The differential metabolite profiles of acute lymphoblastic leukaemic patients treated with 6-mercaptopurine using untargeted metabolomics approach

Circulating Biomarkers Associated with the Diagnosis and Prognosis of B-Cell Progenitor Acute Lymphoblastic Leukemia

Acute lymphoblastic leukemia (ALL) is a hematological disease characterized by the dysfunction of the hematopoietic system that leads to arrest at a specific stage of stem cells development, suppressing the average production of cellular hematologic components. BCP-ALL is a neoplasm of the B-cell lineage progenitor. BCP-ALL is caused and perpetuated by several mechanisms that provide the disease with its tumor potential and genetic and cytological characteristics. These pathological features are used for diagnosis and the prognostication of BCP-ALL. However, most of these paraclinical tools can only be obtained by bone marrow aspiration, which, as it is an invasive study, can delay the diagnosis and follow-up of the disease, in addition to the anesthetic risk it entails for pediatric patients. For this reason, it is crucial to find noninvasive and accessible ways to supply information concerning diagnosis, prognosis, and the monitoring of the disease, such as circulating biomarkers. In oncology, a biomarker is any measurable indicator that demonstrates the presence of malignancy, tumoral behavior, prognosis, or responses to treatments. This review summarizes circulating molecules associated with BCP-ALL with potential diagnostic value, classificatory capacity during monitoring specific clinic features of the disease, and/or capacity to identify each BCP-ALL stage regarding its evolution and outcome of the patients with BCP-ALL. In the same way, we provide and classify biomarkers that may be used in further studies focused on clinical approaches or therapeutic target identification for BCP-ALL.

Using primary teeth and archived dried spots for exposomic studies in children: Exploring new paths in the environmental epidemiology of pediatric cancer

It is estimated that 300,000 children 0-14 years of age are diagnosed with cancer worldwide each year. While the absolute risk of cancer in children is low, it is the leading cause of death due to disease in children in high-income countries. In spite of this, the etiologies of pediatric cancer are largely unknown. Environmental exposures have long been thought to play an etiologic role. However, to date, there are few well-established environmental risk factors for pediatric malignancies, likely due to technical barriers in collecting biological samples prospectively in pediatric populations for direct measurements. In this review, we propose the use of novel or underutilized biospecimens (dried blood spots and teeth) and molecular approaches for exposure assessment (epigenetics, metabolomics, and somatic mutational profiles). Future epidemiologic studies of pediatric cancer should incorporate novel exposure assessment methodologies, data on molecular features of tumors, and a more complete assessment of gene-environment interactions.

Cerebrospinal Fluid Metabolomic Profiles Associated With Fatigue During Treatment for Pediatric Acute Lymphoblastic Leukemia

CONTEXT

Cancer-related fatigue (CRF) is one of the most distressing and persistent symptoms reported during pediatric acute lymphoblastic leukemia (ALL) therapy; however, information on the pathways underlying CRF severity is limited.

OBJECTIVES

We conducted global metabolomics profiling of cerebrospinal fluid (CSF) samples to provide insight into the underlying mechanisms of CRF.

METHODS

Fatigue in pediatric ALL patients (2012-2017) was assessed during postinduction therapy approximately six months after diagnosis. Postinduction CSF was collected from 171 participants, comprising discovery (n = 86) and replication (n = 85) cohorts. We also conducted secondary validation using diagnostic CSF from 48 replication cohort participants. CSF metabolomic profiling was performed using gas chromatography-mass spectrometry (MS) and liquid chromatography-MS/MS. Kendall's rank correlation was used to evaluate associations between metabolite abundance and CRF. False discovery rate was used to account for multiple comparisons.

RESULTS

Participants were 56% males and 59% Hispanic with a mean age at diagnosis of 8.5 years. A total of 274 CSF-derived metabolites were common to the discovery and replication cohorts. Eight metabolites were significantly associated with fatigue in the discovery cohort (P < 0.05), of which three were significant in the replication cohort, including false discovery rate-corrected associations with gamma-glutamylglutamine (P_combined = 6.2E-6) and asparagine (P_combined = 3.5E-4). Notably, the abundance of gamma-glutamylglutamine in diagnostic CSF samples was also significantly associated with fatigue (P = 0.0062).

