Network-Based Biomarkers Enable Cross-Disease Biomarker Discovery

Biomarkers lie at the heart of precision medicine, biodiversity monitoring, agricultural pathogen detection, amongst others. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers almost always involves the application of bespoke techniques that cannot be d...

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Published inbioRxiv
Main Authors Haider, Syed, Yao, Cindy, Sabine, Vicky, Grzadkowski, Michal, Stimper, Vincent, Starmans, Maud Hw, Wang, Jianxin, Nguyen, Francis, Moon, Nathalie C, Lin, Xihui, Drake, Camilla, Crozier, Cheryl A, Brookes, Cassandra, Cornelis Jh Van De Velde, Hasenburg, Annette, Kieback, Dirk, Markopoulos, Christos J, Dirix, Luc Y, Seynaeve, Caroline, Rea, Daniel W, Kasprzyk, Arek, Lio, Pietro, Lambin, Philippe, John Ms Bartlett, Boutros, Paul C
Format Paper
LanguageEnglish
Published Cold Spring Harbor Cold Spring Harbor Laboratory Press 27.03.2018
Subjects
Biodiversity
Biomarkers
Breast cancer
Molecular modelling
Phenotypes
Precision medicine
Prediction models
Tumors
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Summary:Biomarkers lie at the heart of precision medicine, biodiversity monitoring, agricultural pathogen detection, amongst others. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers almost always 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. We therefore created SIMMS: an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We applied SIMMS to multiple data-types across four diseases, and in each it reproducibly identified subtypes, made superior predictions to the best bespoke approaches, and identified known and novel signaling nodes. To demonstrate its ability on a new dataset, we measured 33 genes/nodes of the PIK3CA pathway in 1,734 FFPE breast tumours and created a four-subnetwork prediction model. This model significantly out-performed existing clinically-used molecular tests in an independent 1,742-patient validation cohort. SIMMS is generic and can work with any molecular data or biological network, and is freely available at: https://cran.r-project.org/web/packages/SIMMS.
DOI:10.1101/289934