Systems biology drug screening identifies statins as enhancers of current therapies in chronic lymphocytic leukemia

Obesity and Leukemia: Biological Mechanisms, Perspectives, and Challenges

PURPOSE OF REVIEW

To examine the epidemiological data on obesity and leukemia; evaluate the effect of obesity on leukemia outcomes in childhood acute lymphoblastic leukemia (ALL) survivors; assess the potential mechanisms through which obesity may increase the risk of leukemia; and provide the effects of obesity management on leukemia. Preventive (diet, physical exercise, obesity pharmacotherapy, bariatric surgery) measures, repurposing drugs, candidate therapeutic agents targeting oncogenic pathways of obesity and insulin resistance in leukemia as well as challenges of the COVID-19 pandemic are also discussed.

RECENT FINDINGS

Obesity has been implicated in the development of 13 cancers, such as breast, endometrial, colon, renal, esophageal cancers, and multiple myeloma. Leukemia is estimated to account for approximately 2.5% and 3.1% of all new cancer incidence and mortality, respectively, while it represents the most frequent cancer in children younger than 5 years. Current evidence indicates that obesity may have an impact on the risk of leukemia. Increased birthweight may be associated with the development of childhood leukemia. Obesity is also associated with worse outcomes and increased mortality in leukemic patients. However, there are several limitations and challenges in meta-analyses and epidemiological studies. In addition, weight gain may occur in a substantial number of childhood ALL survivors while the majority of studies have documented an increased risk of relapse and mortality among patients with childhood ALL and obesity. The main pathophysiological pathways linking obesity to leukemia include bone marrow adipose tissue; hormones such as insulin and the insulin-like growth factor system as well as sex hormones; pro-inflammatory cytokines, such as IL-6 and TNF-α; adipocytokines, such as adiponectin, leptin, resistin, and visfatin; dyslipidemia and lipid signaling; chronic low-grade inflammation and oxidative stress; and other emerging mechanisms. Obesity represents a risk factor for leukemia, being among the only known risk factors that could be prevented or modified through weight loss, healthy diet, and physical exercise. Pharmacological interventions, repurposing drugs used for cardiometabolic comorbidities, and bariatric surgery may be recommended for leukemia and obesity-related cancer prevention.

Statin-induced Mitochondrial Priming Sensitizes Multiple Myeloma Cells to BCL2 and MCL-1 Inhibitors

The BCL2 inhibitor venetoclax promotes apoptosis in blood cancer cells and is approved for treatment of chronic lymphocytic leukemia and acute myeloid leukemia. However, multiple myeloma cells are frequently more dependent on MCL-1 for survival, conferring resistance to venetoclax. Here we report that mevalonate pathway inhibition with statins can overcome resistance to venetoclax in multiple myeloma cell lines and primary cells. In addition, statins sensitize to apoptosis induced by MCL-1 inhibitor, S63845. In retrospective analysis of venetoclax clinical studies in multiple myeloma, background statin use was associated with a significantly enhanced rate of stringent complete response and absence of progressive disease. Statins sensitize multiple myeloma cells to venetoclax by upregulating two proapoptotic proteins: PUMA via a p53-independent mechanism and NOXA via the integrated stress response. These findings provide rationale for prospective testing of statins with venetoclax regimens in multiple myeloma.

SIGNIFICANCE

BH3 mimetics including venetoclax hold promise for treatment of multiple myeloma but rational combinations are needed to broaden efficacy. This study presents mechanistic and clinical data to support addition of pitavastatin to venetoclax regimens in myeloma. The results open a new avenue for repurposing statins in blood cancer.