CONCLUSION

The metabolites identified in our assessment have been implicated in neurotransmitter transportation and glutathione recycling, suggesting that glutamatergic pathways or oxidative stress may contribute to ALL-associated CRF. This information could inform targeted therapies for reducing CRF in at-risk individuals.

Metabolomic characterization of colorectal cancer cell lines highlighting stage-specific alterations during cancer progression

Introduction: Metabolomic studies on various colorectal cancer (CRC) cell lines have improved our understanding of the biochemical events underlying the disease. However, the metabolic profile dynamics associated with different stages of CRC progression is still lacking. Such information can provide further insights into the pathophysiology and progression of the disease that will prove useful in identifying specific targets for drug designing and therapeutics. Thus, our study aims to characterize the metabolite profiles in the established cell lines corresponding to different stages of CRC. Methods: Metabolite profiling of normal colon cell lines (CCD 841 CoN) and CRC cell lines corresponding to different stages, i.e., SW 1116 (stage A), HT 29 and SW 480 (stage B), HCT 15 and DLD-1 (stage C), and HCT 116 (stage D), was carried out using liquid chromatography-mass spectrometry (LC-MS). Mass Profiler Professional and Metaboanalyst 4.0 software were used for statistical and pathway analysis. METLIN database was used for the identification of metabolites. Results: We identified 72 differential metabolites compared between CRC cell lines of all the stages and normal colon cells. Principle component analysis and partial least squares discriminant analysis score plot were used to segregate normal and CRC cells, as well as CRC cells in different stages of the disease. Variable importance in projection score identified unique differential metabolites in CRC cells of the different stages. We identified 7 differential metabolites unique to stage A, 3 in stage B, 5 in stage C, and 5 in stage D. Conclusion: This study highlights the differential metabolite profiling in CRC cell lines corresponding to different stages. The identification of the differential metabolites in CRC cells at individual stages will lead to a better understanding of the pathophysiology of CRC development and progression and, hence, its application in treatment strategies.

Global metabolomics profiling of colorectal cancer in Malaysian patients

Introduction: The serum metabolomics approach has been used to identify metabolite biomarkers that can diagnose colorectal cancer (CRC) accurately and specifically. However, the biomarkers identified differ between studies suggesting that more studies need to be performed to understand the influence of genetic and environmental factors. Therefore, this study aimed to identify biomarkers and affected metabolic pathways in Malaysian CRC patients. Methods: Serum from 50 healthy controls and 50 CRC patients were collected at UKM Medical Centre. The samples were deproteinized with acetonitrile and untargeted metabolomics profile determined using liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOFMS, Agilent USA). The data were analysed using Mass Profiler Professional (Agilent, USA) software. The panel of biomarkers determined were then used to identify CRC from a new set of 20 matched samples. Results: Eleven differential metabolites were identified whose levels were significantly different between CRC patients compared to normal controls. Based on the analysis of the area under the curve, 7 of these metabolites showed high sensitivity and specificity as biomarkers. The use of the 11 metabolites on a new set of samples was able to differentiate CRC from normal samples with 80% accuracy. These metabolites were hypoxanthine, acetylcarnitine, xanthine, uric acid, tyrosine, methionine, lysoPC, lysoPE, citric acid, 5-oxoproline, and pipercolic acid. The data also showed that the most perturbed pathways in CRC were purine, catecholamine, and amino acid metabolisms. Conclusion: Serum metabolomics profiling can be used to identify distinguishing biomarkers for CRC as well as to further our knowledge of its pathophysiological mechanisms.

