Dataset Information

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.

SUBMITTER: Haider S

PROVIDER: S-EPMC6232113 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Pathway-based subnetworks enable cross-disease biomarker discovery.

Haider Syed S Yao Cindy Q CQ Sabine Vicky S VS Grzadkowski Michal M Stimper Vincent V Starmans Maud H W MHW Wang Jianxin J Nguyen Francis F Moon Nathalie C NC Lin Xihui X Drake Camilla C Crozier Cheryl A CA Brookes Cassandra L CL van de Velde Cornelis J H CJH Hasenburg Annette A Kieback Dirk G DG Markopoulos Christos J CJ Dirix Luc Y LY Seynaeve Caroline C Rea Daniel W DW Kasprzyk Arek A Lambin Philippe P Lambin Philippe P Lio' Pietro P Bartlett John M S JMS Boutros Paul C PC

Nature communications 20181112 1

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 fragmen ...[more]

PMID: 30420699

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Project description:BackgroundThe interrogation of proteomes ("proteomics") in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology and medicine.Methodology/principal findingsWe present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 µL of serum or plasma). Our current assay measures 813 proteins with low limits of detection (1 pM median), 7 logs of overall dynamic range (~100 fM-1 µM), and 5% median coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding signature of DNA aptamer concentrations, which is quantified on a DNA microarray. Our assay takes advantage of the dual nature of aptamers as both folded protein-binding entities with defined shapes and unique nucleotide sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to rapidly discover unique protein signatures characteristic of various disease states.Conclusions/significanceWe describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine.

Dataset Information

Pathway-based subnetworks enable cross-disease biomarker discovery.

Publications

Pathway-based subnetworks enable cross-disease biomarker discovery.

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