Development of a metabolites risk score for one-year mortality risk prediction in pancreatic adenocarcinoma patients

Development and Validation of a Survival Prediction Model for Patients With Pancreatic Cancer

INTRODUCTION

Patients with pancreatic ductal adenocarcinoma (PDAC) face challenging treatment decisions following their diagnosis. We developed and validated a survival prognostication model using routinely available clinical information, patient-reported symptoms, performance status, and initial cancer-directed treatment.

METHODS

This retrospective cohort study included patients with PDAC from 2007 to 2020 using linked administrative databases in Ontario, Canada. Patients were randomly selected for model development (75%) and validation (25%). Using the development cohort, a multivariable Cox proportional hazards regression with backward stepwise variable selection was used to predict the probability of survival. Model performance was assessed on the validation cohort using the concordance index and calibration plots.

RESULTS

There were 17,450 patients (49% female) with a median age of 72 years (interquartile range 63-81) and a mean survival time of 9 months. In the derivation cohort, 1,469 patients (11%) had early stage, 4,202 (32%) had advanced stage disease, and 7,417 (57%) had unknown stage. The following factors were associated with an increased risk of death by more than 10%: tumor in the tail of the pancreas; advanced stage; hospitalization 3 months before diagnosis; congestive heart failure or dementia; low, moderate, or high pain score; moderate or high appetite score; high dyspnea and tiredness score; and a performance status score of 60-70 or lower. The calibration plot indicated good agreement with a C-index of 0.76.

DISCUSSION

This model accurately predicted one-year survival for PDAC using clinical factors, symptoms, and performance status. This model may foster shared decision making for patients and their providers.

Prognostic models to predict survival in patients with pancreatic cancer: a systematic review

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has poor survival. Current treatments offer little likelihood of cure or long-term survival. This systematic review evaluates prognostic models predicting overall survival in patients diagnosed with PDAC.

METHODS

We conducted a comprehensive search of eight electronic databases from their date of inception through to December 2019. Studies that published models predicting survival in patients with PDAC were identified.

RESULTS

3297 studies were identified; 187 full-text articles were retrieved and 54 studies of 49 unique prognostic models were included. Of these, 28 (57.1%) were conducted in patients with advanced disease, 17 (34.7%) with resectable disease, and four (8.2%) in all patients. 34 (69.4%) models were validated, and 35 (71.4%) reported model discrimination, with only five models reporting values >0.70 in both derivation and validation cohorts. Many (n = 27) had a moderate to high risk of bias and most (n = 33) were developed using retrospective data. No variables were unanimously found to be predictive of survival when included in more than one study.

CONCLUSION

Most prognostic models were developed using retrospective data and performed poorly. Future research should validate instruments performing well locally in international cohorts and investigate other potential predictors of survival.

A prognostic score based on long-term survivor unique transcriptomic signatures predicts patient survival in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is known for its poor prognosis with few long-term survivors. This study aimed to establish a prognostic score using unique transcriptomic profiles of long-term survivors to be used as a patient selection tool for meaningful clinical intervention in PDAC. In TCGA PDAC cohort, 16 genes were significantly upregulated in the long-term survivor tumors. A prognostic score was established using these 16 genes by LASSO Cox regression, and PHKG1, HOXA4, ISL2, DMRT3 and TRA2A gene expressions were included in the score. The prognostic value was confirmed in both testing and validation cohorts. The characteristics of the high score tumor was investigated by bioinformatical approach. The high score tumor was associated with TP53 mutation but not with other commonly enhanced signaling pathways in PDAC. The high score tumor was associated with higher tumor mutational burden and unfavorable tumor microenvironment (TME), such as lower infiltration of CD8-positive T cells and dendritic cells, and less cell composition of mature blood vessels and fibroblasts. The high score tumor was also associated with enhanced cell proliferation and margin positivity after surgery. The impact of score component genes on the cell proliferation was investigated by in vitro experiments. Silencing of the score component genes promoted cell proliferation. In conclusion, the prognostic score predicted PDAC patient survival and was associated with cancer aggressiveness such as unfavorable TME and enhanced cell proliferation.

Perioperative changes in the plasma metabolome of patients receiving general anesthesia for pancreatic cancer surgery

Background: Modern anesthesia strives to offer personalized concepts to meet the patient’s individual needs in sight of clinical outcome. Still, little is known about the impact of anesthesia on the plasma metabolome, although many metabolites have been shown to modulate the function of various immune cells, making it particularly interesting in the context of oncological surgery. In this study longitudinal dynamics in the plasma metabolome during general anesthesia in patients undergoing pancreatic surgery were analyzed.

