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Comprehending Copy Number Variation Header 

Copy number variations (CNVs) hold immense potential to explain genetic diversity, predict disease risk and diagnose complex genomic disorders have long resisted understanding. Now recently developed whole-genome scanning technologies have catalyzed the appreciation of CNVs in the genomic community. Studies linking insertions, deletions, and inversions to disease etiology continue to multiply. As genome-wide scanning techniques become more prevalent in diagnostic laboratories, the major challenge is how to interpret accurately which variations are pathogenic in nature and which are benign.

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Monday, March 14 


8:30 am Short Course Registration

9:00 Short Course Sessions*

*Separate registration required.

12:00 pm Close of Short Courses

11:30 am-2:00 pm Conference Registration

 

CNVs and Cancer 

2:00 Chairperson’s Remarks

Jih-Hsiang Lee, M.D., Associate Investigator, Medical Oncology Branch, NCI NIH

ArrowFeatured Speaker 

2:05 Approaches and Pitfalls for Detecting Genomic Aberrations in Heterogeneous Tumor Samples

David P. Tuck, M.D., Associate Professor, Pathology, Yale University School of Medicine

Whole genome microarray technologies have allowed profiling of copy number alterations and allelic imbalance in cancer genomes providing information about prognosis and treatment response. However, interpretation of tumor samples is challenging due to the combined effects of various factors including polyploidy, normal DNA contamination, GC bias, and tumor clonal heterogeneity. A number of analytic approaches and newer technologies provide solutions for dealing with these issues.

2:50 Functional Copy Number Variants and Cancer Susceptibility

Francesca Demichelis, Ph.D., Assistant Professor, Pathology and Laboratory Medicine, Institute for Computational Biomedicine, Weill Cornell Medical College

Human genetic variation accounts for differences in susceptibility to common diseases including cancer. Emerging data suggests that functional deregulation may be due, in part, by copy number changes in coding and non-coding areas of the genome. This presentation will focus on the role of CNV as a functional risk factor for prostate cancer.

3:25 Refreshment Break

4:00 The Integrative Frameworks for Analysis of the Influence of Tumor Microenvironmental Stresses in Human Cancers

Jen-Tsan Ashley Chi, M.D., Ph.D., Assistant Professor, Institute for Genome Sciences and Policy, Molecular Genetics and Microbiology, Duke Medical Center

Tumor microenvironmental stresses have enormous impact on the tumor phenotypes and treatment responses. But current measurement methods of these stresses are invasive and difficult to integrate with clinical information and other molecular phenotypes. We have used the gene signature approaches to define the influence of these stresses in human cancers. Through such analysis, we have identified the significant contribution of copy number variations to the degrees of hypoxia and lactic acidosis responses in human cancers.

4:35 Exploring Genomic Alteration of Cancers as Tools to Identify Potential Prognostic Biomarkers and Drugable Targets

Jih-Hsiang Lee, M.D., Associate Investigator, Medical Oncology Branch, NCI NIH

Gene amplification and deletions are common in many cancer types. We explored genomic alterations in pulmonary neuroendocrine tumors. We found gene alterations which are common in three distinct neuroendocrine tumors. We further identified potential targets for the treatment of small cell lung cancer.

5:10 Determining Frequent Copy Number Aberrations and Modeling Dysregulation of Gene Expression in Tumors

Christina Leslie, Ph.D., Computational Biologist, Memorial Sloan Kettering Cancer Center

Large-scale cancer genome characterization projects are generating rich and very large tumor profiling data sets, measuring DNA copy number, mRNA expression, and often microRNA expression across tumors. We will discuss two algorithmic approaches for analysis of these large data sets. The first is a new method for identifying frequent copy number aberrations from probe-level aCGH data, i.e. without first segmenting and calling copy number changes on a sample-by-sample basis. The second is an integrative approach for inferring transcriptional and microRNA-mediated regulatory programs in tumors by modeling how gene copy number changes and regulatory elements in the promoter and 3’ UTR impact gene expression. We will describe results of both algorithms on large glioblastoma data sets, including data generated by TCGA.

5:45 Reception in the Exhibit hall

7:15 Close of Day



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