Genome-Wide Association Analyses Identify Variants in IRF4 Associated With Acute Myeloid Leukemia and Myelodysplastic Syndrome Susceptibility

Transcriptomic characterisation of acute myeloid leukemia cell lines bearing the same t(9;11) driver mutation reveals different molecular signatures

BACKGROUND

Acute myeloid leukemia (AML) is the most common type of acute leukemia, accounting for 20% of cases in children and adolescents. Genome-wide studies have identified genes that are commonly mutated in AML, including many epigenetic regulators involved in either DNA methylation (DNMT3A, TET2, IDH1/2) or histone post-translational modifications (ASXL1, EZH2, MLL1). Several cell lines derived from AML patients are widely used in cancer research. Whether important differences in these cell lines exist remains poorly characterised.

RESULTS

Here, we used RNA sequencing (RNA-Seq) to contrast the transcriptome of four commonly used AML-derived cell lines: THP-1, NOMO-1, MOLM-13 bearing the common initiating t(9;11) translocation, and MV4.11 bearing the t(4;11) translocation. Gene set enrichment analyses and comparison of key transcription and epigenetic regulator genes revealed important differences in the transcriptome, distinguishing these AML models. Among these, we found striking differences in the expression of clusters of genes located on chromosome 19 encoding Zinc Finger (ZNF) transcriptional repressors. Low expression of many ZNF genes within these clusters is associated with poor survival in AML patients.

CONCLUSION

The present study offers a valuable resource by providing a detailed comparative characterisation of the transcriptome of cell lines within the same AML subtype used as models for leukemia research.

Research and analysis of differential gene expression in CD34 hematopoietic stem cells in myelodysplastic syndromes

OBJECTIVE

This study aims to investigate and analyze the differentially expressed genes (DEGs) in CD34 + hematopoietic stem cells (HSCs) from patients with myelodysplastic syndromes (MDS) through bioinformatics analysis, with the ultimate goal of uncovering the potential molecular mechanisms underlying pathogenesis of MDS. The findings of this study are expected to provide novel insights into clinical treatment strategies for MDS.

METHODS

Initially, we downloaded three datasets, GSE81173, GSE4619, and GSE58831, from the public Gene Expression Omnibus (GEO) database as our training sets, and selected the GSE19429 dataset as the validation set. To ensure data consistency and comparability, we standardized the training sets and removed batch effects using the ComBat algorithm, thereby integrating them into a unified gene expression dataset. Subsequently, we conducted differential expression analysis to identify genes with significant changes in expression levels across different disease states. In order to enhance prediction accuracy, we incorporated six common predictive models and trained them based on the filtered differential gene expression dataset. After comprehensive evaluation, we ultimately selected three algorithms-Lasso regression, random forest, and support vector machine (SVM)-as our core predictive models. To more precisely pinpoint genes closely related to disease characteristics, we utilized the aforementioned three machine learning methods for prediction and took the intersection of these prediction results, yielding a more robust list of genes associated with disease features. Following this, we conducted in-depth analysis of these key genes in the training set and validated the results independently using the GSE19429 dataset. Furthermore, we performed differential analysis of gene groups, co-expression analysis, and enrichment analysis to delve deeper into the mechanisms underlying the roles of these genes in disease initiation and progression. Through these analyses, we aim to provide new insights and foundations for disease diagnosis and treatment. Figure illustrates the data preprocessing and analysis workflow of this study.

RESULTS

Our analysis of differentially expressed genes (DEGs) in CD34+ hematopoietic stem cells (HSCs) from patients with myelodysplastic syndromes (MDS) revealed significant differences in gene expression patterns compared to the control group (individuals without MDS). Specifically, the expression levels of two key genes, IRF4 and ELANE, were notably downregulated in CD34+ HSCs of MDS patients, indicating their downregulatory roles in the pathological process of MDS.

CONCLUSION

This study sheds light on the potential molecular mechanisms underlying MDS, with a particular focus on the pivotal roles of IRF4 and ELANE as key pathogenic genes. Our findings provide a novel perspective for understanding the complexity of MDS and exploring therapeutic strategies. They may also guide the development of precise and effective treatments, such as targeted interventions directed against these genes.