The Role of Cholesterol in Chronic Lymphocytic Leukemia Development and Pathogenesis

Cholesterol has many critical functions in cells. It is a key component of membranes and cell-signalling processes, and it functions as a chemical precursor in several biochemical pathways, such as Vitamin D and steroid synthesis. Cholesterol has also been implicated in the development and progression of various cancers, in which it is thought to promote cell proliferation, migration, and invasion. Chronic lymphocytic leukemia (CLL) is an example of a lipid-avid cancer that relies on lipid metabolism, rather than glycolysis, to fuel cell proliferation. However, data regarding the role of cholesterol in CLL are conflicting. Studies have shown that dyslipidaemia is more common among CLL patients than age-matched healthy controls, and that CLL patients who take cholesterol-lowering drugs, such as statins, appear to have improved survival rates. Therefore, defining the roles of cholesterol in CLL may highlight the importance of monitoring and managing hyperlipidaemia as part of the routine management of patients with CLL. In this review, we discuss the roles of cholesterol in the context of CLL by examining the literature concerning the trafficking, uptake, endogenous synthesis, and intracellular handling of this lipid. Data from clinical trials investigating various classes of cholesterol and lipid-lowering drugs in CLL are also discussed.

An in silico approach to identify early damage biomarker candidates in metachromatic leukodystrophy

Metachromatic leukodystrophy (MLD) is a rare, autosomal recessive lysosomal storage disease. Deficient activity of arylsulfatase A causes sulfatides to accumulate in cells of different tissues, including those in the central and peripheral nervous systems, leading to progressive demyelination and neurodegeneration. Although there is some association between specific arylsulfatase A alleles and disease severity, genotype-phenotype correlations are not fully understood. We aimed to identify biomarker candidates of early tissue damage in MLD using a modeling approach based on systems biology. A review of the literature was performed in an initial disease characterization step, allowing identification of pathophysiological processes involved in MLD and proteins relating to these processes. Three mathematical models were generated to simulate different stages of MLD at the molecular level: an early pro-inflammatory stage model (including only processes considered to be active in the early stages of disease), a pre-demyelination stage model (including additional processes that are active after some disease progression), and a demyelination stage model (in which all pathophysiological processes are active). The models evaluated 3457 proteins of interest, individually and by pairs through data mining techniques, applying five filters to prioritize biomarkers that could differentiate between the models. Sixteen potential biomarkers were identified, including effectors relating to mitochondrial dysfunction, remyelination, and neurodegeneration. The findings were corroborated in a gene expression data set from T lymphocytes of patients with MLD; all candidates formed combinations that were able to distinguish patients with MLD from controls, and all but one candidate distinguished late-infantile MLD from juvenile MLD as part of a combinatorial biomarker pair. In particular, pro-neuregulin-1 appeared as differential on all comparisons (patients with MLD vs controls and within clinical subtypes); casein kinase II subunit alpha was detected as a potential individual marker within clinical subtypes. These findings provide a panel of biomarker candidates suitable for experimental validation and highlight the utility of mathematical models to identify biomarker candidates of early tissue damage in MLD with a high degree of accuracy and sensitivity.

Antitumor Activity of Simvastatin in Preclinical Models of Mantle Cell Lymphoma

BACKGROUND

Mantle cell lymphoma (MCL) is a rare and aggressive subtype of B-cell non-Hodgkin lymphoma that remains incurable with standard therapy. Statins are well-tolerated, inexpensive, and widely prescribed as cholesterol-lowering agents to treat hyperlipidemia and to prevent cardiovascular diseases through the blockage of the mevalonate metabolic pathway. These drugs have also shown promising anti-cancer activity through pleiotropic effects including the induction of lymphoma cell death. However, their potential use as anti-MCL agents has not been evaluated so far.

AIM

The present study aimed to investigate the activity of simvastatin on MCL cells.

METHODS

We evaluated the cytotoxicity of simvastatin in MCL cell lines by CellTiter-Glo and lactate dehydrogenase (LDH) release assays. Cell proliferation and mitotic index were assessed by direct cell recounting and histone H3-pSer10 immunostaining. Apoptosis induction and reactive oxygen species (ROS) generation were evaluated by flow cytometry. Cell migration and invasion properties were determined by transwell assay. The antitumoral effect of simvastatin in vivo was evaluated in a chick embryo chorioallantoic membrane (CAM) MCL xenograft model.