Metabolomic profiling identifies pathways associated with minimal residual disease in childhood acute lymphoblastic leukaemia

Background

End-induction minimal residual disease (MRD) is the strongest predictor of relapse in paediatric acute lymphoblastic leukaemia (ALL), but an understanding of the biological pathways underlying early treatment response remains elusive. We hypothesized that metabolomic profiling of diagnostic bone marrow plasma could provide insights into the underlying biology of early treatment response and inform treatment strategies for high-risk patients.

Methods

We performed global metabolomic profiling of samples from discovery (N = 93) and replication (N = 62) cohorts treated at Texas Children's Hospital. Next, we tested the cytotoxicity of drugs targeting central carbon metabolism in cell lines and patient-derived xenograft (PDX) cells.

Findings

Metabolite set enrichment analysis identified altered central carbon and amino acid metabolism in MRD-positive patients from both cohorts at a 5% false discovery rate. Metabolites from these pathways were used as inputs for unsupervised hierarchical clustering. Two distinct clusters were identified, which were independently associated with MRD after adjustment for immunophenotype, cytogenetics, and NCI risk group. Three nicotinamide phosphoribosyltransferase (NAMPT) inhibitors, which reduce glycolytic/TCA cycle activities, demonstrated nanomolar-range cytotoxicity in B- and T-ALL cell lines and PDX cells.

Interpretation

This study provides new insights into the role of central carbon metabolism in early treatment response and as a potential targetable pathway in high-risk disease.

Funding

American Society of Hematology; Baylor College of Medicine Department of Paediatrics; Cancer Prevention and Research Institute of Texas; the Lynch family; St. Baldrick's Foundation with support from the Micaela's Army Foundation; United States National Institutes of Health.

Metabolomics toward personalized medicine

Metabolomics, which is the metabolites profiling in biological matrices, is a key tool for biomarker discovery and personalized medicine and has great potential to elucidate the ultimate product of the genomic processes. Over the last decade, metabolomics studies have identified several relevant biomarkers involved in complex clinical phenotypes using diverse biological systems. Most diseases result in signature metabolic profiles that reflect the sums of external and internal cellular activities. Metabolomics has a major role in clinical practice as it represents >95% of the workload in clinical laboratories worldwide. Many of these metabolites require different analytical platforms, such as Nuclear Magnetic Resonance (NMR), Mass Spectrometry (MS), and Ultra Performance Liquid Chromatography (UPLC), while many clinically relevant metabolites are still not routinely amenable to detection using currently available assays. Combining metabolomics with genomics, transcriptomics, and proteomics studies will result in a significantly improved understanding of the disease mechanisms and the pathophysiology of the target clinical phenotype. This comprehensive approach will represent a major step forward toward providing precision medical care, in which individual is accounted for variability in genes, environment, and personal lifestyle. In this review, we compare and evaluate the metabolomics strategies and studies that focus on the discovery of biomarkers that have "personalized" diagnostic, prognostic, and therapeutic value, validated for monitoring disease progression and responses to various management regimens.

Analysis of the Metabolic Characteristics of Serum Samples in Patients With Multiple Myeloma

Aims: This study aimed to identify potential, non-invasive biomarkers for diagnosis and monitoring of the progress in multiple myeloma (MM) patients. Methods: MM patients and age-matched healthy controls (HC) were recruited in Discovery phase and Validation phase, respectively. MM patients were segregated into active group (AG) and responding group (RG). Serum samples were collected were conducted to non-targeted metabolomics analyses. Metabolites which were significantly changed (SCMs) among groups were identified in Discovery phase and was validated in Validation phase. The signaling pathways of these SCMs were enriched. The ability of SCMs to discriminate among groups in Validation phase was analyzed through receiver operating characteristic curve. The correlations between SCMs and clinical features, between SCMs and survival period of MM patients were analyzed. Results: Total of 23 SCMs were identified in AG compared with HC both in Discovery phase and Validation phase. Those SCMs were significantly enriched in arginine and proline metabolism and glycerophospholipid metabolism. 4 SCMs had the discriminatory ability between MM patients and healthy controls in Validation phase. Moreover, 12 SCMs had the ability to discriminate between the AG patients and RG patients in Validation phase. 10 out of 12 SCMs correlated with advanced features of MM. Moreover, 8 out of 12 SCMs had the negative impact on the survival of MM. 5'-Methylthioadenosine may be the only independent prognostic factor in survival period of MM. Conclusion: 10 SCMs identified in our study, which correlated with advanced features of MM, could be potential, novel, non-invasive biomarkers for active disease in MM.

