ArticleBioinformatics (Oxford, England)2021
Quantification of spatial tumor heterogeneity in immunohistochemistry staining images.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Quantile index predictors using R package hyper.gam.Bioinformatics (Oxford, England) · 2025Article
- CytoSpatio: Learning cell type spatial relationships using multirange, multitype point process models.PLoS computational biology · 2025Article
- Functional protein biomarkers based on distributions of expression levels in single-cell imaging data.Bioinformatics (Oxford, England) · 2025Article
- Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.Briefings in bioinformatics · 2024Review
- GammaGateR: semi-automated marker gating for single-cell multiplexed imaging.Bioinformatics (Oxford, England) · 2024Article
- GammaGateR: semi-automated marker gating for single-cell multiplexed imaging.bioRxiv : the preprint server for biology · 2023Article
- Quantile Index Biomarkers Based on Single-Cell Expression Data.Laboratory investigation; a journal of technical methods and pathology · 2023Article
- Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data.Methods in molecular biology (Clifton, N.J.) · 2023Review
- Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis.Cell reports methods · 2022Review
- Orthotopic Versus Allotopic Implantation: Comparison of Radiological and Pathological Characteristics.Journal of magnetic resonance imaging : JMRI · 2022Article
- Review
- Spatial heterogeneity of cancer associated protein expression in immunohistochemically stained images as an improved prognostic biomarker.Frontiers in oncology · 2022Article
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Authors and funding
5 authors.
Funding
Abstract
motivationQuantitative immunofluorescence is often used for immunohistochemistry quantification of proteins that serve as cancer biomarkers. Advanced image analysis systems for pathology allow capturing expression levels in each individual cell or subcellular compartment. However, only the mean signal intensity within the cancer tissue region of interest is usually considered as biomarker completely ignoring the issue of tumor heterogeneity.
resultsWe propose using immunohistochemistry image-derived information on the spatial distribution of cellular signal intensity (CSI) of protein expression within the cancer cell population to quantify both mean expression level and tumor heterogeneity of CSI levels. We view CSI levels as marks in a marked point process of cancer cells in the tissue and define spatial indices based on conditional mean and conditional variance of the marked point process. The proposed methodology provides objective metrics of cell-to-cell heterogeneity in protein expressions that allow discriminating between different patterns of heterogeneity. The prognostic utility of new spatial indices is investigated and compared to the standard mean signal intensity biomarkers using the protein expressions in tissue microarrays incorporating tumor tissues from 1000+ breast cancer patients. AVAILABILITY AND IMPLEMENTATION: THE R CODE FOR COMPUTING THE PROPOSED SPATIAL INDICES IS INCLUDED AS SUPPLEMENTARY MATERIAL: . SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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