Circulating Metabolites and Survival Among Patients With Pancreatic Cancer

Morphomics, Survival, and Metabolites in Patients With Metastatic Pancreatic Cancer

Importance

Associations of body mass index (BMI) with survival in pancreatic ductal adenocarcinoma (PDA) have substantial variability in literature, potentially due to heterogeneous patient populations and retrospective analyses. Additionally, BMI may inadequately describe body composition (ie, morphomics; including subcutaneous and visceral fats, muscle, and fascia), which might have independent biological roles and associations with survival.

Objective

To study the associations of BMI and morphomics with survival and metabolomics in metastatic PDA.

Design, Setting, and Participants

This cohort study prospectively collected patient data, imaging, and serum on the phase 3 trial (Avenger500), which investigated the efficacy and safety of 5-fluorouracil, leucovorin, oxaliplatin, and irinotecan (FOLFIRINOX) versus modified FOLFIRINOX plus devimistat. The randomized trial accrued 528 patients with chemotherapy-naive, metastatic PDA from Europe, Israel, Korea, and the US between 2018 and 2020. In the present study, per-protocol patients with L1 to L4, T10 to T12 vertebral levels were evaluated. Data analysis occurred from January 2023 to April 2024.

Exposure

Patient data were collected by clinical staff. Morphomics were analyzed from baseline imaging. Metabolites were extracted from baseline serum.

Main Outcome and Measures

A multifaceted statistical approach evaluated associations of BMI and morphomics with progression-free survival (PFS) and overall survival (OS). Associations of morphomics with metabolites were also studied.

Results

Of the 528 initial patients, 476 (median [IQR] age, 63 [56-68] years; 280 male [58.8%]; median [IQR] BMI, 25.0 [22.1-25.9]) were evaluable for the present study. BMI (obese [≥30] compared with normal [18.5-24.9]) was not associated with OS (hazard ratio [HR], 0.90; 95% CI, 0.67-1.22; P for trend = .33). More subcutaneous fat was associated with longer OS (HR, 0.62; 95% CI, 0.41-0.94; P for trend = .02). Higher visceral fat density was associated with shorter PFS (HR, 1.74; 95% CI, 1.23-2.48; P for trend = .002) and OS (HR, 1.50; 95% CI, 1.12-2.00; P for trend = .008). A higher muscle-to-fascia ratio was associated with longer PFS (HR, 0.58; 95% CI, 0.40-0.84; P for trend = .005) and OS (HR, 0.56; 95% CI, 0.41-0.75; P for trend = 1.7 × 10-4). Subcutaneous fat was positively associated with long-chain fatty acid metabolism including pristanic acid, decanoylcarnitine, decenoylcarnitine, and octanoylcarnitine. Muscle-to-fascia was positively associated with metabolites including acetylcarnosine (β = 0.34; 95% CI, 0.21-0.47; P = 1.27 × 10-6).

Conclusions and Relevance

In cohort study of patients with metastatic PDA, BMI was not associated with survival. Higher visceral fat density, subcutaneous fat area, and muscle-to-fascia ratio were associated with survival independent of BMI. The latter 2 were associated with higher levels of animal product metabolism. These findings could represent novel focuses for prognostication and intervention to improve survival of patients with PDA.

Excavation of gene markers associated with pancreatic ductal adenocarcinoma based on interrelationships of gene expression

