Sophic Systems Alliance, Inc.
Sophic Systems Alliance, Inc.
Gharbaran R.,Hackensack University Medical Center |
Goy A.,Hackensack University Medical Center |
Tanaka T.,Thomas Jefferson University |
Park J.,Hackensack University Medical Center |
And 9 more authors.
Journal of Hematology and Oncology | Year: 2013
Background: High risk, unfavorable classical Hodgkin lymphoma (cHL) includes those patients with primary refractory or early relapse, and progressive disease. To improve the availability of biomarkers for this group of patients, we investigated both tumor biopsies and peripheral blood leukocytes (PBL) of untreated (chemo-naïve, CN) Nodular Sclerosis Classic Hodgkin Lymphoma (NS-cHL) patients for consistent biomarkers that can predict the outcome prior to frontline treatment. Methods and materials. Bioinformatics data mining was used to generate 151 candidate biomarkers, which were screened against a library of 10 HL cell lines. Expression of FGF2 and SDC1 by CD30+ cells from HL patient samples representing good and poor outcomes were analyzed by qRT-PCR, immunohistochemical (IHC), and immunofluorescence analyses. Results: To identify predictive HL-specific biomarkers, potential marker genes selected using bioinformatics approaches were screened against HL cell lines and HL patient samples. Fibroblast Growth Factor-2 (FGF2) and Syndecan-1 (SDC1) were overexpressed in all HL cell lines, and the overexpression was HL-specific when compared to 116 non-Hodgkin lymphoma tissues. In the analysis of stratified NS-cHL patient samples, expression of FGF2 and SDC1 were 245 fold and 91 fold higher, respectively, in the poor outcome (PO) group than in the good outcome (GO) group. The PO group exhibited higher expression of the HL marker CD30, the macrophage marker CD68, and metastatic markers TGFβ1 and MMP9 compared to the GO group. This expression signature was confirmed by qualitative immunohistochemical and immunofluorescent data. A Kaplan-Meier analysis indicated that samples in which the CD30+ cells carried an FGF2+/SDC1+ immunophenotype showed shortened survival. Analysis of chemo-naive HL blood samples suggested that in the PO group a subset of CD30+ HL cells had entered the circulation. These cells significantly overexpressed FGF2 and SDC1 compared to the GO group. The PO group showed significant down-regulation of markers for monocytes, T-cells, and B-cells. These expression signatures were eliminated in heavily pretreated patients. Conclusion: The results suggest that small subsets of circulating CD30+/CD15+ cells expressing FGF2 and SDC1 represent biomarkers that identify NS-cHL patients who will experience a poor outcome (primary refractory and early relapsing). © 2013 Gharbaran et al.; licensee BioMed Central Ltd.
Blake P.M.,Sophic Systems Alliance, Inc. |
Decker D.A.,Florida Hospital Cancer Institute |
Decker D.A.,University of Central Florida |
Glennon T.M.,Sophic Systems Alliance, Inc. |
And 5 more authors.
Cancer Journal | Year: 2011
Around the world, teams of researchers continue to develop a wide range of systems to capture, store, and analyze data including treatment, patient outcomes, tumor registries, next-generation sequencing, single-nucleotide polymorphism, copy number, gene expression, drug chemistry, drug safety, and toxicity. Scientists mine, curate, and manually annotate growing mountains of data to produce high-quality databases, while clinical information is aggregated in distant systems. Databases are currently scattered, and relationships between variables coded in disparate datasets are frequently invisible. The challenge is to evolve oncology informatics from a "systems" orientation of standalone platforms and silos into an "integrated knowledge environments" that will connect "knowable" research data with patient clinical information. The aim of this article is to review progress toward an integrated knowledge environment to support modern oncology with a focus on supporting scientific discovery and improving cancer care. Copyright © 2011 by Lippincott Williams & Wilkins.
Tamir A.,Hackensack University Medical Center |
Jag U.,Hackensack University Medical Center |
Sarojini S.,Hackensack University Medical Center |
Schindewolf C.,Hackensack University Medical Center |
And 13 more authors.
