Serum NMR metabolomics to differentiate haematologic malignancies

Integrated genomic-metabolic classification of acute myeloid leukemia defines a subgroup with NPM1 and cohesin/DNA damage mutations

Although targeting of cell metabolism is a promising therapeutic strategy in acute myeloid leukemia (AML), metabolic dependencies are largely unexplored. We aimed to classify AML patients based on their metabolic landscape and map connections between metabolic and genomic profiles. Combined serum and urine metabolomics improved AML characterization compared with individual biofluid analysis. At intracellular level, AML displayed dysregulated amino acid, nucleotide, lipid, and bioenergetic metabolism. The integration of intracellular and biofluid metabolomics provided a map of alterations in the metabolism of polyamine, purine, keton bodies and polyunsaturated fatty acids and tricarboxylic acid cycle. The intracellular metabolome distinguished three AML clusters, correlating with distinct genomic profiles: NPM1-mutated(mut), chromatin/spliceosome-mut and TP53-mut/aneuploid AML that were confirmed by biofluid analysis. Interestingly, integrated genomic-metabolic profiles defined two subgroups of NPM1-mut AML. One was enriched for mutations in cohesin/DNA damage-related genes (NPM1/cohesin-mut AML) and showed increased serum choline + trimethylamine-N-oxide and leucine, higher mutation load, transcriptomic signatures of reduced inflammatory status and better ex-vivo response to EGFR and MET inhibition. The transcriptional differences of enzyme-encoding genes between NPM1/cohesin-mut and NPM1-mut allowed in silico modeling of intracellular metabolic perturbations. This approach predicted alterations in NAD and purine metabolism in NPM1/cohesin-mut AML that suggest potential vulnerabilities, worthy of being therapeutically explored.

Polycythemia Vera and Essential Thrombocythemia Patients Exhibit Unique Serum Metabolic Profiles Compared to Healthy Individuals and Secondary Thrombocytosis Patients

Simple Summary

Current diagnosis of myeloproliferative neoplasms (MPNs), including polycythemia vera (PV) and essential thrombocythemia (ET), is controversial due to limitations associated with the lack of reproducibility, subjectivity and the presence of common somatic mutations in the driver genes. Metabolomics represents a powerful approach to identify altered metabolites that can differentiate between disease status at the time of diagnosis. The objective of this study was to characterize the serum metabolic profile of MPNs patients (PV and ET) and compare it with healthy controls (HC) and secondary thrombocytosis (ST) patients. The analysis revealed metabolites following similar trends between PV and ET patients, as well as unique significant differences in the serum metabolite levels of MNPs patients compared to HC and ST patients. These results could contribute to better differentiate patients with these diseases from HC and ST patients.

Abstract

Most common myeloproliferative neoplasms (MPNs) include polycythemia vera (PV) and essential thrombocythemia (ET). Accurate diagnosis of these disorders remains a clinical challenge due to the lack of specific clinical or molecular features in some patients enabling their discrimination. Metabolomics has been shown to be a powerful tool for the discrimination between different hematological diseases through the analysis of patients’ serum metabolic profiles. In this pilot study, the potential of NMR-based metabolomics to characterize the serum metabolic profile of MPNs patients (PV, ET), as well as its comparison with the metabolic profile of healthy controls (HC) and secondary thrombocytosis (ST) patients, was assessed. The metabolic profile of PV and ET patients, compared with HC, exhibited higher levels of lysine and decreased levels of acetoacetic acid, glutamate, polyunsaturated fatty acids (PUFAs), scyllo-inositol and 3-hydroxyisobutyrate. Furthermore, ET patients, compared with HC and ST patients, were characterized by decreased levels of formate, N-acetyl signals from glycoproteins (NAC) and phenylalanine, while the serum profile of PV patients, compared with HC, showed increased concentrations of lactate, isoleucine, creatine and glucose, as well as lower levels of choline-containing metabolites. The overall analysis revealed significant metabolic alterations mainly associated with energy metabolism, the TCA cycle, along with amino acid and lipid metabolism. These results underscore the potential of metabolomics for identifying metabolic alterations in the serum of MPNs patients that could contribute to improving the clinical management of these diseases.

