Review of mass spectrometry-based metabolomics in cancer research

Cancer types investigated

Colorectal Cancer (CRC) was investigated most often. Interestingly, cancers of the urogenital tract were ranked second, presumably because they were hypothesized to have more intensive and direct contact with the urinary matrix. In total, studies involving 21 different types of cancer were reviewed.

Sample types investigated

Conventional clinical sample types were studied most commonly (urine, followed by serum and plasma). Twenty-two studies extracted metabolites from tumor tissues, investigating the metabolome of cancer cells directly. Seven studies used less common samples with direct contact to tumor tissue, i.e. cerebrospinal fluid (glioblastoma) (19, 20), exhaled air (lung cancer) (21), saliva (oral cancer) (22), fecal extracts (23) or volatile signatures derived from skin (melanoma) (24). Wedge et al. compared plasma and serum and described a generally high overlap in metabolites (78%-87%) indicating that overall discriminatory abilities of the two biospecimens are comparable. They concluded that the choice for either plasma or serum is led clinically, rather than analytically (25).

Study populations

The number of patients investigated is shown in Figure 3. Only 15 out of 106 studies (14%) investigated samples from more than 100 cancer patients. Seventy-seven studies (73%) included healthy individuals as controls. Twenty-eight (26%) used additional controls, i.e. patients with benign tumors or other organ specific conditions (i.e. hepatitis for hepatocellular cancer (HCC) control) to enhance the specificity of the evaluated biomarkers. Studies investigating tumor tissue commonly used non-tumorous (“normal”) adjacent tissue specimens as matched control samples.

Instrumentation used

A general definition and overview of the current instrumentation used in mass spectrometry-based metabolomics can be found in the review by Dettmer et al. (26). Liquid (LC) and gas chromatography (GC) were equally often used for metabolite separation involving 61 and 59 studies respectively. Few studies employed other techniques, such as capillary electrophoresis (CE)-MS, direct injection-MS (27, 28) or matrix-assisted-laser-desorption-ionization (MALDI-MS) (29). Accurate mass methodologies such as time of flight (TOF) mass spectrometry dominate the untargeted metabolomic setups with 60 out of 106 studies (57%). Other instruments, i.e. GC-MS with single quadrupole, tandem LC-MS with triple-quadrupole (QqQ), or quadrupole-ion traps (QTRAP) are also frequently used. Figure 3B gives an overview of the analytical setups used in the reviewed studies.

Metabolomics for case-control comparison

The distribution of different study types is shown in Figure 2. The majority of studies, n=77 (73%) examined the use of blood or urine with the intent for future early diagnosis of cancer or a cross-sectional biomarker development. These studies used metabolomics to discriminate cancer cases from healthy individuals. Studies employing this common approach are not discussed in detail here. Most studies used sample sizes of pilot character and did not have independent validation. Eighteen out of 106 studies (17%) studied the metabolome of patients in a discovery and a separate validation set. Out of these studies only 7 had patient numbers >100 (see Table 1). In the following, we will discuss these higher-quality studies and will provide a brief overview of other studies with unusual biospecimen types and aims (tumor tissue profiling, therapy response and prognosis).

