Profiling of serum metabolome of breast cancer: multi-cancer features discriminate between healthy women and patients with breast cancer.

Introduction: The progression of solid cancers is manifested at the systemic level as molecular changes in the metabolome of body fluids, an emerging source of cancer biomarkers. Methods: We analyzed quantitatively the serum metabolite profile using high-resolution mass spectrometry. Metabolic profiles were compared between breast cancer patients (n=112) and two groups of healthy women (from Poland and Norway; n=95 and n=112, respectively) with similar age distributions. Results: Despite differences between both cohorts of controls, a set of 43 metabolites and lipids uniformly discriminated against breast cancer patients and healthy women. Moreover, smaller groups of female patients with other types of solid cancers (colorectal, head and neck, and lung cancers) were analyzed, which revealed a set of 42 metabolites and lipids that uniformly differentiated all three cancer types from both cohorts of healthy women. A common part of both sets, which could be called a multi-cancer signature, contained 23 compounds, which included reduced levels of a few amino acids (alanine, aspartate, glutamine, histidine, phenylalanine, and leucine/isoleucine), lysophosphatidylcholines (exemplified by LPC(18:0)), and diglycerides. Interestingly, a reduced concentration of the most abundant cholesteryl ester (CE(18:2)) typical for other cancers was the least significant in the serum of breast cancer patients. Components present in a multi-cancer signature enabled the establishment of a well-performing breast cancer classifier, which predicted cancer with a very high precision in independent groups of women (AUC>0.95). Discussion: In conclusion, metabolites critical for discriminating breast cancer patients from controls included components of hypothetical multi-cancer signature, which indicated wider potential applicability of a general serum metabolome cancer biomarker.

Association of serum cortisol and cortisone levels and risk of recurrence after endocrine treatment in breast cancer.

Metabolic reprogramming in breast cancer involves changes in steroid hormone synthesis and metabolism. Alterations in estrogen levels in both breast tissue and blood may influence carcinogenesis, breast cancer growth, and response to therapy. Our aim was to examine whether serum steroid hormone concentrations could predict the risk of recurrence and treatment-related fatigue in patients with breast cancer. This study included 66 postmenopausal patients with estrogen receptor-positive breast cancer who underwent surgery, radiotherapy, and adjuvant endocrine treatment. Serum samples were collected at six different time points [before the start of radiotherapy (as baseline), immediately after radiotherapy, and then 3, 6, 12 months, and 7-12 years after radiotherapy]. Serum concentrations of eight steroid hormones (cortisol, cortisone, 17α-hydroxyprogesterone, 17ß-estradiol, estrone, androstenedione, testosterone, and progesterone) were measured using a liquid chromatography-tandem mass spectrometry-based method. Breast cancer recurrence was defined as clinically proven relapse/metastatic breast cancer or breast cancer-related death. Fatigue was assessed with the QLQ-C30 questionnaire. Serum steroid hormone concentrations measured before and immediately after radiotherapy differed between relapse and relapse-free patients [(accuracy 68.1%, p = 0.02, and 63.2%, p = 0.03, respectively, partial least squares discriminant analysis (PLS-DA)]. Baseline cortisol levels were lower in patients who relapsed than in those who did not (p < 0.05). The Kaplan-Meier analysis showed that patients with high baseline concentrations of cortisol (≥ median) had a significantly lower risk of breast cancer recurrence than patients with low cortisol levels (

Association of serum metabolome profile with the risk of breast cancer in participants of the HUNT2 study.

Background: The serum metabolome is a potential source of molecular biomarkers associated with the risk of breast cancer. Here we aimed to analyze metabolites present in pre-diagnostic serum samples collected from healthy women participating in the Norwegian Trøndelag Health Study (HUNT2 study) for whom long-term information about developing breast cancer was available. Methods: Women participating in the HUNT2 study who developed breast cancer within a 15-year follow-up period (BC cases) and age-matched women who stayed breast cancer-free were selected (n=453 case-control pairs). Using a high-resolution mass spectrometry approach 284 compounds were quantitatively analyzed, including 30 amino acids and biogenic amines, hexoses, and 253 lipids (acylcarnitines, glycerides, phosphatidylcholines, sphingolipids, and cholesteryl esters). Results: Age was a major confounding factor responsible for a large heterogeneity in the dataset, hence age-defined subgroups were analyzed separately. The largest number of metabolites whose serum levels differentiated BC cases and controls (82 compounds) were observed in the subgroup of younger women (<45 years old). Noteworthy, increased levels of glycerides, phosphatidylcholines, and sphingolipids were associated with reduced risk of cancer in younger and middle-aged women (≤64 years old). On the other hand, increased levels of serum lipids were associated with an enhanced risk of breast cancer in older women (>64 years old). Moreover, several metabolites could be detected whose serum levels were different between BC cases diagnosed earlier (<5 years) and later (>10 years) after sample collecting, yet these compounds were also correlated with the age of participants. Current results were coherent with the results of the NMR-based metabolomics study performed in the cohort of HUNT2 participants, where increased serum levels of VLDL subfractions were associated with reduced risk of breast cancer in premenopausal women. Conclusions: Changes in metabolite levels detected in pre-diagnostic serum samples, which reflected an impaired lipid and amino acid metabolism, were associated with long-term risk of breast cancer in an age-dependent manner.