Unraveling the genetic landscape of susceptibility to multiple primary cancers

With advances in cancer screening and treatment, there is a growing population of cancer survivors who may develop subsequent primary cancers. While hereditary cancer syndromes account for only a portion of multiple cancer cases, we sought to explore the role of common genetic variation in susceptibility to multiple primary tumors. We conducted a cross-ancestry genome-wide association study (GWAS) and transcriptome-wide association study (TWAS) of 10,983 individuals with multiple primary cancers, 84,475 individuals with single cancer, and 420,944 cancer-free controls from two large-scale studies. Our GWAS identified six lead variants across five genomic regions that were significantly associated (P<5×10-8) with the risk of developing multiple primary tumors (overall and invasive) relative to cancer-free controls (at 3q26, 8q24, 10q24, 11q13.3, and 17p13). We also found one variant significantly associated with multiple cancers when comparing to single cancer cases (at 22q13.1). Multi-tissue TWAS detected associations with genes involved in telomere maintenance in two of these regions (ACTRT3 in 3q26 and SLK and STN1 in 10q24) and the development of multiple cancers. Additionally, the TWAS also identified several novel genes associated with multiple cancers, including two immune-related genes, IRF4 and TNFRSF6B. Telomere maintenance and immune dysregulation emerge as central, common pathways influencing susceptibility to multiple cancers. These findings underscore the importance of exploring shared mechanisms in carcinogenesis, offering insights for targeted prevention and intervention strategies.

The multiple roles of interferon regulatory factor family in health and disease

Interferon Regulatory Factors (IRFs), a family of transcription factors, profoundly influence the immune system, impacting both physiological and pathological processes. This review explores the diverse functions of nine mammalian IRF members, each featuring conserved domains essential for interactions with other transcription factors and cofactors. These interactions allow IRFs to modulate a broad spectrum of physiological processes, encompassing host defense, immune response, and cell development. Conversely, their pivotal role in immune regulation implicates them in the pathophysiology of various diseases, such as infectious diseases, autoimmune disorders, metabolic diseases, and cancers. In this context, IRFs display a dichotomous nature, functioning as both tumor suppressors and promoters, contingent upon the specific disease milieu. Post-translational modifications of IRFs, including phosphorylation and ubiquitination, play a crucial role in modulating their function, stability, and activation. As prospective biomarkers and therapeutic targets, IRFs present promising opportunities for disease intervention. Further research is needed to elucidate the precise mechanisms governing IRF regulation, potentially pioneering innovative therapeutic strategies, particularly in cancer treatment, where the equilibrium of IRF activities is of paramount importance.

Inherited blood cancer predisposition through altered transcription elongation

Despite advances in defining diverse somatic mutations that cause myeloid malignancies, a significant heritable component for these cancers remains largely unexplained. Here, we perform rare variant association studies in a large population cohort to identify inherited predisposition genes for these blood cancers. CTR9, which encodes a key component of the PAF1 transcription elongation complex, is among the significant genes identified. The risk variants found in the cases cause loss of function and result in a ∼10-fold increased odds of acquiring a myeloid malignancy. Partial CTR9 loss of function expands human hematopoietic stem cells (HSCs) by increased super elongation complex-mediated transcriptional activity, which thereby increases the expression of key regulators of HSC self-renewal. By following up on insights from a human genetic study examining inherited predisposition to the myeloid malignancies, we define a previously unknown antagonistic interaction between the PAF1 and super elongation complexes. These insights could enable targeted approaches for blood cancer prevention.

Current Landscape of Genome-Wide Association Studies in Acute Myeloid Leukemia: A Review

Acute myeloid leukemia (AML) is a clonal hematopoietic disease that arises from chromosomal and genetic aberrations in myeloid precursor cells. AML is one of the most common types of acute leukemia in adults; however, it is relatively rare overall, comprising about 1% of all cancers. In the last decade or so, numerous genome-wide association studies (GWAS) have been conducted to screen between hundreds of thousands and millions of variants across many human genomes to discover genetic polymorphisms associated with a particular disease or phenotype. In oncology, GWAS has been performed in almost every commonly occurring cancer. Despite the increasing number of studies published regarding other malignancies, there is a paucity of GWAS studies for AML. In this review article, we will summarize the current status of GWAS in AML.

