Showing papers by "Philip L. Lorenzi published in 2013"
John N. Weinstein1, John N. Weinstein2, Eric A. Collisson3, Gordon B. Mills1 +376 more•Institutions (31)
TL;DR: The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA with a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages.
Abstract: The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile.
5,294 citations
Journal Article•
TL;DR: The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels as mentioned in this paper.
Abstract: The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile.
4,634 citations
TL;DR: It is shown that L-Asparaginase's glutaminase activity is not always required for the enzyme's anticancer effect, suggesting the hypothesis that glutaminases-negative variants of L-ASP would provide larger therapeutic indices than wild-type L-asP for ASNS-negative cancers.
131 citations
TL;DR: It is concluded that DNA concentration is a widely applicable method for normalizing metabolomic data from adherent cell lines.
Abstract: Metabolomics is a rapidly advancing field, and much of our understanding of the subject has come from research on cell lines. However, the results and interpretation of such studies depend on appropriate normalization of the data; ineffective or poorly chosen normalization methods can lead to frankly erroneous conclusions. That is a recurrent challenge because robust, reliable methods for normalization of data from cells have not been established. In this study, we have compared several methods for normalization of metabolomic data from cell extracts. Total protein concentration, cell count, and DNA concentration exhibited strong linear correlations with seeded cell number, but DNA concentration was found to be the most generally useful method for the following reasons: (1) DNA concentration showed the greatest consistency across a range of cell numbers; (2) DNA concentration was the closest to proportional with cell number; (3) DNA samples could be collected from the same dish as the metabolites; and (4)...
94 citations
TL;DR: Although the gene expression data are largely concordant between them, the reported drug-sensitivity measures and subsequently their association with genomic features are highly discordant and the authors call for standardization of drug-response measurements, to aid the discovery of robust biomarkers and mechanisms of drug response and hence progress in personalized cancer medicine.
Abstract: Large panels of human cancer cell lines have been profiled at the DNA, RNA and pharmacological levels to accelerate the search for cancer therapies. But two of those large data sets show only partial concordance. See Analysis p.389
Two large-scale data sets recently catalogued the sensitivity of a large number of cancer cell lines to pharmacological drugs, and integrated the drug-response data with genomic features, such as mutations and gene expression profiles. This Analysis by John Quackenbush and colleagues compares the two studies and finds that although the gene expression data are largely concordant between them, the reported drug-sensitivity measures and subsequently their association with genomic features are highly discordant. The authors call for standardization of drug-response measurements, to aid the discovery of robust biomarkers and mechanisms of drug response and hence progress in personalized cancer medicine.
39 citations