A personalized network framework reveals predictive axis of anti-TNF response across diseases

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.