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The next generation of predictive cancer biomarkers must move away from the reductionist view of biology and become more representative of the complex and functional biology underpinning clinical responses. Cutting-edge spatial biology technologies, such as multiplex immunofluorescence (mIF), are ushering in a new class of digital biomarkers and algorithms that capture the systems-view of disease biology and can ultimately lead to improved patient outcomes. The challenge with adopting new spatial biology methods is managing the massive amounts of data generated from each study and extracting meaningful findings that can inform the development of better treatments and diagnostics.
In this GEN webinar, our speakers will discuss how AI-based spatial analysis of mIF data can accelerate the discovery of clinically relevant biomarkers that can predict response and resistance mechanisms to immunotherapy. They will present a case study where this approach was applied to a cohort of non-small cell lung cancer patients that were treated with checkpoint blockade immunotherapy, leading to the discovery of a unique metabolic signature associated with clinical outcomes and resistance mechanisms.
A live Q&A session followed the presentation, offering a chance to pose questions to our expert panelists.
Director of Biomedical Research
Nucleai
Senior Research Fellow
University of Queensland
Webinar produced with support from:
