Metabolomics in cancer: a bench-to-bedside intersection

Metabolomics and Proteomics in Prostate Cancer Research: Overview, Analytical Techniques, Data Analysis, and Recent Clinical Applications

Prostate cancer (PCa) is a significant global contributor to mortality, predominantly affecting males aged 65 and above. The field of omics has recently gained traction due to its capacity to provide profound insights into the biochemical mechanisms underlying conditions like prostate cancer. This involves the identification and quantification of low-molecular-weight metabolites and proteins acting as crucial biochemical signals for early detection, therapy assessment, and target identification. A spectrum of analytical methods is employed to discern and measure these molecules, revealing their altered biological pathways within diseased contexts. Metabolomics and proteomics generate refined data subjected to detailed statistical analysis through sophisticated software, yielding substantive insights. This review aims to underscore the major contributions of multi-omics to PCa research, covering its core principles, its role in tumor biology characterization, biomarker discovery, prognostic studies, various analytical technologies such as mass spectrometry and Nuclear Magnetic Resonance, data processing, and recent clinical applications made possible by an integrative "omics" approach. This approach seeks to address the challenges associated with current PCa treatments. Hence, our research endeavors to demonstrate the valuable applications of these potent tools in investigations, offering significant potential for understanding the complex biochemical environment of prostate cancer and advancing tailored therapeutic approaches for further development.

Prognostic Factors for Cardiotoxicity among Children with Cancer: Definition, Causes, and Diagnosis with Omics Technologies

Improvements in the treatment of childhood cancer have considerably enhanced survival rates over the last decades to over 80% as of today. However, this great achievement has been accompanied by the occurrence of several early and long-term treatment-related complications major of which is cardiotoxicity. This article reviews the contemporary definition of cardiotoxicity, older and newer chemotherapeutic agents that are mainly involved in cardiotoxicity, routine process diagnoses, and methods using omics technology for early and preventive diagnosis. Chemotherapeutic agents and radiation therapies have been implicated as a cause of cardiotoxicity. In response, the area of cardio-oncology has developed into a crucial element of oncologic patient care, committed to the early diagnosis and treatment of adverse cardiac events. However, routine diagnosis and the monitoring of cardiotoxicity rely on electrocardiography and echocardiography. For the early detection of cardiotoxicity, in recent years, major studies have been conducted using biomarkers such as troponin, N-terminal pro b-natriuretic peptide, etc. Despite the refinements in diagnostics, severe limitations still exist due to the increase in the above-mentioned biomarkers only after significant cardiac damage has occurred. Lately, the research has expanded by introducing new technologies and finding new markers using the omics approach. These new markers could be used not only for early detection but also for the early prevention of cardiotoxicity. Omics science, which includes genomics, transcriptomics, proteomics, and metabolomics, offers new opportunities for biomarker discovery in cardiotoxicity and may provide an understanding of the mechanisms of cardiotoxicity beyond traditional technologies.

Serum Metabolic Disturbances in Lung Cancer Investigated through an Elaborative NMR-Based Serum Metabolomics Approach

Detection of metabolic disturbances in lung cancer (LC) has the potential to aid early diagnosis/prognosis and hence improve disease management strategies through reliable grading, staging, and determination of neoadjuvant status in LC. However, a majority of previous metabolomics studies compare the normalized spectral features which not only provide ambiguous information but further limit the clinical translation of this information. Various such issues can be resolved by performing the concentration profiling of various metabolites with respect to formate as an internal reference using commercial software Chenomx. Continuing our efforts in this direction, the serum metabolic profiles were measured on 39 LC patients and 42 normal controls (NCs, comparable in age/sex) using high-field 800 MHz NMR spectroscopy and compared using multivariate statistical analysis tools to identify metabolic disturbances and metabolites of diagnostic potential. Partial least-squares discriminant analysis (PLS-DA) model revealed a distinct separation between LC and NC groups and resulted in excellent discriminatory ability with the area under the receiver-operating characteristic (AUROC) = 0.97 [95% CI = 0.89-1.00]. The metabolic features contributing to the differentiation of LC from NC samples were identified first using variable importance in projection (VIP) score analysis and then checked for their statistical significance (with p-value < 0.05) and diagnostic potential using the ROC curve analysis. The analysis revealed relevant metabolic disturbances associated with LC. Among various circulatory metabolites, six metabolites, including histidine, glutamine, glycine, threonine, alanine, and valine, were found to be of apposite diagnostic potential for clinical implications. These metabolic alterations indicated altered glucose metabolism, aberrant fatty acid synthesis, and augmented utilization of various amino acids including active glutaminolysis in LC.

Metabolic Phenotyping Predicts Gemcitabine and Cisplatin Chemosensitivity in Patients With Cholangiocarcinoma

Gemcitabine and cisplatin serve as appropriate treatments for patients with cholangiocarcinoma (CCA). Our previous study using histoculture drug response assay (HDRA), demonstrated individual response patterns to gemcitabine and cisplatin. The current study aimed to identify predictive biomarkers for gemcitabine and cisplatin sensitivity in tissues and sera from patients with CCA using metabolomics. Metabolic signatures of patients with CCA were correlated with their HDRA response patterns. The tissue metabolic signatures of patients with CCA revealed the inversion of the TCA cycle that is evident with increased levels of citrate and amino acid backbones as TCA cycle intermediates, and glucose which corresponds to cancer stem cell (CSC) properties. The protein expression levels of CSC markers were examined on tissues and showed the significantly inverse association with the responses of patients to cisplatin. Moreover, the elevation of ethanol level was observed in gemcitabine- and cisplatin-sensitive group. In serum, a lower level of glucose but a higher level of methylguanidine was observed in the gemcitabine-responders as non-invasive predictive biomarker for gemcitabine sensitivity. Collectively, our findings indicate that these metabolites may serve as the predictive biomarkers in clinical practice which not only predict the chemotherapy response in patients with CCA but also minimize the adverse effect from chemotherapy.

Prospective analysis of circulating metabolites and endometrial cancer risk

BACKGROUND

Endometrial cancer is strongly associated with obesity and dysregulation of metabolic factors such as estrogen and insulin signaling are causal risk factors for this malignancy. To identify additional novel metabolic pathways associated with endometrial cancer we performed metabolomic analyses on pre-diagnostic plasma samples from 853 case-control pairs from the European Prospective Investigation into Cancer and Nutrition (EPIC).

METHODS

A total of 129 metabolites (acylcarnitines, amino acids, biogenic amines, glycerophospholipids, hexoses, and sphingolipids) were measured by liquid chromatography-mass spectrometry. Conditional logistic regression estimated the associations of metabolites with endometrial cancer risk. An analysis focusing on clusters of metabolites using the bootstrap lasso method was also employed.

