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- Lunit SCOPE HER2 and Lunit SCOPE ER/PR significantly improve pathologist concordance and accuracy, paving the way for improved breast cancer molecular subtype analysis for informed decision-making and precision patient care SEOUL, South Korea, March 14, 2024 /PRNewswire/ -- Lunit (KRX:328130.KQ), a leading provider of AI-powered solutions for cancer diagnostics and therapeutics, today announced the publication of a new study featuring Lunit SCOPE HER2 and Lunit SCOPE ER/PR, Lunit's AI-powered analyzer of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR). Continue Reading Lunit SCOPE The study, in collaboration with Prof. So-Woon Kim from Kyung Hee University College of Medicine and Prof. Minsun Jung from Yonsei University College of Medicine, was recently published in the Breast Cancer Research (BCR), an international peer-reviewed journal. The study aimed to evaluate the role of Lunit SCOPE in enhancing pathologists' consistency and accuracy of breast cancer molecular subtype classification, including expression levels of HER2, ER, and PR. Breast cancer treatment strategies and clinical outcome predictions heavily rely on the accurate determination of these receptors' expression levels. However, conventional immunohistochemistry (IHC) analysis poses challenges to classification accuracy. There can be variability in interpretation among pathologists, especially when dealing with intermediate expressions. The study highlights Lunit SCOPE HER2 and Lunit SCOPE ER/PR, developed using a dataset of thousands of HER2, ER, and PR-stained IHC breast cancer whole-slide images. An external validation cohort of 201 breast cancer cases underwent analysis using these AI analyzers. Results from the study demonstrated a significant increase in agreement among pathologists on the status of HER2, ER, and PR, especially in cases with intermediate weakly positive expressions. Notably, AI assistance led to an increase in agreement on HER2 status from 49.3% to 74.1%, ER from 93.0% to 96.5%, and PR from 84.6% to 91.5%. This improvement was particularly pronounced in intermediate weakly positive cases with HER2 2+ and HER2 1+, showing concordance increases from 46.2% to 68.4% and 26.5% to 70.7%, respectively. Consequently, with AI assistance, the agreement among pathologists in classification of breast cancer molecular subtypes saw an increase from 58.2% to 78.6%. The study concludes that Lunit SCOPE HER2 and Lunit SCOPE ER/PR significantly improve pathologists' concordance in classifying breast cancer molecular subtypes. These solutions hold immense potential in enhancing treatment strategies and ensuring more accurate and personalized approaches for patients. "In this study, not only do we validate the performance of Lunit SCOPE HER2 and Lunit SCOPE ER/PR, but we also reveal the potential application of AI biomarkers in predicting treatment responses, particularly for HER2-targeted therapies. We also expect this breakthrough to open new avenues for future drug development," said Brandon Suh, CEO of Lunit. "At Lunit, we enhance healthcare providers' accuracy and concordance, and we empower them to make informed decisions that directly impact patients' lives. Our mission is to transform the landscape of personalized cancer diagnostics and therapeutics." About Lunit Founded in 2013, Lunit is a deep learning-based medical AI company on a mission to conquer cancer. We are committed to harnessing AI to ensure accurate diagnosis and optimal treatment for each cancer patient using AI-powered medical image analytics and AI biomarkers. As a medical AI company grounded on clinical evidence, our findings are presented in major peer-reviewed journals, such as the Journal of Clinical Oncology and the Lancet Digital Health, and global conferences, including ASCO and RSNA. After receiving FDA clearance and the CE Mark, our flagship Lunit INSIGHT suite is clinically used in approximately 3,000+ hospitals and medical institutions across 40+ countries. Lunit is headquartered in Seoul, South Korea, with offices and representatives worldwide. For more information, please visit lunit.io. About Lunit SCOPE Lunit SCOPE is a suite of AI-powered software that analyzes tissue slide images for digital pathology and AI biomarker development, aiming to optimize workflow and facilitate more accurate and predictive clinical data for clinicians and researchers. Lunit SCOPE platform offers multiple AI-powered tissue analysis products and assays that can streamline digital pathology workflow and diagnostics and enhance the drug development process. Lunit SCOPE IO analyzes the tumor microenvironment (TME) based on H&E analysis and provides AI-based predictive clinical outcome information. In addition, AI-driven Immunohistochemistry (IHC) slide analysis services are offered through products such as Lunit SCOPE PD-L1, Lunit SCOPE HER2, Lunit SCOPE ER/PR, and others. SOURCE Lunit

