Metabolic Signatures of Human Breast Cancer

Image-guided metabolomics and transcriptomics reveal tumour heterogeneity in luminal A and B human breast cancer beyond glucose tracer uptake

BACKGROUND

Breast cancer is a metabolically heterogeneous disease, and although the concept of heterogeneous cancer metabolism is known, its precise role in human breast cancer is yet to be fully elucidated.

METHODS

We investigated in an explorative approach a cohort of 42 primary mamma carcinoma patients with positron emission tomography/magnetic resonance imaging (PET/MR) prior to surgery, followed by histopathology and molecular diagnosis. From a subset of patients, which showed high metabolic heterogeneity based on tracer uptake and pathology classification, tumour centre and periphery specimen tissue samples were further investigated by a targeted breast cancer gene expression panel and quantitative metabolomics by nuclear magnetic resonance (NMR) spectroscopy. All data were analysed in a combinatory approach.

RESULTS

[18 F]FDG (2-deoxy-2-[fluorine-18]fluoro-d-glucose) tracer uptake confirmed dominance of glucose metabolism in the breast tumour centre, with lower levels in the periphery. Additionally, we observed differences in lipid and proliferation related genes between luminal A and B subtypes in the centre and periphery. Tumour periphery showed elevated acetate levels and enrichment in lipid metabolic pathways genes especially in luminal B. Furthermore, serine was increased in the periphery and higher expression of thymidylate synthase (TYMS) indicated one-carbon metabolism increased in tumour periphery. The overall metabolic activity based on [18 F]FDG uptake of luminal B subtype was higher than that of luminal A and the difference between the periphery and centre increased with tumour grade.

CONCLUSION

Our analysis indicates variations in metabolism among different breast cancer subtypes and sampling locations which details the heterogeneity of the breast tumours. Correlation analysis of [18 F]FDG tracer uptake, transcriptome and tumour metabolites like acetate and serine facilitate the search for new candidates for metabolic tracers and permit distinguishing luminal A and B. This knowledge may help to differentiate subtypes preclinically or to provide patients guide for neoadjuvant therapy and optimised surgical protocols based on individual tumour metabolism.

The Role of Amino Acids in the Diagnosis, Risk Assessment, and Treatment of Breast Cancer: A Review

This review summarizes the role of amino acids in the diagnosis, risk assessment, imaging, and treatment of breast cancer. It was shown that the content of individual amino acids changes in breast cancer by an average of 10-15% compared with healthy controls. For some amino acids (Thr, Arg, Met, and Ser), an increase in concentration is more often observed in breast cancer, and for others, a decrease is observed (Asp, Pro, Trp, and His). The accuracy of diagnostics using individual amino acids is low and increases when a number of amino acids are combined with each other or with other metabolites. Gln/Glu, Asp, Arg, Leu/Ile, Lys, and Orn have the greatest significance in assessing the risk of breast cancer. The variability in the amino acid composition of biological fluids was shown to depend on the breast cancer phenotype, as well as the age, race, and menopausal status of patients. In general, the analysis of changes in the amino acid metabolism in breast cancer is a promising strategy not only for diagnosis, but also for developing new therapeutic agents, monitoring the treatment process, correcting complications after treatment, and evaluating survival rates.

Glutamine Starvation Affects Cell Cycle, Oxidative Homeostasis and Metabolism in Colorectal Cancer Cells

Cancer cells adjust their metabolism to meet energy demands. In particular, glutamine addiction represents a distinctive feature of several types of tumors, including colorectal cancer. In this study, four colorectal cancer cell lines (Caco-2, HCT116, HT29 and SW480) were cultured with or without glutamine. The growth and proliferation rate, colony-forming capacity, apoptosis, cell cycle, redox homeostasis and metabolomic analysis were evaluated by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide test (MTT), flow cytometry, high-performance liquid chromatography and gas chromatography/mass spectrometry techniques. The results show that glutamine represents an important metabolite for cell growth and that its deprivation reduces the proliferation of colorectal cancer cells. Glutamine depletion induces cell death and cell cycle arrest in the GO/G1 phase by modulating energy metabolism, the amino acid content and antioxidant defenses. Moreover, the combined glutamine starvation with the glycolysis inhibitor 2-deoxy-D-glucose exerted a stronger cytotoxic effect. This study offers a strong rationale for targeting glutamine metabolism alone or in combination with glucose metabolism to achieve a therapeutic benefit in the treatment of colon cancer.

E. coli Secretome Metabolically Modulates MDA-MB-231 Breast Cancer Cells' Energy Metabolism

Breast cancer (BC) is commonly diagnosed in women. BC cells are associated with altered metabolism, which is essential to support their energetic requirements, cellular proliferation, and continuous survival. The altered metabolism of BC cells is a result of the genetic abnormalities of BC cells. Risk factors can also enhance it, including age, lifestyle, hormone disturbances, etc. Other unknown BC-promoting risk factors are under scientific investigation. One of these investigated factors is the microbiome. However, whether the breast microbiome found in the BC tissue microenvironment can impact BC cells has not been studied. We hypothesized that E. coli, part of a normal breast microbiome with more presence in BC tissue, secretes metabolic molecules that could alter BC cells' metabolism to maintain their survival. Thus, we directly examined the impact of the E. coli secretome on the metabolism of BC cells in vitro. MDA-MB-231 cells, an in vitro model of aggressive triple-negative BC cells, were treated with the E. coli secretome at different time points, followed by untargeted metabolomics analyses via liquid chromatography-mass spectrometry to identify metabolic alterations in the treated BC cell lines. MDA-MB-231 cells that were not treated were used as controls. Moreover, metabolomic analyses were performed on the E. coli secretome to profile the most significant bacterial metabolites affecting the metabolism of the treated BC cell lines. The metabolomics results revealed about 15 metabolites that potentially have indirect roles in cancer metabolism that were secreted from E. coli in the culture media of MDA-MB-231 cells. The cells treated with the E. coli secretome showed 105 dysregulated cellular metabolites compared to controls. The dysregulated cellular metabolites were involved in the metabolism of fructose and mannose, sphingolipids, amino acids, fatty acids, amino sugar, nucleotide sugar, and pyrimidine, which are vital pathways required for the pathogenesis of BC. Our findings are the first to show that the E. coli secretome modulates the BC cells' energy metabolism, highlighting insights into the possibility of altered metabolic events in BC tissue in the actual BC tissue microenvironment that are potentially induced by the local bacteria. Our study provides metabolic data that could be as a basis for future studies searching for the underlying mechanisms mediated by bacteria and their secretome to alter the metabolism of BC cells.

Targeting cancer-specific metabolic pathways for developing novel cancer therapeutics

Cancer is a heterogeneous disease characterized by various genetic and phenotypic aberrations. Cancer cells undergo genetic modifications that promote their proliferation, survival, and dissemination as the disease progresses. The unabated proliferation of cancer cells incurs an enormous energy demand that is supplied by metabolic reprogramming. Cancer cells undergo metabolic alterations to provide for increased energy and metabolite requirement; these alterations also help drive the tumor progression. Dysregulation in glucose uptake and increased lactate production via "aerobic glycolysis" were described more than 100 years ago, and since then, the metabolic signature of various cancers has been extensively studied. However, the extensive research in this field has failed to translate into significant therapeutic intervention, except for treating childhood-ALL with amino acid metabolism inhibitor L-asparaginase. Despite the growing understanding of novel metabolic alterations in tumors, the therapeutic targeting of these tumor-specific dysregulations has largely been ineffective in clinical trials. This chapter discusses the major pathways involved in the metabolism of glucose, amino acids, and lipids and highlights the inter-twined nature of metabolic aberrations that promote tumorigenesis in different types of cancer. Finally, we summarise the therapeutic interventions which can be used as a combinational therapy to target metabolic dysregulations that are unique or common in blood, breast, colorectal, lung, and prostate cancer.

