Abstract 2492: Enhanced patient selection for anti-PD-L1 treatment in metastatic NSCLC with quantitative continuous scoring of PD-L1

Abstract Background: Anti-PD-L1 therapy has demonstrated clinical activity in patients with metastatic non-small cell lung cancer (mNSCLC). However, only subgroups of patients respond and their identification via PD-L1 as a biomarker remains imperfect. PD-L1 expression is commonly assessed by pathologist tumor cell (TC) scoring of immunohistochemically (IHC) stained tissue. We developed a system for digitally scoring PD-L1 in IHC (PD-L1 QCS), which demonstrated robust scoring across studies 1. Here, we present a comparison of PD-L1 QCS against manual scoring of PD-L1 (SP263 assay, Ventana) in the MYSTIC clinical trial. Methods: PD-L1 QCS on digitized whole slide images (WSI) comprises two deep learning models, enabling segmentation of single TCs followed by PD-L1 expression quantification via their optical density (OD). Positive cells are classified based on an OD threshold, allowing robust digital calculation of the TC percentage 1. The analysis included 502 WSI from the MYSTIC trial (NCT02453282), representing 256 patients treated with anti-PD-L1 therapy and 246 treated with chemotherapy as standard-of-care (SoC) 2. First, an optimal cut-point was determined by optimization against outcome, classifying patients with ≥0. 575% TC as biomarker positive (BM+). Next, the approach was compared against manual TC scoring at 1%, 25% and 50% cut-off. Results: In durvalumab treated patients, median overall survival (mOS) in the PD-L1 QCS BM+ subgroup (prevalence 54. 3%) was 12. 1 months longer than in the BM- subgroup (19. 9m vs. 7. 8m, HR=0. 45, CI 0. 33, 0. 60). Analogous comparison of subgroups based on manually scored TC proportion at 1% (prev. 75. 0%), 25% (prev. 42. 6%) or 50% (prev. 29. 7%) cut-points yielded a mOS difference of 7. 8m (HR=0. 52, CI 0. 38, 0. 72), 8. 3m (HR=0. 61, CI 0. 45, 0. 82) and 11. 0m (HR=0. 55, CI 0. 40, 0. 77) respectively. Comparing durvalumab treatment against SoC within the PD-L1 QCS BM+ subgroup yielded a HR of 0. 62 (CI 0. 46, 0. 82, log rank p=0. 0008, 7. 4m mOS delta). In comparison, a HR of 0. 69 (CI 0. 46, 1. 02, p=0. 0642, 8. 4m mOS delta) was obtained for manual TC scoring at 50%. Conclusion: We compared a computational pathology approach for continuous PD-L1 scoring for the selection of mNSCLC patients for anti-PD-L1 treatment against established manual scoring. Our results suggest that PD-L1 QCS has the potential to identify a larger patient subgroup that retains benefit from anti-PD-L1 treatment and more precisely identifies non-responders. References: 1. Lesniak, Jan, et al. "Quantitative computational assessment of PD-L1 enables robust patient selection for biomarker-informed anti-PD-L1 treatment of NSCLC patients. " J. Immunother. Cancer, Vol. 10. , 2022. 2. Rizvi NA, et al. "Durvalumab with or without tremelimumab vs standard chemotherapy in first-line treatment of mNSCLC: the MYSTIC phase 3 randomized clinical trial. " JAMA Oncology. 2020;6. 5: 661-674. Citation Format: Jan Martin Lesniak, Markus Schick, Thomas Kunzke, Federico Pollastri, Juan Pedro Vigueras-Guillén, Harald Hessel, Susanne Haneder, Pallavi Sontakke, Karma DaCosta, Regina Alleze, Hadassah Sade, J Carl Barrett, Günter Schmidt, Ross Stewart. Enhanced patient selection for anti-PD-L1 treatment in metastatic NSCLC with quantitative continuous scoring of PD-L1 abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 2492.