Abstract CT112: AI-powered and manual assessment of PD-L1 are comparable in predicting response to neoadjuvant atezolizumab in patients (pts) with resectable non-squamous, non-small cell lung cancer (NSCLC)

Background: PD-L1 expression evaluated by immunohistochemistry (IHC) is a well-established predictor of anti-PD-L1/PD-1 cancer immunotherapy (CIT). The Phase II LCMC3 (NCT02927301) study evaluated pre-operative treatment (tx) with atezolizumab (anti-PD-L1) in pts with untreated early stage resectable NSCLC, achieving a 20% major pathologic response (MPR) rate (primary efficacy pts, n=143). A digital PD-L1 scoring method was developed to assess PD-L1 expression as a potential predictive marker for MPR in squamous and non-squamous tumor samples from LCMC3. Methods: Manual scoring was used to determine PD-L1 status on pre-tx biopsy samples using the tumor proportion score (TPS) with a positive threshold of TPS≥50 (22C3). Binary results were correlated with MPR and stratified by squamous/non-squamous histology. A digital pathology workflow for automated PD-L1 scoring was developed to yield a precise continuous PD-L1 TPS. Deep convolutional neural networks trained using pathologist annotations were used to detect individual cells within the tumor and tumor microenvironment and quantify their PD-L1 expression. These cell type predictions were used to compute a digital PD-L1 TPS. LCMC3 pts with available digital and manual PD-L1 scores were then used to assess the role of PD-L1 expression in predicting MPR. Results: PD-L1 scores were available for pre-tx biopsies from 108 pts. No significant difference in scores was seen between histological subtypes. At cutoff (Oct 15, 2021), TPS≥50 was seen in 41 (non-squamous, n=26 [39%]; squamous, n=15 [36%]) of 108 pts and was associated with MPR in non-squamous (odds ratio [OR], 28.6; P Conclusions: These findings support using digitally assessed PD-L1 IHC as a centralized and standardized scoring system and suggest that tumor histological subtype could be an important factor in the utility of PD-L1 as a predictive biomarker for neoadjuvant CIT in early stage NSCLC. Citation Format: John Abel, Christopher Rivard, Filip Kos, Guillaume Chhor, Yi Liu, Jennifer Giltnane, Sara Hoffman, Murray Resnick, Cyrus Hedvat, Amaro Taylor-Weiner, Farah Khalil, Alan Nicholas, Gregory A. Fishbein, Lynette M. Sholl, Natasha Rekhtman, Stephanie Hennek, Ilan Wapinski, Ann Johnson, Michael Montalto, Katja Schulze, Bruce E. Johnson, David P. Carbone, Konstantin Shilo, Andrew H. Beck, Sanja Dacic, William D. Travis, Ignacio Wistuba. AI-powered and manual assessment of PD-L1 are comparable in predicting response to neoadjuvant atezolizumab in patients (pts) with resectable non-squamous, non-small cell lung cancer (NSCLC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr CT112.