Three of the studies with separate validation investigated CRC (30-32). Nishiumi et al. and Ritchie et al. investigated serum of CRC patients in comparison to controls, and both achieved very good discrimination abilities with an area under the receiver operating characteristic curve (AUROC) of >0.90. Ritchie et al. further employed a targeted LC-MS approach focusing on polyunsaturated fatty acids (PUFAS) (30). Although levels of PUFAS were found to be lower in CRC, the discrimination abilities of only this targeted subset of markers was still adequate with an AUROC >0.85 (30). The analysis of urine of CRC patients by Cheng et al. revealed even better AUROC values >0.99 with significantly lower tryptophan downstream metabolites in CRC (tryptophan, kynurenine, 5-hydroxytryptophan, indoleacetate, indole) (32). Budczies et al. characterized the metabolome of breast cancer tissue and normal breast tissue (33), using a more invasive approach. A metabolic map of the breast cancer metabolome showed marked increases in purine and glycerolipid metabolism (33). Maeda et al. measured the amino acid concentrations of a total of 303 patients (34). An AUROC of 0.82 indicated a limited performance of a targeted metabolomics study design, which used amino acids as the only markers (34). Chen et al. identified a previously unknown metabolite as 27-nor-5β-cholestane-3,7,12,24,25 pentol glucuronide (CPG) as a biomarker for ovarian cancer (35). The authors showed in their validation in a cohort of over 600 individuals (>200 ovarian cancer patients) that the AUROC of the single metabolite alone was 0.74 with an accuracy comparable to the standard protein marker cancer antigen 12-5 (CA12-5) (35). However, all of these studies still require marker validation in prospectively collected (non-diseased) individuals to prove that they are able to detect cancers at a very early stage.

Studies employing unusual biospecimen types

Abaffy et al. collected volatile signatures directly from skin of melanoma patients, whereas Gaspar et al. investigated exhaled air from lung cancer patients. Both samples have direct tumor contact and are still non-invasive. An AUROC of 0.94 (melanoma study) and a 100% discriminating ability (lung cancer study) show, that choosing such unusual biospecimens may be particularly useful (21, 24). It should be noted however, that sample sizes in the latter two studies were <20.

Investigation of tumor tissue

Twenty-one studies were categorized as tissue profiling studies, investigating the metabolome of tumor tissue and therefore cancer cells directly. These studies indicate a possible use of metabolomics in a pathological assessment: i.e. Brown et al. investigated 7 prostate and kidney cancer biopsies comparing metabolic profiles to regular histopathological examination (36). The authors noted that metabolomic analysis yielded additional information, distinguishing aggressive vs. mild forms of cancer (36). Two studies by Denkert and coworkers investigated metabolomic profiles of colorectal and ovarian cancer tissues (37, 38). Both studies showed a very high accuracy for the correct classification of tumors (88% and 95%, respectively). Two studies investigating lung (39) and gastric cancer tissue (40) were able to discriminate between tumors graded at an early stage (i.e. T1, T2) vs. late stage (i.e. T3, T4). Lin et al. used a similar approach for renal cell carcinoma (RCC) but in this case serum instead of tissue was investigated (41). These results indicate that the process of tumor grading can be aided by metabolomics, but discrimination between different tumor stages is not always successful (42, 43).

Metabolomics for therapy monitoring

Twelve studies (11%) monitored the impact of a therapeutic intervention (i.e. chemo/-radiotherapy or surgery) on the metabolome. Jiye and coworkers investigated the metabolome of chronic myeloid leukemia patients treated (n=33) / not treated (n=26) with the tyrosine kinase-inhibitor Imatinib (44). The authors showed that the metabolome of patients sensitive to chemotherapy changed, whereas the metabolome of resistant patients was similar to that of untreated controls. Saylor and coworkers investigated the effects of androgen deprivation therapy on prostate cancer patients. As expected, steroid levels were decreased, but also other metabolic effects of the therapy were observed: For example an increase in bile acids and a reduction in lipid oxidation (45). Wibom and coworkers used an unusual approach, measuring the metabolic changes in catheter-microdialysates from glioblastoma patients undergoing radiation therapy. The authors interpreted an increase in glutamate and glutamine after treatment as predictive for tumor proliferation and cell damage. Furthermore, a cross-link towards epigenetic mechanisms such as global DNA-hypomethylation was provided: S-methyl-cysteine, an end-product of demethylation of methylated nucleosides, decreased with treatment. The authors concluded that the demethylation process could be hampered by radiation, proposing a mechanistic explanation as to why combining radiotherapy with Temozolomide (an alkylating drug) works so efficiently (20). Cai et al. monitored metabolic changes in plasma of lung cancer patients treated with radiation, but did not identify possible biomarkers that could distinguish treated from untreated individuals (46). Some studies investigated the effect of surgical intervention on the metabolome (47-51). Most authors report a restoration of the metabolome towards that of healthy controls after surgery, whereas only a few note a clustering into a third group (50, 51). In contrast, a study led by Silva et al. found no differences in pre- and post-surgical serum of patients with ovarian cancer (52).