Effect of Delayed Centrifugation on the Levels of NMR-Measured Lipoproteins and Metabolites in Plasma and Serum Samples.

Metabolic profiling is widely used for large-scale association studies, based on biobank material. The main obstacle to the translation of metabolomic findings into clinical application is the lack of standardization, making validation in independent cohorts challenging. One reason for this is sensitivity of metabolites to preanalytical conditions. We present a systematic investigation of the effect of delayed centrifugation on the levels of NMR-measured metabolites and lipoproteins in serum and plasma samples. Blood was collected from 20 anonymous donors, of which 10 were recruited from an obesity clinic. Samples were stored at room temperature until centrifugation after 30 min, 1, 2, 4, or 8 h, which is within a realistic time scenario in clinical practice. The effect of delaying centrifugation on plasma and serum metabolic concentrations, and on concentrations of lipoprotein subfractions, was investigated. Our results show that lipoproteins are only minimally affected by a delay in centrifugation while metabolite levels are more sensitive to a delay. Metabolites significantly increased or decreased in concentration depending on delay duration. Further, we describe differences in the stability of serum and plasma, showing that plasma is more stable for metabolites, while lipoprotein subfractions are equally stable for both types of matrices.

Lipoprotein and metabolite associations to breast cancer risk in the HUNT2 study.

BACKGROUND: The aim of this study was to gain an increased understanding of the aetiology of breast cancer, by investigating possible associations between serum lipoprotein subfractions and metabolites and the long-term risk of developing the disease. METHODS: From a cohort of 65,200 participants within the Trøndelag Health Study (HUNT study), we identified all women who developed breast cancer within a 22-year follow-up period. Using nuclear magnetic resonance (NMR) spectroscopy, 28 metabolites and 89 lipoprotein subfractions were quantified from prediagnostic serum samples of future breast cancer patients and matching controls (n = 1199 case-control pairs). RESULTS: Among premenopausal women (554 cases) 14 lipoprotein subfractions were associated with long-term breast cancer risk. In specific, different subfractions of VLDL particles (in particular VLDL-2, VLDL-3 and VLDL-4) were inversely associated with breast cancer. In addition, inverse associations were detected for total serum triglyceride levels and HDL-4 triglycerides. No significant association was found in postmenopausal women. CONCLUSIONS: We identified several associations between lipoprotein subfractions and long-term risk of breast cancer in premenopausal women. Inverse associations between several VLDL subfractions and breast cancer risk were found, revealing an altered metabolism in the endogenous lipid pathway many years prior to a breast cancer diagnosis.

Multivariate analysis of NMR-based metabolomic data.

Nuclear magnetic resonance (NMR) spectroscopy allows for simultaneous detection of a wide range of metabolites and lipids. As metabolites act together in complex metabolic networks, they are often highly correlated, and optimal biological insight is achieved when using methods that take the correlation into account. For this reason, latent-variable-based methods, such as principal component analysis and partial least-squares discriminant analysis, are widely used in metabolomic studies. However, with increasing availability of larger population cohorts, and a shift from analysis of spectral data to using quantified metabolite levels, both more traditional statistical approaches and alternative machine learning methods have become more widely used. This review aims at providing an overview of the current state-of-the-art multivariate methods for the analysis of NMR-based metabolomic data as well as alternative methods, highlighting their strengths and limitations.

The effect of morning vs evening exercise training on glycaemic control and serum metabolites in overweight/obese men: a randomised trial.

