A community effort to assess and improve drug sensitivity prediction algorithms
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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A community effort to assess and improve drug sensitivity prediction algorithms. / NCI DREAM Community.
I: Nature Biotechnology, Bind 32, Nr. 12, 12.2014, s. 1202-12.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - A community effort to assess and improve drug sensitivity prediction algorithms
AU - Costello, James C
AU - Heiser, Laura M
AU - Georgii, Elisabeth
AU - Gönen, Mehmet
AU - Menden, Michael P
AU - Wang, Nicholas J
AU - Bansal, Mukesh
AU - Ammad-ud-din, Muhammad
AU - Hintsanen, Petteri
AU - Khan, Suleiman A
AU - Mpindi, John-Patrick
AU - Kallioniemi, Olli
AU - Honkela, Antti
AU - Aittokallio, Tero
AU - Wennerberg, Krister
AU - Collins, James J
AU - Gallahan, Dan
AU - Singer, Dinah
AU - Saez-Rodriguez, Julio
AU - Kaski, Samuel
AU - Gray, Joe W
AU - Stolovitzky, Gustavo
AU - NCI DREAM Community
PY - 2014/12
Y1 - 2014/12
N2 - 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.
AB - 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.
KW - Algorithms
KW - Antineoplastic Agents/adverse effects
KW - Drug Resistance, Neoplasm/genetics
KW - Epigenomics/methods
KW - Gene Expression Profiling
KW - Gene Expression Regulation, Neoplastic/drug effects
KW - Genomics/methods
KW - Humans
KW - Neoplasms/drug therapy
KW - Proteomics/methods
U2 - 10.1038/nbt.2877
DO - 10.1038/nbt.2877
M3 - Journal article
C2 - 24880487
VL - 32
SP - 1202
EP - 1212
JO - Nature Biotechnology
JF - Nature Biotechnology
SN - 1087-0156
IS - 12
ER -
ID: 199430119