RESULTS

We show that treatment with simvastatin induced a 2 to 6-fold LDH release, inhibited more than 50% of cell proliferation, and enhanced the caspase-independent ROS-mediated death of MCL cells. The effective impairment of MCL cell survival was accompanied by the inhibition of AKT and mTOR phosphorylation. Moreover, simvastatin strongly decreased MCL cell migration and invasion ability, leading to a 55% tumor growth inhibition and a consistent diminution of bone marrow and spleen metastasis in vivo.

CONCLUSION

Altogether, these data provide the first preclinical insight into the effect of simvastatin against MCL cells, suggesting that this agent might be considered for repurpose as a precise MCL therapy.

A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects

Background

Machine learning (ML) and deep learning (DL) methods have recently garnered a great deal of attention in the field of cancer research by making a noticeable contribution to the growth of predictive medicine and modern oncological practices. Considerable focus has been particularly directed toward hematologic malignancies because of the complexity in detecting early symptoms. Many patients with blood cancer do not get properly diagnosed until their cancer has reached an advanced stage with limited treatment prospects. Hence, the state-of-the-art revolves around the latest artificial intelligence (AI) applications in hematology management.

Objective

This comprehensive review provides an in-depth analysis of the current AI practices in the field of hematology. Our objective is to explore the ML and DL applications in blood cancer research, with a special focus on the type of hematologic malignancies and the patient’s cancer stage to determine future research directions in blood cancer.

Methods

We searched a set of recognized databases (Scopus, Springer, and Web of Science) using a selected number of keywords. We included studies written in English and published between 2015 and 2021. For each study, we identified the ML and DL techniques used and highlighted the performance of each model.

Results

Using the aforementioned inclusion criteria, the search resulted in 567 papers, of which 144 were selected for review.

Conclusions

The current literature suggests that the application of AI in the field of hematology has generated impressive results in the screening, diagnosis, and treatment stages. Nevertheless, optimizing the patient’s pathway to treatment requires a prior prediction of the malignancy based on the patient’s symptoms or blood records, which is an area that has still not been properly investigated.

Targeting metabolic reprogramming in chronic lymphocytic leukemia

Metabolic reprogramming, fundamentally pivotal in carcinogenesis and progression of cancer, is considered as a promising therapeutic target against tumors. In chronic lymphocytic leukemia (CLL) cells, metabolic abnormalities mediate alternations in proliferation and survival compared with normal B cells. However, the role of metabolic reprogramming is still under investigation in CLL. In this review, the critical metabolic processes of CLL were summarized, particularly glycolysis, lipid metabolism and oxidative phosphorylation. The effects of T cells and stromal cells in the microenvironment on metabolism of CLL were also elucidated. Besides, the metabolic alternation is regulated by some oncogenes and tumor suppressor regulators, especially TP53, MYC and ATM. Thus, the agents targeting metabolic enzymes or signal pathways may impede the progression of CLL. Both the inhibitor of 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR) statins and the lipoprotein lipase inhibitor orlistat induce the apoptosis of CLL cells. In addition, a series of oxidative phosphorylation inhibitors play important roles in decreasing the proliferation of CLL cells. We epitomized recent advancements in metabolic reprogramming in CLL and discussed their clinical potentiality for innovative therapy options. Metabolic reprogramming plays a vital role in the initiation and progression of CLL. Therapeutic approaches targeting metabolism have their advantages in improving the survival of CLL patients. This review may shed novel light on the metabolism of CLL, leading to the development of targeted agents based on the reshaping metabolism of CLL cells.

