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Journal Articles Journal of Computational Geometry Year : 2022

Persistent homology based characterization of the breast cancer immune microenvironment: a feasibility study

Abstract

Persistent homology is a powerful tool in topological data analysis. The main output, persistence diagrams, encode the geometry and topology of given datasets. We present a novel application of persistent homology to characterize the biological environment surrounding breast cancers, known as the tumor microenvironment. Specifically, we will characterize the spatial arrangement of immune and malignant epithelial (tumor) cells within the breast cancer immune microenvironment. Quantitative and robust characterizations are built by computing persistence diagrams from quantitative multiplex immunofluorescence, which is a technology which allows us to obtain spatial coordinates and protein intensities on individual cells. The resulting persistence diagrams are evaluated as characteristic biomarkers predictive of cancer subtype and prognostic of overall survival. For a cohort of approximately 700 breast cancer patients with median 8.5-year clinical follow-up, we show that these persistence diagrams outperform and complement the usual descriptors which capture spatial relationships with nearest neighbor analysis. Our results thus suggest new methods which can be used to build topology-based biomarkers which are characteristic and predictive of cancer subtype and response to therapy as well as prognostic of overall survival.
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Dates and versions

hal-03912324 , version 1 (24-12-2022)

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Andrew Aukerman, Mathieu Carriere, Chao Chen, Kevin Gardner, Raúl Rabadán, et al.. Persistent homology based characterization of the breast cancer immune microenvironment: a feasibility study. Journal of Computational Geometry, 2022, Special Issue of Selected Papers from SoCG 2020, 12 (2), pp.183-206. ⟨10.20382/jocg.v12i2a9⟩. ⟨hal-03912324⟩
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