2026-07-24T06:34:51Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:12169 2024-03-06T10:49:36Z ddc:004 open_access

Persistent Homology Based Characterization of the Breast Cancer Immune Microenvironment: A Feasibility Study Aukerman, Andrew Carrière, Mathieu Chen, Chao Gardner, Kevin Rabadán, Raúl Vanguri, Rami Topological data analysis persistence diagrams Persistent homology is a common tool of topological data analysis, whose main descriptor, the persistence diagram, aims at computing and encoding the geometry and topology of given datasets. In this article, we present a novel application of persistent homology to characterize the spatial arrangement of immune and epithelial (tumor) cells within the breast cancer immune microenvironment. More specifically, quantitative and robust characterizations are built by computing persistence diagrams out of a staining technique (quantitative multiplex immunofluorescence) which allows us to obtain spatial coordinates and stain intensities on individual cells. The resulting persistence diagrams are evaluated as characteristic biomarkers of cancer subtype and prognostic biomarker 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. This provides new insights and possibilities on the general problem of building (topology-based) biomarkers that are characteristic and predictive of cancer subtype, overall survival and response to therapy. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Andrew Aukerman and Mathieu Carrière and Chao Chen and Kevin Gardner and Raúl Rabadán and Rami Vanguri 2020 Is Part Of LIPIcs, Volume 164, 36th International Symposium on Computational Geometry (SoCG 2020) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.SoCG.2020.11 urn:nbn:de:0030-drops-121695 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2020.11 eng https://creativecommons.org/licenses/by/3.0/legalcode