Utility of nuclear magnetic resonance spectroscopy for pancreatic cancer studies

Advances in the Detection of Pancreatic Cancer Through Liquid Biopsy

Pancreatic cancer refers to the development of malignant tumors in the pancreas: it is associated with high mortality rates and mostly goes undetected in its early stages for lack of symptoms. Currently, surgical treatment is the only effective way to improve the survival of pancreatic cancer patients. Therefore, it is crucial to diagnose the disease as early as possible in order to improve the survival rate of patients with pancreatic cancer. Liquid biopsy is a unique in vitro diagnostic technique offering the advantage of earlier detection of tumors. Although liquid biopsies have shown promise for screening for certain cancers, whether they are effective for early diagnosis of pancreatic cancer is unclear. Therefore, we reviewed relevant literature indexed in PubMed and collated updates and information on advances in the field of liquid biopsy with respect to the early diagnosis of pancreatic cancer.

Volatile organic compounds in bile can distinguish pancreatic cancer from chronic pancreatitis: a prospective observational study

BACKGROUND

Early and accurate diagnosis of pancreatic cancer is important. Our aim was to identify potential volatile organic compounds (VOCs) in the bile that can help distinguish pancreatic cancer from chronic pancreatitis.

METHODS

In this prospective observational study, bile was aspirated from patients undergoing endoscopic retrograde cholangiopancreatography for chronic pancreatitis and pancreatic cancer, and the gaseous headspace was analyzed using mass spectrometry.

RESULTS

The study included a discovery cohort of 57 patients (46 pancreatic cancer, 11 chronic pancreatitis) and a validation cohort of 31 patients (19 and 12, respectively). Using logistic regression analysis, the model [0.158 × age + 9.747 × log (ammonia) - 3.994 × log (acetonitrile) + 5.044 × log (trimethylamine) - 30.23] successfully identified patients with pancreatic cancer with a sensitivity of 93.5 % and specificity of 100 % (likelihood ratio 40.9, area under the curve 0.98, 95 % confidence interval 0.95 - 1.00). The diagnostic accuracy of this model was confirmed in the second independent validation cohort.

CONCLUSION

The measurement of VOCs in bile helped to accurately distinguish pancreatic cancer from chronic pancreatitis.

Molecular and Functional Analysis of Choline Transporters and Antitumor Effects of Choline Transporter-Like Protein 1 Inhibitors in Human Pancreatic Cancer Cells

Choline, an organic cation, is one of the biofactors that play an important role in the structure and the function of biological membranes, and it is essential for the synthesis of phospholipids. Choline positron emission tomography-computed tomography (PET/CT) provides useful information for the imaging diagnosis of cancers, and increased choline accumulation has been identified in a variety of tumors. However, the molecular mechanisms of choline uptake and choline transporters in pancreatic cancer have not been elucidated. Here, we examined molecular and functional analyses of choline transporters in human pancreatic-cancer cell line MIA PaCa-2 and the elucidation of the action mechanism behind the antitumor effect of novel choline-transporter-like protein 1 (CTL1) inhibitors, Amb4269951 and its derivative Amb4269675. CTL1 and CTL2 mRNAs were highly expressed in MIA PaCa-2 cells, and CTL1 and CTL2 proteins were localized in the plasma membrane and the intracellular compartments, respectively. Choline uptake was characterized by Na⁺-independence, a single-uptake mechanism, and inhibition by choline-uptake inhibitor HC-3, similar to the function of CTL1. These results suggest that the uptake of extracellular choline in MIA PaCa-2 cells is mediated by CTL1. Choline deficiency and HC-3 treatment inhibited cell viability and increased caspase 3/7 activity, suggesting that the inhibition of CTL1 function, which is responsible for choline transport, leads to apoptosis-induced cell death. Both Amb4269951 and Amb4269675 inhibited choline uptake and cell viability and increased caspase-3/7 activity. Ceramide, which is increased by inhibiting choline uptake, also inhibited cell survival and increased caspase-3/7 activity. Lastly, both Amb4269951 and Amb4269675 significantly inhibited tumor growth in a mouse-xenograft model without any adverse effects such as weight loss. CTL1 is a target molecule for the treatment of pancreatic cancer, and its inhibitors Amb4269951 and Amb4269675 are novel lead compounds.

Differences in lipidomics may be potential biomarkers for early diagnosis of pancreatic cancer

Purpose

To analyze the plasma lipid spectrum between healthy control and patients with pancreatic cancer and to select differentially expressed tumor markers for early diagnosis.

Methods

In total, 20 patents were divided into case group and healthy control group according to surgical pathology. Of almost 1206 plasma lipid molecules harvested from 20 patients were measured by HILIC using the normal phase LC/MS. Heat map presented the relative levels of metabolites and lipids in the healthy control group and patients with pancreatic cancer. The PCA model was constructed to find out the difference in lipid metabolites. The principal components were drawn in a score plot and any clustering tendency could be observed. PLS-DA were performed to distinguish the healthy control group and pancreatic cancer according to the identified lipid profiling datasets. The volcano plot was used to visualize all variables with VIP>1 and presented the important variables with P<0.01 and |FC|>2.

Results

The upregulated lipid metabolites in patients with pancreatic cancer contained 9 lipids; however, the downregulated lipid metabolites contained 79 lipids.

Conclusion

There were lipid metabolomic differences in patients with pancreatic cancer, which could serve as potential tumor markers for pancreatic cancer.

Metabolic profiling of serum in patients with cartilage tumours using 1 H-NMR spectroscopy: A pilot study

Cartilage-forming lesions include tumours that can vary in severity from benign enchondromas to high-grade malignant chondrosarcomas. Chondrosarcoma is the second most frequent malignant bone tumour, accounting for 20-30% of all malignant bone neoplasms. Surgery is the standard treatment for cartilage tumours (CTs); however, their incidental diagnosis and the difficult differentiation of low-grade lesions like chondrosarcoma grade I from benign entities like enchondroma are challenges for clinical management. In this sense, the search for circulating biomarkers for early detection and prognosis is an ongoing interest. Targeted metabolomics is a powerful tool that can propose potential biomarkers in biological fluids as well as help to discover disturbed metabolic pathways to reveal tumour pathogenesis. In this context, the aim of this study was to investigate the ¹ H nuclear magnetic resonance metabolomic serum profile of patients with CTs contrasted with healthy controls. Forty-one metabolites were identified and quantified; the multivariate statistical methods principal component analysis and partial least squares discriminant analysis reveal a clear separation of the CT group, that is, the differential metabolites that were involved in two main metabolic pathways: the taurine and hypotaurine metabolism and synthesis and degradation of ketone bodies. Our results represent preliminary work for emergent serum-based diagnostics or prognostic methods for patients with chondrogenic tumours.

A Prospective Targeted Serum Metabolomics Study of Pancreatic Cancer in Postmenopausal Women

To examine the association between metabolic deregulation and pancreatic cancer, we conducted a two-stage case-control targeted metabolomics study using prediagnostic sera collected one year before diagnosis in the Women's Health Initiative study. We used the LC/MS to quantitate 470 metabolites in 30 matched case/control pairs. From 180 detectable metabolites, we selected 14 metabolites to be validated in additional 18 matched case/control pairs. We used the paired t test to compare the concentrations of each metabolite between cases and controls and used the log fold change (FC) to indicate the magnitude of difference. FDR adjusted q-value < 0.25 was indicated statistically significant. Logistic regression model and ROC curve analysis were used to evaluate the clinical utility of the metabolites. Among 30 case/control pairs, 1-methyl-l-tryptophan (L-1MT) was significantly lower in the cases than in the controls (log₂ FC = -0.35; q-value = 0.03). The area under the ROC curve was 0.83 in the discrimination analysis based on the levels of L-1MT, acadesine, and aspartic acid. None of the metabolites was validated in additional independent 18 case/control pairs. No significant association was found between the examined metabolites and undiagnosed pancreatic cancer.

