Lunit, a leading provider of AI-powered solutions for cancer diagnostics and therapeutics, today announced the presentation of 9 studies at the upcoming European Society for Medical Oncology (ESMO) 2023 Congress, to be held in Madrid, Spain, from October 20-24.

During this year's congress, Lunit plans to highlight the predictive value and analytical power of its Lunit SCOPE suite, offering valuable insights for understanding the tumor microenvironment, predicting treatment responses, and accurately assessing HER2 scores, in various types of cancer such as biliary tract cancer, head and neck squamous cell carcinoma, and non-small cell lung cancer.

A collaborative study indicated that AI-based immune phenotyping can predict therapy outcomes in advanced biliary tract cancer (BTC) patients who are planning to be treated with anti-PD-1 therapy. A total of 337 H&E-stained whole slide images (WSI) were acquired for assessment. In the study, the research team defined the immune phenotype of the WSI samples via Lunit SCOPE IO, Lunit's AI TIL (tumor-infiltrating lymphocytes) analyzer. Among the three immune phenotypes (inamed; immune-excluded; immune-desert), the inflamed group showed enhanced overall survival (12.5 vs. 5.1 months), progression-free survival (5.0 vs. 2.0 months), and objective response rates (27.5% vs. 7.7%), compared to the non-inflamed group.

In another study, it was found that TIL density in tumor microenvironment is highly correlated with favorable treatment response to immune checkpoint inhibitor (ICI) in head and neck squamous cell carcinoma (HNSCC). Assessed by Lunit SCOPE IO, patients with high-TIL showed a higher objective response rate (21.6% vs 5.7%) and more favorable median progression-free survival (3.2 vs 1.6 months).

Lunit also plans to present the results of three trials during this year's congress. A joint trial with the Mayo Clinic unveils that epithelial TIL demonstrated the highest ability to distinguish between MMR-D (Mismatch repair deficiency) and MMR-P (Mismatch repair proficiency) tumors in colon cancer. The post-hoc exploratory analysis results of three clinical trials utilizing Lunit SCOPE IO in Italy and France are also set to be showcased.

In another study, HER2 (human epidermal growth factor receptor-2) scoring in biliary tract cancer was evaluated using Lunit SCOPE HER2. The analysis showed a substantial concordance of 75.3% between AI and human pathologists' assessments.

Another study aimed to predict multiple druggable mutations in non-small cell lung cancer (NSCLC) based on AI analysis of H&E images. More than 3,000 NSCLC samples were used as training data to develop an AI-powered predictive model. In validation in an independent dataset, the model showed robust performance in predicting six types of mutations (EGFR-mt, KRAS-mt, ALK-tr, ROS1-tr, RET-tr, MET-ex). Notably, for MET exon skipping mutations, the model achieved a high positive predictive value (PPV), showing that test-positive patients were three times more likely to have true-MET-ex positive mutations compared to the overall patient population. Moreover, specificity and PPV for identifying patients without mutations (All-WT) were 99.2% and 95.2% respectively, which means with AI assistance unnecessary tests can be avoided. Following the results, it is expected that the newly developed AI genotype predictor, available for multiple genotypes in non-small cell lung cancer, holds immense potential for widespread adoption by clinicians and pharmaceutical industry leaders globally.

'We are thrilled to be at this year's ESMO with nine groundbreaking study results that prove the effectiveness of the Lunit SCOPE AI WSI analyzer and biomarker platform,' said Brandon Suh, CEO of Lunit. 'The study results emphasize the substantial progress made with Lunit SCOPE IO, building compelling evidence of the critical role of immune phenotyping in understanding cancer biology and optimizing treatment strategies. We remain committed to advancing this transformative technology through further research and development.'

Contact:

Email: contact@lunit.io

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