Quantitative proteomic profiling of pancreatic cancer juice

Pancreatic juice is an exceptionally rich source of cancer-specific proteins shed from cancerous ductal cells into the pancreatic juice. Quantitative proteomic analysis of the proteins specific to pancreatic cancer juice has not previously been reported. We used isotope-code affinity tag (ICAT) technology and MS/MS to perform quantitative protein profiling of pancreatic juice from pancreatic cancer patients and normal controls. ICAT technology coupled with MS/MS allows the systematic study of the proteome and measures the protein abundance in pancreatic juice with the potential for development of biomarkers. A total of 105 proteins were identified and quantified in the pancreatic juice from a pancreatic cancer patient, of which 30 proteins showed abundance changes of at least twofold in pancreatic cancer juice compared to normal controls. Many of these proteins have been externally validated. This is the first comprehensive study of the pancreatic juice proteome by quantitative global protein profiling, and the study reveals numerous proteins that are shown for the first time to be associated with pancreatic cancer, providing candidates for diagnostic biomarkers. One of the identified proteins, insulin-like growth factor binding protein-2 was further validated by Western blotting to be elevated in pancreatic cancer juice and overexpressed in pancreatic cancer tissue.     

Comparative proteomic analysis of human pancreatic juice: methodological study

Pancreatic cancer is the most lethal of all the common malignancies. Markers for early detection of this disease are urgently needed. Here, we optimized and applied a proteome analysis of human pancreatic juice to identify biomarkers for pancreatic cancer. Pancreatic juice samples, devoid of blood or bile contamination, were collected from patients with pancreatic cancer (n = 5), benign pancreatic diseases (n = 6), or cholelithiasis (n = 3) during endoscopic retrograde cholangiopancreatography (ERCP). After ultramembrane centrifugation sample preparation, pancreatic juice proteins were separated by 2-DE and identified by MALDI-TOF-MS. A 2-DE dataset of pancreatic juice from patients with cholelithiasis was established, consisting of 76 protein spots representing 22 different proteins. Disease-associated obstruction of the pancreatic duct strongly effected the protein composition of pancreatic juice. Concurrently, pancreatic juice from patients with pancreatic cancer was compared to nonmalignant controls with comparable obstruction of pancreatic ducts. Seven protein spots were identified that consistently appeared at changed levels in pancreatic juice from patients with pancreatic cancer. In conclusion, comparative proteomic analysis of human pancreatic juice can be used to identify biomarkers of pancreatic cancer.     

Biomarker discovery from pancreatic cancer secretome using a differential proteomic approach

Quantitative proteomics can be used as a screening tool for identification of differentially expressed proteins as potential biomarkers for cancers. Candidate biomarkers from such studies can subsequently be tested using other techniques for use in early detection of cancers. Here we demonstrate the use of stable isotope labeling with amino acids in cell culture (SILAC) method to compare the secreted proteins (secretome) from pancreatic cancer-derived cells with that from non-neoplastic pancreatic ductal cells. We identified 145 differentially secreted proteins (>1.5-fold change), several of which were previously reported as either up-regulated (e.g. cathepsin D, macrophage colony stimulation factor, and fibronectin receptor) or down-regulated (e.g. profilin 1 and IGFBP-7) proteins in pancreatic cancer, confirming the validity of our approach. In addition, we identified several proteins that have not been correlated previously with pancreatic cancer including perlecan (HSPG2), CD9 antigen, fibronectin receptor (integrin beta1), and a novel cytokine designated as predicted osteoblast protein (FAM3C). The differential expression of a subset of these novel proteins was validated by Western blot analysis. In addition, overexpression of several proteins not described previously to be elevated in human pancreatic cancer (CD9, perlecan, SDF4, apoE, and fibronectin receptor) was confirmed by immunohistochemical labeling using pancreatic cancer tissue microarrays suggesting that these could be further pursued as potential biomarkers. Lastly the protein expression data from SILAC were compared with mRNA expression data obtained using gene expression microarrays for the two cell lines (Panc1 and human pancreatic duct epithelial), and a correlation coefficient (r) of 0.28 was obtained, confirming previously reported poor associations between RNA and protein expression studies.     