Metabolomic biomarkers of pancreatic cancer: a meta-analysis study

Pancreatic cancer (PC) is an aggressive disease with high mortality rates, however, there is no blood test for early detection and diagnosis of this disease. Several research groups have reported on metabolomics based clinical investigations to identify biomarkers of PC, however there is a lack of a centralized metabolite biomarker repository that can be used for meta-analysis and biomarker validation. Furthermore, since the incidence of PC is associated with metabolic syndrome and Type 2 diabetes mellitus (T2DM), there is a need to uncouple these common metabolic dysregulations that may otherwise diminish the clinical utility of metabolomic biosignatures. Here, we attempted to externally replicate proposed metabolite biomarkers of PC reported by several other groups in an independent group of PC subjects. Our study design included a T2DM cohort that was used as a non-cancer control and a separate cohort diagnosed with colorectal cancer (CRC), as a cancer disease control to eliminate possible generic biomarkers of cancer. We used targeted mass spectrometry for quantitation of literature-curated metabolite markers and identified a biomarker panel that discriminates between normal controls (NC) and PC patients with high accuracy. Further evaluation of our model with CRC, however, showed a drop in specificity for the PC biomarker panel. Taken together, our study underscores the need for a more robust study design for cancer biomarker studies so as to maximize the translational value and clinical implementation.

Serum Metabolomic Profiles for Human Pancreatic Cancer Discrimination

This study evaluated the clinical use of serum metabolomics to discriminate malignant cancers including pancreatic cancer (PC) from malignant diseases, such as biliary tract cancer (BTC), intraductal papillary mucinous carcinoma (IPMC), and various benign pancreaticobiliary diseases. Capillary electrophoresis−mass spectrometry was used to analyze charged metabolites. We repeatedly analyzed serum samples (n = 41) of different storage durations to identify metabolites showing high quantitative reproducibility, and subsequently analyzed all samples (n = 140). Overall, 189 metabolites were quantified and 66 metabolites had a 20% coefficient of variation and, of these, 24 metabolites showed significant differences among control, benign, and malignant groups (p < 0.05; Steel–Dwass test). Four multiple logistic regression models (MLR) were developed and one MLR model clearly discriminated all disease patients from healthy controls with an area under receiver operating characteristic curve (AUC) of 0.970 (95% confidential interval (CI), 0.946–0.994, p < 0.0001). Another model to discriminate PC from BTC and IPMC yielded AUC = 0.831 (95% CI, 0.650–1.01, p = 0.0020) with higher accuracy compared with tumor markers including carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), pancreatic cancer-associated antigen (DUPAN2) and s-pancreas-1 antigen (SPAN1). Changes in metabolomic profiles might be used to screen for malignant cancers as well as to differentiate between PC and other malignant diseases.

Designing a bio-inspired biomimetic in vitro system for the optimization of ex vivo studies of pancreatic cancer

Pancreatic cancer is one of the most aggressive and lethal human malignancies. Drug therapies and radiotherapy are used for treatment as adjuvants to surgery, but outcomes remain disappointing. Advances in tissue engineering suggest that 3D cultures can reflect the in vivo tumor microenvironment and can guarantee a physiological distribution of oxygen, nutrients, and drugs, making them promising low-cost tools for therapy development. Here, we review crucial structural and environmental elements that should be considered for an accurate design of an ex vivo platform for studies of pancreatic cancer. Furthermore, we propose environmental stress response biomarkers as platform readouts for the efficient control and further prediction of the pancreatic cancer response to the environmental and treatment input.

Metabolomics in pancreatic cancer biomarkers research

Pancreatic cancer is one of the worst prognoses of all malignancies. More than 40,000 deaths a year from this disease are observed in European Union alone. The only possibly curative treatment of pancreatic cancer is surgery, yet only 15-20% of patients have operable disease and even patients, which go through surgery and adjuvant chemotherapy, survival is less than 30%. The sensitive and specific biomarkers which could be used for the advance of early diagnostics are needed and constantly researched. Metabolomics is a technology which analyzes the concentrations of low-molecular-weight metabolites (the metabolome) has lately effectively developed due to the improvements in analytical technology. Metabolome analysis can be a one of the useful approaches for the biomarker discovery and disease diagnosis. Here we discuss recent discoveries in the field of pancreatic cancer metabolomics as well as the most promising biomarkers for diagnostics, prognosis and prediction.

