Fibroblast growth factor (FGF) signaling is essential for normal and cancer biology. Mammalian FGF family members participate in multiple signaling pathways by binding to heparan sulfate and FGF receptors (FGFR) with varying affinities. FGF2 is the prototype member of the FGF family and interacts with its receptor to mediate receptor dimerization, phosphorylation, and activation of signaling pathways, such as Ras-MAPK and PI3K pathways. Excessive mitogenic signaling through the FGF/FGFR axis may induce carcinogenic effects by promoting cancer progression and increasing the angiogenic potential, which can lead to metastatic tumor phenotypes. Dysregulated FGF/FGFR signaling is associated with aggressive cancer phenotypes, enhanced chemotherapy resistance and poor clinical outcomes. In vitro experimental settings have indicated that extracellular FGF2 affects proliferation, drug sensitivity, and apoptosis of cancer cells. Therapeutically targeting FGF2 and FGFR has been extensively assessed in multiple preclinical studies and numerous drugs and treatment options have been tested in clinical trials. Diagnostic assays are used to quantify FGF2, FGFRs, and downstream signaling molecules to better select a target patient population for higher efficacy of cancer therapies. This review focuses on the prognostic significance of FGF2 in cancer with emphasis on therapeutic intervention strategies for solid and hematological malignancies.

As research laboratories and clinics collaborate to achieve precision medicine, both communities are required to understand mandated electronic health/medical record (EHR/EMR) initiatives that will be fully implemented in all clinics in the United States by 2015. Stakeholders will need to evaluate current record keeping practices and optimize and standardize methodologies to capture nearly all information in digital format. Collaborative efforts from academic and industry sectors are crucial to achieving higher efficacy in patient care while minimizing costs. Currently existing digitized data and information are present in multiple formats and are largely unstructured. In the absence of a universally accepted management system, departments and institutions continue to generate silos of information. As a result, invaluable and newly discovered knowledge is difficult to access. To accelerate biomedical research and reduce healthcare costs, clinical and bioinformatics systems must employ common data elements to create structured annotation forms enabling laboratories and clinics to capture sharable data in real time. Conversion of these datasets to knowable information should be a routine institutionalized process. New scientific knowledge and clinical discoveries can be shared via integrated knowledge environments defined by flexible data models and extensive use of standards, ontologies, vocabularies, and thesauri. In the clinical setting, aggregated knowledge must be displayed in user-friendly formats so that physicians, non-technical laboratory personnel, nurses, data/research coordinators, and end-users can enter data, access information, and understand the output. The effort to connect astronomical numbers of data points, including ‘-omics’-based molecular data, individual genome sequences, experimental data, patient clinical phenotypes, and follow-up data is a monumental task. Roadblocks to this vision of integration and interoperability include ethical, legal, and logistical concerns. Ensuring data security and protection of patient rights while simultaneously facilitating standardization is paramount to maintaining public support. The capabilities of supercomputing need to be applied strategically. A standardized, methodological implementation must be applied to developed artificial intelligence systems with the ability to integrate data and information into clinically relevant knowledge. Ultimately, the integration of bioinformatics and clinical data in a clinical decision support system promises precision medicine and cost effective and personalized patient care.

