Molecular alterations in exocrine neoplasms of the pancreas

A Transcriptomics-Based Meta-Analysis Combined With Machine Learning Identifies a Secretory Biomarker Panel for Diagnosis of Pancreatic Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is generally incurable due to the late diagnosis and absence of markers that are concordant with expression in several sample sources (i.e., tissue, blood, plasma) and platforms (i.e., Microarray, sequencing). We optimized meta-analysis of 19 PDAC (tissue and blood) transcriptome studies from multiple platforms. The key biomarkers for PDAC diagnosis with secretory potential were identified and validated in different cohorts. Machine learning approach i.e., support vector machine supported by leave-one-out cross-validation was used to build and test the classifier. We identified a 9-gene panel (IFI27, ITGB5, CTSD, EFNA4, GGH, PLBD1, HTATIP2, IL1R2, CTSA) that achieved ∼0.92 average sensitivity and ∼0.90 average specificity in distinguishing PDAC from healthy samples in five training sets using cross-validation. These markers were also validated in proteomics and single-cell transcriptomics studies suggesting their prognostic role in the diagnosis of PDAC. Our 9-gene classifier can not only clearly discriminate between better and poor survivors but can also precisely discriminate PDAC from chronic pancreatitis (AUC = 0.95), early stages of progression [Stage I and II (AUC = 0.82), IPMA and IPMN (AUC = 1), and IPMC (AUC = 0.81)]. The 9-gene marker outperformed the previously known markers in blood studies particularly (AUC = 0.84). The discrimination of PDAC from early precursor lesions in non-malignant tissue (AUC > 0.81) and peripheral blood (AUC > 0.80) may assist in an early diagnosis of PDAC in blood samples and thus will also facilitate risk stratification upon validation in clinical trials.

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.

Genomic Variations in Pancreatic Cancer and Potential Opportunities for Development of New Approaches for Diagnosis and Treatment

Human pancreatic cancer has a very poor prognosis with an overall five-year survival rate of less than 5% and an average median survival time of six months. This is largely due to metastatic disease, which is already present in the majority of patients when diagnosed. Although our understanding of the molecular events underlying multi-step carcinogenesis in pancreatic cancer has steadily increased, translation into more effective therapeutic approaches has been inefficient in recent decades. Therefore, it is imperative that novel and targeted approaches are designed to facilitate the early detection and treatment of pancreatic cancer. Presently, there are numerous ongoing studies investigating the types of genomic variations in pancreatic cancer and their impact on tumor initiation and growth, as well as prognosis. This has led to the development of therapeutics to target these genetic variations for clinical benefit. Thus far, there have been minimal clinical successes directly targeting these genomic alterations; however research is ongoing to ultimately discover an innovative approach to tackle this devastating disease. This review will discuss the genomic variations in pancreatic cancer, and the resulting potential diagnostic and therapeutic implications.

Meta-analysis of transcriptome data identifies a novel 5-gene pancreatic adenocarcinoma classifier

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) is largely incurable due to late diagnosis. Superior early detection biomarkers are critical to improving PDAC survival and risk stratification.

EXPERIMENTAL DESIGN

Optimized meta-analysis of PDAC transcriptome datasets identified and validated key PDAC biomarkers. PDAC-specific expression of a 5-gene biomarker panel was measured by qRT-PCR in microdissected patient-derived FFPE tissues. Cell-based assays assessed impact of two of these biomarkers, TMPRSS4 and ECT2, on PDAC cells.

RESULTS

A 5-gene PDAC classifier (TMPRSS4, AHNAK2, POSTN, ECT2, SERPINB5) achieved on average 95% sensitivity and 89% specificity in discriminating PDAC from non-tumor samples in four training sets and similar performance (sensitivity = 94%, specificity = 89.6%) in five independent validation datasets. This classifier accurately discriminated PDAC from chronic pancreatitis (AUC = 0.83), other cancers (AUC = 0.89), and non-tumor from PDAC precursors (AUC = 0.92) in three independent datasets. Importantly, the classifier distinguished PanIN from healthy pancreas in the PDX1-Cre;LSL-KrasG12D PDAC mouse model. Discriminatory expression of the PDAC classifier genes was confirmed in microdissected FFPE samples of PDAC and matched surrounding non-tumor pancreas or pancreatitis. Notably, knock-down of TMPRSS4 and ECT2 reduced PDAC soft agar growth and cell viability and TMPRSS4 knockdown also blocked PDAC migration and invasion.

CONCLUSIONS

This study identified and validated a highly accurate 5-gene PDAC classifier for discriminating PDAC and early precursor lesions from non-malignant tissue that may facilitate early diagnosis and risk stratification upon validation in prospective clinical trials. Cell-based experiments of two overexpressed proteins encoded by the panel, TMPRSS4 and ECT2, suggest a causal link to PDAC development and progression, confirming them as potential therapeutic targets.

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

PURPOSE

Survival among patients with adenocarcinoma pancreatic cancer (PDCA) is highly variable, which ranges from 0% to 20% at 5 years. Such a wide range is due to tumor size and stage, as well other patients' characteristics. We analyzed alterations in the metabolomic profile, of PDCA patients, which are potentially predictive of patient's one-year mortality.

