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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Vydáno v:Nature communications Ročník 9; číslo 1; s. 4746 - 12
Hlavní autoři: 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.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 12.11.2018
Nature Publishing Group
Nature Portfolio
Témata:
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
Prediction models
Science
Science (multidisciplinary)
Sensory integration
Signal Transduction
Treatment Outcome
Tumors
ISSN:2041-1723, 2041-1723
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Popis
Shrnutí: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.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-018-07021-3