Monitoring tyrosine kinase inhibitor therapeutic responses with a panel of metabolic biomarkers in chronic myeloid leukemia patients

Galaxolide and Irgacure 369 are novel environmental androgens

Endocrine disruptors are environmental chemicals that can interfere with the endocrine system. However, research on endocrine disruptors that interfere with androgen's actions is still limited. The purpose of this study is to use in silico computation, i.e., molecular docking to facilitate the identification of environmental androgens. Computational docking was used to study the binding interactions of environmental/industrial compounds with the three dimensional structure of human androgen receptor (AR). Then reporter assay and cell proliferation assay using AR-expressing LNCaP prostate cancer cells were used to determine their in vitro androgenic activity. Animal studies using immature male rats were also carried out to test their in vivo androgenic activity. Two novel environmental androgens were identified. As a photoinitiator, 2-benzyl-2-(dimethylamino)-4'-morpholinobutyrophenone (Irgacure 369, abbreviated as IC-369) is widely used in the packaging and electronics industries. Galaxolide (HHCB) is widely used in the production of perfume, fabric softeners and detergents. We found that both IC-369 and HHCB could activate AR transcriptional activity and promote cell proliferation in AR-sensitive LNCaP cells. Furthermore, IC-369 and HHCB could induce cell proliferation and histological changes of seminal vesicles in immature rats. RNA sequencing and qPCR analysis showed that androgen-related genes in seminal vesicle tissue were up-regulated by IC-369 and HHCB. In conclusion, IC-369 and HHCB are new environmental androgens that bind AR and induce AR transcriptional activity, thereby exerting toxicological effects on the development of male reproductive organs.

Antileukemic Activity of hsa-miR-203a-5p by Limiting Glutathione Metabolism in Imatinib-Resistant K562 Cells

Imatinib has been the first and most successful tyrosine kinase inhibitor (TKI) for chronic myeloid leukemia (CML), but many patients develop resistance to it after a satisfactory response. Glutathione (GSH) metabolism is thought to be one of the factors causing the emergence of imatinib resistance. Since hsa-miR-203a-5p was found to downregulate Bcr-Abl1 oncogene and also a link between this oncogene and GSH metabolism is reported, the present study aimed to investigate whether hsa-miR-203a-5p could overcome imatinib resistance by targeting GSH metabolism in imatinib-resistant CML cells. After the development of imatinib-resistant K562 (IR-K562) cells by gradually exposing K562 (C) cells to increasing doses of imatinib, resistant cells were transfected with hsa-miR-203a-5p (R+203). Thereafter, cell lysates from various K562 cell sets (imatinib-sensitive, imatinib-resistant, and miR-transfected imatinib-resistant K562 cells) were used for GC-MS-based metabolic profiling. L-alanine, 5-oxoproline (also known as pyroglutamic acid), L-glutamic acid, glycine, and phosphoric acid (Pi)-five metabolites from our data, matched with the enumerated 28 metabolites of the MetaboAnalyst 5.0 for the GSH metabolism. All of these metabolites were present in higher concentrations in IR-K562 cells, but intriguingly, they were all reduced in R+203 and equated to imatinib-sensitive K562 cells (C). Concludingly, the identified metabolites associated with GSH metabolism could be used as diagnostic markers.

Genetic variants in ALDH1L1 and GLDC influence the serine-to-glycine ratio in Hispanic children

BACKGROUND

Glycine is a proteogenic amino acid that is required for numerous metabolic pathways, including purine, creatine, heme, and glutathione biosynthesis. Glycine formation from serine, catalyzed by serine hydroxy methyltransferase, is the major source of this amino acid in humans. Our previous studies in a mouse model have shown a crucial role for the 10-formyltetrahydrofolate dehydrogenase enzyme in serine-to-glycine conversion.

OBJECTIVES

We sought to determine the genomic influence on the serine-glycine ratio in 803 Hispanic children from 319 families of the Viva La Familia cohort.

METHODS

We performed a genome-wide association analysis for plasma serine, glycine, and the serine-glycine ratio in Sequential Oligogenic Linkage Analysis Routines while accounting for relationships among family members.