RESULTS

After adjustment for body mass index, sphingomyelin [SM] C18:0 was positively (OR1SD: 1.18, 95% CI: 1.05-1.33), and glycine, serine, and free carnitine (C0) were inversely (OR1SD: 0.89, 95% CI: 0.80-0.99; OR1SD: 0.89, 95% CI: 0.79-1.00 and OR1SD: 0.91, 95% CI: 0.81-1.00, respectively) associated with endometrial cancer risk. Serine, C0 and two sphingomyelins were selected by the lasso method in >90% of the bootstrap samples. The ratio of esterified to free carnitine (OR1SD: 1.14, 95% CI: 1.02-1.28) and that of short chain to free acylcarnitines (OR1SD: 1.12, 95% CI: 1.00-1.25) were positively associated with endometrial cancer risk. Further adjustment for C-peptide or other endometrial cancer risk factors only minimally altered the results.

CONCLUSION

These findings suggest that variation in levels of glycine, serine, SM C18:0 and free carnitine may represent specific pathways linked to endometrial cancer development. If causal, these pathways may offer novel targets for endometrial cancer prevention.

Plasma metabolite changes in anestrous dairy cows with negative energy balance identified using 1H NMR technology

ABSTRACT The objective of the present study was to investigate the different plasma metabolites between anestrus and estrus postpartum dairy cows and to provide a theoretical basis for prevention of anestrus in dairy farm cows. In the experiment, one hundred and sixty-seven Holstein dairy cows were selected with similar age and parity. According to the concentration of β-hydroxybutyric acid, non-esterified fatty acids and glucose in plasma during 14 to 21 days in milk, all dairy cows were determined as having a status of energy balance. According to the results of clinical symptom, rectal and B ultrasound examination at 60 to 90 days postpartum, these cows were divided into twenty estrus and twenty-four anestrus group, other dairy cows were removed. 1H nuclear magnetic resonance technology was utilized to detect the plasma metabolites changes and screen different plasma metabolites between anestrus and estrus cows. Ten different metabolites including alanine, glutamic acid, asparagine, creatine, choline, phosphocholine, glycerophosphocholine, low-density lipoprotein, and very-low-density lipoprotein were significantly decreased in anestrous cows compared with estrous cows. Metabolic pathway analyses indicated that differential metabolites were primarily involved in amino acid and glycerophospholipid metabolism. These metabolites and their enrichment pathways indicate that reduced steroid hormone synthesis precursors result in lower levels of estradiol and progesterone and cause anestrus in negative energy balance. These data provide a better understanding of the changes that may affect estrus of postpartum dairy cows at NEB status and lay the ground for further research.

A glance at the actual role of glutamine metabolism in thyroid tumorigenesis

Thyroid cancers (TCs) are the most prevalent malignancy of the endocrine system and the seventh most common cancer in women. According to estimates from the Global Cancer Observatory (GCO) in 2020, the incidence of thyroid cancer globally was 586,000 cases. As thyroid cancer incidences have dramatically increased, identifying the most important metabolic pathways and biochemical markers involved in thyroid tumorigenesis can be critical strategies for controlling the prevalence and ultimately treatment of this disease. Cancer cells undergo cellular metabolism and energy alteration in order to promote cell proliferation and invasion. Glutamine is one of the most abundant free amino acids in the human body that contributes to cancer metabolic remodeling as a carbon and nitrogen source to sustain cell growth and proliferation. In the present review, glutamine metabolism and its regulation in cancer cells are highlighted. Thereafter, emphasis is given to the perturbation of glutamine metabolism in thyroid cancer, focusing on metabolomics studies.

Validation of plasma metabolites associated with breast cancer risk among Mexican Americans

In our previous breast cancer case control study in Hispanics, we found 14 metabolites whose levels differed between cases and controls. To validate the results, we carried out a nested case control study of 100 incident breast cancer and 100 matched healthy women identified from the Mano-A-Mano Mexican American Cohort study. With the adjustment of parity, education, birth place, language acculturation, BMI category, smoking, drinking, physical activity, and sitting time, 4 metabolites were associated with breast cancer risk: 3-hydroxyoctanoate (Odds ratio (OR) = 1.51, 95% confidence interval (CI): 1.10, 3.47), 3-hydroxybutyrate (BHBA) (OR = 1.42, 95%CI: 1.01, 3.72), linoleate (18:2n6) (OR = 1.39, 95% CI: 1.07, 4.04), and bilirubin (OR = 0.54, 95%CI: 0.42, 0.95). Then, we used 3 non-redundant metabolites, namely 3-hydroxyoctanoate, linoleate (18:2n6), and bilirubin, to generate a metabolic risk score. Increased metabolites risk score was associated with a 1.67-fold increased risk of breast cancer (OR = 1.67, 95%CI: 1.32, 3.94). And the significant association was more evident among those who were diagnosed with cancer earlier during the follow-up (≤ 5 years) than their counterparts. In conclusion, we identified four significant metabolites which may help elucidate metabolic pathways that contribute to breast carcinogenesis. Our findings warrant further replication efforts.

Transcriptome analysis reveals a reprogramming energy metabolism-related signature to improve prognosis in colon cancer

Although much progress has been made to improve treatment, colon cancer remains a leading cause of cancer death worldwide. Metabolic reprogramming is a significant ability of cancer cells to ensure the necessary energy supply in uncontrolled proliferation. Since reprogramming energy metabolism has emerged as a new hallmark of cancer cells, accumulating evidences have suggested that metabolism-related genes may serve as key regulators of tumorigenesis and potential biomarkers. In this study, we analyzed a set of reprogramming energy metabolism-related genes by transcriptome analysis in colon cancer and revealed a five-gene signature that could significantly predict the overall survival. The reprogramming energy metabolism-related signature could distinguish patients into high-risk and low-risk groups with significantly different survival times (P = 0.0011; HR = 1.92; 95% CI [1.29–2.87]). Its prognostic value was confirmed in another two independent colon cancer cohorts (P = 5.2e–04; HR = 2.09, 95%; CI [1.37–3.2] for GSE17538 and P = 3.8e−04; HR = 2.08, 95% CI [1.37–3.16] for GSE41258). By multivariable analysis, we found that the signature was independent of clinicopathological features. Its power in promoting risk stratification of the current clinical stage was then evaluated by stratified analysis. Moreover, the signature could improve the power of the TNM stage for the prediction of overall survival and could be used in patients who received adjuvant chemotherapy. Overall, our results demonstrated the important role of the reprogramming energy metabolism-related signature in promoting stratification of high-risk patients, which could be diagnostic of adjuvant therapy benefit.

