We are seeking support to establish a Genome Characterization Center, as a part of the Cancer Genome Atlas Project in Boston. The goal of the proposed effort is to analyze 2,000-2,500 tumor samples each year over a five-year period of time and identify a set of genes that can be resequenced by the members of The Cancer Genome Atlas (TCGA) project. It is well established that regions of the cancer genome that are amplified or show loss of heterozygosity or deletion harbor genes that are important for tumor initiation and progression. We will initially identify such regions in the cancer genome by conducting array comparative genomic hybridization (aCGH). Based upon detailed comparisons of many different platforms we have chosen to use the high-density Agilent oligonucleotide arrays for our studies. We have used the Agilent platform to characterize several hundreds of tumors and their corresponding controls, much of it a part of the current TCGA project. We already have the ability to have a throughput of processing four to five thousand samples during the first year of the proposed grant. During the first year we propose to use a sequencing based approach to determine copy number changes in tumors. In the initial phase we will examine 200 samples by a sequence tag counting approach and compare the results with the Agilent and other platforms. Based upon our experience we anticipate that this is eminently feasible and we will gradually switch the copy number analysis to the sequence based platform. In the second year 40% of the samples will be processed by the sequencing platform and the remaining 60% by the array platform. In year three 60% of the samples will be processed by sequencing and in years 4 and 5 we plan to completely switch to the sequencing approach. We have a well established pipeline to collect, store, retrieve and analyze the data from these two platforms. We will develop Level 1-4 data as defined by the consortium and deposit these data in a timely fashion in the Data Coordinating Center. We also propose to use powerful informatics tools that we have developed and propose to improve to analyze the aCGH and the sequence based data and extract a list of most interesting genes for resequencing. We have established an award winning IT infrastructure that will be deployed for LIMS, data storage, data retrieval, data analysis and interface with caBIG. Our proposed approach also has the ability to generate additional useful data for tumor and patient stratification.
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