Metabonomic changes from pancreatic intraepithelial neoplasia to pancreatic ductal adenocarcinoma in tissues from rats

Pancreatic ductal adenocarcinoma (PDAC) is one of the most malignant tumors and is difficult to diagnose in the early phase. This study was aimed at obtaining the metabolic profiles and characteristic metabolites of pancreatic intraepithelial neoplasia (PanIN) and PDAC tissues from Sprague-Dawley (SD) rats to establish metabonomic methods used in the early diagnosis of PDAC. In the present study, the animal models were established by embedding 7,12-dimethylbenzanthracene (DMBA) in the pancreas of SD rats to obtain PanIN and PDAC tissues. After the preprocessing of tissues, (1) H nuclear magnetic resonance (NMR) spectroscopy combined with multivariate and univariate statistical analysis was applied to identify the potential metabolic signatures and the corresponding metabolic pathways. Pattern recognition models were successfully established and differential metabolites, including glucose, amino acids, carboxylic acids and coenzymes, were screened out. Compared with the control, the trends in the variation of several metabolites were similar in both PanIN and PDAC. Kynurenate and methionine levels were elevated in PanIN but decreased in PDAC, thus, could served as biomarkers to distinguish PanIN from PDAC. Our results suggest that NMR-based techniques combined with multivariate statistical analysis can distinguish the metabolic differences among PanIN, PDAC and normal tissues, and, therefore, present a promising approach for physiopathologic metabolism investigations and early diagnoses of PDAC.

Volatile Organic Compounds in Urine for Noninvasive Diagnosis of Malignant Biliary Strictures: A Pilot Study

BACKGROUND

The use of volatile organic compounds (VOCs) in bile was recently studied and appeared promising for diagnosis of malignancy. Noninvasive diagnosis of malignant biliary strictures by using VOCs in urine has not been studied.

AIM

To identify potential VOCs in urine to diagnose malignant biliary strictures.

METHODS

In this prospective cross-sectional study, urine was obtained immediately prior to ERCP from consecutive patients with biliary strictures. Selected-ion flow-tube mass spectrometry was used to analyze the concentration of VOCs in urine samples.

RESULTS

Fifty-four patients with biliary strictures were enrolled. Fifteen patients had malignant stricture [six cholangiocarcinoma (CCA) and nine pancreatic cancer], and 39 patients had benign strictures [10 primary sclerosing cholangitis (PSC) and 29 with benign biliary conditions including chronic pancreatitis and papillary stenosis]. The concentration of several compounds (ethanol and 2-propanol) was significantly different in patients with malignant compared with benign biliary strictures (p < 0.05). Using receiver operating characteristic curve analysis, we developed a model for the diagnosis of malignant biliary strictures adjusted for age and gender based on VOC levels of 2-propranol, carbon disulfide, and trimethyl amine (TMA). The model [-2.4191 * log(2-propanol) + 1.1617 * log(TMA) - 1.2172 * log(carbon disulfide)] ≥ 7.73 identified the patients with malignant biliary stricture [area under the curve (AUC = 0.83)], with 93.3 % sensitivity and 61.5 % specificity (p = 0.009). Comparing patients with CCA and PSC, the model [38.864 * log(ethane) - 3.989 * log(1-octene)] ≤ 169.9 could identify CCA with 80 % sensitivity and 100 % specificity (AUC = 0.9).

CONCLUSIONS

Measurement of VOCs in urine may diagnose malignant biliary strictures noninvasively.

