Artificial intelligence (AI)–based membrane specific PD-L1 and immune subtypes as predictive biomarkers for danburstotug in relapsed or refractory extranodal NK/T cell lymphoma (R/R ENKTL): Insights from the Phase II trial (DISTINKT)

作者：Won Seog Kim, Seok Jin Kim, Jae-Cheol Cho, Sang Eun Yoon, Youngil Koh, Deok‐Hwan Yang, Dok Hyun Yoon, Junhun Cho, Sung Young Lee, Seongyeon Kang, Woochan Hwang, Hyukjung Kim, Heung Tae Kim · 发表于：Blood · 年份：2025 · DOI：10.1182/blood-2025-5426 · 研究领域：Lymphoma Diagnosis and Treatment、Cancer Immunotherapy and Biomarkers、CAR-T cell therapy research

Abstract Introduction: Danburstotug has demonstrated promising efficacy in patients with R/R ENKTL. However, the relationship between PD-L1 expression and treatment outcomes in ENKTL remains poorly defined, partially due to disease's complex and heterogeneous immune microenvironment. To better characterize the tumor immune landscape and identify potential predictive biomarkers, this study incorporated both quantitative AI–based analysis of PD-L1 expression at the single-cell level and manual classification of the tumor immune microenvironment (TIME) based on immune cell markers. We aimed to determine whether the intensity and subcellular localization of PD-L1 staining, as well as TIME subtypes, are associated with clinical response to danburstotug. These findings may offer novel insights into patients' stratification and immune resistance mechanisms in ENKTL. Methods:Patients received danburstotug (20 mg/kg every 2 weeks) without prior biomarker-based selection. Tumor responses were assessed every 12 weeks using the lymphoma response to immunomodulatory therapy criteria (Cheson et al. 2016). A deep learning model utilizing the Lunit SCOPE universal IHC subcellular compartment analysis was applied to segment the membrane, cytoplasm, and nucleus, and to quantify protein expression within each compartment on a continuous scale (0–100). Membrane specificity (MS) was defined as the ratio of membrane intensity to the total intensity across all three compartments, classified to MS-H...