Metabolic reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner

Breast cancer is the leading cause of female cancer-related deaths worldwide. New technologies with enhanced sensitivity and specificity for early diagnosis and monitoring of postoperative recurrence are in critical demand. Automatic breast full volume scanning system (ABVS) is an emerging technology used as an alternative imaging method for breast cancer screening. Despite its improved detection rate of malignant tumors, ABVS cannot accurately stage breast cancer preoperatively in 30–40% of cases. As a major hallmark of breast cancer, the characteristic metabolic reprogramming may provide potential biomarkers as an auxiliary method for ABVS.

Objective

The objective of this study was to identify differential metabolomic signatures between benign and malignant breast tumors and among different subtypes of breast cancer patients based on untargeted metabolomics and improve breast cancer detection rate by combining key metabolites and ABVS.

Methods

Untargeted metabolomics approach was used to profile serum samples from 70 patients with different subtypes of breast cancer and benign breast tumor to determine specific metabolomic profiles through univariate and multivariate statistical data analysis.

Results

Metabolic profiles correctly distinguished benign and malignant breast tumors patients, and a total of 791 metabolites were identified. There were 54 different metabolites between benign and malignant breast tumors and 17 different metabolites between invasive and non-invasive breast cancer. Notably, the missed diagnosis rate of ABVS could be reduced by differential metabolite analysis. Moreover, the diagnostic performance analyses of combined metabolites (pelargonic acid, N-acetylasparagine, and cysteine-S-sulfate) with ABVS performance gave a ROC area under the curve of 0.967 (95% CI: 0.926, 0.993).

Conclusions

Our study identified metabolic features both in benign and malignant breast tumors and in invasive and non-invasive breast cancer. Combined ultrasound ABVS and a panel of differential serum metabolites could further improve the accuracy of preoperative diagnosis of breast cancer and guide surgical therapy.

Immunometabolism characteristics and a potential prognostic risk model associated with TP53 mutations in breast cancer

TP53, a gene with high-frequency mutations, plays an important role in breast cancer (BC) development through metabolic regulation, but the relationship between TP53 mutation and metabolism in BC remains to be explored. Our study included 1,066 BC samples from The Cancer Genome Atlas (TCGA) database, 415 BC cases from the Gene Expression Omnibus (GEO) database, and two immunotherapy cohorts. We identified 92 metabolic genes associated with TP53 mutations by differential expression analysis between TP53 mutant and wild-type groups. Univariate Cox analysis was performed to evaluate the prognostic effects of 24 TP53 mutation-related metabolic genes. By unsupervised clustering and other bioinformatics methods, the survival differences and immunometabolism characteristics of the distinct clusters were illustrated. In a training set from TCGA cohort, we employed the least absolute shrinkage and selection operator (LASSO) regression method to construct a metabolic gene prognostic model associated with TP53 mutations, and the GEO cohort served as an external validation set. Based on bioinformatics, the connections between risk score and survival prognosis, tumor microenvironment (TME), immunotherapy response, metabolic activity, clinical characteristics, and gene characteristics were further analyzed. It is imperative to note that our model is a powerful and robust prognosis factor in comparison to other traditional clinical features and also has high accuracy and clinical usefulness validated by receiver operating characteristic (ROC) and decision curve analysis (DCA). Our findings deepen our understanding of the immune and metabolic characteristics underlying the TP53 mutant metabolic gene profile in BC, laying a foundation for the exploration of potential therapies targeting metabolic pathways. In addition, our model has promising predictive value in the prognosis of BC.

Metabolomic Analysis of Plasma from Breast Cancer Patients Using Ultra-High-Performance Liquid Chromatography Coupled with Mass Spectrometry: An Untargeted Study

Breast cancer (BC) is one of the leading causes of cancer mortality in women worldwide, and therefore, novel biomarkers for early disease detection are critically needed. We performed herein an untargeted plasma metabolomic profiling of 55 BC patients and 55 healthy controls (HC) using ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC/Q-TOF-MS). Pre-processed data revealed 2494 ions in total. Data matrices’ paired t-tests revealed 792 ions (both positive and negative) which presented statistically significant changes (FDR < 0.05) in intensity levels between cases versus controls. Metabolites identified with putative names via MetaboQuest using MS/MS and mass-based approaches included amino acid esters (i.e., N-stearoyl tryptophan, L-arginine ethyl ester), dipeptides (ile-ser, met-his), nitrogenous bases (i.e., uracil derivatives), lipid metabolism-derived molecules (caproleic acid), and exogenous compounds from plants, drugs, or dietary supplements. LASSO regression selected 16 metabolites after several variables (TNM Stage, Grade, smoking status, menopausal status, and race) were adjusted. A predictive conditional logistic regression model on the 16 LASSO selected ions provided a high diagnostic performance with an area-under-the-curve (AUC) value of 0.9729 (95% CI 0.96−0.98) on all 55 samples. This study proves that BC possesses a specific metabolic signature that could be exploited as a novel metabolomics-based approach for BC detection and characterization. Future studies of large-scale cohorts are needed to validate these findings.

Harnessing the Immunogenic Potential of Gold Nanoparticle-Based Platforms as a Therapeutic Strategy in Breast Cancer Immunotherapy: A Mini Review

Breast cancer remains the most common malignancy among women worldwide. Although the implementation of mammography has dramatically increased the early detection rate, conventional treatments like chemotherapy, radiation therapy, and surgery, have significantly improved the prognosis for breast cancer patients. However, about a third of treated breast cancer patients are known to suffer from disease recurrences and progression to metastasis. Immunotherapy has recently gained traction due to its ability to establish long-term immune surveillance, and response for the prevention of disease recurrence and extension of patient survival. Current research findings have revealed that gold nanoparticles can enhance the safety and efficacy of cancer immunotherapy, through their unique intrinsic properties of good biocompatibility, durability, convenient surface modification, as well as enhanced permeability and retention effect. Gold nanoparticles are also able to induce innate immune responses through the process of immunogenic cell death, which can lead to the establishment of lasting adaptive immunity. As such gold nanoparticles are considered as good candidates for next generation immunotherapeutic strategies. This mini review gives an overview of gold nanoparticles and their potential applications in breast cancer immunotherapeutic strategies.

Tripterygium hypoglaucum (Lévl.) Hutch and Its Main Bioactive Components: Recent Advances in Pharmacological Activity, Pharmacokinetics and Potential Toxicity

Tripterygium hypoglaucum (Lévl.) Hutch (THH) is believed to play an important role in health care and disease treatment according to traditional Chinese medicine. Moreover, it is also the representative of medicine with both significant efficacy and potential toxicity. This characteristic causes THH hard for embracing and fearing. In order to verify its prospect for clinic, a wide variety of studies were carried out in the most recent years. However, there has not been any review about THH yet. Therefore, this review summarized its characteristic of components, pharmacological effect, pharmacokinetics and toxicity to comprehensively shed light on the potential clinical application. More than 120 secondary metabolites including terpenoids, alkaloids, glycosides, sugars, organic acids, oleanolic acid, polysaccharides and other components were found in THH based on phytochemical research. All these components might be the pharmacological bases for immunosuppression, anti-inflammatory and anti-tumour effect. In addition, recent studies found that THH and its bioactive compounds also demonstrated remarkable effect on obesity, insulin resistance, fertility and infection of virus. The main mechanism seemed to be closely related to regulation the balance of immune, inflammation, apoptosis and so on in various disease. Furthermore, the study of pharmacokinetics revealed quick elimination of the main component triptolide. The feature of celastrol was also investigated by several models. Finally, the side effect of THH was thought to be the key for its limitation in clinical application. A series of reports indicated that multiple organs or systems including liver, kidney and genital system were involved in the toxicity. Its potential serious problem in liver was paid specific attention in recent years. In summary, considering the significant effect and potential toxicity of THH as well as its components, the combined medication to inhibit the toxicity, maintain effect might be a promising method for clinical conversion. Modern advanced technology such as structure optimization might be another way to reach the efficacy and safety. Thus, THH is still a crucial plant which remains for further investigation.

