Lunit's AACR 2026 presentations showed how AI-driven biomarkers can improve the efficiency of clinical workflows, uncover spatial features of the tumor microenvironment not captured by conventional methods, and enable integrated analysis to better support treatment decision-making. Lunit (KRX:328130), a provider of AI for cancer diagnostics and precision oncology, presented six studies at the American Association for Cancer Research (AACR) Annual Meeting 2026, taking place from April 17 to 22 in San Diego, California. The presentations highlighted Lunit's advancements in AI-driven biomarker development, tumor microenvironment (TME) analysis, and real-world clinical applicability.
Several studies were conducted in collaboration with global partners, including Agilent Technologies. In a study conducted with Agilent Technologies and Ajou University Medical Center, researchers used Lunit SCOPE IO and uIHC to analyze over 25,000 non-small cell lung cancer (NSCLC) samples. The results showed that tumors with high c-MET expression exhibited a significant reduction in immune cell density within 30 µm of tumor cells (p<0.001), revealing a spatial immune exclusion pattern not captured by conventional analysis.
These findings suggest a potential link between c-MET overexpression and immune evasion, supporting combination strategies involving MET-targeted therapy and immunotherapy. Researchers also present findings from an exploratory analysis of the phase II MOUNTAINEER trial, demonstrating that AI- quantified HER2 expression is strongly associated with treatment response in patients with HER2-positive metastatic colorectal cancer treated with tucatinib plus trastuzumab. The overall objective response rate (ORR) was 43.4%, increasing to as high as 80% in patients with higher HER2 expression, indicating a clearer dose-dependent relationship.
Tumor-Infiltrating Lymphocyte (TIL) density independently predicted progression-free survival. Notably, patients with low stromal TIL levels showed no response (ORR 0%) and a significantly higher risk of disease progression. These findings highlight the increasing complexity of biomarker assessment, where both tumor characteristics and immune context need to be considered, underscoring the potential role of AI in supporting treatment decision-making.
In addition to these representative studies, Lunit presented additional research abstracts at AACR 2026, further demonstrating the breadth of its AI-powered oncology research. These include studies on AI-based analysis of tumor-infiltrating lymphocyte in NSCLC in collaboration with Dr. David Rimm's lab at Yale University School of Medicine, AI-based target discovery for bi-specific antibodies, Biomarker research in CD47-targeted therapies.