Metabolomics for patient prognosis

Eight studies (8%) assessed associations of the metabolome with patient prognosis (i.e. cancer recurrence, metastasis and survival). The group of Farshidfar and coworkers used a retrospective approach, profiling the serum of CRC patients with locoregional recurrence, liver metastases, or extra hepatic metastases. These three groups were distinguished by metabolites from the galactose and glutamine/glutamate metabolic pathway indicating an alteration of liver metabolism during metastasis (53). Locasale et al. prospectively followed a group of 10 glioblastoma patients of different stages, by investigating the metabolome of cerebrospinal fluid during lumbar puncture (19). The group was able to discriminate between newly diagnosed cancer patients and those with recurrent disease, indicating a possible role of tryptophan metabolites (19). Wedge et al. described that lower levels of glycerophosphatidylcholines, erythritol, and hexadecanoic acid in plasma and serum were associated with poor survival in lung cancer patients (25). However, no data was provided with respect to patients’ tumor stage as a possible confounder, and only 29 patients were included (25). One of the few prospective studies by Ye et al. followed 19 HCC (stage II and III) patients prospectively and developed a classification model for recurrent disease, using lactic acid and acotinic acid as potential biomarkers. The results of this small pilot study were promising with only 1 out of 7 recurring patients being misclassified (47). Tang et al. investigated serum of nasopharyngeal cancer patients (n=49). Patients were followed prospectively and a set of 4 metabolic markers (kynurenine, two n-acetylglucosamines, and hydroxyphenylpyruvate) increased gradually from hyperplasia towards cancer. This set of biomarkers was further validated within a subsequent targeted analysis. Furthermore, these markers correlated with tumor size and recurrence. Additionally, their levels normalized in patients who underwent radiation therapy (54). Asiago et al. developed a discrimination model for recurrence in breast cancer patients (n=56), where 86% sensitivity and 84% specificity was attained and 55% of the recurrent patients were detected 13 months prior to clinical diagnosis (55).

Analytical assessment

GC-MS is favored for its robustness and its low susceptibility to MS-matrix interferences such as ion-suppression (56). Additionally, well established electron impact databases facilitate the automated identification of metabolites. On the other hand, fewer metabolites are detected by GC-MS, as compared to LC-MS. This is mainly attributed to the derivatization process necessary for GC analysis and the limit in molecular weight of metabolites analyzed by this technique. Therefore, more metabolites are typically covered by LC-MS methods. As a drawback, LC-MS-specific electrospray ionization is more susceptible to matrix interferences such as adduct formation or ion suppression (56). Fourteen studies employed both GC and LC-MS analyses.

Pre-processing and data analysis

Pre-processing is essential in order to analyze and interpret metabolomics data. This step includes the import/conversion of raw data files, the detection of signals (peak picking), the assignment of single ions to the same metabolite (deconvolution), the integration and alignment of chromatographic peaks and eventually different methods for baseline correction, normalization and smoothing (57). Extensive pre-processing software is available, both free-of-charge and commercially (57). Both, the process of pre-processing and available software have been extensively discussed previously (58-60). Appropriate data normalization techniques are critical and the type of procedure is mainly determined by the type of sample or methodology. A special concern arises for urinary metabolomics, where metabolite levels change depending on water intake and kidney function. Studies reviewed in this report generally normalize urine to creatinine levels (51, 61-66), but other types of normalization are also common (normalization to osmolarity or the mean/total peak area of the total ion chromatogram (TIC)). Because normalization methods may influence the results of a study (67, 68), we provide the type of normalization used in studies monitoring urinary metabolite levels in Table 1.

