SEOUL, South Korea, May 26, 2025 -- Lunit (KRX:328130.KQ), a leading provider of AI for cancer diagnostics and therapeutics, today announced that 12 studies featuring its AI-powered digital pathology solution will be presented at the 2025 American Society of Clinical Oncology (ASCO) Annual Meeting, taking place May 30–June 3 in Chicago, IL. Of these, 11 studies will be presented as posters and one as an online publication.
One of the featured studies, conducted with Japan's National Cancer Center Hospital East (NCCE), evaluated HER2 expression in biliary tract cancer (BTC) patients using Lunit's AI-powered analyzer. The resulting AI scores showed strong agreement with pathologist-assigned IHC scores in a 288-patient screening cohort. Among 29 patients treated with trastuzumab deruxtecan (T-DXd), those with higher levels of HER2-intense tumor cells achieved a higher objective response rate (ORR) of 50%, along with significantly longer progression-free survival and overall survival. The study also found that AI-derived "membrane specificity" helped identify additional responders who achieved a 50% ORR and improved survival. This group included not only HER2-intense patients but also some traditionally classified as HER2-low, suggesting that the metric may expand the pool of patients who can benefit from T-DXd. These findings suggest that AI-powered HER2 analysis - especially when incorporating membrane specificity - could expand access to targeted treatment and enable more precise therapy selection in BTC.
A separate prospective study conducted with NCCE evaluated the concordance between pathologist- and AI-assessed PD-L1 expression in lung cancer patients enrolled in LC-SCRUM, one of Japan's largest nationwide observational cohorts, using Lunit SCOPE PD-L1. The study included 847 non-small cell lung cancer (NSCLC) and 102 small cell lung cancer (SCLC) patients. The overall concordance between the AI model and three expert pathologists was 70%. Concordance was particularly high in key subgroups: 84% for TPS ≥50% and 94% for TPS 1–49%. Of the 416 patients initially classified as TPS <1% by pathologists, the AI identified 231 with higher PD-L1 expression. Since PD-L1 scoring is widely used to guide treatment eligibility, these results highlight the potential of AI-powered PD-L1 evaluation to uncover additional candidates for immunotherapy who may have been previously excluded based on low TPS. The high concordance with expert pathologists also reinforces the reliability of the AI model as a clinical decision-support tool.
A third highlighted study introduced an AI model to predict CLDN18.2 expression in gastric cancer. CLDN18.2 is a therapeutic target for zolbetuximab. It is typically assessed using immunohistochemistry (IHC), which is often limited by tissue quantity, cost, and time. To address this, researchers trained the AI on H&E slides and validated it in the external cohort. The model achieved AUROCs over 0.751, suggesting the potential to efficiently pre-screen CLDN18.2-positive patients using only H&E slides. The study also analyzed immune phenotypes using AI-powered whole-slide image analysis to explore treatment implications. Among patients predicted to be CLDN18.2-negative, those with an "inflamed" phenotype—marked by high tumor-infiltrating lymphocyte (TIL) density—showed significantly better outcomes when treated with immune checkpoint inhibitor plus chemotherapy compared to chemotherapy alone. These findings suggest that combining AI-based CLDN18.2 prediction with immune phenotype analysis could guide first-line treatment decisions without additional IHC tests.
"Our ASCO 2025 presentations build on years of work to turn AI into a clinically dependable tool—not just for reading pathology images, but for improving how we select the right treatments. From HER2 scoring in biliary tract cancer to PD-L1 evaluation in lung cancer, our models are helping uncover treatment opportunities for patients who might otherwise be overlooked. This level of precision and reproducibility is exactly what AI needs to deliver real clinical value," said Brandon Suh, CEO of Lunit.
In addition to these three featured studies, Lunit will present 9 additional abstracts covering a wide range of research topics. These include AI-based subcellular profiling to assess the drug-targetability of 74 membrane proteins across 34 cancer types, and deep learning analysis of endothelial cells to understand how the tumor vascular environment influences immunotherapy response.
