Monitoring cancer prognosis, diagnosis and treatment efficacy using metabolomics and lipidomics

Introduction: Cellular metabolism is altered during cancer initiation and progression, which allows cancer cells to increase anabolic synthesis, avoid apoptosis and adapt to low nutrient and oxygen availability. The metabolic nature of cancer enables patient cancer status to be monitored by metabolomics and lipidomics. Additionally, monitoring metabolic status of patients or biological models can be used to greater understand the action of anticancer therapeutics. Objectives: Discuss how metabolomics and lipidomics can be used to (i) identify metabolic biomarkers of cancer and (ii) understand the mechanism-of-action of anticancer therapies. Discuss considerations that can maximize the clinical value of metabolic cancer biomarkers including case–control, prognostic and longitudinal study designs. Methods: A literature search of the current relevant primary research was performed. Results: Metabolomics and lipidomics can identify metabolic signatures that associate with cancer diagnosis, prognosis and disease progression. Discriminatory metabolites were most commonly linked to lipid or energy metabolism. Case–control studies outnumbered prognostic and longitudinal approaches. Prognostic studies were able to correlate metabolic features with future cancer risk, whereas longitudinal studies were most effective for studying cancer progression. Metabolomics and lipidomics can help to understand the mechanism-of-action of anticancer therapeutics and mechanisms of drug resistance. Conclusion: Metabolomics and lipidomics can be used to identify biomarkers associated with cancer and to better understand anticancer therapies

Recent advances in expanding the coverage of the lipidome

The lipidome comprises a large array of molecules with diverse physicochemical properties. Lipids are structural components of cells, act as a source of energy, and function as signaling mediators. Alterations in lipid metabolism are involved in the onset and progression of a variety of diseases, including metabolic syndrome and cancer. Because of this, interest in lipidomics, the comprehensive characterization of the lipidome by mass spectrometry, has intensified in recent years. However, obtaining a truly complete overview of all lipids in a sample has remained very challenging due to their enormous structural diversity. Here, we provide an overview of the collection of analytical approaches used to study various lipid classes, emphasizing innovations in sample preparation and liquid chromatography–mass spectrometry (LC–MS). Additionally, we provide practical suggestions for increasing the coverage of the lipidome

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.11Ysciescopu

Generation and quality control of lipidomics data for the alzheimers disease neuroimaging initiative cohort.

Alzheimers disease (AD) is a major public health priority with a large socioeconomic burden and complex etiology. The Alzheimer Disease Metabolomics Consortium (ADMC) and the Alzheimer Disease Neuroimaging Initiative (ADNI) aim to gain new biological insights in the disease etiology. We report here an untargeted lipidomics of serum specimens of 806 subjects within the ADNI1 cohort (188 AD, 392 mild cognitive impairment and 226 cognitively normal subjects) along with 83 quality control samples. Lipids were detected and measured using an ultra-high-performance liquid chromatography quadruple/time-of-flight mass spectrometry (UHPLC-QTOF MS) instrument operated in both negative and positive electrospray ionization modes. The dataset includes a total 513 unique lipid species out of which 341 are known lipids. For over 95% of the detected lipids, a relative standard deviation of better than 20% was achieved in the quality control samples, indicating high technical reproducibility. Association modeling of this dataset and available clinical, metabolomics and drug-use data will provide novel insights into the AD etiology. These datasets are available at the ADNI repository at http://adni.loni.usc.edu/

Trapped ion mobility spectrometry and PASEF enable in-depth lipidomics from minimal sample amounts

A comprehensive characterization of the lipidome from limited starting material remains very challenging. Here we report a high-sensitivity lipidomics workflow based on nanoflow liquid chromatography and trapped ion mobility spectrometry (TIMS). Taking advantage of parallel accumulation-serial fragmentation (PASEF), we fragment on average 15 precursors in each of 100 ms TIMS scans, while maintaining the full mobility resolution of co-eluting isomers. The acquisition speed of over 100 Hz allows us to obtain MS/MS spectra of the vast majority of isotope patterns. Analyzing 1 mu L of human plasma, PASEF increases the number of identified lipids more than three times over standard TIMS-MS/MS, achieving attomole sensitivity. Building on high intra- and inter-laboratory precision and accuracy of TIMS collisional cross sections (CCS), we compile 1856 lipid CCS values from plasma, liver and cancer cells. Our study establishes PASEF in lipid analysis and paves the way for sensitive, ion mobility-enhanced lipidomics in four dimensions

Cross-Platform Comparison of Untargeted and Targeted Lipidomics Approaches on Aging Mouse Plasma.