Data analysis includes both uni- and multivariate statistical approaches, such as principal component analysis (PCA), some type of discriminant analysis (PLS-DA) or clustering analysis, but also machine-learning techniques such as support vector machines. A good review of metabolomics data analysis was provided by Danielsson et al. (69).

Although guidelines for pre-processing and data analysis of large scale human metabolomics studies have been published recently (70, 71), there is still substantial diversity in the studies reviewed here, with more than 10 different software programs used for pre-processing and for data analysis. PCA, PLS-DA and clustering techniques are most common for studies reviewed here, but some authors favor support vector machines (28, 37, 38, 63, 72-77) or algorithms developed in-house (77). This heterogeneity reflects in part the state of metabolomics as a relatively young discipline, where fully standardized protocols are just beginning to be developed.

Pathways altered by cancer in the human metabolome

Seven out of the 10 most frequently reported metabolites altered in cancer were amino acids. This outlines the importance of an altered protein synthesis in cancer metabolism. Interestingly, essential and aromatic amino acids such as tryptophan, phenylalanine, tyrosine and their downstream metabolites were frequently reported (41, 47, 62, 79-81) (see Supplementary Table 1). For example, many catabolites of tryptophan, i.e. several indole derivatives, nicotinuric acid and especially kynurenine were altered in various cancer types (54, 62, 73). Chen et al. concluded that the unusual increases of kynurenine and nicotinuric acid in urine of breast cancer patients might be a consequence of elevated estrogen levels (62), because of the hormonal regulation of tryptophan oxygenase, the initial enzyme catabolizing tryptophan (62). Conversely, Cheng et al. reported lower kynurenine concentrations in the urine of CRC patients, compared to controls (32) – this is consistent with opposing effects of estrogen on colorectal and breast carcinogenesis, as noted in the epidemiology of these diseases (83, 84). Recently kynurenine has been described as an endogenous ligand of the human aryl hydrocarbon receptor, providing a mechanism for important carcinogenic characteristics, such as tumor cell survival, motility and immune suppression (85). Downstream metabolites of phenylalanine, such as hippurate or p-cresol were frequently altered (32, 72, 80, 86). The latter metabolite has been controversial in the literature: Cheng et al. reported a decrease in urine of CRC patients compared to controls (32), whereas Qui et al. observed an opposite trend (80). The above mentioned metabolites are produced by the gut microbiota. A number of other metabolites most likely of microbial origin have also been described in various publications (Supplementary table 1). These metabolites may play a role in cancer etiology, especially regarding CRC pathogenesis, as discussed in more detail elsewhere (87-89). Other members of the pathway, such as phenylacetylglutamine, a downstream metabolite of phenylacetic acid and glutamine were higher in urine of CRC patients. Phenylacetylglutamine has been found to be cancer suppressive by initiating apoptosis. Its upregulation may reflect the organism’s response to the presence of a tumor. (65)

Alanine, aspartate and glutamate are also frequently reported to be changed and link multiple pathways in cancer metabolism: i.e., they fulfill anaplerotic reactions for the tricarboxylic acid (TCA) cycle, providing an alternative energy source for cancer cells, which predominantly use energy produced by glycolysis rather than the TCA cycle and oxidative phosphorylation. This effect can be observed by the metabolic pathway analysis (lactate is in the top 10 metabolites altered) and has long been known as the Warburg effect (90).

Alterations in arginine and proline metabolism are furthermore linked to the urea cycle that has also been discussed in the review by Ng et al (82). Impaired amino acid metabolism is therefore not only a consequence of protein synthesis. An increased use of amino acids for energy production in cancers may explain the excretion of ammonia and therefore the impairment of the urea cycle. Because of the importance of amino acids in cancer metabolism, several targeted studies, measuring their exact concentrations, have been conducted (34, 91, 92). One large scale study pooled over 900 patients with different cancer types together. Despite this, an AUROC close to 0.75 indicates a limited value of amino acids as sole biomarkers (92).

