Accumulated Metabolites of Hydroxybutyric Acid Serve as Diagnostic and Prognostic Biomarkers of Ovarian High-Grade Serous Carcinomas

A hypergraph-based method for large-scale dynamic correlation study at the transcriptomic scale

Background

The biological regulatory system is highly dynamic. Correlations between functionally related genes change over different biological conditions, which are often unobserved in the data. At the gene level, the dynamic correlations result in three-way gene interactions involving a pair of genes that change correlation, and a third gene that reflects the underlying cellular conditions. This type of ternary relation can be quantified by the Liquid Association statistic. Studying these three-way interactions at the gene triplet level have revealed important regulatory mechanisms in the biological system. Currently, due to the extremely large amount of possible combinations of triplets within a high-throughput gene expression dataset, no method is available to examine the ternary relationship at the biological system level and formally address the false discovery issue.

Results

Here we propose a new method, Hypergraph for Dynamic Correlation (HDC), to construct module-level three-way interaction networks. The method is able to present integrative uniform hypergraphs to reflect the global dynamic correlation pattern in the biological system, providing guidance to down-stream gene triplet-level analyses. To validate the method’s ability, we conducted two real data experiments using a melanoma RNA-seq dataset from The Cancer Genome Atlas (TCGA) and a yeast cell cycle dataset. The resulting hypergraphs are clearly biologically plausible, and suggest novel relations relevant to the biological conditions in the data.

Conclusions

We believe the new approach provides a valuable alternative method to analyze omics data that can extract higher order structures. The software is at https://github.com/yunchuankong/HypergraphDynamicCorrelation.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5787-x) contains supplementary material, which is available to authorized users.

PML-Regulated Mitochondrial Metabolism Enhances Chemosensitivity in Human Ovarian Cancers

High-grade serous ovarian cancer (HGSOC) remains an unmet medical challenge. Here, we unravel an unanticipated metabolic heterogeneity in HGSOC. By combining proteomic, metabolomic, and bioergenetic analyses, we identify two molecular subgroups, low- and high-OXPHOS. While low-OXPHOS exhibit a glycolytic metabolism, high-OXPHOS HGSOCs rely on oxidative phosphorylation, supported by glutamine and fatty acid oxidation, and show chronic oxidative stress. We identify an important role for the PML-PGC-1α axis in the metabolic features of high-OXPHOS HGSOC. In high-OXPHOS tumors, chronic oxidative stress promotes aggregation of PML-nuclear bodies, resulting in activation of the transcriptional co-activator PGC-1α. Active PGC-1α increases synthesis of electron transport chain complexes, thereby promoting mitochondrial respiration. Importantly, high-OXPHOS HGSOCs exhibit increased response to conventional chemotherapies, in which increased oxidative stress, PML, and potentially ferroptosis play key functions. Collectively, our data establish a stress-mediated PML-PGC-1α-dependent mechanism that promotes OXPHOS metabolism and chemosensitivity in ovarian cancer.

Ovarian tumours of different histologic type and clinical stage induce similar changes in lipid metabolism

Background

Previous results obtained from serum samples of late-stage, high-grade serous ovarian carcinoma patients showed large alterations in lipid metabolism. To validate and extend the results, we studied lipidomic changes in early-stage ovarian tumours. In addition to serous ovarian cancer, we investigated whether these changes occur in mucinous and endometrioid histological subtypes as well.

Methods

Altogether, 354 serum or plasma samples were collected from three centres, one from Germany and two from Finland. We performed lipidomic analysis of samples from patients with malignant (N = 138) or borderline (N = 25) ovarian tumours, and 191 controls with benign pathology. These results were compared to previously published data.

Results

We found 39 lipids that showed consistent alteration both in early- and late-stage ovarian cancer patients as well as in pre- and postmenopausal women. Most of these changes were already significant at an early stage and progressed with increasing stage. Furthermore, 23 lipids showed similar alterations in all investigated histological subtypes.

Conclusions

Changes in lipid metabolism due to ovarian cancer occur in early-stage disease but intensify with increasing stage. These changes occur also in other histological subtypes besides high-grade serous carcinoma. Understanding lipid metabolism in ovarian cancer may lead to new therapeutic and diagnostic alternatives.

