Reconstructing disease dynamics for mechanistic insights and clinical benefit

Amit Frishberg, Neta Milman, Ayelet Alpert, Hannah Spitzer, Ben Asani, Johannes B. Schiefelbein, Evgeny Bakin, Karen Regev-Berman, Siegfried G. Priglinger, Joachim L. Schultze, Fabian J. Theis and Shai S. Shen-Orr ()
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Amit Frishberg: Technion-Israel Institute of Technology
Neta Milman: Technion-Israel Institute of Technology
Ayelet Alpert: Technion-Israel Institute of Technology
Hannah Spitzer: Helmholtz Center Munich
Ben Asani: Ludwig-Maximilians-University
Johannes B. Schiefelbein: Ludwig-Maximilians-University
Evgeny Bakin: CytoReason
Karen Regev-Berman: CytoReason
Siegfried G. Priglinger: Ludwig-Maximilians-University
Joachim L. Schultze: Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)
Fabian J. Theis: Helmholtz Center Munich
Shai S. Shen-Orr: Technion-Israel Institute of Technology

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Diseases change over time, both phenotypically and in their underlying molecular processes. Though understanding disease progression dynamics is critical for diagnostics and treatment, capturing these dynamics is difficult due to their complexity and the high heterogeneity in disease development between individuals. We present TimeAx, an algorithm which builds a comparative framework for capturing disease dynamics using high-dimensional, short time-series data. We demonstrate the utility of TimeAx by studying disease progression dynamics for multiple diseases and data types. Notably, for urothelial bladder cancer tumorigenesis, we identify a stromal pro-invasion point on the disease progression axis, characterized by massive immune cell infiltration to the tumor microenvironment and increased mortality. Moreover, the continuous TimeAx model differentiates between early and late tumors within the same tumor subtype, uncovering molecular transitions and potential targetable pathways. Overall, we present a powerful approach for studying disease progression dynamics—providing improved molecular interpretability and clinical benefits for patient stratification and outcome prediction.

Date: 2023
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DOI: 10.1038/s41467-023-42354-8

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