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A Network-Based Framework to Discover Treatment-Response–Predicting Biomarkers for Complex Diseases
Title / Series / Name
Publication Volume
Publication Issue
Pages
Editors
Keywords
Humans
Biomarkers
Protein Interaction Maps
Machine Learning
Arthritis, Rheumatoid/drug therapy
Precision Medicine/methods
Colitis, Ulcerative/drug therapy
Infliximab/therapeutic use
Crohn Disease/genetics
Autoimmune Diseases/diagnosis
Gene Expression Profiling/methods
Pathology and Forensic Medicine
Molecular Medicine
SDG 3 - Good Health and Well-being
Biomarkers
Protein Interaction Maps
Machine Learning
Arthritis, Rheumatoid/drug therapy
Precision Medicine/methods
Colitis, Ulcerative/drug therapy
Infliximab/therapeutic use
Crohn Disease/genetics
Autoimmune Diseases/diagnosis
Gene Expression Profiling/methods
Pathology and Forensic Medicine
Molecular Medicine
SDG 3 - Good Health and Well-being
URI
https://hdl.handle.net/20.500.14018/27114
Abstract
The potential of precision medicine to transform complex autoimmune disease treatment is often challenged by limited data availability and inadequate sample size when compared with the number of molecular features found in high-throughput multi-omics data sets. To address this issue, the novel framework PRoBeNet (Predictive Response Biomarkers using Network medicine) was developed. PRoBeNet operates under the hypothesis that the therapeutic effect of a drug propagates through a protein-protein interaction network to reverse disease states. PRoBeNet prioritizes biomarkers by considering i) therapy-targeted proteins, ii) disease-specific molecular signatures, and iii) an underlying network of interactions among cellular components (the human interactome). PRoBeNet helped discover biomarkers predicting patient responses to both an established autoimmune therapy (infliximab) and an investigational compound (a mitogen-activated protein kinase 3/1 inhibitor). The predictive power of PRoBeNet biomarkers was validated with retrospective gene-expression data from patients with ulcerative colitis and rheumatoid arthritis and prospective data from tissues from patients with ulcerative colitis and Crohn disease. Machine-learning models using PRoBeNet biomarkers significantly outperformed models using either all genes or randomly selected genes, especially when data were limited. These results illustrate the value of PRoBeNet in reducing features and for constructing robust machine-learning models when data are limited. PRoBeNet may be used to develop companion and complementary diagnostic assays, which may help stratify suitable patient subgroups in clinical trials and improve patient outcomes.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2024-10
Language
ISBN
Identifiers
10.1016/j.jmoldx.2024.06.008