Survival differences and associated molecular signatures of DNMT3A-mutant acute myeloid leukemia patients

Single-Cell DNA Sequencing Reveals an Evolutionary Pattern of CHIP in Transplant Eligible Multiple Myeloma Patients

Clonal hematopoiesis of indeterminate potential (CHIP) refers to the phenomenon where a hematopoietic stem cell acquires fitness-increasing mutation(s), resulting in its clonal expansion. CHIP is frequently observed in multiple myeloma (MM) patients, and it is associated with a worse outcome. High-throughput amplicon-based single-cell DNA sequencing was performed on circulating CD34+ cells collected from twelve MM patients before autologous stem cell transplantation (ASCT). Moreover, in four MM patients, longitudinal samples either before or post-ASCT were collected. Single-cell sequencing and data analysis were assessed using the MissionBio Tapestri® platform, with a targeted panel of 20 leukemia-associated genes. We detected CHIP pathogenic mutations in 6/12 patients (50%) at the time of transplant. The most frequently mutated genes were TET2, EZH2, KIT, DNMT3A, and ASXL1. In two patients, we observed co-occurring mutations involving an epigenetic modifier (i.e., DNMT3A) and/or a gene involved in splicing machinery (i.e., SF3B1) and/or a tyrosine kinase receptor (i.e., KIT) in the same clone. Longitudinal analysis of paired samples revealed a positive selection of mutant high-fitness clones over time, regardless of their affinity with a major or minor sub-clone. Copy number analysis of the panel of all genes did not show any numerical alterations present in stem cell compartment. Moreover, we observed a tendency of CHIP-positive patients to achieve a suboptimal response to therapy compared to those without. A sub-clone dynamic of high-fitness mutations over time was confirmed.

Epigenetic regulation in hematopoiesis and its implications in the targeted therapy of hematologic malignancies

Hematologic malignancies are one of the most common cancers, and the incidence has been rising in recent decades. The clinical and molecular features of hematologic malignancies are highly heterogenous, and some hematologic malignancies are incurable, challenging the treatment, and prognosis of the patients. However, hematopoiesis and oncogenesis of hematologic malignancies are profoundly affected by epigenetic regulation. Studies have found that methylation-related mutations, abnormal methylation profiles of DNA, and abnormal histone deacetylase expression are recurrent in leukemia and lymphoma. Furthermore, the hypomethylating agents and histone deacetylase inhibitors are effective to treat acute myeloid leukemia and T-cell lymphomas, indicating that epigenetic regulation is indispensable to hematologic oncogenesis. Epigenetic regulation mainly includes DNA modifications, histone modifications, and noncoding RNA-mediated targeting, and regulates various DNA-based processes. This review presents the role of writers, readers, and erasers of DNA methylation and histone methylation, and acetylation in hematologic malignancies. In addition, this review provides the influence of microRNAs and long noncoding RNAs on hematologic malignancies. Furthermore, the implication of epigenetic regulation in targeted treatment is discussed. This review comprehensively presents the change and function of each epigenetic regulator in normal and oncogenic hematopoiesis and provides innovative epigenetic-targeted treatment in clinical practice.

Computational gene expression analysis reveals distinct molecular subgroups of T-cell prolymphocytic leukemia