Metabolic Flexibility Is a Determinant of Breast Cancer Heterogeneity and Progression

Breast cancer progression is characterized by changes in cellular metabolism that contribute to enhanced tumour growth and adaptation to microenvironmental stresses. Metabolic changes within breast tumours are still poorly understood and are not as yet exploited for therapeutic intervention, in part due to a high level of metabolic heterogeneity within tumours. The metabolic profiles of breast cancer cells are flexible, providing dynamic switches in metabolic states to accommodate nutrient and energy demands and further aggravating the challenges of targeting metabolic dependencies in cancer. In this review, we discuss the intrinsic and extrinsic factors that contribute to metabolic heterogeneity of breast tumours. Next, we examine how metabolic flexibility, which contributes to the metabolic heterogeneity of breast tumours, can alter epigenetic landscapes and increase a variety of pro-tumorigenic functions. Finally, we highlight the difficulties in pharmacologically targeting the metabolic adaptations of breast tumours and provide an overview of possible strategies to sensitize heterogeneous breast tumours to the targeting of metabolic vulnerabilities.

Prognostic Significance of O-GlcNAc and PKM2 in Hormone Receptor-Positive and HER2-Nonenriched Breast Cancer

Predictive metabolic biomarkers for the recurrent luminal breast cancer (BC) with hormone receptor (HR)-positive and human epidermal growth factor receptor type 2 (HER2)-negative are lacking. High levels of O-GlcNAcylation (O-GlcNAc) and pyruvate kinase isoenzyme M2 (PKM2) are associated with malignancy in BC; however, the association with the recurrence risk remains unclear. We first conduct survival analysis by using the METABRIC dataset to assess the correlation of PKM2 expression with BC clinical outcomes. Next, patients with HR⁺/HER2- luminal BC were recruited for PKM2/O-GlcNAc testing. Logistic regression and receiver operating characteristic curve analysis were performed to evaluate the 10-year DFS predicted outcome. Survival analysis of the METABRIC dataset revealed that high expression of PKM2 was significantly associated with worse overall survival in luminal BC. The high expression of O-GlcNAc or PKM2 was a significant independent marker for poor 10-year DFS using immunohistochemical analysis. The PKM2 or O-GlcNAc status was a significant predictor of DFS, with the combination of PKM2–O-GlcNAc status and T stage greatly enhancing the predictive outcome potential. In summary, O-GlcNAc, PKM2, and T stage serve as good prognostic discriminators in HR⁺/HER2⁻ luminal BC.

Gold Nanoparticles; Potential Nanotheranostic Agent in Breast Cancer: A Comprehensive Review with Systematic Search Strategy

BACKGROUND

Breast cancer is a heterogeneous disease typically prevalent among women and is the second-largest cause of death worldwide. Early diagnosis is the key to minimize the cancer-induced complication, however, the conventional diagnostic strategies have been sluggish, complex, and, to some extent, non-specific. Therapeutic tools are not so convenient and side effects of current therapies offer the development of novel theranostic tool to combat this deadly disease.

OBJECTIVE

This article aims to summarize the advances in the diagnosis and treatment of breast cancer with gold nanoparticles (GNP or AuNP).

METHODS

A systematic search was conducted in the three popular electronic online databases including PubMed, Google Scholar, and Web of Science, regarding GNP as breast cancer theranostics.

RESULTS

Published literature demonstrated that GNPs tuned with photosensitive moieties, nanomaterials, drugs, peptides, nucleotide, peptides, antibodies, aptamer, and other biomolecules improve the conventional diagnostic and therapeutic strategies of breast cancer management with minimum cytotoxic effect. GNP derived diagnosis system assures reproducibility, reliability, and accuracy cost-effectively. Additionally, surface-modified GNP displayed theranostic potential even in the metastatic stage of breast cancer.

CONCLUSION

Divergent strategies have shown the theranostic potential of surface tuned GNPs against breast cancer even in the metastatic stage with minimum cytotoxic effects both in vitro and in vivo.

Metabolic control of cancer progression as novel targets for therapy

Metabolism is an important part of tumorigenesis as well as progression. The various cancer metabolism pathways, such as glucose metabolism and glutamine metabolism, directly regulate the development and progression of cancer. The pathways by which the cancer cells rewire their metabolism according to their needs, surrounding environment and host tissue conditions are an important area of study. The regulation of these metabolic pathways is determined by various oncogenes, tumor suppressor genes, as well as various constituent cells of the tumor microenvironment. Expanded studies on metabolism will help identify efficient biomarkers for diagnosis and strategies for therapeutic interventions and countering ways by which cancers may acquire resistance to therapy.

Metabolomics-guided global pathway analysis reveals better insights into the metabolic alterations of breast cancer

Accurate metabolome measurements are critical for improved insights into breast cancer metabolic disturbances and enhanced exploration of novel therapeutic targets. Nevertheless, conventional functional interpretation is limited by metabolite identification capacity, which diminishes the scientific value of untargeted metabolomics analyses. In this study, we conducted a metabolomics-guided global pathway meta-analysis to investigate the metabolic alterations of breast cancer. Metabolic features were directly investigated in the pathway meta-analysis to identify breast cancer-associated metabolic processes. Conventional pathway analysis was also conducted involving identified metabolites alone. Comparison of the two strategies revealed that the global pathway meta-analysis approach could avoid the loss of functionally relevant information, relative to the conventional analysis findings. Furthermore, the pathway meta-analysis accurately captured alterations in the following components of the breast cancer metabolome: central carbon metabolism, oxidative glutamine metabolism, purine metabolism, nonessential amino acid metabolism, and glutathione metabolism. There were also substantial alterations of fatty acyl carnitine species and fatty acid β-oxidation processes. These pathways contribute to breast cancer initiation, progression, metastasis, and drug resistance. In conclusion, we suggest that global pathway analysis and the conventional approach with identified metabolites should be employed together to maximize the exploration of breast cancer's metabolic landscape.

Fecal Metabolic Profiling of Breast Cancer Patients during Neoadjuvant Chemotherapy Reveals Potential Biomarkers

Breast cancer (BC) is the most common form of cancer among women worldwide. Despite the huge advancements in its treatment, the exact etiology of breast cancer still remains unresolved. There is an increasing interest in the role of the gut microbiome in modulating the anti-cancer therapeutic response. It seems that alteration of the microbiome-derived metabolome potentially promotes carcinogenesis. Taken together, metabolomics has arisen as a fascinating new omics field to screen promising metabolic biomarkers. In this study, fecal metabolite profiling was performed using NMR spectroscopy, to identify potential biomarker candidates that can predict response to neoadjuvant chemotherapy (NAC) for breast cancer. Metabolic profiles of feces from patients (n = 8) following chemotherapy treatment cycles were studied. Interestingly, amino acids were found to be upregulated, while lactate and fumaric acid were downregulated in patients under the second and third cycles compared with patients before treatment. Furthermore, short-chain fatty acids (SCFAs) were significantly differentiated between the studied groups. These results strongly suggest that chemotherapy treatment plays a key role in modulating the fecal metabolomic profile of BC patients. In conclusion, we demonstrate the feasibility of identifying specific fecal metabolic profiles reflecting biochemical changes that occur during the chemotherapy treatment. These data give an interesting insight that may complement and improve clinical tools for BC monitoring.

