Journal article
A community effort to assess and improve drug sensitivity prediction algorithms
Nature biotechnology, v 32(12), pp 1202-1212
Dec 2014
PMID: 24880487
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
Metrics
Details
- Title
- A community effort to assess and improve drug sensitivity prediction algorithms
- Creators
- James C Costello - 1] Howard Hughes Medical Institute, Boston University, Boston, Massachusetts, USA. Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA.Laura M Heiser - 1] Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA.Elisabeth Georgii - 1] Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, Espoo, Finland.Mehmet Gönen - Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, Espoo, FinlandMichael P Menden - European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UKNicholas J Wang - Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USAMukesh Bansal - Department of Systems Biology, Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, USAMuhammad Ammad-ud-din - Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, Espoo, FinlandPetteri Hintsanen - Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, FinlandSuleiman A Khan - Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, Espoo, FinlandJohn-Patrick Mpindi - Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, FinlandOlli Kallioniemi - Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, FinlandAntti Honkela - Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, FinlandTero Aittokallio - Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, FinlandKrister Wennerberg - Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, FinlandJames J Collins - 1] Howard Hughes Medical Institute, Boston University, Boston, Massachusetts, USA. Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA. Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, USADan Gallahan - National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USADinah Singer - National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USAJulio Saez-Rodriguez - European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UKSamuel Kaski - 1] Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, Espoo, Finland. Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, FinlandJoe W Gray - Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USAGustavo Stolovitzky - IBM T.J. Watson Research Center, IBM, Yorktown Heights, New York, USANCI DREAM CommunityAdam Michael Ertel - School of Biomedical Engineering, Science, and Health Systems (1997-)
- Publication Details
- Nature biotechnology, v 32(12), pp 1202-1212
- Publisher
- Springer Nature
- Grant note
- U54 CA121852 / NCI NIH HHS U54 CA112970 / NCI NIH HHS P30 CA016672 / NCI NIH HHS Howard Hughes Medical Institute 5U54CA121852-08 / NCI NIH HHS P50 CA058207 / NCI NIH HHS U54 CA 112970 / NCI NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000346156800022
- Scopus ID
- 2-s2.0-84906549588
- Other Identifier
- 991019176647304721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Industry collaboration
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biotechnology & Applied Microbiology