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Please note this is a comparison between Version 1 by Judith Buentzel and Version 2 by Beatrix Zheng.
Modifying and changing (energy) metabolism is a hall mark of cancer. But how to appraise metabolic changes in cancer patients? Cancer and benign cells shed microvesicles (MV) into the blood. These MV can be easily extracted and isolated. Targeted mass spectrometry of MV is able to differentiate not only between healthy controls and cancer patients, but between molecular breast cancer subtypes. Changes detected in some of these metabolites are indicators for a worse prognosis. In summary, metabolic profiling of MV yields promising biomarkers for diagnosis and prognosis of breast cancer.
- metabolic profiling
- breast cancer
- extracellular vesicles
- biomarker
- molecular breast cancer subtypes
1. Introduction
The communication between cancer cells and their microenvironment is an essential mechanism supporting tumor progression, invasion and metastasis. This crosstalk is mediated in part by secreted soluble messengers but also by extracellular vesicles (EV), which are produced by tumor cells in high numbers. EV have recently attracted increasing attention since they are able to regulate dynamic crosstalk between cancerous, immune and stromal cells in establishing the tumorigenic microenvironment [1]. Apart from apoptotic bodies (500–4000 nm), which represent products of dying cells, there are two main types of EV: microvesicles (MV, 100–1000 nm), which are shed directly from the outer plasma membrane, as well as the small EV (sEV, also called exosomes, 50–150 nm), which are of endosomal origin [2][3][4][2,3,4]. Both MV and sEV carry not only DNA, mRNA or miRNA, but also cytosolic and membrane proteins originating from the mother cell. This cargo can then be transferred to the recipient cells and modulate their biological characteristics [1][5][1,5]. Detection of EV in nearly all biological fluids, such as blood, urine or saliva, demonstrates not only the advantage of isolating them from various bodily fluids, but also indicates their crucial role supporting malignant invasion, immune escape, angiogenesis, creating a hospital environment to support cancer growth and prompting therapy escape [6][7][6,7].
MV can easily be harvested and analyzed from cancer patients’ blood due to their large size. They can be collected without the need for ultracentrifugation and can subsequently be characterized by flow cytometry. The blood-derived “microvesiculome” mostly comprises MV originating from blood and endothelial cells, while only a minor part or percentage of these MV is tumor derived [8]. Despite this fact, we have shown that cancer patients’ MV strongly induce tumor invasiveness in vitro, while MV from healthy controls do not [8]. This evaluation of MV as a biomarker has recently aroused broader scientific interest.
Until now, the potential of EV as biomarkers in breast cancer patients has mainly been analyzed in small patient cohorts with regard to the proteome and miRNA content of sEV [9][10][9,10]. However, many of the EV effects can only partially be explained by pro-invasive proteins or (mi)RNAs. EV also contain lipids and other small metabolites, such as sphingomyelin and ceramide signaling, known to be involved in pro-invasive signaling cascades. So far, only few studies have focused on the metabolomic profiling of body fluids and cancer tissue. In breast cancer patients, specific features in the lipid composition have been demonstrated for blood plasma [11][12][11,12], tissue [13][14][15][13,14,15], saliva [16] or urine [17]. Only two studies concentrated on the metabolome of purified EV [18][19][18,19]; however, these were isolated from breast cancer cell lines and not from patients’ blood. Nishida-Aoki et al. investigated the metabolite differences of sEV in two triple-negative breast cancer cell lines and found a higher diacylglycerol content in the more metastatic cell line [18]. Roberg-Larsen et al. described an enrichment of 27-hydroxycholesterol in sEV from estrogen-receptor-positive cells [19]. The metabolome of sEV has also been analyzed in urine or tissue of prostate [20][21][22][20,21,22] or blood plasma of lung [23] and pancreatic [24] cancer patients. Together, these preliminary data clearly support the hypothesis that the metabolome of EV mirrors the biological characteristics of the tumor cells and may yield biomarkers to predict and follow the clinical course of the disease.
Since we have shown that MV can be easily and reliably extracted from blood plasma of cancer patients [25], we set out to characterize the metabolomic profile of blood-derived MV in a cohort of 78 breast cancer patients and 30 controls with complete clinical annotation. Using mass spectrometry and a targeted metabolomics approach, we found that a combination of metabolites discriminates between breast cancer patients and healthy controls as well as between breast cancer molecular subtypes and, additionally, is prognostic regarding overall survival.
