Novel targets identified by integrated cancer-stromal interactome analysis of pancreatic adenocarcinoma
Yukihiko HiroshimaRika KasajimaYayoi KimuraDaisuke KomuraShumpei IshikawaYasushi IchikawaMichael BouvetNaoto YamamotoTakashi OshimaSoichiro MorinagaShree Ram SinghRobert M. HoffmanItaru EndoYohei Miyagi
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Laser capture microdissection
Druggability
Tissue microarray
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Proteome
Human proteome project
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Background: Pancreatic cancer is an extremely aggressive malignancy that is characterised by an intense desmoplastic response, the role of which remains unknown. Proteomic studies allow the analysis of the protein content of a cell or tissue and offer a more functional perspective than traditional gene based studies. Recent work has suggested that the surrounding stromal tissue actively participates in tumour progression and invasion and this may be detectable by critical changes in protein expression. We aim to characterize the protein expression in the pancreatic cancer microenvironment using a proteomic-based approach. Methods: Pancreatectomy specimens obtained following surgery were sectioned, stained and specific cell populations obtained using laser capture microdissection (LCM). We obtained malignant ductal cells and stromal cells both immediately adjacent to tumour (juxtatumoral stroma) and distant to the tumour (panstroma). Proteins from these cell populations were subsequently separated using two-dimensional gel electrophoresis (2DE) and visualized using silver staining. Spot profiles from each were compared to identify differentially expressed spots and mass spectrometry used to identify proteins from these spots of interest. Results: Four groups of protein samples have been acquired, each gel displaying in excess of 700 spots. Comparison of the spot patterns revealed nine consistent differences between ductal cells and stromal cells. Five of these differences were limited to the juxtatumoral stromal cells, and appear to represent novel proteins previously unreported in pancreatic stromal cells. Conclusions: By employing proteomic-based technologies, we have demonstrated differential protein expression between pancreatic stromal cells and malignant ductal epithelium. Furthermore, we have shown that some of these proteins are limited to those stromal cells lying immediately adjacent to the malignant ducts. Such proteins may represent future novel biomarkers or therapeutic targets.
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One difficulty in studying epithelial tumors has been the inability to isolate pure samples for DNA and RNA analysis. Prostate cancer, with its infiltrative nature, is particularly challenging. The Combination of several new technologies should help overcome these hurdles. Laser capture microdissection uses a laser beam to achieve transfer of pure cell populations for isolation of DNA, RNA, and protein. High-throughput analysis of these samples can be achieved by using cDNA expression microarrays. High-density tissue microarrays should allow for validation of differentially expressed genes. This review will concentrate on the application of laser capture microdissection, cDNA microarrays, and tissue microarrays in the area of prostate cancer research. Copyright © 2001 John Wiley & Sons, Ltd.
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This chapter contains sections titled: Introduction Druggability: Ligand Properties Druggability: Ligand Binding Druggability Prediction by Protein Class Druggability Predictions: Experimental Methods Druggability Predictions: Computational Methods A Test Case: PTP1B Outlook and Concluding Remarks References
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Protein tyrosine phosphatases (PTP) play important roles in the pathogenesis of many diseases. The fact that no PTP inhibitors have reached the market so far has raised many questions about their druggability. In this study, the active sites of 17 PTPs were characterized and assessed for its ability to bind drug-like molecules. Consequently, PTPs were classified according to their druggability scores into four main categories. Only four members showed intermediate to very druggable pocket; interestingly, the rest of them exhibited poor druggability. Particularly focusing on PTP1B, we also demonstrated the influence of several factors on the druggability of PTP active site. For instance, the open conformation showed better druggability than the closed conformation, while the tight-bound water molecules appeared to have minimal effect on the PTP1B druggability. Finally, the allosteric site of PTP1B was found to exhibit superior druggability compared to the catalytic pocket. This analysis can prove useful in the discovery of new PTP inhibitors by assisting researchers in predicting hit rates from high throughput or virtual screening and saving unnecessary cost, time, and efforts via prioritizing PTP targets according to their predicted druggability.
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Abstract Druggability refers to the capacity of a cellular target to be modulated by a small-molecule drug. To date, druggability is mainly studied by focusing on direct binding interactions between a drug and its target. However, druggability is impacted by cellular networks connected to a drug target. Here, we use computational approaches to reveal basic principles of network motifs that modulate druggability. Through quantitative analysis, we find that inhibiting self-positive feedback loop is a more robust and effective treatment strategy than inhibiting other regulations, and adding direct regulations to a drug-target generally reduces its druggability. The findings are explained through analytical solution of the motifs. Furthermore, we find that a consensus topology of highly druggable motifs consists of a negative feedback loop without any positive feedback loops, and consensus motifs with low druggability have multiple positive direct regulations and positive feedback loops. Based on the discovered principles, we predict potential genetic targets in Escherichia coli that have either high or low druggability based on their network context. Our work establishes the foundation toward identifying and predicting druggable targets based on their network topology.
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The druggable subset of the human genome, termed the "druggable genome," provides the pharmaceutical industry with a unique opportunity for the advancement of new therapeutic interventions for a multitude of diseases and disorders. To date, there is no systematic assessment of the evolutionary history and nature of the defined druggable proteins derived from the contemporary druggable genome (i.e., proteins that bind or are predicted to bind with high affinity to a biologic). An understanding of drug-protein target interactions in specific cellular compartments is crucial for the optimal therapeutic delivery of pharmaceutical agents, as well as for preclinical drug trials in model animals. This study applied the concept of pharmacophylogenomics, the study of genes, evolution, and drug targets, to conduct an evolutionary survey of drug targets with respect to their subcellular localizations. Using multiple models and modes of druggable genome comparison, the results concordantly indicated that orthologous drug targets with a nuclear localization in the human, macaque, mouse, and rat showed a higher trend for evolutionary conservation compared with drug targets in the cell membrane and the extracellular compartment. As such, this study provides important information regarding druggable protein targets and the druggable genome at the pharmacophylogenomics level.
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Incorporation of early druggability assessment in the drug discovery process provides a means to prioritize target proteins for high-throughput screening. We present chemical fragment arrays as a method that is capable of determining the druggability of a given target with low protein and compound consumption, enabling rapid decision making during early phases of drug discovery.
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