Combined Immunohistochemistry And NGS-Based Patient Profiling For Predicting Anti-PD-1/PD-L1 Therapy Response

Key Takeaways

  • Successful pathology AI does not replace, but assist pathologists in high-complexity tissue analysis
  • Transparency and control is retained in a pathologist-centric AI-based system
  • Pathology AI enables cost effective healthcare by testing for multiple factors on a single sample
  • Correlating rich information about a tissue type with clinical outcomes allows for creation of Diagnostics (Dx), Prognostics (Px), and Companion Diagnostics (CDx)


There are several different modalities of predictive tests which support response to anti-PD-1/PD-L1 inhibitors therapy, including PD-L1 expression by immunohisto chemistry (PD-L1 IHC), mismatch repair deficiency (dMMR), microsatellite instability (MSI), and recently emerging tumor mutation burden (TMB), and Gene Expression Panels (GEP). Each of these methods capture different facets of the immune system: TMB and MSI evaluates mutational/neoantigen load which can stimulate the immune system; GEP establishes a profile of immune response, and whereas PD-L1 IHC directly evaluates the state of checkpoint inhibition in the tumor and tumor microenvironment (TME). We constructed a compound testing paradigm for immune system monitoring called PredicineX, which combines genome analysis which relies on tissue or blood-derived nucleic acids and advanced tissue context analytics based on PD-L1 IHC in solid tissue biopsies to create a comprehensive patient profile to support anti-PD/PD-L1 therapy decision making.