Global variation in copy number in the human genome

Nature 444, 444-454 (23 November 2006) | doi:10.1038/nature05329; Received 13 June 2006; Accepted 10 October 2006 

Copy number variation (CNV) of DNA sequences is functionally significant but has yet to be fully ascertained. We have constructed a first-generation CNV map of the human genome through the study of 270 individuals from four populations with ancestry in Europe, Africa or Asia (the HapMap collection). DNA from these individuals was screened for CNV using two complementary technologies: single-nucleotide polymorphism (SNP) genotyping arrays, and clone-based comparative genomic hybridization. A total of 1,447 copy number variable regions (CNVRs), which can encompass overlapping or adjacent gains or losses, covering 360 megabases (12% of the genome) were identified in these populations. These CNVRs contained hundreds of genes, disease loci, functional elements and segmental duplications. Notably, the CNVRs encompassed more nucleotide content per genome than SNPs, underscoring the importance of CNV in genetic diversity and evolution. The data obtained delineate linkage disequilibrium patterns for many CNVs, and reveal marked variation in copy number among populations. We also demonstrate the utility of this resource for genetic disease studies.

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Genomic signatures to guide the use of chemotherapeutics

Nature Medicine – 12, 1294 – 1300 (2006)
Published online: 22 October 2006; | doi:10.1038/nm1491

Anil Potti1, 2, Holly K Dressman1, 3, Andrea Bild1, 3, Richard F Riedel1, 2, Gina Chan4, Robyn Sayer4, Janiel Cragun4, Hope Cottrill4, Michael J Kelley2, Rebecca Petersen5, David Harpole5, Jeffrey Marks5, Andrew Berchuck1, 6, Geoffrey S Ginsburg1, 2, Phillip Febbo1, 2, 3, Johnathan Lancaster4 & Joseph R Nevins1, 2, 3

Using in vitro drug sensitivity data coupled with Affymetrix microarray data, we developed gene expression signatures that predict sensitivity to individual chemotherapeutic drugs. Each signature was validated with response data from an independent set of cell line studies. We further show that many of these signatures can accurately predict clinical response in individuals treated with these drugs. Notably, signatures developed to predict response to individual agents, when combined, could also predict response to multidrug regimens. Finally, we integrated the chemotherapy response signatures with signatures of oncogenic pathway deregulation to identify new therapeutic strategies that make use of all available drugs. The development of gene expression profiles that can predict response to commonly used cytotoxic agents provides opportunities to better use these drugs, including using them in combination with existing targeted therapies.

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