On October 16, 2023 Lunit (KRX:328130.KQ), a leading provider of AI-powered solutions for cancer diagnostics and therapeutics, reported the presentation of 9 studies at the upcoming European Society for Medical Oncology (ESMO) (Free ESMO Whitepaper) 2023 Congress, to be held in Madrid, Spain, from October 20-24 (Press release, Lunit, OCT 16, 2023, View Source;findings-to-be-presented-at-the-esmo-2023-301957513.html [SID1234636024]).

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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 (inflamed; 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 (Free ESMO Whitepaper) 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."

For inquiries or to schedule a meeting with the Lunit team, please contact [email protected].

Lunit’s Abstracts at ESMO (Free ESMO Whitepaper) 2023

No.

Poster
No. #

Title

Type

1

118P

Artificial intelligence (AI)-powered spatial analysis of tumor-
infiltrating lymphocytes (TILs) as a predictive biomarker for anti-PD-
1 in advanced biliary tract cancer (BTC)

Poster

2

874P

Artificial intelligence (AI)-powered spatial tumor-infiltrating
lymphocyte (TIL) analysis in recurrent/metastatic (r/m) head and
neck squamous cell carcinoma (HNSCC) patients treated with
immune checkpoint inhibitor (ICI) treatment

Poster

3

569P

Clinical Trial of artificial intelligence for detection of mismatch
repair deficiency in colon carcinomas (alliance)

Poster

4

366P

Artificial Intelligence(AI)-powered Assessment of Complete and
Intense Human Epidermal Growth Factor Receptor 2 (HER2)-Positive
Tumor Cell Proportion in Breast Cancer: Predicting Fluorescence In
Situ Hybridization (FISH) Positivity and Response to HER2-Targeted
Therapy

Poster

5

1930P

Phase II clinical trial of avelumab in combination with gemcitabine
in advanced leiomyosarcoma as a second-line treatment (KCSG
UN18-06)

Poster

6

123P

Artificial intelligence (AI)-powered analysis of human epidermal
growth factor receptor-2 (HER2) and tumor-infiltrating lymphocytes
(TILs) in advanced biliary tract cancer (BTC)

Poster

7

571P

Artificial intelligence-powered analysis of tumor lymphocytes
infiltration: a translational analysis of AtezoTRIBE clinical trial

Poster

8

2330P

Pre-test prediction of multiple druggable mutations based on H&E
image artificial intelligence (AI) analysis may enable more efficient
clinical workflow for treatment decisions in non-small cell lung
cancer (NSCLC)

Poster

9

–

Late Breaking Abstract