In this era of AI and big data, discovery science can no longer rely on simply feeding millions—or even billions—of data points into machine learning pipelines and passively waiting for insights to emerge. Instead, strategic foresight and thoughtful design are essential.
This webinar highlights a groundbreaking study recently published as a Nature cover story (April 2025). Dr. Joe Yeong will describe how a pre-designed analytical framework—driven by well-defined clinical and scientific questions—enabled the extraction of meaningful insights from over a trillion data points. This comprehensive dataset encompassed 300 patients, three distinct tissue locations, over 100,000 single cells per tissue, more than 18,000-plex spatial measurements capturing both gene and protein expression, and over 100 clinical-pathological parameters.
Dr. Yeong will also explain how a smart clinical scoring system was developed using AI, incorporating population and demographic signatures to define robust, population-aware cutoffs—avoiding the common dilemma of marginal thresholds such as 0.49 vs. 0.51.
The webinar will explore how initial spatial discoveries were validated through spatial proteomics, using both mass spectrometry and multiplex immunofluorescence (mIF/IHC). Building on this foundation, AI was applied—drawing inspiration from H&E 2.0—to create a clinically implementable scoring system that outperforms existing evaluation tools. Treatment escalation may offer significant benefit to this high-risk patient group, as demonstrated across ex vivo studies, in vivo models, and multiple patient cohorts.
Could this mark the dawn of spatial medicine?
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