Genomic data sets associated with each cell line
RNA based expression profiling. The Ethier lab has performed expression profiling of each of the cell lines using several platforms, including Affymetrix arrays, Illumina bead arrays, and more recently, by RNA-sequencing. This latter technology allows for the analysis of expression of the entire transcriptome and provides information on the relative level of expression of individual splice isoforms of every gene. Thus, this data base will allow users to query the expression characteristics of their gene(s) of interest in each of the cell lines.
DNA based copy number analysis. Array comparative genomic hybridization has been performed on three different platforms, BAC arrays, Agilent arrays, and Illumia high density SNP arrays. These analyses provide genome wide, detailed and high resolution information on copy number gains, focal amplifications, and allele-specific deletions in each cell line.
Exome sequencing. Exome sequencing of the cell line panel has been performed to identify sequence variations in the exomes of each cell line. Exome sequencing not only provides information on all known somatic variants in each cell line, and comparing the variants in each cell line with other cancer databases, such as COSMIC, allows instant identification of cancer causing mutations in each cell line. In addition, exome sequencing allows for identification of internal deletions (indels) and fusion transcripts present in the cell lines.
Genome scale shRNA screening. Our lab has performed genome scale shRNA screening on all of the SUM lines, on MCF-10A cells, MCF-7 cells, and MCF-7LTED cells from the Daniel Birnbaum lab, using the Cellecta library of 81,000 shRNAs that covers nearly 16,000 well annotated human genes. The data sets derived from these analysis provides rich functional data that can be used in conjunction with gene expression and genomic alteration data sets to identify key functionally relevant genes and pathways operative in each cell line. We have used these data sets to identify functional oncogene signatures in each of the SUM lines, and have identified drug targets that have allowed us to elucidate rational combinatorial drug strategies that have profound effects on cancer cell viability. Thus, the shRNA screening data sets are a linchpin of the Knowledge Base and will allow users to perform detailed bioinformatic analyses in each line based on the functional genomic data.
Functional oncogene signatures. Functional oncogene signatures have been elucidated for each cell line by combining the results of the shRNA screen with data from copy number analysis, point mutation analysis, gene expression profiling, and other cancer genome knowledge bases, to identify the small signature of functional driving oncogenes in each cell line. These signatures are highly predictive for sensitivity to targeted drugs and are predictive for targeted drug combinations that are highly effective at killing cancer cells that are addicted to these oncogenes. Orthogonal analyses of these data sets have also allowed us to identify interactions between oncogenes in each signature, as well as genes within the oncogene interaction networks that are druggable.
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