CBIIT Speaker Series Page

SYNOPSIS:

Cancer is a complex group of diseases with many causes. Genetic changes and epigenetic alterations in somatic tissues, as well as germ line mutations or risk alleles, all contribute to cancer development and progression. Dr. Lee will present integrated studies of breast, esophageal, and gastric cancers through collaborative research with many investigators in CCR and DCEG. He will discuss a broad range of topics: from GWAS to whole genome sequencing studies; from genomics to functional studies; from genes to signature studies; from experiments to computational analyses; and from data to knowledge discovery. Dr. Lee will talk about how interweaving high-throughput data with bioinformatics analyses enables us to gain a better understanding of cancer biology and etiology.

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SYNOPSIS:

For most people health occurs in every-day living, not in hospitals and doctor's offices.  They must remember to take medications, monitor healing progress, note changes from normal, or get up and exercise — all information intensive and cognitively demanding activities.  Through Project HealthDesign, an eight-year initiative funded by the Robert Wood Johnson Foundation, we learned that there is so much more to health information than what is generated in the course of care and recorded in the electronic health record.  In addition to lab values and blood pressures, people attend to a wide range of data that informs them about their health status and drives them toward healthy behaviors.  We call these novel data types "observations of daily living" (ODLs).  ODLs represent the sensations, behaviors, attitudes, thoughts, and exposures to which people attend and draw interpretations about their health situation.

Patient-generated data includes ODLs, as well as a full range of parameters that only the individual person can provide. Programs like PCORI and the NQF Patient-reported outcomes measures project show that patient-generated data not only is useful for augmenting clinical signs and assessments in evaluating a patient's health needs, but also can be used to determine the effectiveness of care. There is growing acceptance of the importance and relevance of patient-reported data and an uptake in the sophistication of the tools used to create, store, report, and analyze it.  In this presentation, Dr. Brennan will introduce the concept of patient-generated data, provide an elaboration of one novel type (ODLs), and explore the ethical and policy issues related to capture and use of patient-reported data.

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SYNOPSIS:

The Computational Genomics Research group at NCI CBIIT participated in the HPN-DREAM Breast Cancer Network Inference Challenge for the first time in 2013. The challenge comprises network inference, time-course prediction, and visualization of time-course data derived from proteomic experiments in cancer cell lines and in silico simulation. The group developed a novel approach to construct consensus networks and predict phosphoprotein trajectories under the influence of each inhibitor. The time-series data were visualized with in-house R package OmicCircos which is available at bioconductor.org. This novel diagram not only maintains the network structure but also displays the time-course change, biological annotations, and topological features. The group submitted results to Synapse.org, a site that Sage Bionetworks has developed for data sharing and live scoring. Our prediction result of 20 phosphoproteins and 10 time points for the sub-challenge 2B "In silico time-course prediction" has been ranked as the best predictor.

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SYNOPSIS:

Of all the genomic aberrations recently observed in tumors by the TCGA program, which are expressed at the protein level? The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has begun to answer this question. With this endeavor come a number of informatics challenges, not the least of which is integrating genomic and proteomic data from a common tumor. Dr. Kinsinger will highlight findings, software tools, and data resources developed to support the next era of omics research.

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