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  • 1
    Publication Date: 2012-03-31
    Description: The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of 'personalized' therapeutic regimens.〈br /〉〈br /〉〈a href="" target="_blank"〉〈img src="" border="0"〉〈/a〉   〈a href="" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Barretina, Jordi -- Caponigro, Giordano -- Stransky, Nicolas -- Venkatesan, Kavitha -- Margolin, Adam A -- Kim, Sungjoon -- Wilson, Christopher J -- Lehar, Joseph -- Kryukov, Gregory V -- Sonkin, Dmitriy -- Reddy, Anupama -- Liu, Manway -- Murray, Lauren -- Berger, Michael F -- Monahan, John E -- Morais, Paula -- Meltzer, Jodi -- Korejwa, Adam -- Jane-Valbuena, Judit -- Mapa, Felipa A -- Thibault, Joseph -- Bric-Furlong, Eva -- Raman, Pichai -- Shipway, Aaron -- Engels, Ingo H -- Cheng, Jill -- Yu, Guoying K -- Yu, Jianjun -- Aspesi, Peter Jr -- de Silva, Melanie -- Jagtap, Kalpana -- Jones, Michael D -- Wang, Li -- Hatton, Charles -- Palescandolo, Emanuele -- Gupta, Supriya -- Mahan, Scott -- Sougnez, Carrie -- Onofrio, Robert C -- Liefeld, Ted -- MacConaill, Laura -- Winckler, Wendy -- Reich, Michael -- Li, Nanxin -- Mesirov, Jill P -- Gabriel, Stacey B -- Getz, Gad -- Ardlie, Kristin -- Chan, Vivien -- Myer, Vic E -- Weber, Barbara L -- Porter, Jeff -- Warmuth, Markus -- Finan, Peter -- Harris, Jennifer L -- Meyerson, Matthew -- Golub, Todd R -- Morrissey, Michael P -- Sellers, William R -- Schlegel, Robert -- Garraway, Levi A -- DP2 OD002750/OD/NIH HHS/ -- DP2 OD002750-01/OD/NIH HHS/ -- R33 CA126674/CA/NCI NIH HHS/ -- R33 CA126674-04/CA/NCI NIH HHS/ -- R33 CA155554/CA/NCI NIH HHS/ -- R33 CA155554-02/CA/NCI NIH HHS/ -- England -- Nature. 2012 Mar 28;483(7391):603-7. doi: 10.1038/nature11003.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉The Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="" target="_blank"〉PubMed〈/a〉
    Keywords: Antineoplastic Agents/pharmacology ; Cell Line, Tumor ; Cell Lineage ; Chromosomes, Human/genetics ; Clinical Trials as Topic/methods ; *Databases, Factual ; Drug Screening Assays, Antitumor/*methods ; *Encyclopedias as Topic ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Genes, ras/genetics ; Genome, Human/genetics ; Genomics ; Humans ; Mitogen-Activated Protein Kinase Kinases/antagonists & inhibitors/metabolism ; *Models, Biological ; Neoplasms/*drug therapy/genetics/metabolism/*pathology ; Pharmacogenetics ; Plasma Cells/cytology/drug effects/metabolism ; Precision Medicine/methods ; Receptor, IGF Type 1/antagonists & inhibitors/metabolism ; Receptors, Aryl Hydrocarbon/genetics/metabolism ; Sequence Analysis, DNA ; Topoisomerase Inhibitors/pharmacology
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
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  • 2
    ISSN: 1432-1238
    Keywords: Key words Severity of illness ; index ; Intensive care ; Critical care ; Mortality prediction ; Simplified Acute Physiology Score (SAPS II) ; Acute Physiology and Chronic Health Evaluation (APACHE II)
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine
    Notes: Abstract Objective: To compare the performance of the New Simplified Acute Physiology Score (SAPS II) and Acute Physiology and Chronic Health Evaluation (APACHE) II in an independent database, using formal statistical assessment. Design: Analysis of the database of a multicentre, prospective study. Setting: 19 intensive care units (ICUs) in Portugal. Patients: Data for 1094 patients consecutively admitted to the ICUs were collected over a period of 4 months. Following the original SAPS II and APACHE II criteria, the analysis excluded patients younger than 18 years of age, readmissions, acute myocardial infarction, burns, patients in the post-operative period after coronary artery bypass surgery, and patients with a length of stay in the ICU of less than 24 h. The group analysed comprised 982 patients. Interventions: Collection of the first 24 h admission data necessary for the calculation of SAPS II, APACHE II, Therapeutic Intervention Scoring System (TISS), Simplified TISS, organ system failure and basic demographic statistics. Vital status at discharge from the hospital was registered. Measurements and results: In this cohort, discrimination was better for SAPS II than for APACHE II (SAPS II: area under the receiver operating characteristic curve 0.817, standard error 0.015; APACHE II: 0.787, 0.015; p 〈 0.001); however, both models presented a poor calibration, with significant differences between observed and predicted mortality (Hosmer-Lemeshow goodness-of-fit tests H and C, p 〈 0.001). In a stratified analysis, this study was unable to demonstrate any definite pattern of association between the poor performance of the models and specific subgroups of patients except for the most severely ill patients, where both models overestimated mortality. Conclusions: SAPS II performed better than APACHE II in this independent database, but the results do not allow its use, at least without being customised, to analyse quality of care or performance among ICUs in the target population.
    Type of Medium: Electronic Resource
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