2. Current Insights
Reprogramming of cellular energy metabolism is one of the hallmarks of cancer [26][35]. Changes in cancer metabolism should be reflected in the metabolite composition of cancer tissue or blood samples of patients, which has been shown for several cancer entities [27][28][29][30][36,37,38,39]. However, metabolic profiling of body fluids such as plasma is challenging since the metabolite content is influenced by factors such as diet, microbiome and exposome [31][40], and, in particular, by the stability of the compounds in an aqueous solution. Identifying (onco) metabolites in the more sheltered matrix of EV, which originate at least in part directly from the tumor cells, therefore seems enticing. Additionally, since EV from breast cancer patients have been shown to be critical for conferring invasiveness [8], it can be assumed that the most important compounds for these effects are associated with EV. Nevertheless, surprisingly few studies [20][21][22][32][20,21,22,41] have focused on the metabolome of EV in cancer patients, most of them characterizing either EV as a whole or exclusively sEV. In contrast, our analysis was performed on the larger MV, which confer the crucial advantage of rapid and simple harvest from peripheral blood [25].
Since we were interested in a high-throughput tool, the first question we asked was whether the AbsoluteIDQ® p180 kit (Biocrates) was suitable for this matrix. Our study is the first to demonstrate the application of the AbsoluteIDQ® p180 kit (Biocrates, Innsbruck, Austria) for analyzing MV. Validation by parallel analysis via an untargeted metabolomics approach on a QToF mass spectrometer showed that most of the significant metabolites were also detected with the second method. Although the kit detected a broad variety of compounds, we found mostly lipids, such as (lyso)PCs and SMs, as the predominant metabolites. This is not surprising, since these compounds represent key players in energy housekeeping, membrane structure and signal transduction [33][34][42,43] and are to be expected in high abundance in the large membrane-enclosed MV. In contrast, the absence of other metabolites that are highly abundant in blood plasma, e.g., amino acids and carnitines, underlines the quality of our preparation protocol for EV. However, some metabolites of low abundance, but nevertheless of functional importance, may have been missed using the standardized approach, suggesting that the application of specifically optimized mass spectrometry methods would be necessary.
Through metabolic profiling of blood plasma several authors have previously identified cancer-specific metabolites that differentiate between healthy subjects and lung [35][36][37][44,45,46] or breast cancer patients [11][12][11,12]. Now, we demonstrate for the first time that the metabolome of blood-derived MV from breast cancer patients differs significantly from that of healthy controls. We describe eight lipids that are present in higher concentrations in cancer patients. The profile in our study is partially superimposable with the metabolic profile identified by Qiu et al. [12] in plasma samples of breast cancer patients (6/8 metabolites). Likewise, 11/24 metabolites in the cohort luminal B vs. controls were identical with the Qiu data. This is hardly surprising, given that plasma contains EV in addition to soluble molecules. Depending on the sample preparation before mass spectrometry, EV can also be lysed and included in subsequent analyses. The fact that we observed higher lipid concentrations of all metabolites than Qiu et al. is in line with this assumption.
Some of the identified compounds were not only suitable discriminators between cancer patients and controls, but also markers of poor clinical outcome. Higher concentrations of the phosphatidylcholine (PC) “PC aa C38:5” as well as the lysoPC “lysoPC a C26:0” were associated with significantly shorter OS, thus underlining the prognostic relevance of this finding. In accordance, Guo and coworkers had previously described that deviations of lysoPCs were associated with cancer progression [35][44]. Interestingly, this association differs between cancer entities. Prostate cancer patients showing high amounts of lysoPCs before the onset of disease seem to have a lower risk for advanced disease at the time of diagnosis [38][47].
Comparison of the MV metabolome from breast cancer patients with different molecular subtypes revealed distinct features predominantly for the two large groups of luminal A and B versus each other as well as luminal B versus controls. Despite some variations regarding single metabolites, we could not detect significantly different profiles for the Her2-enriched and the basal-like cohort, although their diverging biological characteristics would strongly suggest such differences. However, both subcohorts comprised only few patients (basal-like: 9 patients; Her2-enriched: 4 patients); thus, the statistical power to detect significant differences was not reached. The reason for the small numbers was that only a minority of the patients in these clinically aggressive subgroups could be included in our study, since most of them were under cancer-specific (chemo)therapy that had been established as an exclusion criterion to avoid contamination with apoptotic bodies.
