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bioRxiv · 10.1101/2021.06.16.448558

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

Gerassy-Vainberg, S.·Starosvetsky, E.·Gaujoux, R.·Blatt, A.·Maimon, N.·Gorelik, Y.·Pressman, S.·Alpert, A.·Bar-Yoseph, H.·Dubovik, T.·Perets, B.·Katz, A.·Milman, N.·Chowers, Y.·Shen-Orr, S. S.
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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.

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Gerassy-Vainberg, S., Starosvetsky, E., Gaujoux, R., Blatt, A., Maimon, N., Gorelik, Y., Pressman, S., Alpert, A., Bar-Yoseph, H., Dubovik, T., Perets, B., Katz, A., Milman, N., Chowers, Y., Shen-Orr, S. S.. 2021-06-18. A personalized network framework reveals predictive axis of anti-TNF response across diseases. https://doi.org/10.1101/2021.06.16.448558

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