OMICS insights into cancer histology; Metabolomics and proteomics approach

Metabolomics as a post-genomic research area comprising different analytical methods for small molecules analysis. One of the underlying applications of metabolomics technology for better disease diagnosis and prognosis is discovering the metabolic pathway differences between healthy individuals and patients. On the other hand, the other noteworthy applications of metabolomics include its effective role in biomarker screening for cancer detection, monitoring, and prediction. In other words, emerging of the metabolomics field can be hopeful to provide a suitable alternative for the common current cancer diagnostic methods especially histopathological tests. Indeed, cancer as a major global issue places a substantial burden on the health care system. Hence, proper management can be beneficial. In this respect, formalin-fixed paraffin-embedded tissue specimens (in histopathological tests) are considered as a valuable source for metabolomics investigations. Interestingly, formalin-fixed paraffin-embedded tissue specimens can provide informative data for cancer management. In general, using these specimens, determining the cancer stage, individual response to the different therapies, personalized risk prediction are possible and high-quality clinical services are the promise of OMICS technologies for cancer disease. However, considering all of these beneficial characteristics, there are still some limitations in this area that need to be addressed in order to optimize the metabolomics utilizations and advancement.

Plasma Metabolic Profiling of Human Thyroid Nodules by Gas Chromatography-Mass Spectrometry (GC-MS)-Based Untargeted Metabolomics

One of the challenges in the area of diagnostics of human thyroid cancer is a preoperative diagnosis of thyroid nodules with indeterminate cytology. Herein, we report an untargeted metabolomics analysis to identify circulating thyroid nodule metabolic signatures, to find new novel metabolic biomarkers. Untargeted gas chromatography-quadrupole-mass spectrometry was used to ascertain the specific plasma metabolic changes of thyroid nodule patients, which consisted of papillary thyroid carcinoma (PTC; n = 19), and multinodular goiter (MNG; n = 16), as compared to healthy subjects (n = 20). Diagnostic models were constructed using multivariate analyses such as principal component analysis, orthogonal partial least squares-discriminant analysis, and univariate analysis including One-way ANOVA and volcano plot by MetaboAnalyst and SIMCA software. Because of the multiple-testing issue, false discovery rate p-values were also computed for these functions. A total of 60 structurally annotated metabolites were subjected to statistical analysis. A combination of univariate and multivariate statistical analyses revealed a panel of metabolites responsible for the discrimination between thyroid nodules and healthy subjects, with variable importance in the projection (VIP) value greater than 0.8 and p-value less than 0.05. Significantly altered metabolites between thyroid nodules versus healthy persons are those associated with amino acids metabolism, the tricarboxylic acid cycle, fatty acids, and purine and pyrimidine metabolism, including cysteine, cystine, glutamic acid, α-ketoglutarate, 3-hydroxybutyric acid, adenosine-5-monophosphate, and uracil, respectively. Further, sucrose metabolism differed profoundly between thyroid nodule patients and healthy subjects. Moreover, according to the receiver operating characteristic (ROC) curve analysis, sucrose could discriminate PTC from MNG (area under ROC curve value = 0.92). This study enhanced our understanding of the distinct metabolic pathways associated with thyroid nodules, which enabled us to distinguish between patients and healthy subjects. In addition, our study showed extensive sucrose metabolism in the plasma of thyroid nodule patients, which provides a new metabolic signature of the thyroid nodule’s tumorigenesis. Accordingly, it suggests that sucrose can be considered as a circulating biomarker for differential diagnosis between malignancy and benignity in indeterminate thyroid nodules.

Metabolomic studies of breast cancer in murine models: A review

BACKGROUND

Metabolomic strategies have been extensively used to search for biomarkers of disease, including cancer, in biological complex mixtures such as cells, tissues and biofluids. In breast cancer research, murine models are of great value and metabolomics has been increasingly applied to characterize tumor or organ tissues, or biofluids, for instance to follow-up metabolism during cancer progression or response to specific therapies.

SCOPE OF REVIEW

This review briefly introduces the different murine models used in breast cancer research and proceeds to present the metabolomic studies reported so far to describe the deviant metabolic behavior associated to breast cancer, in each type of model: xenografts (cell- or patient-derived), spontaneous (naturally-occurring or genetically engineered) and carcinogen-induced. The type of sample and strategies followed are identified, as well as the main findings from of study.

MAJOR CONCLUSIONS

Metabolomics has gradually become relevant in characterizing murine models of breast cancer, using either Nuclear Magnetic Resonance (NMR) or Mass Spectromety (MS). Both tissue and biofluids are matrixes of interest in this context, although in some type of models, reports have focused primarily on the former. The aims of tissue studies have comprised the search for mechanistic knowledge of carcinogenesis, metastasis development and response/resistance to therapies. Biofluid metabolomics has mainly aimed at finding non-invasive biomarkers for early breast cancer detection or prognosis determination.

GENERAL SIGNIFICANCE

Metabolomics provides exquisite detail on murine tumor and systemic metabolism of breast cancer. This knowledge paves the way for the discovery of new biomarkers, potentially translatable to in vivo non-invasive patient follow-up.

Active surveillance as a management strategy for papillary thyroid microcarcinoma

Active surveillance (AS) can be considered as a treatment strategy for low risk papillary thyroid microcarcinoma (PTMC), with the absence of clinically apparent lymph nodes, extrathyroidal extensions, and distant metastasis. After reviewing the reports on AS of low risk PTMCs worldwide, we introduced AS, and discussed the selection criteria for active surveillance candidates based on different guidelines and the follow-up schedules. Moreover, the requirement of cytological diagnosis, progression evaluation methods, necessity of thyrotropin suppression, and medical costs were issues that both clinicians and patients considered. The usefulness of AS for low risk PTMC patients depended on accurate and confidential evaluation of patient risk. Clinicians may adopt measures like dynamic monitoring, risk stratification, and making personal follow-up schedules to minimize these potential risks. By appropriately selecting PTMC patients, AS can be an effective alternative treatment to immediate surgery.

Metabolic profiles of serum samples from ground glass opacity represent potential diagnostic biomarkers for lung cancer

Background

Lung cancer is a leading cause of cancer deaths worldwide. Low-dose computed tomography (LDCT) screening trials indicated that LDCT is effective for the early detection of lung cancer, but the findings were accompanied by high false positive rates. Therefore, the detection of lung cancer needs complementary blood biomarker tests to reduce false positive rates.

Methods

In order to evaluate the potential of metabolite biomarkers for diagnosing lung cancer and increasing the effectiveness of clinical interventions, serum samples from subjects participating in a low-dose CT-scan screening were analyzed by using untargeted liquid chromatography-hybrid quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS). Samples were acquired from 34 lung patients with ground glass opacity diagnosed lung cancer and 39 healthy controls.

