Lunit to Present 16 Abstracts at the 2023 ASCO Annual Meeting

Lunit (KRX:328130.KQ), a leading global provider of AI-powered cancer diagnostic solutions, today announced the presentation of 16 abstracts featuring its AI-biomarker platform at the American Society of Clinical Oncology (ASCO) Annual Meeting, to be held in Chicago, Illinois, on June 2-6.

Lunit AI-biomarker platform 'Lunit SCOPE IO' and AI-based PD-L1 scoring algorithm 'Lunit SCOPE PD-L1'

Since 2019, the company has been releasing groundbreaking findings based on its AI-powered tissue analysis platform, Lunit SCOPE, at the ASCO annual meeting. At this year’s ASCO, Lunit plans to showcase the largest number of studies in the company’s history, including 10 poster presentations and 6 online publications. The studies will cover a range of topics, from predicting clinical outcomes to analyzing tumor microenvironments in various cancer types using the Lunit SCOPE suite.

Some of Lunit’s highlighted abstracts at ASCO 2023 include:

  • A study conducted in collaboration with the National Cancer Center Hospital East (NCCE) found a correlation between tumor-infiltrating lymphocyte (TIL) density change during chemoradiotherapy (CRT) and pathologic complete response (pCR) rate in locally advanced rectal cancer (LARC), indicating the potential of Lunit SCOPE IO to predict favorable outcomes
  • Lunit SCOPE IO analyzed TILs in head and neck squamous cell carcinoma (HNSCC) patients treated with durvalumab +/- tremelimumab, showing improved outcomes due to increased immune inflammation
  • Lunit SCOPE IO validated the efficacy of neoadjuvant HPV vaccine and immunotherapy of HPV+ head and neck squamous cell carcinoma (HNSCC) patients
  • A collaborative study led by Mayo Clinic predicted prognosis in patients with colon cancer by applying AI-derived immune phenotypes
  • A study showing how Lunit SCOPE IO predicts the efficacy of immunotherapy based on the patient’s Transforming growth factor-beta (TGFβ) level
  • The use of Lunit SCOPE IO to predict MET pathogenic mutations in non-small cell lung cancer
  • The use of Lunit SCOPE UIHC (Universal Immunohistochemistry) for exploring target cancer types and predicting response to novel tumor-associated antigens (TAA) targeted agents
  • Using AI to analyze the spatial arrangement of macrophages within tumor environments and how it relates to the Interferon Gamma (IFNG) signature and immune phenotype across different types of cancer

“We are thrilled to showcase the largest number of studies in the company’s history at ASCO 2023, demonstrating Lunit SCOPE’s efficacy,” said Brandon Suh, CEO of Lunit. “Through our novel academic research using Lunit SCOPE suite across various cancer types and treatment settings, we aim to bring cutting-edge technology to the forefront of optimized cancer treatment for all patients.”

