Metabolomics approaches in pancreatic adenocarcinoma: tumor metabolism profiling predicts clinical outcome of patients

Taurine Synthesis by 2-Aminoethanethiol Dioxygenase as a Vulnerable Metabolic Alteration in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) exhibits an altered metabolic profile compared to normal pancreatic tissue. However, studies on actual pancreatic tissues are limited. Untargeted metabolomics analysis was conducted on 54 pairs of tumor and matched normal tissues. Taurine levels were validated via immunohistochemistry (IHC) on separate PDAC and normal tissues. Bioinformatics analysis of transcriptomics and proteomics data evaluated genes associated with taurine metabolism. Identified taurine-associated gene was validated through gene modulation. Clinical implications were evaluated using patient data. Metabolomics analysis showed a 2.51-fold increase in taurine in PDAC compared to normal tissues (n=54). IHC confirmed this in independent samples (n=99 PDAC, 19 normal). Bioinformatics identified 2-aminoethanethiol dioxygenase (ADO) as a key gene modulating taurine metabolism. IHC on a tissue microarray (39 PDAC, 10 normal) confirmed elevated ADO in PDAC. The ADO-Taurine axis correlated with PDAC recurrence and disease-free survival. ADO knockdown reduced cancer cell proliferation and tumor growth in a mouse xenograft model. The MEK-related signaling pathway is suggested to be modulated by ADO-Taurine metabolism. Our multi-omics investigation revealed elevated taurine synthesis mediated by ADO upregulation in PDAC. The ADO-Taurine axis may serve as a biomarker for PDAC prognosis and a therapeutic target.

Metabolite Biomarkers for Early Detection of Pancreatic Ductal Adenocarcinoma: A Systematic Review

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a poor prognosis. This poor prognosis is largely attributed to a late-stage diagnosis. Recent advancements in metabolomics have emerged as a promising avenue for biomarker discovery in PDAC. This systematic review evaluates the potential of metabolite biomarkers for early detection of PDAC. Four studies meeting the inclusion criteria were analyzed, encompassing experimental, case-control, and prospective cohort designs. Key findings include the identification of distinct metabolic subtypes in PDAC with varying sensitivities to metabolic inhibitors. A biomarker signature comprising nine metabolites plus CA19-9 showed high accuracy in distinguishing PDAC from chronic pancreatitis, outperforming CA19-9 alone. Another study identified a five-metabolite signature demonstrating high diagnostic accuracy for pancreatic cancer, differentiating it from type 2 diabetes mellitus. A two-metabolite model (isoleucine and adrenic acid) showed superior performance in detecting stage-I PDAC compared to CA19-9. These studies consistently demonstrate altered metabolic pathways in PDAC patients compared to healthy controls and those with benign pancreatic conditions. Integrating metabolomic data with other molecular profiling approaches has become a powerful strategy for improving diagnostic accuracy. However, challenges remain, including the influence of confounding factors, the need for large-scale validation studies, and the standardization of metabolomic methods. The potential of artificial intelligence in interpreting complex metabolomic data offers promising avenues for future research. This review highlights the significant potential of metabolite biomarkers in early PDAC detection while emphasizing the need for further validation and refinement of these approaches.

Comprehensive multi-omics profiling identifies novel molecular subtypes of pancreatic ductal adenocarcinoma

Pancreatic cancer, a highly fatal malignancy, is predicted to rank as the second leading cause of cancer-related death in the next decade. This highlights the urgent need for new insights into personalized diagnosis and treatment. Although molecular subtypes of pancreatic cancer were well established in genomics and transcriptomics, few known molecular classifications are translated to guide clinical strategies and require a paradigm shift. Notably, chronically developing and continuously improving high-throughput technologies and systems serve as an important driving force to further portray the molecular landscape of pancreatic cancer in terms of epigenomics, proteomics, metabonomics, and metagenomics. Therefore, a more comprehensive understanding of molecular classifications at multiple levels using an integrated multi-omics approach holds great promise to exploit more potential therapeutic options. In this review, we recapitulated the molecular spectrum from different omics levels, discussed various subtypes on multi-omics means to move one step forward towards bench-to-beside translation of pancreatic cancer with clinical impact, and proposed some methodological and scientific challenges in store.

Serum Carbohydrate Antigen 19-9 and Metabolite Hypotaurine Are Predictive Markers for Early Recurrence of Pancreatic Ductal Adenocarcinoma

OBJECTIVE

A significant number of patients experience early recurrence after surgical resection for pancreatic ductal adenocarcinoma (PDAC), negating the benefit of surgery. The present study conducted clinicopathologic and metabolomic analyses to explore the factors associated with the early recurrence of PDAC.

MATERIALS AND METHODS

Patients who underwent pancreatectomy for PDAC at Kagawa University Hospital between 2011 and 2020 were enrolled. Tissue samples of PDAC and nonneoplastic pancreas were collected and frozen immediately after resection. Charged metabolites were quantified by capillary electrophoresis-mass spectrometry. Patients who relapsed within 1 year were defined as the early recurrence group.

RESULTS

Frozen tumor tissue and nonneoplastic pancreas were collected from 79 patients. The clinicopathologic analysis identified 11 predictive factors, including preoperative carbohydrate antigen 19-9 levels. The metabolomic analysis revealed that only hypotaurine was a significant risk factor for early recurrence. A multivariate analysis, including clinical and metabolic factors, showed that carbohydrate antigen 19-9 and hypotaurine were independent risk factors for early recurrence ( P = 0.045 and P = 0.049, respectively). The recurrence-free survival rate 1 year after surgery with both risk factors was only 25%.

CONCLUSIONS

Our results suggested that tumor hypotaurine is a potential metabolite associated with early recurrence. Carbohydrate antigen 19-9 and hypotaurine showed a vital utility for predicting early recurrence.

Microbiome and pancreatic cancer: time to think about chemotherapy

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer characterized by late diagnosis, rapid progression, and a high mortality rate. Its complex biology, characterized by a dense, stromal tumor environment with an immunosuppressive milieu, contributes to resistance against standard treatments like chemotherapy and radiation. This comprehensive review explores the dynamic role of the microbiome in modulating chemotherapy efficacy and outcomes in PDAC. It delves into the microbiome's impact on drug metabolism and resistance, and the interaction between microbial elements, drugs, and human biology. We also highlight the significance of specific bacterial species and microbial enzymes in influencing drug action and the immune response in the tumor microenvironment. Cutting-edge methodologies, including artificial intelligence, low-biomass microbiome analysis and patient-derived organoid models, are discussed, offering insights into the nuanced interactions between microbes and cancer cells. The potential of microbiome-based interventions as adjuncts to conventional PDAC treatments are discussed, paving the way for personalized therapy approaches. This review synthesizes recent research to provide an in-depth understanding of how the microbiome affects chemotherapy efficacy. It focuses on elucidating key mechanisms and identifying existing knowledge gaps. Addressing these gaps is crucial for enhancing personalized medicine and refining cancer treatment strategies, ultimately improving patient outcomes.

