Immune Phenotyping Identified as Promising Predictive Biomarker by Lunit AI in Biliary Tract Cancer - New publication in CCR

AI-based tumor microenvironment classification utilizing Lunit SCOPE IO® shows promise for guiding treatment decisions in advanced BTC

September 5, 2024 -- Lunit, a leading provider of AI-powered solutions for cancer diagnostics and therapeutics, today announced that Lunit SCOPE IO® has demonstrated significant potential in predicting immunotherapy response for patients with advanced biliary tract cancer (BTC), via AI-powered analysis of immune phenotype and tumor-infiltrating lymphocytes (TILs). The groundbreaking study, conducted in collaboration with researchers from Asan Medical Center and Severance Hospital in Seoul, Korea, was recently published in Clinical Cancer Research (CCR), an AACR journal.

BTC is known for its poor prognosis, with limited treatment options available. While recent studies have shown promise in combining immunotherapy, such as anti-PD-1 inhibitors with standard chemotherapy, there has been a lack of effective predictive tools to guide treatment decisions.

The study analyzed pre-treatment pathology samples (H&E slides) from 339 patients with advanced BTC who received anti-PD-1 monotherapy as second-line or later treatment. Using Lunit SCOPE IO, researchers performed a detailed analysis of the tumor microenvironment, classifying patients' immune phenotypes into three categories: inflamed (high intratumoral TIL), immune-excluded (low intratumoral TIL and high stromal TIL), and immune desert (low TIL overall). Immune phenotypes are an emerging pan-cancer biomarker with support from leaders of the immuno-oncology community.[1]

Key findings include:

• Patients classified as having an "inflamed" immune phenotype showed significantly better treatment outcomes compared to those with non-inflamed phenotypes, as consistent with previously published studies.[2]
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• The inflamed group demonstrated both longer overall survival (12.6 vs. 5.1 months) and progression-free survival (4.5 vs. 1.9 months), as well as higher overall response rates (27.5% vs. 7.7%).
• Lunit SCOPE IO provided objective and efficient assessment of the tumor microenvironment, overcoming limitations of manual evaluation demonstrating high feasibility for AI-powered analysis of immune phenotype and TIL.

 Notably, this study suggests immune phenotyping can serve as a predictive biomarker for possible response to immunotherapy in BTC, addressing a long-standing gap in personalized treatment approaches for this class of cancers with a high unmet need.

“Lunit SCOPE IO represents a significant advancement in the precision medicine landscape for cancer treatment,” said Brandon Suh, CEO at Lunit. “By providing a deeper understanding of the tumor microenvironment, particularly immune phenotyping, our AI technology empowers clinicians to make informed treatment decisions, identifying patients most likely to benefit from immunotherapy and opening new avenues for personalized treatment strategies in challenging cancer types.”

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