Metabolic profiling and novel plasma biomarkers for predicting survival in epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is one of the most lethal gynecological malignancies around the world, and patients with ovarian cancer always have an extremely poor chance of survival. Therefore, it is meaningful to develop a highly efficient model that can predict the overall survival for EOC. In order to investigate whether metabolites could be used to predict the survival of EOC, we performed a metabolic analysis of 98 plasma samples with follow-up information, based on the ultra-performance liquid chromatography mass spectrometry (UPLC/MS) systems in both positive (ESI+) and negative (ESI-) modes. Four metabolites: Kynurenine, Acetylcarnitine, PC (42:11), and LPE(22:0/0:0) were selected as potential predictive biomarkers. The AUC value of metabolite-based risk score, together with pathological stages in predicting three-year survival rate was 0.80. The discrimination performance of these four biomarkers between short-term mortality and long-term survival was excellent, with an AUC value of 0.82. In conclusion, our plasma metabolomics study presented the dysregulated metabolism related to the survival of EOC, and plasma metabolites could be utilized to predict the overall survival and discriminate the short-term mortality and long-term survival for EOC patients. These results could provide supplementary information for further study about EOC survival mechanism and guiding the appropriate clinical treatment.

Lipoproteins for therapeutic delivery: recent advances and future opportunities

The physiological role(s) of mammalian plasma lipoproteins is to transport hydrophobic molecules (primarily cholesterol and triacylglycerols) to their respective destinations. Lipoproteins have also been studied as drug-delivery agents due to their advantageous payload capacity, long residence time in the circulation and biocompatibility. The purpose of this review is to briefly discuss current findings with the focus on each type of formulation's potential for clinical applications. Regarding utilizing lipoprotein type formulation for cancer therapeutics, their potential for tumor-selective delivery is also discussed.

Metabolic Heterogeneity Evidenced by MRS among Patient-Derived Glioblastoma Multiforme Stem-Like Cells Accounts for Cell Clustering and Different Responses to Drugs

Clustering of patient-derived glioma stem-like cells (GSCs) through unsupervised analysis of metabolites detected by magnetic resonance spectroscopy (MRS) evidenced three subgroups, namely clusters 1a and 1b, with high intergroup similarity and neural fingerprints, and cluster 2, with a metabolism typical of commercial tumor lines. In addition, subclones generated by the same GSC line showed different metabolic phenotypes. Aerobic glycolysis prevailed in cluster 2 cells as demonstrated by higher lactate production compared to cluster 1 cells. Oligomycin, a mitochondrial ATPase inhibitor, induced high lactate extrusion only in cluster 1 cells, where it produced neutral lipid accumulation detected as mobile lipid signals by MRS and lipid droplets by confocal microscopy. These results indicate a relevant role of mitochondrial fatty acid oxidation for energy production in GSCs. On the other hand, further metabolic differences, likely accounting for different therapy responsiveness observed after etomoxir treatment, suggest that caution must be used in considering patient treatment with mitochondria FAO blockers. Metabolomics and metabolic profiling may contribute to discover new diagnostic or prognostic biomarkers to be used for personalized therapies.

Functionalized gold nanoparticle-enhanced competitive assay for sensitive small-molecule metabolite detection using surface plasmon resonance

A functionalized gold nanoparticle-enhanced competitive assay was developed to overcome the sensitivity challenge associated with direct SPR sensing of small-molecule metabolites.

Usefulness of Amino Acid Profiling in Ovarian Cancer Screening with Special Emphasis on Their Role in Cancerogenesis

The aim of this study was to quantitate 42 serum-free amino acids, propose the biochemical explanation of their role in tumor development, and identify new ovarian cancer (OC) biomarkers for potential use in OC screening. The additional value of this work is the schematic presentation of the interrelationship between metabolites which were identified as significant for OC development and progression. The liquid chromatography-tandem mass spectrometry technique using highly-selective multiple reaction monitoring mode and labeled internal standards for each analyzed compound was applied. Performed statistical analyses showed that amino acids are potentially useful as OC biomarkers, especially as variables in multi-marker models. For the distinguishing metabolites the following metabolic pathways involved in cancer growth and development were proposed: histidine metabolism; tryptophan metabolism; arginine biosynthesis; arginine and proline metabolism; and alanine, aspartate and glutamine metabolism. The presented research identifies histidine and citrulline as potential new OC biomarkers. Furthermore, it provides evidence that amino acids are involved in metabolic pathways related to tumor growth and play an important role in cancerogenesis.

