Metabolic profiling of colorectal cancer organoids: A comparison between high-resolution magic angle spinning magnetic resonance spectroscopy and solution nuclear magnetic resonance spectroscopy of polar extracts.

Patient-derived cancer cells cultured in vitro are a cornerstone of cancer metabolism research. More recently, the introduction of organoids has provided the research community with a more versatile model system. Physiological structure and organization of the cell source tissue are maintained in organoids, representing a closer link to in vivo tumor models. High-resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS) is a commonly applied analytical approach for metabolic profiling of intact tissue, but its use has not been reported for organoids. The aim of the current work was to compare the performance of HR MAS MRS and extraction-based nuclear magnetic resonance (NMR) in metabolic profiling of wild-type and tumor progression organoids (TPOs) from human colon cancer, and further to investigate how the sequentially increased genetic alterations of the TPOs affect the metabolic profile. Sixteen metabolites were reliably identified and quantified both in spectra based on NMR of extracts and HR MAS MRS of intact organoids. The metabolite concentrations from the two approaches were highly correlated (r = 0.94), and both approaches were able to capture the systematic changes in metabolic features introduced by the genetic alterations characteristic of colorectal cancer progression (e.g., increased levels of lactate and decreased levels of myo-inositol and phosphocholine with an increasing number of mutations). The current work highlights that HR MAS MRS is a well-suited method for metabolic profiling of intact organoids, with the additional benefit that the nondestructive nature of HR MAS enables subsequent recovery of the organoids for further analyses based on nucleic acids or proteins.

Biodistribution of Poly(alkyl cyanoacrylate) Nanoparticles in Mice and Effect on Tumor Infiltration of Macrophages into a Patient-Derived Breast Cancer Xenograft.

We have investigated the biodistribution and tumor macrophage infiltration after intravenous injection of the poly(alkyl cyanoacrylate) nanoparticles (NPs): PEBCA (poly(2-ethyl-butyl cyanoacrylate), PBCA (poly(n-butyl cyanoacrylate), and POCA (poly(octyl cyanoacrylate), in mice. These NPs are structurally similar, have similar PEGylation, and have previously been shown to give large variations in cellular responses in vitro. The PEBCA NPs had the highest uptake both in the patient-derived breast cancer xenograft MAS98.12 and in lymph nodes, and therefore, they are the most promising of these NPs for delivery of cancer drugs. High-resolution magic angle spinning magnetic resonance (HR MAS MR) spectroscopy did not reveal any differences in the metabolic profiles of tumors following injection of the NPs, but the PEBCA NPs resulted in higher tumor infiltration of the anti-tumorigenic M1 macrophages than obtained with the two other NPs. The PEBCA NPs also increased the ratio of M1/M2 (anti-tumorigenic/pro-tumorigenic) macrophages in the tumors, suggesting that these NPs might be used both as a vehicle for drug delivery and to modulate the immune response in favor of enhanced therapeutic effects.

Biomarker Discovery Using NMR-Based Metabolomics of Tissue.

NMR-based metabolomics has shown promise in the diagnosis of diseases as it enables identification and quantification of metabolic biomarkers. Using high-resolution magic-angle-spinning (HR-MAS) NMR spectroscopy, metabolic profiles from intact tissue specimens can be obtained with high spectral resolution. In addition, HR-MAS NMR requires minimal sample preparation and the sample is kept intact for subsequent analyses. In this chapter, we describe a typical protocol for NMR-based metabolomics of tissue samples. We cover all major steps ranging from tissue sample collection to determination of biomarkers, including experimental precautions taken to ensure reproducible and reliable reporting of data in the area of clinical application.

Glutamine to proline conversion is associated with response to glutaminase inhibition in breast cancer.

