What You Should Know:
– Lunit, a provider of AI for cancer diagnostics and precision oncology, and Labcorp, a global leader in innovative and comprehensive laboratory services, today announced a strategic collaborative initiative. The partnership is designed to accelerate innovation in digital pathology (DP) and artificial intelligence (AI) for both oncology research and clinical care.
– The core of the collaboration is leveraging Labcorp’s extensive clinical and pathology expertise alongside Lunit’s cutting-edge AI algorithms. By combining high-resolution whole-slide imaging with AI-powered spatial profiling, the initiative seeks to generate new insights. These insights are expected to enhance biomarker discovery and guide precision immuno-oncology strategies.
AI Uncovers Predictive Biomarkers in NSCLC
The first outcomes of the collaboration were showcased at two prominent scientific conferences, demonstrating the predictive power of AI-based spatial profiling in non-small cell lung cancer (NSCLC).
- SITC (Society for Immunotherapy of Cancer): A study used Lunit SCOPE IO® to analyze over 370 pathology slides. The research demonstrated how AI-based spatial profiling and machine learning can identify immune-active subtypes of NSCLC tumors with the MET exon 14 skipping mutation, which are associated with improved immunotherapy outcomes. Immune gene expression analysis validated these AI-defined immune phenotypes.
- AMP (Association for Molecular Pathology): A related study highlighted distinct tumor-immune microenvironments linked to different MET alterations in NSCLC. Specifically, it revealed immune-desert phenotypes in MET-amplified tumors and inflamed phenotypes in tumors with MET exon 14 skipping.
Expanding AI Across Cancer Types
The initial studies serve as a clear example of how digital pathology and AI can advance precision oncology understanding, bridging discovery research and real-world clinical care. Lunit and Labcorp plan to further broaden their collaboration by applying digital pathology AI to additional cancer types and genomic correlations. Their collective mission is to turn complex pathology data into meaningful, actionable insights.
