Abstract
Personalized treatment of complex diseases is an unmet medical need pushing towards drug biomarker identification of one drug-disease combination at a time. Here, we used a novel computational approach for modeling cell-centered individual-level network dynamics from high-dimensional blood data to predict infliximab response and uncover individual variation of non-response. We identified and validated that the RAC1-PAK1 axis is predictive of infliximab response in inflammatory bowel disease. Intermediate monocytes, which closely correlated with inflammation 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 pan-disease drug response diagnostics from blood, implicating common mechanisms of drug response or failure across diseases.
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.