RESULTS

All 3 parameters were significantly heritable (h2 = 0.22-0.78; P < 0.004). The strongest associations for the serine-glycine ratio were with single nucleotide polymorphisms (SNPs) in aldehyde dehydrogenase 1 family member L1 (ALDH1L1) and glycine decarboxylase (GLDC) and for glycine with GLDC (P < 3.5 × 10-8; effect sizes, 0.03-0.07). No significant associations were found for serine. We also conducted a targeted genetic analysis with ALDH1L1 exonic SNPs and found significant associations between the serine-glycine ratio and rs2886059 (β = 0.68; SE, 0.25; P = 0.006) and rs3796191 (β = 0.25; SE, 0.08; P = 0.003) and between glycine and rs3796191 (β = -0.08; SE, 0.02; P = 0.0004). These exonic SNPs were further associated with metabolic disease risk factors, mainly adiposity measures (P < 0.006). Significant genetic and phenotypic correlations were found for glycine and the serine-glycine ratio with metabolic disease risk factors, including adiposity, insulin sensitivity, and inflammation-related phenotypes [estimate of genetic correlation = -0.37 to 0.35 (P < 0.03); estimate of phenotypic correlation = -0.19 to 0.13 (P < 0.006)]. The significant genetic correlations indicate shared genetic effects among glycine, the serine-glycine ratio, and adiposity and insulin sensitivity phenotypes.

CONCLUSIONS

Our study suggests that ALDH1L1 and GLDC SNPs influence the serine-to-glycine ratio and metabolic disease risk.

Integration of Transcriptomics Data and Metabolomic Data Using Biomedical Literature Mining and Pathway Analysis

Recent progress in omics technologies such as transcriptomics and metabolomics offers an unprecedented opportunity to understand the disease mechanisms and determines the associated biomedical entities using biomedical literature mining. Tremendous data available in the biomedical literature helps in addressing complex biomedical problems. Advancements in genomics and transcriptomics helps in decoding the genetic information obtained from various high throughput techniques for its use in personalized medicine and therapeutics. Integration of data from biomedical literature and data from large-scale genomic studies aids in the determination of the etiology of a disease and drug targets. This chapter addresses the perspectives of transcriptomics and metabolomics in biomedical literature mining and gives an overview of state-of-the-art techniques in this field.

A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity

Endocrine disrupting compounds (EDCs) are a persistent threat to humans and wildlife due to their ability to interfere with endocrine signaling pathways. Inspired by previous work to improve chemical hazard identification through the use of toxicogenomics data, we developed a genomic-oriented data space for profiling the molecular activity of EDCs in an in silico manner, and for creating predictive models that identify and prioritize EDCs. Predictive models of EDCs, derived from gene expression data from rats (in vivo and in vitro primary hepatocytes) and humans (in vitro primary hepatocytes and HepG2), achieve testing accuracy greater than 90%. Negative test sets indicate that known safer chemicals are not predicted as EDCs. The rat in vivo-based classifiers achieve accuracy greater than 75% when tested for invitro to in vivoextrapolation. This study reveals key metabolic pathways and genes affected by EDCs together with a set of predictive models that utilize these pathways to prioritize EDCs in dose/time dependent manner and to predict EDCevokedmetabolic diseases.

Metabolic shift of chronic myeloid leukemia patients under imatinib-pioglitazone regimen and discontinuation

The Estudo de Descontinuação de Imatinibe após Pioglitazona (EDI-PIO) is a single-center, longitudinal, prospective, phase 2, non-randomized, open, clinical trial (NCT02852486, August 2, 2016 retrospectively registered) for the discontinuation of imatinib after concomitant use of pioglitazone, being the first of its kind in a Brazilian population with chronic myeloid leukemia. Due to remaining of leukemic quiescent cells that are not affected by tyrosine kinase inhibitors, it has been suggested the use of pioglitazone, a PPARγ agonist, together with imatinib as a strategy for the maintenance of deep molecular response. The clinical benefit to this association is still controversial, and the metabolic alteration along this process remains unclear. Therefore, we applied a metabolomic protocol using high-resolution mass spectrometry to profile plasmatic metabolic response of a prospective cohort of ten individuals under discontinuation of imatinib and pioglitazone protocol. By comparing patients under pioglitazone and imatinib treatment with imatinib monotherapy and discontinuation phase, we were able to annotate 41 and 36 metabolites, respectively. The metabolic alterations observed during imatinib-pioglitazone combined therapy are associated with an extensive lipid remodeling, with activation of β-oxidation pathway, in addition to the presence of markers that suggest mitochondrial dysfunction.

