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 Seynaeve; Arek Kasprzyk; Philippe Lambin; Pietro Lio'; John M.S. Bartlett; Paul C. Boutros

doi:10.1038/s41467-018-07021-3

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

Cancer Clinical Trials Unit

Research output: Contribution to journal › Article › peer-review

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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.

Original language	English
Article number	4746
Journal	Nature Communications
Volume	9
Issue number	1
DOIs	https://doi.org/10.1038/s41467-018-07021-3
Publication status	Published - 12 Nov 2018

ASJC Scopus subject areas

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

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Haider_etal_Pathway-based_subnetworks_enable_cross-disease_biomarker_discovery_Nature_Communications
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Cite this

Haider, S., Yao, C. Q., Sabine, V. S., Grzadkowski, M., Stimper, V., Starmans, M. H. W., Wang, J., Nguyen, F., Moon, N. C., Lin, X., Drake, C., Crozier, C. A., Brookes, C. L., van de Velde, C. J. H., Hasenburg, A., Rea, D., Kieback, D. G., Markopoulos, C. J., Dirix, L. Y., ... Boutros, P. C. (2018). Pathway-based subnetworks enable cross-disease biomarker discovery. Nature Communications, 9(1), Article 4746. https://doi.org/10.1038/s41467-018-07021-3

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title = "Pathway-based subnetworks enable cross-disease biomarker discovery",

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.",

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

year = "2018",

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Haider, S, Yao, CQ, Sabine, VS, Grzadkowski, M, Stimper, V, Starmans, MHW, Wang, J, Nguyen, F, Moon, NC, Lin, X, Drake, C, Crozier, CA, Brookes, CL, van de Velde, CJH, Hasenburg, A, Rea, D, Kieback, DG, Markopoulos, CJ, Dirix, LY, Seynaeve, C, Kasprzyk, A, Lambin, P, Lio', P, Bartlett, JMS & Boutros, PC 2018, 'Pathway-based subnetworks enable cross-disease biomarker discovery', Nature Communications, vol. 9, no. 1, 4746. https://doi.org/10.1038/s41467-018-07021-3

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T1 - Pathway-based subnetworks enable cross-disease biomarker discovery

AU - Haider, Syed

AU - Yao, Cindy Q.

AU - Sabine, Vicky S.

AU - Grzadkowski, Michal

AU - Stimper, Vincent

AU - Starmans, Maud H.W.

AU - Wang, Jianxin

AU - Nguyen, Francis

AU - Moon, Nathalie C.

AU - Lin, Xihui

AU - Drake, Camilla

AU - Crozier, Cheryl A.

AU - Brookes, Cassandra L.

AU - van de Velde, Cornelis J.H.

AU - Hasenburg, Annette

AU - Rea, Daniel

AU - Kieback, Dirk G.

AU - Markopoulos, Christos J.

AU - Dirix, Luc Y.

AU - Seynaeve, Caroline

AU - Kasprzyk, Arek

AU - Lambin, Philippe

AU - Lio', Pietro

AU - Bartlett, John M.S.

AU - Boutros, Paul C.

PY - 2018/11/12

Y1 - 2018/11/12

N2 - 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.

AB - 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.

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DO - 10.1038/s41467-018-07021-3

M3 - Article

C2 - 30420699

AN - SCOPUS:85056317417

SN - 2041-1723

VL - 9

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 4746

ER -

Pathway-based subnetworks enable cross-disease biomarker discovery

Abstract

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