Fingerprinting and profiling in metabolomics of biosamples

This review focuses on metabolomics from an NMR point of view. It attempts to cover the broad scope of metabolomics and describes the NMR experiments that are most suitable for each sample type. It is addressed not only to NMR specialists, but to all researchers who wish to approach metabolomics with a clear idea of what they wish to achieve but not necessarily with a deep knowledge of NMR. For this reason, some technical parts may seem a bit naïve to the experts. The review starts by describing standard metabolomics procedures, which imply the use of a dedicated 600 MHz instrument and of four properly standardized 1D experiments. Standardization is a must if one wants to directly compare NMR results obtained in different labs. A brief mention is also made of standardized pre-analytical procedures, which are even more essential. Attention is paid to the distinction between fingerprinting and profiling, and the advantages and disadvantages of fingerprinting are clarified. This aspect is often not fully appreciated. Then profiling, and the associated problems of signal assignment and quantitation, are discussed. We also describe less conventional approaches, such as the use of different magnetic fields, the use of signal enhancement techniques to increase sensitivity, and the potential of field-shuttling NMR. A few examples of biomedical applications are also given, again with the focus on NMR techniques that are most suitable to achieve each particular goal, including a description of the most common heteronuclear experiments. Finally, the growing applications of metabolomics to foodstuffs are described.

Characteristics of Cancer Epidemiology Studies That Employ Metabolomics: A Scoping Review

An increasing number of cancer epidemiology studies use metabolomics assays. This scoping review characterizes trends in the literature in terms of study design, population characteristics, and metabolomics approaches and identifies opportunities for future growth and improvement. We searched PubMed/MEDLINE, Embase, Scopus, and Web of Science: Core Collection databases and included research articles that used metabolomics to primarily study cancer, contained a minimum of 100 cases in each main analysis stratum, used an epidemiologic study design, and were published in English from 1998 to June 2021. A total of 2,048 articles were screened, of which 314 full texts were further assessed resulting in 77 included articles. The most well-studied cancers were colorectal (19.5%), prostate (19.5%), and breast (19.5%). Most studies used a nested case-control design to estimate associations between individual metabolites and cancer risk and a liquid chromatography-tandem mass spectrometry untargeted or semi-targeted approach to measure metabolites in blood. Studies were geographically diverse, including countries in Asia, Europe, and North America; 27.3% of studies reported on participant race, the majority reporting White participants. Most studies (70.2%) included fewer than 300 cancer cases in their main analysis. This scoping review identified key areas for improvement, including needs for standardized race and ethnicity reporting, more diverse study populations, and larger studies.

Plasma Metabolomics Predicts Chemotherapy Response in Advanced Pancreatic Cancer

Pancreatic cancer (PC) is one of the deadliest cancers. Developing biomarkers for chemotherapeutic response prediction is crucial for improving the dismal prognosis of advanced-PC patients (pts). To evaluate the potential of plasma metabolites as predictors of the response to chemotherapy for PC patients, we analyzed plasma metabolites using high-performance liquid chromatography-mass spectrometry from 31 cachectic, advanced-PC subjects enrolled into the PANCAX-1 (NCT02400398) prospective trial to receive a jejunal tube peptide-based diet for 12 weeks and who were planned for palliative chemotherapy. Overall, there were statistically significant differences in the levels of intermediates of multiple metabolic pathways in pts with a partial response (PR)/stable disease (SD) vs. progressive disease (PD) to chemotherapy. When stratified by the chemotherapy regimen, PD after 5-fluorouracil-based chemotherapy (e.g., FOLFIRINOX) was associated with decreased levels of amino acids (AAs). For gemcitabine-based chemotherapy (e.g., gemcitabine/nab-paclitaxel), PD was associated with increased levels of intermediates of glycolysis, the TCA cycle, nucleoside synthesis, and bile acid metabolism. These results demonstrate the feasibility of plasma metabolomics in a prospective cohort of advanced-PC patients for assessing the effect of enteral feeding as their primary source of nutrition. Metabolic signatures unique to FOLFIRINOX or gemcitabine/nab-paclitaxel may be predictive of a patient's response and warrant further study.

High-resolution magic angle spinning NMR for intact biological specimen analysis: Initial discovery, recent developments, and future directions

High-resolution magic angle spinning (HRMAS) NMR, an approach for intact biological material analysis discovered more than 25 years ago, has been advanced by many technical developments and applied to many biomedical uses. This article provides a history of its discovery, first by explaining the key scientific advances that paved the way for HRMAS NMR's invention, and then by turning to recent developments that have profited from applying and advancing the technique during the last 5 years. Developments aimed at directly impacting healthcare include HRMAS NMR metabolomics applications within studies of human disease states such as cancers, brain diseases, metabolic diseases, transplantation medicine, and adiposity. Here, the discussion describes recent HRMAS NMR metabolomics studies of breast cancer and prostate cancer, as well as of matching tissues with biofluids, multimodality studies, and mechanistic investigations, all conducted to better understand disease metabolic characteristics for diagnosis, opportune windows for treatment, and prognostication. In addition, HRMAS NMR metabolomics studies of plants, foods, and cell structures, along with longitudinal cell studies, are reviewed and discussed. Finally, inspired by the technique's history of discoveries and recent successes, future biomedical arenas that stand to benefit from HRMAS NMR-initiated scientific investigations are presented.

Untargeted metabolomics characterization of the resectable pancreatic ductal adenocarcinoma

BACKGROUND

Diagnosis of pancreatic ductal adenocarcinoma (PDAC) is difficult due to the lack of specific symptoms and screening methods. Only less than 10% of PDAC patients are candidates for surgery at the time of diagnosis. Thus, there is a great global unmet need for valuable biomarkers that could improve the opportunity to detect PDAC at the resectable stage. This study aimed to develop a potential biomarker model for the detection of resectable PDAC by tissue and serum metabolomics.

METHODS

Ultra-high-performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS/MS) was performed for metabolome quantification in 98 serum samples (49 PDAC patients and 49 healthy controls (HCs)) and 20 pairs of matched pancreatic cancer tissues (PCTs) and adjacent noncancerous tissues (ANTs) from PDAC patients. Univariate and multivariate analyses were used to profile the differential metabolites between PDAC and HC.

RESULTS

A total of 12 differential metabolites were present in both serum and tissue samples of PDAC. Among them, a total of eight differential metabolites showed the same expressional levels, including four upregulated and four downregulated metabolites. Finally, a panel of three metabolites including 16-hydroxypalmitic acid, phenylalanine, and norleucine was constructed by logistic regression analysis. Notably, the panel was capable of distinguishing resectable PDAC from HC with an AUC value of 0.942. Additionally, a multimarker model based on the 3-metabolites-based panel and CA19-9 showed a better performance than the metabolites panel or CA19-9 alone (AUC: 0.968 vs. 0.942, 0.850).

CONCLUSIONS

Taken together, the resectable early-stage PDAC has unique metabolic features in serum and tissue samples. The defined panel of three metabolites has the potential value for early screening of PDAC at the resectable stage.

Tumor metabolic alterations after neoadjuvant chemoradiotherapy predict postoperative recurrence in patients with pancreatic cancer

OBJECTIVE

We investigated the metabolic changes in pancreatic ductal adenocarcinoma to identify the mechanisms of treatment response of neoadjuvant chemoradiation therapy.

METHODS

Frozen tumor and non-neoplastic pancreas tissues were prospectively obtained from 88 patients with pancreatic ductal adenocarcinoma who underwent curative-intent surgery. Sixty-two patients received neoadjuvant chemoradiation therapy and 26 patients did not receive neoadjuvant therapy (control group). Comprehensive analysis of metabolites in tumor and non-neoplastic pancreatic tissue was performed by capillary electrophoresis-mass spectrometry.

