ArticleLaboratory investigation; a journal of technical methods and pathology2023
Quantile Index Biomarkers Based on Single-Cell Expression Data.
Article in Laboratory investigation; a journal of technical methods and pathology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Quantile index predictors using R package hyper.gam.Bioinformatics (Oxford, England) · 2025Article
- Functional protein biomarkers based on distributions of expression levels in single-cell imaging data.Bioinformatics (Oxford, England) · 2025Article
- Using random forests to uncover the predictive power of distance-varying cell interactions in tumor microenvironments.PLoS computational biology · 2024Article
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10 authors.
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Abstract
Current histocytometry methods enable single-cell quantification of biomolecules in tumor tissue sections by multiple detection technologies, including multiplex fluorescence-based immunohistochemistry or in situ hybridization. Quantitative pathology platforms can provide distributions of cellular signal intensity (CSI) levels of biomolecules across the entire cell populations of interest within the sampled tumor tissue. However, the heterogeneity of CSI levels is usually ignored, and the simple mean signal intensity value is considered a cancer biomarker. Here we consider the entire distribution of CSI expression levels of a given biomolecule in the cancer cell population as a predictor of clinical outcome. The proposed quantile index (QI) biomarker is defined as the weighted average of CSI distribution quantiles in individual tumors. The weight for each quantile is determined by fitting a functional regression model for a clinical outcome. That is, the weights are optimized so that the resulting QI has the highest power to predict a relevant clinical outcome. The proposed QI biomarkers were derived for proteins expressed in cancer cells of malignant breast tumors and demonstrated improved prognostic value compared with the standard mean signal intensity predictors. The R package Qindex implementing QI biomarkers has been developed. The proposed approach is not limited to immunohistochemistry data and can be based on any cell-level expressions of proteins or nucleic acids.
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