T-cell prolymphocytic leukemia (T-PLL) is a rare blood cancer with poor prognosis. Overexpression of the proto-oncogene TCL1A and missense mutations of the tumor suppressor ATM are putative main drivers of T-PLL development, but so far only little is known about the existence of T-PLL gene expression subtypes. We performed an in-depth computational reanalysis of 68 gene expression profiles of one of the largest currently existing T-PLL patient cohorts. Hierarchical clustering combined with bootstrapping revealed three robust T-PLL gene expression subgroups. Additional comparative analyses revealed similarities and differences of these subgroups at the level of individual genes, signaling and metabolic pathways, and associated gene regulatory networks. Differences were mainly reflected at the transcriptomic level, whereas gene copy number profiles of the three subgroups were much more similar to each other, except for few characteristic differences like duplications of parts of the chromosomes 7, 8, 14, and 22. At the network level, most of the 41 predicted potential major regulators showed subgroup-specific expression levels that differed at least in comparison to one other subgroup. Functional annotations suggest that these regulators contribute to differences between the subgroups by altering processes like immune responses, angiogenesis, cellular respiration, cell proliferation, apoptosis, or migration. Most of these regulators are known from other cancers and several of them have been reported in relation to leukemia (e.g. AHSP, CXCL8, CXCR2, ELANE, FFAR2, G0S2, GIMAP2, IL1RN, LCN2, MBTD1, PPP1R15A). The existence of the three revealed T-PLL subgroups was further validated by a classification of T-PLL patients from two other smaller cohorts. Overall, our study contributes to an improved stratification of T-PLL and the observed subgroup-specific molecular characteristics could help to develop urgently needed targeted treatment strategies.

Clonal hematopoiesis: Mutation-specific adaptation to environmental change

Clonal hematopoiesis of indeterminate potential (CHIP) describes a widespread expansion of genetically variant hematopoietic cells that increases exponentially with age and is associated with increased risks of cancers, cardiovascular disease, and other maladies. Here, we discuss how environmental contexts associated with CHIP, such as old age, infections, chemotherapy, or cigarette smoking, alter tissue microenvironments to facilitate the selection and expansion of specific CHIP mutant clones. Further, we consider major remaining gaps in knowledge, including intrinsic effects, clone size thresholds, and factors affecting clonal competition, that will determine future application of this field in transplant and preventive medicine.

Clinical Utility of Implementing a Frontline NGS-Based DNA and RNA Fusion Panel Test for Patients with Suspected Myeloid Malignancies

BACKGROUND

The use of molecular genetic biomarkers is rapidly advancing to aid diagnosis, prognosis, and clinical management of hematological disorders. We have implemented a next-generation sequencing (NGS) assay for detection of genetic variants and fusions as a frontline test for patients suspected with myeloid malignancy. In this study, we summarize the findings and assess the clinical impact in the first 1613 patients tested.

METHODS

All patients were assessed using NGS based Oncomine Myeloid Research Assay (ThermoFisher) including 40 genes (17 full genes and 23 genes with clinically relevant "hotspot" regions), along with a panel of 29 fusion driver genes (including over fusion 600 partners).

RESULTS

Among 1613 patients with suspected myeloid malignancy, 43% patients harbored at least one clinically relevant variant: 91% (90/100) in acute myeloid leukemia patients, 71.7% (160/223) in myelodysplastic syndrome (MDS), 77.5% (308/397) in myeloproliferative neoplasm (MPN), 83% (34/41) in MPN/MDS, and 100% (40/40) in chronic myeloid leukemia patients. Comparison of NGS and cytogenetics results revealed a high degree of concordance in gene fusion detection.

CONCLUSIONS

Our findings demonstrate clinical utility and feasibility of integrating a NGS-based gene mutation and fusion testing assay as a frontline diagnostic test in a large reported cohort of patients with suspected myeloid malignancy, in a clinical laboratory setting. Overlap with cytogenetic test results provides opportunity for testing reduction and streamlining.

A novel nine-microRNA-based model to improve prognosis prediction of renal cell carcinoma

Background

With the improved knowledge of disease biology and the introduction of immune checkpoints, there has been significant progress in treating renal cell carcinoma (RCC) patients. Individual treatment will differ according to risk stratification. As the clinical course varies in RCC, it has developed different predictive models for assessing patient’s individual risk. However, among other prognostic scores, no transparent preference model was given. MicroRNA as a putative marker shown to have prognostic relevance in RCC, molecular analysis may provide an innovative benefit in the prophetic prediction and individual risk assessment. Therefore, this study aimed to establish a prognostic-related microRNA risk score model of RCC and further explore the relationship between the model and the immune microenvironment, immune infiltration, and immune checkpoints. This practical model has the potential to guide individualized surveillance protocols, patient counseling, and individualized treatment decision for RCC patients and facilitate to find more immunotherapy targets.