AIMS/HYPOTHESIS: We determined whether the time of day of exercise training (morning vs evening) would modulate the effects of consumption of a high-fat diet (HFD) on glycaemic control, whole-body health markers and serum metabolomics. METHODS: In this three-armed parallel-group randomised trial undertaken at a university in Melbourne, Australia, overweight/obese men consumed an HFD (65% of energy from fat) for 11 consecutive days. Participants were recruited via social media and community advertisements. Eligibility criteria for participation were male sex, age 30-45 years, BMI 27.0-35.0 kg/m2 and sedentary lifestyle. The main exclusion criteria were known CVD or type 2 diabetes, taking prescription medications, and shift-work. After 5 days, participants were allocated using a computer random generator to either exercise in the morning (06:30 hours), exercise in the evening (18:30 hours) or no exercise for the subsequent 5 days. Participants and researchers were not blinded to group assignment. Changes in serum metabolites, circulating lipids, cardiorespiratory fitness, BP, and glycaemic control (from continuous glucose monitoring) were compared between groups. RESULTS: Twenty-five participants were randomised (morning exercise n = 9; evening exercise n = 8; no exercise n = 8) and 24 participants completed the study and were included in analyses (n = 8 per group). Five days of HFD induced marked perturbations in serum metabolites related to lipid and amino acid metabolism. Exercise training had a smaller impact than the HFD on changes in circulating metabolites, and only exercise undertaken in the evening was able to partly reverse some of the HFD-induced changes in metabolomic profiles. Twenty-four-hour glucose concentrations were lower after 5 days of HFD compared with the participants' habitual diet (5.3 ± 0.4 vs 5.6 ± 0.4 mmol/l, p = 0.001). There were no significant changes in 24 h glucose concentrations for either exercise group but lower nocturnal glucose levels were observed in participants who trained in the evening, compared with when they consumed the HFD alone (4.9 ± 0.4 vs 5.3 ± 0.3 mmol/l, p = 0.04). Compared with the no-exercise group, peak oxygen uptake improved after both morning (estimated effect 1.3 ml min-1 kg-1 [95% CI 0.5, 2.0], p = 0.003) and evening exercise (estimated effect 1.4 ml min-1 kg-1 [95% CI 0.6, 2.2], p = 0.001). Fasting blood glucose, insulin, cholesterol, triacylglycerol and LDL-cholesterol concentrations decreased only in participants allocated to evening exercise training. There were no unintended or adverse effects. CONCLUSIONS/INTERPRETATION: A short-term HFD in overweight/obese men induced substantial alterations in lipid- and amino acid-related serum metabolites. Improvements in cardiorespiratory fitness were similar regardless of the time of day of exercise training. However, improvements in glycaemic control and partial reversal of HFD-induced changes in metabolic profiles were only observed when participants exercise trained in the evening. TRIAL REGISTRATION: anzctr.org.au registration no. ACTRN12617000304336. FUNDING: This study was funded by the Novo Nordisk Foundation (NNF14OC0011493).

Serum levels of inflammation-related markers and metabolites predict response to neoadjuvant chemotherapy with and without bevacizumab in breast cancers.

Angiogenesis is necessary for tumor growth and has been targeted in breast cancer; however, it is unclear which patients will respond and benefit from antiangiogenic therapy. We report noninvasive monitoring of patient response to neoadjuvant chemotherapy given alone or in combination with anti-vascular endothelial growth factor (bevacizumab) in a randomized clinical trial. At four time points during neoadjuvant chemotherapy ± bevacizumab of receptor tyrosine-protein kinase erbB-2-negative breast cancers, we measured metabolites and inflammation-related markers in patient's serum. We report significant changes in the levels of several molecules induced by bevacizumab, the most prominent being an increase in pentraxin 3 (PTX3) and von Willebrand factor (VWF). Serum levels of AXL, VWF and pulmonary and activation-regulated cytokine (PARC/CCL18) reflected response to chemotherapy alone or in combination with bevacizumab. We further analyzed serum cytokines in relation to tumor characteristics such as gene expression, tumor metabolites and tumor infiltrating leukocytes. We found that VWF and growth-differentiation factor 15 tumor mRNA levels correlated with their respective serum protein levels suggesting that these cytokines may be produced by tumors and outflow to the bloodstream while influencing the tumor microenvironment locally. Finally, we used binomial logistic regression which allowed to predict patient's response using only 10 noninvasive biomarkers. Our study highlights the potential of monitoring circulating levels of cytokines and metabolites during breast cancer therapy.