RESULTS

Capillary electrophoresis-mass spectrometry detected 90 metabolites for analysis among more than 500 ionic metabolites quantified. There were significant differences in 27 tumor metabolites between the neoadjuvant chemoradiation therapy and control groups. There were significant differences in eight metabolites [1-MethylnNicotinamide, Carnitine, Glucose, Glutathione (red), N-acetylglucosamine 6-phosphate, N-acetylglucosamine 1-phosphate, UMP, Phosphocholine] between good responder and poor responder for neoadjuvant chemoradiation therapy. Among these metabolites, phosphocholine, Carnitine and Glutathione were associated with recurrence-free survival only in the neoadjuvant chemoradiation therapy group. Microarray confirmed marked gene suppression of choline transporters [CTL1-4 (SLC44A1-44A4)] in pancreatic ductal adenocarcinoma tissue of neoadjuvant chemoradiation therapy group.

CONCLUSION

The present study identifies several important metabolic consequences and potential neoadjuvant chemoradiation therapy targets in pancreatic ductal adenocarcinoma. Choline metabolism is one of the key pathways involved in recurrence of the patients with pancreatic ductal adenocarcinoma who received neoadjuvant chemoradiation therapy.

Gut Microbial Profile in Patients with Pancreatic Cancer

Background: Pancreatic cancer is a lethal tumor with a poor prognosis. The connection between pancreatic cancer and gut microbiota is less reported. Objectives: This study analyzed microbial characteristics in patients with pancreatic cancer from the tropical area of China and explored the potential impact of the characteristic microflora on pancreatic cancer. Methods: Stool samples and blood test indices of participants were collected in Hainan, China. Metagenomic sequencing was used to analyze the gut microbiota characteristics. The R corrplot package was used to analyze the correlation between gut microbiota and blood test indices. Results: The microbial community in pancreatic cancer were clustered together and significantly separated from controls. The Simpson index was increased significantly in pancreatic cancer compared to controls. The abundances of butyrate-producing bacteria (Anaerostipes hadrus, Lachnoclostridium phocaeense, and Romboutsia ilealis), Bifidobacteria, and [Eubacterium] eligens were significantly decreased, while Fusobacterium, Enterobacter, and Enterococcus were significantly increased in pancreatic cancer. Prevotella copri may have a vital role in the bacterial interaction network. Pathways connected to metabolism, environment (bacterial secretion system), genetic information (protein export and ribosome), and human diseases (infectious diseases and drug resistance) were increased in the pancreatic cancer group. Butyrate-producing bacteria (butyrate-producing bacterium SS3/4, A. hadrus, R. intestinalis, and Faecalibacterium prausnitzii) and Bifidobacteria were significantly negatively correlated with the neutrophil-to-lymphocyte ratio. Conclusions: The gut microbiome was distinct in patients with pancreatic cancer from the tropical area of China. Changes in intestinal flora abundance and metabolic pathways may play an essential role in the occurrence and development of pancreatic cancer.

Targeted metabolomics analyses for brain tumor margin assessment during surgery

MOTIVATION

Identification and removal of micro-scale residual tumor tissue during brain tumor surgery are key for survival in glioma patients. For this goal, High-Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HRMAS NMR) spectroscopy-based assessment of tumor margins during surgery has been an effective method. However, the time required for metabolite quantification and the need for human experts such as a pathologist to be present during surgery are major bottlenecks of this technique. While machine learning techniques that analyze the NMR spectrum in an untargeted manner (i.e. using the full raw signal) have been shown to effectively automate this feedback mechanism, high dimensional and noisy structure of the NMR signal limits the attained performance.

RESULTS

In this study, we show that identifying informative regions in the HRMAS NMR spectrum and using them for tumor margin assessment improves the prediction power. We use the spectra normalized with the ERETIC (electronic reference to access in vivo concentrations) method which uses an external reference signal to calibrate the HRMAS NMR spectrum. We train models to predict quantities of metabolites from annotated regions of this spectrum. Using these predictions for tumor margin assessment provides performance improvements up to 4.6% the Area Under the ROC Curve (AUC-ROC) and 2.8% the Area Under the Precision-Recall Curve (AUC-PR). We validate the importance of various tumor biomarkers and identify a novel region between 7.97 ppm and 8.09 ppm as a new candidate for a glioma biomarker.

AVAILABILITY AND IMPLEMENTATION

The code is released at https://github.com/ciceklab/targeted_brain_tumor_margin_assessment. The data underlying this article are available in Zenodo, at https://doi.org/10.5281/zenodo.5781769.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Emerging Role for 7T MRI and Metabolic Imaging for Pancreatic and Liver Cancer

Advances in magnet technologies have led to next generation 7T magnetic resonance scanners which can fit in the footprint and price point of conventional hospital scanners (1.5−3T). It is therefore worth asking if there is a role for 7T magnetic resonance imaging and spectroscopy for the treatment of solid tumor cancers. Herein, we survey the medical literature to evaluate the unmet clinical needs for patients with pancreatic and hepatic cancer, and the potential of ultra-high field proton imaging and phosphorus spectroscopy to fulfil those needs. We draw on clinical literature, preclinical data, nuclear magnetic resonance spectroscopic data of human derived samples, and the efforts to date with 7T imaging and phosphorus spectroscopy. At 7T, the imaging capabilities approach histological resolution. The spectral and spatial resolution enhancements at high field for phospholipid spectroscopy have the potential to reduce the number of exploratory surgeries due to tumor boundaries undefined at conventional field strengths. Phosphorus metabolic imaging at 7T magnetic field strength, is already a mainstay in preclinical models for molecular phenotyping, energetic status evaluation, dosimetry, and assessing treatment response for both pancreatic and liver cancers. Metabolic imaging of primary tumors and lymph nodes may provide powerful metrics to aid staging and treatment response. As tumor tissues contain extreme levels of phospholipid metabolites compared to the background signal, even spectroscopic volumes containing less than 50% tumor can be detected and/or monitored. Phosphorus spectroscopy allows non-invasive pH measurements, indicating hypoxia, as a predictor of patients likely to recur. We conclude that 7T multiparametric approaches that include metabolic imaging with phosphorus spectroscopy have the potential to meet the unmet needs of non-invasive location-specific treatment monitoring, lymph node staging, and the reduction in unnecessary surgeries for patients undergoing resections for pancreatic cancer. There is also potential for the use of 7T phosphorous spectra for the phenotyping of tumor subtypes and even early diagnosis (<2 mL). Whether or not 7T can be used for all patients within the next decade, the technology is likely to speed up the translation of new therapeutics.

Complex roles of nicotinamide N-methyltransferase in cancer progression

Nicotinamide N-methyltransferase (NNMT) is an intracellular methyltransferase, catalyzing the N-methylation of nicotinamide (NAM) to form 1-methylnicotinamide (1-MNAM), in which S-adenosyl-l-methionine (SAM) is the methyl donor. High expression of NNMT can alter cellular NAM and SAM levels, which in turn, affects nicotinamide adenine dinucleotide (NAD⁺)-dependent redox reactions and signaling pathways, and remodels cellular epigenetic states. Studies have revealed that NNMT plays critical roles in the occurrence and development of various cancers, and analysis of NNMT expression levels in different cancers from The Cancer Genome Atlas (TCGA) dataset indicated that NNMT might be a potential biomarker and therapeutic target for tumor diagnosis and treatment. This review provides a comprehensive understanding of recent advances on NNMT functions in different tumors and deciphers the complex roles of NNMT in cancer progression.