Results

In total, we identified 9 metabolites in electron spray ionization (ESI)(+) mode and 7 metabolites in ESI(-) mode. L-(+)-gulose, phosphatidylethanolamine (PE)(22:2(13Z,16Z)/15:0), cysteinyl-glutamine, S-japonin, threoninyl-glutamine, chlorate, 3-oxoadipic acid, dukunolide A, and malonic semialdehyde levels were observed to be elevated in serum samples of lung cancer cases when compared to those of healthy controls. By contrast, 1-(2-furanylmethyl)-1H-pyrrole, 2,4-dihydroxybenzoic acid, monoethyl carbonate, guanidinosuccinic acid, pseudouridine, DIMBOA-Glc, and 4-feruloyl-1,5-quinolactone levels were lower in serum samples of lung cancer cases compared with those of healthy controls.

Conclusions

This study demonstrates evidence of early metabolic alterations that can possibly distinguish malignant ground glass opacity from benign ground glass opacity. Further studies in larger pools of samples are warranted.

Breast cancer risk in relation to plasma metabolites among Hispanic and African American women

PURPOSE

The metabolic etiology of breast cancer has been explored in the past several years using metabolomics. However, most of these studies only included non-Hispanic White individuals.

METHODS

To fill this gap, we performed a two-step (discovery and validation) metabolomics profiling in plasma samples from 358 breast cancer patients and 138 healthy controls. All study subjects were either Hispanics or non-Hispanic African Americans.

RESULTS

A panel of 14 identified metabolites significantly differed between breast cancer cases and healthy controls in both the discovery and validation sets. Most of these identified metabolites were lipids. In the pathway analysis, citrate cycle (TCA cycle), arginine and proline metabolism, and linoleic acid metabolism pathways were observed, and they significantly differed between breast cancer cases and healthy controls in both sets. From those 14 metabolites, we selected 9 non-correlated metabolites to generate a metabolic risk score. Increased metabolites risk score was associated with a 1.87- and 1.63-fold increased risk of breast cancer in the discovery and validation sets, respectively (Odds ratio (OR) 1.87, 95% Confidence interval (CI) 1.50, 2.32; OR 1.63, 95% CI 1.36, 1.95).

CONCLUSIONS

In summary, our study identified metabolic profiles and pathways that significantly differed between breast cancer cases and healthy controls in Hispanic or non-Hispanic African American women. The results from our study might provide new insights on the metabolic etiology of breast cancer.

Advances in metabolomics of thyroid cancer diagnosis and metabolic regulation

Thyroid cancers (TCs) are the most frequent endocrine malignancy with an unpredictable fast-growing incidence, especially in females all over the world. Fine-needle aspiration biopsy (FNAB) analysis is an accurate diagnostic method for detecting thyroid nodules and classification of TC. Though simplicity, safety, and accuracy of FNAB, 15-30% of cases are indeterminate, and it is not possible to determine the exact cytology of the specimen. This demands the need for innovative methods capable to find crucial biomarkers with adequate sensitivity for diagnosis and prediction in TC researches. Cancer-based metabolomics is a vast emerging field focused on the detection of a large set of metabolites extracted from biofluids or tissues. Using analytical chemistry procedures allows for the potential recognition of cancer-based metabolites for the purposes of advancing the era of personalized medicine. Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) coupled with separation techniques e.g., gas chromatography (GC) and liquid chromatography (LC) are the main approaches for metabolic studies in cancers. The immense metabolite profiling has provided a chance to discover novel biomarkers for early detection of thyroid cancer and reduce unnecessary aggressive surgery. In this review, we recapitulate the recent advances and developed methods of diverse metabolomics tools and metabolic phenotypes of thyroid cancer, following a brief discussion of recent challenges in the thyroid cancer diagnosis.

Current state and future direction of screening tool for colorectal cancer

As the second-most-common cause of cancer death, colorectal cancer (CRC) has been recognized as one of the biggest health concerns in advanced countries. The 5-year survival rate for patients with early-stage CRC is significantly better than that for patients with CRC detected at a late stage. The primary target for CRC screening and prevention is advanced neoplasia, which includes both CRC itself, as well as benign but histologically advanced adenomas that are at increased risk for progression to malignancy. Prevention of CRC through detection of advanced adenomas is important. It is, therefore, necessary to develop more efficient detection methods to enable earlier detection and therefore better prognosis. Although a number of CRC diagnostic methods are currently used for early detection, including stool-based tests, traditional colonoscopy, etc., they have not shown optimal results due to several limitations. Hence, development of more reliable screening methods is required in order to detect the disease at an early stage. New screening tools also need to be able to accurately diagnose CRC and advanced adenoma, help guide treatment, and predict the prognosis along with being relatively simple and non-invasive. As part of such efforts, many proposals for the early detection of colorectal neoplasms have been introduced. For example, metabolomics, referring to the scientific study of the metabolism of living organisms, has been shown to be a possible approach for discovering CRC-related biomarkers. In addition, a growing number of high-performance screening methodologies could facilitate biomarker identification. In the present, evidence-based review, the authors summarize the current state as recognized by the recent guideline recommendation from the American Cancer Society, US Preventive Services Task Force and the United States Multi-Society Task Force and discuss future direction of screening tools for colorectal cancer. Further, we highlight the most interesting publications on new screening tools, like molecular biomarkers and metabolomics, and discuss these in detail.

Breast Cancer Metabolomics: From Analytical Platforms to Multivariate Data Analysis. A Review

Cancer is a major health issue worldwide for many years and has been increasing significantly. Among the different types of cancer, breast cancer (BC) remains the leading cause of cancer-related deaths in women being a disease caused by a combination of genetic and environmental factors. Nowadays, the available diagnostic tools have aided in the early detection of BC leading to the improvement of survival rates. However, better detection tools for diagnosis and disease monitoring are still required. In this sense, metabolomic NMR, LC-MS and GC-MS-based approaches have gained attention in this field constituting powerful tools for the identification of potential biomarkers in a variety of clinical fields. In this review we will present the current analytical platforms and their applications to identify metabolites with potential for BC biomarkers based on the main advantages and advances in metabolomics research. Additionally, chemometric methods used in metabolomics will be highlighted.

Metabolomics Biomarkers for Detection of Colorectal Neoplasms: A Systematic Review

BACKGROUND

Several approaches have been suggested to be useful in the early detection of colorectal neoplasms. Since metabolites are closely related to the phenotype and are available from different human bio-fluids, metabolomics are candidates for non-invasive early detection of colorectal neoplasms.

OBJECTIVES

We aimed to summarize current knowledge on performance characteristics of metabolomics biomarkers that are potentially applicable in a screening setting for the early detection of colorectal neoplasms.

DESIGN

We conducted a systematic literature search in PubMed and Web of Science and searched for biomarkers for the early detection of colorectal neoplasms in easy-to-collect human bio-fluids. Information on study design and performance characteristics for diagnostic accuracy was extracted.

