Molecular epidemiology in clinical tissues with multiparameter IHC

A43

Background:
 A common goal in pharmaceutical research is to discover correlations between clinical outcomes and complex protein expression and interaction patterns in tissue sections. Correlations inform target validation, trial design, patient selection, response assessment, and, if trials are successful, the diagnostic component of theranostics. However, to successfully detect multiple, often weakly-expressed targets in clinical tissue sections requires appropriate staining protocols, advanced instrumentation and powerful software.
 Objective:
 After developing multi-label immunohistochemical staining methods that were quantitative, independent, and specific, the goal was to create and validate an automated, whole-slide scanning imaging system that could be used to capture and distinguish multiple labels. Image analysis algorithms were used to differentiate relevant tissue regions (e.g., malignant and normal epithelia, stroma, necrosis, etc.) and segment cellular compartments (nuclei, cytoplasm, and membrane) to allow for detailed, spatially resolved multiparameter quantitation. Such as system would have an immediate application to signal-transduction research applied to conventional tissue sections. Here we describe this platform, and present results obtained from analysis of breast cancer tissue microarrays (TMAs). A key technological component of this project was multispectral imaging, which enabled the multiplexed detection.
 Methods and Materials:
 Multispectral imaging, using a spectrally enabled whole-slide scanning system, was performed on two sets of a 712-core TMA (356 patients represented in duplicate). The first set was stained for ER, PR, and Her2, with a counterstain of hematoxylin, and the second stained for PR, Her1, and Her2, with a counterstain of hematoxylin. Single-stain data for comparison was obtained from previously stained and analyzed TMAs. IHC signals were spectrally unmixed and isolated from each other and the hematoxylin counterstain. Machine-learning-based automated image analysis was performed to locate cancer cells, segment subcellular compartments, and extract IHC signals on a per-cell basis. Relative stain intensities on a per-cell basis were analyzed with flow-cytometry analysis software.
 Results and Discussion: Multispectral 20x images obtained of each TMA core were acquired and spectrally unmixed at a rate of about three cores per minute. Automated image analysis, using algorithms that could be developed by end-users in under 2 hours, took approximately 10 seconds per core, segmenting cancer-containing regions and extracting signals from relevant cell compartments. Concordance (r) between visual scores of single stain sections and the semi-automated scores of triple-stained samples indicate equivalency (r values ranging from 0.65 to 0.85), thus validating that multiplexed IHC faithfully reflects data obtainable with single-stains. Thus, multiplexed staining and detection, coupled with flow-cytometry analysis tools can be used to explore multiple protein expression patterns on a cell-by-cell basis, something that cannot be accomplished with serial single stains. Together, the innovative multispectral platform and software provide can capture cellular and subcellular expression details in an intact tissue architectural context.