Cell-based quantification of molecular biomarkers in histopathology specimens

Abstract

Aims To investigate the use of a computer-assisted technology for objective, cell-based quantification of molecular biomarkers in specified cell types in histopathology specimens, with the aim of advancing current visual estimation or pixel-level (rather than cell-based) quantification methods. Methods and results Tissue specimens were multiplex-immunostained to reveal cell structures, cell type markers, and analytes, and imaged with multispectral microscopy. The image data were processed with novel software that automatically delineates and types each cell in the field, measures morphological features, and quantifies analytes in different subcellular compartments of specified cells. The methodology was validated with the use of cell blocks composed of differentially labelled cultured cells mixed in known proportions, and evaluated on human breast carcinoma specimens for quantifying human epidermal growth factor receptor 2, oestrogen receptor, progesterone receptor, Ki67, phospho-extracellular signal-related kinase, and phospho-S6. Automated cell-level analyses closely matched human assessments, but, predictably, differed from pixel-level analyses of the same images. Conclusions Our method reveals the type, distribution, morphology and biomarker state of each cell in the field, and allows multiple biomarkers to be quantified over specified cell types, regardless of abundance. It is ideal for studying specimens from patients in clinical trials of targeted therapeutic agents, for investigating minority stromal cell subpopulations, and for phenotypic characterization to personalize therapy and prognosis.

An Active Learning Approach for Rapid Characterization of Endothelial Cells in Human Tumors

“…We previously developed FARSIGHT to analyze images and classify carcinoma cells based on morphometric characteristics and association with epithelial cytokeratin or other biomarker staining [25]. When we used similar association rules to classify EC on a subset of ccRCC images, the resulting classification had many errors when compared to expert human EC classification of the same images.…”

Immediate‐early gene Homer1a intranuclear transcription focus intensity as a measure of relative neural activation

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Pointwise mutual information quantifies intratumor heterogeneity in tissue sections labeled with multiple fluorescent biomarkers

“…We applied the segmentation algorithm developed by GE to output the cellular data into a comma separated value file containing the spatial location and the biomarker intensity for each cell in the TMA [Figure 2]. [3247] To partition the data into high- and low-intensity signals (L1 and L2, respectively [Figure 4a]), we applied a threshold value as determined by the elbow found in the probability distribution of the intensities of each biomarker channel. For biomarker pattern recognition via K-SVD, we used Ron Rubenstein's MATLAB implementation from http://www.cs.technion.ac.il/~ronrubin/Software/ksvdbo×13.zip [Figure 4b and c].…”