Pancreatic ductal adenocarcinoma (PDAC) accounts for 95% of all pancreatic cancer cases, posing grave challenges to its diagnosis and treatment. Timely diagnosis is pivotal for improving patient survival, necessitating the discovery of precise biomarkers. An innovative approach was introduced to identify gene markers for precision PDAC detection. The core idea of our method is to discover gene pairs that display consistent opposite relative expression and differential co-expression patterns between PDAC and normal samples. Reversal gene pair analysis and differential partial correlation analysis were performed to determine reversal differential partial correlation (RDC) gene pairs. Using incremental feature selection, the authors refined the selected gene set and constructed a machine-learning model for PDAC recognition. As a result, the approach identified 10 RDC gene pairs. And the model could achieve a remarkable accuracy of 96.1% during cross-validation, surpassing gene expression-based models. The experiment on independent validation data confirmed the model's performance. Enrichment analysis revealed the involvement of these genes in essential biological processes and shed light on their potential roles in PDAC pathogenesis. Overall, the findings highlight the potential of these 10 RDC gene pairs as effective diagnostic markers for early PDAC detection, bringing hope for improving patient prognosis and survival.

Integrated analysis identifies cuproptosis-related gene DLAT and its competing endogenous RNAs network to predict the prognosis of pancreatic adenocarcinoma patients

Pancreatic adenocarcinoma (PAAD) is a highly malignant tumor with poor prognosis. However, the relationship between cuproptosis-related genes (CRGs) and its competing endogenous RNA (ceRNA) network with the prognosis of PAAD patients remains unclear. To investigate this relationship, we calculated the difference in CRGs between PAAD tissues and normal tissues using the 'limma' R package. Additionally, we employed least absolute shrinkage and selection operator (LASSO) Cox regression analysis to construct a prognostic signature for CRGs. Survival analysis of patients with PAAD was performed using Kaplan-Meier analysis. Furthermore, we used bioinformatics tools to screen for CRGs-related MicroRNA (miRNA) and lncRNAs. To validate these findings, we conducted real-time quantitative polymerase chain reaction (RT-qPCR), CCK-8, colony formation, and Transwell assays to assess the effect of DLAT in vitro. Our results revealed a cuproptosis-related prognostic signature consisting of 3 prognostic genes (DLAT, LIAS, and LIPT1). Notably, patients with a high-risk score for the CRGs signature exhibited poor prognosis in terms of overall survival (OS) (P < .05). The receiver operating characteristic (ROC) curve was used to evaluate the prognostic signature of CRGs. The results showed that the 1-year, 3-year, and 5-year area under the curve values for predicting OS were 0.62, 0.66, and 0.79, respectively. Additionally, the CRGs-related ceRNA network revealed the regulatory axis of LINC00857/has-miR-1179/DLAT in PAAD. In vitro experiments demonstrated that knockdown of LINC00857 and DLAT inhibited the growth and invasion of PAAD cells. This study identified a CRG-related prognostic signature consisting of 3 biomarkers (DLAT, LIAS, and LIPT1) for PAAD. Furthermore, ceRNA network analysis suggested the involvement of the LINC00857/has-miR-1179/DLAT axis in the development of PAAD. Overall, this study provides theoretical support for the investigation of diagnostic and prognostic biomarkers as well as potential therapeutic targets in PAAD.

Associations between genetically predicted levels of blood metabolites and pancreatic cancer risk

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive solid malignancies, which is featured by systematic metabolism. Thus, a better understanding of metabolic dysregulation in PDAC is important to better characterize its etiology. Here, we performed a large metabolome-wide association study (MWAS) to systematically explore associations between genetically predicted metabolite levels in blood and PDAC risk. Using data from 881 subjects of European descent in the TwinsUK Project, comprehensive genetic models were built to predict serum metabolite levels. These prediction models were applied to the genetic data of 8275 cases and 6723 controls included in the PanScan (I, II and III) and PanC4 consortia. After assessing the metabolite-PDAC risk associations by a slightly modified TWAS/FUSION framework, we identified five metabolites (including two dipeptides) showing significant associations with PDAC risk at false discovery rate (FDR) <0.05. Integrated with gut microbial information, two-sample Mendelian randomization (MR) analyses were further performed to investigate the relationship among serum metabolites, gut microbiome features and PDAC. The flavonoid-degrading bacteria Flavonifractor sp90199495 was found to be associated with metabolite X-21849 and it was also shown to be associated with PDAC risk. Collectively, our study identified novel candidate metabolites for PDAC risk, which could lead to new insights into the etiology of PDAC and improved treatment options.

