Leslie R. Euceda

Metabolic clusters of breast cancer in relation to gene- and protein expression subtypes

BackgroundThe heterogeneous biology of breast cancer leads to high diversity in prognosis and response to treatment, even for patients with similar clinical diagnosis, histology, and stage of disease. Identifying mechanisms contributing to this heterogeneity may reveal new cancer targets or clinically relevant subgroups for treatment stratification. In this study, we have merged metabolite, protein, and gene expression data from breast cancer patients to examine the heterogeneity at a molecular level.MethodsThe study included primary tumor samples from 228 non-treated breast cancer patients. High-resolution magic-angle spinning magnetic resonance spectroscopy (HR MAS MRS) was performed to extract the tumors metabolic profiles further used for hierarchical cluster analysis resulting in three significantly different metabolic clusters (Mc1, Mc2, and Mc3). The clusters were further combined with gene and protein expression data.ResultsOur result revealed distinct differences in the metabolic profile of the three metabolic clusters. Among the most interesting differences, Mc1 had the highest levels of glycerophosphocholine (GPC) and phosphocholine (PCho), Mc2 had the highest levels of glucose, and Mc3 had the highest levels of lactate and alanine. Integrated pathway analysis of metabolite and gene expression data uncovered differences in glycolysis/gluconeogenesis and glycerophospholipid metabolism between the clusters. All three clusters had significant differences in the distribution of protein subtypes classified by the expression of breast cancer-related proteins. Genes related to collagens and extracellular matrix were downregulated in Mc1 and consequently upregulated in Mc2 and Mc3, underpinning the differences in protein subtypes within the metabolic clusters. Genetic subtypes were evenly distributed among the three metabolic clusters and could therefore contribute to additional explanation of breast cancer heterogeneity.ConclusionsThree naturally occurring metabolic clusters of breast cancer were detected among primary tumors from non-treated breast cancer patients. The clusters expressed differences in breast cancer-related protein as well as genes related to extracellular matrix and metabolic pathways known to be aberrant in cancer. Analyses of metabolic activity combined with gene and protein expression provide new information about the heterogeneity of breast tumors and, importantly, the metabolic differences infer that the clusters may be susceptible to different metabolically targeted drugs.

Preprocessing of NMR metabolomics data

Abstract Metabolomics involves the large scale analysis of metabolites and thus, provides information regarding cellular processes in a biological sample. Independently of the analytical technique used, a vast amount of data is always acquired when carrying out metabolomics studies; this results in complex datasets with large amounts of variables. This type of data requires multivariate statistical analysis for its proper biological interpretation. Prior to multivariate analysis, preprocessing of the data must be carried out to remove unwanted variation such as instrumental or experimental artifacts. This review aims to outline the steps in the preprocessing of NMR metabolomics data and describe some of the methods to perform these. Since using different preprocessing methods may produce different results, it is important that an appropriate pipeline exists for the selection of the optimal combination of methods in the preprocessing workflow.

Metabolic Response to Everolimus in Patient-Derived Triple-Negative Breast Cancer Xenografts

Patients with triple-negative breast cancer (TNBC) are unresponsive to endocrine and anti-HER2 pharmacotherapy, limiting their therapeutic options to chemotherapy. TNBC is frequently associated with abnormalities in the PI3K/AKT/mTOR signaling pathway; drugs targeting this pathway are currently being evaluated in these patients. However, the response is variable, partly due to heterogeneity within TNBC, conferring a need to identify biomarkers predicting response and resistance to targeted therapy. In this study, we used a metabolomics approach to assess response to the mTOR inhibitor everolimus in a panel of TNBC patient-derived xenografts (PDX) (n = 103 animals). Tumor metabolic profiles were acquired using high-resolution magic angle spinning magnetic resonance spectroscopy. Partial least-squares-discriminant analysis on relative metabolite concentrations discriminated treated xenografts from untreated controls with an accuracy of 67% (p = 0.003). Multilevel linear mixed-effects models (LMM) indicated reduced glycolytic lactate production and glutaminolysis after treatment, consistent with PI3K/AKT/mTOR pathway inhibition. Although inherent metabolic heterogeneity between different PDX models seemed to hinder prediction of treatment response, the metabolic effects following treatment were more pronounced in responding xenografts compared to nonresponders. Additionally, the metabolic information predicted p53 mutation status, which may provide complementary insight into the interplay between PI3K signaling and other drivers of disease progression.

