Altered metabolism is a key area of cancer research and metabolomics and has become an important application in its own right—cancer metabolism on a system-wide scale. There have been numerous discoveries and publications focused on understanding the mechanisms of carcinogenesis and proliferation, as well as the identification of putative biomarkers in biofluid and biopsy samples. With the potential to measure the phenome on a system-wide level, metabolomics has become the tool of choice, complementing genomics, transcriptomics, and proteomics in the field of oncology and advancing the understanding of cellular transformation.
Cancer has always been recognized as a problem of abnormal growth and cell proliferation. Otto Warburg uncovered alterations in the intermediary metabolism of cancer cells that enabled cancer cell growth. This Warburg effect1 is defined as increased glucose uptake and lactate production through the fermentation of glucose in the presence of oxygen.
Cancer cells and their metabolic regulation rely on a variety of metabolic fuels and nutrients. In cancer cells, metabolism is dramatically altered relative to that of normal cells, supporting accelerated cell proliferation, adaption to the tumor microenvironment, and survival.
The recent interest in alterations of metabolism and how cancer cells transform from normal cells into cancer is also coinciding with our ability to instantaneously profile thousands of endogenous biochemical metabolites in cells using an approach called metabolomics.
Untargeted metabolomics is an excellent tool for probing cancer-altered biochemical pathways. For example, enhanced glycolysis is often the result of cellular transformation, leading to changes in endogenous biochemicals that can then be readily quantified to gain insights into the cancer's pathogenesis and to identify biomarkers and/or therapeutic targets.
Additionally, because many metabolic pathways are altered in cancer cells, a global approach that quantifies changes in both polar and non-polar biochemical pathways offers a comprehensive view of metabolism and carcinogenesis.
Oral cancer (osteosarcoma cancer or OSCC) is a type of head and neck cancer (head and neck squamous cell carcinoma or HNSCC). Because OSCC is often discovered late in its development and has a high risk of producing tumors, the death rate is high.
An untargeted (differential) metabolomics analysis of head and neck cancer cells (HNCCs) and cancer stem-like cells (CSCs) was performed to identify significant changes in energy metabolism pathways including glycolysis and the tricarboxylic acid (TCA) cycle.
Differential analysis of capillary IC-MS data was performed using three osteosarcoma cancer cell (OSCC) lines: UMSCC1, UMSCC5, and CSCs. When combined with pathway mapping and meta-analysis, significant changes in both glycolysis and TCA energy metabolism pathways were discovered. One significant finding derived from pathway analysis was that the sugar phosphates in oral CSCs and NSCCs showed significantly higher changes than typical intermediates found in the glycolysis pathway.
A highly sensitive platform that coupled capillary ion chromatography (cap IC) with a high-resolution accurate mass (HRAM) Orbitrap Q Exactive mass spectrometer was used during the metabolic profiling of oral cancer cells. The outstanding resolution of cap IC enabled the separation of isomeric polar metabolites and isobaric metabolites with identical MS/MS spectra and their identification based on retention time matches with standard compounds.
Enhanced separation and detection of polar anionic metabolites established capillary IC-HRAM Orbitrap MS analysis as a technique that complements hydrophilic interaction liquid chromatography (HILIC)-HRAM and reversed phase ultra-high pressure liquid chromatography (RP-UHPLC)-HRAM analyses for metabolomics applications.
Preliminary findings using IC-MS and an untargeted metabolomics approach have led to insights into the metabolic reprogramming of the sugar and TCA metabolism pathways of CSCs.
Preliminary findings using IC-MS in an untargeted metabolomics approach have led to insights into the metabolic reprogramming of the sugar and TCA metabolism pathways of cancer stem cells. Targeted metabolomics is a quantitative approach wherein a set of known targeted metabolites is quantified based on their relative abundances when compared to internal or external reference standards. The resulting data are then used for pathway analysis or as input variables for statistical analysis.
The following study design and workflow are shown for targeted metabolomics analysis:
In this study, a targeted metabolomics approach was utilized for the analysis of cancer cells. This targeted approach was based on high-performance ion chromatography (IC) separation while using a Q Exactive HF MS for high-resolution accurate mass (HRAM) measurement. Stable isotope-labeled internal standards were employed for absolute quantitation.
The IC/Q Exactive HF MS achieved wide dynamic ranges of five orders of magnitude for six targeted metabolites: pyruvate, succinic acid, malic acid, citric acid, fumaric acid, and alpha-ketoglutaric acid, with an R2 ≈ 0.99. Using this platform, metabolites were simultaneously quantified from low fmol/μL to nmol/μL levels in cellular samples. This metabolomics approach has been successfully applied to the analysis of targeted metabolites in head and neck cancer cells as well as cancer stem-like cells (CSCs), and the findings indicate that metabolic phenotypes may be distinct between high and low invasive head and neck cancer cells and between CSCs and non-stem cancer cells (NSCCs).