The Metabolic Mechanisms of Breast Cancer Metastasis

Breast cancer is one of the most common malignancy among women worldwide. Metastasis is mainly responsible for treatment failure and is the cause of most breast cancer deaths. The role of metabolism in the progression and metastasis of breast cancer is gradually being emphasized. However, the regulatory mechanisms that conduce to cancer metastasis by metabolic reprogramming in breast cancer have not been expounded. Breast cancer cells exhibit different metabolic phenotypes depending on their molecular subtypes and metastatic sites. Both intrinsic factors, such as MYC amplification, PIK3CA, and TP53 mutations, and extrinsic factors, such as hypoxia, oxidative stress, and acidosis, contribute to different metabolic reprogramming phenotypes in metastatic breast cancers. Understanding the metabolic mechanisms underlying breast cancer metastasis will provide important clues to develop novel therapeutic approaches for treatment of metastatic breast cancer.

Management of Brain and Leptomeningeal Metastases from Breast Cancer

The management of breast cancer (BC) has rapidly evolved in the last 20 years. The improvement of systemic therapy allows a remarkable control of extracranial disease. However, brain (BM) and leptomeningeal metastases (LM) are frequent complications of advanced BC and represent a challenging issue for clinicians. Some prognostic scales designed for metastatic BC have been employed to select fit patients for adequate therapy and enrollment in clinical trials. Different systemic drugs, such as targeted therapies with either monoclonal antibodies or small tyrosine kinase molecules, or modified chemotherapeutic agents are under investigation. Major aims are to improve the penetration of active drugs through the blood-brain barrier (BBB) or brain-tumor barrier (BTB), and establish the best sequence and timing of radiotherapy and systemic therapy to avoid neurocognitive impairment. Moreover, pharmacologic prevention is a new concept driven by the efficacy of targeted agents on macrometastases from specific molecular subgroups. This review aims to provide an overview of the clinical and molecular factors involved in the selection of patients for local and/or systemic therapy, as well as the results of clinical trials on advanced BC. Moreover, insight on promising therapeutic options and potential directions of future therapeutic targets against BBB and microenvironment are discussed.

Specific urinary metabolites in canine mammary gland tumors

The identification of biomarkers that distinguish diseased from healthy individuals is of great interest in human and veterinary fields. In this research area, a metabolomic approach and its related statistical analyses can be useful for biomarker determination and allow non-invasive discrimination of healthy volunteers from breast cancer patients. In this study, we focused on the most common canine neoplasm, mammary gland tumor, and herein, we describe a simple method using ultra-high-performance liquid chromatography to determine the levels of tyrosine and its metabolites (epinephrine, 3,4-dihydroxy-L-phenylalanine, 3,4-dihydroxyphenylacetic acid, and vanillylmandelic acid), tryptophan and its metabolites (5-hydroxyindolacetic acid, indoxyl sulfate, serotonin, and kynurenic acid) in canine mammary cancer urine samples. Our results indicated significantly increased concentrations of three tryptophan metabolites, 5-hydroxyindolacetic acid (p < 0.001), serotonin, indoxyl sulfate (p < 0.01), and kynurenic acid (p < 0.05), and 2 tyrosine metabolites, 3,4-dihydroxy-L-phenylalanine (p < 0.001), and epinephrine (p < 0.05) in urine samples from the mammary gland tumor group compared to concentrations in urine samples from the healthy group. The results indicate that select urinary tyrosine and tryptophan metabolites may be useful as non-invasive diagnostic markers as well as in developing a therapeutic strategy for canine mammary gland tumors.

Study on Urinary Candidate Metabolome for the Early Detection of Breast Cancer

A metabolomic study for determination of certain urinary metabolomes, 1-methyladenosine (1-MA), 1-methylguanosine (1-MG), and 8-hydroxy-2' deoxyguanosine (8-OHdG) in urine specimens of breast cancer patients. The accuracy of these metabolites and their combined score with cancer antigen 15-3 (CA15-3) was developed to improve the early detection of breast cancer. This study recruited 52 healthy individuals, 47 benign breast tumors, and 167 malignant breast tumor patients. Urine samples were handled to adjust the creatinine concentrations to 8 mg/dL (0.7 mmol/L) and analyzed using GC-MS to detect and quantify the selected urinary metabolomes in urine samples of all participants. The accuracy of individual urinary metabolomes and their combination with CA15-3 were evaluated using multivariate statistical analysis. The cutoff value of CA15-3 was 32.5 U/mL. Cutoff values of 1-MA, 1-MG, and 8-OHdG were 2.19, 2.1, and 7.3 µmol/mmol creatinine, respectively. The concentrations of 1-MA, 1-MG, and 8-OHdG were significantly higher in breast cancer patients, especially in the early-stage. The combination of three urinary metabolomes with CA15-3 improves the diagnostic sensitivity of breast cancer. For the combined score, the area under the curve (AUC) value of combined score ranged from 0.820 to 0.950, with high accuracy, ranged from 77.0 to 95.5%. The most significant AUC (0.973), sensitivity (90.1%), selectivity (94.0%) was recorded at comparing the healthy control with the early-stage of malignant breast cancer. In conclusion, the combination of three urinary metabolomes with serum CA15-3 improves the diagnostic sensitivity of breast cancer.

Untargeted Urinary 1H NMR-Based Metabolomic Pattern as a Potential Platform in Breast Cancer Detection

Breast cancer (BC) remains the second leading cause of death among women worldwide. An emerging approach based on the identification of endogenous metabolites (EMs) and the establishment of the metabolomic fingerprint of biological fluids constitutes a new frontier in medical diagnostics and a promising strategy to differentiate cancer patients from healthy individuals. In this work we aimed to establish the urinary metabolomic patterns from 40 BC patients and 38 healthy controls (CTL) using proton nuclear magnetic resonance spectroscopy (1H-NMR) as a powerful approach to identify a set of BC-specific metabolites which might be employed in the diagnosis of BC. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied to a 1H-NMR processed data matrix. Metabolomic patterns distinguished BC from CTL urine samples, suggesting a unique metabolite profile for each investigated group. A total of 10 metabolites exhibited the highest contribution towards discriminating BC patients from healthy controls (variable importance in projection (VIP) >1, p < 0.05). The discrimination efficiency and accuracy of the urinary EMs were ascertained by receiver operating characteristic curve (ROC) analysis that allowed the identification of some metabolites with the highest sensitivities and specificities to discriminate BC patients from healthy controls (e.g. creatine, glycine, trimethylamine N-oxide, and serine). The metabolomic pathway analysis indicated several metabolism pathway disruptions, including amino acid and carbohydrate metabolisms, in BC patients, namely, glycine and butanoate metabolisms. The obtained results support the high throughput potential of NMR-based urinary metabolomics patterns in discriminating BC patients from CTL. Further investigations could unravel novel mechanistic insights into disease pathophysiology, monitor disease recurrence, and predict patient response towards therapy.

