On October 14, 2025 Lunit (KRX:328130.KQ), a leading provider of AI for cancer diagnostics and therapeutics, reported the presentation of three studies at the European Society for Medical Oncology (ESMO) (Free ESMO Whitepaper) Congress 2025, taking place October 17–21 in Berlin, Germany. All three studies highlight how Lunit’s AI pathology solution, Lunit SCOPE IO, can identify biomarkers that predict response to immune checkpoint inhibitors (ICIs), offering new potential to guide more effective and personalized cancer treatment.

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Among these, the first study, selected for oral presentation, was conducted in collaboration with Chiara Cremolini, MD, PhD, Professor of Medical Oncology at the University of Pisa, Italy. Researchers applied Lunit SCOPE IO to pre-treatment H&E slides from patients with proficient mismatch repair (pMMR) metastatic colorectal cancer (mCRC) enrolled in the AtezoTRIBE and AVETRIC trials. The AI quantified multiple cell types within the tumor microenvironment, generating a biomarker that stratified patients into "biomarker-high" and "biomarker-low" groups.

In the AtezoTRIBE trial, biomarker-high patients treated with atezolizumab plus FOLFOXIRI/bevacizumab showed significantly improved progression-free survival (PFS) and overall survival (OS) compared to biomarker-low patients, while no such benefit was observed in the control arm. Validation in the AVETRIC cohort confirmed improved survival outcomes for biomarker-high patients receiving ICI-based therapy, with better PFS and OS compared to biomarker-low patients. These results suggest that an AI-derived tumor microenvironment biomarker could help identify patients with pMMR mCRC who are most likely to benefit from immunotherapy combinations—an urgent unmet need in this population.

A collaborative study with Yonsei University College of Medicine, Korea, evaluated whether AI-defined immune phenotypes (IP) could predict outcomes in patients with advanced clear cell renal cell carcinoma (ccRCC) treated with either nivolumab plus ipilimumab (NIVO+IPI) or sunitinib (SUN). Using Lunit SCOPE IO, tumors were classified as inflamed or non-inflamed, based on the density and spatial distribution of tumor-infiltrating lymphocytes.

Patients with inflamed tumors treated with NIVO+IPI demonstrated significantly longer PFS, OS, and higher response rates (60.5% vs. 23.2%) compared to those with non-inflamed tumors. No such benefit was observed in the SUN arm. Findings were validated in an independent ccRCC cohort and aligned with inflamed gene expression signatures from The Cancer Genome Atlas, supporting AI-based immune phenotyping as a promising biomarker to guide treatment selection between immunotherapy combinations and targeted therapies in first-line treatment for ccRCC.

The third study, conducted in collaboration with the National Cancer Center Hospital East (NCCHE) Japan, further validated the predictive power of Lunit SCOPE IO for ICI treatment response in non-small cell lung cancer (NSCLC) patients. In this multicenter prospective study, tumors classified as inflamed showed significantly better responses and longer survival with ICI therapy compared to non-inflamed tumors, a difference not observed among patients treated with cytotoxic chemotherapy. This result strengthens the evidence supporting Lunit SCOPE IO as a predictive biomarker for immunotherapy benefit in NSCLC.

"At ESMO (Free ESMO Whitepaper) 2025, we are demonstrating how AI can unlock predictive biomarkers directly from routine pathology slides," said Brandon Suh, CEO of Lunit. "These findings show the potential of Lunit SCOPE IO to help identify patients who will truly benefit from immunotherapy—whether in colorectal or kidney cancer—and to guide treatment strategies that can make cancer care more precise and effective."

Lunit’s Featured Presentations at ESMO (Free ESMO Whitepaper) 2025

Oral Presentation [#725O/Berlin Auditorium – Hub 27, Oct.20, 09:10-20 AM]: Leveraging Artificial Intelligence to predict immune checkpoint inhibitor (ICI) efficacy in proficient MMR mCRC: translational analyses of AtezoTRIBE and AVETRIC trials — Chiara Cremolini, University of Pisa, Italy
Poster Presentation [#1912P]: Inflamed immune phenotype as a novel predictive marker of immune checkpoint inhibitors for non-small cell lung cancer — Yoshitaka Zenke, National Cancer Center Hospital East, Japan
Poster Presentation [#2624P]: Artificial intelligence (AI)-powered immune phenotype predicts differential benefit from nivolumab plus ipilimumab versus sunitinib in advanced clear cell renal cell carcinoma — Chang Gon Kim, Yonsei University College of Medicine, Korea
Visit Lunit at booth #3017 to learn more about its latest AI-powered cancer research and innovations in digital pathology.

(Press release, Lunit, OCT 14, 2025, View Source [SID1234656654])