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
February 16, 2024 -- Lunit, 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.
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 brief
Lunit says a study published in the Journal for ImmunoTherapy of Cancer found that its Lunit SCOPE IO tool could classify immune phenotypes from H&E-stained whole-slide images and identify patients with different outcomes after immune checkpoint inhibitor treatment. The analysis covered 1,806 treated patients across 27 tumor types. Patients classified as having an Inflamed Immune Phenotype had higher objective response rates and longer progression-free and overall survival than patients without that classification, according to the study. The reported differences held regardless of immunotherapy regimen or PD-L1 expression. Lunit says it plans further validation and deployment of the tool, which analyzes tumor-infiltrating lymphocytes to classify tumors as Inflamed, Immune Excluded or Immune Desert.
Key takeaways
- The study evaluated 1,806 immune checkpoint inhibitor-treated patients across 27 tumor types.
- Lunit SCOPE IO classifies tumors into Inflamed, Immune Excluded or Immune Desert phenotypes using H&E slide images.
- Inflamed-phenotype patients had more favorable response and survival results, according to the study.
- Lunit says it plans to continue validating and deploying the tool.
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.
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Questions around this news
How does Lunit SCOPE IO assess a tumor’s immune phenotype?
According to the study, the AI model analyzes H&E-stained whole-slide images, identifying cancer areas, surrounding cancer stroma, and tumor-infiltrating lymphocytes. It then classifies tumors as Inflamed, Immune Excluded, or Immune Desert based on where lymphocytes are found and their density. The release says the approach uses image analysis rather than requiring a separate immune-phenotyping stain.
What outcomes did the study report for patients with an Inflamed Immune Phenotype?
The release reports that patients classified as Inflamed had more favorable outcomes after immune checkpoint inhibitor treatment. It gives objective response rates of 26.3% versus 15.8%, progression-free survival of 5.3 versus 3.1 months, and overall survival of 25.3 versus 13.6 months. The reported differences were irrespective of treatment regimen or PD-L1 expression.
What are immune checkpoint inhibitors?
Immune checkpoint inhibitors are medicines that block certain proteins involved in regulating immune responses. By interrupting these signals, they can help immune cells recognize and attack cancer cells. They are used for some cancers, though whether a treatment is suitable or effective depends on the specific cancer and patient.
What does progression-free survival measure?
Progression-free survival is the length of time during and after treatment that a patient lives without the cancer getting worse. It is commonly used in clinical studies to assess treatment outcomes. It differs from overall survival, which measures time until death from any cause.
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