Methods

Downloaded data of RCC from the TCGA database for difference analysis and divided it into a training set and validation set. Then the prognostic genes were screened out by Cox and Lasso regression analysis. Multivariate Cox regression analysis was used to establish a predictive model that divided patients into high-risk and low-risk groups. The ENCORI online website and the results of the RCC difference analysis were used to search for hub genes of miRNA. Estimate package and TIMER database were used to evaluate the relationship between risk score and tumor immune microenvironment (TME) and immune infiltration. Based on Kaplan-Meier survival analysis, search for immune checkpoints related to the prognosis of RCC.

Results

There were nine miRNAs in the established model, with a concordance index of 0.702 and an area under the ROC curve of 0.701. Nine miRNAs were strongly correlated with the prognosis (P < 0.01), and those with high expression levels had a poor prognosis. We found a common target gene PDGFRA of hsa-miR-6718, hsa-miR-1269b and hsa-miR-374c, and five genes related to ICGs (KIR2DL3, TNFRSF4, LAG3, CD70 and TNFRSF9). The immune/stromal score, immune infiltration, and immune checkpoint genes of RCC were closely related to its prognosis and were positively associated with a risk score.

Conclusions

The established nine-miRNAs prognostic model has the potential to facilitate prognostic prediction. Moreover, this model was closely related to the immune microenvironment, immune infiltration, and immune checkpoint genes of RCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09322-9.

RNAi-Mediated Screen of Primary AML Cells Nominates MDM4 as a Therapeutic Target in NK-AML with DNMT3A Mutations

DNA-methyltransferase 3A (DNMT3A) mutations belong to the most frequent genetic aberrations found in adult acute myeloid leukemia (AML). Recent evidence suggests that these mutations arise early in leukemogenesis, marking leukemic progenitors and stem cells, and persist through consolidation chemotherapy, providing a pool for AML relapse. Currently, there are no therapeutic approaches directed specifically against this cell population. To unravel therapeutically actionable targets in mutant DNMT3A-driven AML cells, we have performed a focused RNAi screen in a panel of 30 primary AML samples, all carrying a DNMT3A R882 mutation. As one of the strongest hits, we identified MDM4 as a gene essential for proliferation of primary DNMT3A^WT/R882X AML cells. We analyzed a publicly available RNA-Seq dataset of primary normal karyotype (NK) AML samples and found a trend towards MDM4 transcript overexpression particularly in DNMT3A-mutant samples. Moreover, we found that the MDM2/4 inhibitor ALRN-6924 impairs growth of DNMT3A^WT/R882X primary cells in vitro by inducing cell cycle arrest through upregulation of p53 target genes. Our results suggest that MDM4 inhibition is a potential target in NK-AML patients bearing DNMT3A R882X mutations.

Integration of Multimodal Data from Disparate Sources for Identifying Disease Subtypes

Simple Summary

The diagnostic and treatment strategies of cancer remain generally suboptimal resulting in over-diagnosis or under-treatment. Though many attempts on optimizing treatment decisions by early prediction of disease progression have been undertaken, these efforts yielded only modest success so far due to the heterogeneity of cancer with multifactorial etiology. Here, we propose a deep-learning based data integration model capable of predicting disease progression by integrating collective information available through multiple studies with different cohorts and heterogeneous data types. The results have shown that the proposed data integration pipeline is able to identify disease progression with higher accuracy and robustness compared to using a single cohort, by offering a more complete picture of the specific disease on patients with brain, blood, and pancreatic cancers.

Abstract

Studies over the past decade have generated a wealth of molecular data that can be leveraged to better understand cancer risk, progression, and outcomes. However, understanding the progression risk and differentiating long- and short-term survivors cannot be achieved by analyzing data from a single modality due to the heterogeneity of disease. Using a scientifically developed and tested deep-learning approach that leverages aggregate information collected from multiple repositories with multiple modalities (e.g., mRNA, DNA Methylation, miRNA) could lead to a more accurate and robust prediction of disease progression. Here, we propose an autoencoder based multimodal data fusion system, in which a fusion encoder flexibly integrates collective information available through multiple studies with partially coupled data. Our results on a fully controlled simulation-based study have shown that inferring the missing data through the proposed data fusion pipeline allows a predictor that is superior to other baseline predictors with missing modalities. Results have further shown that short- and long-term survivors of glioblastoma multiforme, acute myeloid leukemia, and pancreatic adenocarcinoma can be successfully differentiated with an AUC of 0.94, 0.75, and 0.96, respectively.