Applications of Metabolomics in Cancer Studies

Since the start of metabolomics as a field of research, the number of studies related to cancer has grown to such an extent that cancer metabolomics now represents its own discipline. In this chapter, the applications of metabolomics in cancer studies are explored. Different approaches and analytical platforms can be employed for the analysis of samples depending on the goal of the study and the aspects of the cancer metabolome being investigated. Analyses have concerned a range of cancers including lung, colorectal, bladder, breast, gastric, oesophageal and thyroid, amongst others. Developments in these strategies and methodologies that have been applied are discussed, in addition to exemplifying the use of cancer metabolomics in the discovery of biomarkers and in the assessment of therapy (both pharmaceutical and nutraceutical). Finally, the application of cancer metabolomics in personalised medicine is presented.

Overexpression and Suppression of Artemisia annua 4-Hydroxy-3-Methylbut-2-enyl Diphosphate Reductase 1 Gene (AaHDR1) Differentially Regulate Artemisinin and Terpenoid Biosynthesis

4-Hydroxy-3-methylbut-2-enyl diphosphate reductase (HDR) catalyzes the last step of the 2-C-methyl-D-erythritol 4- phosphate (MEP) pathway to synthesize isopentenyl pyrophosphate (IPP) and dimethylallyl diphosphate (DMAPP). To date, little is known regarding effects of an increase or a decrease of a HDR expression on terpenoid and other metabolite profiles in plants. In our study, an Artemisia annua HDR cDNA (namely AaHDR1) was cloned from leaves. Expression profiling showed that it was highly expressed in leaves, roots, stems, and flowers with different levels. Green florescence protein fusion and confocal microscope analyses showed that AaHDR1 was localized in chloroplasts. The overexpression of AaHDR1 increased contents of artemisinin, arteannuin B and other sesquiterpenes, and multiple monoterpenes. By contrast, the suppression of AaHDR1 by anti-sense led to opposite results. In addition, an untargeted metabolic profiling showed that the overexpression and suppression altered non-polar metabolite profiles. In conclusion, the overexpression and suppression of AaHDR1 protein level in plastids differentially affect artemisinin and other terpenoid biosynthesis, and alter non-polar metabolite profiles of A. annua. Particularly, its overexpression leading to the increase of artemisinin production is informative to future metabolic engineering of this antimalarial medicine.

Multicomponent, multitarget integrated adjustment - Metabolomics study of Qizhiweitong particles curing gastrointestinal motility disorders in mice induced by atropine

ETHNOPHARMACOLOGICAL RELEVANCE

Qizhiweitong particles (QZWT) which is derived from the Sinisan decoction in Shang Han Za Bing Lun, composed of Bupleurum chinenis, Paeonia obovata, Citrus aurantium L., Glycyrrhiza uralensis Fisch., Cyperus rotundus and Rhizoma Corydalis is a traditional Chinese medicine (TCM) treating gastrointestinal diseases. It have been used in clinical for years. It have been used in clinical for years. According to previous research, Bupleurum chinenis, Citrus aurantium, Cyperus rotundus in QZWT play the role of promoting gastric peristalsis, which consist of complex chemical constituents. The aim of this study is to probe the multiple effective components with gastrointestinal prokinetic efficacy in QZWT and investigate the multitarget integrated adjustment mechanism of QZWT curing atropine-induced gastrointestinal motility dysfunction mice.

