Software for Predicting Immunotherapy Response in Lung Cancer


Software to predict immunotherapy response and overall survival in patients with non-small cell lung cancer.

Key Benefits

  • Maps the complex relationships between multiple cell types within the tumor microenvironment to a pathomic signature for predicting immunotherapy outcome.
  • Interprets these inter- and intra- relationships to predict immunotherapy treatment outcomes.
  • Outperforms current image biomarkers, machine learning and deep learning-based models in predicting response to immunotherapy and overall survival.

Market Summary

Non-small cell lung cancer (NSCLC) accounts for up to 85% of lung cancer, and 40% of NSCLCs spread beyond the lungs by the time it is diagnosed. Immunotherapy is the standard treatment for patients with advanced NSCLC, but less than 50% of patients respond to this treatment. There is a need for better algorithms and improved biomarkers for identifying cancer patient populations that are most likely to respond to immunotherapy prior to treatment.

Technical Summary

Emory researchers, in partnership with Yale and Case Western, developed TriAnGIL: Triangular Analysis of Geographical Interplay of Lymphocytes, which is software to predict immunotherapy response in lung cancer. TriAnGIL analyzes pre-treatment biopsy specimens from lung cancer patients and identifies triangular spatial relationships between cell types within the tumor microenvironment. It synthesizes this information to predict how well patients might respond to immunotherapy.

Patent Information

Tech ID: 23112
Published: 12/21/2023