Materials and Methods: Prospective, observational study with 10 patients diagnosed with pancreatic (pre-) malignancy and subjected to elective resection surgery under general anesthesia. Plasma metabolites (n = 630) were quantified at eight consecutive perioperative timepoints using mass spectrometry-based targeted metabolomics.

Results: 39 metabolites significantly changed during the perioperative period. Tryptophan concentrations decreased by 45% with the maximum decrease after anesthesia induction (p = 6.24E-07), while taurine synthesis increased (p = 1.46E-04). Triacylglycerides and lysophosphatidylcholines were significantly reduced with increased liberation of free monounsaturated fatty acids (p = 0.03). Carnitine levels decreased significantly (p = 9.30E-04).

Conclusions: The major finding of this study was perioperative tryptophan depletion and increased taurine synthesis. Both are essential for immune cell function and are therefore of significant interest for perioperative management. Further studies are needed to identify influencing anesthetic factors.

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.

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.

A PD-L2-based immune marker signature helps to predict survival in resected pancreatic ductal adenocarcinoma

BACKGROUND

Programmed cell death protein 1 (PD-1) is a key immune checkpoint that regulates peripheral tolerance and protects against autoimmunity. Programmed death ligand-2 (PD-L2) is a less studied ligand to PD-1 and has yet to be fully explored, especially in pancreatic ductal adenocarcinoma (PDAC).

METHODS

In this study, we performed immunohistochemistry to detect the PD-L2, CD3, CD8, transforming growth factor-β2 (TGF-β2) and FOXP3 levels in paraffin sections from 305 patients with resected PDAC as a training set. Expression levels of intratumoral and stromal immune markers were compared in relation to survival using Kaplan-Meier curves, random survival forest model and survival tree analysis. A multivariable Cox proportional-hazards model of associated markers was used to calculate the risk scores.

RESULTS

PD-L2 was expressed in 71.5% of PDAC samples and showed strong correlations with CD3+, CD8+ T cells and FOXP3+ regulatory T cell densities. High levels of intratumoral PD-L2 and FOXP3 were related to poor survival; only stromal FOXP3 overexpression was associated with worse prognosis. Four patterns generated from survival tree analysis demonstrated that PD-L2lowstromalFOXP3low patients had the longest survival, while PD-L2highintratumoralCD3low patients had the shortest survival (P <  0.001). The area under the curve was 0.631(95% confidence interval (CI): 0.447-0.826) for the immune marker-based signature and 0.549 (95% CI: 0.323-0.829; P <  0.001) for the clinical parameter-based signature, which was consistent with the results in the validation set including 150 patients (P <  0.001). A higher risk score indicated shorter survival and could serve as an independent prognostic factor. PD-L2 was also showed associated with TGF-β2 and other immune molecules based on bioinformatics analysis.

CONCLUSIONS

Our work highlighted PD-L2 as a promising immunotherapeutic target with prognostic value combined with complex tumor infiltrating cells in PDAC.

Review and Comparison of Cancer Biomarker Trends in Urine as a Basis for New Diagnostic Pathways

Cancer is one of the major causes of mortality worldwide and its already large burden is projected to increase significantly in the near future with a predicted 22 million new cancer cases and 13 million cancer-related deaths occurring annually by 2030. Unfortunately, current procedures for diagnosis are characterized by low diagnostic accuracies. Given the proved correlation between cancer presence and alterations of biological fluid composition, many researchers suggested their characterization to improve cancer detection at early stages. This paper reviews the information that can be found in the scientific literature, regarding the correlation of different cancer forms with the presence of specific metabolites in human urine, in a schematic and easily interpretable form, because of the huge amount of relevant literature. The originality of this paper relies on the attempt to point out the odor properties of such metabolites, and thus to highlight the correlation between urine odor alterations and cancer presence, which is proven by recent literature suggesting the analysis of urine odor for diagnostic purposes. This investigation aims to evaluate the possibility to compare the results of studies based on different approaches to be able in the future to identify those compounds responsible for urine odor alteration.

Identification of ENTPD8 and cytidine in pancreatic cancer by metabolomic and transcriptomic conjoint analysis

To identify metabolic pathways that were perturbed in pancreatic cancer (PC), we investigated gene‐metabolite networks by integration of metabolomic and transcriptomic. In this research, we undertook the metabolomic study of 43 paired human PC samples, aiming to identify key metabolic alterations in PC. We also carried out in vitro experiments to validate that the key metabolite cytidine and its related gene ENTPD8 played an important role in PC cell proliferation. We screened out 13 metabolites differentially expressed in PC tissue (PCT) by liquid chromatography/mass spectrometry analysis on 34 metabolites, and the partial least square discrimination analysis results revealed that 9 metabolites among them were remarkably altered in PCT compared to adjacent noncancerous tissue (variable importance in projection >1, P < .05). Among the 9 metabolites, 7 might be potential biomarkers. The most significantly enriched metabolic pathway was pyrimidine metabolism. We analyzed 351 differentially expressed genes from The Cancer Genome Atlas and intersected them with Kyoto Encyclopedia of Genes and Genomes metabolic pathways. We found that ENTPD8 had a gene‐metabolite association with cytidine in the CTP dephosphorylation pathway. We verified by in vitro experiments that the CTP dephosphorylation pathway was changed in PCT compared with adjacent noncancerous tissue. ENTPD8 was downregulated in PCT, causing a reduction in cytidine formation and hence weakened CTP dephosphorylation in pyrimidine metabolism.

