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Characterization of glioblastoma (GBM) vasculature and protein expression of surrounding tumor cells on single FFPE sections with a multicycle multiplexed in situ immunofluorescent staining technology Jingyu Zhang 1 , Colin McCulloch 1 , Yunxia Sui 1 , Sean Dinn 1 , Qing Li 1 , Alberto Santamaria-Pang 1 , Christopher J Sevinsky 1 , Jeremy R Graff 2 , Lawrence Weiss 3 , Teng Jin Ong 3 , and Fiona Ginty 1 Background: GBM is the most common brain tumor in humans and has a dismal prognosis. Although antiangiogenic therapy (bevacizumab) is an option for GBM, there is still unmet need to understand tumor pathophysiology and predictive biomarkers. We built a tissue based multiplexed immunofluorescent assays and developed algorithms to identify and quantify tumor vasculature, that enabled quantification, visualization, and colocalization of multiple protein in surrounding tumor cells at single cell and subcellular levels. This assay provides unique opportunity to explore tumor heterogeneity of tissue morphology and their relationships to vasculature, and is a novel tool for biomarker and treatment discovery. Method: Tissue micro arrays (TMAs) were constructed from 141 GBM patients. Fluorescent dye labeled antibodies against 18 biomarkers were sequentially applied on single sections of these TMAs. Metrics were built to identify vessels, quantify distance of tumor cells to vessels, and analyze expression profiles of biomarkers, including signaling molecules in EGFR, PI3K/AKT, TGF-beta pathways, and hypoxia marker Glut1, in proximity to blood vessels. Results: CD31 was successfully used to identify blood vessels in GMB. Vessel segmentation and quantification were performed on all of the images. Biomarker profiling in the context of blood vessels demonstrated different patterns in close proximity to vessels, with some biomarkers showing increased levels (e.g. SMA, EGFR, pS6), some showing decreased levels (e.g. p4EBP), and others remain the same (FOXO3a, S6). Quantification of biomarkers in different cellular compartments showed heterogeneous expression within the same sample and across the cohort. In addition, co-localization of the above markers was visualized and demonstrated on single cell and subcellular levels. 1 General Electric Global Research Center, Niskayuna, NY USA; 2 Lilly Oncology Research, Indianapolis, IN 46236; 3 General Electric Healthcare, Princeton, NJ 08540 Abstract Experimental Design Multiplex Immunofluorescent Staining Vessel Identification and Proximity Measurement Figure 3. Vessels are identified with CD31 positive staining. Vessel proximity quantification for each cell measures the shortest distance from the closest vessel In this presented study, we used a novel fluorescent multiplexing technology (MultiOmyx TM ) to study GBM biology. This technology allowed simultaneous analyses of multiple biomarkers, and provides new insights on the relationship of markers to each other, tumor heterogeneity and angiogenesis. Conclusion Cohort 141 GBM patients Sample 7 TMAs, >600 cores Replicates 4 replicates per patient Biomarker 18 biomarkers Table 1 Tissue information Table 2 Multiplexed targets Ribosomal protein S6 pCREB NaKATPase p4EBP CD31 pmTOR SMA pGSK-3b GFAP PTEN EGFR pS6 pEGFR FOXO3a AKT pSMA2/3 pAKT Glut-1 Figure 1. Schematic overview of multiplex analysis. Fluorescent dye conjugated antibodies are applied to tissue, and images are acquired. Dye inactivation allows sequential staining and imaging using a discrete set of fluorophores. Image registration allows alignment of fluorescent images from multiple steps of staining to ensure the same FOVs are being captured and analyzed. Images are also processed to remove autofluorescent signals. Single cells and subcellular compartments are identified based on staining of specific markers. Single cell features are extracted and analyzed. The above table lists 18 biomarkers that were sequentially stained on single tissue sections, including structural markers (CD31 specific for vessels, S6 specific for cytoplasm, NaKATPase specific for cell membrane, and GFAP specific for astrocytes) and signaling molecules on AKT, TGF-beta, and EGFR pathways. S6 DAPI Cytoplasm Nucleus CD31 GFAP DAPI SMA Glut-1 DAPI NaKATPase pCREB DAPI p4EBP pS6 DAPI pEGFR EGFR DAPI pAKT AKT DAPI PTEN pSMAD2/3 DAPI Figure 2. Multiplex immunofluoresent images from one field of view demonstrate geometrical location of markers relative to each other and subcellular co-localization of them. Ribosomal protein S6 and DAPI were used to define cytoplasm and nucleus, respectively (upper left). The resulting segmentation mask of the image is shown in the upper middle picture. 2R 8R 0 0.2 0.3 0.4 4R CD31 pS6 EGFR pCREB p4EBP Figure 5. Tumor cells biomarker profiling in proximity to blood vessels. Vessels were identified by CD31 positive staining as demonstrated in Figure 3. Each plot shows the median intensity of each biomarker in proximity to blood vessels. An exemplary image of each biomarker is shown to the right of each plot. Figure 4. An example of percentage of cells within certain distance of vessels. Each blue dot indicates one core. Each red line connects four replicates of each subject, therefore the length of red lines indicates heterogeneity of numbers of vessels for each subject. 4 fold SMA pSMAD2/3 pS6 Biomarker Profiling FOXO3a Glut1 S6 © 2014 General Electric Company ― All rights reserved. GE and the GE Monogram are trademarks of General Electric Company. December 2014 JB26528US For Research Use Only