Ex vivo metabolic fingerprinting identifies biomarkers predictive of prostate cancer recurrence following radical prostatectomy

Background:Robust biomarkers that identify prostate cancer patients with high risk of recurrence will improve personalised cancer care. In this study, we investigated whether tissue metabolites detectable by high-resolution magic angle spinning magnetic resonance spectroscopy (HR-MAS MRS) were associated with recurrence following radical prostatectomy.Methods:We performed a retrospective ex vivo study using HR-MAS MRS on tissue samples from 110 radical prostatectomy specimens obtained from three different Norwegian cohorts collected between 2002 and 2010. At the time of analysis, 50 patients had experienced prostate cancer recurrence. Associations between metabolites, clinicopathological variables, and recurrence-free survival were evaluated using Cox proportional hazards regression modelling, Kaplan–Meier survival analyses and concordance index (C-index).Results:High intratumoural spermine and citrate concentrations were associated with longer recurrence-free survival, whereas high (total-choline+creatine)/spermine (tChoCre/Spm) and higher (total-choline+creatine)/citrate (tChoCre/Cit) ratios were associated with shorter time to recurrence. Spermine concentration and tChoCre/Spm were independently associated with recurrence in multivariate Cox proportional hazards modelling after adjusting for clinically relevant risk factors (C-index: 0.769; HR: 0.72; P=0.016 and C-index: 0.765; HR: 1.43; P=0.014, respectively).Conclusions:Spermine concentration and tChoCre/Spm ratio in prostatectomy specimens were independent prognostic markers of recurrence. These metabolites can be noninvasively measured in vivo and may thus offer predictive value to establish preoperative risk assessment nomograms.

Evaluation of metabolomic changes during neoadjuvant chemotherapy combined with bevacizumab in breast cancer using MR spectroscopy

IntroductionMetabolomics investigates biochemical processes directly, potentially complementing transcriptomics and proteomics in providing insight into treatment outcome.ObjectivesThis study aimed to use magnetic resonance (MR) spectroscopy on breast tumor tissue to explore the effect of neoadjuvant therapy on metabolic profiles, determine metabolic effects of the antiangiogenic drug bevacizumab, and investigate metabolic differences between responders and non-responders.MethodsBreast tumors from 122 patients were profiled using high resolution magic angle spinning MR spectroscopy. All patients received neoadjuvant chemotherapy, and were randomized to receive bevacizumab or not. Tumors were biopsied prior, during, and after treatment.ResultsPrincipal component analysis showed clear metabolic changes indicating a decline in glucose consumption and a transition to normal breast adipose tissue as an effect of chemotherapy. Partial least squares-discriminant analysis revealed metabolic differences between pathological minimal residual disease patients and pathological non-responders after treatment (accuracy of 77%, p < 0.001), but not before or during treatment. Lower glucose and higher lactate was observed in patients exhibiting a good response (≥90% tumor reduction) compared to those with no response (≤10% tumor reduction) before treatment, while the opposite was observed after treatment. Bevacizumab-receiving and chemotherapy-only patients could not be discriminated at any time point. Linear mixed-effects models revealed a significant interaction between time and bevacizumab for glutathione, indicating higher levels of this antioxidant in chemotherapy-only patients than in bevacizumab receivers after treatment.ConclusionMR spectroscopy showed potential in detecting metabolic response to treatment and complementing other molecular assays for the elucidation of underlying mechanisms affecting pathological response.

Metabolic Portraits of Breast Cancer by HR MAS MR Spectroscopy of Intact Tissue Samples

Despite progress in early detection and therapeutic strategies, breast cancer remains the second leading cause of cancer-related death among women globally. Due to the heterogeneity and complexity of tumor biology, breast cancer patients with similar diagnosis might have different prognosis and response to treatment. Thus, deeper understanding of individual tumor properties is necessary. Cancer cells must be able to convert nutrients to biomass while maintaining energy production, which requires reprogramming of central metabolic processes in the cells. This phenomenon is increasingly recognized as a potential target for treatment, but also as a source for biomarkers that can be used for prognosis, risk stratification and therapy monitoring. Magnetic resonance (MR) metabolomics is a widely used approach in translational research, aiming to identify clinically relevant metabolic biomarkers or generate novel understanding of the molecular biology in tumors. Ex vivo proton high-resolution magic angle spinning (HR MAS) MR spectroscopy is widely used to study central metabolic processes in a non-destructive manner. Here we review the current status for HR MAS MR spectroscopy findings in breast cancer in relation to glucose, amino acid and choline metabolism.