Comprehensive proteomic analysis of human pancreatic juice

Proteomic technologies provide an excellent means for analysis of body fluids for cataloging protein constituents and identifying biomarkers for early detection of cancers. The biomarkers currently available for pancreatic cancer, such as CA19-9, lack adequate sensitivity and specificity contributing to late diagnosis of this deadly disease. In this study, we carried out a comprehensive characterization of the "pancreatic juice proteome" in patients with pancreatic adenocarcinoma. Pancreatic juice was first fractionated by 1-dimensional gel electrophoresis and subsequently analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS). A total of 170 unique proteins were identified including known pancreatic cancer tumor markers (e.g., CEA, MUC1) and proteins overexpressed in pancreatic cancers (e.g., hepatocarcinoma-intestine-pancreas/pancreatitis-associated protein (HIP/PAP) and lipocalin 2). In addition, we identified a number of proteins that have not been previously described in pancreatic juice (e.g., tumor rejection antigen (pg96) and azurocidin). Interestingly, a novel protein that is 85% identical to HIP/PAP was identified, which we have designated as PAP-2. The proteins identified in this study could be directly assessed for their potential as biomarkers for pancreatic cancer by quantitative proteomics methods or immunoassays.     

Proteomics in pancreatic cancer research

In this review, we give an overview of the actual role of proteomic technologies in the study of pancreatic cancers (PCs). We describe PC proteomics on the basis of sample origins, i.e. tissues, body fluids, and PC cell lines. As regards PC tissues, we report the identification of a number of candidate biomarkers of precursor lesions that may allow early diagnosis of this neoplasia. Moreover, we describe cytoskeletal and hypoxia-regulated proteins that confirm the involvement of cytoskeleton modifications and metabolism adaptations in carcinogenesis. We also discuss the most important biomarkers identified by proteomic analysis involved in local invasion and distant metastasis, and in the cross-talk between pancreatic tumor and the surrounding stroma. Furthermore, we report novel candidate biomarkers identified in serum, plasma, and pancreatic juice of cancer patients compared with cancer-free controls. Proteomic alterations in PC cell line models as compared to normal controls and studies on cell lines treated with drugs or new agents to understand their mechanism of pharmacological action or the onset of drug resistance are also presented. Finally, we discuss the recent improvements obtained in classical 2-DE and high-throughput proteomic strategies able to allow the overcoming of relevant proteomic drawbacks.     

Proteomics analysis of conditioned media from three breast cancer cell lines: a mine for biomarkers and therapeutic targets

A "bottom-up" proteomics approach and a two-dimensional (strong cation exchange followed by reversed-phase) LC-MS/MS strategy on a linear ion trap (LTQ) were utilized to identify and compare expressions of extracellular and membrane-bound proteins in the conditioned media of three breast cell lines (MCF-10A, BT474, and MDA-MB-468). Proteomics analysis of the media identified in excess of 600, 500, and 700 proteins in MCF-10A, BT474, and MDA-MB-468, respectively. We successfully identified the internal control proteins, kallikreins 5, 6, and 10 (ranging in concentration from 2 to 50 microg/liter) in MDA-MB-468 conditioned medium as validated by ELISA and confidently identified Her-2/neu in BT474 cells. Subcellular localization was determined based on Genome Ontology terms for all the 1,139 proteins of which 34% were classified as extracellular and membrane-bound. Proteomics analysis of MDA-MB-468 cell lysate demonstrated that only 5% of all identified proteins were extracellular. This confirmed our hypothesis that examining the CM of cell lines, as opposed to the cell lysates, leads to a significant enrichment in secreted proteins. Tissue specificity, functional classifications, and spectral counting were performed. Elafin, a protease inhibitor, identified in the conditioned media of BT474 and MDA-MB-468 and the three kallikreins (KLK5, KLK6, and KLK10) were validated using an immunoassay on various serum and biological samples. Some of the secreted proteins identified have established roles in breast cancer development (cell growth, differentiation, and metastasis) and/or are linked to early onset breast cancer. Our approach to mining for low abundance molecules could identify proteins in various stages of breast cancer development. Many of the identified proteins are potentially useful to investigate as circulating serum breast cancer biomarkers.     

