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Quantile Index Biomarkers Based on Single-Cell Expression Data.

Misung Yi1, Tingting Zhan1, Amy R Peck2

  • 1Division of Biostatistics, Department of Pharmacology and Experimental Therapeutics, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania.

Laboratory Investigation; a Journal of Technical Methods and Pathology
|April 23, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new biomarker, the quantile index (QI), which analyzes the full distribution of biomolecule expression in cancer cells. This QI biomarker improves prediction of clinical outcomes in breast cancer compared to traditional methods.

Keywords:
cancer biomarkerdistribution quantileslinear functional Cox modelmultiplex immunofluorescence-immunohistochemistrysingle-cell imaging

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Area of Science:

  • Biomedical science
  • Computational pathology
  • Cancer research

Background:

  • Histocytometry enables single-cell biomolecule quantification in tumors.
  • Current methods often ignore expression heterogeneity, using only mean signal intensity.
  • This overlooks valuable information within the cellular signal intensity (CSI) distribution.

Purpose of the Study:

  • To develop a novel biomarker utilizing the entire CSI distribution for clinical outcome prediction.
  • To assess the prognostic value of this new biomarker in malignant breast tumors.
  • To provide a computational tool for implementing the new biomarker.

Main Methods:

  • Defined a quantile index (QI) biomarker as a weighted average of CSI distribution quantiles.
  • Optimized quantile weights using functional regression models for clinical outcome prediction.
  • Applied QI biomarkers to protein expression data from breast cancer patients.

Main Results:

  • QI biomarkers demonstrated improved prognostic value over standard mean signal intensity predictors.
  • The method effectively captures and utilizes expression heterogeneity for prediction.
  • An R package, Qindex, was developed for QI biomarker implementation.

Conclusions:

  • The quantile index (QI) offers a more powerful approach to cancer biomarker development by considering expression distribution.
  • This method enhances prediction of clinical outcomes in breast cancer.
  • The approach is versatile and applicable to various cell-level expression data beyond immunohistochemistry.