Application of Proteomics in Pancreatic Ductal Adenocarcinoma Biomarker Investigations: A Review

Pancreatic ductal adenocarcinoma (PDAC), a highly aggressive malignancy with a poor prognosis is usually detected at the advanced stage of the disease. The only US Food and Drug Administration-approved biomarker that is available for PDAC, CA 19-9, is most useful in monitoring treatment response among PDAC patients rather than for early detection. Moreover, when CA 19-9 is solely used for diagnostic purposes, it has only a recorded sensitivity of 79% and specificity of 82% in symptomatic individuals. Therefore, there is an urgent need to identify reliable biomarkers for diagnosis (specifically for the early diagnosis), ascertain prognosis as well as to monitor treatment response and tumour recurrence of PDAC. In recent years, proteomic technologies are growing exponentially at an accelerated rate for a wide range of applications in cancer research. In this review, we discussed the current status of biomarker research for PDAC using various proteomic technologies. This review will explore the potential perspective for understanding and identifying the unique alterations in protein expressions that could prove beneficial in discovering new robust biomarkers to detect PDAC at an early stage, ascertain prognosis of patients with the disease in addition to monitoring treatment response and tumour recurrence of patients.

1 H NMR-based metabolomics of paired esophageal tumor tissues and serum samples identifies specific serum biomarkers for esophageal cancer

Serum metabolites of healthy controls and esophageal cancer (EC) patients have previously been compared to predict cancer-specific profiles. However, the association between metabolic alterations in serum samples and esophageal tissues in EC patients remains unclear. Here, we analyzed 50 pairs of EC tissues and distant noncancerous tissues, together with patient-matched serum samples, using ¹ H NMR spectroscopy and pattern recognition algorithms. EC patients could be differentiated from the controls based on the metabolic profiles at tissue and serum levels. Some overlapping discriminatory metabolites, including valine, alanine, glucose, acetate, citrate, succinate and glutamate, were identified in both matrices. These results suggested deregulation of metabolic pathways, and potentially revealed the links between EC and several metabolic pathways, such as the tricarboxylic acid cycle, glutaminolysis, short-chain fatty acid metabolism, lipometabolism and pyruvate metabolism. Perturbation of the pyruvate metabolism was most strongly associated with EC progression. Consequently, an optimal serum metabolite biomarker panel comprising acetate and pyruvate was developed, as these two metabolites are involved in pyruvate metabolism, and changes in their serum levels were significantly correlated with alterations in the levels of some other esophageal tissue metabolites. In comparison with individual biomarkers, this panel exhibited better diagnostic efficiency for EC, with an AUC of 0.948 in the test set, and a good predictive ability of 82.5% in the validation set. Analysis of key genes related to pyruvate metabolism in EC patients revealed patterns corresponding to the changes in serum pyruvate and acetate levels. These correlation analyses demonstrate that there were distinct metabolic characteristics and pathway aberrations in the esophageal tumor tissue and in the serum. Changes in the serum metabolic signatures could reflect the alterations in the esophageal tumor profile, thereby emphasizing the importance of distinct serum metabolic profiles as potential noninvasive biomarkers for EC.

Stratified 3D Microtumors as Organotypic Testing Platforms for Screening Pancreatic Cancer Therapies

Cancer-associated pancreatic stellate cells installed in periacinar/periductal regions are master players in generating the characteristic biophysical shield found in pancreatic ductal adenocarcinoma (PDAC). Recreating this unique PDAC stromal architecture and its desmoplastic microenvironment in vitro is key to discover innovative treatments. However, this still remains highly challenging to realize. Herein, organotypic 3D microtumors that recapitulate PDAC-stroma spatial bioarchitecture, as well as its biomolecular, metabolic, and desmoplastic signatures, are bioengineered. Such newly engineered platforms, termed stratified microenvironment spheroid models - STAMS - mimic the spatial stratification of cancer-stromal cells, exhibit a reproducible morphology and sub-millimeter size. In culture, 3D STAMS secrete the key molecular biomarkers found in human pancreatic cancer, namely TGF-β, FGF-2, IL-1β, and MMP-9, among others. This is accompanied by an extensive desmoplastic reaction where collagen and glycosaminoglycans (GAGs) de novo deposition is observed. These stratified models also recapitulate the resistance to various chemotherapeutics when compared to standard cancer-stroma random 3D models. Therapeutics resistance is further evidenced upon STAMS inclusion in a tumor extracellular matrix (ECM)-mimetic hydrogel matrix, reinforcing the importance of mimicking PDAC-stroma bioarchitectural features in vitro. The 3D STAMS technology represents a next generation of biomimetic testing platforms with improved potential for advancing high-throughput screening and preclinical validation of innovative pancreatic cancer therapies.

Current State of "Omics" Biomarkers in Pancreatic Cancer

Pancreatic cancer is one of the most fatal malignancies and the seventh leading cause of cancer-related deaths related to late diagnosis, poor survival rates, and high incidence of metastasis. Unfortunately, pancreatic cancer is predicted to become the third leading cause of cancer deaths in the future. Therefore, diagnosis at the early stages of pancreatic cancer for initial diagnosis or postoperative recurrence is a great challenge, as well as predicting prognosis precisely in the context of biomarker discovery. From the personalized medicine perspective, the lack of molecular biomarkers for patient selection confines tailored therapy options, including selecting drugs and their doses or even diet. Currently, there is no standardized pancreatic cancer screening strategy using molecular biomarkers, but CA19-9 is the most well known marker for the detection of pancreatic cancer. In contrast, recent innovations in high-throughput techniques have enabled the discovery of specific biomarkers of cancers using genomics, transcriptomics, proteomics, metabolomics, glycomics, and metagenomics. Panels combining CA19-9 with other novel biomarkers from different "omics" levels might represent an ideal strategy for the early detection of pancreatic cancer. The systems biology approach may shed a light on biomarker identification of pancreatic cancer by integrating multi-omics approaches. In this review, we provide background information on the current state of pancreatic cancer biomarkers from multi-omics stages. Furthermore, we conclude this review on how multi-omics data may reveal new biomarkers to be used for personalized medicine in the future.

Comprehensive metabolomic profiling of osteosarcoma based on UHPLC-HRMS

INTRODUCTION

Osteosarcoma (OS) is the most common primary malignant bone tumor in children and adolescents. An increasing number of studies have demonstrated that tumor proliferation and metastasis are closely related to complex metabolic reprogramming. However, there are limited data to provide a comprehensive metabolic picture of osteosarcoma.

OBJECTIVES

Our study aims to identify aberrant metabolic pathways and seek potential adjuvant biomarkers for osteosarcoma.

METHODS

Serum samples were collected from 65 osteosarcoma patients and 30 healthy controls. Nontargeted metabolomic profiling was performed by liquid chromatography-mass spectrometry (LC-MS) based on univariate and multivariate statistical analyses.

RESULTS

The OPLS-DA model analysis identified clear separations among groups. We identified a set of differential metabolites such as higher serum levels of adenosine-5-monophosphate, inosine-5-monophosphate and guanosine monophosphate in primary OS patients compared to healthy controls, and higher serum levels of 5-aminopentanamide, 13(S)-HpOTrE (FA 18:3 + 2O) and methionine sulfoxide in lung metastatic OS patients compared to primary OS patients, revealing aberrant metabolic features during the proliferation and metastasis of osteosarcoma. We found a group of metabolites especially lactic acid and glutamic acid, with AUC values of 0.97 and 0.98, which could serve as potential adjuvant diagnostic biomarkers for primary osteosarcoma, and a panel of 2 metabolites, 5-aminopentanamide and 13(S)-HpOTrE (FA 18:3 + 2O), with an AUC value of 0.92, that had good monitoring ability for lung metastases.