Journal of ovarian research | Year: 2014
BACKGROUND: Early detection of ovarian cancer remains a challenge due to widespread metastases and a lack of biomarkers for early-stage disease. This study was conducted to identify relevant biomarkers for both laparoscopic and serum diagnostics in ovarian cancer.METHODS: Bioinformatics analysis and expression screening in ovarian cancer cell lines were employed. Selected biomarkers were further validated in bio-specimens of diverse cancer types and ovarian cancer subtypes. For non-invasive detection, biomarker proteins were evaluated in serum samples from ovarian cancer patients.RESULTS: Two kallikrein (KLK) serine protease family members (KLK6 and KLK7) were found to be significantly overexpressed relative to normal controls in most of the ovarian cancer cell lines examined. Overexpression of KLK6 and KLK7 mRNA was specific to ovarian cancer, in particular to serous and papillary serous subtypes. In situ hybridization and histopathology further confirmed significantly elevated levels of KLK6 and KLK7 mRNA and proteins in tissue epithelium and a lack of expression in neighboring stroma. Lastly, KLK6 and KLK7 protein levels were significantly elevated in serum samples from serous and papillary serous subtypes in the early stages of ovarian cancer, and therefore could potentially decrease the high "false negative" rates found in the same patients with the common ovarian cancer biomarkers human epididymis protein 4 (HE4) and cancer antigen 125 (CA-125).CONCLUSION: KLK6 and KLK7 mRNA and protein overexpression is directly associated with early-stage ovarian tumors and can be measured in patient tissue and serum samples. Assays based on KLK6 and KLK7 expression may provide specific and sensitive information for early detection of ovarian cancer.
Suh K.S.,Hackensack University Medical Center |
Park S.W.,Hackensack University Medical Center |
Castro A.,Hackensack University Medical Center |
Patel H.,Hackensack University Medical Center |
And 3 more authors.
Expert Review of Molecular Diagnostics | Year: 2010
Multiple omics researches in the past two decades have identified over 200 potential biomarkers for ovarian cancer. Discoveries during the 1990s were more focused on clinicopathology-based biomarkers that were targeted to support diagnosis, but the emphasis has shifted to the identification of prognostic biomarkers in the past 10 years. The post-genomic era has opened the door for personalized cancer treatments and the trend of discovery is moving forward to identify more stratified biomarkers to accurately predict the progression of disease, as well as efficacy biomarkers to precisely determine drug response. To better meet future challenges, biomedical research needs the reformed and standardized infrastructure of tissue banks/biorepositories, with national and international initiatives. Of the hundreds of biomarker candidates for ovarian cancer, only a small number are actively being validated with clinical samples, owing to the lack of biomaterials that are linked with accurate clinical data. The purpose of this article is to present selected biomarkers from the past 20 years of ovarian cancer research, placing special emphasis on biomarkers that are strongly associated with positive or negative clinical outcomes. The article also presents a global view of all known potential biomarkers and mutations for ovarian cancer from NCIs Cancer Gene Index developed by Sophic, and Sangers Catalogue of Somatic Mutations in Cancer database. © 2010 Expert Reviews Ltd.
Suh K.S.,University Medical Center |
Sarojini S.,University Medical Center |
Youssif M.,University Medical Center |
Nalley K.,Sophic Systems Alliance, Inc. |
And 6 more authors.
Journal of Oncology | Year: 2013
Personalized medicine promises patient-tailored treatments that enhance patient care and decrease overall treatment costs by focusing on genetics and "-omics" data obtained from patient biospecimens and records to guide therapy choices that generate good clinical outcomes. The approach relies on diagnostic and prognostic use of novel biomarkers discovered through combinations of tissue banking, bioinformatics, and electronic medical records (EMRs). The analytical power of bioinformatic platforms combined with patient clinical data from EMRs can reveal potential biomarkers and clinical phenotypes that allow researchers to develop experimental strategies using selected patient biospecimens stored in tissue banks. For cancer, high-quality biospecimens collected at diagnosis, first relapse, and various treatment stages provide crucial resources for study designs. To enlarge biospecimen collections, patient education regarding the value of specimen donation is vital. One approach for increasing consent is to offer publically available illustrations and game-like engagements demonstrating how wider sample availability facilitates development of novel therapies. The critical value of tissue bank samples, bioinformatics, and EMR in the early stages of the biomarker discovery process for personalized medicine is often overlooked. The data obtained also require cross-disciplinary collaborations to translate experimental results into clinical practice and diagnostic and prognostic use in personalized medicine. © 2013 K. Stephen Suh et al.
