Visit us at CAP19.
Explore the next revolution in pathology.
Recent studies have demonstrated that spatial biomarkers have better predictive power than PD-L1 IHC in predicting response to anti-PD-1/anti-PD-L1 treatments1. Multiplex IHC/IF enables a more comprehensive study of the tumor microenvironment than single-marker tests or multiplex methods that homogenize the sample without preserving critical spatial context.
Stop by booth #406 to learn more about how we are revolutionizing the field of pathology with our spatial biology tools – The CODEX® and Phenoptics™ Solutions.
Featured products in the booth
Come by our booth to get a demo of our solutions for multiplexed IHC and tissue imaging.
Vectra® Polaris™ Quantitative Pathology System:
- The only end-to-end multiplex tissue imaging solution for high throughput applications
- Image up to 8 markers per tissue and load up to 80 slides per run
- Comprehensive workflow that includes instruments, Opal™ IHC staining kits and inForm® Tissue Finder
inForm® and phenoptrReports for Spatial Reporting:
inForm enables the separation and measurement of weak and spectrally overlapping markers in multiplexed assays to accurately phenotype cells labeled with multiple biomarkers in tissue. phenoptrReports provides tools to analyze spatial relationships between these phenotypes and visualize their relationships overlaid tissues.
Multi-institutional study evaluates reproducibility of the Phenoptics™ platform
Download our latest poster, encompassing data gathered in a joint collaboration involving six institutions, which discusses the development and validation of an automated 6-plex, 7-color assay assessing inter- and intra-site reproducibility, with emphasis on the PD-1/ PD-L1 axis and %PD-L1 expression by immune cells.
Using the Leica BOND RX autostainer and the Phenoptics™ workflow, a 7-color multiplex immunofluorescence panel (PD-L1, PD-1, CD8, CD68, FoxP3, Cytokeratin, and DAPI) was optimized and stained on serial sections of tonsil and lung TMA at each site. The implementation of these methodologies may help to reduce data variability and increase translational confidence in using this technology for clinical trials.