Proteomics Profiling of Pancreatic Cancer and Pancreatitis for Biomarkers Discovery

Functional Imaging and Inhibitor Screening of Human Pancreatic Lipase by a Resorufin-Based Fluorescent Probe

Human pancreatic lipase (hPL) is a crucial digestive enzyme responsible for the digestion of dietary lipids in humans, and inhibition of hPL is effective in reducing triglyceride intake, thereby preventing and treating obesity. In this study, a series of fatty acids with different carbon chain lengths were constructed to the fluorophore resorufin based on the substrate preference of hPL. Among them, RLE was found to have the best combination of stability, specificity, sensitivity and reactivity towards hPL. Under physiological conditions, RLE can be rapidly hydrolyzed by hPL and released to resorufin, which triggered approximately 100-fold fluorescence enhancement at 590 nm. RLE was successfully applied for sensing and imaging of endogenous PL in living systems with low cytotoxicity and high imaging resolution. Moreover, a visual high-throughput screening platform was established using RLE, and the inhibitory effects of hundreds of drugs and natural products toward hPL were evaluated. Collectively, this study reports a novel and highly specific enzyme-activatable fluorogenic substrate for hPL that could serve as a powerful tool for monitoring hPL activity in complex biological systems and showcases the potential to explore physiological functions and rapid screening of inhibitors.

Identification of Novel Ligands for Targeted Antifibrotic Therapy of Chronic Pancreatitis

PURPOSE

Chronic pancreatitis (CP) is an inflammatory disorder of the pancreas that leads to impaired pancreatic function. The limited therapeutic options and the lack of molecular targeting ligands or non-serum-based biomarkers hinder the development of target-specific drugs. Thus, there is a need for an unbiased, comprehensive discovery and evaluation of pancreatitis-specific ligands.

METHODS

This study utilized a computational-guided in vivo phage display approach to select peptide ligands selective for cellular components in the caerulein-induced mouse model of CP. The identified peptides were conjugated to pegylated DOPC liposomes via the reverse-phase evaporation method, and the in vivo specificity and pharmacokinetics were determined. As proof of concept, CP-targeted liposomes were used to deliver an antifibrotic small molecular drug, apigenin. Antifibrotic effects determined by pancreatic histology, fibronectin expression, and collagen deposition were evaluated.

RESULTS

We have identified five peptides specific for chronic pancreatitis and demonstrated selectivity to activated pancreatic stellate cells, acinar cells, macrophages, and extracellular matrix, respectively. MDLSLKP-conjugated liposomes demonstrated an increased particle accumulation by 1.3-fold in the inflamed pancreas compared to the control liposomes. We also observed that targeted delivery of apigenin resulted in improved acini preservation, a 37.2% and 33.1% respective reduction in collagen and fibronectin expression compared to mice receiving the free drug, and reduced oxidative stress in the liver.

CONCLUSION

In summary, we have developed a systematic approach to profile peptide ligands selective for cellular components of complex disease models and demonstrated the biomedical applications of the identified peptides to improve tissue remodeling in the inflamed pancreas.

Engineering of biomaterials for tumor modeling

Development of biomaterials mimicking tumor and its microenvironment has recently emerged for the use of drug discovery, precision medicine, and cancer biology. These biomimetic models have developed by reconstituting tumor and stroma cells within the 3D extracellular matrix. The models are recently extended to recapitulate the in vivo tumor microenvironment, including biological, chemical, and mechanical conditions tailored for specific cancer type and its microenvironment. In spite of the recent emergence of various innovative engineered tumor models, many of these models are still early stage to be adapted for cancer research. In this article, we review the current status of biomaterials engineering for tumor models considering three main aspects - cellular engineering, matrix engineering, and engineering for microenvironmental conditions. Considering cancer-specific variability in these aspects, our discussion is focused on pancreatic cancer, specifically pancreatic ductal adenocarcinoma (PDAC). In addition, we further discussed the current challenges and future opportunities to create reliable and relevant tumor models.

Transcriptome analysis identifies putative multi-gene signature distinguishing benign and malignant pancreatic head mass

Background

Most often, the patients with pancreatic diseases are presented with a mass in pancreatic head region and existing methods of diagnosis fail to confirm whether the head mass is malignant or benign. As subsequent management of the disease hugely depends on the correct diagnosis, we wanted to explore possible biomarkers which could distinguish benign and malignant pancreatic head masses.

Methods

In order to address that gap, we performed a case–control study to identify genome-wide differentially expressed coding and noncoding genes between pancreatic tissues collected from benign and malignant head masses. These genes were next shortlisted using stringent criteria followed by selection of top malignancy specific genes. They subsequently got validated by quantitative RT-PCR and also in other patient cohorts. Survival analysis and ROC analysis were also performed.

Results

We identified 55 coding and 13 noncoding genes specific for malignant pancreatic head masses. Further shortlisting and validation, however, resulted in 5 coding genes as part of malignancy specific multi-gene signature, which was validated in three independent patient cohorts of 145 normal and 153 PDAC patients. We also found that overexpression of these genes resulted in survival disadvantage in the patients and ROC analysis identified that combination of 5 coding genes had the AUROC of 0.94, making them potential biomarker.

Conclusions

Our study identified a multi-gene signature comprising of 5 coding genes (CDCA7, DLGAP5, FOXM1, TPX2 and OSBPL3) to distinguish malignant head masses from benign ones.