Lunit to Showcase 7 Studies at ASCO 2024, including AI Innovations in HER2 Quantification, and Multimodal Predictive Models for Immunotherapy Response

- Lunit's ASCO 2024 presentations to highlight advances including HER2 ultra-low detection and AI-powered ICI response prediction models for NSCLC,demonstrating the impact of Lunit SCOPE suite on precision oncology

SEOUL,South Korea,May 24,2024 -- Lunit (KRX:328130.KQ),a leading provider of AI-powered solutions for cancer diagnostics and therapeutics,today announced the presentation of seven studies at the American Society of Clinical Oncology (ASCO) 2024 Annual Meeting in Chicago,from May 31 to June 4. Lunit will present detailed findings on several innovative studies,including the identification of HER2 ultra-low expression in breast cancer using AI-based quantification,and a deep learning-based model integrating chest CT and histopathology analysis for predicting immunotherapy response in non-small cell lung cancer (NSCLC).

Visit Lunit at booth IH22 to discover how the Lunit SCOPE suite is revolutionizing oncology research and clinical practice.

In a poster presentation,Lunit's AI-powered HER2 analyzer,Lunit SCOPE HER2,demonstrated the ability to identify HER2 ultra-low expression and differentiate it from true HER2-negative cases in breast cancer patients using continuous subcellular quantification from HER2 immunohistochemistry (IHC) images.

According to findings presented at ASCO 2022,HER2-targeted antibody-drug conjugates (ADCs) can effectively target tumor cells even in HER2-low breast cancers. This highlights the importance of accurately identifying HER2-low and HER2 ultra-low expression in breast cancer,especially for patients previously classified as HER2-negative. In response,Lunit developed an AI-based whole-slide image (WSI) analyzer for IHC-stained slides to differentiate between true HER2-negative and HER2 ultra-low cases. The AI model evaluated over 67 million tumor cells and 119 million non-tumor cells from 401 WSIs,identifying a significant proportion of HER2 ultra-low cases among pathologist-assessed HER2 score 0 cases. This AI-powered analysis could expand and refine treatment options for patients with HER2-targeted therapies,as demonstrated by the 23.6% of HER2 score 0 cases identified as HER2 ultra-low by AI,and the 51.9% of HER2 score 1+ cases classified as HER2 low by AI,comparable to the 52.3% objective response rate to a HER2-targeted ADC observed in another clinical trial.

In another study,Lunit developed and validated an AI model that analyzes patients' chest CT images alone and in combination with pathology images to predict Immune Checkpoint Inhibitor (ICI) response in NSCLC patients. Lunit's deep learning-based chest CT prediction model,developed using data from 1,876 NSCLC patients treated with ICIs,predicted treatment response based on pre-treatment chest CT scans,along with PD-L1 status and immune phenotype. The model demonstrated significant predictive power as an independent biomarker. Patients predicted as responders by the AI model showed significantly longer median time to the next treatment (TTNT; 7 months vs. 2.5 months) and a longer overall survival (OS; 16.5 months vs. 7.6 months) compared to patients predicted as non-responders. Combining the AI CT model with histopathologic biomarkers such as PD-L1 expression and tumor-infiltrating lymphocytes (TILs) further enhanced prediction accuracy,highlighting the complementary strengths of imaging and pathology data in improving predictive models for ICI response.

A collaborative study with Stanford University School of Medicine examined the association of immune phenotypes with outcomes after immunotherapy in metastatic melanoma,highlighting the heterogeneity of immune phenotypes across melanoma subtypes.

Another study with Northwestern University utilized AI-powered analysis of tertiary lymphoid structures (TLS) in H&E whole-slide images to predict immunotherapy response in NSCLC patients. This demonstrated AI's potential in identifying predictive biomarkers for survival outcomes.

"At ASCO 2024,Lunit proudly presents seven groundbreaking studies that illustrate our pioneering role in AI-driven precision oncology," said Brandon Suh,CEO of Lunit. "From HER2 quantification to predictive models for immunotherapy response,our work is transforming oncology by making cancer treatment not just personalized but predictive,ensuring the best possible outcomes for patients worldwide."

In addition to the studies above,Lunit will present three more studies at this year's ASCO,demonstrating the diverse capabilities of the Lunit SCOPE suite. The studies include comprehensive histopathomic prediction models for early breast cancer,and hypothetical test-and-control group generation for treatment selection in TPS-high NSCLC.

Visit Lunit at booth IH22 to discover how the Lunit SCOPE suite is revolutionizing oncology research and clinical practice.

Presentations at ASCO 2024 featuring Lunit SCOPE include:

"Identification of HER2 ultra-low based on an artificial intelligence (AI)-powered HER2 subcellular quantification from HER2 immunohistochemistry images" (1115,Poster Board #93)

"Deep learning–based chest CT model to predict treatment response to immune checkpoint inhibitors in non-small cell lung cancer independently and additively to histopathological biomarkers"(8536,Poster Board #400)

"Artificial intelligence (AI) –powered H&E whole-slide image (WSI) analysis to predict recurrence in hormone receptor positive (HR+) early breast cancer (EBC)"(571,Poster Board #163)

"Immune phenotype profiling based on anatomic origin of melanoma and impact on clinical outcomes of immune checkpoint inhibitor treatment"(9569,Poster Board #353)

"Artificial intelligence (AI) -powered H&E whole-slide image (WSI) analysis of tertiary lymphoid structure (TLS) to predict response to immunotherapy in non-small cell lung cancer (NSCLC)"(3135,Poster Board #280)

"Updated safety,efficacy,pharmacokinetics,and biomarkers from the phase 1 study of IMC-002,a novel anti-CD47 monoclonal antibody,in patients with advanced solid tumors"(2642,Poster Board #121)

"Relationship between immune phenotype and treatment selection of Chemo-IO vs. IO-only in TPS-high NSCLC using hypothetical test-and-control group generation based on survival data extracted from phase III trials" (e13569)

About Lunit

Founded in 2013,Lunit is a medical AI company on a mission to conquer cancer. We harness AI-powered medical image analytics and AI biomarkers to ensure accurate diagnosis and optimal treatment for each cancer patient. Our FDA-cleared Lunit INSIGHT suite for cancer screening serves over 3,000 hospitals and medical institutions across 40+ countries.

Our clinical findings are featured in top journals,including the Journal of Clinical Oncology and the Lancet Digital Health,and presented at global conferences such as the ASCO and RSNA.

In 2024,Lunit acquired Volpara Health Technologies,setting the stage for unparalleled synergy and accuracy,particularly in breast health and screening technologies.

Headquartered in Seoul,with a global network of offices,Lunit leads in medical AI innovation. Discover more atlunit.io.