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

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-1/PD-L1 therapy decision making.

CONCLUSIONS

Combining contextual tissue and IHC analysis with comprehensive genomic alteration profiling and TMB Scoring creates an intricate composite biomarker profile for each tissue sample which can inform and predict clinical endpoints

  • Using Flagship Biosciences cTA® tissue image analysis technology, we created an artificial intelligence (AI) based PD-L1 IHC scoring and morphometric analysis platform which provides both current PD-L1 IHC scoring paradigms and novel computational scores from the rich tissue context data profile created from PD-L1 IHC slides
  • Using Predicine’s PredicineATLAS NGS technology, we combined contextual tissue data with a tissue-based NGS assay to capture genomic alterations in cancer genes including tumor mutation burden (TMB) and gene mutation data
  • We demonstrate the synergistic value of combining genomic based TMB and GEP immune profiles with contextual information from PD-L1 IHC slides in patient biopsies. The high complexity gene profile, combined with the rich tissue context data, provided a novel means to stratify patients into 4 categories:
    1) mutation high/immune competent;
    2) mutation low/immune deficient;
    3) mutation high/immune deficient; and
    4) mutation low/immune competent
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