Cancer sniffer dogs: how can we translate this peculiarity in laboratory medicine? Results of a pilot study on gastrointestinal cancers

BACKGROUND

Identification of cancer biomarkers to allow early diagnosis is an urgent need for many types of tumors, whose prognosis strongly depends on the stage of the disease. Canine olfactory testing for detecting cancer is an emerging field of investigation. As an alternative, here we propose to use GC-Olfactometry (GC/O), which enables the speeding up of targeted biomarker identification and analysis. A pilot study was conducted in order to determine odor-active compounds in urine that discriminate patients with gastrointestinal cancers from control samples (healthy people).

METHODS

Headspace solid phase microextraction (HS-SPME)-GC/MS and GC-olfactometry (GC/O) analysis were performed on urine samples obtained from gastrointestinal cancer patients and healthy controls.

RESULTS

In total, 91 key odor-active compounds were found in the urine samples. Although no odor-active biomarkers present were found in cancer carrier's urine, significant differences were discovered in the odor activities of 11 compounds in the urine of healthy and diseased people. Seven of above mentioned compounds were identified: thiophene, 2-methoxythiophene, dimethyl disulphide, 3-methyl-2-pentanone, 4-(or 5-)methyl-3-hexanone, 4-ethyl guaiacol and phenylacetic acid. The other four compounds remained unknown.

CONCLUSIONS

GC/O has a big potential to identify compounds not detectable using untargeted GC/MS approach. This paves the way for further research aimed at improving and validating the performance of this technique so that the identified cancer-associated compounds may be introduced as biomarkers in clinical practice to support early cancer diagnosis.

Development and validation of a multivariate predictive model for rheumatoid arthritis mortality using a machine learning approach

We developed and independently validated a rheumatoid arthritis (RA) mortality prediction model using the machine learning method Random Survival Forests (RSF). Two independent cohorts from Madrid (Spain) were used: the Hospital Clínico San Carlos RA Cohort (HCSC-RAC; training; 1,461 patients), and the Hospital Universitario de La Princesa Early Arthritis Register Longitudinal study (PEARL; validation; 280 patients). Demographic and clinical-related variables collected during the first two years after disease diagnosis were used. 148 and 21 patients from HCSC-RAC and PEARL died during a median follow-up time of 4.3 and 5.0 years, respectively. Age at diagnosis, median erythrocyte sedimentation rate, and number of hospital admissions showed the higher predictive capacity. Prediction errors in the training and validation cohorts were 0.187 and 0.233, respectively. A survival tree identified five mortality risk groups using the predicted ensemble mortality. After 1 and 7 years of follow-up, time-dependent specificity and sensitivity in the validation cohort were 0.79–0.80 and 0.43–0.48, respectively, using the cut-off value dividing the two lower risk categories. Calibration curves showed overestimation of the mortality risk in the validation cohort. In conclusion, we were able to develop a clinical prediction model for RA mortality using RSF, providing evidence for further work on external validation.

Computational Model Predicts the Effects of Targeting Cellular Metabolism in Pancreatic Cancer

Reprogramming of energy metabolism is a hallmark of cancer that enables the cancer cells to meet the increased energetic requirements due to uncontrolled proliferation. One prominent example is pancreatic ductal adenocarcinoma, an aggressive form of cancer with an overall 5-year survival rate of 5%. The reprogramming mechanism in pancreatic cancer involves deregulated uptake of glucose and glutamine and other opportunistic modes of satisfying energetic demands in a hypoxic and nutrient-poor environment. In the current study, we apply systems biology approaches to enable a better understanding of the dynamics of the distinct metabolic alterations in KRAS-mediated pancreatic cancer, with the goal of impeding early cell proliferation by identifying the optimal metabolic enzymes to target. We have constructed a kinetic model of metabolism represented as a set of ordinary differential equations that describe time evolution of the metabolite concentrations in glycolysis, glutaminolysis, tricarboxylic acid cycle and the pentose phosphate pathway. The model is comprised of 46 metabolites and 53 reactions. The mathematical model is fit to published enzyme knockdown experimental data. We then applied the model to perform in silico enzyme modulations and evaluate the effects on cell proliferation. Our work identifies potential combinations of enzyme knockdown, metabolite inhibition, and extracellular conditions that impede cell proliferation. Excitingly, the model predicts novel targets that can be tested experimentally. Therefore, the model is a tool to predict the effects of inhibiting specific metabolic reactions within pancreatic cancer cells, which is difficult to measure experimentally, as well as test further hypotheses toward targeted therapies.

