Pathway-based subnetworks enable cross-disease biomarker discovery

Biomarkers lie at the heart of precision medicine. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers usually involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic meth...

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Published inNature communications Vol. 9; no. 1; pp. 4746 - 12
Main Authors Haider, Syed, Yao, Cindy Q., Sabine, Vicky S., Grzadkowski, Michal, Stimper, Vincent, Starmans, Maud H. W., Wang, Jianxin, Nguyen, Francis, Moon, Nathalie C., Lin, Xihui, Drake, Camilla, Crozier, Cheryl A., Brookes, Cassandra L., van de Velde, Cornelis J. H., Hasenburg, Annette, Kieback, Dirk G., Markopoulos, Christos J., Dirix, Luc Y., Seynaeve, Caroline, Rea, Daniel W., Kasprzyk, Arek, Lambin, Philippe, Lio’, Pietro, Bartlett, John M. S., Boutros, Paul C.
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 12.11.2018
Nature Publishing Group
Nature Portfolio
Subjects
1-Phosphatidylinositol 3-kinase
38/61
38/91
49/23
631/1647/48
639/705/1042
692/4028/67/327
692/4028/67/69
692/53/2423
Algorithms
Benchmarking
Biomarkers
Biomarkers, Tumor - analysis
Breast cancer
Cell Proliferation
Data integration
Data processing
Heart
Humanities and Social Sciences
Humans
Mathematical models
Metabolic Networks and Pathways
Molecular modelling
multidisciplinary
Neoplasms - metabolism
Phenotypes
Precision medicine
Science
Science (multidisciplinary)
Sensory integration
Signal Transduction
Treatment Outcome
Tumors
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Abstract Biomarkers lie at the heart of precision medicine. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers usually involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. Here we present SIMMS (Subnetwork Integration for Multi-Modal Signatures): an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We apply SIMMS to multiple data types across five diseases, and in each it reproducibly identifies known and novel subtypes, and makes superior predictions to the best bespoke approaches. To demonstrate its ability on a new dataset, we profile 33 genes/nodes of the PI3K pathway in 1734 FFPE breast tumors and create a four-subnetwork prediction model. This model out-performs a clinically-validated molecular test in an independent cohort of 1742 patients. SIMMS is generic and enables systematic data integration for robust biomarker discovery. Accurate and actionable biomarkers that integrate diverse molecular, functional and clinical information hold great promise in precision medicine. Here, the authors develop SIMMS, a method for pathway-based cross-disease biomarker discovery.
AbstractList Biomarkers lie at the heart of precision medicine. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers usually involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. Here we present SIMMS (Subnetwork Integration for Multi-Modal Signatures): an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We apply SIMMS to multiple data types across five diseases, and in each it reproducibly identifies known and novel subtypes, and makes superior predictions to the best bespoke approaches. To demonstrate its ability on a new dataset, we profile 33 genes/nodes of the PI3K pathway in 1734 FFPE breast tumors and create a four-subnetwork prediction model. This model out-performs a clinically-validated molecular test in an independent cohort of 1742 patients. SIMMS is generic and enables systematic data integration for robust biomarker discovery. Accurate and actionable biomarkers that integrate diverse molecular, functional and clinical information hold great promise in precision medicine. Here, the authors develop SIMMS, a method for pathway-based cross-disease biomarker discovery.
Biomarkers lie at the heart of precision medicine. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers usually involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. Here we present SIMMS (Subnetwork Integration for Multi-Modal Signatures): an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We apply SIMMS to multiple data types across five diseases, and in each it reproducibly identifies known and novel subtypes, and makes superior predictions to the best bespoke approaches. To demonstrate its ability on a new dataset, we profile 33 genes/nodes of the PI3K pathway in 1734 FFPE breast tumors and create a four-subnetwork prediction model. This model out-performs a clinically-validated molecular test in an independent cohort of 1742 patients. SIMMS is generic and enables systematic data integration for robust biomarker discovery.
Accurate and actionable biomarkers that integrate diverse molecular, functional and clinical information hold great promise in precision medicine. Here, the authors develop SIMMS, a method for pathway-based cross-disease biomarker discovery.
ArticleNumber 4746
Author Grzadkowski, Michal
Yao, Cindy Q.
Markopoulos, Christos J.
Boutros, Paul C.
Lio’, Pietro
Kieback, Dirk G.
Wang, Jianxin
Lin, Xihui
Nguyen, Francis
Moon, Nathalie C.
van de Velde, Cornelis J. H.
Lambin, Philippe
Bartlett, John M. S.
Stimper, Vincent
Brookes, Cassandra L.
Drake, Camilla
Seynaeve, Caroline
Haider, Syed
Starmans, Maud H. W.
Sabine, Vicky S.
Hasenburg, Annette
Dirix, Luc Y.
Kasprzyk, Arek
Rea, Daniel W.
Crozier, Cheryl A.
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  surname: Brookes
  fullname: Brookes, Cassandra L.
  organization: Cancer Research UK Clinical Trials Unit, University of Birmingham
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  givenname: Cornelis J. H.
  surname: van de Velde
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  organization: Erasmus Medical Center-Daniel den Hoed
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  organization: Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Department of Medical Biophysics, University of Toronto, Department of Pharmacology and Toxicology, University of Toronto
BackLink https://www.ncbi.nlm.nih.gov/pubmed/30420699$$D View this record in MEDLINE/PubMed
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Snippet Biomarkers lie at the heart of precision medicine. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers usually...
Accurate and actionable biomarkers that integrate diverse molecular, functional and clinical information hold great promise in precision medicine. Here, the...
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Algorithms
Benchmarking
Biomarkers
Biomarkers, Tumor - analysis
Breast cancer
Cell Proliferation
Data integration
Data processing
Heart
Humanities and Social Sciences
Humans
Mathematical models
Metabolic Networks and Pathways
Molecular modelling
multidisciplinary
Neoplasms - metabolism
Phenotypes
Precision medicine
Science
Science (multidisciplinary)
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Signal Transduction
Treatment Outcome
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Title Pathway-based subnetworks enable cross-disease biomarker discovery
URI https://link.springer.com/article/10.1038/s41467-018-07021-3
https://www.ncbi.nlm.nih.gov/pubmed/30420699
https://www.proquest.com/docview/2132250578
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Volume 9
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