The respective amines of aspartate and glutamate (asparagine and glutamine) are important amino-donors. Glutamine’s role as an energy source for cancer cells, entering the TCA cycle via α-ketoglutarate has been previously discussed by several authors (82, 93). Both amines, being nitrogen-donors, can furthermore link amino acid to nucleoside synthesis (see Figure 4). Some studies also investigated this impaired nucleoside metabolism, focusing on several modified nucleosides excreted in urine of breast cancer patients (75, 94). A targeted study led by Yoo et al. showed that a decrease in downstream metabolites hypoxanthine and xanthine were suitable markers for non-Hodgkin Lymphoma with AUROC values of 0.85 and 0.83, respectively (29).

Another hallmark of cancer is impaired lipid metabolism: The chemical group of lysophosphatidylcholines and lysoethanolamines were prime markers of this pathway. Changes in fatty acid profiles and various carnitines were also frequently reported by multiple groups, indicating increased membrane synthesis and cellular turnover (95-99).

Myo-inositol, which was increased in various types of cancers (48, 64, 100), might provide a link to the phosphoinositide-3-kinase (PI3K) pathway in cancer. This indicates that well known cancer pathways, such as PI3K, not only show their effects in the proteome, but also in the metabolome.

Metabolic markers can be organ- and therefore cancer-specific

Metabolites that are exclusively synthesized within specific organs/tissues can be considered as good candidates for markers of the respective cancer, because of their specificity. This becomes evident in studies with a focus on HCC (liver) and adrenocortical cancer (adrenal glands):

Because of the liver’s prime role in human metabolism, metabolic markers of liver disease, e.g. bilirubin, have a long clinical history. Therefore, most HCC studies included diseased controls (liver cirrhosis, hepatitis) for the development of biomarkers (23, 101-104). Liver-specific bile metabolites have been reported very frequently, impacting both bile acid and taurine metabolism. The decrease in bile acids is in line with a loss of the organ’s function in HCC. Moreover, taurine is considered as an important antioxidant (105) highlighting the role of oxidative stress in cancer. Cao and coworkers also reported a decrease in bile acids and an increase in lysophosphatidylcholines (23). Tan et al. showed that an increase of three markers, namely taurocholic acid, lysophosphatidylcholine 22:5, and lysophosphoethanolamine 16:0, were enough to discriminate HCC from patients with hepatitis B or cirrhosis with a sensitivity of 87.5% and a specificity of 72.3% (103).

Adrenocortical cancer is also characterized by specific metabolite patterns, because of the adrenal gland’s function which influences the steroidal metabolome. Arlt and coworkers studied urinary steroid levels and showed that they are of high value in discriminating cancer patients from patients with adenomas. Sensitivity and specificity in the range of 90% could be attained (106). Similar observations have been made by Kotlowska et al. (107).

Metabolic alterations are only part of the systemic alterations present in cancer

One challenge of metabolomics is to elucidate the importance of altered metabolites in a biological system. Different theoretical options exist:

1. A metabolite reflects a change in the genome, epigenome, transcriptome, or proteome. For example a mutation affecting an enzyme’s activity may translate into a change in metabolite levels.

2. A metabolite can be a driver, i.e. the altered metabolite levels may inhibit an enzyme or activate a receptor (see kynurenine (85)) or have epigenetic effects.

The question, whether or not a metabolite can be considered a) or b) is often difficult to answer and would most often require additional experiments. Ultimately, it is the entire biological system which is changed: Elevated levels of 2-hydroxyglutarate for example, are the consequence of mutated isocitratedehydrogenase(s) (a), but the metabolite itself may promote epigenetically-driven pathogenesis (b) (108).

