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Digital SP263 PD-L1 tumor cell scoring in non-small cell lung cancer achieves comparable outcome prediction to manual pathology scoring


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 developed1. 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)

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Hen Prizant1*, John Shamshoian2*, John Abel2, Andrew Beck2, Laura Chambre2, Stephanie Hennek2, Hartmut Koeppen1, Daniel Ruderman1, Meghna Das Thakur1, Michael Montalto2, Ben Trotter2, Ilan Wapinski2, Wei Zou1, Minu K. Srivastava1#, Jennifer M. Giltnane1#

1Genentech, Inc, South San Francisco, CA, USA; 2PathAI, Boston, MA USA; *Co-first author, #Co-senior author