12–17 Jul 2026
University of Graz
Europe/Vienna timezone

Quantifying Tissue Architecture in the Tumour Microenvironment with Multi-Parameter Persistent Homology

MS69-04
13 Jul 2026, 11:40
20m
15.04 - HS (University of Graz)

15.04 - HS

University of Graz

195

Speaker

Kylie Savoye (1) School of Mathematics, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, UK; 2) School of Physics and Astronomy, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, UK)

Description

Spatial transcriptomics has revolutionised our ability to measure gene expression while preserving tissue architecture. Yet extracting meaningful patterns from the complex interaction of spatial organisation and molecular profiles remains challenging, particularly in the heterogeneous tumour microenvironment (TME). Here we apply Multi-Parameter Persistent Homology (MPH), a topological data analysis framework that can simultaneously track cellular organisation patterns across spatial proximity and gene expression levels, to reveal disease-relevant tissue architecture in cancer that may be invisible to conventional methods.

MPH constructs two-parameter filtrations combining spatial distance with gene expression gradients, enabling quantitative characterisation of topological features as they emerge and persist across both parameters. This approach captures how cells organise relative to both their neighbours and their molecular states, which provides a unified signature of tissue structure well suited to characterise the spatial and molecular heterogeneity central to tumour plasticity.

We demonstrate MPH's capabilities in a cancer cell line derived from colorectal cancer tumour, revealing spatial immune and stromal compartmentalisation patterns relevant to understanding TME organisation. Notably, we find differences in topological signatures across fibroblast populations that are not observed in macrophage populations, suggesting that stromal heterogeneity in the TME may have structure beyond purely spatial organisation. We are currently extending this framework to additional human cancer datasets, with ongoing data collection and methods development aimed at broadening its applicability to clinical contexts.

MPH's quantitative characterisation of tissue architecture offers potential for identifying pathology-specific spatial patterns that may inform treatment outcome prediction and disease progression monitoring. Topological approaches like MPH provide a bridge between molecular measurements and the architectural context essential for understanding tumour plasticity, stromal remodelling, and therapeutic response.

Author

Kylie Savoye (1) School of Mathematics, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, UK; 2) School of Physics and Astronomy, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, UK)

Co-authors

Alaide Morcavallo (In Vivo Pharmacology, Oncology Targeted Discovery, Oncology, AstraZeneca, UK) Arthur Lewis (Pathology, Clinical Pharmacology & Safety Sciences, R&D BioPharmaceuticals, AstraZeneca, UK) Azam Hamidinekoo (Pathology, Clinical Pharmacology & Safety Sciences, R&D BioPharmaceuticals, AstraZeneca, UK) Fabian Spill (School of Mathematics, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, UK) Semiramis Popova (Discovery Bioanalysis Spatial & Quantitative Omics, Clinical Pharmacology & Safety Sciences, R&D BioPharmaceuticals, AstraZeneca, UK)

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