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A discovery pipeline in triple-negative breast cancer identified a novel macrophage subset and led to two patented assets. Now, the same approach is being applied to MASH. Discover what made this pipeline successful and how disease-focused data and knowledge graphs could help identify promising targets across diseases.
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Finding a promising therapeutic target requires more than generating large amounts of biological data. What matters is whether that data is built around a clear biological question and can provide evidence strong enough to support validation.
A compelling example comes from research in triple-negative breast cancer. Dr Jit Sarkar and colleagues at King's College London used clinically annotated, multi-modal data to identify a novel macrophage subset. By validating its role in functional models, the team advanced the research into two patented assets: a first-in-class immunotherapy candidate and a biomarker panel to identify patients most likely to respond.
The same discovery pipeline is now being applied to MASH (metabolic dysfunction-associated steatohepatitis), where disease heterogeneity makes identifying effective therapeutic targets particularly challenging. The question is how an approach demonstrated in one disease can help generate stronger evidence for target discovery in another.
Join Dr Jit Sarkar to explore what made this pipeline work and how it could be adapted across diseases. The session will also examine how Elucidata combines data from research partners with broader scientific evidence to build disease knowledge graphs and support target identification. We'll discuss how pharma teams can apply this approach using their existing data or gain access to purpose-built research cohorts.
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