Single-EV analysis (sEVA) of mutated proteins allows detection of stage 1 pancreatic cancer

Tumor cell-derived extracellular vesicles (EVs) are being explored as circulating biomarkers, but it is unclear whether bulk measurements will allow early cancer detection. We hypothesized that a single-EV analysis (sEVA) technique could potentially improve diagnostic accuracy. Using pancreatic cancer (PDAC), we analyzed the composition of putative cancer markers in 11 model lines. In parental PDAC cells positive for KRASmut and/or P53mut proteins, only ~40% of EVs were also positive. In a blinded study involving 16 patients with surgically proven stage 1 PDAC, KRASmut and P53mut protein was detectable at much lower levels, generally in <0.1% of vesicles. These vesicles were detectable by the new sEVA approach in 15 of the 16 patients. Using a modeling approach, we estimate that the current PDAC detection limit is at ~0.1-cm3 tumor volume, below clinical imaging capabilities. These findings establish the potential for sEVA for early cancer detection.

Citrate synthase desuccinylation by SIRT5 promotes colon cancer cell proliferation and migration

Citrate synthase (CS), the rate-limiting enzyme in the tricarboxylic acid (TCA) cycle catalyzes the first step of the cycle, namely, the condensation of oxaloacetate and acetyl-CoA to produce citrate. The expression and enzymatic activity of CS are altered in cancers, but posttranslational modification (PTM) of CS and its regulation in tumorigenesis remain largely obscure. SIRT5 belongs to the nicotinamide adenine dinucleotide (NAD)+-dependent deacetylase sirtuin family and plays vital roles in multiple biological processes via modulating various substrates. Here, we show that SIRT5 interacts with CS and that SIRT5 desuccinylates CS at the evolutionarily conserved residues K393 and K395. Moreover, hypersuccinylation of CS at K393 and K395 dramatically reduces its enzymatic activity and suppresses colon cancer cell proliferation and migration. These results provide experimental evidence in support of a potential therapeutic approach for colon cancer.

Modeling EV Kinetics for Use in Early Cancer Detection

Tumor-derived extracellular vesicles (EVs) represent promising biomarkers for monitoring cancers. Technological advances have improved the ability to measure EV reliably in blood using protein, RNA, or lipid detection methods. However, it is less clear how efficacious current EV assays are for the early detection of small and thus curable tumors. Here, a mathematical model is developed to estimate key parameter values and future requirements for EV testing. Tumor volumes in mice correlate well with increases in total number of circulating EV allowing the researchers to calculate EV shed rates for four different published cancer models. Model extrapolations to human physiology show good agreement with published clinical data. Specifically, it is shown that current bulk EV detection systems are ≈104 -fold too insensitive to detect human cancers of ≈1 cm3 . Conversely, it is predicted that emerging single EV methods will allow blood-based detection of cancers of <1 mm3 in humans.

Survival estimation through the cumulative hazard with monotone natural cubic splines using convex optimization-the HCNS approach

BACKGROUND AND OBJECTIVES

In survival analysis both the Kaplan-Meier estimate and the Cox model enjoy a broad acceptance. We present an improved spline-based survival estimate and offer a fully automated software for its implementation. We explore the use of natural cubic splines that are constrained to be monotone. Apart from its superiority over the Kaplan Meier estimator our approach overcomes limitations of other known smoothing approaches and can accommodate covariates. Unlike other spline methods, concerns of computational problems and issues of overfitting are resolved since no attempt is made to maximize a likelihood once the Kaplan-Meier estimator is obtained. An application to laryngeal cancer data, a simulation study and illustrations of the broad application of the method and its software are provided. In addition to presenting our approaches, this work contributes to bridging a communication gap between clinicians and statisticians that is often apparent in the medical literature.

