>status variable for EFSTM</td> <td>num</td> <td>0 'censored' 1 'event'</td> </tr> <tr> <td>EFSTM</td> <td>event free survival time</td> <td>num</td> <td>in months</td> </tr> <tr> <td>ETV6</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>EXAML</td> <td>extramedullary AML</td> <td>char</td> <td>0 'No', 1 'Yes'</td> </tr> <tr> <td>EZH2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>FBXW7</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>FLT3I</td> <td>FLT3-ITD mutation status</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>FLT3T</td> <td>FLT3-TKD mutation status</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>GATA2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>GNAS</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>HB</td> <td>hemoglobin</td> <td>num</td> <td>in mmol/l</td> </tr> <tr> <td>HRAS</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>IDH1</td> <td>IDH1 mutation status</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>IDH2</td> <td>IDH2 mutation status</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>IKZF1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>JAK2</td> <td>Jak2 Mutation</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>KDM6A</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>KIT</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>KRAS</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>MPL</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>MYD88</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>NOTCH1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>NPM1</td> <td>NPM1 mutation status</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>NRAS</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>OSSTAT</td> <td>status variable for OSTM</td> <td>num</td> <td>0 'censored' 1 'event'</td> </tr> <tr> <td>OSTM</td> <td>overall survival time</td> <td>num</td> <td>in months</td> </tr> <tr> <td>PDGFRA</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>PHF6</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>PLT</td> <td>platelet count</td> <td>num</td> <td>in 10⁶/l</td> </tr> <tr> <td>PTEN</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>PTPN11</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>RAD21</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>RUNX1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SETBP1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SEX</td> <td>sex</td> <td>char</td> <td>f 'female', m 'male'</td> </tr> <tr> <td>SF3B1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SMC1A</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SMC3</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SRSF2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>STAG2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SUBJID</td> <td>subject identifier</td> <td>char</td> <td> </td> </tr> <tr> <td>TET2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>TP53</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>U2AF1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>WBC</td> <td>white blood count</td> <td>num</td> <td>in 10⁶/l</td> </tr> <tr> <td>WT1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>ZRSR2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>inv16_t16.16</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t8.21</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.6.9..p23.q34.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>inv.3..q21.q26.2.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>minus.5</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>del.5q.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.9.22..q34.q11.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>minus.7</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>minus.17</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.v.11..v.q23.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>abn.17p.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.9.11..p21.23.q23.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.3.5.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.6.11.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.10.11.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.11.19..q23.p13.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>del.7q.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>del.9q.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>trisomy 8</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>trisomy 21</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>minus.Y</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>minus.X</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> </tbody> </table>
A human genome editing-based MLL-AF4 acute lymphoblastic leukemia model recapitulates key cellular and molecular leukemogenic features. (Processed data)
<p>The prognosis of infant B-cell acute lymphoblastic leukemia (iB-ALL) remains dismal, especially in patients harboring the MLL-AF4 (KTM2A-AFF1) rearrangement, which arises prenatally in early hematopoietic stem/progenitor cells (HSPCs) and accounts for 80% of iB-ALL and 10% of non-infant cases. MLL-AF4+ B-ALL shows a bimodal localization of the MLL gene breakpoint within the MLL break cluster region, and two subgroups of patients based on the gene expression pattern of the HOXA/MEIS cluster have been identified. The pathogenic mechanisms in MLL- AF4+ B-ALL are challenging to study functionally due to the absence of faithful human cellular models recapitulating the disease phenotype and latency. Here, we assess the molecular contribution and leukemogenic capacity of MLL breakpoints occurring in either intron 10 (MLL i10 , centromeric) or intron 12 (MLL i12 , telomeric) in ontogenically-different human HSPCs sourced prenatally (fetal liver) and neonatally (cord blood). CRISPR-Cas9-induced MLL-AF4 (MA) targeting either MLL i10 (M i10 A) or MLL i12 (M i12 A) causes MA-driven in vitro myeloid immortalization in both fetal liver- and cord blood-CD34+ HSPCs. The centromeric location of the MLL breakpoint, but not the cellular ontogeny, determined the expression of HOXA/MEIS1 genes in MLL-edited cells. Centromeric MLL breakpoints endowed enhanced myeloid clonogenic replating to MLL- edited CD34+ HSPCs. The cellular ontogeny and the location of the MLL breakpoint also influenced the capacity of MLL-edited CD34+ HSPCs to initiate pro-B-ALL in vivo, which faithfully recapitulated the molecular, transcriptomic and methylome profiles of patients with primary MA+ iB-ALL. Our data provide key insights into the cellular and molecular leukemogenic determinants of MA+ iB-ALL. This dataset contains processed RNAseq and DNA methylation data from the abovementioned study.</p>
Progenitor like cell type of an MLL-EDC4 fusion in acute myeloid leukemia
<p>Transcriptome analysis by single cell sequencing provides valuable information on intratumor heterogeneity and developmental stages of acute myeloid leukemia (AML) as well as interactions of tumor cells with the microenvironment. However, it has been hardly applied to the subgroup of cases with translocations of the mixed lineage leukemia (<i>MLL</i>) gene for which the enhancer of mRNA decapping 4 (<i>EDC4</i>) gene was recently identified as a novel fusion partner <i>(MLL-EDC4</i>). In our study published in Blood Advances (Schuster et al., 2023; https://doi.org/10.1182/bloodadvances.2022009096), we compared different <i>MLL</i> translocations by single cell RNA sequencing of cells derived from peripheral blood or bone marrow. The <i>MLL</i>-<i>EDC4</i> positive cells almost exclusively showed a transcriptional profile of hematopoietic progenitor cells while leukemic cells of <i>MLL-MLLT3</i> and <i>MLL-ELL</i> fusions exhibited a more differentiated phenotype. The <i>MLL</i>-<i>EDC4</i> progenitor state was characterized by the upregulation of key transcriptional regulators in AML (<i>RUNX1, SOX4, HOPX</i>), target genes of <i>MYC</i> and interferon signaling as well as other genes known to play a critical role in hematopoiesis or leukemic stem cell activation (<i>CDK6, FLT3, NPM1</i>). Here, the scRNA-seq dataset of our study is provided. It contains six scRNA-seq read count matrices of the AML samples with MLL fusions as described in the table scRNA-seq_samples.xlsx. Further details are given in the publication associated with this dataset. </p>
Study of Efficacy and Safety of Asciminib in Combination With Imatinib in Patients With Chronic Myeloid Leukemia in Chronic Phase (CML-CP)
ClinicalTrials.gov study NCT03578367. IPD Sharing: YES. Countries: 15. Publications: 1.
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.