Lunit’s Abstracts at ASCO 2023

No.Abstract No. #TitleType
13608Predictive value of tumor-infiltrating lymphocyte (TIL) dynamics in the tumor microenvironment (TME) during preoperative chemoradiotherapy (CRT) on pathologic complete response (pCR) in microsatellite-stable (MSS) locally advanced rectal cancer (LARC)Poster
22578Dynamic change of immune phenotype assessed by artificial intelligence (AI)-powered tumor-infiltrating lymphocytes (TILs) analysis during neoadjuvant durvalumab with or without tremelimumab (D+/-T) in head and neck squamous cell carcinoma (HNSCC)Poster
36075Neoadjuvant pembrolizumab, GX-188E, and GX-I7 in patients with human papilloma virus-16- and/or 18-positive head and neck squamous cell carcinoma: single-arm, phase 2 trial with single cell transcriptomic analysis and artificial intelligence-powered spatial analysisPoster
43542Artificial Intelligence-Derived Immune Phenotypes for Prediction of Prognosis in Patients with Stage III Colon Cancer (NCCTG N0147) [Alliance]Poster
52585Tumor microenvironment (TME)-based histomic TGFβ signature (TGFBs) reveals stromal fibroblast recruitment and exclusion of immune cells as immunotherapy resistance mechanismsPoster
6e13578Deep learning-based ensemble model using hematoxylin and eosin (H&E) whole slide images (WSIs) for the prediction of MET mutations in non-small cell lung cancer (NSCLC)Online Publication
73135Exploring expression levels of HER2, HER3, MET, Claudin18.2, and MUC16 across 16 cancer types using an artificial intelligence-powered immunohistochemistry analyzerPoster
82621Artificial intelligence (AI) –powered spatial analysis of macrophages in tumor microenvironment and its association with interferon-gamma (IFNG) signature and immune phenotype (IP) in pan-cancer datasetPoster
9e20520Artificial intelligence (AI) –powered H&E whole-slide image (WSI) analysis of tertiary lymphoid structure (TLS) correlates with immune phenotype and related molecular signatures in non–small-cell lung cancerOnline Publication
101049Artificial intelligence–powered tumor-infiltrating lymphocytes analyzer to reveal distinct immune landscapes in breast cancer by molecular subtype and HER2 scorePoster
11e21179Immune phenotype-driven treatment outcome of IO-only versus chemo-IO in PD-L1-high, first-line, advanced non-small cell lung cancer (NSCLC)Online Publication
124162Artificial intelligence (AI) –powered spatial analysis of tumor-infiltrating lymphocytes (TILs) for prediction of prognosis in resectable pancreatic adenocarcinoma (PDAC)Poster
136100Artificial intelligence (AI) analysis of tumor-infiltrating lymphocytes (TILs) in hematoxylin and eosin (H&E) slides to explore immune phenotypes in papillary thyroid cancerPoster
14e14657Correlation of fragmented pattern of tumor mass captured by artificial intelligence (AI)-powered whole-slide image (WSI) analysis with biased fibroblast expansion over tumor growth and distinct mutational signaturesOnline Publication
15e13553Performance validation of an artificial intelligence-powered PD-L1 combined positive score analyzer in six cancer typesOnline Publication
16e13546Effect of an artificial intelligence–powered programmed death-ligand 1 combined positive score analyzer in urothelial cancer on inter-observer and inter-site variabilityOnline Publication

About Lunit

With AI, Lunit aims to ‘conquer cancer,’ one of the leading causes of death worldwide. Lunit is an AI software company devoted to developing AI solutions for precision diagnostics and therapeutics, to find the right diagnosis at the right cost, and the right treatment for the right patients. Lunit, a portmanteau of ‘learning unit,’ is a deep learning-based medical AI company devoted to developing advanced medical image analytics and data-driven imaging biomarkers via cutting-edge technology.|

Founded in 2013, Lunit has been acknowledged around the world for its advanced, state-of-the-art technology and its application in medical images. Its technology has been recognized at international AI competitions surpassing top companies like Google, IBM, and Microsoft. As a medical AI company with a focus on clinical evidence, the company’s findings are presented in major peer-reviewed journals such as the Journal of Clinical Oncology and JAMA Network Open, and global conferences including ASCO and AACR.

After receiving FDA clearance and the CE Mark, Lunit INSIGHT CXR and MMG are clinically used in approximately 2,000 hospitals and medical institutions across more than 40 countries. Lunit SCOPE PD-L1 is CE marked in Europe and has not been cleared or approved by the US Food and Drug Administration (FDA) and is intended for Research Use Only. Lunit is headquartered in Seoul, South Korea with offices and representatives around the world.

About Lunit SCOPE

Lunit SCOPE is a suite of AI-powered software that analyzes tissue slide images for digital pathology and AI biomarker development, with the aim of optimizing workflow and facilitating more accurate and predictive clinical data for clinicians and researchers.

Lunit SCOPE platform offers multiple tissue analysis AI software products and assays that can streamline digital pathology workflow and diagnostics, as well as enhance the drug development process.

The flagship product, Lunit SCOPE IO, generates immune phenotype reports based on H&E analysis and provides AI-based predictive clinical outcome information. In addition, services are also offered by assessing protein expression via Immunohistochemistry (IHC) slide analysis, through products such as Lunit SCOPE PD-L1, Lunit SCOPE HER2, Lunit SCOPE ER/PR, and others.

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