February 20, 2012
Scientists from the Covance Biomarker Center of Excellence along with other business units within the Discovery and Translational Services group, including Discovery Services, the Covance Genomics Laboratory and Antibody Products, will be showcasing their company's R&D capabilities, talent and technologies at the 2012 Molecular Med Tri-Con this week in San Francisco, CA.
The Molecular Med Tri-Con brings together the therapeutic and diagnostic aspects of the life sciences industry in one place. From Sunday, February 19th to Thursday, February 23rd, over 3,000 delegates from 1,200 companies will engage in interactive lectures, discussions, and various receptions on diagnostics, drug discovery and development, informatics and cancer.
Scientists from the Biomarker Center of Excellence will be available at the conference to discuss Covance's spectrum of services and expertise, including the company's new pathway tool, BioPathways, which integrates Covance assay capabilities and antibody products with biological and disease pathways. BioPathways can be accessed free of charge at covance.com/biopathways.
Molecular Med Tri-Con attendees can visit the Covance booth 206/208 in the Exhibit Hall and register to win an Apple iPad. Scientific experts in biomarkers, companion diagnostics, computational biology, discovery services, genomics, oncology studies and translational medicine will be available to discuss how Covance can help their clients make faster and more effective decisions about their portfolios.
This year at the Molecular Med Tri-Con, Covance and Ingenuity Systems will demonstrate the latest tools in next-generation sequencing data analysis and interpretation. On Tuesday, February 21st, from 12:40 pm - 4:20 pm, Covance and Ingenuity will demonstrate Ingenuity Variant Analysis, a web application that helps researchers studying human disease to identify causal variants from human resequencing data in just minutes.
Also on Tuesday afternoon, from 12:40 pm - 1:40 pm, Covance and Affymetrix will hold a lunch seminar, featuring two speakers who will discuss the clinical applications of pharmacogenomics:
- Mark Parrish, Senior Manager of Assay Development and the Covance Genomics Laboratory will discuss clinical applications of pharmacogenomics and present on high-throughput DMET profiling for drug development.
- William Douglas Figg Sr., Pharm.D., M.B.A, Senior Scientist and Head of the Clinical Pharmacology Program and Molecular Pharmacology Section, Medical Oncology Branch and the Center for Cancer Research, National Cancer Institute and National Institutes of Health, will talk about moving pharmacogenetics from the laboratory to the clinic.
Lastly, four scientists from Covance Discovery and Translational Services will be presenting posters on biomarker discovery, next gen sequencing and genomics, and miRNA extraction.
Walter J. Jessen, Ph.D., Covance Biomarker Center of Excellence
Biomedical knowledge is growing exponentially; however, meta-knowledge around the data is often lacking. PubMed is a database comprising more than 21 million citations for biomedical literature from MEDLINE and additional life science journals dating back to the 1950s. To explore the use and frequency of biomarkers across human disease, we mined PubMed for biomarker-disease associations. We then ranked the top 100 linked diseases by relevance and mapped them to medical subject headings (MeSH) and, subsequently, to the Disease Ontology. To identify biomarkers for each disease, we queried Covance BioPathways, an online data resource that maps commercial biomarker assays to biological and disease pathways. We then integrated pathways-based information to describe both known and potential biomarkers as well as disease-associated genes/proteins for select diseases. This approach identifies therapeutic areas with candidate or validated biomarkers, and highlights those areas where a paucity of biomarkers exists.
Optimizing Therapeutic Outcomes for Glioblastoma Multiforme Leveraging Whole Exome Sequencing
Anup Madan, Ph.D., Covance Genomics Laboratory
Glioblastoma (GBM) is the most common malignant primary brain tumor in adults and one of the most lethal of all cancers. Current standard care for patients diagnosed with GBM includes surgical resection, radiotherapy with concomitant temozolomide (TMZ) chemotherapy, followed by adjuvant TMZ chemotherapy. Response to TMZ has been shown to be heterogeneous and MGMT promoter methylation has been identified as a predictive marker of response to TMZ in patients with newly diagnosed GBM. However, MGMT methylation has not been used widely to stratify GBM patients in a clinical setting because of several issues such as cost, turn around time and lack of precision. To identify other genomic markers that correlate with TMZ response and are easily tested in a clinical setting, we performed whole exome sequence analysis on cell lines with differential MGMT methylation and response to TMZ treatment. Next Generation Sequencing data were analyzed using various open source tools to identify genomic aberrations in these cell lines. Ingenuity’s Variant Analysis and IPA® software (www.ingenuity.com) was then used to identify variants that can potentially predict response to TMZ treatment. These are being further tested in clinical samples for their ability to optimize TMZ effectiveness in individual patients.
Mapping the Molecular Genomic Network of Glioblastoma Multiforme
Anup Madan, Ph.D., Covance Genomics Laboratory
Glioblastoma Multiforme (GBM) is a highly aggressive malignant primary brain tumor, characterized by rapid growth, diffused infiltration of cells into both adjacent and remote brain regions, and a generalized resistance to currently available treatment modalities. To date, much remains unknown about the pathology of disease onset and the mechanism of response to therapy despite intensive clinical and basic science research. Next Generation Sequencing technology offers the opportunity to characterize cancer genomes and transcriptomes at unprecedented depth and sensitivity. To generate new insights into glioma biology, we have performed systematic analysis of transcriptomic datasets from 9 immortalized glioma cell lines and normal human astrocytes. In this analysis, RNA-Seq data was simultaneously interrogated for gene expression levels, expressed sequence mutations, gene fusion and alternative splicing events in these cell lines. We then used the Nanostring nCounter Analysis System to further validate RNA-Seq data and determine the expression levels of identified genes in a panel of distinct FFPE tumor samples. The dataset is being further analyzed leveraging Ingenuity IPA® and iReport™ software to investigate their ability to stratify or define discrete subtypes of tumor heterogeneity and potentially provide targets for patient stratification (diagnostics) and/or therapies.
Optimization of miRNA Extraction from Serum
Zinaida Sergueeva, Ph.D., Covance Genomics Laboratory
The profiling of circulating nucleic acids is a key step toward the development of noninvasive, blood-based molecular diagnostic tests. However, the clinical effectiveness of circulating microRNAs as biomarkers is likely to be affected by a range of pre-analytical variables such as RNA extraction efficiency and methodological issues involved in platform-specific sample preparation for miRNA profiling. Reproducible isolation of cell-free miRNAs is a technical challenge for a number of reasons. First, plasma and serum are biospecimens that have a very high concentration of protein that could potentially interfere with sample preparation and the detection assay. Secondly, the yield of RNA from small volume plasma or serum samples (< 1 mL) usually falls below the limit of accurate quantification by spectrometry and calls for an alternative way to assess the efficiency of RNA recovery. In order to develop an optimized protocol, we compared the performance of three different extraction protocols and included several synthetic miRNA spike-ins for sample quality and extraction efficiency assessment. Further, we compared the ability to profile the miRNAs from these samples using both microarray and LNA-based qRT-PCR platforms. Our data shows that the protocol used for miRNA extraction can interact with the detection platform. We were able to develop an optimized protocol that shows good alignment between microarray data and RT-PCR assays (miRCURY RT-PCR assays).