Abstract
Personalized treatment of complex diseases has been mostly predicated on biomarker identification of one drug-disease combination at a time. Here, we used a novel computational approach termed Disruption Networks to generate a new data type, contextualized by cell-centered individual-level networks, that captures biology otherwise overlooked when performing standard statistics. The new 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 network 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.
Competing Interest Statement
These authors disclose the following: Y.C received consulting fees from AbbVie, Janssen, Takeda, Pfizer and CytoReason; speaker fees from AbbVie, Janssen, and Takeda; and grants from AbbVie, Takeda and Janssen. S.S.S-O received grant fees from Takeda, S.S.S.-O, E.S. and R.G declares CytoReason equity and advisory fees. N. Ma and A.K are employees at CytoReason. S.G.V declares CytoReason advisory fees. The remaining authors disclose no conflicts.
Footnotes
on behalf of the Israeli IBD research Network (IIRN)