Plasma metabolite biomarkers for the detection of pancreatic cancer

Patients with pancreatic cancer (PC) are usually diagnosed at late stages, when the disease is nearly incurable. Sensitive and specific markers are critical for supporting diagnostic and therapeutic strategies. The aim of this study was to use a metabonomics approach to identify potential plasma biomarkers that can be further developed for early detection of PC. In this study, plasma metabolites of newly diagnosed PC patients (n = 100) and age- and gender-matched controls (n = 100) from Connecticut (CT), USA, and the same number of cases and controls from Shanghai (SH), China, were profiled using combined gas and liquid chromatography mass spectrometry. The metabolites consistently expressed in both CT and SH samples were used to identify potential markers, and the diagnostic performance of the candidate markers was tested in two sample sets. A diagnostic model was constructed using a panel of five metabolites including glutamate, choline, 1,5-anhydro-d-glucitol, betaine, and methylguanidine, which robustly distinguished PC patients in CT from controls with high sensitivity (97.7%) and specificity (83.1%) (area under the receiver operating characteristic curve [AUC] = 0.943, 95% confidence interval [CI] = 0.908-0.977). This panel of metabolites was then tested with the SH data set, yielding satisfactory accuracy (AUC = 0.835; 95% CI = 0.777-0.893), with a sensitivity of 77.4% and specificity of 75.8%. This model achieved a sensitivity of 84.8% in the PC patients at stages 0, 1, and 2 in CT and 77.4% in the PC patients at stages 1 and 2 in SH. Plasma metabolic signatures show promise as biomarkers for early detection of PC.

Volatile organic compounds in bile can diagnose malignant biliary strictures in the setting of pancreatic cancer: a preliminary observation

BACKGROUND

Ascertaining the nature of biliary strictures is challenging. The role of volatile organic compounds (VOCs) in bile in determining the cause of biliary strictures is not known.

OBJECTIVE

To identify potential VOCs in the headspaces (gas above the sample) of bile in patients with malignant biliary strictures from pancreatic cancer.

DESIGN

Prospective cross-sectional study.

SETTING

Referral center.

PATIENTS

Prospective study in which bile was aspirated in 96 patients undergoing ERCP for benign and malignant conditions.

MAIN OUTCOME MEASUREMENTS

Selected ion flow tube mass spectrometry (VOICE200R SIFT-MS instrument; Syft Technologies Ltd, Christchurch, New Zealand) was used to analyze the headspace and to build a predictive model for pancreatic cancer.

RESULTS

The headspaces from 96 bile samples were analyzed, including 24 from patients with pancreatic cancer and 72 from patients with benign biliary conditions. The concentrations of 6 compounds (acetaldehyde, acetone, benzene, carbon disulfide, pentane, and trimethylamine [TMA]) were increased in patients with pancreatic cancer compared with controls (P < .05). By using receiver-operating characteristic curve analysis, we developed a model for the diagnosis of pancreatic cancer based on the levels of TMA, acetone, isoprene, dimethyl sulfide, and acetaldehyde. The model [10.94 + 1.8229* log (acetaldehyde) + 0.7600* log (acetone) - 1.1746* log (dimethyl sulfide) + 1.0901* log (isoprene) - 2.1401 * log (trimethylamine) ≥ 10] identified the patients with pancreatic cancer (area under the curve = 0.85), with 83.3% sensitivity and 81.9% specificity.

LIMITATIONS

Sample size.

CONCLUSIONS

The measurement of biliary fluid VOCs may help to distinguish malignant from benign biliary strictures. Further studies are warranted to validate these observations. (Clinical Trial Registration Number NCT01565460.).

MRS-based Metabolomics in Cancer Research

Metabolomics is a relatively new technique that is gaining importance very rapidly. MRS-based metabolomics, in particular, is becoming a useful tool in the study of body fluids, tissue biopsies and whole organisms. Advances in analytical techniques and data analysis methods have opened a new opportunity for such technology to contribute in the field of diagnostics. In the MRS approach to the diagnosis of disease, it is important that the analysis utilizes all the essential information in the spectra, is robust, and is non-subjective. Although some of the data analytic methods widely used in chemical and biological sciences are sketched, a more extensive discussion is given of a 5-stage Statistical Classification Strategy. This proposes powerful feature selection methods, based on, for example, genetic algorithms and novel projection techniques. The applications of MRS-based metabolomics in breast cancer, prostate cancer, colorectal cancer, pancreatic cancer, hepatobiliary cancers, gastric cancer, and brain cancer have been reviewed. While the majority of these applications relate to body fluids and tissue biopsies, some in vivo applications have also been included. It should be emphasized that the number of subjects studied must be sufficiently large to ensure a robust diagnostic classification. Before MRS-based metabolomics can become a widely used clinical tool, however, certain challenges need to be overcome. These include manufacturing user-friendly commercial instruments with all the essential features, and educating physicians and medical technologists in the acquisition, analysis, and interpretation of metabolomics data.