METHODS

We employ a two-stage approach: first obtain the stepwise cumulative hazard and then consider a natural cubic spline to smooth its steps under restrictions of monotonicity between any consecutive knots. The underlying region of monotonicity corresponds to a non-linear region that encompasses the full family of monotone third-degree polynomials. We approximate it linearly and reduce the problem to a restricted least squares one under linear restrictions. This ensures convexity. We evaluate our method through simulations against competitive traditional approaches.

RESULTS

Our method is compared to the popular Kaplan Meier estimate both in terms of mean squared error and in terms of coverage. Over-fitting is avoided by construction, as our spline attempts to approximate the empirical estimate of the cumulative hazard itself, and is not fitted directly on the data.

CONCLUSIONS

The proposed approach will enable clinical researchers to obtain improved survival estimates and valid confidence intervals over the full spectrum of the range of the survival data. Our methods outperform conventional approaches and can be readily utilized in settings beyond survival analysis such as diagnostic testing.

Metabolomics identified new biomarkers for the precise diagnosis of pancreatic cancer and associated tissue metastasis

Pancreatic cancer (PC) is one of the most aggressive malignancies with high mortality due to a complex and latent pathogenesis leading to the severe lack of early diagnosis methods. To improve clinical diagnosis and enhance therapeutic outcome, we employed the newly developed precision-targeted metabolomics method to identify and validate metabolite biomarkers from the plasma samples of patients with pancreatic cancer that can sensitively and efficiently diagnose the onsite progression of the disease. Many differential metabolites have the capacity to markedly distinguish patients with pancreatic cancer (n = 60) from healthy controls (n = 60). To further enhance the specificity and selectivity of metabolite biomarkers, a dozen tumor tissues from PC patients and paired normal tissues were used to clinically validate the biomarker performance. We eventually verified five new metabolite biomarkers in plasma (creatine, inosine, beta-sitosterol, sphinganine and glycocholic acid), which can be used to readily diagnose pancreatic cancer in a clinical setting. Excitingly, we proposed a panel biomarker by integrating these five individual metabolites into one pattern, demonstrating much higher accuracy and specificity to precisely diagnose pancreatic cancer than conventional biomarkers (CA125, CA19-9, CA242 and CEA); moreover, this plasma panel biomarker used for PC diagnosis is also quite convenient to implement in clinical practice. Using the same metabolomics method, we characterized succinic acid and gluconic acid as having a great capability to monitor the progression and metastasis of pancreatic cancer at different stages. Taken together, this metabolomics method was used to identify and validate metabolite biomarkers that can precisely and sensitively diagnose the onsite progression and metastasis of pancreatic cancer in a clinical setting. Furthermore, such effort should leave clinicians with the correct time frame to facilitate early and efficient therapeutic interventions, which could largely improve the five-year survival rate of PC patients by significantly lowering clinical mortality.

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.

Prediagnostic Leukocyte Telomere Length and Pancreatic Cancer Survival

BACKGROUND

Leukocyte telomere length has been associated with risk of subsequent pancreatic cancer. Few prospective studies have evaluated the association of prediagnostic leukocyte telomere length with pancreatic cancer survival.

METHODS

We prospectively examined the association of prediagnostic leukocyte telomere length with overall survival (OS) time among 423 participants diagnosed with pancreatic adenocarcinoma between 1984 and 2008 within the Health Professionals Follow-up Study, Nurses' Health Study, Physicians' Health Study, and Women's Health Initiative. We measured prediagnostic leukocyte telomere length in banked blood samples using quantitative PCR. Cox proportional hazards models were used to estimate HRs for OS with adjustment for potential confounders. We also evaluated 10 SNPs at the telomerase reverse transcriptase locus.