Shared graft-versus-leukemia minor histocompatibility antigens in DISCOVeRY-BMT

T-cell responses to minor histocompatibility antigens (mHAs) mediate graft-versus-leukemia (GVL) effects and graft-versus-host disease (GVHD) in allogeneic hematopoietic cell transplantation. Therapies that boost T-cell responses improve allogeneic hematopoietic cell transplant (alloHCT) efficacy but are limited by concurrent increases in the incidence and severity of GVHD. mHAs with expression restricted to hematopoietic tissue (GVL mHAs) are attractive targets for driving GVL without causing GVHD. Prior work to identify mHAs has focused on a small set of mHAs or population-level single-nucleotide polymorphism-association studies. We report the discovery of a large set of novel GVL mHAs based on predicted immunogenicity, tissue expression, and degree of sharing among donor-recipient pairs (DRPs) in the DISCOVeRY-BMT data set of 3231 alloHCT DRPs. The total number of predicted mHAs varied by HLA allele, and the total number and number of each class of mHA significantly differed by recipient genomic ancestry group. From the pool of predicted mHAs, we identified the smallest sets of GVL mHAs needed to cover 100% of DRPs with a given HLA allele. We used mass spectrometry to search for high-population frequency mHAs for 3 common HLA alleles. We validated 24 predicted novel GVL mHAs that are found cumulatively within 98.8%, 60.7%, and 78.9% of DRPs within DISCOVeRY-BMT that express HLA-A∗02:01, HLA-B∗35:01, and HLA-C∗07:02, respectively. We confirmed the immunogenicity of an example novel mHA via T-cell coculture with peptide-pulsed dendritic cells. This work demonstrates that the identification of shared mHAs is a feasible and promising technique for expanding mHA-targeting immunotherapeutics.

Bioinformatics analysis deciphering the transcriptomic signatures associated with signalling pathways and prognosis in the myelodysplastic syndromes

BACKGROUND

Several studies scatteredly identified the myelodysplastic syndromes' transcriptomic profiles (MDS). However, the exploration of transcriptional signatures, key signalling pathways, and their association with prognosis and diagnosis in the integrated multiple datasets remains lacking.

METHODS

We integrated the GSE4619, GSE19429, GSE30195, and GSE58831 microarray datasets of CD34 + cells for identifying the differentially expressed genes (DEGs) in the MDS. The series of bioinformatics methods are applied to identify the key hub genes, gene clusters, prognostic hub genes, and genes associated with diagnostic efficacy. Finally, we validated the expression differences of hub genes in the GSE114922 dataset.

RESULTS

We explored the DEGs related to gene ontology enrichment and KEGG pathways. We identified significant hub genes, including 168 upregulated hub genes (such as STAT1, IFIH1, EPRS, GRB2, RAC2, MAPK14, CASP1, and SPI1) and 52 downregulated hub genes (such as CREBBP, HIF1A, PIK3CA, EZH2, PIK3R1, MDM2, IRF4, CXCR4, PCNA, and CD19) in the MDS. In addition, we identified six significant molecular complex detection (MCODE)-derived upregulated gene clusters and one downregulated gene cluster, respectively. Moreover, we found that the higher expression level of MX2, GBP2, PXN, IFI44, FDXR, PLCB2, ASS1, ERCC4, PML, and RRAGD and the lower expression level of CD19, PAX5, TCF3, LEF1, NUSAP1, and TIMELESS hub genes are significantly correlated with shorter survival times of MDS patients. Furthermore, the area value under the ROC curve (AUC) of PXN, FDXR, PLCB2, PML, CD19, PAX5, and LEF1 prognostic genes are more than 0.80, indicating that these genes could be effectively used for the diagnostic efficacy of MDS patients.

