A community effort to assess and improve drug sensitivity prediction algorithms

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

A community effort to assess and improve drug sensitivity prediction algorithms. / NCI DREAM Community.

In: Nature Biotechnology, Vol. 32, No. 12, 12.2014, p. 1202-12.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

NCI DREAM Community 2014, 'A community effort to assess and improve drug sensitivity prediction algorithms', Nature Biotechnology, vol. 32, no. 12, pp. 1202-12. https://doi.org/10.1038/nbt.2877

APA

NCI DREAM Community (2014). A community effort to assess and improve drug sensitivity prediction algorithms. Nature Biotechnology, 32(12), 1202-12. https://doi.org/10.1038/nbt.2877

Vancouver

NCI DREAM Community. A community effort to assess and improve drug sensitivity prediction algorithms. Nature Biotechnology. 2014 Dec;32(12):1202-12. https://doi.org/10.1038/nbt.2877

Author

NCI DREAM Community. / A community effort to assess and improve drug sensitivity prediction algorithms. In: Nature Biotechnology. 2014 ; Vol. 32, No. 12. pp. 1202-12.

Bibtex

@article{011192c4481f4f13a5c32331d2d12d55,
title = "A community effort to assess and improve drug sensitivity prediction algorithms",
abstract = "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. ",
keywords = "Algorithms, Antineoplastic Agents/adverse effects, Drug Resistance, Neoplasm/genetics, Epigenomics/methods, Gene Expression Profiling, Gene Expression Regulation, Neoplastic/drug effects, Genomics/methods, Humans, Neoplasms/drug therapy, Proteomics/methods",
author = "Costello, {James C} and Heiser, {Laura M} and Elisabeth Georgii and Mehmet G{\"o}nen and Menden, {Michael P} and Wang, {Nicholas J} and Mukesh Bansal and Muhammad Ammad-ud-din and Petteri Hintsanen and Khan, {Suleiman A} and John-Patrick Mpindi and Olli Kallioniemi and Antti Honkela and Tero Aittokallio and Krister Wennerberg and Collins, {James J} and Dan Gallahan and Dinah Singer and Julio Saez-Rodriguez and Samuel Kaski and Gray, {Joe W} and Gustavo Stolovitzky and {NCI DREAM Community}",
year = "2014",
month = dec,
doi = "10.1038/nbt.2877",
language = "English",
volume = "32",
pages = "1202--12",
journal = "Nature Biotechnology",
issn = "1087-0156",
publisher = "nature publishing group",
number = "12",

}

RIS

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