Amino Acid Metabolic Vulnerabilities in Acute and Chronic Myeloid Leukemias

Amino acid (AA) metabolism plays an important role in many cellular processes including energy production, immune function, and purine and pyrimidine synthesis. Cancer cells therefore require increased AA uptake and undergo metabolic reprogramming to satisfy the energy demand associated with their rapid proliferation. Like many other cancers, myeloid leukemias are vulnerable to specific therapeutic strategies targeting metabolic dependencies. Herein, our review provides a comprehensive overview and TCGA data analysis of biosynthetic enzymes required for non-essential AA synthesis and their dysregulation in myeloid leukemias. Furthermore, we discuss the role of the general control nonderepressible 2 (GCN2) and-mammalian target of rapamycin (mTOR) pathways of AA sensing on metabolic vulnerability and drug resistance.

Metabolomic Analysis of Patients with Chronic Myeloid Leukemia and Cardiovascular Adverse Events after Treatment with Tyrosine Kinase Inhibitors

Background: Cardiovascular adverse events (CV-AEs) are considered critical complications in chronic myeloid leukemia (CML) patients treated with second- and third-generation tyrosine kinase inhibitors (TKIs). The aim of our study was to assess the correlation between metabolic profiles and CV-AEs in CML patients treated with TKIs. Methods: We investigated 39 adult CML patients in chronic-phase (mean age 49 years, range 24–70 years), with no comorbidities evidenced at baseline, who were consecutively identified with CML and treated with imatinib, nilotinib, dasatinib, and ponatinib. All patients performed Gas-Chromatography-Mass-Spectrometry-based metabolomic analysis and were divided into two groups (with and without CV-AEs). Results: Ten CV-AEs were documented. Seven CV-AEs were rated as 3 according to the Common Toxicity Criteria, and one patient died of a dissecting aneurysm of the aorta. The patients’ samples were clearly separated into two groups after analysis and the main discriminant metabolites were tyrosine, lysine, glutamic acid, ornithine, 2-piperdinecarboxylic acid, citric acid, proline, phenylalanine, threonine, mannitol, leucine, serine, creatine, alanine, and 4-hydroxyproline, which were more abundant in the CV-AE group. Conversely, myristic acid, oxalic acid, arabitol, 4-deoxy rithronic acid, ribose, and elaidic acid were less represented in the CV-AE group. Conclusions: CML patients with CV-AEs show a different metabolic profile, suggesting probable mechanisms of endothelial damage.

An omics perspective on drug target discovery platforms

The drug discovery process starts with identification of a disease-modifying target. This critical step traditionally begins with manual investigation of scientific literature and biomedical databases to gather evidence linking molecular target to disease, and to evaluate the efficacy, safety and commercial potential of the target. The high-throughput and affordability of current omics technologies, allowing quantitative measurements of many putative targets (e.g. DNA, RNA, protein, metabolite), has exponentially increased the volume of scientific data available for this arduous task. Therefore, computational platforms identifying and ranking disease-relevant targets from existing biomedical data sources, including omics databases, are needed. To date, more than 30 drug target discovery (DTD) platforms exist. They provide information-rich databases and graphical user interfaces to help scientists identify putative targets and pre-evaluate their therapeutic efficacy and potential side effects. Here we survey and compare a set of popular DTD platforms that utilize multiple data sources and omics-driven knowledge bases (either directly or indirectly) for identifying drug targets. We also provide a description of omics technologies and related data repositories which are important for DTD tasks.

Tyrosine Kinase Inhibitor-Induced Hypertension: Role of Hypertension as a Biomarker in Cancer Treatment

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Cancer treatment is an area of continuous improvement. Therapy is becoming more targeted and the use of anti-angiogenic agents in multiple cancers, specifically tyrosine kinase inhibitors (TKIs), has demonstrated prolonged survival outcomes compared with previous drugs. Therefore, they have become a well-established part of the treatment.