EXPERIMENTAL DESIGN

A targeted metabolomic assay was conducted on serum samples of patients diagnosed with pancreatic cancer. Statistical analyses were performed only for those 27 patients with information on vital status at follow-up and baseline clinical features. Random Forest analysis was performed to identify all metabolites and clinical variables with the best capability to predict patient's mortality risk at one year. Regression coefficients were estimated from multivariable Weibull survival model, which included the most associated metabolites. Such coefficients were used as weights to build a metabolite risk score (MRS) which ranged from 0 (lowest mortality risk) to 1 (highest mortality risk). The stability of these weights were evaluated performing 10,000 bootstrap resamplings.

RESULTS

MRS was built as a weighted linear combination of the following five metabolites: Valine (HR = 0.62, 95%CI: 0.11-1.71 for each standard deviation (SD) of 98.57), Sphingomyeline C24:1 (HR = 2.66, 95%CI: 1.30-21.09, for each SD of 20.67), Lysine (HR = 0.36, 95%CI: 0.03-0.77, for each SD of 51.73), Tripentadecanoate TG15 (HR = 0.25, 95%CI: 0.01-0.82, for each SD of 2.88) and Symmetric dimethylarginine (HR = 2.24, 95%CI: 1.28-103.08, for each SD of 0.62), achieving a very high discrimination ability (survival c-statistic of 0.855, 95%CI: 0.816-0.894). Such association was still present even after adjusting for the most associated clinical variables (confounders).

CONCLUSIONS

The mass spectrometry-based metabolomic profiling of serum represents a valid tool for discovering novel candidate biomarkers with prognostic ability to predict one-year mortality risk in patients with pancreatic adenocarcinoma.

Multiplex targeted proteomic assay for biomarker detection in plasma: a pancreatic cancer biomarker case study

Biomarkers are most frequently proteins that are measured in the blood. Their development largely relies on antibody creation to test the protein candidate performance in blood samples of diseased versus nondiseased patients. The creation of such antibody assays has been a bottleneck in biomarker progress due to the cost, extensive time, and effort required to complete the task. Targeted proteomics is an emerging technology that is playing an increasingly important role to facilitate disease biomarker development. In this study, we applied a SRM-based targeted proteomics platform to directly detect candidate biomarker proteins in plasma to evaluate their clinical utility for pancreatic cancer detection. The characterization of these protein candidates used a clinically well-characterized cohort that included plasma samples from patients with pancreatic cancer, chronic pancreatitis, and healthy age-matched controls. Three of the five candidate proteins, including gelsolin, lumican, and tissue inhibitor of metalloproteinase 1, demonstrated an AUC value greater than 0.75 in distinguishing pancreatic cancer from the controls. In addition, we provide an analysis of the reproducibility, accuracy, and robustness of the SRM-based proteomics platform. This information addresses important technical issues that could aid in the adoption of the targeted proteomics platform for practical clinical utility.

Analogs of vitamin E epitomized by alpha-tocopheryl succinate for pancreatic cancer treatment: in vitro results induce caution for in vivo applications

OBJECTIVES

alpha-Tocopheryl succinate (alpha-TOS) is thought to be toxic only for cancer cells. We ascertained in vitro alpha-TOS effects on pancreatic cancer (PC) and normal cell growth and verified whether the combination of nontoxic alpha-TOS and 5-fluorouracil (5-FU) doses causes cancer cell death and whether alpha-TOS effects are mediated by the proapoptotic proteins Bax/Bak and/or SMAD4/DPC4 status.

METHODS

Five PC cell lines, myoblasts, normal monocytes, wild-type (WT) and Bax/Bak double knockout mouse embryonic fibroblast (MEF) cells, and permanently SMAD4/DPC4-transfected PSN1 cells were cultured in 1% and 10% fetal calf serums (FCSs), without or with alpha-TOS (5-500 micromol/L). Nontoxic 5-FU (0.0001 mmol/L) and alpha-TOS alone or in combination were also evaluated.

RESULTS

Only PSN1 PC cell line, which had SMAD4/DPC4 homozygous deletion, was sensitive to nontoxic alpha-TOS doses (5 micromol/L in 1% FCS and 50 micromol/L in 10% FCS). A 20-micromol/L alpha-TOS inhibited MEF-WT, not MEF-double knockout growth. Only PSN1 cells were sensitive to nontoxic 5-FU and alpha-TOS combination. SMAD4/DPC4 transfection restored PSN1 resistance to the effects of combined 5-FU and alpha-TOS effects.

CONCLUSIONS

Only a minority of PC cells are sensitive to the antiproliferative effects of alpha-TOS, any sensitivity appearing to be correlated with SMAD4/DPC4 homozygous deletion and Bax/Bak expression.

Comprehensive mass spectrometry based metabolic profiling of blood plasma reveals potent discriminatory classifiers of pancreatic cancer

Poor outcome of pancreatic cancer necessitates development of an early diagnostic method to reduce mortality. No reliable early diagnostic test for pancreatic cancer detection has been developed and validated to date. In the current study, metabolic profiling of plasma samples from selected cancer patients and noncancerous controls was performed to seek novel metabolic biomarkers of pancreatic cancer. A comprehensive mass spectrometry based analytical platform established at the Metabolomics Core of the UC Davis Genome Center allowed detection of multiple compounds previously unreported in plasma from pancreatic cancer patients. It was found that selective amino acids, bile acids, and polar lipids were detected with increased or decreased levels in pancreatic cancer samples compared to controls. These findings on blood plasma levels of the relevant metabolites might be very useful clinical parameters for early diagnosis of pancreatic cancer.