Digital Pathology
Tumor tissue slides are scanned at high resolution and then analyzed using deep learning algorithms. These artificial intelligence (AI) algorithms can quantify biomarkers, detect subtle anomalies, and automatically segment tumor and immune structures.
⟶ Provides a standardized, reproducible, and more detailed interpretation than conventional pathology.
In the OASIS program, immunohistochemistry (IHC) with digital pathology and multiplex immunofluorescence (IF) are used to study the spatial organization and protein expression within tumor tissues. These complementary approaches provide critical information on the tumor microenvironment, including the presence, distribution, and interactions of different cell populations. Together, they help elucidate mechanisms of response and resistance to antibody–drug conjugates (ADCs) and support a more integrated understanding of tumor biology.
How does it work?
Immunohistochemistry (IHC) and digital pathology
Immunohistochemistry is a well-established technique that uses specific antibodies to detect target proteins directly in tumor tissue sections. In OASIS, IHC slides are digitized and analyzed using digital pathology tools, enabling standardized, quantitative, and reproducible assessment of protein expression. Advanced image analysis and artificial intelligence–based algorithms allow detailed evaluation of staining intensity, cell density, and spatial patterns across the tissue.
Multiplex immunofluorescence (IF) using COMET technology
Multiplex immunofluorescence in OASIS is performed using the COMET technology, which enables the simultaneous detection of multiple proteins on a single tissue section. By labeling different targets with distinct fluorescent signals, this approach allows high-resolution mapping of cell types and functional states within the tumor microenvironment. COMET provides detailed spatial information at the single-cell level, supporting in-depth analysis of tumor heterogeneity and immune contexture.
Picture from Lunaphore