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Pathway-based subnetworks enable cross-disease biomarker discovery.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Yao, Cindy Q 
Sabine, Vicky S 
Grzadkowski, Michal 
Stimper, Vincent 

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.

Description

Keywords

Algorithms, Benchmarking, Biomarkers, Tumor, Cell Proliferation, Humans, Metabolic Networks and Pathways, Neoplasms, Signal Transduction, Treatment Outcome

Journal Title

Nat Commun

Conference Name

Journal ISSN

2041-1723
2041-1723

Volume Title

9

Publisher

Springer Science and Business Media LLC