A personalized network framework reveals predictive axis of anti-TNF response across diseases
Shiran Gerassy-Vainberg, Elina Starosvetsky, Renaud Gaujoux, Alexandra Blatt, Naama Maimon, Yuri Gorelik, Sigal Pressman, Ayelet Alpert, Haggai Bar‐Yoseph, Tania Dubovik, Benny Perets, Adir Katz, Neta Milman, Meital Segev, Yehuda Chowers, Shai S. Shen-Orr · Cell Reports Medicine · 2023
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.