Comparative Gene Expression Analysis Reveals Similarities and Differences of Chronic Myeloid Leukemia Phases

Chronic myeloid leukemia (CML) is a slowly progressing blood cancer that primarily affects elderly people. Without successful treatment, CML progressively develops from the chronic phase through the accelerated phase to the blast crisis, and ultimately to death. Nowadays, the availability of targeted tyrosine kinase inhibitor (TKI) therapies has led to long-term disease control for the vast majority of patients. Nevertheless, there are still patients that do not respond well enough to TKI therapies and available targeted therapies are also less efficient for patients in accelerated phase or blast crises. Thus, a more detailed characterization of molecular alterations that distinguish the different CML phases is still very important. We performed an in-depth bioinformatics analysis of publicly available gene expression profiles of the three CML phases. Pairwise comparisons revealed many differentially expressed genes that formed a characteristic gene expression signature, which clearly distinguished the three CML phases. Signaling pathway expression patterns were very similar between the three phases but differed strongly in the number of affected genes, which increased with the phase. Still, significant alterations of MAPK, VEGF, PI3K-Akt, adherens junction and cytokine receptor interaction signaling distinguished specific phases. Our study also suggests that one can consider the phase-wise CML development as a three rather than a two-step process. This is in accordance with the phase-specific expression behavior of 24 potential major regulators that we predicted by a network-based approach. Several of these genes are known to be involved in the accumulation of additional mutations, alterations of immune responses, deregulation of signaling pathways or may have an impact on treatment response and survival. Importantly, some of these genes have already been reported in relation to CML (e.g., AURKB, AZU1, HLA-B, HLA-DMB, PF4) and others have been found to play important roles in different leukemias (e.g., CDCA3, RPL18A, PRG3, TLX3). In addition, increased expression of BCL2 in the accelerated and blast phase indicates that venetoclax could be a potential treatment option. Moreover, a characteristic signaling pathway signature with increased expression of cytokine and ECM receptor interaction pathway genes distinguished imatinib-resistant patients from each individual CML phase. Overall, our comparative analysis contributes to an in-depth molecular characterization of similarities and differences of the CML phases and provides hints for the identification of patients that may not profit from an imatinib therapy, which could support the development of additional treatment strategies.

Eight Differential miRNAs in DN Identified by Microarray Analysis as Novel Biomarkers

BACKGROUND

Diabetic nephropathy (DN) is the common cause of renal diseases such as end-stage renal disease (ESRD) and chronic kidney disease (CKD). Various diagnostic applications and treatment methods are used for clinical but remain some prognosis issues. To avoid morbidity and mortality related to DN, early detection of disease complications as well as targeted therapeutic strategies is essential. Considerable evidence indicates that non-coding RNA plays a vital role in the biological processes of various diseases, used as biomarkers and therapeutic targets. And the most known ncRNAs are the microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs).

MATERIALS AND METHODS

Our study aimed to identify potential prognostic ncRNAs involved in DN by bioinformatics analysis and validated expression levels through quantitative polymerase chain reaction (qPCR) and GEO database. Our research focuses on differential expression miRNAs (DEmiRNAs) in DN and their interactions with critical genes.

RESULTS

We identified 8 up-regulated DEmiRNAs, including miR-103a-2-5p, miR-297, miR-548x-3p, miR-604, miR-644a, miR-1256, miR-3911 and miR-5047 finally. We further validated these miRNAs in a murine model.

CONCLUSION

Identifying these up-regulated genes and elucidating these miRNAs regulatory network will contribute to a better understanding of the molecular mechanism of DN and how they can be used as new biomarkers and potential therapeutic targets for DN.