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 749.11K | Year: 2010
To date, there is no single, well-maintained, up-to-date repository containing all clinically relevant cancer biomarker information. Researchers often face the daunting, tedious task of searching increasing numbers of databases that often provide inaccurate, incomplete, out-of-date, fragmented information. This directly results in wasted time and delays in finding cures for cancer. In Phase I, Sophic scientists developed a prototype central biomarker repository, the Integrated Cancer Biomarker KnowledgeBase (ICSK) that can mitigate this problem. In Phase" Sophic will continue to collaborate with a scientific advisory team of respected cancer researchers who will provide recommendations, feedback on the project. The advisors will help maintain the scientific Integrity of the ICBK in a cancer community that is in constant flux. Prototype Sophic Cancer BIomarker Objects (SCBDs) will be extended and enriched with biomarker related molecular information mined from target sources and curated by Sophic Scientists. The 2,116 biomarker genes mined from IBM Medline Abstracts and manually curated by PhD. scientists during the 5-year Ncr Cancer Gene Index Project will be the foundation for the SCBOs. Enriched SCBDs will be integrated In lCBK and provide scientists with detailed molecular information on Individual biomarker genes and panels of genes. A powerful, easy to use Knowledge Management System will be configured allow non-technical researchers to mine. explore and graphically display complex networks of biomarker, disease and scientific element relationships. The aims of the project are to improve the accuracy of disease diagnosis, increase the effectiveness of treatments and accelerate the discovery of drugs to cure cancer.
PubMed | Sophic Systems Alliance, Inc.
Type: Journal Article | Journal: Cancer journal (Sudbury, Mass.) | Year: 2011
Around the world, teams of researchers continue to develop a wide range of systems to capture, store, and analyze data including treatment, patient outcomes, tumor registries, next-generation sequencing, single-nucleotide polymorphism, copy number, gene expression, drug chemistry, drug safety, and toxicity. Scientists mine, curate, and manually annotate growing mountains of data to produce high-quality databases, while clinical information is aggregated in distant systems. Databases are currently scattered, and relationships between variables coded in disparate datasets are frequently invisible. The challenge is to evolve oncology informatics from a systems orientation of standalone platforms and silos into an integrated knowledge environments that will connect knowable research data with patient clinical information. The aim of this article is to review progress toward an integrated knowledge environment to support modern oncology with a focus on supporting scientific discovery and improving cancer care.
Sophic Systems Alliance, Inc. | Date: 2011-11-16
Software applications in the life sciences field. Providing business consultation services in the field of health care; management of grants and research for others in the medical research field. Providing software as a service (SAAS) services featuring software for the mining, reporting and analysis of health information; providing database services for others; scientific and medical research in the field of biomarker discovery, characterization and development.
Sophic Systems Alliance, Inc. | Date: 2011-11-16
Software applications in the life sciences field for use in research and data management, data integration, data analysis, data sorting and searching, and project management. Providing business consultation services in the field of health care; management of medical grants and medical research for others, namely conducting and configuring research studies, implementation of research, reporting of research findings, training and support related thereto. Providing software as a service (SAAS) services featuring software for the mining, reporting and analysis of health information; providing an online database in the field of medical and scientific research in the field of biomarker discovery, characterization and development; scientific and medical research in the field of biomarker discovery, characterization and development.
PubMed | Hackensack University Medical Center and Sophic Systems Alliance, Inc.
Type: | Journal: Journal of clinical bioinformatics | Year: 2015
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.