RESULTS

Finally, we included 41 studies in our analysis investigating biomarkers in different bio-fluids (blood, urine, and feces). Although single metabolites mostly had limited ability to distinguish people with and without colorectal neoplasms, promising results were reported for metabolite panels, especially amino acid panels in blood samples, as well as nucleosides in urine samples in several studies. However, validation of the results is limited.

CONCLUSIONS

Panels of metabolites consisting of amino acids in blood and nucleosides in urinary samples might be useful biomarkers for early detection of advanced colorectal neoplasms. However, to make metabolomic biomarkers clinically applicable, future research in larger studies and external validation of the results is required.

Identification of metabolites in plasma for predicting survival in glioblastoma

Circulating metabolomics profiling holds prognostic potential. However, such efforts have not been extensively carried out in glioblastoma. In this study, two-step (training and testing) metabolomics profiling was conducted from the plasma samples of 159 glioblastoma patients. Metabolomics profiling was tested for correlation with 2-year overall and disease-free survivals. Arginine, methionine, and kynurenate levels were significantly associated with 2-year overall survival in both the training and testing sets. In the combined sets, elevated levels of arginine and methionine were associated with a 34% and 37% increased probability whereas kynurenate was associated with a 55% decreased probability of 2-year overall survival. These three metabolites were also significantly associated with 2-year disease-free survival. Risk scores were generated using the linear combination of levels of these significant metabolites. Glioblastoma patients with a high-risk score exhibited a 2.41-fold decreased probability of 2-year overall survival (hazard ratio (HR) = 2.41; 95% Confidence Interval (CI) = 1.20-4.93) and a 3.17-fold decreased probability of 2-year disease free survival (HR = 3.17, 95%CI = 1.42-7.54) relative to those with a low-risk score. In conclusion, we identified a unique plasma metabolite profile that is predictive of glioblastoma prognosis.

Metabolomics reveals novel blood plasma biomarkers associated to the BRCA1-mutated phenotype of human breast cancer

Hereditary breast and ovarian cancer syndrome (HBOC) is partly due to the presence of mutations in the BRCA genes. Triple-negative (TN) breast cancer (BC) shares histological characteristics with germline BRCA1 mutation-associated tumours. We have investigated the metabolic profiles of human breast cancer (BC) cell lines carrying BRCA1 pathogenic mutations by non-targeted liquid chromatography coupled to mass spectrometry technology. Based on our in vitro results, we performed a targeted metabolomic analysis of plasma samples from TN HBOC patients taking into account their BRCA1 genotype. BRCA1 promoter hypermethylation and the BRCAness phenotype of BC cell lines were also studied. The purpose of this study was to determine the metabolic signature of HBOC syndrome and TNBC patients and to evaluate the potential contribution of the metabolites identified to the genetic diagnosis of breast cancer. The present results show the existence of a differential metabolic signature for BC cells based on the BRCA1 functionality. None of the studied BC cell lines presented hypermethylation of the BRCA1 promoter region. We provide evidence of the existence of free methylated nucleotides capable of distinguishing plasma samples from HBOC patients as BRCA1-mutated and BRCA1 non-mutated, suggesting that they might be considered as BRCA1-like biomarkers for TNBC and HBOC syndrome.

Circulating metabolite profiles to predict overall survival in advanced non-small cell lung cancer patients receiving first-line chemotherapy

OBJECTIVES

The prognosis for advanced-stage non-small cell lung cancer (NSCLC) is usually poor. However, survival may be variable and difficult to predict. In the current study, we aimed to identify circulating metabolites as potential predictive biomarkers for overall survival of advanced-stage (III/IV) NSCLC patients treated with first-line platinum-based chemotherapy.

MATERIALS AND METHODS

Using two-stage study design, we performed global metabolomic profiling in blood of 220 advanced-stage NSCLC patients, including 110 with poor survival and 110 with good survival. Metabolomic profiling was conducted using Metabolon platform. The association of each metabolite with survival was assessed by Cox proportional hazard regression model with adjustment for covariates.

RESULTS AND CONCLUSION

We found levels of 4 metabolites, caffeine, paraxanthine, stachydrine, and methyl glucopyranoside (alpha+beta), differed significantly between NSCLC patients with poor and good survival in both discovery and validation phases (P<0.05). Interestingly, majority of the identified metabolites are involved in caffeine metabolism, and 2 metabolites are related to coffee intake. In fact, caffeine metabolism pathway was the only significant pathway identified which significantly differed between NSCLC patients with poor and good survival (P=1.48E-07) in the pathway analysis. We also found 4 metabolites whose levels were significantly associated with good survival in both discovery and validation phases. Strong cumulative effects on overall survival were observed for these 4 metabolites. In conclusion, we identified a panel of metabolites including metabolites in caffeine metabolism pathway that may predict survival outcome in advanced-stage NSCLC patients. The identified small metabolites may be useful biomarker candidates to help identify patients who may benefit from platinum-based chemotherapy.

Nuclear Magnetic Resonance technique in tumor metabolism

Cancer is one of the most serious diseases that cause an enormous number of deaths all over the world. Tumor metabolism has great discrimination from that of normal tissues. Exploring the tumor metabolism may be one of the best ways to find biomarkers for cancer detection, diagnosis and to provide novel insights into internal physiological state where subtle changes may happen in metabolite concentrations. Nuclear Magnetic Resonance (NMR) technique nowadays is a popular tool to analyze cell extracts, tissues and biological fluids, etc, since it is a relatively fast and an accurate technique to supply abundant biochemical information at molecular levels for tumor research. In this review, approaches in tumor metabolism are discussed, including sample collection, data profiling and multivariate data analysis methods etc. Some typical applications of NMR are also summarized in tumor metabolism.

Serum phosphatidylethanolamine levels distinguish benign from malignant solitary pulmonary nodules and represent a potential diagnostic biomarker for lung cancer

BACKGROUND

Recent computed tomography (CT) screening trials showed that it is effective for early detection of lung cancer, but were plagued by high false positive rates. Additional blood biomarker tests designed to complement CT screening and reduce false positive rates are highly desirable.

OBJECTIVE

Identify blood-based metabolite biomarkers for diagnosing lung cancer.

MEHTODS

Serum samples from subjects participating in a CT screening trial were analyzed using untargeted GC-TOFMS and HILIC-qTOFMS-based metabolomics. Samples were acquired prior to diagnosis (pre-diagnostic, n= 17), at-diagnosis (n= 25) and post-diagnosis (n= 19) of lung cancer and from subjects with benign nodules (n= 29).

