Leukemia cells display lower levels of intracellular cholesterol irrespective of the exogenous cholesterol availability

The RORɣ/SREBP2 pathway is a master regulator of cholesterol metabolism and serves as potential therapeutic target in t(4;11) leukemia

Dysregulated cholesterol homeostasis promotes tumorigenesis and progression. Therefore, metabolic reprogramming constitutes a new hallmark of cancer. However, until today, only few therapeutic approaches exist to target this pathway due to the often-observed negative feedback induced by agents like statins leading to controversially increased cholesterol synthesis upon inhibition. Sterol regulatory element-binding proteins (SREBPs) are key transcription factors regulating the synthesis of cholesterol and fatty acids. Since SREBP2 is difficult to target, we performed pharmacological inhibition of retinoic acid receptor (RAR)-related orphan receptor gamma (RORγ), which acts upstream of SREBP2 and serves as master regulator of the cholesterol metabolism. This resulted in an inactivated cholesterol-related gene program with significant downregulation of cholesterol biosynthesis. Strikingly, these effects were more pronounced than the effects of fatostatin, a direct SREBP2 inhibitor. Upon RORγ inhibition, RNA sequencing showed strongly increased cholesterol efflux genes leading to leukemic cell death and cell cycle changes in a dose- and time-dependent manner. Combinatorial treatment of t(4;11) cells with the RORγ inhibitor showed additive effects with cytarabine and even strong anti-leukemia synergism with atorvastatin by circumventing the statin-induced feedback. Our results suggest a novel therapeutic strategy to inhibit tumor-specific cholesterol metabolism for the treatment of t(4;11) leukemia.

An Overview of Conventional Drugs and Nano Therapeutic Options for the Treatment and Management of Pediatric Acute Lymphoblastic Leukemia

Acute lymphoblastic leukemia (ALL) is a common form of pediatric cancer affecting the lymphoblast, a type of white blood cell found in the bone marrow. In this disease, the normal lymphoblast cells transform into leukemic cells and subsequently enter the bloodstream. Leukemic cells found in patients with ALL have shown differences in cholesterol uptake and utilization. Current treatment consists of chemotherapy, chimeric antigen receptor (CAR) therapy, and hematopoietic stem cell transplantation (HSCT). In addition, minimal residual disease (MRD) has become an effective tool in measuring treatment efficacy and the potential for relapse. Chemotherapy resistance remains a significant barrier in the treatment of ALL. Biomarkers such as an upregulated Akt signaling pathway and an overexpressed VLA-4 integrin-protein have been associated with drug resistance. Nanoparticles have been used to favorably alter the pharmacokinetic profile of conventional drug agents. These drug-delivery systems are designed to selectively deliver their drug payloads to desired targets. Therefore, nanoparticles offer advantages such as improved efficacy and reduced toxicity. This review highlights conventional treatment options, distinctive characteristics of pediatric ALL, therapeutic challenges encountered during therapy, and the key role that nanotherapeutics play in the treatment of ALL.

The role of cholesterol metabolism in leukemia

Leukemia is a common hematological malignancy with overall poor prognosis. Novel therapies are needed to improve the outcome of leukemia patients. Cholesterol metabolism reprogramming is a featured alteration in leukemia. Many metabolic-related genes and metabolites are essential to the progress and drug resistance of leukemia. Exploring potential therapeutical targets related to cholesterol homeostasis is a promising area. This review summarized the functions of cholesterol and its derived intermediate metabolites, and also discussed potential agents targeting this metabolic vulnerability in leukemia.

Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine

Acute myeloid leukemia (AML) is a type of blood cancer characterized by the rapid growth of immature white blood cells from the bone marrow. Therapy resistance resulting from the persistence of leukemia stem cells (LSCs) are found in numerous patients. Comparative transcriptome studies have been previously conducted to analyze differentially expressed genes between LSC⁺ and LSC^- cells. However, these studies mainly focused on a limited number of genes with the most obvious expression differences between the two cell types. We developed a computational approach incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), support vector machine (SVM), Repeated Incremental Pruning to Produce Error Reduction (RIPPER), to identify gene expression features specific to LSCs. One thousand 0ne hudred fifty-nine features (genes) were first identified, which can be used to build the optimal SVM classifier for distinguishing LSC⁺ and LSC^- cells. Among these 1159 genes, the top 17 genes were identified as LSC-specific biomarkers. In addition, six classification rules were produced by RIPPER algorithm. The subsequent literature review on these features/genes and the classification rules and functional enrichment analyses of the 1159 features/genes confirmed the relevance of extracted genes and rules to the characteristics of LSCs.

Lipid metabolism in cancer cells under metabolic stress

Cancer cells are often exposed to a metabolically challenging environment with scarce availability of oxygen and nutrients. This metabolic stress leads to changes in the balance between the endogenous synthesis and exogenous uptake of fatty acids, which are needed by cells for membrane biogenesis, energy production and protein modification. Alterations in lipid metabolism and, consequently, lipid composition have important therapeutic implications, as they affect the survival, membrane dynamics and therapy response of cancer cells. In this article, we provide an overview of recent insights into the regulation of lipid metabolism in cancer cells under metabolic stress and discuss how this metabolic adaptation helps cancer cells thrive in a harsh tumour microenvironment.

Low Density Lipoproteins Amplify Cytokine-signaling in Chronic Lymphocytic Leukemia Cells

Recent studies suggest there is a high incidence of elevated low-density lipoprotein (LDL) levels in Chronic Lymphocytic Leukemia (CLL) patients and a survival benefit from cholesterol-lowering statin drugs. The mechanisms of these observations and the kinds of patients they apply to are unclear. Using an in vitro model of the pseudofollicles where CLL cells originate, LDLs were found to increase plasma membrane cholesterol, signaling molecules such as tyrosine-phosphorylated STAT3, and activated CLL cell numbers. The signaling effects of LDLs were not seen in normal lymphocytes or glycolytic lymphoma cell-lines but were restored by transduction with the nuclear receptor PPARδ, which mediates metabolic activity in CLL cells. Breakdown of LDLs in lysosomes was required for the amplification effect, which correlated with down-regulation of HMGCR expression and long lymphocyte doubling times (LDTs) of 53.6 ± 10.4 months. Cholesterol content of circulating CLL cells correlated directly with blood LDL levels in a subgroup of patients. These observations suggest LDLs may enhance proliferative responses of CLL cells to inflammatory signals. Prospective clinical trials are needed to confirm the therapeutic potential of lowering LDL concentrations in CLL, particularly in patients with indolent disease in the “watch-and-wait” phase of management.

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Slow-growing CLL cells use lysosomal lipase to break low density lipoproteins (LDLs) into free fatty acids and cholesterol.

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LdL degradation products increase survival of proliferating CLL cells.

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LDLs decrease oxidative stress and increase plasma membrane cholesterol.

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LDLs amplify signaling responses to cytokines but not antigens in proliferating CLL cells.

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Rapidly growing CLL cells, acute leukemia cells, and normal lymphocytes do not exhibit this dependence on LDLs.