Quantile Index Biomarkers Based on Single-Cell Expression Data.

Autor:	Yi M; Division of Biostatistics, Department of Pharmacology and Experimental Therapeutics, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania. Electronic address: misung.yi@jefferson.edu., Zhan T; Division of Biostatistics, Department of Pharmacology and Experimental Therapeutics, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania., Peck AR; Department of Pathology, Medical College of Wisconsin, Milwaukee, Wisconsin., Hooke JA; John P. Murtha Cancer Center, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, Maryland., Kovatich AJ; John P. Murtha Cancer Center, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, Maryland., Shriver CD; John P. Murtha Cancer Center, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, Maryland., Hu H; Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, Pennsylvania., Sun Y; Department of Pathology, Medical College of Wisconsin, Milwaukee, Wisconsin., Rui H; Department of Pathology, Medical College of Wisconsin, Milwaukee, Wisconsin., Chervoneva I; Division of Biostatistics, Department of Pharmacology and Experimental Therapeutics, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania. Electronic address: inna.chervoneva@jefferson.edu.
Jazyk:	angličtina
Zdroj:	Laboratory investigation; a journal of technical methods and pathology [Lab Invest] 2023 Aug; Vol. 103 (8), pp. 100158. Date of Electronic Publication: 2023 Apr 22.
DOI:	10.1016/j.labinv.2023.100158
Abstrakt:	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. (Copyright © 2023 United States & Canadian Academy of Pathology. All rights reserved.)
Databáze:	MEDLINE
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