NMR-based metabolomics of biofluids in cancer

Metabolomics is the branch of “omics” technologies that involves high‐throughput identification and quantification of small‐molecule metabolites in the metabolome. NMR‐based spectroscopy of biofluids represents a potential method for non‐invasive characterization of cancer. While the metabolism of cancer cells is altered compared with normal non‐proliferating cells, the metabolome of several biofluids (e.g. blood and urine) reflects the metabolism of the entire organism. This review provides an update on the current status of NMR metabolomics analysis of biofluids with respect to: (i) cancer risk assessment; (ii) cancer detection; (iii) disease characterization and prognosis; and (iv) treatment monitoring. We conclude that many studies show impressive associations between biofluid metabolomics and cancer progression, and suggest that NMR metabolomics can be used to provide information with prognostic or predictive value. However, translation of these findings to clinical practice is currently hindered by a lack of validation, difficulties in biological interpretation, and non‐standardized analytical procedures.

Prediction of Clinical Endpoints in Breast Cancer Using NMR Metabolic Profiles

Metabolic profiles reflect biological conditions as a result of biochemical changes within a living system. It is therefore possible to associate metabolic signatures with clinical endpoints of diseases, such as breast cancer. Nuclear magnetic resonance (NMR) spectroscopy is one of the most common techniques used for metabolic profiling, and produces high dimensional datasets from which meaningful biological information can be extracted. Here, we present an overview of data analysis techniques used to achieve this, describing key steps in the procedure. Moreover, examples of clinical endpoints of interest are provided. Although these are specific for breast cancer, the procedures for the analysis of NMR spectra as described here are applicable to any type of cancer and to other diseases.

Non-Invasive Prostate Cancer Characterization with Diffusion-Weighted MRI: Insight from In silico Studies of a Transgenic Mouse Model

Diffusion-weighted magnetic resonance imaging (DWI) enables non-invasive, quantitative staging of prostate cancer via measurement of the apparent diffusion coefficient (ADC) of water within tissues. In cancer, more advanced disease is often characterized by higher cellular density (cellularity), which is generally accepted to correspond to a lower measured ADC. A quantitative relationship between tissue structure and in vivo measurements of ADC has yet to be determined for prostate cancer. In this study, we establish a theoretical framework for relating ADC measurements with tissue cellularity and the proportion of space occupied by prostate lumina, both of which are estimated through automatic image processing of whole-slide digital histology samples taken from a cohort of six healthy mice and nine transgenic adenocarcinoma of the mouse prostate (TRAMP) mice. We demonstrate that a significant inverse relationship exists between ADC and tissue cellularity that is well characterized by our model, and that a decrease of the luminal space within the prostate is associated with a decrease in ADC and more aggressive tumor subtype. The parameters estimated from our model in this mouse cohort predict the diffusion coefficient of water within the prostate-tissue to be 2.18 × 10−3 mm2/s (95% CI: 1.90, 2.55). This value is significantly lower than the diffusion coefficient of free water at body temperature suggesting that the presence of organelles and macromolecules within tissues can drastically hinder the random motion of water molecules within prostate tissue. We validate the assumptions made by our model using novel in silico analysis of whole-slide histology to provide the simulated ADC (sADC); this is demonstrated to have a significant positive correlation with in vivo measured ADC (r2 = 0.55) in our mouse population. The estimation of the structural properties of prostate tissue is vital for predicting and staging cancer aggressiveness, but prostate tissue biopsies are painful, invasive, and are prone to complications such as sepsis. The developments made in this study provide the possibility of estimating the structural properties of prostate tissue via non-invasive virtual biopsies from MRI, minimizing the need for multiple tissue biopsies and allowing sequential measurements to be made for prostate cancer monitoring.

NMR-Based Prostate Cancer Metabolomics

Prostate cancer is the second most common malignancy, and the fifth leading cause of cancer-related death among men, worldwide. A major unsolved clinical challenge in prostate cancer is the ability to accurately distinguish indolent cancer types from the aggressive ones. Reprogramming of metabolism is now a widely accepted hallmark of cancer development, where cancer cells must be able to convert nutrients to biomass while maintaining energy production. Metabolomics is the large-scale study of small molecules, commonly known as metabolites, within cells, biofluids, tissues, or organisms. Nuclear magnetic resonance (NMR) spectroscopy is commonly applied in metabolomics studies of cancer. This chapter provides protocols for NMR-based metabolomics of cell cultures, biofluids (serum and urine), and intact tissue, with concurrent advice for optimal biobanking and sample preparation procedures.