LOCATOR: feature extraction and spatial analysis of the cancer tissue microenvironment using mass cytometry imaging technologies.

Autor: Ehsani R; Department of Informatics, Computational Biology Unit, University of Bergen, Bergen N-5020, Norway.; Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, Bergen N-5020, Norway., Jonassen I; Department of Informatics, Computational Biology Unit, University of Bergen, Bergen N-5020, Norway.; Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, Bergen N-5020, Norway., Akslen LA; Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, Bergen N-5020, Norway.; Department of Pathology, Haukeland University Hospital, Bergen N-5020, Norway., Kleftogiannis D; Department of Informatics, Computational Biology Unit, University of Bergen, Bergen N-5020, Norway.; Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, Bergen N-5020, Norway.
Jazyk: angličtina
Zdroj: Bioinformatics advances [Bioinform Adv] 2023 Oct 11; Vol. 3 (1), pp. vbad146. Date of Electronic Publication: 2023 Oct 11 (Print Publication: 2023).
DOI: 10.1093/bioadv/vbad146
Abstrakt: Motivation: Recent advances in highly multiplexed imaging have provided unprecedented insights into the complex cellular organization of tissues, with many applications in translational medicine. However, downstream analyses of multiplexed imaging data face several technical limitations, and although some computational methods and bioinformatics tools are available, deciphering the complex spatial organization of cellular ecosystems remains a challenging problem.
Results: To mitigate this problem, we develop a novel computational tool, LOCATOR (anaLysis Of CAncer Tissue micrOenviRonment), for spatial analysis of cancer tissue microenvironments using data acquired from mass cytometry imaging technologies. LOCATOR introduces a graph-based representation of tissue images to describe features of the cellular organization and deploys downstream analysis and visualization utilities that can be used for data-driven patient-risk stratification. Our case studies using mass cytometry imaging data from two well-annotated breast cancer cohorts re-confirmed that the spatial organization of the tumour-immune microenvironment is strongly associated with the clinical outcome in breast cancer. In addition, we report interesting potential associations between the spatial organization of macrophages and patients' survival. Our work introduces an automated and versatile analysis tool for mass cytometry imaging data with many applications in future cancer research projects.
Availability and Implementation: Datasets and codes of LOCATOR are publicly available at https://github.com/RezvanEhsani/LOCATOR.
Competing Interests: None declared.
(© The Author(s) 2023. Published by Oxford University Press.)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje