Chronic myelomonocytic leukemia - a review

Discovery and design of dual inhibitors targeting Sphk1 and Sirt1

CONTEXT

Leukaemia has become a serious threat to human health. Although tyrosine kinase inhibitors (TKIs) have been developed as targets for the remedy of leukaemia, drug resistance occurs. Research demonstrated that the simultaneous targeting of sphingosine kinase 1 (Sphk1) and Sirtuin 1 (Sirt1) can downregulate myeloid cell leukaemia-1 (MCL-1), overcome the resistance of tyrosine kinase inhibitors, and play a synergistic inhibitory impact on leukaemia treatment.

METHODS

In this study, virtual screening of 7.06 million small molecules was done by sphingosine kinase 1 and Sirtuin 1 pharmacophore models using Schrödinger version 2019; after that, ADME and Toxicity molecule properties were predicted using Discovery Studio. Molecular docking using Schrödinger selected five molecules, which have the best binding affinity with sphingosine kinase 1 and Sirtuin 1. The five molecules and reference inhibitors were constructed with a total of 12 systems with GROMACS that carried out 100 ns molecular dynamics simulation and molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) calculation. Due to compound 3 has the lowest binding energy, its structure was modified. A series of compounds docked with sphingosine kinase 1 and Sirtuin 1, respectively. Among them, QST-LC03, QST-LD05, QST-LE03, and QST-LE04 have the better binding affinity than reference inhibitors. Moreover, the SwissADME and PASS platforms predict that 1, 3, QST-LC03, and QST-LE04 have further study value.

Synergistic effect of HDAC inhibitor Chidamide with Cladribine on cell cycle arrest and apoptosis by targeting HDAC2/c-Myc/RCC1 axis in acute myeloid leukemia

BACKGROUND

More effective targeted therapy and new combination regimens are needed for Acute myeloid leukemia (AML), owing to the unsatisfactory long-term prognosis of the disease. Here, we investigated the synergistic effect and the mechanism of a histone deacetylase inhibitor, Chidamide in combination with Cladribine, a purine nucleoside antimetabolite analog in the disease.

METHODS

Cell counting kit-8 assays and Chou-Talalay's combination index were used to examine the synergistic effect of Chidamide and Cladribine on AML cell lines (U937, THP-1, and MV4-11) and primary AML cells. PI and Annexin-V/PI assays were used to detect the cell cycle effect and apoptosis effect, respectively. Global transcriptome analysis, RT-qPCR, c-MYC Knockdown, western blotting, co-immunoprecipitation, and chromatin immunoprecipitation assays were employed to explore the molecule mechanisms.

RESULTS

The combination of Chidamide with Cladribine showed a significant increase in cell proliferation arrest, the G0/G1 phase arrest, and apoptosis compared to the single drug control in AML cell lines along with upregulated p21^Waf1/Cip1 expression and downregulated CDK2/Cyclin E2 complex, and elevated cleaved caspase-9, caspase-3, and PARP. The combination significantly suppresses the c-MYC expression in AML cells, and c-MYC knockdown significantly increased the sensitivity of U937 cells to the combination compared to single drug control. Moreover, we observed HDAC2 interacts with c-Myc in AML cells, and we further identified that c-Myc binds to the promoter region of RCC1 that also could be suppressed by the combination through c-Myc-dependent. Consistently, a positive correlation of RCC1 with c-MYC was observed in the AML patient cohort. Also, RCC1 and HDAC2 high expression are associated with poor survival in AML patients. Finally, we also observed the combination significantly suppresses cell growth and induces the apoptosis of primary cells in AML patients with AML1-ETO fusion, c-KIT mutation, MLL-AF6 fusion, FLT3-ITD mutation, and in a CMML-BP patient with complex karyotype.

CONCLUSIONS

Our results demonstrated the synergistic effect of Chidamide with Cladribine on cell growth arrest, cell cycle arrest, and apoptosis in AML and primary cells with genetic defects by targeting HDAC2/c-Myc/RCC1 signaling in AML. Our data provide experimental evidence for the undergoing clinical trial (Clinical Trial ID: NCT05330364) of Chidamide plus Cladribine as a new potential regimen in AML.

Mutational landscape of chronic myelomonocytic leukemia in Chinese patients

BACKGROUND

Chronic myelomonocytic leukemia (CMML) is a rare and heterogeneous hematological malignancy. It has been shown that the molecular abnormalities such as ASXL1, TET2, SETBP1, and SRSF2 mutations are common in Caucasian population.

METHODS

We retrospectively analyzed 178 Chinese CMML patients. The targeted next generation sequencing (NGS) was used to evaluate 114 gene variations, and the prognostic factors for OS were determined by COX regression analysis.

RESULTS

The CMML patients showed a unique mutational spectrum, including TET2 (36.5%), NRAS (31.5%), ASXL1 (28.7%), SRSF2 (24.7%), and RUNX1 (21.9%). Of the 102 patients with clonal analysis, the ancestral events preferentially occurred in TET2 (18.5%), splicing factors (16.5%), RAS (14.0%), and ASXL1 (7.8%), and the subclonal genes were mainly ASXL1, TET2, and RAS. In addition, the secondary acute myeloid leukemia (sAML) transformed from CMML often had mutations in DNMT3A, ETV6, FLT3, and NPM1, while the primary AML (pAML) demonstrated more mutations in CEBPA, DNMT3A, FLT3, IDH1/2, NPM1, and WT1. It was of note that a series of clones were emerged during the progression from CMML to AML, including DNMT3A, FLT3, and NPM1. By univariate analysis, ASXL1 mutation, intermediate- and high-risk cytogenetic abnormality, CMML-specific prognostic scoring system (CPSS) stratifications (intermediate-2 and high group), and treatment options (best supportive care) predicted for worse OS. Multivariate analysis revealed a similar outcome.

CONCLUSIONS

The common mutations in Chinese CMML patients included epigenetic modifiers (TET2 and ASXL1), signaling transduction pathway components (NRAS), and splicing factor (SRSF2). The CMML patients with DNMT3A, ETV6, FLT3, and NPM1 mutations tended to progress to sAML. ASXL1 mutation and therapeutic modalities were independent prognostic factors for CMML.

Chronic Neutrophilic Leukemia: A Comprehensive Review of Clinical Characteristics, Genetic Landscape and Management

Chronic neutrophilic leukemia (CNL) represents a rare disease, that has been classified among the BCR/ABL-negative myeloproliferative neoplasms. The disease is characterized by marked leukocytosis with absolute neutrophilia and its clinical presentation may vary from asymptomatic to highly symptomatic with massive splenomegaly and constitutional symptoms. CNL prognosis remains relatively poor, as most patients succumb to disease complications or transform to acute myeloid leukemia. Recent studies have demonstrated that CSF3R mutations drive the disease, albeit the presence of other secondary mutations perplex the genetic landscape of the disease. Notably, the presence of CSF3R mutations has been adopted as a criterion for diagnosis of CNL. Despite the vigorous research, the management of the disease remains suboptimal. Allogeneic stem cell transplantation represents the only treatment that could lead to cure; however, it is accompanied by high rates of treatment-related mortality. Recently, ruxolitinib has shown significant responses in patients with CNL; however, emergence of resistance might perturbate long-term management of the disease. The aim of this review is to summarize the clinical course and laboratory findings of CNL, highlight its pathogenesis and complex genetic landscape, and provide the context for the appropriate management of patients with CNL.

A deep learning method and device for bone marrow imaging cell detection

BACKGROUND

Morphological analysis of bone marrow cells is considered as the gold standard for the diagnosis of leukemia. However, due to the diverse morphology of bone marrow cells, extensive experience and patience are needed for morphological examination. automatic diagnosis system through the comprehensive application of image analysis and pattern recognition technology is urgently needed to reduce work intensity, error probability and improves work efficiency.

METHODS

In this article, we establish a new morphological diagnosis system for bone marrow cell detection based on the deep learning object detection framework. The model is based on the Faster Region-Convolutional Neural Network (R-CNN), a classical object detection model. The system automatically detects bone marrow cells and determines their types. As specimens have severe long-tail distribution, i.e., the frequency of different types of cells varies dramatically, we proposed a general score ranking loss to solve such a problem. The general score ranking loss considers the ranking relationship between positive and negative samples and optimizes the positive sample with a higher classification probability value.

RESULTS

We verified this system with 70 bone marrow specimens of leukemia patients, which proved that it can realize intelligent recognition with high efficiency. The software is finally integrated into the microscope system to build an augmented reality system.

CONCLUSIONS

Clinical tests show that the response speed of the newly developed diagnostic system is faster than that of trained diagnostic experts.