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)

Abstract

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.

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