Effect of Repeated Freeze-Thaw Cycles on NMR-Measured Lipoproteins and Metabolites in Biofluids.

Metabolic profiling of biofluids by nuclear magnetic resonance (NMR) spectroscopy serves as an important tool in disease characterization, and its accuracy largely depends on the quality of samples. We aimed to explore possible effects of repeated freeze-thaw cycles (FTCs) on concentrations of lipoprotein parameters in serum and metabolite concentrations in serum and urine samples. After one to five FTCs, serum and urine samples (n= 20) were analyzed by NMR spectroscopy, and 112 lipoprotein parameters, 20 serum metabolites, and 35 urine metabolites were quantified by a commercial analytical platform. Principal component analysis showed no systematic changes related to FTCs, and samples from the same donor were closely clustered, showing a higher between-subject variation than within-subject variation. The coefficients of variation were small (medians of 4.3%, 11.0%, and 4.9%  for lipoprotein parameters and serum and urine metabolites, respectively). Minor, but significant accumulated freeze-thaw effects were observed for 32 lipoprotein parameters and one serum metabolite (acetic acid) when comparing FTC1 to further FTCs. Remaining lipoprotein and metabolite concentrations showed no significant change. In conclusion, five FTCs did not significantly alter the concentrations of urine metabolites and introduced only minor changes to serum lipoprotein parameters and metabolites evaluated by the NMR-based platform.

Assessing Treatment Response and Prognosis by Serum and Tissue Metabolomics in Breast Cancer Patients.

Patients with locally advanced breast cancer have a worse prognosis compared to patients with localized tumors and require neoadjuvant treatment before surgery. The aim of this study was to characterize the systemic metabolic effect of neoadjuvant chemotherapy in patients with large primary breast cancers and to relate these changes to treatment response and long-term survival. This study included 132 patients with large primary breast tumors randomized to receive neoadjuvant chemotherapy with or without the addition of the antiangiogenic drug Bevacizumab. Tumor biopsies and serum were collected before and during treatment and, serum additionally 6 weeks after surgery. Samples were analyzed by nuclear magnetic resonance spectroscopy (NMR). Correlation analysis showed low correlations between metabolites measured in cancer tissue and serum. Multilevel partial least squares discriminant analysis (PLS-DA) showed clear changes in serum metabolite levels during treatment (p-values ≤ 0.001), including unfavorable changes in lipid levels. PLS-DA revealed metabolic differences between tissue samples from survivors and nonsurvivors collected 12 weeks into treatment with an accuracy of 72% (p-value = 0.005); however, this was not evident in serum samples. Our results demonstrate a potential clinical application for serum-metabolomics for patient monitoring during and after treatment, and indicate potential for tissue NMR spectroscopy for predicting patient survival.

Circulating microRNAs as predictive biomarkers of myocardial infarction: Evidence from the HUNT study.

BACKGROUND AND AIMS: Several risk prediction models for coronary heart disease (CHD) are available today, however, they only explain a modest proportion of the incidence. Circulating microRNAs (miRs) have recently been associated with processes in CHD development, and may therefore represent new potential risk markers. The aim of the study was to assess the incremental value of adding circulating miRs to the Framingham Risk Score (FRS). METHODS: This is a case-control study with a 10-year observation period, with fatal and non-fatal myocardial infarction (MI) as endpoint. At baseline, ten candidate miRs were quantified by real-time polymerase chain reaction in serum samples from 195 healthy participants (60-79 years old). During the follow-up, 96 participants experienced either a fatal (n = 36) or a non-fatal MI (n = 60), whereas the controls (n = 99) remained healthy. By using best subset logistic regression, we identified the miRs that together with the FRS for hard CHD best predicted future MI. The model evaluation was performed by 10-fold cross-validation reporting area under curve (AUC) from the receiver operating characteristic curve (ROC). RESULTS: The best miR-based logistic regression risk-prediction model for MI consisted of a combination of miR-21-5p, miR-26a-5p, mir-29c-3p, miR-144-3p and miR-151a-5p. By adding these 5 miRs to the FRS, AUC increased from 0.66 to 0.80. In comparison, adding other important CHD risk factors (waist-hip ratio, triglycerides, glucose, creatinine) to the FRS only increased AUC from 0.66 to 0.68. CONCLUSIONS: Circulating levels of miRs can add value on top of traditional risk markers in predicting future MI in healthy individuals.