Rapidly dividing cancer cells, such as HeLa cells, undergo Warburg metabolism in which an increased amount of glucose is taken up and utilized compared to normal differentiated cells. A large proportion of glucose is secreted as lactate, but that is not the only fate of the glucose carbon.
To study the entire fate of glucose, two complementary metabolomic technologies were utilized: solid-state nuclear magnetic resonance (NMR) and high-resolution LC-MS. This study focused on HeLa cancer cells cultured in uniformly labeled [13C]glucose ([U-13C]glucose). Intracellular metabolites that become enriched after being labeled for 48 hours were cataloged using LC-MS, and the fraction of consumed glucose that became incorporated into proteins, peptides, sugars/glycerol, and lipids was quantified with NMR.
When using LC-MS, the label was identified in glycolytic intermediates as well as in all tricarboxylic acid (TCA) cycle metabolites, pentose phosphate pathway intermediates, and lipids such as fatty acids, phosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, and phosphatidylserines.
This study comprehensively characterized the fates of glucose into various biomass components by integrating LC-MS and solid-state NMR technologies.
Lipid droplets (LDs) are cellular organelles that store neutral lipids and are important for energy and lipid metabolism. The accumulation of lipid droplets is a hallmark of obesity, metabolic syndrome, and type II diabetes. It has been shown that in many carcinomas, LDs accumulate; furthermore, aberrant lipid metabolism is connected to prostate cancer and renal clear cell carcinoma.
This lipidomics study tested the role of lipid droplet associated hydrolase (LDAH) in cholesterol ester hydrolysis and the metabolism of other neutral lipids. A knockout mouse model lacking this enzyme was compared to a control mouse with the LDAH. Using a high-resolution Orbitrap LC-MS, lipids from extracted liver were detected and identified with LipidSearch software (from a database of >1,000,000 entries) and through searches of both positive and negative ion adducts. The accurate mass–extracted ion chromatograms were integrated for each identified lipid species and peak areas obtained for quantitation. An internal standard for phosphatidylinositol (PI 17:0-20:4) was spiked in prior to extraction and used for normalization.
This study utilized enzyme activity assays alongside Orbitrap mass spectrometry–based lipidomics and proteomics approaches. It confirmed that LDAH is localized to LDs in several model systems, that there is no evidence for LDAH having an effect on cholesterol ester or triacylglycerol metabolism in vivo. Furthermore, it was found that LDAH does not play a role in energy or glucose metabolism. These data suggest that an alternative metabolic function other than cholesterol ester hydrolysis may be responsible for the development of prostate cancer.
The field of metabolomics could reveal new cancer biomarkers that prove useful for improving diagnosis, discovering better therapeutic targets, and predicting disease development. There have been many examples of metabolomics applications that revealed potential biomarkers in different cancers; such an approach is invaluable because early detection is still the most effective way to improve disease outcome. Because the metabolome represents the endpoint of the omics cascade and strongly correlates with the biological phenotype, metabolomics is a useful approach for finding effective cancer biomarkers.
Hepatocellular carcinoma develops rapidly and undergoes early metastasis; therefore, the disease has a poor prognosis. It is the fifth most common cancer in the world; in fact, in China, a large portion of the population is infected with hepatitis B virus (HBV), the primary risk factor for hepatocellular carcinoma. As a result, studying the hepatocarcinogenesis mechanism is important for decreasing the incidence and mortality of this disease.
Untargeted metabolomics approaches are often used to investigate chronic liver diseases (CLD) and hepatocellular carcinoma, with the potential for discovering new biomarkers and investigating carcinogenesis mechanisms.
In one study, a high-resolution Orbitrap–based LC-MS approach was used to characterize the metabolic features of hepatocellular carcinoma (liver tissues) using an untargeted metabolomics approach. Fifty sets of matched hepatocellular carcinoma tissues, including hepatocellular carcinoma tissue (HCT), adjacent noncancerous tissue (ANT), and distal noncancerous tissue (DNT), were collected. The study also focused on the impact of tumors on surrounding tissue. By analyzing defined differential metabolites, metabolic pathways and correlation networks were investigated and their potential for use in clinical diagnostics was investigated.
Once again, energy metabolism observations correlated with those observed during the Warburg effect. Overall, tumor metabolism was modified to promote cell proliferation or escape from apoptosis. When combined with further studies of serum, betaine, and propionylcarnitine, two metabolites were identified as potential hepatocellular carcinoma biomarkers.