1H NMR-Based Metabolomics Reveals the Antitumor Mechanisms of Triptolide in BALB/c Mice Bearing CT26 Tumors

Triptolide, the main active ingredient in Tripterygium wilfordii Hook. f. (Celastraceae), has shown promising effects against a variety of tumors. However, the molecular pharmacological mechanisms explaining the action of triptolide remain unknown. In this study, the CT26 colon tumor cell line was inoculated subcutaneously into BALB/c mice, and plasma samples were subjected to ¹H NMR metabolomics analysis. The metabolic signature identified five metabolites whose levels were lower and 15 whose levels were higher in CT26 tumor-bearing mice than in normal control mice. Triptolide treatment significantly reversed the levels of nine of these metabolites, including isoleucine, glutamine, methionine, proline, 3-hydroxybutyric acid, 2-hydroxyisovalerate, 2-hydroxyisobutyrate, and low-density lipoprotein/very low-density lipoprotein. Based on the identities of these potential biomarkers, we conclude that the antitumor mechanism of triptolide might rely on correcting perturbations in branched-chain amino acid metabolism, serine/glycine/methionine biosynthesis, and ketone bodies metabolism.

Liver- and Microbiome-derived Bile Acids Accumulate in Human Breast Tumors and Inhibit Growth and Improve Patient Survival

PURPOSE

Metabolomics is a discovery tool for novel associations of metabolites with disease. Here, we interrogated the metabolome of human breast tumors to describe metabolites whose accumulation affects tumor biology.

EXPERIMENTAL DESIGN

We applied large-scale metabolomics followed by absolute quantification and machine learning-based feature selection using LASSO to identify metabolites that show a robust association with tumor biology and disease outcome. Key observations were validated with the analysis of an independent dataset and cell culture experiments.

RESULTS

LASSO-based feature selection revealed an association of tumor glycochenodeoxycholate levels with improved breast cancer survival, which was confirmed using a Cox proportional hazards model. Absolute quantification of four bile acids, including glycochenodeoxycholate and microbiome-derived deoxycholate, corroborated the accumulation of bile acids in breast tumors. Levels of glycochenodeoxycholate and other bile acids showed an inverse association with the proliferation score in tumors and the expression of cell-cycle and G₂-M checkpoint genes, which was corroborated with cell culture experiments. Moreover, tumor levels of these bile acids markedly correlated with metabolites in the steroid metabolism pathway and increased expression of key genes in this pathway, suggesting that bile acids may interfere with hormonal pathways in the breast. Finally, a proteome analysis identified the complement and coagulation cascade as being upregulated in glycochenodeoxycholate-high tumors.

CONCLUSIONS

We describe the unexpected accumulation of liver- and microbiome-derived bile acids in breast tumors. Tumors with increased bile acids show decreased proliferation, thus fall into a good prognosis category, and exhibit significant changes in steroid metabolism.

Prioritization of metabolic genes as novel therapeutic targets in estrogen-receptor negative breast tumors using multi-omics data and text mining

Estrogen-receptor negative (ERneg) breast cancer is an aggressive breast cancer subtype in the need for new therapeutic options. We have analyzed metabolomics, proteomics and transcriptomics data for a cohort of 276 breast tumors (MetaCancer study) and nine public transcriptomics datasets using univariate statistics, meta-analysis, Reactome pathway analysis, biochemical network mapping and text mining of metabolic genes. In the MetaCancer cohort, a total of 29% metabolites, 21% proteins and 33% transcripts were significantly different (raw p <0.05) between ERneg and ERpos breast tumors. In the nine public transcriptomics datasets, on average 23% of all genes were significantly different (raw p <0.05). Specifically, up to 60% of the metabolic genes were significantly different (meta-analysis raw p <0.05) across the transcriptomics datasets. Reactome pathway analysis of all omics showed that energy metabolism, and biosynthesis of nucleotides, amino acids, and lipids were associated with ERneg status. Text mining revealed that several significant metabolic genes and enzymes have been rarely reported to date, including PFKP, GART, PLOD1, ASS1, NUDT12, FAR1, PDE7A, FAHD1, ITPK1, SORD, HACD3, CDS2 and PDSS1. Metabolic processes associated with ERneg tumors were identified by multi-omics integration analysis of metabolomics, proteomics and transcriptomics data. Overall results suggested that TCA anaplerosis, proline biosynthesis, synthesis of complex lipids and mechanisms for recycling substrates were activated in ERneg tumors. Under-reported genes were revealed by text mining which may serve as novel candidates for drug targets in cancer therapies. The workflow presented here can also be used for other tumor types.

The decrease of some serum free amino acids can predict breast cancer diagnosis and progression

This study was targeted on a metabolomic approach to compare the blood serum free amino acid profiles and concentration of confirmed breast cancer (stages I-III) patients to healthy controls in order to establish reliable biomarkers of early detection and prediction of breast cancer. The ultra-high-performance liquid chromatography coupled with mass spectrometry using positive ionization electrospray was applied for the picoline-derivatized serum free amino acids using the EZ:faastTM kit. Multivariate statistical analysis principal component analysis, partial least squares discrimination analysis and univariate analysis were applied in order to discriminate between patient groups and putative amino acid biomarkers for breast cancer. A significant decrease of amino acid concentrations between the breast cancer group and the control group was positively correlated with breast cancer progression. Arginine, Alanine, Isoleucine, Tyrosine and Tryptophan were identified as being good potential discriminants (AUROC ≥0.85) and suitable candidates to diagnose and predict the breast cancer progression.

Serum metabolomic alterations in multiple myeloma revealed by targeted and untargeted metabolomics approaches: a pilot study

Multiple myeloma (MM) is the second most prevalent hematological malignancy characterized by rapid proliferation of plasma cells, which leads to overproduction of antibodies. MM affects around 15% of all hemato-oncology cases across the world. The present study involves identification of metabolomic alterations in the serum of an MM cohort compared to healthy controls using both LC-MRM/MS based targeted and GC-MS based untargeted approaches. Several MM specific serum metabolomic signatures were observed in this study. A total of 54 metabolites were identified as being significantly altered in MM cohort, out of which, 26 metabolites were identified from LC-MRM/MS based targeted analysis, whereas 28 metabolites were identified from the GC-MS based untargeted analysis. Receiver operating characteristic (ROC) curve analysis demonstrated that six metabolites each from both the datasets can be projected as marker metabolites to discriminate MM subjects with higher specificity and sensitivity. Moreover, pathway analysis deciphered that several metabolic pathways were altered in MM including pyrimidine metabolism, purine metabolism, amino acid metabolism, nitrogen metabolism, sulfur metabolism, and the citrate cycle. Comprehensively, this study contributes valuable information regarding MM induced serum metabolite alterations and their pathways, which could offer further insights into this cancer.

This study presents the potential of serum metabolomics approach towards the segregation of multiple myeloma cohort from healthy controls.

Metabolite normalization with local radiotherapy following breast tumor resection

The aims of this study were to investigate changes in energy balance-associated metabolites associated with radiotherapy in patients with breast cancer, and to relate these changes to the clinical and pathological response-to-treatment. We studied 151 women with breast cancer who received radiotherapy following surgical excision of the tumor. Blood was obtained before and after the irradiation procedure. The control group was composed of 44 healthy women with a similar age distribution to that of the patients. We analyzed the concentrations of metabolites involved in glycolysis, citric acid cycle and amino acid metabolism using targeted quantitative metabolomics. Post-surgery, pre-radiotherapy, patients had major alterations in the serum concentrations of products of glycolysis, citric acid cycle and amino acid metabolism. The strongest alterations were decreases in serine, leucine and isoleucine concentrations. Alterations in metabolite levels were partially, or totally, reversed after irradiation; the concentrations of serine, leucine and isoleucine approached equivalence to those of the control group. Estrogen receptor-positive patients were those with lower concentrations, while triple negative patients had higher concentrations of these amino acids. The normalization of the amino acids serine, leucine and isoleucine concentrations could be clinically relevant because the normalization of these energy-balance metabolites would suggest that residual micro-metastatic disease had been effectively diminished by the radiotherapy, and may be an indicator of its efficacy.