RESULTS

Shorter prediagnostic leukocyte telomere length was associated with reduced OS among patients with pancreatic cancer (P trend = 0.04). The multivariable-adjusted HR for OS comparing the lowest with highest quintiles of leukocyte telomere length was 1.39 (95% confidence interval, 1.01-1.93), corresponding to a 3-month difference in median OS time. In an analysis excluding cases with blood collected within 2 years of cancer diagnosis, the association was moderately stronger (HR, 1.55; 95% confidence interval, 1.09-2.21; comparing the lowest with highest quintiles; P trend = 0.01). No prognostic association or effect modification for the prognostic association of prediagnostic leukocyte telomere length was noted in relation to the studied SNPs.

CONCLUSIONS

Prediagnostic leukocyte telomere length was associated with pancreatic cancer survival.

IMPACT

Prediagnostic leukocyte telomere length can be a prognostic biomarker in pancreatic cancer.

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.

Circulating tumor DNA: clinical roles in diffuse large B cell lymphoma

Diffuse large B cell lymphoma (DLBCL), the most common non-Hodgkin lymphoma (NHL), is a clinically and molecularly heterogeneous malignant lymphoproliferative disease. In the era of personalized medicine, genetic information is critical to early diagnosis, aiding risk stratification, directing therapeutic option, and monitoring disease relapse. However, lacking a circulating disease with most DLBCL cases hampers the acquisition of tumor genomic landscapes and disease dynamics. Circulating tumor DNA (ctDNA) is a novel noninvasive, real-time, and tumor-specific biomarker, reliably reflecting the comprehensive tumor genetic profiles, thus holds great promise in individualized medicine, including precise diagnosis and prognosis, response monitoring, and relapse detection of DLBCL. Here, we reviewed the recent advances of ctDNA in DLBCL and discussed its clinical values at different time points during the disease courses by comparing with the current routine methods in clinical practice. Collectively, we anticipated that ctDNA will ultimately be integrated into the management of DLBCL to facilitate precision medicine.

Epidemiology of Moderate Alcohol Consumption and Breast Cancer: Association or Causation?

Epidemiological studies have been used to show associations between modifiable lifestyle habits and the incidence of breast cancer. Among such factors, a history of alcohol use has been reported in multiple studies and meta-analyses over the past decades. However, associative epidemiological studies that were interpreted as evidence that even moderate alcohol consumption increases breast cancer incidence have been controversial. In this review, we consider the literature on the relationship between moderate or heavy alcohol use, both in possible biological mechanisms and in variations in susceptibility due to genetic or epigenetic factors. We argue that there is a need to incorporate additional approaches to move beyond the associations that are reported in traditional epidemiological analyses and incorporate information on molecular pathologic signatures as a requirement to posit causal inferences. In particular, we point to the efforts of the transdisciplinary field of molecular pathological epidemiology (MPE) to evaluate possible causal relationships, if any, of alcohol consumption and breast cancer. A wider application of the principles of MPE to this field would constitute a giant step that could enhance our understanding of breast cancer and multiple modifiable risk factors, a step that would be particularly suited to the era of "personalized medicine".

Circulating biomarkers for early diagnosis of pancreatic cancer: facts and hopes

Pancreatic cancer (PC) is characterized by extremely high mortality and poor prognosis, which are largely ascribed to difficulties in early diagnosis and limited therapeutics. Although there is a sufficient window for intervention before preneoplastic lesions progress to invasive disease, effective early detection of PC remains difficult using current biomarkers and imaging techniques. Biomarkers with satisfactory diagnostic efficacy and convenient analysis methods are urgently required. In this review, we summarized recent advances in the identification of biomarkers in circulation for early detection of PC. A number of novel circulating biomarkers, such as metabolites, cell-free DNA (cfDNA), noncoding RNA, and exosomes, that show promising diagnostic value have been discovered using advances in sequencing techniques and "omics" analyses. Panels comprising several biomarkers may also exhibit better diagnostic performance. In the future, we need more efficient circulating biomarkers for the identification of noninvasive precursor lesions and early disease. Collaborative large-scale studies are also required to show the clinical validity and applicability of potential biomarkers.

