Disease proteomics

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62680/1/nature01514.pd

Genomic and oncoproteomic advances in detection and treatment of colorectal cancer

<p>Abstract</p> <p>Aims</p> <p>We will examine the latest advances in genomic and proteomic laboratory technology. Through an extensive literature review we aim to critically appraise those studies which have utilized these latest technologies and ascertain their potential to identify clinically useful biomarkers.</p> <p>Methods</p> <p>An extensive review of the literature was carried out in both online medical journals and through the Royal College of Surgeons in Ireland library.</p> <p>Results</p> <p>Laboratory technology has advanced in the fields of genomics and oncoproteomics. Gene expression profiling with DNA microarray technology has allowed us to begin genetic profiling of colorectal cancer tissue. The response to chemotherapy can differ amongst individual tumors. For the first time researchers have begun to isolate and identify the genes responsible. New laboratory techniques allow us to isolate proteins preferentially expressed in colorectal cancer tissue. This could potentially lead to identification of a clinically useful protein biomarker in colorectal cancer screening and treatment.</p> <p>Conclusion</p> <p>If a set of discriminating genes could be used for characterization and prediction of chemotherapeutic response, an individualized tailored therapeutic regime could become the standard of care for those undergoing systemic treatment for colorectal cancer. New laboratory techniques of protein identification may eventually allow identification of a clinically useful biomarker that could be used for screening and treatment. At present however, both expression of different gene signatures and isolation of various protein peaks has been limited by study size. Independent multi-centre correlation of results with larger sample sizes is needed to allow translation into clinical practice.</p

Prognostic molecular markers in early breast cancer

A multitude of molecules involved in breast cancer biology have been studied as potential prognostic markers. In the present review we discuss the role of established molecular markers, as well as potential applications of emerging new technologies. Those molecules used routinely to make treatment decisions in patients with early-stage breast cancer include markers of proliferation (e.g. Ki-67), hormone receptors, and the human epidermal growth factor receptor 2. Tumor markers shown to have prognostic value but not used routinely include cyclin D(1 )and cyclin E, urokinase-like plasminogen activator/plasminogen activator inhibitor, and cathepsin D. The level of evidence for other molecular markers is lower, in part because most studies were retrospective and not adequately powered, making their findings unsuitable for choosing treatments for individual patients. Gene microarrays have been successfuly used to classify breast cancers into subtypes with specific gene expression profiles and to evaluate prognosis. RT-PCR has also been used to evaluate expression of multiple genes in archival tissue. Proteomics technologies are in development

PDZ domains and their binding partners: structure, specificity, and modification

PDZ domains are abundant protein interaction modules that often recognize short amino acid motifs at the C-termini of target proteins. They regulate multiple biological processes such as transport, ion channel signaling, and other signal transduction systems. This review discusses the structural characterization of PDZ domains and the use of recently emerging technologies such as proteomic arrays and peptide libraries to study the binding properties of PDZ-mediated interactions. Regulatory mechanisms responsible for PDZ-mediated interactions, such as phosphorylation in the PDZ ligands or PDZ domains, are also discussed. A better understanding of PDZ protein-protein interaction networks and regulatory mechanisms will improve our knowledge of many cellular and biological processes

Cell cycle and aging, morphogenesis, and response to stimuli genes are individualized biomarkers of glioblastoma progression and survival