RESULTS

Univariate analysis identified 40, 102 and 30 features which were significantly different between subjects with malignant (pre-, at- and post-diagnosis) solitary pulmonary nodules (SPNs) and benign SPNs, respectively. Ten metabolites were consistently different between subjects presenting malignant (pre- and at-diagnosis) or benign SPNs. Three of these 10 metabolites were phosphatidylethanolamines (PE) suggesting alterations in lipid metabolism. Accuracies of 77%, 83% and 78% in the pre-diagnosis group and 69%, 71% and 67% in the at-diagnosis group were determined for PE(34:2), PE(36:2) and PE(38:4), respectively.

CONCLUSIONS

This study demonstrates evidence of early metabolic alterations that can possibly distinguish malignant from benign SPNs. Further studies in larger pools of samples are warranted.

Metabolomics as a Challenging Approach for Medicinal Chemistry and Personalized Medicine

"Omics" sciences have been developed to provide a holistic point of view of biology and to better understand the complexity of an organism as a whole. These systems biology approaches can be examined at different levels, starting from the most fundamental, i.e., the genome, and finishing with the most functional, i.e., the metabolome. Similar to how genomics is applied to the exploration of DNA, metabolomics is the qualitative and quantitative study of metabolites. This emerging field is clearly linked to genomics, transcriptomics, and proteomics. In addition, metabolomics provides a unique and direct vision of the functional outcome of an organism's activities that are required for it to survive, grow, and respond to internal and external stimuli or stress, e.g., pathologies and drugs. The links between metabolic changes, patient phenotype, physiological and/or pathological status, and treatment are now well established and have opened a new area for the application of metabolomics in the drug discovery process and in personalized medicine.

Hypoxia-induced autophagy as an additional mechanism in human osteosarcoma radioresistance

Osteosarcoma (OS) responds poorly to radiotherapy, but the mechanism is unclear. We found OS tumor tissues expressed high level of protein HIF-1α, a common biological marker indicative of hypoxia. It is known that hypoxic cells are generally radioresistant because of reduced production of irradiation-induced DNA-damaging reactive oxygen species (ROS) in the anaerobic condition. Here we report another mechanism how hypoxia induces radioresistance. In MG-63 human osteosarcoma cells, hypoxic pretreatment increased the cellular survival in irradiation. These hypoxia-exposed cells displayed compartmental recruitment of GFP-tagged LC3 and expression of protein LC3-II, and restored the radiosensitivity upon autophagy inhibition. The following immunohistochemistry of OS tumor tissue sections revealed upregulated LC3 expression in a correlation with HIF-1α protein level, implying the possibly causative link between hypoxia and autophagy. Further studies in MG-63 cells demonstrated hypoxic pretreatment reduced cellular and mitochondrial ROS production during irradiation, while inhibition of autophagy re-elicited them. Taken together, our study suggests hypoxia can confer cells resistance to irradiation through activated autophagy to accelerate the clearance of cellular ROS products. This might exist in human osteosarcoma as an additional mechanism for radioresistance.

A Pilot Study on the Utility of Serum Metabolomics in Neuroblastoma Patients and Xenograft Models

BACKGROUND

Improved prediction of neuroblastoma (NB) behavior is needed to detect treatment-refractory disease and may allow further reduction in therapy for some patients. In this regard, serum metabolomic analysis has proven utility in several cancer types. We hypothesize that serum metabolomic analysis will correlate with risk-group classification for patients with NB, and sensitively detect NB in murine xenograft models.

PROCEDURE

A pilot study was done on Children's Oncology Group (COG) tumor bank sera from 10 patients (five high-, five low-risk). An institutional pilot study was carried out on five patients comparing sera obtained during active versus minimal disease (complete response/very good partial response; CR/VGPR).

XENOGRAFT

Flank tumors were established in Nu/Nu mice by injection of NB cell lines (IMR-32, SH-EP, SK-N-AS). Serum for comparison was drawn pre-injection, at 1 week after injection when there was no visible tumor, and again once tumors were grossly visible. Comparisons were also made between tumor bearing mouse serum and supernatants from NB cell lines.

METABOLOMIC ANALYSIS

Samples were analyzed by nuclear magnetic resonance and/or gas chromatography-mass spectrometry. Multivariate data analysis was conducted using SIMCA-P (Umetrics).

RESULTS

Serum metabolomic analysis differentiated high- and low-risk patients as well as active disease from CR/VGPR. Differences were in nitrogen, amino acid, and carbohydrate metabolism, as well as ketosis. The serum metabolomic signature in murine xenograft models sensitively detected NB cells and correlated with disease burden. Similar metabolic changes attributable to NB were noted in both human and murine serum.

CONCLUSIONS

Serum metabolomic analysis can distinguish several characteristics of NB. A larger analysis of COG banked sera is warranted.

Investigation of metabolomic blood biomarkers for detection of adenocarcinoma lung cancer

BACKGROUND

Untargeted metabolomics was used in case-control studies of adenocarcinoma (ADC) lung cancer to develop and test metabolite classifiers in serum and plasma as potential biomarkers for diagnosing lung cancer.

METHODS

Serum and plasma were collected and used in two independent case-control studies (ADC1 and ADC2). Controls were frequency matched for gender, age, and smoking history. There were 52 adenocarcinoma cases and 31 controls in ADC1 and 43 adenocarcinoma cases and 43 controls in ADC2. Metabolomics was conducted using gas chromatography time-of-flight mass spectrometry. Differential analysis was performed on ADC1 and the top candidates (FDR < 0.05) for serum and plasma used to develop individual and multiplex classifiers that were then tested on an independent set of serum and plasma samples (ADC2).

RESULTS

Aspartate provided the best accuracy (81.4%) for an individual metabolite classifier in serum, whereas pyrophosphate had the best accuracy (77.9%) in plasma when independently tested. Multiplex classifiers of either 2 or 4 serum metabolites had an accuracy of 72.7% when independently tested. For plasma, a multimetabolite classifier consisting of 8 metabolites gave an accuracy of 77.3% when independently tested. Comparison of overall diagnostic performance between the two blood matrices yielded similar performances. However, serum is most ideal given higher sensitivity for low-abundant metabolites.

CONCLUSION

This study shows the potential of metabolite-based diagnostic tests for detection of lung adenocarcinoma. Further validation in a larger pool of samples is warranted.

IMPACT

These biomarkers could improve early detection and diagnosis of lung cancer.