High-grade ovarian serous carcinoma patients exhibit profound alterations in lipid metabolism

Ovarian cancer is a very severe type of disease with poor prognosis. Treatment of ovarian cancer is challenging because of the lack of tests for early detection and effective therapeutic targets. Thus, new biomarkers are needed for both diagnostics and better understanding of the cellular processes of the disease. Small molecules, consisting of metabolites or lipids, have shown emerging potential for ovarian cancer diagnostics. Here we performed comprehensive lipidomic profiling of serum and tumor tissue samples from high-grade serous ovarian cancer patients to find lipids that were altered due to cancer and also associated with progression of the disease. Ovarian cancer patients exhibited an overall reduction of most lipid classes in their serum as compared to a control group. Despite the overall reduction, there were also specific lipids showing elevation, and especially alterations in ceramide and triacylglycerol lipid species were dependent on their fatty acyl side chain composition. Several lipids showed progressive alterations in patients with more advanced disease and poorer overall survival, and outperformed CA-125 as prognostic markers. The abundance of many serum lipids correlated with their abundance in tumor tissue samples. Furthermore, we found a negative correlation of serum lipids with 3-hydroxybutyric acid, suggesting an association between decreased lipid levels and fatty acid oxidation. In conclusion, here we present a comprehensive analysis of lipid metabolism alterations in ovarian cancer patients, with clinical implications.

Metabolomic characterization of experimental ovarian cancer ascitic fluid

INTRODUCTION

Malignant ascites (MA) is a major cause of morbidity that occurs in 37% of ovarian cancer patients. The accumulation of MA in the peritoneal cavity due to cancer results in debilitating symptoms and extremely poor quality of life. There is an urgent unmet need to expand the understanding of MA to design effective treatment strategies, and to improve MA diagnosis.

OBJECTIVE

Our purpose here is to contribute to a better characterization of MA metabolic composition in ovarian cancer.

METHOD

We determined the metabolic composition of ascitic fluids resulting from orthotopic growth of two ovarian cancer cell lines, the mouse ID8-vascular endothelial growth factor (VEGF)-Defb29 cell line and the human OVCAR3 cell line using high-resolution ¹H MRS. ID8-VEGF-Defb29 tumors induce large volumes of ascites, while OVCAR3 tumors induce ascites less frequently and at smaller volumes. To better understand the factors driving the metabolic composition of the fluid, we characterized the metabolism of these ovarian cancer cells in culture by analyzing cell lysates and conditioned culture media with ¹H NMR.

RESULTS

Distinct metabolite patterns were detected in ascitic fluid collected from OVCAR3 and ID8-VEGF-Defb29 tumor bearing mice that were not reflected in the corresponding cell culture or conditioned medium.

CONCLUSION

High-resolution ¹H NMR metabolic markers of MA can be used to improve characterization and diagnosis of MA. Metabolic characterization of MA can provide new insights into how MA fluid supports cancer cell growth and resistance to treatment, and has the potential to identify metabolic targeting strategies to reduce or eliminate the formation of MA.

