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Detecting Clinically Relevant Topological Structures In Multiplexed Spatial Proteomics Using Topkat.

Home
Detecting Clinically Relevant Topological Structures In Multiplexed Spatial Proteomics Using Topkat.

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Detecting clinically relevant topological structures in multiplexed spatial proteomics using TopKAT.

Sarah Samorodnitsky^1,2, Katie Campbell³, Amarise Little^1,2

¹Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.

Patterns (New York, N.Y.)

|January 26, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

We developed a new statistical method, the topological kernel association test (TopKAT), to link cell geometry in the tumor microenvironment to patient outcomes. TopKAT analyzes spatial cell structures to predict clinical results, improving cancer prognosis and treatment response analysis.

Keywords:

cell-level imaging kernel association testing kernel machine regression multiplexed spatial proteomics persistent homology topological data analysis tumor microenvironment

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Area of Science:

Computational biology
Cancer research
Statistical methods

Background:

Multiplexed spatial proteomics reveals complex cell arrangements within the tumor microenvironment (TME).
Cellular spatial organization in the TME is clinically significant, impacting disease prognosis and treatment efficacy.
Existing statistical methods are insufficient for analyzing cell-level spatial data against patient-level outcomes.

Purpose of the Study:

To introduce a novel statistical framework, the topological kernel association test (TopKAT), for analyzing spatial proteomics data.
To assess the association between geometric cell structures in the TME and patient outcomes (continuous, binary, survival).
To provide a robust method for predicting clinical outcomes based on spatial cell topology.

Main Methods:

TopKAT integrates persistent homology to quantify topological features of cell arrangements using persistence diagrams.
It employs kernel testing to compare similarities between persistence diagrams based on homology count and lifespan.
The method analyzes cell-level spatial data to identify associations with patient-level clinical outcomes.

Main Results:

TopKAT demonstrates potential for increased statistical power compared to existing methods, especially for ring-like cellular structures.
The study validates TopKAT's utility in analyzing breast and colorectal cancer datasets.
The method effectively links spatial cell topology to patient outcomes.

Conclusions:

TopKAT offers a powerful new approach for analyzing complex spatial proteomics data in cancer research.
This method can improve the prediction of patient prognosis and treatment response by incorporating spatial cell information.
TopKAT provides a valuable tool for understanding the clinical implications of cellular geometry in the TME.