Next-generation diagnostics for precision oncology: Preanalytical considerations, technical challenges, and available technologies

Molecular diagnostics as the centrepiece of precision oncology has gone through revolutionary developments over the last decade, becoming tremendously broad, deep and precise with still ongoing advancements. In the majority of scenarios, treatment selection for cancer patients without any type of molecular characterization is no longer conceivable. Considering the impact of sample quality on the reliability of molecular analyses and the importance of the results for the fate of an individual patient, it is surprising how sparsely preanalytical and analytical requirements are addressed scientifically. Standardization and rigorous quality assessment continue to play only a marginal role in the field. Within this review, we will systematically discuss influencing preanalytic parameters and technology setups affecting molecular test results. We will shed light on the specifics of different analytes, technical modalities, and analysis pipelines. The review will have a certain focus on broad molecular genetic tumour testing with next generation sequencing but will go beyond that including other molecular diagnostic modalities and will give a glimpse into the future of molecular testing.

Serum metabolomics approach to monitor the changes in metabolite profiles following renal transplantation

Systemic metabolic changes after renal transplantation reflect the key processes that are related to graft accommodation. In order to describe and better understand these changes, the ¹HNMR based metabolomics approach was used. The changes of 47 metabolites in the serum samples of 19 individuals were interpreted over time with respect to their levels prior to transplantation. Considering the specific repeated measures design of the experiments, data analysis was mainly focused on the multiple analyses of variance (ANOVA) methods such as ANOVA simultaneous component analysis and ANOVA-target projection. We also propose here the combined use of ANOVA and classification and regression trees (ANOVA-CART) under the assumption that a small set of metabolites the binary splits on which may better describe the graft accommodation processes over time. This assumption is very important for developing a medical protocol for evaluating a patient's health state. The results showed that besides creatinine, which is routinely used to monitor renal activity, the changes in levels of hippurate, mannitol and alanine may be associated with the changes in renal function during the post-transplantation recovery period. Specifically, the level of hippurate (or histidine) is more sensitive to any short-term changes in renal activity than creatinine.

Metabolomics analysis identifies lysine and taurine as candidate prognostic biomarkers for AML-M2 patients

There is an ongoing search for potential biomarkers for acute myeloid leukemia (AML) patients using metabolic analysis. However, only few studies to date have focused on bone marrow samples or a specific subtype of AML. In the present study, we used gas chromatography time-of-flight mass spectrometry of plasma and bone marrow supernatants to compare the metabolic characteristics of patients with AML with maturation (AML-M2). This approach identified significantly altered metabolites. We next performed pathway analysis and determined relative mRNA expression by qRT-PCR. Our results show that lysine, methionine and serine were significantly decreased in AML-M2 patients compared with healthy control. Moreover, plasma abundance of lysine was negatively associated with patients' risk stratification. Taurine had higher plasma abundance in AML-M2 patients and plasma level of taurine was positively related with AML-M2 risk status, while the expression level of taurine transporter showed a negative correlation. Receiver operating characteristic curve analysis showed these four metabolites had high diagnostic value with lysine showing the highest sensitivity and specificity. These results suggest that plasma abundances of lysine and taurine may serve as potential metabolic biomarkers for the prognosis of patients with AML-M2.

The differential activation of metabolic pathways in leukemic cells depending on their genotype and micro-environmental stress

INTRODUCTION

Acute myeloid leukemia (AML) is characterized by a set of malignant proliferations leading to an accumulation of blasts in the bone marrow and blood. The prognosis is pejorative due to the molecular complexity and pathways implicated in leukemogenesis.

OBJECTIVES

Our research was focused on comparing the metabolic profiles of leukemic cells in basal culture and deprivation conditions to investigate their behaviors under metabolic stress.