Lipidomics - the global assessment of lipids - can be performed using a variety of mass spectrometry (MS)-based approaches. However, choosing the optimal approach in terms of lipid coverage, robustness and throughput can be a challenging task. Here, we compare a novel targeted quantitative lipidomics platform known as the Lipidyzer to a conventional untargeted liquid chromatography (LC)-MS approach. We find that both platforms are efficient in profiling more than 300 lipids across 11 lipid classes in mouse plasma with precision and accuracy below 20% for most lipids. While the untargeted and targeted platforms detect similar numbers of lipids, the former identifies a broader range of lipid classes and can unambiguously identify all three fatty acids in triacylglycerols (TAG). Quantitative measurements from both approaches exhibit a median correlation coefficient (r) of 0.99 using a dilution series of deuterated internal standards and 0.71 using endogenous plasma lipids in the context of aging. Application of both platforms to plasma from aging mouse reveals similar changes in total lipid levels across all major lipid classes and in specific lipid species. Interestingly, TAG is the lipid class that exhibits the most changes with age, suggesting that TAG metabolism is particularly sensitive to the aging process in mice. Collectively, our data show that the Lipidyzer platform provides comprehensive profiling of the most prevalent lipids in plasma in a simple and automated manner

Identification of plasma lipid biomarkers for prostate cancer by lipidomics and bioinformatics

Background: Lipids have critical functions in cellular energy storage, structure and signaling. Many individual lipid molecules have been associated with the evolution of prostate cancer; however, none of them has been approved to be used as a biomarker. The aim of this study is to identify lipid molecules from hundreds plasma apparent lipid species as biomarkers for diagnosis of prostate cancer. Methodology/Principal Findings: Using lipidomics, lipid profiling of 390 individual apparent lipid species was performed on 141 plasma samples from 105 patients with prostate cancer and 36 male controls. High throughput data generated from lipidomics were analyzed using bioinformatic and statistical methods. From 390 apparent lipid species, 35 species were demonstrated to have potential in differentiation of prostate cancer. Within the 35 species, 12 were identified as individual plasma lipid biomarkers for diagnosis of prostate cancer with a sensitivity above 80%, specificity above 50% and accuracy above 80%. Using top 15 of 35 potential biomarkers together increased predictive power dramatically in diagnosis of prostate cancer with a sensitivity of 93.6%, specificity of 90.1% and accuracy of 97.3%. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) demonstrated that patient and control populations were visually separated by identified lipid biomarkers. RandomForest and 10-fold cross validation analyses demonstrated that the identified lipid biomarkers were able to predict unknown populations accurately, and this was not influenced by patient's age and race. Three out of 13 lipid classes, phosphatidylethanolamine (PE), ether-linked phosphatidylethanolamine (ePE) and ether-linked phosphatidylcholine (ePC) could be considered as biomarkers in diagnosis of prostate cancer. Conclusions/Significance: Using lipidomics and bioinformatic and statistical methods, we have identified a few out of hundreds plasma apparent lipid molecular species as biomarkers for diagnosis of prostate cancer with a high sensitivity, specificity and accuracy

Substrate-Specific Inhibition Constants for Phospholipase A2 Acting on Unique Phospholipid Substrates in Mixed Micelles and Membranes Using Lipidomics.

Assaying lipolytic enzymes is extremely challenging because they act on water-insoluble lipid substrates, which are normally components of micelles, vesicles, and cellular membranes. We extended a new lipidomics-based liquid chromatographic-mass spectrometric assay for phospholipases A2 to perform inhibition analysis using a variety of commercially available synthetic and natural phospholipids as substrates. Potent and selective inhibitors of three recombinant human enzymes, including cytosolic, calcium-independent, and secreted phospholipases A2 were used to establish and validate this assay. This is a novel use of dose-response curves with a mixture of phospholipid substrates, not previously feasible using traditional radioactive assays. The new application of lipidomics to developing assays for lipolytic enzymes revolutionizes in vitro testing for the discovery of potent and selective inhibitors using mixtures of membranelike substrates

Untargeted Lipidomic Analysis to Broadly Characterize the Effects of Pathogenic and Non-Pathogenic Staphylococci on Mammalian Lipids

Modification of the host lipidome via secreted enzymes is an integral, but often overlooked aspect of bacterial pathogenesis. In the current era of prevalent antibiotic resistance, knowledge regarding critical host pathogen lipid interactions has the potential for use in developing novel antibacterial agents. While most studies to date on this matter have focused on specific lipids, or select lipid classes, this provides an incomplete picture. Modern methods of untargeted lipidomics have the capacity to overcome these gaps in knowledge and provide a comprehensive understanding of the role of lipid metabolism in the pathogenesis of infections. In an attempt to determine the role of lipid modifying enzymes produced by staphylococci, we exposed bovine heart lipids, a standardized model for the mammalian lipidome, to spent medium from staphylococcal cultures, and analyzed lipid molecular changes by MS/MSALLshotgun lipidomics. We elucidate distinct effects of different staphylococcal isolates, including 4 clinical isolates of the pathogenic species Staphylococcus aureus, a clinical isolate of the normally commensal species S. epidermidis, and the non-pathogenic species S. carnosus. Two highly virulent strains of S. aureus had a more profound effect on mammalian lipids and modified more lipid classes than the other staphylococcal strains. Our studies demonstrate the utility of the applied untargeted lipidomics methodology to profile lipid changes induced by different bacterial secretomes. Finally, we demonstrate the promise of this lipidomics approach in assessing the specificity of bacterial enzymes for mammalian lipid classes. Our data suggests that there may be a correlation between the bacterial expression of lipid-modifying enzymes and virulence, and could facilitate the guided discovery of lipid pathways required for bacterial infections caused by S. aureus and thereby provide insights into the generation of novel antibacterial agents