CONCLUSIONS

Identifying key hub genes and their association with the prognosis and diagnostic efficacy may provide substantial clues for the treatment and diagnosis of MDS patients.

The prognosis biomarkers based on m6A-related lncRNAs for myeloid leukemia patients

Background

Chronic myeloid leukemia (CML) and acute myeloid leukemia (AML) are two common malignant disorders in leukemia. Although potent drugs are emerging, CML and AML may still relapse after the drug treatment is stopped. N6-methyladenosine (m6A) and lncRNAs play certain roles in the occurrence and development of tumors, but m6A-modified LncRNAs in ML remain to be further investigated.

Methods

In this study, we extracted and analyzed the TCGA gene expression profile of 151 ML patients and the clinical data. On this basis, we then evaluated the immune infiltration capacity of ML and LASSO-penalized Cox analysis was applied to construct the prognostic model based on m6A related lncRNAs to verify the prognostic risk in clinical features of ML. Quantitative reverse transcription PCR was used to detect the expression level of LncRNA in in ML cell lines K562, MOLM13 and acute monocytic leukemia cell line THP-1.

Results

We found 70 m6A-related lncRNAs that were related to prognosis, and speculated that the content of stromal cells and immune cells would correlate with the survival of patients with ML. Next, Prognostic risk model of m6A-related lncRNAs was validated to have excellent consistency in clinical features of ML. Finally, we verified the expression levels of CRNDE, CHROMR and NARF-IT1 in ML cell lines K562, MOLM13 and acute monocytic leukemia cell line THP-1, which were significant.

Conclusions

The research provides clues for the prognosis prediction of ML patients by using the m6A-related lncRNAs model we have created, and clarifies the accuracy and authenticity of it.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02428-3.

Novel genetic variants associated with mortality after unrelated donor allogeneic hematopoietic cell transplantation

BACKGROUND

Identification of non-human leukocyte antigen (HLA) genetic risk factors could improve survival after allogeneic blood or marrow transplant (BMT) through matching at additional loci or individualizing risk prediction. We hypothesized that non-HLA loci contributed significantly to 1-year overall survival (OS), disease related mortality (DRM) or transplant related mortality (TRM) after unrelated donor (URD)BMT.

METHODS

We performed a genome-wide association study (GWAS) in 2,887 acute myeloid leukemia (AML), myelodysplastic syndrome (MDS) and acute lymphoblastic leukemia (ALL) patients and their ≥8/8 HLA-matched URDs comprising two independent cohorts treated from 2000-2011.

FINDINGS

Using meta-analyses of both cohorts, genome-wide significant associations (p < 5 × 10-8) were identified in: recipient genomes with OS at MBNL1 (rs9990017, HR = 1.4, 95% CI 1.24-1.56, p = 3.3 × 10-8) and donor-recipient genotype mismatch with OS at LINC02774 (rs10927108, HR = 1.34, 95% CI 1.21-1.48, p = 2.0 × 10-8); donor genomes with DRM at PCNX4 (rs79076914, HR = 1.7, 95% CI 1.41-2.05, p = 3.15 × 10-8), LINC01194 (rs79498125, HR = 1.86, 95% CI 1.49-2.31, p = 2.84 × 10-8), ARID5B (rs2167710, HR = 1.5, 95% CI 1.31-1.73, p = 6.9 × 10-9) and CT49 (rs32250, HR = 1.44, 95% CI1.26-1.64, p = 2.6 × 10-8); recipient genomes at PILRB with TRM (rs141591562, HR = 2.33, 95% CI 1.74-3.12, p = 1.26 × 10-8) and donor-recipient genotype mismatch between EPGN and MTHF2DL with TRM (rs75868097, HR = 2.66, 95% CI 1.92-3.58, p = 4.6 × 10-9). Results publicly available at https://fuma.ctglab.nl/browse.

INTERPRETATION

These data provide the first evidence that non-HLA common genetic variation at novel loci with biochemical function significantly impacts 1-year URD-BMT survival. Our findings have implications for donor selection, could guide treatment strategies and provide individualized risk prediction after future validation and functional studies.

FUNDING

This project was funded by grants from the National Institutes of Health, USA.