Metabolic Targeting of Breast Cancer Cells With the 2-Deoxy-D-Glucose and the Mitochondrial Bioenergetics Inhibitor MDIVI-1

Breast cancer cells have different requirements on metabolic pathways in order to sustain their growth. Triple negative breast cancer (TNBC), an aggressive breast cancer subtype relies mainly on glycolysis, while estrogen receptor positive (ER+) breast cancer cells possess higher mitochondrial oxidative phosphorylation (OXPHOS) levels. However, breast cancer cells generally employ both pathways to sustain their metabolic needs and to compete with the surrounding environment. In this study, we demonstrate that the mitochondrial fission inhibitor MDIVI-1 alters mitochondrial bioenergetics, at concentrations that do not affect mitochondrial morphology. We show that this effect is accompanied by an increase in glycolysis consumption. Dual targeting of glycolysis with 2-deoxy-D-glucose (2DG) and mitochondrial bioenergetics with MDIVI-1 reduced cellular bioenergetics, increased cell death and decreased clonogenic activity of MCF7 and HDQ-P1 breast cancer cells. In conclusion, we have explored a novel and effective combinatorial regimen for the treatment of breast cancer.

Activation of pro-survival metabolic networks by 1,25(OH)2D3 does not hamper the sensitivity of breast cancer cells to chemotherapeutics

BACKGROUND

We have previously identified 1,25-dihydroxyvitamin D3 [1,25(OH)2D3], the bioactive form of vitamin D3, as a potent regulator of energy-utilization and nutrient-sensing pathways in prostate cancer cells. In the current study, we investigated the effects of 1,25(OH)2D3 on breast cancer (BCa) cell metabolism using cell lines representing distinct molecular subtypes, luminal (MCF-7 and T-47D), and triple-negative BCa (MDA-MB-231, MDA-MB-468, and HCC-1143).

METHODS

1,25(OH)2D3's effect on BCa cell metabolism was evaluated by employing a combination of real-time measurements of glycolysis/oxygen consumption rates using a biosensor chip system, GC/MS-based metabolomics, gene expression analysis, and assessment of overall energy levels. The influence of treatment on energy-related signaling molecules was investigated by immunoblotting.

RESULTS

We show that 1,25(OH)2D3 significantly induces the expression and activity of the pentose phosphate pathway enzyme glucose-6-phosphate dehydrogenase (G6PD) in all BCa cell lines, however differentially influences glycolytic and respiratory rates in the same cells. Although 1,25(OH)2D3 treatment was found to induce seemingly anti-oxidant responses in MCF-7 cells, such as increased intracellular serine levels, and reduce the expression of its putative target gene thioredoxin-interacting protein (TXNIP), intracellular reactive oxygen species levels were found to be elevated. Serine accumulation in 1,25(OH)2D3-treated cells was not found to hamper the efficacy of chemotherapeutics, including 5-fluorouracil. Detailed analyses of the nature of TXNIP's regulation by 1,25(OH)2D3 included genetic and pharmacological inhibition of signaling molecules and metabolic enzymes including AMP-activated protein kinase and G6PD, as well as by studying the ITCH (E3 ubiquitin ligase)-TXNIP interaction. While these investigations demonstrated minimal involvement of such pathways in the observed non-canonical regulation of TXNIP, inhibition of estrogen receptor (ER) signaling by tamoxifen mirrored the reduction of TXNIP levels by 1,25(OH)2D3, demonstrating that the latter's negative regulation of ER expression is a potential mechanism of TXNIP modulation.

CONCLUSIONS

Altogether, we propose that regulation of energy metabolism contributes to 1,25(OH)2D3's anti-cancer effects and that combining 1,25(OH)2D3 with drugs targeting metabolic networks in tumor cells may lead to synergistic effects.

ADHFE1 is a MYC-linked oncogene that induces metabolic reprogramming and cellular de-differentiation in breast cancer

The oncometabolite, D-2-hydroxyglutarate, accumulates in various cancers because of acquired mutations in isocitrate dehydrogenase 1 & 2. Here, we describe a new mechanism for D-2-hydroxyglutarate accumulation in breast cancer. It involves c-Myc signaling and alcohol dehydrogenase, iron-containing protein 1 (ADHFE1) and leads to metabolic reprogramming, de-differentiation, and increased mammary tumorigenesis.

Correlated metabolomic, genomic, and histologic phenotypes in histologically normal breast tissue

Breast carcinogenesis is a multistep process accompanied by widespread molecular and genomic alterations, both in tumor and in surrounding microenvironment. It is known that tumors have altered metabolism, but the metabolic changes in normal or cancer-adjacent, nonmalignant normal tissues and how these changes relate to alterations in gene expression and histological composition are not well understood. Normal or cancer-adjacent normal breast tissues from 99 women of the Normal Breast Study (NBS) were evaluated. Data of metabolomics, gene expression and histological composition was collected by mass spectrometry, whole genome microarray, and digital image, respectively. Unsupervised clustering analysis determined metabolomics-derived subtypes. Their association with genomic and histological features, as well as other breast cancer risk factors, genomic and histological features were evaluated using logistic regression. Unsupervised clustering of metabolites resulted in two main clusters. The metabolite differences between the two clusters suggested enrichment of pathways involved in lipid metabolism, cell growth and proliferation, and migration. Compared with Cluster 1, subjects in Cluster 2 were more likely to be obese (body mass index ≥30 kg/m2, p<0.05), have increased adipose proportion (p<0.01) and associated with a previously defined Active genomic subtype (p<0.01). By the integrated analyses of histological, metabolomics and transcriptional data, we characterized two distinct subtypes of non-malignant breast tissue. Further research is needed to validate our findings, and understand the potential role of these alternations in breast cancer initiation, progression and recurrence.

Evidence-based approaches to reduce cancer health disparities: Discover, develop, deliver, and disseminate

The Texas Center for Health Disparities (TCHD) at the University of North Texas Health Science Center is a National Institute on Minority Health and Health Disparities-funded, specialized center of excellence for health disparities. TCHD organized its 12^th annual conference focusing on "Evidence-Based Approaches to Reduce Cancer Health Disparities: Discover, Develop, Deliver, and Disseminate." At this conference, experts in health care, biomedical sciences, and public health gathered to discuss the current status and strategies for reducing cancer health disparities. The meeting was conducted in three sessions on breast cancer, prostate cancer, and colorectal cancer disparities, in addition to roundtable discussions and a poster session. Each session highlighted differences in the effects of cancer, based on factors such as race/ethnicity, gender, socioeconomic status, and geographical location. In each session, expert speakers presented their findings, and this was followed by a discussion panel made up of experts in that field and cancer survivors, who responded to questions from the audience. This article summarizes the approaches to fundamental, translational, clinical, and public health issues in cancer health disparities discussed at the conference.

Applications of RNA Indexes for Precision Oncology in Breast Cancer

Precision oncology aims to offer the most appropriate treatments to cancer patients mainly based on their individual genetic information. Genomics has provided numerous valuable data on driver mutations and risk loci; however, it remains a formidable challenge to transform these data into therapeutic agents. Transcriptomics describes the multifarious expression patterns of both mRNAs and non-coding RNAs (ncRNAs), which facilitates the deciphering of genomic codes. In this review, we take breast cancer as an example to demonstrate the applications of these rich RNA resources in precision medicine exploration. These include the use of mRNA profiles in triple-negative breast cancer (TNBC) subtyping to inform corresponding candidate targeted therapies; current advancements and achievements of high-throughput RNA interference (RNAi) screening technologies in breast cancer; and microRNAs as functional signatures for defining cell identities and regulating the biological activities of breast cancer cells. We summarize the benefits of transcriptomic analyses in breast cancer management and propose that unscrambling the core signaling networks of cancer may be an important task of multiple-omic data integration for precision oncology.

