Pathway-based subnetworks enable cross-disease biomarker discovery

Syed Haider, Cindy Q. Yao, Vicky S. Sabine, Michal Grzadkowski, Vincent Stimper, Maud H.W. Starmans, Jianxin Wang, Francis Nguyen, Nathalie C. Moon, Xihui Lin, Camilla Drake, Cheryl A. Crozier, Cassandra L. Brookes, Cornelis J.H. van de Velde, Annette Hasenburg, Daniel Rea, Dirk G. Kieback, Christos J. Markopoulos, Luc Y. Dirix, Caroline SeynaeveArek Kasprzyk, Philippe Lambin, Pietro Lio', John M.S. Bartlett, Paul C. Boutros

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)
169 Downloads (Pure)


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.

Original languageEnglish
Article number4746
JournalNature Communications
Issue number1
Publication statusPublished - 12 Nov 2018

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)


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