Metabolic biomarker signature to differentiate pancreatic ductal adenocarcinoma from chronic pancreatitis

OBJECTIVE

Current non-invasive diagnostic tests can distinguish between pancreatic cancer (pancreatic ductal adenocarcinoma (PDAC)) and chronic pancreatitis (CP) in only about two thirds of patients. We have searched for blood-derived metabolite biomarkers for this diagnostic purpose.

DESIGN

For a case-control study in three tertiary referral centres, 914 subjects were prospectively recruited with PDAC (n=271), CP (n=282), liver cirrhosis (n=100) or healthy as well as non-pancreatic disease controls (n=261) in three consecutive studies. Metabolomic profiles of plasma and serum samples were generated from 477 metabolites identified by gas chromatography-mass spectrometry and liquid chromatography-tandem mass spectrometry.

RESULTS

A biomarker signature (nine metabolites and additionally CA19-9) was identified for the differential diagnosis between PDAC and CP. The biomarker signature distinguished PDAC from CP in the training set with an area under the curve (AUC) of 0.96 (95% CI 0.93-0.98). The biomarker signature cut-off of 0.384 at 85% fixed specificity showed a sensitivity of 94.9% (95% CI 87.0%-97.0%). In the test set, an AUC of 0.94 (95% CI 0.91-0.97) and, using the same cut-off, a sensitivity of 89.9% (95% CI 81.0%-95.5%) and a specificity of 91.3% (95% CI 82.8%-96.4%) were achieved, successfully validating the biomarker signature.

CONCLUSIONS

In patients with CP with an increased risk for pancreatic cancer (cumulative incidence 1.95%), the performance of this biomarker signature results in a negative predictive value of 99.9% (95% CI 99.7%-99.9%) (training set) and 99.8% (95% CI 99.6%-99.9%) (test set). In one third of our patients, the clinical use of this biomarker signature would have improved diagnosis and treatment stratification in comparison to CA19-9.

Generation of an oxoglutarate dehydrogenase knockout rat model and the effect of a high-fat diet

Although abnormal metabolism in metabolic syndrome and tumours has been well described, the relationship between oxoglutarate dehydrogenase (OGDH) and obesity-related diseases is still largely unknown. This study aimed to investigate whether it was possible to use transcription activator-like effector nuclease (TALEN) technology to establish OGDH^−/− rats and then study the effect of a high-fat diet (HFD) on these rats. However, after OGDH^+/−rats were generated, we were unable to identify any OGDH^−/− rats by performing mating experiments with the OGDH^+/− rats for almost one year. During the past three years, only OGDH^+/− rats were stably established, and correspondingly reduced OGDH expression in the tissues of the OGDH^+/− rats was verified. No significant abnormal behaviour was observed in the OGDH^+/− rats compared to the wild-type (WT) control rats. However, the OGDH^+/− rats were revealed to have higher body weight, and the difference was even significantly greater under the HFD condition. Furthermore, blood biochemical and tissue histological examinations uncovered no abnormalities with normal diets, but a HFD resulted in liver dysfunction with pathological alterations in the OGDH^+/− rats. Our results strongly indicate that OGDH homologous knockout is lethal in rats but heterologous OGDH knockout results in vulnerable liver lesions with a HFD. Therefore, the current study may provide a useful OGDH^+/− rat model for further investigations of metabolic syndrome and obesity-related hepatic carcinogenesis.

Although abnormal metabolism in metabolic syndrome and tumours has been well described, the relationship between oxoglutarate dehydrogenase (OGDH) and obesity-related diseases is still largely unknown.