Immunoassay-based proteome profiling of 24 pancreatic cancer cell lines

Pancreatic ductal adenocarcinoma is one of the most deadly forms of cancers, with a mortality that is almost identical to incidence. The inability to predict, detect or diagnose the disease early and its resistance to all current treatment modalities but surgery are the prime challenges to changing the devastating prognosis. Also, relatively little is known about pancreatic carcinogenesis. In order to better understand relevant aspects of pathophysiology, differentiation, and transformation, we analysed the cellular proteomes of 24 pancreatic cancer cell lines and two controls using an antibody microarray that targets 741 cancer-related proteins. In this analysis, 72 distinct disease marker proteins were identified that had not been described before. Additionally, categorizing cancer cells in accordance to their original location (primary tumour, liver metastases, or ascites) was made possible. A comparison of the cells' degree of differentiation (well, moderately, or poorly differentiated) resulted in unique marker sets of high relevance. Last, 187 proteins were differentially expressed in primary versus metastatic cancer cells, of which the majority is functionally related to cellular movement.     

Serum proteomic-based analysis of pancreatic carcinoma for the identification of potential cancer biomarkers

To identify new biomarkers that improve the early diagnosis and lead to possible therapeutic targets in pancreatic carcinoma, we performed a proteomic approach to compare serum protein expression patterns of pancreatic carcinoma patients with that of gastric cancer patients, other pancreatic disease patients, and healthy volunteers. By two-dimensional gel electrophoresis (2-DE) analyses and mass spectroscopic identification, 10 protein spots were found significantly changed in pancreatic carcinoma and 5 proteins including cyclin I, Rab GDP dissociation inhibitor beta (GDI2), alpha-1 antitrypsin precursor, Haptoglobin precursor, and Serotransferrin precursor were successfully identified. The increased levels of cyclin I and GDI2 found to be associated with pancreatic carcinoma were further confirmed by Western blot analyses in an independent series of serum samples and/or pancreatic juice samples. Applying immunohistochemistry, we further validated expression of cyclin I and GDI2 in additional pancreatic carcinomas. These results indicate that cyclin I and GDI2 may be potential molecular targets for pancreatic cancer diagnostics and therapeutics.     

Glioblastoma cell secretome: analysis of three glioblastoma cell lines reveal 148 non-redundant proteins

Glioblastoma multiforme (GBM) is the most aggressive among human gliomas with poor prognosis. Study of tumor cell secretome - proteins secreted by cancer cell lines, is a powerful approach to discover potential diagnostic or prognostic biomarkers. Here we report, for the first time, proteins secreted by three GBM cell lines, HNGC2, LN229 and U87MG. Analysis of the conditioned media of these cell lines by LC-MS/MS using ESI-IT mass spectrometer (LTQ) resulted in the confident identification of 102, 119 and 64 proteins, respectively. Integration of the results from all the three cell lines lead to a dataset of 148 non-redundant proteins. Subcellular classification using Genome Ontology indicated that 42% of the proteins identified belonged to extracellular or membrane proteins, viz. Vinculin, Tenascin XB, SERPIN F1 and TIMP-1. 52 proteins matched with the secretomes of 11 major cancer types reported earlier whereas remaining 96 are unique to our study. 25 protein identifications from the dataset represent proteins related to GBM or other cancer tissues as per Human Protein Atlas; at least 22 are detectable in plasma, 11 of them being reported even in cerebrospinal fluid. Our study thus provides a valuable resource of GBM cell secretome with potential for further investigation as GBM biomarkers.     

Proteomic strategies in the search for novel pancreatic cancer biomarkers and drug targets: recent advances and clinical impact

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers; despite a low incidence rate it is the fourth leading cause of cancer-related death in the world. Improvement of the diagnosis, prognosis and treatment remains the main focus of pancreatic cancer research. Rapid developments in proteomic technologies has improved our understanding of the pancreatic cancer proteome. Here, the authors summarise the recent proteomic strategies undertaken in the search for: novel biomarkers for early diagnosis, pancreatic cancer-specific proteins which may be used for novel targeted therapies and proteins which may be useful for monitoring disease progression post-therapy. Recent advances and findings discussed here provide great promise of having a significant clinical impact and improving the outcome of patients with this malignancy.     