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

BACKGROUND

Pancreatic adenocarcinomas (PAs) have very poor prognoses even when surgery is possible. Currently, there are no tissular biomarkers to predict long-term survival in patients with PA. The aims of this study were to (1) describe the metabolome of pancreatic parenchyma (PP) and PA, (2) determine the impact of neoadjuvant chemotherapy on PP and PA, and (3) find tissue metabolic biomarkers associated with long-term survivors, using metabolomics analysis.

METHODS

1H high-resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) spectroscopy using intact tissues was applied to analyze metabolites in PP tissue samples (n = 17) and intact tumor samples (n = 106), obtained from 106 patients undergoing surgical resection for PA.

RESULTS

An orthogonal partial least square-discriminant analysis (OPLS-DA) showed a clear distinction between PP and PA. Higher concentrations of myo-inositol and glycerol were shown in PP, whereas higher levels of glucose, ascorbate, ethanolamine, lactate, and taurine were revealed in PA. Among those metabolites, one of them was particularly obvious in the distinction between long-term and short-term survivors. A high ethanolamine level was associated with worse survival. The impact of neoadjuvant chemotherapy was higher on PA than on PP.

CONCLUSIONS

This study shows that HRMAS NMR spectroscopy using intact tissue provides important and solid information in the characterization of PA. Metabolomics profiling can also predict long-term survival: the assessment of ethanolamine concentration can be clinically relevant as a single metabolic biomarker. This information can be obtained in 20 min, during surgery, to distinguish long-term from short-term survival.

Metabolomic profile in pancreatic cancer patients: a consensus-based approach to identify highly discriminating metabolites

Purpose

pancreatic adenocarcinoma is the fourth leading cause of cancer related deaths due to its aggressive behavior and poor clinical outcome. There is a considerable variability in the frequency of serum tumor markers in cancer' patients. We performed a metabolomics screening in patients diagnosed with pancreatic cancer.

Experimental Design

Two targeted metabolomic assays were conducted on 40 serum samples of patients diagnosed with pancreatic cancer and 40 healthy controls. Multivariate methods and classification trees were performed.

Materials and Methods

Sparse partial least squares discriminant analysis (SPLS-DA) was used to reduce the high dimensionality of a pancreatic cancer metabolomic dataset, differentiating between pancreatic cancer (PC) patients and healthy subjects. Using Random Forest analysis palmitic acid, 1,2-dioleoyl-sn-glycero-3-phospho-rac-glycerol, lanosterol, lignoceric acid, 1-monooleoyl-rac-glycerol, cholesterol 5α,6α epoxide, erucic acid and taurolithocholic acid (T-LCA), oleoyl-L-carnitine, oleanolic acid were identified among 206 metabolites as highly discriminating between disease states. Comparison between Receiver Operating Characteristic (ROC) curves for palmitic acid and CA 19-9 showed that the area under the ROC curve (AUC) of palmitic acid (AUC=1.000; 95% confidence interval) is significantly higher than CA 19-9 (AUC=0.963; 95% confidence interval: 0.896-1.000).

Conclusion

Mass spectrometry-based metabolomic profiling of sera from pancreatic cancer patients and normal subjects showed significant alterations in the profiles of the metabolome of PC patients as compared to controls. These findings offer an information-rich matrix for discovering novel candidate biomarkers with diagnostic or prognostic potentials.

Pancreatic cancer screening using a multiplatform human serum metabolomics system

Aim: To examine a novel screening method for pancreatic cancer involving gas chromatography/mass spectrometry and liquid chromatography/mass spectrometry-based metabolomics analysis. Materials & methods: Sera from pancreatic cancer patients (n = 59) and healthy volunteers (n = 59) were allocated to the training set or validation set. Serum metabolome analysis was carried out using our multiplatform metabolomics system. A diagnostic model was constructed using a two-phase screening method that was newly advocated. Results: When the training set was used, the constructed diagnostic model exhibited high sensitivity (100%) and specificity (80%) for pancreatic cancer. When the validation set was used, the model displayed high sensitivity (84.1%) and specificity (84.1%). Conclusion: We successfully developed a diagnostic model for pancreatic cancer using a multiplatform serum metabolomics system.