Study Background
Tumor cell (TC) PD-L1 expression is predictive of response to PD-L1-targeted immunotherapy, and accurate scoring is crucial for treatment selection. Scoring relies on manual assessment of immunohistochemically labeled tissue and is subject to subjective variation due to pathologist assessment. As a digital alternative, a clone-agnostic AI-based model for PD-L1 quantification in non-small cell lung cancer (AIM-PD-L1 NSCLC) was developed 1. AIM-PD-L1 was deployed on samples from a Phase 3 study of anti-PD-L1 atezolizumab combination therapy with carboplatin and paclitaxel, and/or bevacizumab in Stage IV NSCLC (IMpower150; NCT02366143). Digital and manual PD-L1 TC scores were compared and interrogated for their respective potential to predict efficacy to atezolizumab combination treatments.1 Griffin et al., Proceedings of the American Association for Cancer Research Annual Meeting 2022 Cancer Res 2022;82(12_Suppl)
Hen Prizant 1*, John Shamshoian 2*, John Abel 2, Andrew Beck 2, Laura Chambre 2, Stephanie Hennek 2, Hartmut Koeppen 1, Daniel Ruderman 1, Meghna Das Thakur 1, Michael Montalto 2, Ben Trotter 2, Ilan Wapinski 2, Wei Zou 1, Minu K. Srivastava 1#, Jennifer M. Giltnane 1#1Genentech, Inc, South San Francisco, CA, USA; 2PathAI, Boston, MA USA; *Co-first author, #Co-senior author