Demonstrating Caris' impact on precision medicine, research results to be presented include the first study to report an association between clinical outcomes and molecular sub-types of uterine carcinosarcoma IRVING, Texas, March 13, 2024 /PRNewswire/ -- Caris Life Sciences®(Caris), the leading next-generation AI TechBio company and precision medicine pioneer that is actively developing and delivering innovative solutions to revolutionize healthcare and improve the human condition using molecular science and AI, announced today that the company and collaborators within the Caris Precision Oncology Alliance™ (POA) will collectively present three studies across multiple gynecological tumor types at the 2024 SGO Annual Meeting on Women's Cancer, March 16-18, 2024, in San Diego. The findings demonstrate the power of Caris' comprehensive multi-modal database, generated by examining every gene, to enable novel insights into cancer that could have profound effects on a patient's diagnosis, prognosis, care plan and response to treatment. Continue Reading "We are proud to return and present an array of precision oncology research at this year's SGO Annual Meeting. The findings represent important observations in various gynecological malignancies, including the effect of molecular subtype on survival in uterine carcinosarcoma, identification of potential prognostic biomarkers in ovarian cancer, and an exploration of the frequency and outcomes of endometrial tumors with alterations in multiple molecular classifiers," said Caris EVP and Chief Medical Officer George W. Sledge, Jr., MD. "We are confident that Caris' powerful comprehensive molecular profiling, matched clinical outcomes data and in-depth collaborative efforts within our POA member institutions will lead to novel insights into cancer biology, new targeted therapies and ultimately improved outcomes for all patients with gynecological cancers." The findings demonstrate the power of Caris' comprehensive multi-modal database, generated by examining every gene Post this Survival outcomes according to molecular classification of uterine carcinosarcoma Walking Poster Session | Walking Poster Group 5: Cancer Care Delivery March 17: 8:15 – 8:45 AM PST Uterine carcinosarcoma (UCS) is an aggressive cancer with a poor prognosis, yet there are limited studies evaluating its molecular subtypes and their association with survival outcomes. In this new study, more than 4,000 UCS and endometrioid endometrial cancer (EEC) tumors were evaluated using next-generation sequencing (NGS) to determine if UCS molecular subtype governs survival. This is the first study to report survival outcomes according to molecular classification in UCS compared to an EEC cohort. Frequency and outcomes of co-mutations according to ProMisE classifiers in endometrial cancer Poster Session I March 17: 1:15 – 1:45 PM PST The Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE) criteria classify four molecular subtypes of endometrial tumors (ET). However, there is limited understanding of how the presence of alterations in multiple classifiers affects ET prognosis. Whole exome sequencing of over 5,100 ETs revealed that some ETs have overlapping molecular subtypes. Molecular characterization of squamous cell ovarian cancers for identification of therapeutic targets Poster Session I March 17: 1:15 – 1:45 PM PST Not all ovarian cancers have the same prognosis; ovarian squamous cell carcinoma (OSCC), for example, is associated with a worse prognosis than high-grade serous ovarian cancer (HGSOC). Through NGS of DNA and RNA from ovarian tumors (OSCC, HGSOC, and Brenner's tumors), this study sought to identify prognostic factors and molecular markers associated with OSCC. High tumor mutational burden (TMB) and lack of ER, PR expression were found to be molecular characteristics of OSCC, along with other distinct patterns of molecular and immune markers. Further characterization of OSCC may lead to the identification of therapeutic targets. Poster and abstract summaries highlighting the Caris research presented at SGO 2024 will be available onsite at Caris' booth (#601). The full abstracts will be available on the Caris website beginning on March 16. The POA includes 91 cancer centers, academic institutions, research consortia and healthcare systems, including 43 NCI-designated cancer centers, collaborating to advance precision oncology and biomarker-driven research. POA members work together to establish and optimize standards of care for molecular testing through innovative research focused on predictive and prognostic markers that improve the clinical outcomes for cancer patients. About Caris Life Sciences Caris Life Sciences® (Caris) is the leading next-generation AI TechBio company and precision medicine pioneer that is actively developing and delivering innovative solutions to revolutionize healthcare and improve the human condition. Through comprehensive molecular profiling (Whole Exome and Whole Transcriptome Sequencing) and the application of advanced AI and machine learning algorithms, Caris has created the large-scale, multi-modal database and computing capability needed to analyze and unravel the molecular complexity of disease. This convergence of sequencing power, big data and AI technologies provides an unmatched platform to deliver the next generation of precision medicine tools for early detection, diagnosis, monitoring, therapy selection and drug development. Headquartered in Irving, Texas, Caris has offices in Phoenix, New York, Cambridge (MA), Tokyo, Japan and Basel, Switzerland. Caris or its distributor partners provide services in the U.S., Europe, Asia and other international markets. To learn more, please visit CarisLifeSciences.com. Caris Life Sciences Media Contact: Lisa Burgner [email protected] 214.294.5606 SOURCE Caris Life Sciences

