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, Dirk G. Kieback, Christos J. Markopoulos, Luc Y. Dirix, Caroline Seynaeve, Daniel W. Rea, Arek Kasprzyk, Philippe Lambin, Pietro Lio’, John M. S. Bartlett () and Paul C. Boutros ()
Additional contact information
Syed Haider: Ontario Institute for Cancer Research
Cindy Q. Yao: Ontario Institute for Cancer Research
Vicky S. Sabine: Ontario Institute for Cancer Research
Michal Grzadkowski: Ontario Institute for Cancer Research
Vincent Stimper: Ontario Institute for Cancer Research
Maud H. W. Starmans: Ontario Institute for Cancer Research
Jianxin Wang: Ontario Institute for Cancer Research
Francis Nguyen: Ontario Institute for Cancer Research
Nathalie C. Moon: Ontario Institute for Cancer Research
Xihui Lin: Ontario Institute for Cancer Research
Camilla Drake: Ontario Institute for Cancer Research
Cheryl A. Crozier: Ontario Institute for Cancer Research
Cassandra L. Brookes: University of Birmingham
Cornelis J. H. van de Velde: Leiden University Medical Center
Annette Hasenburg: University Hospital
Dirk G. Kieback: Klinikum Vest Medical Center
Christos J. Markopoulos: Athens University Medical School
Luc Y. Dirix: St. Augustinus Hospital
Caroline Seynaeve: Erasmus Medical Center-Daniel den Hoed
Daniel W. Rea: University of Birmingham
Arek Kasprzyk: Ontario Institute for Cancer Research
Philippe Lambin: Maastricht University Medical Center
Pietro Lio’: University of Cambridge
John M. S. Bartlett: Ontario Institute for Cancer Research
Paul C. Boutros: Ontario Institute for Cancer Research

Nature Communications, 2018, vol. 9, issue 1, 1-12

Abstract: 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.

Date: 2018
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DOI: 10.1038/s41467-018-07021-3

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