Epiregulin is Up-regulated in pancreatic cancer and stimulates pancreatic cancer cell growth

Epiregulin belongs to the epidermal growth factor (EGF) family of polypeptides. Previous studies have underscored the important role of the EGF family of ligands and receptors in the pathology of pancreatic ductal adenocarcinoma (PDAC) and chronic pancreatitis (CP). It is not known, however, whether epiregulin may also have a role in these diseases. Therefore, in the present study we investigated the expression and function of epiregulin in five pancreatic cancer cell lines and in PDAC and CP tissue samples. Epiregulin mRNA was present at high (MIA-PaCa-2 cells) or moderate levels (ASPC-1, CAPAN-1, and T3M4) in most cells, but was below detection levels in PANC-1 cells. All the cell lines exhibited a dose-dependent increase in growth in response to recombinant human epiregulin. Epiregulin mRNA levels were increased 2.1-fold in PDAC samples (P < 0.01) and 1.7-fold in CP samples (P < 0.01), when compared with the normal controls. There was no correlation between epiregulin mRNA levels and tumor stage or grade. By in situ hybridization, a moderate to intense epiregulin mRNA signal was present in most pancreatic cancer cells in PDAC. In contrast, only a weak (normal pancreas) to moderate (CP) signals were present in the ductal and acinar cells in CP. These findings suggest that epiregulin may contribute to the pathobiology of PDAC, and may also have a role in CP.     

Gene cloning of immunogenic antigens overexpressed in pancreatic cancer

The serological analysis of recombinant cDNA expression libraries (SEREX) by utilizing a library derived from a human pancreatic adenocarcinoma cell line and IgG antibodies from an allogeneic patient serum led to the identification of 18 genes: 13 of these were known genes, and 5 were unknown genes. In Northern and RT-PCR analyses, we found that the expression of mRNA of 14 genes was elevated in pancreatic cancer cell lines compared with the levels in normal pancreatic tissues. In addition, the expression of mRNA of hsp105 in colon cancer was greater than that in normal colon tissue. Immunohistochemical analysis using anti-hsp105 antibody revealed that an increased expression of hsp105 is a characteristic feature of pancreatic ductal and colon adenocarcinoma. Furthermore, hsp105 immunoreactivity in some cases of gastric, esophageal, and hepatocellular carcinoma was much stronger than that in normal corresponding tissues. These molecules identified may provide good diagnostic markers for cancer cells.     

Extracellular vesicles in pancreatic cancer progression and therapies

Pancreatic cancer (PC) is one of the leading causes of cancer-related death worldwide due to delayed diagnosis and limited treatments. More than 90% of all pancreatic cancers are pancreatic ductal adenocarcinoma (PDAC). Extensive communication between tumour cells and other cell types in the tumour microenvironment have been identified which regulate cancer hallmarks during pancreatic tumorigenesis via secretory factors and extracellular vesicles (EVs). The EV-capsuled factors not only facilitate tumour growth locally, but also enter circulation and reach distant organs to construct a pre-metastatic niche. In this review, we delineate the key factors in pancreatic ductal adenocarcinoma derived EVs that mediate different tumour processes. Also, we highlight the factors that are related to the crosstalk with cancer stem cells/cancer-initiating cells (CSC/CIC), the subpopulation of cancer cells that can efficiently metastasize and resist currently used chemotherapies. Lastly, we discuss the potential of EV-capsuled factors in early diagnosis and antitumour therapeutic strategies.Subject terms: Cancer microenvironment, Cancer stem cells     

Secretome compartment is a valuable source of biomarkers for cancer-relevant pathways

