Advancing the development of next-gen cancer treatments like antibody-drug conjugates (ADCs), bispecific antibodies, and immunotherapy requires the analysis of complex patterns hidden in a patient’s tissue biopsy and drawing clinically relevant biomarker signatures that can predict treatment response. Novel artificial intelligence (AI) algorithms, based on advancements in military intelligence-grade geospatial analysis, are providing the ability to analyze all this biological complexity in an automated, precise, and scalable fashion, accelerating the development of novel therapeutics.
In this webinar, Justin Lucas, head of translational research at Adlai Nortye, and Ken Bloom, head of pathology at Nucleai will share insights on how AI-driven spatial biomarker analysis can extract predictive signatures across multiple pathology modalities such as H&E-stained slides, immunohistochemistry (IHC), multiplex immunofluorescence (mIF i.e., spatial proteomics), and spatial transcriptomics.