Identifying the metabolomic fingerprint of high and low flavonoid consumers

High flavonoid consumption can improve vascular health. Exploring flavonoid–metabolome relationships in population-based settings is challenging, as: (i) there are numerous confounders of the flavonoid–metabolome relationship; and (ii) the set of dependent metabolite variables are inter-related, highly variable and multidimensional. The Metabolite Fingerprint Score has been developed as a means of approaching such data. This study aims to compare its performance with that of more traditional methods, in identifying the metabolomic fingerprint of high and low flavonoid consumers. This study did not aim to identify biomarkers of intake, but rather to explore how systemic metabolism differs in high and low flavonoid consumers. Using liquid chromatography–tandem MS, 174 circulating plasma metabolites were profiled in 584 men and women who had complete flavonoid intake assessment. Participants were randomised to one of two datasets: (a) training dataset, to determine the models for the discrimination variables (n 399); and (b) validation dataset, to test the capacity of the variables to differentiate higher from lower total flavonoid consumers (n 185). The stepwise and full canonical variables did not discriminate in the validation dataset. The Metabolite Fingerprint Score successfully identified a unique pattern of metabolites that discriminated high from low flavonoid consumers in the validation dataset in a multivariate-adjusted setting, and provides insight into the relationship of flavonoids with systemic lipid metabolism. Given increasing use of metabolomics data in dietary association studies, and the difficulty in validating findings using untargeted metabolomics, this paper is of timely importance to the field of nutrition. However, further validation studies are required.

Multiparametric plasma EV profiling facilitates diagnosis of pancreatic malignancy

Pancreatic ductal adenocarcinoma (PDAC) is usually detected late in the disease process. Clinical workup through imaging and tissue biopsies is often complex and expensive due to a paucity of reliable biomarkers. We used an advanced multiplexed plasmonic assay to analyze circulating tumor-derived extracellular vesicles (tEVs) in more than 100 clinical populations. Using EV-based protein marker profiling, we identified a signature of five markers (PDACEV signature) for PDAC detection. In our prospective cohort, the accuracy for the PDACEV signature was 84% [95% confidence interval (CI), 69 to 93%] but only 63 to 72% for single-marker screening. One of the best markers, GPC1 alone, had a sensitivity of 82% (CI, 60 to 95%) and a specificity of 52% (CI, 30 to 74%), whereas the PDACEV signature showed a sensitivity of 86% (CI, 65 to 97%) and a specificity of 81% (CI, 58 to 95%). The PDACEV signature of tEVs offered higher sensitivity, specificity, and accuracy than the existing serum marker (CA 19-9) or single-tEV marker analyses. This approach should improve the diagnosis of pancreatic cancer.

Diversity of Precursor Lesions For Pancreatic Cancer: The Genetics and Biology of Intraductal Papillary Mucinous Neoplasm

Pancreatic ductal adenocarcinoma (PDA), one of the most lethal cancers worldwide, is associated with two main types of morphologically distinct precursors—pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasm (IPMN). Although the progression of PanIN into invasive cancer has been well characterized, there remains an urgent need to understand the biology of IPMNs, which are larger radiographically detectable cystic tumors. IPMNs comprise a number of subtypes with heterogeneous histopathologic and clinical features. Although frequently remaining benign, a significant proportion exhibits malignant progression. Unfortunately, there are presently no accurate prognosticators for assessing cancer risk in individuals with IPMN. Moreover, the fundamental mechanisms differentiating PanIN and IPMN remain largely obscure, as do those that distinguish IPMN subtypes. Recent studies, however, have identified distinct genetic profiles between PanIN and IPMN, providing a framework to better understand the diversity of the precursors for PDA. Here, we review the clinical, biological, and genetic properties of IPMN and discuss various models for progression of these tumors to invasive PDA.

Early detection of pancreatic cancer: Where are we now and where are we going?