INTRODUCTION: Glutaminase inhibitors target cancer cells by blocking the conversion of glutamine to glutamate, thereby potentially interfering with anaplerosis and synthesis of amino acids and glutathione. The drug CB-839 has shown promising effects in preclinical experiments and is currently undergoing clinical trials in several human malignancies, including triple-negative breast cancer (TNBC). However, response to glutaminase inhibitors is variable and there is a need for identification of predictive response biomarkers. The aim of this study was to determine how glutamine is utilized in two patient-derived xenograft (PDX) models of breast cancer representing luminal-like/ER+ (MAS98.06) and basal-like/triple-negative (MAS98.12) breast cancer and to explore the metabolic effects of CB-839 treatment. EXPERIMENTAL: MAS98.06 and MAS98.12 PDX mice received CB-839 (200 mg/kg) or drug vehicle two times daily p.o. for up to 28 days (n = 5 per group), and the effect on tumor growth was evaluated. Expression of 60 genes and seven glutaminolysis key enzymes were determined using gene expression microarray analysis and immunohistochemistry (IHC), respectively, in untreated tumors. Uptake and conversion of glutamine were determined in the PDX models using HR MAS MRS after i.v. infusion of [5-13C] glutamine when the models had received CB-839 (200 mg/kg) or vehicle for 2 days (n = 5 per group). RESULTS: Tumor growth measurements showed that CB-839 significantly inhibited tumor growth in MAS98.06 tumors, but not in MAS98.12 tumors. Gene expression and IHC analysis indicated a higher proline synthesis from glutamine in untreated MAS98.06 tumors. This was confirmed by HR MAS MRS of untreated tumors demonstrating that MAS98.06 used glutamine to produce proline, glutamate, and alanine, and MAS98.12 to produce glutamate and lactate. In both models, treatment with CB-839 resulted in accumulation of glutamine. In addition, CB-839 caused depletion of alanine, proline, and glutamate ([1-13C] glutamate) in the MAS98.06 model. CONCLUSION: Our findings indicate that TNBCs may not be universally sensitive to glutaminase inhibitors. The major difference in the metabolic fate of glutamine between responding MAS98.06 xenografts and non-responding MAS98.12 xenografts is the utilization of glutamine for production of proline. We therefore suggest that addiction to proline synthesis from glutamine is associated with response to CB-839 in breast cancer. The effect of glutaminase inhibition in two breast cancer patient-derived xenograft (PDX) models. 13C HR MAS MRS analysis of tumor tissue from CB-839-treated and untreated models receiving 13C-labeled glutamine ([5-13C] Gln) shows that the glutaminase inhibitor CB-839 is causing an accumulation of glutamine (arrow up) in two PDX models representing luminal-like breast cancer (MAS98.06) and basal-like breast cancer (MAS98.12). In MAS98.06 tumors, CB-839 is in addition causing depletion of proline ([5-13C] Pro), alanine ([1-13C] Ala), and glutamate ([1-13C] Glu), which could explain why CB-839 causes tumor growth inhibition in MAS98.06 tumors, but not in MAS98.12 tumors.

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.

Interplay of choline metabolites and genes in patient-derived breast cancer xenografts.

INTRODUCTION: Dysregulated choline metabolism is a well-known feature of breast cancer, but the underlying mechanisms are not fully understood. In this study, the metabolomic and transcriptomic characteristics of a large panel of human breast cancer xenograft models were mapped, with focus on choline metabolism. METHODS: Tumor specimens from 34 patient-derived xenograft models were collected and divided in two. One part was examined using high-resolution magic angle spinning (HR-MAS) MR spectroscopy while another part was analyzed using gene expression microarrays. Expression data of genes encoding proteins in the choline metabolism pathway were analyzed and correlated to the levels of choline (Cho), phosphocholine (PCho) and glycerophosphocholine (GPC) using Pearson's correlation analysis. For comparison purposes, metabolic and gene expression data were collected from human breast tumors belonging to corresponding molecular subgroups. RESULTS: Most of the xenograft models were classified as basal-like (N = 19) or luminal B (N = 7). These two subgroups showed significantly different choline metabolic and gene expression profiles. The luminal B xenografts were characterized by a high PCho/GPC ratio while the basal-like xenografts were characterized by highly variable PCho/GPC ratio. Also, Cho, PCho and GPC levels were correlated to expression of several genes encoding proteins in the choline metabolism pathway, including choline kinase alpha (CHKA) and glycerophosphodiester phosphodiesterase domain containing 5 (GDPD5). These characteristics were similar to those found in human tumor samples. CONCLUSION: The higher PCho/GPC ratio found in luminal B compared with most basal-like breast cancer xenograft models and human tissue samples do not correspond to results observed from in vitro studies. It is likely that microenvironmental factors play a role in the in vivo regulation of choline metabolism. Cho, PCho and GPC were correlated to different choline pathway-encoding genes in luminal B compared with basal-like xenografts, suggesting that regulation of choline metabolism may vary between different breast cancer subgroups. The concordance between the metabolic and gene expression profiles from xenograft models with breast cancer tissue samples from patients indicates that these xenografts are representative models of human breast cancer and represent relevant models to study tumor metabolism in vivo.