- A new study published in the Journal for ImmunoTherapy of Cancer (JITC) shows that Lunit's AI-powered biomarker "Lunit SCOPE IO" can predict favorable outcomes of Inflamed Immune Phenotype patients treated with Immune Checkpoint Inhibitor across multiple cancer types SEOUL, South Korea, Feb. 16, 2024 /PRNewswire/ -- Lunit (KRX:328130.KQ), a leading provider of AI-powered solutions for cancer diagnostics and therapeutics, today announced a groundbreaking study published in the Journal for ImmunoTherapy of Cancer (JITC). The study demonstrates the ability of Lunit SCOPE IO, to enable quantitative immune phenotyping from H&E stained slides as a broadly accessible biomarker for immunotherapy. Continue Reading Lunit's AI-powered biomarker "Lunit SCOPE IO" The study, conducted on a real-world multicenter cohort of 1,806 Immune Checkpoint Inhibitor (ICI)-treated patients across 27 tumor types, showcases a correlation between the Inflamed Immune Phenotype and positive ICI treatment responses. There is an unmet need for improved immunotherapy biomarkers, and this study highlights the importance of Lunit SCOPE IO's ability to quantify immune phenotype (IP) as Inflamed, Excluded, or Desert, purely from H&E whole slide images (WSIs). Utilizing advanced machine learning (ML) models, Lunit SCOPE IO segments tissue into cancer area (CA) and cancer stroma (CS) within WSIs. The model also detects Tumor-Infiltrating Lymphocytes (TILs) using a cell detection model trained on over 17,000 WSIs spanning multiple solid tumor types. Based on TIL density, the model classifies the tumor into one of three immune phenotypes: Inflamed (IIP; high TIL density within CA), Immune Excluded (IEP; TILs within CS but excluded from CA), and Immune Desert (IDP; low TIL density within both CA and CS). In an independent real-world dataset of ICI-treated patients, Lunit SCOPE IO demonstrated predictive power for clinical outcomes, including objective response rates (ORR), progression-free survival (PFS), and overall survival (OS). In the study, IIP patients showed significantly favorable clinical outcomes post-ICI treatment. More favorable ORRs (26.3% vs 15.8%), prolonged PFS (5.3 vs. 3.1 months) and OS (25.3 vs. 13.6 months) were observed in IIP patients, irrespective of ICI regimen or programmed death-ligand 1 (PD-L1) expression. The dataset reflected global diversity, with data coming from Stanford University, Samsung Medical Center, Seoul National University Bundang Hospital, Chonnam National University Hospital, and Northwestern Memorial Hospital, and more. This study paves the way for more precise patient selection with a time-efficient and labor-efficient analysis at scale in immunotherapy. Lunit plans to further validate and deploy Lunit SCOPE IO, ultimately enabling more personalized and effective immunotherapy strategies, especially under the current limitations of traditional biomarkers. "This study marks a major step towards better biomarkers for immunotherapy driven by AI, analyzing the tumor microenvironment to determine immune phenotype quantitatively and predict patient responses to ICI therapy," said Brandon Suh CEO of Lunit. "Our commitment to advancing cancer care through innovation has never been clearer. By providing a robust tool for personalized treatment strategies, Lunit SCOPE IO promises improved outcomes and could redefine the standard of care for patients in several cancer types where predictive biomarkers are lacking." Published in the JITC, the official journal of the Society for Immunotherapy of Cancer (SITC), the study also contributes to SITC's mission of enhancing cancer patient outcomes by advancing the science and application of cancer immunotherapy. SITC is the world's leading member-driven organization that includes over 4,650 members from 35 medical specialties across 63 countries worldwide. About Lunit Founded in 2013, Lunit is a deep learning-based medical AI company on a mission to conquer cancer. We are committed to harnessing AI to ensure accurate diagnosis and optimal treatment for each cancer patient using AI-powered medical image analytics and AI biomarkers. As a medical AI company grounded on clinical evidence, our findings are presented in major peer-reviewed journals, such as the Journal of Clinical Oncology and the Lancet Digital Health, and global conferences, including ASCO and RSNA. After receiving FDA clearance and the CE Mark, our flagship Lunit INSIGHT suite is clinically used in approximately 3,000+ hospitals and medical institutions across 40+ countries. Lunit is headquartered in Seoul, South Korea, with offices and representatives worldwide. For more information, please visit lunit.io. About Lunit SCOPE Lunit SCOPE is a suite of AI-powered software that analyzes tissue slide images for digital pathology and AI biomarker development, aiming to optimize workflow and facilitate more accurate and predictive clinical data for clinicians and researchers. Lunit SCOPE platform offers multiple AI-powered tissue analysis products and assays that can streamline digital pathology workflow and diagnostics and enhance the drug development process. Lunit SCOPE IO analyzes the tumor microenvironment (TME) based on H&E analysis and provides AI-based predictive clinical outcome information. In addition, AI-driven Immunohistochemistry (IHC) slide analysis services are offered through products such as Lunit SCOPE PD-L1, Lunit SCOPE HER2, Lunit SCOPE ER/PR, and others. SOURCE Lunit