Acute Myeloid Leukemia Research

The paper shows that combining DNA demethylating agents with UPR targeting improves AML cell suppression beyond single-agent therapies.

AMLdb is a multi-omics platform for AML research that identifies potential therapeutic targets, including five genes, with ENO1 as a novel candidate.

The paper develops a k-mer based transcriptomics analysis to distinguish NPM1-mutated from non-mutated AML patients, achieving over 95% accuracy in three scenarios.

This study identifies DACT3 as an epigenetically silenced tumor suppressor in AML, showing its role in Wnt signaling and potential as a therapeutic target when combined with DNMT and HDAC inhibitors.

This study identifies 12,754 epigenetic fusion gene (EFG) isoforms in 390 AML patients, showing they can distinguish AML patients from healthy individuals.

This study uses CRISPR/Cas9 to knockout CLL-1 in AML cells, identifies differentially expressed genes, and finds hub genes involved in chemokine signaling and ubiquitination.

The paper identifies 243 hereditary fusion genes associated with acute myeloid leukemia inheritance from 926 potential candidates, supporting the two-hit hypothesis and suggesting AML's complexity.

This study evaluates ex vivo drug sensitivity profiling in AML patients to predict survival and chemotherapy response, showing its robustness in mapping treatment response signatures.

This study uses single cell multiomic analyses to show DNMT3A mutations in clonal hematopoiesis worsen inflammatory outcomes in COVID-19 by increasing IL32 expression and chromatin accessibility.

This study analyzes clonal hematopoiesis in over a million diverse individuals, identifying 72 genome-wide significant loci and linking CHIP to various phenotypes.

This study evaluates the clinical utility of optical genome mapping and a 523-gene NGS panel for comprehensive genomic profiling of myeloid cancers, showing higher sensitivity and resolution compared to standard-of-care methods.

This study uses exome sequencing of 628,388 individuals to identify genetic variants associated with clonal hematopoiesis and its health outcomes.

This study examines clonal hematopoiesis risk in individuals with germline ANKRD26 or ETV6 mutations, finding that ANKRD26 carriers have elevated risk while ETV6 carriers do not.

The paper predicts ex vivo drug sensitivity in AML patients using proteomic and phosphoproteomic signatures, showing better accuracy than mutation or mRNA-based signatures.

The paper shows that in individuals with obesity, mutation-driven haematopoietic clones grow faster with insulin resistance and low HDL-C, which are cardiovascular risk factors.

This study evaluates optical genome mapping (OGM) for detecting genomic aberrations in AML, finding it identifies all clinically relevant SVs and CNVs and detects additional variants missed by standard methods.

This study uses deep learning to analyze bone marrow histopathology in MDS patients, linking morphological features to genetic and clinical outcomes.

The paper identifies microhomology-mediated end joining (MMEJ) as the mechanism behind recurrent deletions in myeloid malignancies and shows that these deletions occur in multipotent hematopoietic stem cells.

The paper shows oncologic therapy increases clonal hematopoiesis with DDR mutations, which predicts risk of therapy-related myeloid neoplasms.

The paper identifies germline genetic variants associated with clonal hematopoiesis of indeterminate potential (CHIP) using whole genome sequences from the TOPMed program.

This study analyzes genomic mutations in clonal hematopoiesis across 179,417 Japanese individuals, revealing population-specific patterns and differences in cancer risk.

The paper uses a consensus machine learning approach to identify gene targets associated with pediatric AML risk, finding recurrently selected genes like HOXA9 and MEIS1 across multiple algorithms.

This paper develops an AML classification model using flow cytometry data by selecting important markers and identifying decision-support ranges through machine learning.

The paper quantifies fitness advantages of pathogenic variants in clonal haematopoiesis using blood sequencing data from ~50,000 individuals.

This study examines the role of EED gene expression in acute myeloid leukemia and diffuse large B cell lymphoma using TCGA data, finding associations with prognosis, risk, and regulatory mechanisms.