13C high-resolution-magic angle spinning MRS reveals differences in glucose metabolism between two breast cancer xenograft models with different gene expression patterns.

Tumor cells have increased glycolytic activity, and glucose is mainly used to form lactate and alanine, even when high concentrations of oxygen are present (Warburg effect). The purpose of the present study was to investigate glucose metabolism in two xenograft models representing basal-like and luminal-like breast cancer using (13) C high-resolution-magic angle spinning (HR-MAS) MRS and gene expression analysis. Tumor tissue was collected from two groups for each model: untreated mice (n=19) and a group of mice (n=16) that received an injection of [1-(13) C]-glucose 10 or 15 min before harvesting the tissue. (13) C HR-MAS MRS was performed on the tumor samples and differences in the glucose/alanine (Glc/Ala), glucose/lactate (Glc/Lac) and alanine/lactate (Ala/Lac) ratios between the models were studied. The expression of glycolytic genes was studied using tumor tissue from the same models. In the natural abundance MR spectra, a significantly lower Glc/Ala and Glc/Lac ratio (p<0.001) was observed in the luminal-like model compared with the basal-like model. In the labeled samples, the predominant glucose metabolites were lactate and alanine. Significantly lower Glc/Ala and Glc/Lac ratios were observed in the luminal-like model (p<0.05). Most genes contributing to glycolysis were expressed at higher levels in the luminal-like model (fdr<0.001). The lower Glc/Ala and Glc/Lac ratios and higher glycolytic gene expression observed in the luminal-like model indicates that the transformation of glucose to lactate and alanine occurred faster in this model than in the basal-like model, which has a growth rate several times faster than that of the luminal-like model. The results from the present study suggest that the tumor growth rate is not necessarily a determinant of glycolytic activity.

Multivariate modeling and prediction of breast cancer prognostic factors using MR metabolomics.

Axillary lymph node status together with estrogen and progesterone receptor status are important prognostic factors in breast cancer. In this study, the potential of using MR metabolomics for prediction of these prognostic factors was evaluated. Biopsies from breast cancer patients (n = 160) were excised during surgery and analyzed by high resolution magic angle spinning MR spectroscopy (HR MAS MRS). The spectral data were preprocessed and variable stability (VAST) scaled, and training and test sets were generated using the Kennard-Stone and SPXY sample selection algorithms. The data were analyzed by partial least-squares discriminant analysis (PLS-DA), probabilistic neural networks (PNNs) and Bayesian belief networks (BBNs), and blind samples (n = 50) were predicted for verification. Estrogen and progesterone receptor status was successfully predicted from the MR spectra, and were best predicted by PLS-DA with a correct classification of 44 of 50 and 39 of 50 samples, respectively. Lymph node status was best predicted by BBN with 34 of 50 samples correctly classified, indicating a relationship between metabolic profile and lymph node status. Thus, MR profiles contain prognostic information that may be of benefit in treatment planning, and MR metabolomics may become an important tool for diagnosis of breast cancer patients.