METHODS

We performed untargeted metabolomics using ¹H HRMAS-NMR. Five human leukemic cell lines-KG1, K562, HEL, HL60 and OCIAML3-were studied in the basal and nutrient deprivation states. A multivariate analysis of the metabolic profile was performed to find over- or under- expressed metabolites in the different cell lines, depending on the experimental conditions.

RESULTS

In the basal state, each leukemic cell line exhibited a specific metabolic signature related to the diversity of AML subtypes represented and their phenotypes. When cultured in a serum-free medium, they showed quick metabolic adaptation and continued to proliferate and survive despite the lack of nutrients. Low apoptosis was observed. Increased phosphocholine and glutathione was a common feature of all the observed cell lines, with the maximum increase in these metabolites at 24 h of culture, suggesting the involvement of lipid metabolism and oxidative stress regulators in the survival mechanism developed by the leukemic cells.

CONCLUSIONS

Our study provides new insights into the metabolic mechanisms in leukemogenesis and suggests a hierarchy of metabolic pathways activated within leukemic cells, some dependent on their genotypes and others conserved among the subtypes but commonly induced under micro-environmental stress.

The Metabolomic Profile of Lymphoma Subtypes: A Pilot Study

Lymphoma defines a group of different diseases. This study examined pre-treatment plasma samples from 66 adult patients (aged 20–74) newly diagnosed with any lymphoma subtype, and 96 frequency matched population controls. We used gas chromatography-mass spectrometry (GC-MS) to compare the metabolic profile by case/control status and across the major lymphoma subtypes. We conducted univariate and multivariate analyses, and partial least square discriminant analysis (PLS-DA). When compared to the controls, statistically validated models were obtained for diffuse large B-cell lymphoma (DLBCL), chronic lymphocytic leukemia (CLL), multiple myeloma (MM), and Hodgkin lymphoma (HL), but not follicular lymphoma (FL). The metabolomic analysis highlighted interesting differences between lymphoma patients and population controls, allowing the discrimination between pathologic and healthy subjects: Important metabolites, such as hypoxanthine and elaidic acid, were more abundant in all lymphoma subtypes. The small sample size of the individual lymphoma subtypes prevented obtaining PLS-DA validated models, although specific peculiar features of each subtype were observed; for instance, fatty acids were most represented in MM and HL patients, while 2-aminoadipic acid, 2-aminoheptanedioic acid, erythritol, and threitol characterized DLBCL and CLL. Metabolomic analysis was able to highlight interesting differences between lymphoma patients and population controls, allowing the discrimination between pathologic and healthy subjects. Further studies are warranted to understand whether the peculiar metabolic patterns observed might serve as early biomarkers of lymphoma.

Stratification of cancer and diabetes based on circulating levels of formate and glucose

BACKGROUND

Serum and urine metabolites have been investigated for their use as cancer biomarkers. The specificity of candidate metabolites can be limited by the impact of other disorders on metabolite levels. In particular, the increasing incidence of obesity could become a significant confounding factor.

METHODS

Here we developed a multinomial classifier for the stratification of cancer, obesity and healthy phenotypes based on circulating glucose and formate levels. We quantified the classifier performance from the retrospective analysis of samples from breast cancer, lung cancer, obese individuals and healthy controls.

RESULTS

We discovered that circulating formate levels are significantly lower in breast and lung cancer patients than in healthy controls. However, the performance of a cancer classifier based on formate levels alone is limited because obese patients also have low serum formate levels. By introducing a multinomial classifier based on circulating glucose and formate levels, we were able to improve the classifier performance, reaching a true positive rate of 79% with a false positive rate of 8%.

CONCLUSIONS

Circulating formate is reduced in HER2+ breast cancer, non-small cell lung cancer and highly obese patients relative to healthy controls. Further studies are required to determine the relevance of these observations in other cancer types and diseases.