HMGA1 drives metabolic reprogramming of intestinal epithelium during hyperproliferation, polyposis, and colorectal carcinogenesis

Although significant progress has been made in the diagnosis and treatment of colorectal cancer (CRC), it remains a leading cause of cancer death worldwide. Early identification and removal of polyps that may progress to overt CRC is the cornerstone of CRC prevention. Expression of the High Mobility Group A1 (HMGA1) gene is significantly elevated in CRCs as compared with adjacent, nonmalignant tissues. We investigated metabolic aberrations induced by HMGA1 overexpression in small intestinal and colonic epithelium using traveling wave ion mobility mass spectrometry (TWIMMS) in a transgenic model in which murine Hmga1 was misexpressed in colonic epithelium. To determine if these Hmga1-induced metabolic alterations in mice were relevant to human colorectal carcinogenesis, we also investigated tumors from patients with CRC and matched, adjacent, nonmalignant tissues. Multivariate statistical methods and manual comparisons were used to identify metabolites specific to Hmga1 and CRC. Statistical modeling of data revealed distinct metabolic patterns in Hmga1 transgenics and human CRC samples as compared with the control tissues. We discovered that 13 metabolites were specific for Hmga1 in murine intestinal epithelium and also found in human CRC. Several of these metabolites function in fatty acid metabolism and membrane composition. Although further validation is needed, our results suggest that high levels of HMGA1 protein drive metabolic alterations that contribute to CRC pathogenesis through fatty acid synthesis. These metabolites could serve as potential biomarkers or therapeutic targets.

Application of metabolomics in drug resistant breast cancer research

The metabolic profiles of breast cancer cells are different from normal mammary epithelial cells. Breast cancer cells that gain resistance to therapeutic interventions can reprogram their endogenous metabolism in order to adapt and proliferate despite high oxidative stress and hypoxic conditions. Drug resistance in breast cancer, regardless of subgroups, is a major clinical setback. Although recent advances in genomics and proteomics research has given us a glimpse into the heterogeneity that exists even within subgroups, the ability to precisely predict a tumor’s response to therapy remains elusive. Metabolomics as a quantitative, high through put technology offers promise towards devising new strategies to establish predictive, diagnostic and prognostic markers of breast cancer. Along with other “omics” technologies that include genomics, transcriptomics, and proteomics, metabolomics fits into the puzzle of a comprehensive systems biology approach to understand drug resistance in breast cancer. In this review, we highlight the challenges facing successful therapeutic treatment of breast cancer and the innovative approaches that metabolomics offers to better understand drug resistance in cancer.

Cancer metabolomics in basic science perspective

As metabolomics investigates metabolic pathways with the focus on metabolites, it is a suitable approach to address the complex metabolic alteration in cancer. In addition, metabolic profiles are affected by environmental and post-natal changes, and therefore, directly measuring many metabolites may provide epigenetically relevant information in cancer. Despite much development in our understanding of cancer metabolism, focus is often directed to signaling or metabolic proteins that modulate the metabolite levels. In this review, we discuss the "metabolite-oriented view" on cancer metabolism. We cover how metabolomics research contributed to our current insights into the basic mechanism of metabolic alterations leading to cancer. Then, we discuss specific metabolites and related enzymatic pathways directly related with tumorigenesis. We particularly pay attention to how metabolites regulate signaling proteins and metabolic enzymes ultimately leading to cancer phenotypes. Finally, we address future prospects and challenges of metabolomics in cancer research.

Perioperative dynamics and significance of amino acid profiles in patients with cancer

Background

Metabolome analysis including amino acid profile is under investigation as an approach in cancer screening. The present study aims to analyze plasma free amino acid (PFAA) profiles in cancer patients and investigate their potential as biomarkers of malignancy.

Methods

Plasma samples from 56 gastric cancer patients, 28 breast cancer patients, 33 thyroid cancer patients, and 137 age-matched healthy controls were collected in the study. PFAA levels were measured and their perioperative alterations were analyzed. Biological effects of ten cancer-related amino acids were further validated in gastric and breast cancer cells.

Results

We found that PFAA profiles of cancer patients differed significantly from those of healthy controls. Decreased concentrations of PFAAs were associated with lymph node metastases in gastric cancer. Levels of PFAAs such as aspartate and alanine increased after tumor resection. PFAA levels correlated with clinical tumor markers in gastric cancer patients and pathological immunohistochemistry markers in breast cancer patients. Specifically, alanine, arginine, aspartate and cysteine had proliferative effects on breast cancer cells. Proliferation of gastric cancer cells was promoted by cysteine, but inhibited by alanine and glutamic acid. Furthermore, alanine treatment decreased total and stable fraction of gastric cancer cells, and alanine and glutamic acid induced apoptosis of gastric cancer cells.

Conclusions

PFAA patterns in cancer patients are altered perioperatively. Tumor-related amino acids identified by dynamic study of PFAA patterns may have the potential to be developed as novel biomarkers for diagnosis and prognosis of cancer patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-015-0408-1) contains supplementary material, which is available to authorized users.

Metabolic characterization of triple negative breast cancer

Background

The aims of this study were to characterize the metabolite profiles of triple negative breast cancer (TNBC) and to investigate the metabolite profiles associated with human epidermal growth factor receptor-2/neu (HER-2) overexpression using ex vivo high resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS). Metabolic alterations caused by the different estrogen receptor (ER), progesterone receptor (PgR) and HER-2 receptor statuses were also examined. To investigate the metabolic differences between two distinct receptor groups, TNBC tumors were compared to tumors with ER^pos/PgR^pos/HER-2^pos status which for the sake of simplicity is called triple positive breast cancer (TPBC).

Methods

The study included 75 breast cancer patients without known distant metastases. HR MAS MRS was performed for identification and quantification of the metabolite content in the tumors. Multivariate partial least squares discriminant analysis (PLS-DA) modeling and relative metabolite quantification were used to analyze the MR data.

Results

Choline levels were found to be higher in TNBC compared to TPBC tumors, possibly related to cell proliferation and oncogenic signaling. In addition, TNBC tumors contain a lower level of Glutamine and a higher level of Glutamate compared to TPBC tumors, which indicate an increase in glutaminolysis metabolism. The development of glutamine dependent cell growth or “Glutamine addiction” has been suggested as a new therapeutic target in cancer. Our results show that the metabolite profiles associated with HER-2 overexpression may affect the metabolic characterization of TNBC. High Glycine levels were found in HER-2^pos tumors, which support Glycine as potential marker for tumor aggressiveness.

Conclusions

Metabolic alterations caused by the individual and combined receptors involved in breast cancer progression can provide a better understanding of the biochemical changes underlying the different breast cancer subtypes. Studies are needed to validate the potential of metabolic markers as targets for personalized treatment of breast cancer subtypes.

Molecular profiling and companion diagnostics: where is personalized medicine in cancer heading?

The goal of personalized medicine is to use the right drug at the right dose - with minimal or no toxicity - for the right patient at the right time. Recent advances in understanding cell biology and pathways, and in using molecular 'omics' technologies to diagnose cancer, offer a strategic bridge to personalized medicine in cancer. Modern personalized medicine takes into account an individual's genetic makeup and disease history before developing a treatment regimen. The future of clinical oncology will be based on the use of predictive and prognostic biomarkers in patient management. Once implemented widely, personalized medicine will benefit patients and the healthcare system greatly.