Roles of Histone Deacetylases in Acute Myeloid Leukemia With Fusion Proteins

Accurate orchestration of gene expression is critical for the process of normal hematopoiesis, and dysregulation is closely associated with leukemogenesis. Epigenetic aberration is one of the major causes contributing to acute myeloid leukemia (AML), where chromosomal rearrangements are frequently found. Increasing evidences have shown the pivotal roles of histone deacetylases (HDACs) in chromatin remodeling, which are involved in stemness maintenance, cell fate determination, proliferation and differentiation, via mastering the transcriptional switch of key genes. In abnormal, these functions can be bloomed to elicit carcinogenesis. Presently, HDAC family members are appealing targets for drug exploration, many of which have been deployed to the AML treatment. As the majority of AML events are associated with chromosomal translocation resulting in oncogenic fusion proteins, it is valuable to comprehensively understand the mutual interactions between HDACs and oncogenic proteins. Therefore, we reviewed the process of leukemogenesis and roles of HDAC members acting in this progress, providing an insight for the target anchoring, investigation of hyperacetylated-agents, and how the current knowledge could be applied in AML treatment.

In Silico Methods for the Identification of Diagnostic and Favorable Prognostic Markers in Acute Myeloid Leukemia

Acute myeloid leukemia (AML), the most common type of acute leukemia in adults, is mainly asymptomatic at early stages and progresses/recurs rapidly and frequently. These attributes necessitate the identification of biomarkers for timely diagnosis and accurate prognosis. In this study, differential gene expression analysis was performed on large-scale transcriptomics data of AML patients versus corresponding normal tissue. Weighted gene co-expression network analysis was conducted to construct networks of co-expressed genes, and detect gene modules. Finally, hub genes were identified from selected modules by applying network-based methods. This robust and integrative bioinformatics approach revealed a set of twenty-four genes, mainly related to cell cycle and immune response, the diagnostic significance of which was subsequently compared against two independent gene expression datasets. Furthermore, based on a recent notion suggesting that molecular characteristics of a few, unusual patients with exceptionally favorable survival can provide insights for improving the outcome of individuals with more typical disease trajectories, we defined groups of long-term survivors in AML patient cohorts and compared their transcriptomes versus the general population to infer favorable prognostic signatures. These findings could have potential applications in the clinical setting, in particular, in diagnosis and prognosis of AML.

miR-6718-5p and miR-4329 can be used as potential biomarkers for acute myocardial infarction

BACKGROUND

The prevention and prognosis of the onset or recurrence of acute myocardial infarction (AMI) is a difficult problem in contemporary research.

METHODS

In this study, peripheral blood samples were collected from seven patients with AMI and nine healthy adults, and exosome microRNAs (miRNAs) were extracted. The miRNA differential expression profiles of serum exosomes in patients with AMI were obtained by using the next-generation sequencing technology combined with bioinformatics analysis. Reverse-transcriptase polymerase chain reaction (RT-PCR) was used to verify the primary screening of differential exosome miRNAs to reveal the possible mechanism of their action on AMI.

RESULTS

Compared with healthy individuals, 544 miRNAs were upregulated and 518 miRNAs were downregulated in AMI patients preoperatively. Among these miRNAs, we selected miR-6718 and miR-4329 for qPCR verification. The expression of miR6718 and miR-4329 in patients with myocardial infarction was significantly lower than that in normal controls.

Gene Transcription as a Therapeutic Target in Leukemia

Blood malignancies often arise from undifferentiated hematopoietic stem cells or partially differentiated stem-like cells. A tight balance of multipotency and differentiation, cell division, and quiescence underlying normal hematopoiesis requires a special program governed by the transcriptional machinery. Acquisition of drug resistance by tumor cells also involves reprogramming of their transcriptional landscape. Limiting tumor cell plasticity by disabling reprogramming of the gene transcription is a promising strategy for improvement of treatment outcomes. Herein, we review the molecular mechanisms of action of transcription-targeted drugs in hematological malignancies (largely in leukemia) with particular respect to the results of clinical trials.