IntroductionMantle cell lymphoma (MCL) is an aggressive and mostly incurable B-cell malignancy accounting for 5% of non-Hodgkin lymphomas (NHLs). MCL arises from naive B cells (NBCs) in the mantle zone of lymph node follicles and is characterized by the t(11,14) chromosomal translocation leading to overexpression of cyclin D1 (CCND1). 1 However, murine models overexpressing CCND1 in the absence of other oncogenes, such as MYC, do not develop lymphoma, 2 implying that additional pathogenic mechanisms are involved in MCL. Cip/Kip proteins have an important role in the formation of active CDK4/cyclin D complexes. In NHLs other than MCL, p27(Kip) protein expression is inversely related to the proliferation activity of the tumors. 3 Apoptosis-related genes such as BCL2 have also been found to be altered in MCL with the use of different approaches. Homozygous deletions of BIM, a member of the BCL2 family, have also been found in MCL. 4 Epigenetic changes, such as methylation of gene promoters, have been shown to contribute to the pathogenesis of both solid and hematologic malignancies. 5 Single-locus studies analyzing methylation in MCL patient samples have shown hypermethylation of key genes, such as cell-cycle regulators p14 ARF and CDKN2A,6,7 protein phosphatase SHP-1 8 and Rho-adenosine triphosphatase PARG-1. 9 However these studies did not compare the methylation to NBCs, the normal counterparts of these malignant cells. 10 Several lines of evidence conclusively show NBCs to be the cell of origin of MCL, including immunoglobulin heavy chain mutation status, t (11,14) chromosomal breakpoint analysis, 11 and gene expression microarrays. 12,13 To develop a more comprehensive understanding of aberrant DNA methylation in MCL, we performed a genomewide analysis of MCL DNA methylation and gene expression with the use of purified normal NBCs as controls. We report that promoter DNA methylation is aberrantly distributed in the MCL genome compared with normal NBCs, and we identified aberrantly hypermethylated and hypomethylated genes that provide a basis for rational targeted therapy in this disease. Methods Patient samplesTissues and blood samples were obtained from patients newly diagnosed with MCL before any treatment after informed consent in accordance with the Declaration of Helsinki. Sample collection and laboratory studies were in compliance with institutional review board and Helsinki protocols. CD19 ϩ cells from 22 patients treated at the National Institutes of Health were purified by magnetic bead sorting from peripheral blood or pheresis products before freezing to ensure greater than 90% purity for HpaII tiny The publisher or recipient acknowledges right of the US government to retain a nonexclusive, royalty-free license in and to any copyright covering the article.The online version of this article contains a data supplement.The publication costs of this article were defrayed in part by page charge payment. Therefore, and solely to indicate this fact, this article is hereby marked ''advertisement'' in accorda...

Despite the widespread use of conventional and contemporary methods to detect ovarian cancer development, ovarian cancer remains a common and commonly fatal gynecological malignancy. The identification and validation of early detection biomarkers highly specific to ovarian cancer, which would permit development of minimally invasive screening methods for detecting early onset of the disease, are urgently needed. Current practices for early detection of ovarian cancer include transvaginal ultrasonography, biomarker analysis, or a combination of both. In this paper we review recent research on novel and robust biomarkers for early detection of ovarian cancer and provide specific details on their contributions to tumorigenesis. Promising biomarkers for early detection of ovarian cancer include KLK6/7, GSTT1, PRSS8, FOLR1, ALDH1, and miRNAs.

Syndecan-1 (SDC1, CD138) is a key cell surface adhesion molecule essential for maintaining cell morphology and interaction with the surrounding microenvironment. Deregulation of SDC1 contributes to cancer progression by promoting cell proliferation, metastasis, invasion and angiogenesis, and is associated with relapse through chemoresistance. SDC1 expression level is also associated with responses to chemotherapy and with prognosis in multiple solid and hematological cancers, including multiple myeloma and Hodgkin lymphoma. At the tissue level, the expression levels of SDC1 and the released extracellular domain of SDC1 correlate with tumor malignancy, phenotype, and metastatic potential for both solid and hematological tumors in a tissue-specific manner. The SDC1 expression profile varies among cancer types, but the differential expression signatures between normal and cancer cells in epithelial and stromal compartments are directly associated with aggressiveness of tumors and patient's clinical outcome and survival. Therefore, relevant biomarkers of SDC signaling may be useful for selecting patients that would most likely respond to a particular therapy at the time of diagnosis or perhaps for predicting relapse. In addition, the reciprocal expression signature of SDC between tumor epithelial and stromal compartments may have synergistic value for patient selection and the prediction of clinical outcome.