MATERIALS AND METHODS

One hundred and thirty two male mice were randomly divided into 11 groups, including control group, model group, Domperidone group, Mosapride group, QZWT group and six components groups. With gastric retention rate, rate of small intestine propulsion, serum content of GAS and MTL as indexes to evaluate the curing effect on gastrointestinal movement disorders caused by atropine in mice. A serum metabonomics method based on the ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) had been established to investigate the mechanism of QZWT and these components, and PCA and PLS-DA have been used to distinguish different groups and found potential biomarkers.

RESULTS

Four components from six present good prokinetic effects, including Bupleurum Polysaccharide, Citrus aurantium flavonoid, Citrus aurantium essential oil and Cyperus rotundus flavonoids. These components and QZWT regulate 5 potential biomarkers in the body, and primarily involved in 5 metabolic pathways. These potential biomarkers possess direct or indirect connections, each biomarker regulated by multiple components, each component adjusting multiple targets, and QZWT is nearly the sum of its components.

CONCLUSIONS

This experiment deepened our understanding of insufficient gastrointestinal dynamics, confirmed that QZWT treating gastrointestinal disorders was through multicomponent, multitarget ways. These results fully reflect the multiple targets synergy characteristics of TCM.

A Genome-Wide Scenario of Terpene Pathways in Self-pollinated Artemisia annua

Scenarios of genes to metabolites in Artemisia annua remain uninvestigated. Here, we report the use of an integrated approach combining metabolomics, transcriptomics, and gene function analyses to characterize gene-to-terpene and terpene pathway scenarios in a self-pollinating variety of this species. Eighty-eight metabolites including 22 sesquiterpenes (e.g., artemisinin), 26 monoterpenes, two triterpenes, one diterpene and 38 other non-polar metabolites were identified from 14 tissues. These metabolites were differentially produced by leaves and flowers at lower to higher positions. Sequences from cDNA libraries of six tissues were assembled into 18 871 contigs and genome-wide gene expression profiles in tissues were strongly associated with developmental stages and spatial specificities. Sequence mining identified 47 genes that mapped to the artemisinin, non-amorphadiene sesquiterpene, monoterpene, triterpene, 2-C-methyl-D-erythritol 4-phosphate and mevalonate pathways. Pearson correlation analysis resulted in network integration that characterized significant correlations of gene-to-gene expression patterns and gene expression-to-metabolite levels in six tissues simultaneously. More importantly, manipulations of amorpha-4,11-diene synthase gene expression not only affected the activity of this pathway toward artemisinin, artemisinic acid, and arteannuin b but also altered non-amorphadiene sesquiterpene and genome-wide volatile profiles. Such gene-to-terpene landscapes associated with different tissues are fundamental to the metabolic engineering of artemisinin.

Metabolomics and partial least square discriminant analysis to predict history of myocardial infarction of self-claimed healthy subjects: validity and feasibility for clinical practice

Background

The dynamics of metabolomics in establishing a prediction model using partial least square discriminant analysis have enabled better disease diagnosis; with emphasis on early detection of diseases. We attempted to translate the metabolomics model to predict the health status of the Orang Asli community whom we have little information. The metabolite expressions of the healthy vs. diseased patients (cardiovascular) were compared. A metabotype model was developed and validated using partial least square discriminant analysis (PLSDA). Cardiovascular risks of the Orang Asli were predicted and confirmed by biochemistry profiles conducted concurrently.

Results

Fourteen (14) metabolites were determined as potential biomarkers for cardiovascular risks with receiver operating characteristic of more than 0.7. They include 15S-HETE (AUC = 0.997) and phosphorylcholine (AUC = 0.995). Seven Orang Asli were clustered with the patients’ group and may have ongoing cardiovascular risks and problems. This is supported by biochemistry tests results that showed abnormalities in cholesterol, triglyceride, HDL and LDL levels.

Conclusions

The disease prediction model based on metabolites is a useful diagnostic alternative as compared to the current single biomarker assays. The former is believed to be more cost effective since a single sample run is able to provide a more comprehensive disease profile, whilst the latter require different types of sampling tubes and blood volumes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13336-015-0018-4) contains supplementary material, which is available to authorized users.