Lunit will be exhibiting at Booth #26149, where attendees can learn more about the studies and AI solutions featured at this year's ASCO.
Lunit's featured presentations at ASCO 2025 include:
- [Poster #4047/337] Artificial intelligence-based prediction of claudin 18.2 expression and immune phenotype to guide treatment decisions in patients with gastric cancer, May 31, 9:00 AM – 12:00 PM CDT, Hall A - Posters and Exhibits
- [Poster #3084/399] Artificial intelligence (AI)-powered evaluation of protein drug-targetability through subcellular-level expression profiling from immunohistochemistry (IHC) images, June 2, 1:30 PM – 4:30 PM CDT, Hall A - Posters and Exhibits
- [Poster #4097/387] Membrane-specific HER2 expression by artificial intelligence-based quantitative scoring for prediction of efficacy of trastuzumab deruxtecan in biliary tract cancer (HERB trial): Exploratory analysis of a multicenter, single arm, phase II trial, May 31, 9:00 AM – 12:00 PM CDT, Hall A - Posters and Exhibits
- [e13628] Deep learning to predict treatment response of immune checkpoint inhibitors from pretreatment chest X-rays in non–small-cell lung cancer, Online
- [Poster #593/186] Use of artificial intelligence (AI)–powered spatial analysis to predict pathologic complete response (pCR) in HR+ HER2- breast cancer (BC) patients treated with neoadjuvant chemotherapy (NAC), June 2, 9:00 AM – 12:00 PM CDT, Hall A - Posters and Exhibits
- [Poster #2578/225] Deep learning–powered H&E whole-slide image analysis of endothelial cells to characterize tumor vascular environment and correlate treatment outcome to immunotherapy, June 2, 1:30 PM – 4:30 PM CDT, Hall A - Posters and Exhibits
- [Poster #8572/52] Artificial intelligence-powered spatial analysis of tumor infiltrating lymphocytes and tertiary lymphoid structures in non-small cell lung cancer patients treated with immune-checkpoint inhibitors±chemotherapy, May 31, 1:30 PM – 4:30 PM CDT, Hall A - Posters and Exhibits
- [Poster #8536/16] Artificial intelligence-powered spatial analysis of tumor microenvironment in non-small cell lung cancer patients who acquired resistance after EGFR tyrosine kinase inhibitors, May 31, 1:30 PM – 4:30 PM CDT, Hall A - Posters and Exhibits
- [Poster #8535/15] A large validation study of AI-powered PD-L1 analyzer compared to pathologists' assessment of PD-L1 expression in lung cancer, May 31, 1:30 PM – 4:30 PM CDT, Hall A - Posters and Exhibits
- [Poster #1110/89] Artificial Intelligence-based tumor microenvironment and PD-L1 analysis using digital pathology to predict pembrolizumab response in metastatic triple-negative breast cancer, June 2, 9:00 AM – 12:00 PM CDT, Hall A - Posters and Exhibits
- [Poster #4137/427] Use of artificial intelligence-powered spatial analysis of tumor microenvironment to predict the prognosis in resected gallbladder cancer, May 31, 9:00 AM – 12:00 PM CDT, Hall A - Posters and Exhibits
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About Lunit
Founded in 2013, Lunit (KRX:328130.KQ) is a global leader in AI for cancer diagnostics and therapeutics. With a mission to conquer cancer through AI, Lunit develops AI-powered solutions for medical imaging and biomarker analysis to enable precise diagnosis and personalized treatment. Lunit's FDA-cleared Lunit INSIGHT suite supports cancer screening at over 4,800 medical institutions in more than 55 countries. Lunit clinical studies have been featured in top-tier journals—including The Lancet Digital Health and Journal of Clinical Oncology—and presented at major conferences such as ASCO and RSNA. Headquartered in Seoul with global offices, Lunit is driving the worldwide fight against cancer. Learn more at lunit.io.
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