ArticleCell reports. Medicine2024
A personalized network framework reveals predictive axis of anti-TNF response across diseases.
Article in Cell reports. Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- The Israeli IBD Research Nucleus: collaborative advancements in Israeli IBD research.Therapeutic advances in gastroenterology · 2026Review
- Predictive biomarkers for precision treatment in rheumatoid arthritis: are we there yet?Frontiers in pharmacology · 2026Review
- Review
- The Constrained Disorder Principle: A Paradigm Shift for Accurate Interactome Mapping and Information Analysis in Complex Biological Systems.Bioengineering (Basel, Switzerland) · 2025Review
- Molecular profiling for advancing precision rheumatology.Nature reviews. Rheumatology · 2025Article
- Challenges in IBD Research 2024: Precision Medicine.Inflammatory bowel diseases · 2024Review
- Inflammation and bacteriophages affect DNA inversion states and functionality of the gut microbiota.Cell host & microbe · 2024Article
- Prediction of response to anti-TNFα using integrative computational approaches in Crohn's disease-Needle in a haystack or a promising biomarker?Cell reports. Medicine · 2024Article
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Authors and funding
16 authors.
Funding
Abstract
Personalized treatment of complex diseases has been mostly predicated on biomarker identification of one drug-disease combination at a time. Here, we use a computational approach termed Disruption Networks to generate a data type, contextualized by cell-centered individual-level networks, that captures biology otherwise overlooked when performing standard statistics. This data type extends beyond the "feature level space", to the "relations space", by quantifying individual-level breaking or rewiring of cross-feature relations. Applying Disruption Networks to dissect high-dimensional blood data, we discover and validate that the RAC1-PAK1 axis is predictive of anti-TNF response in inflammatory bowel disease. Intermediate monocytes, which correlate with the inflammatory state, play a key role in the RAC1-PAK1 responses, supporting their modulation as a therapeutic target. This axis also predicts response in rheumatoid arthritis, validated in three public cohorts. Our findings support blood-based drug response diagnostics across immune-mediated diseases, implicating common mechanisms of non-response.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.