The RUNX1-ETO fusion protein trans-activates c-KIT expression by recruiting histone acetyltransferase P300 on its promoter

Deciphering Acute Myeloid Leukemia Associated Transcription Factors in Human Primary CD34+ Hematopoietic Stem/Progenitor Cells

Hemato-oncological diseases account for nearly 10% of all malignancies and can be classified into leukemia, lymphoma, myeloproliferative diseases, and myelodysplastic syndromes. The causes and prognosis of these disease entities are highly variable. Most entities are not permanently controllable and ultimately lead to the patient's death. At the molecular level, recurrent mutations including chromosomal translocations initiate the transformation from normal stem-/progenitor cells into malignant blasts finally floating the patient's bone marrow and blood system. In acute myeloid leukemia (AML), the so-called master transcription factors such as RUNX1, KMT2A, and HOX are frequently disrupted by chromosomal translocations, resulting in neomorphic oncogenic fusion genes. Triggering ex vivo expansion of primary human CD34+ stem/progenitor cells represents a distinct characteristic of such chimeric AML transcription factors. Regarding oncogenic mechanisms of AML, most studies focus on murine models. However, due to biological differences between mice and humans, findings are only partly transferable. This review focuses on the genetic manipulation of human CD34+ primary hematopoietic stem/progenitor cells derived from healthy donors to model acute myeloid leukemia cell growth. Analysis of defined single- or multi-hit human cellular AML models will elucidate molecular mechanisms of the development, maintenance, and potential molecular intervention strategies to counteract malignant human AML blast cell growth.

H3K27 acetylation-induced FSTL1 upregulation by P300/RUNX1 co-activation exacerbated autophagy-mediated neuronal damage and NF-κB-stimulated inflammation in Alzheimer's disease

Follistatin-like protein 1 (FSTL1) has been demonstrated to participate in the pathogenesis of several neurological diseases. The current study informed the role of H3K27 acetylation-induced FSTL1 upregulation in Alzheimer's disease (AD). Our investigation discovered the upregulated FSTL1 expression and enhanced autophagy activity in AD. FSTL1 knockdown successfully attenuated the injuries of Aβ1-42-challenged SH-SY5Y cells through the inhibition of autophagy activity. Besides, FSTL1 deficiency suppresses the inflammatory response and NF-κB signaling in AD. Moreover, it was found that p300 was recruited by transcriptional factor RUNX1 to stimulate the H3K27 acetylation in FSTL1 promoter region, which caused the upregulation of FSTL1 in AD. To summarize, p300 acted as a co-activator of RUNX1 to trigger the activation of FSTL1 in AD, resulting in the exacerbated injuries and inflammatory responses of Aβ1-42-induced SH-SY5Y cells.

Bayesian variable selection for high-dimensional data with an ordinal response: identifying genes associated with prognostic risk group in acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML) is a heterogeneous cancer of the blood, though specific recurring cytogenetic abnormalities in AML are strongly associated with attaining complete response after induction chemotherapy, remission duration, and survival. Therefore recurring cytogenetic abnormalities have been used to segregate patients into favorable, intermediate, and adverse prognostic risk groups. However, it is unclear how expression of genes is associated with these prognostic risk groups. We postulate that expression of genes monotonically associated with these prognostic risk groups may yield important insights into leukemogenesis. Therefore, in this paper we propose penalized Bayesian ordinal response models to predict prognostic risk group using gene expression data. We consider a double exponential prior, a spike-and-slab normal prior, a spike-and-slab double exponential prior, and a regression-based approach with variable inclusion indicators for modeling our high-dimensional ordinal response, prognostic risk group, and identify genes through hypothesis tests using Bayes factor.

RESULTS

Gene expression was ascertained using Affymetrix HG-U133Plus2.0 GeneChips for 97 favorable, 259 intermediate, and 97 adverse risk AML patients. When applying our penalized Bayesian ordinal response models, genes identified for model inclusion were consistent among the four different models. Additionally, the genes included in the models were biologically plausible, as most have been previously associated with either AML or other types of cancer.

CONCLUSION

These findings demonstrate that our proposed penalized Bayesian ordinal response models are useful for performing variable selection for high-dimensional genomic data and have the potential to identify genes relevantly associated with an ordinal phenotype.

AML1/ETO and its function as a regulator of gene transcription via epigenetic mechanisms

The chromosomal translocation t(8;21) and the resulting oncofusion gene AML1/ETO have long served as a prototypical genetic lesion to model and understand leukemogenesis. In this review, we describe the wide-ranging role of AML1/ETO in AML leukemogenesis, with a particular focus on the aberrant epigenetic regulation of gene transcription driven by this AML-defining mutation. We begin by analyzing how structural changes secondary to distinct genomic breakpoints and splice changes, as well as posttranscriptional modifications, influence AML1/ETO protein function. Next, we characterize how AML1/ETO recruits chromatin-modifying enzymes to target genes and how the oncofusion protein alters chromatin marks, transcription factor binding, and gene expression. We explore the specific impact of these global changes in the epigenetic network facilitated by the AML1/ETO oncofusion on cellular processes and leukemic growth. Furthermore, we define the genetic landscape of AML1/ETO-positive AML, presenting the current literature concerning the incidence of cooperating mutations in genes such as KIT, FLT3, and NRAS. Finally, we outline how alterations in transcriptional regulation patterns create potential vulnerabilities that may be exploited by epigenetically active agents and other therapeutics.