A Metabolomic Approach to Predict Breast Cancer Behavior and Chemotherapy Response

Although the classification of breast carcinomas into molecular or immunohistochemical subtypes has contributed to a better categorization of women into different therapeutic regimens, breast cancer nevertheless still progresses or recurs in a remarkable number of patients. Identifying women who would benefit from chemotherapy could potentially increase treatment effectiveness, which has important implications for long-term survival. Metabolomic analyses of fluids and tissues from cancer patients improve our knowledge of the reprogramming of metabolic pathways involved in resistance to chemotherapy. This review evaluates how recent metabolomic approaches have contributed to understanding the relationship between breast cancer and the acquisition of resistance. We focus on the advantages and challenges of cancer treatment and the use of new strategies in clinical care, which helps us comprehend drug resistance and predict responses to treatment.

Innovative methods for biomarker discovery in the evaluation and development of cancer precision therapies

The discovery of biomarkers able to detect cancer at an early stage, to evaluate its aggressiveness, and to predict the response to therapy remains a major challenge in clinical oncology and precision medicine. In this review, we summarize recent achievements in the discovery and development of cancer biomarkers. We also highlight emerging innovative methods in biomarker discovery and provide insights into the challenges faced in their evaluation and validation.

Metabolomics profiling in plasma samples from glioma patients correlates with tumor phenotypes

Background

Tumor-based molecular biomarkers have redefined in the classification gliomas. However, the association of systemic metabolomics with glioma phenotype has not been explored yet.

Methods

In this study, we conducted two-step (discovery and validation) metabolomic profiling in plasma samples from 87 glioma patients. The metabolomics data were tested for correlation with glioma grade (high vs low), glioblastoma (GBM) versus malignant gliomas, and IDH mutation status.

Results

Five metabolites, namely uracil, arginine, lactate, cystamine, and ornithine, significantly differed between high- and low-grade glioma patients in both the discovery and validation cohorts. When the discovery and validation cohorts were combined, we identified 29 significant metabolites with 18 remaining significant after adjusting for multiple comparisons. Those 18 significant metabolites separated high- from low-grade glioma patients with 91.1% accuracy. In the pathway analysis, a total of 18 significantly metabolic pathways were identified. Similarly, we identified 2 and 6 metabolites that significantly differed between GBM and non-GBM, and IDH mutation positive and negative patients after multiple comparison adjusting. Those 6 significant metabolites separated IDH1 mutation positive from negative glioma patients with 94.4% accuracy. Three pathways were identified to be associated with IDH mutation status. Within arginine and proline metabolism, levels of intermediate metabolites in creatine pathway were all significantly lower in IDH mutation positive than in negative patients, suggesting an increased activity of creatine pathway in IDH mutation positive tumors.

Conclusion

Our findings identified metabolites and metabolic pathways that differentiated tumor phenotypes. These may be useful as host biomarker candidates to further help glioma molecular classification.

Cambogin exerts anti-proliferative and pro-apoptotic effects on breast adenocarcinoma through the induction of NADPH oxidase 1 and the alteration of mitochondrial morphology and dynamics

Cambogin, a bioactive polycyclic polyprenylated acylphoroglucinol (PPAP) derived from the Garcinia genus, possesses proapoptotic effect in medulloblastoma and breast cancer cells. We have previously demonstrated that the proapoptotic effect of cambogin is driven by the production of reactive oxygen species (ROS). Here we have shown that the inhibitory effect of cambogin on cell proliferation is associated with the loss of mitochondrial transmembrane potential (ΔΨ_m) and mitochondrial fragmentation. Cambogin also promotes the mutual complex formation of the membrane-bound subunit p22^phox of NADPH oxidase 1 (NOX1), as well as the phosphorylation of the cytosolic subunit p47^phox, subsequently enhancing membrane-bound NOX1 activity, which leads to increases in intracellular and mitochondrial levels of O₂^.- and H₂O₂. Pharmacological inhibition of NOX1 using apocynin (pan-NOX inhibitor), ML171 (NOX1 inhibitor) or siRNA against NOX1 prevents the increases in O₂^.- and H₂O₂ levels and the anti-proliferative effect of cambogin. Antioxidants, including SOD (superoxide dismutase), CAT (catalase) and EUK-8, are also able to restore cell viability in the presence of cambogin. Besides, cambogin increases the dissociation of thioredoxin-1 (Trx1) from ASK1, switching the inactive form of ASK1 to the active kinase, subsequently leads to the phosphorylation of JNK/SAPK, which is abolished upon ML171 treatment. The proapoptotic effect of cambogin in breast cancer cells is also aggravated upon knocking down Trx1 in MCF-7 cells. Taken in conjunction, these data indicate that the anti-proliferative and pro-apoptotic effect of cambogin is mediated via inducing NOX1-dependent ROS production and the dissociation of ASK1 and Trx1.

ADHFE1 is a breast cancer oncogene and induces metabolic reprogramming

Metabolic reprogramming in breast tumors is linked to increases in putative oncogenic metabolites that may contribute to malignant transformation. We previously showed that accumulation of the oncometabolite, 2-hydroxyglutarate (2HG), in breast tumors was associated with MYC signaling, but not with isocitrate dehydrogenase (IDH) mutations, suggesting a distinct mechanism for increased 2HG in breast cancer. Here, we determined that D-2HG is the predominant enantiomer in human breast tumors and show that the D-2HG-producing mitochondrial enzyme, alcohol dehydrogenase, iron-containing protein 1 (ADHFE1), is a breast cancer oncogene that decreases patient survival. We found that MYC upregulates ADHFE1 through changes in iron metabolism while coexpression of both ADHFE1 and MYC strongly enhanced orthotopic tumor growth in MCF7 cells. Moreover, ADHFE1 promoted metabolic reprogramming with increased formation of D-2HG and reactive oxygen, a reductive glutamine metabolism, and modifications of the epigenetic landscape, leading to cellular dedifferentiation, enhanced mesenchymal transition, and phenocopying alterations that occur with high D-2HG levels in cancer cells with IDH mutations. Together, our data support the hypothesis that ADHFE1 and MYC signaling contribute to D-2HG accumulation in breast tumors and show that D-2HG is an oncogenic metabolite and potential driver of disease progression.

A plasma metabolomic signature discloses human breast cancer

PURPOSE

Metabolomics is the comprehensive global study of metabolites in biological samples. In this retrospective pilot study we explored whether serum metabolomic profile can discriminate the presence of human breast cancer irrespective of the cancer subtype.

METHODS

Plasma samples were analyzed from healthy women (n = 20) and patients with breast cancer after diagnosis (n = 91) using a liquid chromatography-mass spectrometry platform. Multivariate statistics and a Random Forest (RF) classifier were used to create a metabolomics panel for the diagnosis of human breast cancer.

RESULTS

Metabolomics correctly distinguished between breast cancer patients and healthy control subjects. In the RF supervised class prediction analysis comparing breast cancer and healthy control groups, RF accurately classified 100% both samples of the breast cancer patients and healthy controls. So, the class error for both group in and the out-of-bag error were 0. We also found 1269 metabolites with different concentration in plasma from healthy controls and cancer patients; and basing on exact mass, retention time and isotopic distribution we identified 35 metabolites. These metabolites mostly support cell growth by providing energy and building stones for the synthesis of essential biomolecules, and function as signal transduction molecules. The collective results of RF, significance testing, and false discovery rate analysis identified several metabolites that were strongly associated with breast cancer.