Another option to assess the functional role of metabolites is to embed them into pathways and their alterations. This question is not easily addressed by analyses of urine or plasma/serum levels, but tissue analyses are more promising. For example, Budczies et al. constructed a metabolic map of breast cancer, based on their tissue metabolomics data to reveal alterations in energy, amino acid and nucleotide metabolism (33).

Some of the included studies have used cross-omic approaches involving both metabolomics and proteomics (64, 81, 109). Two studies focused on changes in the metabolome based on altered protein levels (110, 111). For example Brockmöller and coworkers correlated the expression levels of the enzyme glycerol-3-phophate acyltransferase with increased levels of phospholipids and phosphatidylcholines (110). This emphasizes, that increased membrane synthesis, accompanied by high cellular turnover is a hallmark of cancer and can be mirrored both on the metabolic as well as on the protein level. In the latter study is an example for a). In contrast, changes in the metabolome can also influence other cellular layers (b), for example the epigenome: pathways corresponding to epigenetic mechanisms, such as S-adenosylmethionine (SAM) and methylated nucleosides indicate connections between epigenomic and metabolic alterations in cancer. Henneges et al. investigated urinary nucleoside levels of breast cancer patients in a targeted fashion. They reported that methyltransferase activity was altered by monitoring pathophysiological patterns of methylated nucleosides and an increase of S-adenosylhomocysteine (SAH), the product of methylation reactions (75). Additionally, SAH is a potent inhibitor of methyltransferase activity. Its increased excretion suggests the ongoing methylation capacity of cancer cells. Woo et al. also found altered methylated nucleosides in the urine of breast, ovary and cervical cancer patients (112). Chen and coworkers reported only 1-methyladenosine together with two unknown components to be upregulated in HCC (113). A comprehensive study by Putluri and coworkers investigated tissue and urine from bladder cancer patients and revealed cross connections between elevated SAM and DNA methylation patterns (81). Furthermore the authors measured altered promotor-methylation of genes involved in xenobiotic metabolism (i.e. CYP1A1 and CYPB1) and their consequent silencing by epigenetic mechanisms. A very comprehensive study led by Sreekumar, analyzed urine, tissue, and plasma samples of prostate cancer patients (73). They could show that a single metabolite, namely sarcosine, was increased in prostate cancer progression and metastasis. The authors were further able to show that sarcosine is involved in the mechanism of a tumor’s invasiveness, by performing cell culture and knockout experiments of the enzymes involved in sarcosine metabolism. In contrast, Struys et al. showed that serum sarcosine levels alone, were not enough to distinguish controls from patients with elevated prostate specific antigen (PSA) or prostate cancer (114). Moreover, Jentzmik et al. reported that urinary sarcosine did not improve the discrimination of prostate cancer patients from patients with no evidence of malignancy (115). They also reported no association with tumor stage or Gleason score. The relevance of sarcosine as a marker for prostate cancer is therefore controversial.

1. Replication against statistical overfitting

Multivariate statistical approaches such as PCA or PLS-DA/ OPLS-DA are very frequently used to differentiate between groups, i.e. cancer vs. control. Due to the generally low sample numbers compared to the numbers of metabolites, overfitting is a common problem for supervised statistical methods. PLS-DA models were reported to be especially susceptible (38). It is therefore important to keep in mind that independent validation of putative markers is essential. This can be achieved by a replication study: Within the present work, only a minority of 18 out of 106 studies (17%) further validated their set of discovered biomarkers in a separate study population.

2. Prospective study designs for predictive markers

Many studies claim to aim for the early detection of cancer, but it is important to note that no study used a prospective study design, following a cohort of healthy individuals over time until the onset of cancer. This type of validation within a prospective screening is essential for early detection, as outlined in the five phases of screening for biomarkers approach by the Early Detection Research Network (ERDN) of the National Cancer Institute (122). A prospective design for patient prognosis was also implemented in only a few studies (19, 47, 54). Validation in prospectively collected samples will be essential for clinical translation.