CONCLUSIONS

Our study provides new insight into the aberrant metabolic features of osteosarcoma. The potential biomarkers identified here may have translational significance.

Recent Discoveries of Diagnostic, Prognostic and Predictive Biomarkers for Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with a dismal prognosis that is frequently diagnosed at an advanced stage. Although less common than other malignant diseases, it currently ranks as the fourth most common cause of cancer-related death in the European Union with a five-year survival rate of below 9%. Surgical resection, followed by adjuvant chemotherapy, remains the only potentially curative treatment but only a minority of patients is diagnosed with locally resectable, non-metastatic disease. Patients with advanced disease are treated with chemotherapy but high rates of treatment resistance and unfavorable side-effect profiles of some of the used regimens remain major challenges. Biomarkers reflect pathophysiological or physiological processes linked to a disease and can be used as diagnostic, prognostic and predictive tools. Thus, accurate biomarkers can allow for better patient stratification and guide therapy choices. Currently, the only broadly used biomarker for PDAC, CA 19-9, has multiple limitations and the need for novel biomarkers is urgent. In this review, we highlight the current situation, recent discoveries and developments in the field of biomarkers of PDAC and their potential clinical applications.

Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy

Complete resection of the tumor is important for survival in glioma patients. Even if the gross total resection was achieved, left-over micro-scale tissue in the excision cavity risks recurrence. High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HRMAS NMR) technique can distinguish healthy and malign tissue efficiently using peak intensities of biomarker metabolites. The method is fast, sensitive and can work with small and unprocessed samples, which makes it a good fit for real-time analysis during surgery. However, only a targeted analysis for the existence of known tumor biomarkers can be made and this requires a technician with chemistry background, and a pathologist with knowledge on tumor metabolism to be present during surgery. Here, we show that we can accurately perform this analysis in real-time and can analyze the full spectrum in an untargeted fashion using machine learning. We work on a new and large HRMAS NMR dataset of glioma and control samples (n = 565), which are also labeled with a quantitative pathology analysis. Our results show that a random forest based approach can distinguish samples with tumor cells and controls accurately and effectively with a median AUC of 85.6% and AUPR of 93.4%. We also show that we can further distinguish benign and malignant samples with a median AUC of 87.1% and AUPR of 96.1%. We analyze the feature (peak) importance for classification to interpret the results of the classifier. We validate that known malignancy biomarkers such as creatine and 2-hydroxyglutarate play an important role in distinguishing tumor and normal cells and suggest new biomarker regions. The code is released at http://github.com/ciceklab/HRMAS_NC.

Metabolomic profiling of gastric cancer tissues identified potential biomarkers for predicting peritoneal recurrence

BACKGROUND

Metabolomics is useful for analyzing the nutrients necessary for cancer progression, as the proliferation is regulated by available nutrients. We studied the metabolomic profile of gastric cancer (GC) tissue to elucidate the associations between metabolism and recurrence.

METHODS

Cancer and adjacent non-cancerous tissues were obtained in a pair-wise manner from 140 patients with GC who underwent gastrectomy. Frozen tissues were homogenized and analyzed by capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS). Metabolites were further assessed based on the presence or absence of recurrence.

RESULTS

Ninety-three metabolites were quantified. In cancer tissues, the lactate level was significantly higher and the adenylate energy charge was lower than in non-cancerous tissues. The Asp, β-Ala, GDP, and Gly levels were significantly lower in patients with recurrence than in those without. Based on ROC analyses to determine the cut-off values of the four metabolites, patients were categorized into groups at high risk and low risk of peritoneal recurrence. Logistic regression and Cox proportional hazard analyses identified β-Ala as an independent predictor of peritoneal recurrence (hazard ratio [HR] 5.21 [95% confidence interval 1.07-35.89], p = 0.029) and an independent prognostic factor for the overall survival (HR 3.44 [95% CI 1.65-7.14], p < 0.001).

CONCLUSIONS

The metabolomic profiles of cancer tissues differed from those of non-cancerous tissues. In addition, four metabolites were significantly associated with recurrence in GC. β-Ala was both a significant predictor of peritoneal recurrence and a prognostic factor.

Magnetic resonance imaging biomarkers for pulsed focused ultrasound treatment of pancreatic ductal adenocarcinoma

BACKGROUND

The robust fibroinflammatory stroma characteristic of pancreatic ductal adenocarcinoma (PDA) impedes effective drug delivery. Pulsed focused ultrasound (pFUS) can disrupt this stroma and has improved survival in an early clinical trial. Non-invasive methods to characterize pFUS treatment effects are desirable for advancement of this promising treatment modality in larger clinical trials.

AIM

To identify promising, non-invasive pre-clinical imaging methods to characterize acute pFUS treatment effects for in vivo models of PDA.

METHODS

We utilized quantitative magnetic resonance imaging methods at 14 tesla in three mouse models of PDA (subcutaneous, orthotopic and transgenic - Kras^LSL-G12D/+, Trp53^LSL-R172H/+, Cre or “KPC”) to assess immediate tumor response to pFUS treatment (VIFU 2000 Alpinion Medical Systems; 475 W peak electric power, 1 ms pulse duration, 1 Hz, duty cycle 0.1%) vs sham therapy, and correlated our results with histochemical data. These pFUS treatment parameters were previously shown to enhance tumor permeability to chemotherapeutics. T1 and T2 relaxation maps, high (126, 180, 234, 340, 549) vs low (7, 47, 81) b-value apparent diffusion coefficient (ADC) maps, magnetization transfer ratio (MTR) maps, and chemical exchange saturation transfer (CEST) maps for the amide proton spectrum (3.5 parts per million or “ppm”) and the glycosaminoglycan spectrum (0.5-1.5 ppm) were generated and analyzed pre-treatment, and immediately post-treatment, using ImageJ. Animals were sacrificed immediately following post-treatment imaging. The whole-tumor was selected as the region of interest for data analysis and subsequent statistical analysis. T-tests and Pearson correlation were used for statistical inference.

RESULTS

Mean high-b value ADC measurements increased significantly with pFUS treatment for all models. Mean glycosaminoglycan CEST and T2 measurements decreased significantly post-treatment for the KPC group. Mean MTR and amide CEST values increased significantly for the KPC group. Hyaluronic acid focal intensities in the treated regions were significantly lower following pFUS treatment for all animal models. The magnetic resonance imaging changes observed acutely following pFUS therapy likely reflect: (1) Sequelae of variable degrees of microcapillary hemorrhage (T1, MTR and amide CEST); (2) Lower PDA glycosaminoglycan content and associated water content (glycosaminoglycan CEST, T2 and hyaluronic acid focal intensity); and (3) Improved tumor diffusivity (ADC) post pFUS treatment.

CONCLUSION

T2, glycosaminoglycan CEST, and ADC maps may provide reliable quantitation of acute pFUS treatment effects for patients with PDA.