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Despite good results, there is a broad range of moderate to severe adverse effects associated with treatment. Hypertension (HTN) is one of the most frequent adverse effects and has been associated with favourable outcomes (in terms of cancer treatment) of TKI treatment.

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High blood pressure is considered a class effect of TKI treatment, although the mechanisms have not been fully described. Three current hypotheses of TKI-associated HTN are highlighted in this narrative review. These include nitric oxide decrease, a change in endothelin-1 levels and capillary rarefaction.

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Several studies have investigated HTN as a potential biomarker of TKI efficacy. HTN is easy to measure and adding this factor to prognostic models has been shown to improve specificity. HTN may become a potential biomarker in clinical practice involving treating advanced cancers. However, data are currently limited by the number of studies and knowledge of the mechanism of action.

Metabolic underpinnings of leukemia pathology and treatment

BACKGROUND

Carcinogenic transformation of white blood cells during hematopoiesis leads to the development of leukemia, a cancer characterized by incompetent immune cells and a disruption of normal bone marrow function. Leukemias are diverse in type, affected population, prognosis, and treatment regimen, yet a common theme in leukemia is the dysregulated metabolism of leukemic cells and leukemic stem cells with respect to their noncancerous counterparts.

RECENT FINDINGS

In this review, we highlight current findings that elucidate metabolic traits unique to the four major types of leukemia, which confer carcinogenic survival but can be potentially exploited for therapeutic intervention. These metabolic features can work in conjunction with or be independent of unique aspects of the bone marrow microenvironment that can also influence cell survival and proliferation, thus sustaining carcinogenesis.

CONCLUSION

Deepening our understanding of the interactions of leukemias with their niche environments in vivo will inform future treatments for leukemia, particularly for those that are refractive to tyrosine kinase inhibitors and other therapeutic mainstays.

A review of validated biomarkers obtained through metabolomics

INTRODUCTION

Studying changes in the whole set of small molecules, final products of biochemical reactions in living systems or metabolites, is extremely appealing because they represent the best approach to identifying what occurs in an organism when samples are collected. However, their usefulness as potential biomarkers is limited by discoveries obtained in small groups without proper validation or even confirmation of the chemical structure. Areas covered: During the past 5 years, more than 900 papers have been published on metabolomics for biomarker discovery, but the numbers are much lower when some criteria of validation are applied. In total, 102 papers have been included in this review. The most frequent disease areas in which these markers have been discovered include the following: cancer, diabetes, and related diseases and neurodegenerative, cardiovascular, autoimmune, liver, and kidney diseases. Expert commentary: Metabolomics has been demonstrated as rapidly growing due to the improvements in instrumentation, mainly mass spectrometry, and data mining software. For application in the clinic, the results should be validated in different stages, from analytical validation to validation in independent sets of samples, using thousands of samples from different sources.

Monitoring tyrosine kinase inhibitor therapeutic responses with a panel of metabolic biomarkers in chronic myeloid leukemia patients

The aim of this study is to investigate the potential biomarkers associated with chronic myeloid leukemia (CML), reveal the metabolite changes related to the continuous phases of tyrosine kinase inhibitors (TKIs), and find the potential biomarkers associated with treatment effects. Fifty‐two patients with CML and 26 matched healthy people were enrolled as the discovery set. Another 194 randomly selected CML patients treated with TKI were chosen as the external validation set. Plasma samples from the patients and controls were profiled using the gas chromatography‐mass spectrometry‐based metabonomic approach. Multivariate and univariate statistical analyses were combined to select the differential metabolic features. The gas chromatography‐mass spectrometry‐based metabolomics showed a clear clustering and separation of metabolic patterns from healthy controls and pre‐ and post‐TKI treatment CML patients in the discovery set. We identified 9 metabolites that differentiated CML patients from healthy controls, including lactic acid, isoleucine, glycerol, glycine, myristic acid, d‐sorbitol, d‐galactose, d‐glucose, and myo‐inositol. Among the 9 markers, glycerol and myristic acid had the most significant association with TKI treatment effects in both discovery and external validation sets. In the receiver operating characteristic analysis, the combination of glycerol and myristic acid showed a better discrimination performance compared to a single biomarker. The results indicated that metabolic profiling has the potential for diagnosis of CML and the panel of biomarkers including myristic acid and glycerol could be useful in monitoring TKI therapeutic responses.