There are only limited data on the breast cancer metabolome in the literature and most of these are derived from tissue analysis. In accordance with our results, Ide and coworkers [14] described an elevated content of saturated and unsaturated PC in breast cancer tissue, in particular PC aa C32:1, PC aa C34:1 and PC aa C36:0, which were significant metabolites in the luminal B subtype profile we observed. Similarly, PC aa C32:1 was found by other authors in the tissues of breast cancer and oral squamous carcinoma [13][39][13,27]. Only two recent studies aimed to identify metabolome signatures specific for the various molecular subtypes by analyzing blood plasma [11][40][11,48]. Similar to our data, Dìaz-Béltran et al. found several candidates that were able to discriminate between the subtypes; however, there is no overlap between our profile for luminal B and the one proposed by that study. Fan et al. were able to distinguish between molecular subtypes using eight metabolites. However, none of these metabolites overlapped with our data. A distinct classification of molecular subtypes may explain these findings, in addition to the fact that our source was MV rather than plasma. On a second note, we identified several lipids not previously described by metabolic studies investigating tissue samples. This is to be expected, since MV extracted from the blood represent a mixture of MV from platelets, red blood, endothelial and immune cells, with only a minority of tumor MV. Cancer cells and their MV influence the microenvironment by reprogramming the innate and adaptive immune cells [41][42][49,50]. Thus, our approach offers the advantage to detect specific metabolites not only in the tumor MV but also to characterize metabolic changes in the whole blood MV “reactome”, allowing a broader, holistic view on cancer metabolism.
Lipids are critically involved in cancer-related signaling cascades. Accordingly, pathway analysis of our detected metabolites yielded two significantly altered metabolic pathways. Distinct changes in the glycerophospholipid metabolism were found in breast cancer MV samples in comparison to healthy controls as well as in the luminal B subtype MV versus controls. Within this pathway, particularly the conversion of the PC with the KEGG identifier C00157 (e.g., PC aa C32:1) into lysoPCs with the KEGG identifiers C04230 and C04233 (e.g., lysoPC a C16:0) and back were involved. These reactions are catalyzed by the enzymes lecithin–cholesterol acyltransferase (LCAT) and lysophosphatidylcholine acyltransferase (LPCAT), respectively. An accumulation of PC (16:0/16:1), a potential isomer of PC aa C32:1, and the dysregulation of the responsible enzyme has also been observed by others in colorectal cancer tissues [43][31]. The second significantly involved pathway, the ether lipid metabolism, did not discriminate between the whole cohort of cancer patients and the controls but predominantly between the luminal B subtype and the controls. The proposed relevant metabolites were the KEGG identifiers C05212 and C04598, which comprise the compounds PC ae C36:0 and PC ae C34:0 found in our analyses. These isomers are substrates for phospholipases and the enzyme LPCAT2. Changes in ether lipid metabolism have recently been described on the genomic and metabolic level in vitro as well as in vivo for breast cancer patients [44][45][46][32,33,34]. There are first pre-clinical efforts to target ether lipid metabolism in cancer cells [47][51].
Taken together, it's demonstrated that metabolomic profiling of blood-derived MV is feasible using a standardized high-throughput tool for mass spectrometry (AbsoluteIDQ® p180 kit; Biocrates, Innsbruck, Austria). Analysis of MV and other vesicles, in contrast to whole plasma, is of particular interest for the understanding of breast cancer progression, since we have shown earlier that vesicles, and not soluble plasma compounds, mediate the invasiveness of breast cancer cells [8]. The identified lipid biomarkers did not only discriminate between breast cancer patients and controls but were also prognostic markers of clinical outcome. Additionally, a distinct metabolic profile was described for the luminal B subtype, which was very different from the controls and the luminal A cohort. This is in line with the biological behavior of these subtypes, the luminal A group representing the most favorable variant with the highest similarities to the original ductal tissue [48][30]. Assumedly, the aggressive Her2-positive and triple-negative subtypes display distinct metabolic features but our subcohorts were too small for statistically reliable conclusions.
In summary, metabolic profiling of MV yields promising biomarkers for diagnosis and prognosis of breast cancer. Further studies are necessary to determine whether they are indeed of functional relevance and to clarify their potential mode of action.
