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A community effort to assess and improve drug sensitivity prediction algorithms

Costello, James C ; Heiser, Laura M ; Georgii, Elisabeth ; Gönen, Mehmet ; Menden, Michael P ; Wang, Nicholas J ; Bansal, Mukesh ; Ammad-ud-din, Muhammad ; Hintsanen, Petteri ; Khan, Suleiman A ; Mpindi, John-Patrick ; Kallioniemi, Olli ; Honkela, Antti ; Aittokallio, Tero ; Wennerberg, Krister ; Collins, James J ; Gallahan, Dan ; Singer, Dinah ; Saez-Rodriguez, Julio ; Kaski, Samuel ; Gray, Joe W ; Stolovitzky, Gustavo

Nature biotechnology, 2014-12, Vol.32 (12), p.1202-1212 [Periódico revisado por pares]

United States: Nature Publishing Group

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Título:
A community effort to assess and improve drug sensitivity prediction algorithms
Autor: Costello, James C ; Heiser, Laura M ; Georgii, Elisabeth ; Gönen, Mehmet ; Menden, Michael P ; Wang, Nicholas J ; Bansal, Mukesh ; Ammad-ud-din, Muhammad ; Hintsanen, Petteri ; Khan, Suleiman A ; Mpindi, John-Patrick ; Kallioniemi, Olli ; Honkela, Antti ; Aittokallio, Tero ; Wennerberg, Krister ; Collins, James J ; Gallahan, Dan ; Singer, Dinah ; Saez-Rodriguez, Julio ; Kaski, Samuel ; Gray, Joe W ; Stolovitzky, Gustavo
Assuntos: Algorithms ; Antineoplastic Agents - adverse effects ; Antineoplastic Agents - therapeutic use ; Breast cancer ; Chemotherapy ; Community ; Drug Resistance, Neoplasm - genetics ; Drug therapy ; Epigenetic inheritance ; Epigenomics - methods ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic - drug effects ; Genetic research ; Genomics ; Genomics - methods ; Humans ; Life Sciences ; Neoplasms - drug therapy ; Neoplasms - genetics ; Proteomics - methods ; Quantitative Methods ; Sensitivity analysis
É parte de: Nature biotechnology, 2014-12, Vol.32 (12), p.1202-1212
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
PMCID: PMC4547623
Present address: Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
These authors contributed equally to this work.
Descrição: Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.
Editor: United States: Nature Publishing Group
Idioma: Inglês

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A community effort to assess and improve drug sensitivity prediction algorithms

United States: Nature Publishing Group

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