Pancreatic carcinoma, pancreatitis, and healthy controls: metabolite models in a three-class diagnostic dilemma

Metabolomics as one of the most rapidly growing technologies in the "-omics" field denotes the comprehensive analysis of low molecular-weight compounds and their pathways. Cancer-specific alterations of the metabolome can be detected by high-throughput mass-spectrometric metabolite profiling and serve as a considerable source of new markers for the early differentiation of malignant diseases as well as their distinction from benign states. However, a comprehensive framework for the statistical evaluation of marker panels in a multi-class setting has not yet been established. We collected serum samples of 40 pancreatic carcinoma patients, 40 controls, and 23 pancreatitis patients according to standard protocols and generated amino acid profiles by routine mass-spectrometry. In an intrinsic three-class bioinformatic approach we compared these profiles, evaluated their selectivity and computed multi-marker panels combined with the conventional tumor marker CA 19-9. Additionally, we tested for non-inferiority and superiority to determine the diagnostic surplus value of our multi-metabolite marker panels. Compared to CA 19-9 alone, the combined amino acid-based metabolite panel had a superior selectivity for the discrimination of healthy controls, pancreatitis, and pancreatic carcinoma patients [Formula: see text] We combined highly standardized samples, a three-class study design, a high-throughput mass-spectrometric technique, and a comprehensive bioinformatic framework to identify metabolite panels selective for all three groups in a single approach. Our results suggest that metabolomic profiling necessitates appropriate evaluation strategies and-despite all its current limitations-can deliver marker panels with high selectivity even in multi-class settings.

A novel serum metabolomics-based diagnostic approach to pancreatic cancer

BACKGROUND

To improve the prognosis of patients with pancreatic cancer, more accurate serum diagnostic methods are required. We used serum metabolomics as a diagnostic method for pancreatic cancer.

METHODS

Sera from patients with pancreatic cancer, healthy volunteers, and chronic pancreatitis were collected at multiple institutions. The pancreatic cancer and healthy volunteers were randomly allocated to the training or the validation set. All of the chronic pancreatitis cases were included in the validation set. In each study, the subjects' serum metabolites were analyzed by gas chromatography mass spectrometry (GC/MS) and a data processing system using an in-house library. The diagnostic model constructed via multiple logistic regression analysis in the training set study was evaluated on the basis of its sensitivity and specificity, and the results were confirmed by the validation set study.

RESULTS

In the training set study, which included 43 patients with pancreatic cancer and 42 healthy volunteers, the model possessed high sensitivity (86.0%) and specificity (88.1%) for pancreatic cancer. The use of the model was confirmed in the validation set study, which included 42 pancreatic cancer, 41 healthy volunteers, and 23 chronic pancreatitis; that is, it displayed high sensitivity (71.4%) and specificity (78.1%); and furthermore, it displayed higher sensitivity (77.8%) in resectable pancreatic cancer and lower false-positive rate (17.4%) in chronic pancreatitis than conventional markers.

CONCLUSIONS

Our model possessed higher accuracy than conventional tumor markers at detecting the resectable patients with pancreatic cancer in cohort including patients with chronic pancreatitis.

IMPACT

It is a promising method for improving the prognosis of pancreatic cancer via its early detection and accurate discrimination from chronic pancreatitis.

Advances in magnetic resonance molecular and functional imaging to diagnose pancreatic cancer

Pancreatic cancer has a high mortality rate, which is generally related to the initial diagnosis coming at late stage disease combined with a lack of effective diagnostic techniques. Over the past few years, molecular-functional imaging, which can be defined as the in vivo characterization and measurement of biologic processes at the molecular and gene levels, has developed rapidly and allows diagnosing pancreatic cancer more early and specifically. Magnetic resonance (MR) imaging is widely used for molecular imaging because of the high spatial resolution. This paper reviews recent advances in MR molecular and functional imaging to diagnose pancreatic cancer.