Metabolomic analysis of the saliva of Japanese patients with oral squamous cell carcinoma

The aim of the present study was to characterize the metabolic systems in Japanese patients with oral squamous cell carcinoma (OSCC) using capillary electrophoresis-mass spectrometry (CE-MS) metabolome analysis of saliva samples. A previous study showed variations among ethnicities and tumor sites in the saliva metabolome of patients with OSCC using CE-MS. In the present study, saliva was obtained from 22 Japanese patients with OSCC and from 21 healthy controls who visited the Department of Dentistry, Oral and Maxillofacial Surgery, Tokyo Dental Collage Ichikawa General Hospital, Tokyo, Japan, and all samples were subject to comprehensive quantitative metabolome analysis using CE-MS. A total of 499 metabolites were detected as CE-MS peaks in the saliva tested from the two groups. A total of 25 metabolites were revealed as potential markers to discriminate between patients with OSCC and healthy controls: Choline, p-hydroxyphenylacetic acid, and 2-hydroxy-4-methylvaleric acid (P<0.001); valine, 3-phenyllactic acid, leucine, hexanoic acid, octanoic acid, terephthalic acid, γ-butyrobetaine, and 3-(4-hydroxyphenyl)propionic acid (P<0.01); and isoleucine, tryptophan, 3-phenylpropionic acid, 2-hydroxyvaleric acid, butyric acid, cadaverine, 2-oxoisovaleric acid, N6,N6,N6-trimethyllysine, taurine, glycolic acid, 3-hydroxybutyric acid, heptanoic acid, alanine, and urea (P<0.05, according to the Wilcoxon rank sum test). A previous study by Sugimoto and co-workers detected 24 discriminatory metabolites, 7 of which (taurine, valine, leucine, isoleucine, choline, cadaverine, and tryptophan) were also detected in the present study. In the present study, however, choline, metabolites in the branched chain amino acids (BCAA) cycle, urea, and 3-hydroxybutyric acid were also characterized. Choline and metabolites of the BCAA cycle have previously been reported in OSCC using metabolome analysis. To the best of our knowledge, no previous reports have identified urea and 3-hydroxybuyric acid in the metabolome of patients with OSCC. These findings suggest the usefulness of metabolites as salivary biomarkers for Japanese patients with OSCC. Further studies using larger patient cohorts should be conducted to validate these results.

Imaging Tumor Metabolism to Assess Disease Progression and Treatment Response

Changes in tumor metabolism may accompany disease progression and can occur following treatment, often before there are changes in tumor size. We focus here on imaging methods that can be used to image various aspects of tumor metabolism, with an emphasis on methods that can be used for tumor grading, assessing disease progression, and monitoring treatment response. Clin Cancer Res; 22(21); 5196-203. ©2016 AACR.

Metabolomics of biomarker discovery in ovarian cancer: a systematic review of the current literature

INTRODUCTION

Metabolomics is the emerging member of "omics" sciences advancing the understanding, diagnosis and treatment of many cancers, including ovarian cancer (OC).

OBJECTIVES

To systematically identify the metabolomic abnormalities in OC detection, and the dominant metabolic pathways associated with the observed alterations.

METHODS

An electronic literature search was performed, up to and including January 15th 2016, for studies evaluating the metabolomic profile of patients with OC compared to controls. QUADOMICS tool was used to assess the quality of the twenty-three studies included in this systematic review.

RESULTS

Biological samples utilized for metabolomic analysis include: serum/plasma (n = 13), urine (n = 4), cyst fluid (n = 3), tissue (n = 2) and ascitic fluid (n = 1). Metabolites related to cellular respiration, carbohydrate, lipid, protein and nucleotide metabolism were significantly altered in OC. Increased levels of tricarboxylic acid cycle intermediates and altered metabolites of the glycolytic pathway pointed to perturbations in cellular respiration. Alterations in lipid metabolism included enhanced fatty acid oxidation, abnormal levels of glycerolipids, sphingolipids and free fatty acids with common elevations of palmitate, oleate, and myristate. Increased levels of glutamine, glycine, cysteine and threonine were commonly reported while enhanced degradations of tryptophan, histidine and phenylalanine were found. N-acetylaspartate, a brain amino acid, was found elevated in primary and metastatic OC tissue and ovarian cyst fluid. Further, elevated levels of ketone bodies including 3-hydroxybutyrate were commonly reported. Increased levels of nucleotide metabolites and tocopherols were consistent through out the studies.

CONCLUSION

Metabolomics presents significant new opportunities for diagnostic biomarker development, elucidating previously unknown mechanisms of OC pathogenesis.