CONCLUSIONS

In breast cancer a metabolomics signature of cancer exists and can be detected in patient plasma irrespectively of the breast cancer type.

Metabolic advantages and vulnerabilities in brain metastases

Metabolic adaptations permit tumor cells to metastasize to and thrive in the brain. Brain metastases continue to present clinical challenges due to rising incidence and resistance to current treatments. Therefore, elucidating altered metabolic pathways in brain metastases may provide new therapeutic targets for the treatment of aggressive disease. Due to the high demand for glucose in the brain, increased glycolytic activity is favored for energy production. Primary tumors that undergo Warburg-like metabolic reprogramming become suited to growth in the brain microenvironment. Indeed, elevated metabolism is a predictor of metastasis in many cancer subtypes. Specifically, metabolic alterations are seen in primary tumors that are associated with the formation of brain metastases, namely breast cancer, lung cancer, and melanoma. Because of this selective pressure, inhibitors of key metabolic factors may reduce tumor cell viability, thus exploiting metabolic pathways for cancer therapeutics. This review summarizes the metabolic advantages and vulnerabilities of brain metastases.

Metabolomics Applications in Precision Medicine: An Oncological Perspective

Nowadays, cancer therapy remains limited by the conventional one-size-fits-all approach. In this context, treatment decisions are based on the clinical stage of disease but fail to ascertain the individual´s underlying biology and its role in driving malignancy. The identification of better therapies for cancer treatment is thus limited by the lack of sufficient data regarding the characterization of specific biochemical signatures associated with each particular cancer patient or group of patients. Metabolomics approaches promise a better understanding of cancer, a disease characterized by significant alterations in bioenergetic metabolism, by identifying changes in the pattern of metabolite expression in addition to changes in the concentration of individual metabolites as well as alterations in biochemical pathways. These approaches hold the potential of identifying novel biomarkers with different clinical applications, including the development of more specific diagnostic methods based on the characterization of metabolic subtypes, the monitoring of currently used cancer therapeutics to evaluate the response and the prognostic outcome with a given therapy, and the evaluation of the mechanisms involved in disease relapse and drug resistance. This review discusses metabolomics applications in different oncological processes underlining the potential of this omics approach to further advance the implementation of precision medicine in the oncology area.

The branched-chain amino acid transaminase 1 sustains growth of antiestrogen-resistant and ERα-negative breast cancer

Antiestrogen-resistant and triple-negative breast tumors pose a serious clinical challenge because of limited treatment options. We assessed global gene expression changes in antiestrogen-sensitive compared with antiestrogen-resistant (two tamoxifen resistant and two fulvestrant resistant) MCF-7 breast cancer cell lines. The branched-chain amino acid transaminase 1 (BCAT1), which catalyzes the first step in the breakdown of branched-chain amino acids, was among the most upregulated transcripts in antiestrogen-resistant cells. Elevated BCAT1 expression was confirmed in relapsed tamoxifen-resistant breast tumor specimens. High intratumoral BCAT1 levels were associated with a reduced relapse-free survival in adjuvant tamoxifen-treated patients and overall survival in unselected patients. On a tissue microarray (n=1421), BCAT1 expression was detectable in 58% of unselected primary breast carcinomas and linked to a higher Ki-67 proliferation index, as well as histological grade. Interestingly, BCAT1 was predominantly expressed in estrogen receptor-α-negative/human epidermal growth factor receptor-2-positive (ERα-negative/HER-2-positive) and triple-negative breast cancers in independent patient cohorts. The inverse relationship between BCAT1 and ERα was corroborated in various breast cancer cell lines and pharmacological long-term depletion of ERα induced BCAT1 expression in vitro. Mechanistically, BCAT1 indirectly controlled expression of the cell cycle inhibitor p27^Kip1 thereby affecting pRB. Correspondingly, phenotypic analyses using a lentiviral-mediated BCAT1 short hairpin RNA knockdown revealed that BCAT1 sustains proliferation in addition to migration and invasion and that its overexpression enhanced the capacity of antiestrogen-sensitive cells to grow in the presence of antiestrogens. Importantly, silencing of BCAT1 in an orthotopic triple-negative xenograft model resulted in a massive reduction of tumor volume in vivo, supporting our findings that BCAT1 is necessary for the growth of hormone-independent breast tumors.

Racial disparity in metabolic regulation of cancer

Genetic mutations and metabolic reprogramming are two key hallmarks of cancer, required for proliferation, invasion, and metastasis of the disease. While genetic mutations, whether inherited or acquired, are critical for the initiation of tumor development, metabolic reprogramming is an effector mechanism imperative for adaptational transition during the progression of cancer. Recent findings in the literature emphasize the significance of molecular cross-talk between these two cellular processes in regulating signaling and differentiation of cancer cells. Genome-wide sequencing analyses of cancer genomes have highlighted the association of various genic mutations in predicting cancer risk and survival. Oncogenic mutational frequency is heterogeneously distributed among various cancer types in different populations, resulting in varying susceptibility to cancer risk. In this review, we explore and discuss the role of genetic mutations in metabolic enzymes and metabolic oncoregulators to stratify cancer risk in persons of different racial backgrounds.

The Impact of Soy Isoflavones on MCF-7 and MDA-MB-231 Breast Cancer Cells Using a Global Metabolomic Approach

Despite substantial research, the understanding of the chemopreventive mechanisms of soy isoflavones remains challenging. Promising tools, such as metabolomics, can provide now a deeper insight into their biochemical mechanisms. The purpose of this study was to offer a comprehensive assessment of the metabolic alterations induced by genistein, daidzein and a soy seed extract on estrogen responsive (MCF-7) and estrogen non-responsive breast cancer cells (MDA-MB-231), using a global metabolomic approach. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay showed that all test compounds induced a biphasic effect on MCF-7 cells and only a dose-dependent inhibitory effect on MDA-MB-231 cells. Proton nuclear magnetic resonance (¹H-NMR) profiling of extracellular metabolites and gas chromatography-mass spectrometry (GC-MS) profiling of intracellular metabolites confirmed that all test compounds shared similar metabolic mechanisms. Exposing MCF-7 cells to stimulatory concentrations of isoflavones led to increased intracellular levels of 6-phosphogluconate and ribose 5-phosphate, suggesting a possible upregulation of the pentose phosphate pathway. After exposure to inhibitory doses of isoflavones, a significant decrease in glucose uptake was observed, especially for MCF-7 cells. In MDA-MB-231 cells, the glutamine uptake was significantly restricted, leading to alterations in protein biosynthesis. Understanding the metabolomic alterations of isoflavones represents a step forward in considering soy and soy derivates as functional foods in breast cancer chemoprevention.

In situ biomarker discovery and label-free molecular histopathological diagnosis of lung cancer by ambient mass spectrometry imaging

Sensitive and spatial exploration of the metabolism of tumors at the metabolome level is highly challenging. In this study, we developed an in situ metabolomics method based on ambient mass spectrometry imaging using air flow-assisted desorption electrospray ionization (AFADESI), which can spatially explore the alteration of global metabolites in tissues with high sensitivity. Using this method, we discovered potential histopathological diagnosis biomarkers (including lipids, amino acids, choline, peptides, and carnitine) from 52 postoperative lung cancer tissue samples and then subsequently used these biomarkers to generate images for rapid and label-free histopathological diagnosis. These biomarkers were validated with a sensitivity and a specificity of 93.5% and 100%, respectively. Moreover, a single imaging analysis of a cryosection that visualized all these biomarkers, taking tens of minutes, revealed the type and subtype of the cancer. This method could potentially be used as a molecular pathological tool for rapid clinical lung cancer diagnosis and immediate image-guided surgery.