<p>Abstract</p> <p>Background</p> <p>Glioblastoma is a complex multifactorial disorder that has swift and devastating consequences. Few genes have been consistently identified as prognostic biomarkers of glioblastoma survival. The goal of this study was to identify general and clinical-dependent biomarker genes and biological processes of three complementary events: lifetime, overall and progression-free glioblastoma survival.</p> <p>Methods</p> <p>A novel analytical strategy was developed to identify general associations between the biomarkers and glioblastoma, and associations that depend on cohort groups, such as race, gender, and therapy. Gene network inference, cross-validation and functional analyses further supported the identified biomarkers.</p> <p>Results</p> <p>A total of 61, 47 and 60 gene expression profiles were significantly associated with lifetime, overall, and progression-free survival, respectively. The vast majority of these genes have been previously reported to be associated with glioblastoma (35, 24, and 35 genes, respectively) or with other cancers (10, 19, and 15 genes, respectively) and the rest (16, 4, and 10 genes, respectively) are novel associations. <it>Pik3r1</it>, <it>E2f3, Akr1c3</it>, <it>Csf1</it>, <it>Jag2</it>, <it>Plcg1</it>, <it>Rpl37a</it>, <it>Sod2</it>, <it>Topors</it>, <it>Hras</it>, <it>Mdm2, Camk2g</it>, <it>Fstl1</it>, <it>Il13ra1</it>, <it>Mtap </it>and <it>Tp53 </it>were associated with multiple survival events.</p> <p>Most genes (from 90 to 96%) were associated with survival in a general or cohort-independent manner and thus the same trend is observed across all clinical levels studied. The most extreme associations between profiles and survival were observed for <it>Syne1</it>, <it>Pdcd4</it>, <it>Ighg1</it>, <it>Tgfa</it>, <it>Pla2g7</it>, and <it>Paics</it>. Several genes were found to have a cohort-dependent association with survival and these associations are the basis for individualized prognostic and gene-based therapies. <it>C2</it>, <it>Egfr</it>, <it>Prkcb</it>, <it>Igf2bp3</it>, and <it>Gdf10 </it>had gender-dependent associations; <it>Sox10</it>, <it>Rps20</it>, <it>Rab31</it>, and <it>Vav3 </it>had race-dependent associations; <it>Chi3l1</it>, <it>Prkcb</it>, <it>Polr2d</it>, and <it>Apool </it>had therapy-dependent associations. Biological processes associated glioblastoma survival included morphogenesis, cell cycle, aging, response to stimuli, and programmed cell death.</p> <p>Conclusions</p> <p>Known biomarkers of glioblastoma survival were confirmed, and new general and clinical-dependent gene profiles were uncovered. The comparison of biomarkers across glioblastoma phases and functional analyses offered insights into the role of genes. These findings support the development of more accurate and personalized prognostic tools and gene-based therapies that improve the survival and quality of life of individuals afflicted by glioblastoma multiforme.</p

The Current State of Proteomics in GI Oncology

Proteomics refers to the study of the entire set of proteins in a given cell or tissue. With the extensive development of protein separation, mass spectrometry, and bioinformatics technologies, clinical proteomics has shown its potential as a powerful approach for biomarker discovery, particularly in the area of oncology. More than 130 exploratory studies have defined candidate markers in serum, gastrointestinal (GI) fluids, or cancer tissue. In this article, we introduce the commonly adopted proteomic technologies and describe results of a comprehensive review of studies that have applied these technologies to GI oncology, with a particular emphasis on developments in the last 3 years. We discuss reasons why the more than 130 studies to date have had little discernible clinical impact, and we outline steps that may allow proteomics to realize its promise for early detection of disease, monitoring of disease recurrence, and identification of targets for individualized therapy

SPARC: a matricellular regulator of tumorigenesis

Although many clinical studies have found a correlation of SPARC expression with malignant progression and patient survival, the mechanisms for SPARC function in tumorigenesis and metastasis remain elusive. The activity of SPARC is context- and cell-type-dependent, which is highlighted by the fact that SPARC has shown seemingly contradictory effects on tumor progression in both clinical correlative studies and in animal models. The capacity of SPARC to dictate tumorigenic phenotype has been attributed to its effects on the bioavailability and signaling of integrins and growth factors/chemokines. These molecular pathways contribute to many physiological events affecting malignant progression, including extracellular matrix remodeling, angiogenesis, immune modulation and metastasis. Given that SPARC is credited with such varied activities, this review presents a comprehensive account of the divergent effects of SPARC in human cancers and mouse models, as well as a description of the potential mechanisms by which SPARC mediates these effects. We aim to provide insight into how a matricellular protein such as SPARC might generate paradoxical, yet relevant, tumor outcomes in order to unify an apparently incongruent collection of scientific literature