Profiling of somatic mutations and fusion genes in acute myeloid leukemia patients with FLT3-ITD or FLT3-TKD mutation at diagnosis reveals distinct evolutionary patterns

Background

The receptor tyrosine kinase FLT3 with internal tandem duplications within the juxtamembrane domain (FLT3-ITD) is a poor prognostic factor; however, the prognostic significance of missense mutation in the tyrosine kinase domain (FLT3-TKD) is controversial. Furthermore, the accompanying mutations and fusion genes with FLT3 mutations are unclear in acute myeloid leukemia (AML).

Methods

We investigated FLT3 mutations and their correlation with other gene mutations and gene fusions through two RNA-seq based next-generation sequencing (NGS) method and prognostic impact in 207 de novo AML patients.

Results

FLT3-ITD mutations were positive in 58 patients (28%), and FLT3-TKD mutations were positive in 20 patients (9.7%). FLT3-ITD was associated with a higher white blood cell count (WBC, mean 72.9 × 10⁹/L vs. 24.2 × 10⁹/L, P = 0.000), higher bone marrow blasts (mean 65.9% vs. 56.0%, P = 0.024), and NK-AML (normal karyotype) (64.8% vs. 48.4%, P = 0.043). NPM1 and DNMT3A mutations were enriched in FLT3-ITD (53.5% vs. 15.3%, P = 0.000; 34.6% vs. 13%, P = 0.003). However, the mutations of CEBPA were excluded in FLT3-AML (3.8% vs. 0% vs. 19.8%, P = 0.005). Mutations of Ras and TP53 were unlikely associated with FLT3-ITD (1.9% vs. 20.6%, P = 0.006; 0% vs. 6.1%, P = 0.04). The common fusion genes (> 10%) in FLT3-ITD had MLL-rearrangement and NUP98-rearrangement, while the common fusion genes in FLT3-TKD had AML1-ETO and MLL-rearrangement. Two novel fusion genes PRDM16-SKI and EFAN2-ZNF238 were identified in FLT3-ITD patients. Gene fusions and NPM1 mutation were mutually excluded in FLT3-ITD and FLT3-TKD patients. Their patterns of mutual exclusivity and cooperation among mutated genes suggest that additional driver genetic alterations are required and reveal two evolutionary patterns of FLT3 pathogenesis. Patients with FLT3-ITD had a lower CR (complete remission) rate, lower 3-year OS (overall survival), DFS (disease-free survival), and EFS (event-free survival) compared to FLT3_wtAML. NK-AML with FLT3-ITD had a lower 3-year OS, DFS, and EFS than those without, while FLT3-TKD did not influence the survival in whole cohort and NK-AML. Besides, we found that FLT3-ITD/TET2 bimutation defined a poor prognostic subgroup.

Conclusions

Our study offers deep insights into the molecular pathogenesis and biology of AML with FLT3-ITD and FLT3-TKD by providing the profiles of concurrent molecular alterations and the clinical impact of FLT3-ITD and FLT3-TKD on AML patients.

t(8;21) Acute Myeloid Leukemia as a Paradigm for the Understanding of Leukemogenesis at the Level of Gene Regulation and Chromatin Programming

Acute myeloid leukemia (AML) is a heterogenous disease with multiple sub-types which are defined by different somatic mutations that cause blood cell differentiation to go astray. Mutations occur in genes encoding members of the cellular machinery controlling transcription and chromatin structure, including transcription factors, chromatin modifiers, DNA-methyltransferases, but also signaling molecules that activate inducible transcription factors controlling gene expression and cell growth. Mutant cells in AML patients are unable to differentiate and adopt new identities that are shaped by the original driver mutation and by rewiring their gene regulatory networks into regulatory phenotypes with enhanced fitness. One of the best-studied AML-subtypes is the t(8;21) AML which carries a translocation fusing the DNA-binding domain of the hematopoietic master regulator RUNX1 to the ETO gene. The resulting oncoprotein, RUNX1/ETO has been studied for decades, both at the biochemical but also at the systems biology level. It functions as a dominant-negative version of RUNX1 and interferes with multiple cellular processes associated with myeloid differentiation, growth regulation and genome stability. In this review, we summarize our current knowledge of how this protein reprograms normal into malignant cells and how our current knowledge could be harnessed to treat the disease.