Περίληψη:
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.
Συγγραφείς:
Haider, Syed
Yao, Cindy Q.
Sabine, Vicky S.
Grzadkowski,
Michal
Stimper, Vincent
Starmans, Maud H. W.
Wang, Jianxin
and 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. and
Markopoulos, Christos J.
Dirix, Luc Y.
Seynaeve, Caroline and
Rea, Daniel W.
Kasprzyk, Arek
Lambin, Philippe
Lio', Pietro
and Bartlett, John M. S.
Boutros, Paul C.