Identification of Potential Muscle Biomarkers in McArdle Disease: Insights from Muscle Proteome Analysis

Glycogen storage disease type V (GSDV, McArdle disease) is a rare genetic myopathy caused by deficiency of the muscle isoform of glycogen phosphorylase (PYGM). This results in a block in the use of muscle glycogen as an energetic substrate, with subsequent exercise intolerance. The pathobiology of GSDV is still not fully understood, especially with regard to some features such as persistent muscle damage (i.e., even without prior exercise). We aimed at identifying potential muscle protein biomarkers of GSDV by analyzing the muscle proteome and the molecular networks associated with muscle dysfunction in these patients. Muscle biopsies from eight patients and eight healthy controls showing none of the features of McArdle disease, such as frequent contractures and persistent muscle damage, were studied by quantitative protein expression using isobaric tags for relative and absolute quantitation (iTRAQ) followed by artificial neuronal networks (ANNs) and topology analysis. Protein candidate validation was performed by Western blot. Several proteins predominantly involved in the process of muscle contraction and/or calcium homeostasis, such as myosin, sarcoplasmic/endoplasmic reticulum calcium ATPase 1, tropomyosin alpha-1 chain, troponin isoforms, and alpha-actinin-3, showed significantly lower expression levels in the muscle of GSDV patients. These proteins could be potential biomarkers of the persistent muscle damage in the absence of prior exertion reported in GSDV patients. Further studies are needed to elucidate the molecular mechanisms by which PYGM controls the expression of these proteins.

Targeting cholesterol homeostasis in hematopoietic malignancies

Cholesterol is a vital lipid for cellular functions. It is necessary for membrane biogenesis, cell proliferation and differentiation. In addition to maintaining cell integrity and permeability, increasing evidence indicates a strict link between cholesterol homeostasis, inflammation and haematological tumors. This makes cholesterol homeostasis an optimal therapeutic target for hematopoietic malignancies. Manipulating cholesterol homeostasis either interfering with its synthesis or activating the reverse cholesterol transport via the engagement of liver X receptors (LXRs), affects the integrity of tumor cells both in vitro and in vivo. Cholesterol homeostasis has also been manipulated to restore antitumor immune responses in preclinical models. These observations have prompted clinical trials in acute myeloid leukemia (AML) to test the combination of chemotherapy with drugs interfering with cholesterol synthesis, i.e. statins. We review the role of cholesterol homeostasis in hematopoietic malignancies, as well as in cells of the tumor microenvironment, and discuss the potential use of lipid modulators for therapeutic purposes.

Proteomics and Drug Repurposing in CLL towards Precision Medicine

Simple Summary

Despite continued efforts, the current status of knowledge in CLL molecular pathobiology, diagnosis, prognosis and treatment remains elusive and imprecise. Proteomics approaches combined with advanced bioinformatics and drug repurposing promise to shed light on the complex proteome heterogeneity of CLL patients and mitigate, improve, or even eliminate the knowledge stagnation. In relation to this concept, this review presents a brief overview of all the available proteomics and drug repurposing studies in CLL and suggests the way such studies can be exploited to find effective therapeutic options combined with drug repurposing strategies to adopt and accost a more “precision medicine” spectrum.

Abstract

CLL is a hematological malignancy considered as the most frequent lymphoproliferative disease in the western world. It is characterized by high molecular heterogeneity and despite the available therapeutic options, there are many patient subgroups showing the insufficient effectiveness of disease treatment. The challenge is to investigate the individual molecular characteristics and heterogeneity of these patients. Proteomics analysis is a powerful approach that monitors the constant state of flux operators of genetic information and can unravel the proteome heterogeneity and rewiring into protein pathways in CLL patients. This review essences all the available proteomics studies in CLL and suggests the way these studies can be exploited to find effective therapeutic options combined with drug repurposing approaches. Drug repurposing utilizes all the existing knowledge of the safety and efficacy of FDA-approved or investigational drugs and anticipates drug alignment to crucial CLL therapeutic targets, leading to a better disease outcome. The drug repurposing studies in CLL are also discussed in this review. The next goal involves the integration of proteomics-based drug repurposing in precision medicine, as well as the application of this procedure into clinical practice to predict the most appropriate drugs combination that could ensure therapy and the long-term survival of each CLL patient.