In principle, targeted therapies have optimal activity against a specific subset of tumors that depend upon the targeted molecule or pathway for growth, survival, or metastasis. Consequently, it is important in drug development and clinical practice to have predictive biomarkers that can reliably identify patients who will benefit from a given therapy. We analyzed tumor cell-line secretomes (conditioned cell media) to look for predictive biomarkers; secretomes represent a potential source for potential biomarkers that are expressed in intracellular signaling and therefore may reflect changes induced by targeted therapy. Using Gene Ontology, we classified by function the secretome proteins of 12 tumor cell lines of different histotypes. Representations and hierarchical relationships among the functional groups differed among the cell lines. Using bioinformatics tools, we identified proteins involved in intracellular signaling pathways. For example, we found that secretome proteins related to TGF-beta signaling in thyroid cancer cells, such as vasorin, CD109, and βIG-H3 (TGFBI), were sensitive to RPI-1 and dasatinib treatments, which have been previously demonstrated to be effective in blocking cell proliferation. The secretome may be a valuable source of potential biomarkers for detecting cancer and measuring the effectiveness of cancer therapies.     

Autophagy in pancreatic cancer

Pancreatic cancer, the fourth leading cause of cancer-related death in the United States, is resistant to current chemotherapies. Therefore, identification of different pathways of cell death is important to develop novel therapeutics. Our previous study has shown that triptolide, a diterpene triepoxide, inhibits the growth of pancreatic cancer cells in vitro and prevents tumor growth in vivo. However, the mechanism by which triptolide kills pancreatic cancer cells was not known, hence, this study aimed at elucidating it. Our study reveals that triptolide kills diverse types of pancreatic cancer cells by two different pathways; it induces caspase-dependent apoptotic death in some cell lines and death via a caspase-independent autophagic pathway in the other cell lines tested. Triptolide-induced autophagy requires autophagy-specific genes, atg5 or beclin 1, and its inhibition results in cell death via the apoptotic pathway, whereas inhibition of both autophagy and apoptosis rescues triptolide-mediated cell death. Our study shows for the first time that induction of autophagy by triptolide has a pro-death role in pancreatic cancer cells. Since triptolide kills diverse pancreatic cancer cells by different mechanisms, it makes an attractive chemotherapeutic agent for future use against a broad spectrum of pancreatic cancers.     

Elevating pancreatic cystic lesion stratification: Current and future pancreatic cancer biomarker(s)

Pancreatic ductal adenocarcinoma (PDAC) is an incredibly deadly disease with a 5-year survival rate of 9%. The presence of pancreatic cystic lesions (PCLs) confers an increased likelihood of future pancreatic cancer in patients placing them in a high-risk category. Discerning concurrent malignancy and risk of future PCL progression to cancer must be carefully and accurately determined to improve survival outcomes and avoid unnecessary morbidity of pancreatic resection. Unfortunately, current image-based guidelines are inadequate to distinguish benign from malignant lesions. There continues to be a need for accurate molecular and imaging biomarker(s) capable of identifying malignant PCLs and predicting the malignant potential of PCLs to enable risk stratification and effective intervention management. This review provides an update on the current status of biomarkers from pancreatic cystic fluid, pancreatic juice, and seromic molecular analyses and discusses the potential of radiomics for differentiating PCLs harboring cancer from those that do not.     

Glycoproteomic analyses of ovarian cancer cell lines and sera from ovarian cancer patients show distinct glycosylation changes in individual proteins

