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

doi:10.1016/j.xcrm.2023.101300

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 journal › Article › peer-review

4 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 language	English
Article number	101300
Journal	Cell Reports Medicine
Volume	5
Issue number	1
DOIs	https://doi.org/10.1016/j.xcrm.2023.101300
State	Published - 16 Jan 2024
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2023 The Authors

Funding

This work was supported by a grant from the Leona M. and Harry B. Helmsley Charitable Trust (G-2018PG-CD009) to Y.C. and S.S.S.-O. We thank T. Shvedov for contribution to patient enrollment and clinical data collection; S. Pollok, L. Pinzur, N. Molshatzki, and Y. Benita for fruitful discussions and advice on computational methodology; and V. Barsan for his insightful comments in reviewing the manuscript. We thank Yoav Schumacher and Yuval Abraham for the graphical design of the manuscript figures and graphical abstract. S.S.S.-O. and Y.C. conceived the idea; S.S.S.-O. Y.C. S.G.V. E.S. and R.G. designed the analyses; S.S.S.-O. Y.C. and S.G.V. performed the interpretation; S.G.V. and R.G. performed the design and development of the computational pipeline and validation; A.K. B.P. Y.G. and A.A. performed development of the computational methodology; N. Maimon, A.B. S.P. and E.S. counseled regarding the biological interpretation; E.S. performed the experimental design of the collected cohort; E.S. N. Maimon, A.A. and T.D. performed the data generation; A.B. and N. Milman performed the experimental validation; A.B. and S.P. performed the sample collection; Y.C. H.B.Y. and Y.G. performed patient enrollment and clinical characterization; and S.S.S.-O. Y.C. and S.G.V. wrote the manuscript. 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. R.G. and Y.C. declare CytoReason equity. S.S.S.-O. declares CytoReason advisory fees. Y.C. N.Maimon, and A.K. are employees at CytoReason. S.G.V. declares CytoReason advisory fees. S.S.S.-O. Y.C. S.G.V. A.B. E.S. R.G. and N.Maimon, have a National Phase Patent: WO2022/264134 A1 (METHOD FOR DETERMINING SUITABILITY TO ANTI-TNF ALPHA THERAPY, published as of December 22, 2022). The grant support did not affect study design at any stage. This work was supported by a grant from the Leona M. and Harry B. Helmsley Charitable Trust ( G-2018PG-CD009 ) to Y.C. and S.S.S.-O. 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., R.G., and Y.C. declare CytoReason equity. S.S.S.-O. declares CytoReason advisory fees. Y.C., N.Maimon, and A.K. are employees at CytoReason. S.G.V. declares CytoReason advisory fees. S.S.S.-O., Y.C., S.G.V., A.B., E.S., R.G., and N.Maimon, have a National Phase Patent: WO2022/264134 A1 (METHOD FOR DETERMINING SUITABILITY TO ANTI-TNF ALPHA THERAPY, published as of December 22, 2022). The grant support did not affect study design at any stage.

Funders	Funder number
CytoReason	WO2022/264134 A1
Leona M. and Harry B. Helmsley Charitable Trust

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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10.1016/j.xcrm.2023.101300

Cite this

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., Segev, M., Chowers, Y., & Shen-Orr, S. S. (2024). A personalized network framework reveals predictive axis of anti-TNF response across diseases. Cell Reports Medicine, 5(1), Article 101300. https://doi.org/10.1016/j.xcrm.2023.101300

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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 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.",

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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, Segev, M, Chowers, Y & Shen-Orr, SS 2024, 'A personalized network framework reveals predictive axis of anti-TNF response across diseases', Cell Reports Medicine, vol. 5, no. 1, 101300. https://doi.org/10.1016/j.xcrm.2023.101300

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AU - Gerassy-Vainberg, Shiran

AU - Starosvetsky, Elina

AU - Gaujoux, Renaud

AU - Blatt, Alexandra

AU - Maimon, Naama

AU - Gorelik, Yuri

AU - Pressman, Sigal

AU - Alpert, Ayelet

AU - Bar-Yoseph, Haggai

AU - Dubovik, Tania

AU - Perets, Benny

AU - Katz, Adir

AU - Milman, Neta

AU - Segev, Meital

AU - Chowers, Yehuda

AU - Shen-Orr, Shai S.

PY - 2024/1/16

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AB - 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.

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

Abstract

Bibliographical note

Funding

Keywords

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