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

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Article number101300
JournalCell Reports Medicine
Volume5
Issue number1
DOIs
StatePublished - 16 Jan 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 The Authors

Keywords

  • anti-TNF antibodies
  • drug response
  • immune-mediated diseases
  • individual-level network analysis
  • inflammatory bowel disease
  • infliximab
  • pan-disease drug response diagnostics
  • precision medicine
  • rheumatoid arthritis

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