TY - JOUR
T1 - A Network-Based Framework to Discover Treatment-Response–Predicting Biomarkers for Complex Diseases
AU - Shanthamallu, Uday S.
AU - Kilpatrick, Casey
AU - Jones, Alex
AU - Rubin, Jonathan
AU - Saleh, Alif
AU - Barabási, Albert László
AU - Akmaev, Viatcheslav R.
AU - Ghiassian, Susan D.
N1 - Copyright © 2024 Association for Molecular Pathology and American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.
PY - 2024/10
Y1 - 2024/10
N2 - 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.
AB - 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.
KW - Humans
KW - Biomarkers
KW - Protein Interaction Maps
KW - Machine Learning
KW - Arthritis, Rheumatoid/drug therapy
KW - Precision Medicine/methods
KW - Colitis, Ulcerative/drug therapy
KW - Infliximab/therapeutic use
KW - Crohn Disease/genetics
KW - Autoimmune Diseases/diagnosis
KW - Gene Expression Profiling/methods
UR - http://www.scopus.com/inward/record.url?scp=85204017378&partnerID=8YFLogxK
U2 - 10.1016/j.jmoldx.2024.06.008
DO - 10.1016/j.jmoldx.2024.06.008
M3 - Article
C2 - 39067570
AN - SCOPUS:85204017378
SN - 1525-1578
VL - 26
SP - 917
EP - 930
JO - Journal of Molecular Diagnostics
JF - Journal of Molecular Diagnostics
IS - 10
ER -