Predicting Carbon Spectrum in Heteronuclear Single Quantum Coherence Spectroscopy for Online Feedback During Surgery

¹H High-Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) is a reliable technology used for detecting metabolites in solid tissues. Fast response time enables guiding surgeons in real time, for detecting tumor cells that are left over in the excision cavity. However, severe overlap of spectral resonances in 1D signal often render distinguishing metabolites impossible. In that case, Heteronuclear Single Quantum Coherence Spectroscopy (HSQC) NMR is applied which can distinguish metabolites by generating 2D spectra ( ¹H- ¹³C). Unfortunately, this analysis requires much longer time and prohibits real time analysis. Thus, obtaining 2D spectrum fast has major implications in medicine. In this study, we show that using multiple multivariate regression and statistical total correlation spectroscopy, we can learn the relation between the ¹H and ¹³C dimensions. Learning is possible with small sample sizes and without the need for performing the HSQC analysis, we can predict the ¹³C dimension by just performing ¹H HRMAS NMR experiment. We show on a rat model of central nervous system tissues (80 samples, 5 tissues) that our methods achieve 0.971 and 0.957 mean R² values, respectively. Our tests on 15 human brain tumor samples show that we can predict 104 groups of 39 metabolites with 97 percent accuracy. Finally, we show that we can predict the presence of a drug resistant tumor biomarker (creatine) despite obstructed signal in ¹H dimension. In practice, this information can provide valuable feedback to the surgeon to further resect the cavity to avoid potential recurrence.

Tissue-based metabolomics reveals potential biomarkers for cervical carcinoma and HPV infection

Aberrant metabolic regulation has been observed in human cancers, but the corresponding regulation in human papillomavirus (HPV) infection-associated cervical cancer is not well understood. Here, we explored potential biomarkers for the early prediction of cervical carcinoma based on the metabolic profile of uterine cervical tissue specimens that were positive for HPV16 infection. Fifty-two fresh cervical tissues were collected from women confirmed to have cervical squamous cell carcinoma (SCC; n = 21) or cervical intraepithelial neoplasia (CIN) stages II-III (n = 20). Eleven healthy women constituted the controls (negative controls [NCs]). Real-time polymerase chain reaction (PCR) was performed to detect HPV infection in the tissues. High-resolution magic angle spinning nuclear magnetic resonance was utilized for the analysis of the metabolic profile in the tissues. The expression of rate-limiting enzymes involved in key metabolic pathways was detected by reverse-transcription quantitative PCR. An independent immunohistochemical analysis was performed using 123 cases of paraffin-embedded cervical specimens. A profile of 17 small molecular metabolites that showed differential expression in HPV16-positive cervical SCC or CIN II-III compared with HPV-negative NC group was identified. According to the profile, the levels of α- and β-glucose decreased, those of lactate and low-density lipoproteins increased, and the expression of multiple amino acids was altered. Significantly increased transcript and protein levels of glycogen synthase kinase 3 beta (GSK3β) and glutamate decarboxylase 1 (GAD1) and decreased transcript and protein levels of pyruvate kinase muscle isozyme 2 (PKM2) and carnitine palmitoyltransferase 1A (CPT1A) were observed in the patient group (p < 0.05). HPV infection and cervical carcinogenesis drive metabolic modifications that might be associated with the aberrant regulation of enzymes related to metabolic pathways.

Comparative metabolome analysis of serum changes in sheep under overgrazing or light grazing conditions

Background

Overgrazing is a primary contributor to severe reduction in forage quality and production in Inner Mongolia, leading to extensive ecosystem degradation, sheep health impairment and growth performance reduction. Further studies to identify serum biomarkers that reflect changes in sheep health and nutritional status following overgrazing would be beneficial. We hereby hypothesize that reduced sheep growth performance under overgrazing conditions would be associated with metabolic and immune response alterations. This study used an untargeted metabolomics analysis by conducting ultra-high-performance liquid chromatography combined with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) of sheep serum under overgrazing and light grazing conditions to identify metabolic disruptions in response to overgrazing.

Results

The sheep body weight gains as well as serum biochemical variables associated with immune responses and nutritional metabolism (immunoglobulin G, albumin, glucose, and nonesterified fatty acids) were significantly decreased with overgrazing compared with light grazing condition. In contrast, other serum parameters such as alanine and aspartate aminotransferase, alkaline phosphatase, total bilirubin, blood urea nitrogen, and interleukin-8 were markedly higher in the overgrazing group. Principal component analysis discriminated the metabolomes of the light grazing from the overgrazing group. Multivariate and univariate analyses revealed changes in the serum concentrations of 15 metabolites (9 metabolites exhibited a marked increase, whereas 6 metabolites showed a significant decrease) in the overgrazing group. Major changes of fatty acid oxidation, bile acid biosynthesis, and purine and protein metabolism were observed.

Conclusions

These findings offer metabolic evidence for putative biomarkers for overgrazing-induced changes in serum metabolism. Target-identification of these particular metabolites may potentially increase our knowledge of the molecular mechanisms of altered immune responses, nutritional metabolism, and reduced sheep growth performance under overgrazing conditions.

Metabolomics of Small Intestine Neuroendocrine Tumors and Related Hepatic Metastases

To assess the metabolomic fingerprint of small intestine neuroendocrine tumors (SI-NETs) and related hepatic metastases, and to investigate the influence of the hepatic environment on SI-NETs metabolome. Ninety-four tissue samples, including 46 SI-NETs, 18 hepatic NET metastases and 30 normal SI and liver samples, were analyzed using 1H-magic angle spinning (HRMAS) NMR nuclear magnetic resonance (NMR) spectroscopy. Twenty-seven metabolites were identified and quantified. Differences between primary NETs vs. normal SI and primary NETs vs. hepatic metastases, were assessed. Network analysis was performed according to several clinical and pathological features. Succinate, glutathion, taurine, myoinositol and glycerophosphocholine characterized NETs. Normal SI specimens showed higher levels of alanine, creatine, ethanolamine and aspartate. PLS-DA revealed a continuum-like distribution among normal SI, G1-SI-NETs and G2-SI-NETs. The G2-SI-NET distribution was closer and clearly separated from normal SI tissue. Lower concentration of glucose, serine and glycine, and increased levels of choline-containing compounds, taurine, lactate and alanine, were found in SI-NETs with more aggressive tumors. Higher abundance of acetate, succinate, choline, phosphocholine, taurine, lactate and aspartate discriminated liver metastases from normal hepatic parenchyma. Higher levels of alanine, ethanolamine, glycerophosphocholine and glucose was found in hepatic metastases than in primary SI-NETs. The present work gives for the first time a snapshot of the metabolomic characteristics of SI-NETs, suggesting the existence of complex metabolic reality, maybe characteristic of different tumor evolution.

Developing a preoperative serum metabolome-based recurrence-predicting nomogram for patients with resected pancreatic ductal adenocarcinoma

We investigated the potential application of preoperative serum metabolomes in predicting recurrence in patients with resected pancreatic cancer. From November 2012 to June 2014, patients who underwent potentially curative pancreatectomy for pancreatic ductal adenocarcinoma were examined. Among 57 patients, 32 were men; 42 had pancreatic head cancers. The 57 patients could be clearly categorized into two main clusters using 178 preoperative serum metabolomes. Patients within cluster 2 showed earlier tumor recurrence, compared with those within cluster 1 (p = 0.034). A nomogram was developed for predicting the probability of early disease-free survival in patients with resected pancreatic cancer. Preoperative cancer antigen (CA) 19-9 levels and serum metabolomes PC.aa.C38_4, PC.ae.C42_5, and PC.ae.C38_6 were the most powerful preoperative clinical variables with which to predict 6-month and 1-year cancer recurrence-free survival after radical pancreatectomy, with a Harrell's concordance index of 0.823 (95% CI: 0.750-0.891) and integrated area under the curve of 0.816 (95% CI: 0.736-0.893). Patients with resected pancreatic cancer could be categorized according to their different metabolomes to predict early cancer recurrence. Preoperative detectable parameters, serum CA 19-9, PC.aa.C38_4, PC.ae.C42_5, and PC.ae.C38_6 were the most powerful predictors of early recurrence of pancreatic cancer.