Elevation of circulating branched-chain amino acids is an early event in human pancreatic adenocarcinoma development

Most patients with pancreatic ductal adenocarcinoma (PDAC) are diagnosed with advanced disease and survive less than 12 months. PDAC has been linked with obesity and glucose intolerance, but whether changes in circulating metabolites are associated with early cancer progression is unknown. To better understand metabolic derangements associated with early disease, we profiled metabolites in prediagnostic plasma from individuals with pancreatic cancer (cases) and matched controls from four prospective cohort studies. We find that elevated plasma levels of branched-chain amino acids (BCAAs) are associated with a greater than twofold increased risk of future pancreatic cancer diagnosis. This elevated risk was independent of known predisposing factors, with the strongest association observed among subjects with samples collected 2 to 5 years before diagnosis, when occult disease is probably present. We show that plasma BCAAs are also elevated in mice with early-stage pancreatic cancers driven by mutant Kras expression but not in mice with Kras-driven tumors in other tissues, and that breakdown of tissue protein accounts for the increase in plasma BCAAs that accompanies early-stage disease. Together, these findings suggest that increased whole-body protein breakdown is an early event in development of PDAC.

The utility of metabolomics in natural product and biomarker characterization

BACKGROUND

Metabolomics is a well-established rapidly developing research field involving quantitative and qualitative metabolite assessment within biological systems. Recent improvements in metabolomics technologies reveal the unequivocal value of metabolomics tools in natural products discovery, gene-function analysis, systems biology and diagnostic platforms.

SCOPE OF REVIEW

We review here some of the prominent metabolomics methodologies employed in data acquisition and analysis of natural products and disease-related biomarkers.

MAJOR CONCLUSIONS

This review demonstrates that metabolomics represents a highly adaptable technology with diverse applications ranging from environmental toxicology to disease diagnosis. Metabolomic analysis is shown to provide a unique snapshot of the functional genetic status of an organism by examining its biochemical profile, with relevance toward resolving phylogenetic associations involving horizontal gene transfer and distinguishing subgroups of genera possessing high genetic homology, as well as an increasing role in both elucidating biosynthetic transformations of natural products and detecting preclinical biomarkers of numerous disease states.

GENERAL SIGNIFICANCE

This review expands the interest in multiplatform combinatorial metabolomic analysis. The applications reviewed range from phylogenetic assignment, biosynthetic transformations of natural products, and the detection of preclinical biomarkers.

Metabonomics of human colorectal cancer: new approaches for early diagnosis and biomarker discovery

Colorectal cancer (CRC) is one of the most common cancers in the world, having both high prevalence and mortality. It is usually diagnosed at advanced stages due to the limitations of current screening methods used in the clinic. There is an urgent need to develop new biomarkers and modalities to detect, diagnose, and monitor the disease. Metabonomics, an approach that involves the comprehensive profiling of the full complement of endogenous metabolites in a biological system, has demonstrated its great potential for use in the early diagnosis and personalized treatment of various cancers including CRC. By applying advanced analytical techniques and bioinformatics tools, the metabolome is mined for biomarkers that are associated with carcinogenesis and prognosis. This review provides an overview of the metabonomics workflow and studies, with a focus on recent advances and findings in biomarker discovery for the early diagnosis and prognosis of CRC.

The continued evidence from overviews: what is the clinical utility?

The Oxford Overview process has provided us with extremely high-powered meta-analyses assessing the role of adjuvant chemotherapy in early breast cancer. From the most recent publication, the proportional benefits from chemotherapy are relatively equivalent across all patient subgroups, a finding contradictory to our growing understanding of the role of tumour biology in dictating chemosensitivity. Several factors, including heterogeneity of patient groups and chemotherapy regimens, lack of data on underlying tumour biological subtypes, and confounding effect of chemotherapy-induced ovarian suppression in premenopausal women with hormone receptor positive breast cancer, impact on the applicability and clinical utility of the Overview in current and future oncological practice. With these considerations, the Overview has become less clinically relevant as a tool for guiding adjuvant chemotherapy treatment decisions, and a new direction is required.

Translational medicine as a new clinical tool and application which improves metabolic diseases: perspectives from 2012 Sino-American symposium on clinical and translational medicine

Because of the economic growth and changes in lifestyle, metabolic diseases have become a major public health problem, which impose heavy economic burdens on individuals, families and health systems. However, its precise mediators and mechanisms remain to be fully understood. Clinical translational medicine (CTM) is an emerging area comprising multidisciplinary research from basic science to medical applications and as a new tool to improve human health by reducing disease incidence, morbidity and mortality. It can bridge knowledge of metabolic diseases processes, gained by in vitro and experimental animal models, with the disease pathways found in humans, further to identify their susceptibility genes and enable patients to achieve personalized medicament treatment. Thus, we have the reasons to believe that CTM will play even more roles in the development of new diagnostics, therapies, healthcare, and policies and the Sino-American Symposium on Clinical and Translational Medicine (SAS-CTM) will become a more and more important platform for exchanging ideas on clinical and translational research and entails a close collaboration among hospital, academia and industry.

Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

Metabolism represents the 'sharp end' of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule 'drug' transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology.

Plasma metabolomic profiles in breast cancer patients and healthy controls: by race and tumor receptor subtypes

BACKGROUND

A few studies in the last several years have shown that metabolomics, the study of metabolites and small intermediate molecules, may help better understand the breast carcinogenesis. However, breast cancer is a heterogeneous disease with different subtypes. Additionally, there is a significant racial difference in terms of breast cancer incidence and mortality. Few, if any, metabolomics studies in breast cancer have considered race and tumor subtypes in the study design.

METHODS

We performed a global metabolomic profiling using mass spectrometry and samples from 60 breast cancer cases and 60 matched controls.

RESULTS

A total of 375 named metabolites were observed, with 117 metabolites whose levels were significantly different between African American and Caucasian American women (P < .05 and q < 0.10) and 78 that differed between breast cancer cases and healthy controls (P < .05 and q < 0.10). Most of those differentiated metabolites belong to amino acids, fatty acids, and lysolipids. In the pathway-based analysis, we found that plasma levels of many amino acids were statistically significantly lower in patients with breast cancer, especially those with triple-negative breast cancer, than healthy controls. However, plasma levels of many FAs related to β-oxidation were statistically significantly higher in patients with breast cancer than healthy controls, suggesting the possibility of altered FA β-oxidation in patients with breast cancer.

CONCLUSIONS

Because of small sample size, the clinical usage of the metabolites from this study is unclear. Further validation of those significant metabolites is warranted, especially with the consideration of racial difference.