Pancreatic cancer (PC) is one of the most lethal malignancies. Recent studies indicate that patients with incidentally diagnosed PC have better prognosis than those with symptoms and that there is a sufficient window for early detection. However, effective early diagnosis remains difficult and depends mainly on imaging modalities and the development of screening methodologies with highly sensitive and specific biomarkers. This review summarizes recent advances in effective screening for early diagnosis of PC using imaging modalities and novel molecular biomarkers discovered from various "omics" studies including genomics, epigenomics, non-coding RNA, metabonomics, liquid biopsy (CTC, ctDNA and exosomes) and microbiomes, and their use in body fluids (feces, urine and saliva). Although many biomarkers for early detection of PC have been discovered through various methods, larger scale and rigorous validation is required before their application in the clinic. In addition, more effective and specific biomarkers of PC are urgently needed.

Energy sources identify metabolic phenotypes in pancreatic cancer

Metabolic reprogramming is one of the emerging hallmarks of cancers. As a highly malignant tumor, pancreatic ductal adenocarcinoma (PDA) is not only a metabolic disease but also a heterogeneous disease. Heterogeneity induces PDA dependence on distinct nutritive substrates, thereby inducing different metabolic phenotypes. We stratified PDA into four phenotypes with distinct types of energy metabolism, including a Warburg phenotype, a reverse Warburg phenotype, a glutaminolysis phenotype, and a lipid-dependent phenotype. The four phenotypes possess distinct metabolic features and reprogram their metabolic pathways to adapt to stress. The metabolic type present in PDA should prompt differential imaging and serologic metabolite detection for diagnosis and prognosis. The targeting of an individual metabolic phenotype with corresponding metabolic inhibitors is considered a promising therapeutic approach and, in combination with chemotherapy, is expected to be a novel strategy for PDA treatment.

Nurses' Health Study Contributions on the Epidemiology of Less Common Cancers: Endometrial, Ovarian, Pancreatic, and Hematologic

OBJECTIVES

To review the contributions of the Nurses' Health Study (NHS) to epidemiologic knowledge of endometrial, ovarian, pancreatic, and hematologic cancers.

METHODS

We reviewed selected NHS publications from 1976 to 2016, including publications from consortia and other pooled studies.

RESULTS

NHS studies on less common cancers have identified novel risk factors, such as a reduced risk of endometrial cancer in women of advanced age at last birth, and have clarified or prospectively confirmed previously reported associations, including an inverse association between tubal ligation and ovarian cancer. Through biomarker research, the NHS has furthered understanding of the pathogenesis of rare cancers, such as the role of altered metabolism in pancreatic cancer risk and survival. NHS investigations have also demonstrated the importance of the timing of exposure, such as the finding of a positive association of early life body fatness, but not of usual adult body mass index, with non-Hodgkin lymphoma risk.

CONCLUSIONS

Evidence from the NHS has informed prevention strategies and contributed to improved survival from less common but often lethal malignancies, including endometrial, ovarian, pancreatic, and hematologic cancers.

Genomics, Telomere Length, Epigenetics, and Metabolomics in the Nurses' Health Studies

OBJECTIVES

To review the contribution of the Nurses' Health Study (NHS) and NHS II to genomics, epigenetics, and metabolomics research.

METHODS

We performed a narrative review of the publications of the NHS and NHS II between 1990 and 2016 based on biospecimens, including blood and tumor tissue, collected from participants.

RESULTS

The NHS has contributed to the discovery of genetic loci influencing more than 45 complex human phenotypes, including cancers, diabetes, cardiovascular disease, reproductive characteristics, and anthropometric traits. The combination of genomewide genotype data with extensive exposure and lifestyle data has enabled the evaluation of gene-environment interactions. Furthermore, data suggest that longer telomere length increases risk of cancers not related to smoking, and that modifiable factors (e.g., diet) may have an impact on telomere length. "Omics" research in the NHS continues to expand, with epigenetics and metabolomics becoming greater areas of focus.

CONCLUSIONS

The combination of prospective biomarker data and broad exposure information has enabled the NHS to participate in a variety of "omics" research, contributing to understanding of the epidemiology and biology of multiple complex diseases.