Metabolomic prostate cancer fields in HRMAS MRS-profiled histologically benign tissue vary with cancer status and distance from cancer

In this article, we review the state of the field of high resolution magic angle spinning MRS (HRMAS MRS)-based cancer metabolomics since its beginning in 2004; discuss the concept of cancer metabolomic fields, where metabolomic profiles measured from histologically benign tissues reflect patient cancer status; and report our HRMAS MRS metabolomic results, which characterize metabolomic fields in prostatectomy-removed cancerous prostates. Three-dimensional mapping of cancer lesions throughout each prostate enabled multiple benign tissue samples per organ to be classified based on distance from and extent of the closest cancer lesion as well as the Gleason score (GS) of the entire prostate. Cross-validated partial least squares-discriminant analysis separations were achieved between cancer and benign tissue, and between cancer tissue from prostates with high (≥4 + 3) and low (≤3 + 4) GS. Metabolomic field effects enabled histologically benign tissue adjacent to cancer to distinguish the GS and extent of the cancer lesion itself. Benign samples close to either low GS cancer or extensive cancer lesions could be distinguished from those far from cancer. Furthermore, a successfully cross-validated multivariate model for three benign tissue groups with varying distances from cancer lesions within one prostate indicates the scale of prostate cancer metabolomic fields. While these findings could, at present, be potentially useful in the prostate cancer clinic for analysis of biopsy or surgical specimens to complement current diagnostics, the confirmation of metabolomic fields should encourage further examination of cancer fields and can also enhance understanding of the metabolomic characteristics of cancer in myriad organ systems. Our results together with the success of HRMAS MRS-based cancer metabolomics presented in our literature review demonstrate the potential of cancer metabolomics to provide supplementary information for cancer diagnosis, staging, and patient prognostication.

Integrative analysis of the inverse expression patterns in pancreas development and cancer progression

BACKGROUND

As the malignant tumor, pancreatic cancer with a meager 5-years survival rate has been widely concerning. However, the molecular mechanisms that result in malignant transformation of pancreatic cells remain elusive.

AIM

To investigate the gene expression profiles in normal or malignant transformed pancreas development.

METHODS

MaSigPro and ANOVA were performed on two pancreas development datasets downloaded from the Gene Expression Omnibus database. Six pancreatic cancer datasets collected from TCGA database were used to establish differentially expressed genes related to pancreas development and pancreatic cancer. Moreover, gene clusters with highly similar interpretation patterns between pancreas development and pancreatic cancer progression were established by self-organizing map and singular value decomposition. Additionally, the hypergeometric test was performed to compare the corresponding interpretation patterns. Abnormal regions of metabolic pathway were analyzed using the Sub-pathway-GM method.

RESULTS

This study established the continuously upregulated and downregulated genes at different stages in pancreas development and progression of pancreatic cancer. Through analysis of the differentially expressed genes, we established the inverse and consistent direction development-cancer pattern associations. Based on the application of the Subpathway-GM analysis, we established 17 significant metabolic sub-pathways that were closely associated with pancreatic cancer. Of note, the most significant metabolites sub-pathway was related to glycerophospholipid metabolism.

CONCLUSION

The inverse and consistent direction development-cancer pattern associations were established. There was a significant correlation in the inverse patterns, but not consistent direction patterns.

Ex vivo metabolite profiling of paediatric central nervous system tumours reveals prognostic markers

Brain tumours are the most common cause of cancer death in children. Molecular studies have greatly improved our understanding of these tumours but tumour metabolism is underexplored. Metabolites measured in vivo have been reported as prognostic biomarkers of these tumours but analysis of surgically resected tumour tissue allows a more extensive set of metabolites to be measured aiding biomarker discovery and providing validation of in vivo findings. In this study, metabolites were quantified across a range of paediatric brain tumours using 1H-High-Resolution Magic Angle Spinning nuclear magnetic resonance spectroscopy (HR-MAS) and their prognostic potential investigated. HR-MAS was performed on pre-treatment frozen tumour tissue from a single centre. Univariate and multivariate Cox regression was used to examine the ability of metabolites to predict survival. The models were cross validated using C-indices and further validated by splitting the cohort into two. Higher concentrations of glutamine were predictive of a longer overall survival, whilst higher concentrations of lipids were predictive of a shorter overall survival. These metabolites were predictive independent of diagnosis, as demonstrated in multivariate Cox regression models. Whilst accurate quantification of metabolites such as glutamine in vivo is challenging, metabolites show promise as prognostic markers due to development of optimised detection methods and increasing use of 3 T clinical scanners.

Integrated targeted metabolomic and lipidomic analysis: A novel approach to classifying early cystic precursors to invasive pancreatic cancer

Pancreatic cystic neoplasms (PCNs) are a highly prevalent disease of the pancreas. Among PCNs, Intraductal Papillary Mucinous Neoplasms (IPMNs) are common lesions that may progress from low-grade dysplasia (LGD) through high-grade dysplasia (HGD) to invasive cancer. Accurate discrimination of IPMN-associated neoplastic grade is an unmet clinical need. Targeted (semi)quantitative analysis of 100 metabolites and >1000 lipid species were performed on peri-operative pancreatic cyst fluid and pre-operative plasma from IPMN and serous cystic neoplasm (SCN) patients in a pancreas resection cohort (n = 35). Profiles were correlated against histological diagnosis and clinical parameters after correction for confounding factors. Integrated data modeling was used for group classification and selection of the best explanatory molecules. Over 1000 different compounds were identified in plasma and cyst fluid. IPMN profiles showed significant lipid pathway alterations compared to SCN. Integrated data modeling discriminated between IPMN and SCN with 100% accuracy and distinguished IPMN LGD or IPMN HGD and invasive cancer with up to 90.06% accuracy. Free fatty acids, ceramides, and triacylglycerol classes in plasma correlated with circulating levels of CA19-9, albumin and bilirubin. Integrated metabolomic and lipidomic analysis of plasma or cyst fluid can improve discrimination of IPMN from SCN and within PMNs predict the grade of dysplasia.

A metabolic database for biomedical studies of biopsy specimens by high-resolution magic angle spinning nuclear MR: a qualitative and quantitative tool

PURPOSE

The aim of this study is to generate a metabolic database for biomedical studies of biopsy specimens by high-resolution magic angle spinning (HRMAS) nuclear MR (NMR).

METHODS

Seventy-six metabolites, classically found in human biopsy samples, were prepared in aqueous solution at a known concentration and analyzed by HRMAS NMR. The spectra were recorded under the same conditions as the ones used for the analysis of biopsy specimens routinely performed in our hospital.

RESULTS

For each metabolite, a complete set of NMR spectra (1D ¹ H, 1D ¹ H-CPMG, 2D J-Resolved, 2D TOCSY, and 2D ¹ H-¹³ C HSQC) was recorded at 500 MHz and 277 K. All spectra were manually assigned using the information contained in the different spectra and existing databases. Experiments to measure the T₁ and the T₂ of the different protons present in the 76 metabolites were also recorded.

CONCLUSION

This new HRMAS metabolic database is a useful tool for all scientists working on human biopsy specimens, particularly in the field of oncology. It will make the identification of metabolites in biopsy specimens faster and more reliable. Additionally, the knowledge of the T₁ and T₂ values will allow to obtain a more accurate quantification of the metabolites present in biopsy specimens.