Prioritizing Molecular Biomarkers in Asthma and Respiratory Allergy Using Systems Biology

Highly prevalent respiratory diseases such as asthma and allergy remain a pressing health challenge. Currently, there is an unmet need for precise diagnostic tools capable of predicting the great heterogeneity of these illnesses. In a previous study of 94 asthma/respiratory allergy biomarker candidates, we defined a group of potential biomarkers to distinguish clinical phenotypes (i.e. nonallergic asthma, allergic asthma, respiratory allergy without asthma) and disease severity. Here, we analyze our experimental results using complex algorithmic approaches that establish holistic disease models (systems biology), combining these insights with information available in specialized databases developed worldwide. With this approach, we aim to prioritize the most relevant biomarkers according to their specificity and mechanistic implication with molecular motifs of the diseases. The Therapeutic Performance Mapping System (Anaxomics’ TPMS technology) was used to generate one mathematical model per disease: allergic asthma (AA), non-allergic asthma (NA), and respiratory allergy (RA), defining specific molecular motifs for each. The relationship of our molecular biomarker candidates and each disease was analyzed by artificial neural networks (ANNs) scores. These analyses prioritized molecular biomarkers specific to the diseases and to particular molecular motifs. As a first step, molecular characterization of the pathophysiological processes of AA defined 16 molecular motifs: 2 specific for AA, 2 shared with RA, and 12 shared with NA. Mechanistic analysis showed 17 proteins that were strongly related to AA. Eleven proteins were associated with RA and 16 proteins with NA. Specificity analysis showed that 12 proteins were specific to AA, 7 were specific to RA, and 2 to NA. Finally, a triggering analysis revealed a relevant role for AKT1, STAT1, and MAPK13 in all three conditions and for TLR4 in asthmatic diseases (AA and NA). In conclusion, this study has enabled us to prioritize biomarkers depending on the functionality associated with each disease and with specific molecular motifs, which could improve the definition and usefulness of new molecular biomarkers.

A Multi-Objective Approach for Drug Repurposing in Preeclampsia

Preeclampsia is a hypertensive disorder that occurs during pregnancy. It is a complex disease with unknown pathogenesis and the leading cause of fetal and maternal mortality during pregnancy. Using all drugs currently under clinical trial for preeclampsia, we extracted all their possible targets from the DrugBank and ChEMBL databases and labeled them as "targets". The proteins labeled as "off-targets" were extracted in the same way but while taking all antihypertensive drugs which are inhibitors of ACE and/or angiotensin receptor antagonist as query molecules. Classification models were obtained for each of the 55 total proteins (45 targets and 10 off-targets) using the TPOT pipeline optimization tool. The average accuracy of the models in predicting the external dataset for targets and off-targets was 0.830 and 0.850, respectively. The combinations of models maximizing their virtual screening performance were explored by combining the desirability function and genetic algorithms. The virtual screening performance metrics for the best model were: the Boltzmann-Enhanced Discrimination of ROC (BEDROC)α=160.9 = 0.258, the Enrichment Factor (EF)1% = 31.55 and the Area Under the Accumulation Curve (AUAC) = 0.831. The most relevant targets for preeclampsia were: AR, VDR, SLC6A2, NOS3 and CHRM4, while ABCG2, ERBB2, CES1 and REN led to the most relevant off-targets. A virtual screening of the DrugBank database identified estradiol, estriol, vitamins E and D, lynestrenol, mifrepristone, simvastatin, ambroxol, and some antibiotics and antiparasitics as drugs with potential application in the treatment of preeclampsia.