Application of metabolite profiling tools and time-of-flight mass spectrometry in the identification of transformation products of iopromide and iopamidol during advanced oxidation

The efficiency of wastewater treatment systems in removing pharmaceuticals is often assessed on the basis of the decrease in the concentration of the parent compound. However, what is perceived as "removal" during treatment may not necessarily mean mineralization of the pharmaceutical compound but simply conversion into different transformation products (TPs). Using liquid chromatography coupled to a quadrupole time-of-flight mass spectrometer (LC-QToF-MS), we demonstrated conversion of iopromide in wastewater to at least 14 TPs after an advanced oxidation process (AOP) using UV (fluence = 1500 mJ/cm(2)) and H2O2 (10 mg/L). Due to the complexity of the wastewater matrix, the initial experiments were performed using a high concentration (10 mg/L) of iopromide in order to facilitate the identification of TPs. Despite the high concentration of iopromide used, cursory inspection of UV and mass spectra only revealed four TPs in the chromatograms of the post-AOP samples. However, the use of METLIN database and statistics-based profiling tools commonly used in metabolomics proved effective in discriminating between background signals and TPs derived from iopromide. High-resolution mass data allowed one to predict molecular formulas of putative TPs with errors below 5 ppm relative to the observed m/z. Tandem mass spectrometry (MS/MS) data and isotope pattern comparisons provided necessary information that allowed one to elucidate the structure of iopromide TPs. The presence of the proposed iopromide TPs was determined in unspiked wastewater from a municipal wastewater treatment plant, but no iopromide and TPs were detected. Using analogous structural modifications and oxidation that results from the AOP treatment of iopromide, the potential TPs of iopamidol (a structurally similar compound to iopromide) were predicted. The same mass fragmentation pattern observed in iopromide TPs was applied to the predicted iopamidol TPs. LC-QToF-MS revealed the presence of two iopamidol TPs in unspiked AOP-treated wastewater.

Serum peptidome based biomarkers searching for monitoring minimal residual disease in adult acute lymphocytic leukemia

BACKGROUND

The persistence of minimal residual disease (MRD) during therapy is the strongest adverse prognostic factor in acute lymphocytic leukemia (ALL). This study was to identify serum candidate peptides for monitoring MRD in adult ALL.

RESULTS

A total of 33 peptides in the molecular weight range of 1000-10000 Da were detected using ClinProt system and statistically different between adult patients with ALL and healthy controls. Quick classifier (QC) algorithm was used to obtain a diagnostic model consisting of five peptides that could discriminate patients with ALL from controls with a high sensitivity (100%) and specificity (96.67%). The peptides in the QC model were identified as fibrinogen alpha chain (FGA), glutathione S-transferase P1 (GSTP1), isoform 1 of fibrinogen alpha chain precursor, platelet factor 4 (PF4) by high pressure/performance liquid chromatography mass spectrometry/mass spectrometry. Relative intensities of the five peptides were compared among ALL different groups for the potential importance of MRD evaluation in ALL. The peptides with increased relative intensities in newly diagnosed (ND) ALL patients were found to be decreased in their relative intensities after complete remission (CR) of adult ALL. When ALL patients were refractory & relapsed (RR), relative intensities of the peptides were elevated again. Peptides with decreased relative intensities in ND and RR ALL patients were found to be increased in their relative intensities when ALL patients achieved CR. The findings were validated by ELISA and western blot. Further linear regression analyses were performed to eliminate the influence of platelet and white blood cell counts on serum protein contents and indicated that there were no correlations between the contents of all four proteins (PF4, connective tissue active peptide III, FGA and GSTP1) and white blood cell or platelet counts in ALL different groups and healthy control.

CONCLUSIONS

We speculate the five peptides, FGA, isoform 1 of fibrinogen alpha chain precursor, GSTP1, PF4 and connective tissue active peptide III would be potential biomarkers for forecasting relapse, monitoring MRD and evaluating therapeutic response in adult ALL.