High-Throughput Metabolomics by 1D NMR

Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the -omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population-wide health screening technique.

Pathology assessment of pancreatic cancer following neoadjuvant treatment: Time to move on

Neoadjuvant treatment has increasingly become an integral part of the multimodal management of patients with pancreatic cancer. In patients who are able to undergo surgery following preoperative therapy, tumour regression grading remains the diagnostic gold standard for the histomorphological assessment of the effect of neoadjuvant treatment. In recent years, however, there has been growing concern about inherent flaws of tumour regression grading systems as well as their imprecise and impractical criteria that result in divergence of practice and lack of interobserver agreement. Furthermore, existing tumour regression systems differ in their defining criteria and thresholds, leading to incomparability of data. In this review, the principles and limitations of the main existing tumour regression systems are discussed, and potential alternative assessment approaches and novel markers are presented.

Multiparametric PET/MR imaging biomarkers are associated with overall survival in patients with pancreatic cancer

PURPOSE

To correlate the overall survival (OS) with the imaging biomarkers of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), diffusion-weighted imaging (DWI), magnetic resonance spectroscopy, and glucose metabolic activity derived from integrated fluorine 18 fluorodeoxyglucose positron emission tomography (18F-FDG PET)/MRI in patients with pancreatic cancer.

METHODS

This prospective study was approved by the institutional review board and informed consent was obtained from all participants. Sixty-three consecutive patients (mean age, 62.7 ± 12 y; men/women, 40/23) with pancreatic cancer underwent PET/MRI before treatment. The imaging biomarkers were comprised of DCE-MRI parameters (peak, IAUC 60 , K trans , k ep , v e ), the minimum apparent diffusion coefficient (ADCmin), choline level, standardized uptake values, metabolic tumor volume, and total lesion glycolysis (TLG) of the tumors. The relationships between these imaging biomarkers with OS were evaluated with the Kaplan-Meier and Cox proportional hazard models.

RESULTS

Seventeen (27%) patients received curative surgery, with the median follow-up duration being 638 days. Univariate analysis showed that patients at a low TNM stage (≦3, P = 0.041), high peak (P = 0.006), high ADCmin (P = 0.002) and low TLG (P = 0.01) had better OS. Moreover, high TLG/peak ratio was associated with poor OS (P = 0.016). Multivariate analysis indicated that ADCmin (P = 0.011) and TLG/peak ratio (P = 0.006) were independent predictors of OS after adjustment for age, gender, tumor size, and TNM stage. The TLG/peak ratio was an independent predictor of OS in a subgroup of patients who did not receive curative surgery (P = 0.013).

CONCLUSION

The flow-metabolism mismatch reflected by the TLG/peak ratio may better predict OS than other imaging biomarkers from PET/MRI in pancreatic cancer patients.

Preanalytical Processing and Biobanking Procedures of Biological Samples for Metabolomics Research: A White Paper, Community Perspective (for "Precision Medicine and Pharmacometabolomics Task Group"-The Metabolomics Society Initiative)

BACKGROUND

The metabolome of any given biological system contains a diverse range of low molecular weight molecules (metabolites), whose abundances can be affected by the timing and method of sample collection, storage, and handling. Thus, it is necessary to consider the requirements for preanalytical processes and biobanking in metabolomics research. Poor practice can create bias and have deleterious effects on the robustness and reproducibility of acquired data.

CONTENT

This review presents both current practice and latest evidence on preanalytical processes and biobanking of samples intended for metabolomics measurement of common biofluids and tissues. It highlights areas requiring more validation and research and provides some evidence-based guidelines on best practices.

SUMMARY

Although many researchers and biobanking personnel are familiar with the necessity of standardizing sample collection procedures at the axiomatic level (e.g., fasting status, time of day, "time to freezer," sample volume), other less obvious factors can also negatively affect the validity of a study, such as vial size, material and batch, centrifuge speeds, storage temperature, time and conditions, and even environmental changes in the collection room. Any biobank or research study should establish and follow a well-defined and validated protocol for the collection of samples for metabolomics research. This protocol should be fully documented in any resulting study and should involve all stakeholders in its design. The use of samples that have been collected using standardized and validated protocols is a prerequisite to enable robust biological interpretation unhindered by unnecessary preanalytical factors that may complicate data analysis and interpretation.

Metabolic Profile of Oral Squamous Carcinoma Cell Lines Relies on a Higher Demand of Lipid Metabolism in Metastatic Cells

Tumor cells are subjected to a broad range of selective pressures. As a result of the imposed stress, subpopulations of surviving cells exhibit individual biochemical phenotypes that reflect metabolic reprograming. The present work aimed at investigating metabolic parameters of cells displaying increasing degrees of metastatic potential. The metabolites present in cell extracts fraction of tongue fibroblasts and of cell lines derived from human tongue squamous cell carcinoma lineages displaying increasing metastatic potential (SCC9 ZsG, LN1 and LN2) were analyzed by ¹H NMR (nuclear magnetic resonance) spectroscopy. Living, intact cells were also examined by the non-invasive method of fluorescence lifetime imaging microscopy (FLIM) based on the auto fluorescence of endogenous NADH. The cell lines reproducibly exhibited distinct metabolic profiles confirmed by Partial Least-Square Discriminant Analysis (PLS-DA) of the spectra. Measurement of endogenous free and bound NAD(P)H relative concentrations in the intact cell lines showed that ZsG and LN1 cells displayed high heterogeneity in the energy metabolism, indicating that the cells would oscillate between glycolysis and oxidative metabolism depending on the microenvironment’s composition. However, LN2 cells appeared to have more contributions to the oxidative status, displaying a lower NAD(P)H free/bound ratio. Functional experiments of energy metabolism, mitochondrial physiology, and proliferation assays revealed that all lineages exhibited similar energy features, although resorting to different bioenergetics strategies to face metabolic demands. These differentiated functions may also promote metastasis. We propose that lipid metabolism is related to the increased invasiveness as a result of the accumulation of malonate, methyl malonic acid, n-acetyl and unsaturated fatty acids (CH₂)_n in parallel with the metastatic potential progression, thus suggesting that the NAD(P)H reflected the lipid catabolic/anabolic pathways.

Mechanisms of anti-cancer effects of ascorbate: Cytotoxic activity and epigenetic modulation

Vitamin C (Vit C or Ascorbate) is essential for many fundamental biochemical processes. Vit C is an essential nutrient with redox functions at normal physiologic concentrations. The main physiologic function of this vitamin is related to its capacity to act as a co-factor for a large family of enzymes, collectively known as Fe and 2-oxoglutarate-dependent dioxygenases. It also modulates epigenetic gene expression through the control of TET enzymes activity. Vit C also has several biological properties allowing to restore the deregulated epigenetic response observed in many tumors. High-dose Vit C has been investigated as a treatment for cancer patients since the 1969. Pharmacologic ascorbate acts as a pro-drug for hydrogen peroxide formation (H₂O₂) and, through this mechanism, kills cancer cells. To achieve high in vivo concentrations, Ascorbate must be injected by i.v. route. Initial clinical studies of Ascorbate cancer treatment have provided encouraging results, not confirmed in subsequent studies. Recent clinical studies using i.v. injection of high-dose Ascorbate have renewed the interest in the field, showing that significant anti-tumor activity. Pre-clinical studies have led to identify tumors sensitive to Ascorbate that could potentially benefit from this treatment either through an epigenetic modulator effect or through tumor killing by oxidative stress.