DNA methylation analysis improves the prognostication of acute myeloid leukemia

Identification of Novel DNA Methylation Prognostic Biomarkers for AML With Normal Cytogenetics

PURPOSE

AML is a hematologic cancer that is clinically heterogeneous, with a wide range of clinical outcomes. DNA methylation changes are a hallmark of AML but are not routinely used as a criterion for risk stratification. The aim of this study was to explore DNA methylation markers that could risk stratify patients with cytogenetically normal AML (CN-AML), currently classified as intermediate-risk.

MATERIALS AND METHODS

DNA methylation profiles in whole blood samples from 77 patients with CN-AML in The Cancer Genome Atlas (LAML cohort) were analyzed. Individual 5'-cytosine-phosphate-guanine-3' (CpG) sites were assessed for their ability to predict overall survival. The output was validated using DNA methylation profiles from bone marrow samples of 79 patients with CN-AML in a separate data set from the Gene Expression Omnibus.

RESULTS

In the training set, using DNA methylation data derived from the 450K array, we identified 2,549 CpG sites that could potentially distinguish patients with CN-AML with an adverse prognosis (intermediate-poor) from those with a more favorable prognosis (intermediate-favorable) independent of age. Of these, 25 CpGs showed consistent prognostic potential across both the 450K and 27K array platforms. In a separate validation data set, nine of these 25 CpGs exhibited statistically significant differences in 2-year survival. These nine validated CpGs formed the basis for a combined prognostic biomarker panel, which includes an 8-CpG Somatic Panel and the methylation status of cg23947872. This panel displayed strong predictive ability for 2-year survival, 2-year progression-free survival, and complete remission in the validation cohort.

CONCLUSION

This study highlights DNA methylation profiling as a promising approach to enhance risk stratification in patients with CN-AML, potentially offering a pathway to more personalized treatment strategies.

Integrating DNA methylation and gene expression data in a single gene network using the iNETgrate package

Analyzing different omics data types independently is often too restrictive to allow for detection of subtle, but consistent, variations that are coherently supported based upon different assays. Integrating multi-omics data in one model can increase statistical power. However, designing such a model is challenging because different omics are measured at different levels. We developed the iNETgrate package ( https://bioconductor.org/packages/iNETgrate/ ) that efficiently integrates transcriptome and DNA methylation data in a single gene network. Applying iNETgrate on five independent datasets improved prognostication compared to common clinical gold standards and a patient similarity network approach.

"iNETgrate": integrating DNA methylation and gene expression data in a single gene network

Integrating multi-omics data in one model can increase statistical power. However, designing such a model is challenging because different omics are measured at different levels. We developed the iNETgrate package (https://bioconductor.org/packages/iNETgrate/) that efficiently integrates transcriptome and DNA methylation data in a single gene network. Applying iNETgrate on five independent datasets improved prognostication compared to common clinical gold standards and a patient similarity network approach.

The pathogenesis and development of targeted drugs in acute T lymphoblastic leukaemia

Acute lymphoblastic leukaemia (ALL) is mainly classified into acute T- and B-lymphoblastic leukaemia according to the source of its lymphocytes, thymus and bone. Among them, the incidence of adult T-cell accounts for about 25% of adult acute lymphoblastic leukaemia, but the degree of malignancy is high and the treatment rate and prognosis are poor. At this stage, there are few targeted drugs and the commonly used broad-spectrum chemotherapeutic drugs have poor efficacy and many adverse drug reactions. Understanding and investigating the pathogenesis of T-acute lymphoblastic leukaemia is very important for further developing new targeting drugs and improving existing drugs. Dysregulated signalling pathways are the main aetiological factors of T-acute lymphoblastic leukaemia. They play crucial roles in promoting tumour initiation, progression, drug design and therapy responses. This is primarily because signalling pathways are indispensable for many cellular biological processes, including tumour growth, migration, invasion, metastasis and others. As a result, small molecule inhibitors targeting the major kinase components of the signalling pathway have received a lot of attention and have been developed and evaluated in preclinical models and clinical trials. Already marketed drugs are also being repurposed in combination therapies to further improve efficacy and overcome tumour cell resistance. In this review, we have aimed to examine the latest and most classical signalling pathways in the aetiology of T-acute lymphoblastic leukaemia and shed light on potential targets for novel therapeutic agents to act on.

Twist1 Promoter Methylation Regulates the Proliferation and Apoptosis of Acute Myeloid Leukemia Cells via PI3K/AKT Pathway

Twist-related protein 1 (Twist1) is a widely recognized oncogene in acute myeloid leukemia (AML), and its promoter methylation is related with the progression of solid tumors. However, the association between Twist1 promoter methylation and AML has not been well studied. Twist1 mRNA expression was detected using quantitative real-time polymerase chain reaction (qRT-PCR). The protein levels of Twist1 and phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) signal were measured via western blotting. Methylation-specific PCR was performed to detect the methylation status of Twist1 promoter. CCK-8 assay and flow cytometry were used to reveal cellular biological effects. Twist1 expression and promoter methylation level were significantly upregulated in AML tissues and cell lines and were further downregulated in demethylating agent 5'-azacitidine (5-Aza)-treated cells. Ectopic expression of Twist1 increased AML cell viability, while reducing apoptosis, and attenuated the effects of 5-Aza on the proliferation and apoptosis. We also found that the PI3K/AKT signaling pathway was positively regulated by Twist1. Our findings revealed that Twist1 accelerates the tumorigenesis of AML cells by promoting its promoter methylation via the activation of PI3K/AKT signaling pathway.

Profiling senescent cells in human brains reveals neurons with CDKN2D/p19 and tau neuropathology

Senescent cells contribute to pathology and dysfunction in animal models1. Their sparse distribution and heterogenous phenotype have presented challenges for detecting them in human tissues. We developed a senescence eigengene approach to identify these rare cells within large, diverse populations of postmortem human brain cells. Eigengenes are useful when no single gene reliably captures a phenotype, like senescence; they also help to reduce noise, which is important in large transcriptomic datasets where subtle signals from low-expressing genes can be lost. Each of our eigengenes detected ~2% senescent cells from a population of ~140,000 single nuclei derived from 76 postmortem human brains with various levels of Alzheimer's disease (AD) pathology. More than 97% of the senescent cells were excitatory neurons and overlapped with tau-containing neurofibrillary tangles (NFTs). Cyclin dependent kinase inhibitor 2D (CDKN2D/p19) was predicted as the most significant contributor to the primary senescence eigengene. RNAscope and immunofluorescence confirmed its elevated expression in AD brain tissue whereby p19-expressing neurons had 1.8-fold larger nuclei and significantly more cells with lipofuscin than p19-negative neurons. These hallmark senescence phenotypes were further elevated in the presence of NFTs. Collectively, CDKN2D/p19-expressing neurons with NFTs represent a unique cellular population in human AD with a senescence phenotype. The eigengenes developed may be useful in future senescence profiling studies as they accurately identified senescent cells in snRNASeq datasets and predicted biomarkers for histological investigation.