Ovarian cancer is difficult to diagnose in women because symptoms of the disease are often not noticed until the disease has progressed to an advanced untreatable stage. Although a serum test, CA125, is currently available to assist with monitoring treatment of ovarian cancer, this test lacks the necessary specificity and sensitivity for early detection. Therefore, better biomarkers of ovarian cancer are needed. A glycoprotein analysis approach was undertaken using high resolution Fourier transform ion cyclotron resonance mass spectrometry to analyze glycosylated proteins present in the conditioned media of ovarian cancer cell lines and in sera obtained from ovarian cancer patients and normal controls. In this study, glycosylated proteins were separated by gel electrophoresis, and individual glycoproteins were selected for glycosylation analysis and protein identification. The attached glycans from each protein were released and profiled by mass spectrometry. Glycosylation of a mucin protein and a large glycosylated protein isolated from the ES2 ovarian cancer cell line was determined to consist of mostly O-linked glycans. Four prominent glycoproteins of approximate 517, 370, 250, 163 kDa from serum samples were identified as two forms of apolipoprotein B-100, fibronectin, and immunoglobulin A1, respectively. Mass spectrometric analysis of glycans isolated from apolipoprotein B-100 (517 kD) showed the presence of small, specific O-linked oligosaccharides. In contrast, analysis of fibronectin (250 kD) and immunoglobulin A1 (163 kD) produced N-linked glycan fragments in forms that were sufficiently different from the glycans obtained from the corresponding protein band present in the normal serum samples. This study shows that not only a single protein but several are aberrantly glycosylated, and those abnormal glycosylation changes can be detected and may ultimately serve as glycan biomarkers for ovarian cancer.     

Characterization of proteins in human pancreatic cancer serum using differential gel electrophoresis and tandem mass spectrometry

The purpose of this study was to develop techniques for identifying cancer biomarkers in human serum using differential in-gel electrophoresis (DIGE), and characterizing the protein biomarkers using tandem mass spectrometry (MS/MS). A major problem in profiling protein expression by DIGE comes from the presence of high concentrations of a small number of proteins. Therefore, serum samples were first chromatographed using an immunoaffinity HPLC column (Agilent Technologies), to selectively remove albumin, immunoglobulins, transferrin, haptoglobin, and antitrypsin. Serum samples from three individuals with pancreatic cancer and three individuals without cancer were compared. Serum samples were processed using the immunoaffinity column. Differential protein analysis was performed using DIGE. A total of 56 protein spot-features were found to be significantly increased and 43 significantly decreased in cancer serum samples. These spot features were excised, trypsin digested, and analyzed by MALDI/TOF/TOF (4700 Proteomics Analyzer, Applied Biosystems). We identified 24 unique proteins that were increased and 17 unique proteins that were decreased in cancer serum samples. Western blot analysis confirmed increased levels of several of these proteins in the pancreatic cancer serum samples. In an independent series of serum samples from 20 patients with pancreatic cancer and 14 controls, increased levels of apolipoprotein E, alpha-1-antichymotrypsin, and inter-alpha-trypsin inhibitor were found to be associated with pancreatic cancer. These results suggest that affinity column enrichment and 2-D DIGE can be used to identify numerous proteins differentially expressed in serum from individuals with pancreatic cancer.     

Identification of Prognostic Biomarkers by Combined mRNA and miRNA Expression Microarray Analysis in Pancreatic Cancer

Pancreatic cancer is the fourth leading cause for cancer-related death, and early diagnosis is one key to improve the survival rate of this disease. Molecular biomarkers are an important method for diagnostic use in pancreatic cancer. We used data from three mRNA microarray datasets and a microRNA dataset (GSE16515, GSE15471, GSE28735, and GSE41372) to identify potential key genes. Differentially expressed genes (DEGs) and microRNAs (DEMs) were identified. Functional, pathway enrichment, and protein-protein interaction analyses were performed on common DEGs across all datasets. The target genes of the DEMs were identified. DEMs targets that were also DEGs were further scrutinized using overall survival analysis. A total of 236 DEGs and 21 DEMs were identified. There were a total of four DEGs (ECT2, NR5A2, NRP2, and TGFBI), which were also predicted target genes of DEMs. Overall survival analysis showed that high expression levels of three of these genes (ECT2, NRP2, and TGFBI) were associated with poor overall survival for pancreatic cancer patients. The basic expression of DEGs in pancreas stood lower level in various organ tissues. The expression of ECT2 and NRP2 was higher in different pancreatic cancer cell lines than normal pancreas cell line. Knockout of ECT2 by Crispr Cas9 gene editing system decreased proliferation and migration ability in pancreatic cancer cell line MiaPaCa2. In conclusion, we think that data mining method can do well in biomarker screening, and ECT2 and NRP2 can play as potential biomarker or therapy target by Crispr Cas9 in pancreatic cancer.