Background Pathologist-based scoring of PD-L1 expression on tumor cells using IHC1 has shown clinical utility in predicting favorable overall survival in advanced non-small cell lung cancer (NSCLC) patients treated with anti-PD-(L)1 therapies including durvalumab.2-3Quantitative Continuous Scoring (QCS)4 enables the continuous measurement of the PD-L1 expression on single cells and the selection of the PD-L1 expression cutoff that best stratifies anti-PD-L1-treated patients with respect to prevalence and log-rank test p-value.5 We present here the extension of QCS to PD-L1 measured by multiplex immunofluorescence (mIF)6 to evaluate its ability to optimize patient stratification.
Methods Pre-treatment tumor samples from advanced NSCLC patients enrolled in durvalumab nonrandomized phase 1/2 trial (CP1108/NCT01693562) 2, were stained by mIF panel containing PD-L1. 6 Similarly to IHC PD-L1 QCS, mIF PD-L1 QCS consists of two deep-learning models, first to segment epithelium regions and second to detect membrane, cytoplasm and nuclei of each epithelium cell, transferring for the second model annotations from IHC to mIF domain. 7 The mIF images are normalized based on batch statistics prior to image analysis. PD-L1 expression is measured for each epithelium cell as the average of the PD-L1 signal in the segmented membrane. Cells with expression higher than an expression threshold (TPDL1) are considered positive. A slide is considered QCS-positive if it comprises a greater percentage of PD-L1 positive cells (QCS-score) than a cutoff value (CoV).
Results The QCS-scores are computed on 119 NSCLC patients treated with durvalumab. As a first proof of concept that QCS-scoring can replicate tumor proportion scoring (TPS), we optimize TPDL1 as to maximize the correlation between QCS and TPS scores (figure 1). Second, we estimate for different combinations of (TPDL1, CoV) the log rank p-value associated with the stratification between patients with low and high QCS scores. A subregion of the parameter space was identified for which the stratification is significant (p<0.01) with more than 50% prevalence in the positive subgroup (figure 2). The p-value is minimized (p=7.2 10-5) for (TPD-L1=37, CoV=0.75%), yielding a median OS of 5.58 months and 13.44 months in the QCS negative and positive subgroups respectively, similar to those of IHC PD-L1 manual scoring with 25% cutoff.
Conclusions The extension of QCS to mIF imaging provides opportunities to evaluate continuous PD-L1 expression of single tumor cells in relation to spatial distribution of other cells (e.g. PD1+ CD8+ T cells) and identify predictive biomarkers of tumor-immune cell interactions of anti-PD-(L)1 therapies.
Trial Registration CP1108/NCT01693562
Rebelatto M, et al. Development of a programmed cell death ligand-1 immunohistochemical assay validated for analysis of non-small cell lung cancer and head and neck squamous cell carcinoma. Diagnostic Pathology 2016 Oct 8;11(1):95.
Antonia S, et al. Clinical Activity, Tolerability, and Long-Term Follow-Up of Durvalumab in Patients With Advanced NSCLC Journal of thoracic Oncology 2019 Oct ; 14 (10):1794–1806
Rizvi NA, et al. Durvalumab With or Without Tremelimumab vs Standard Chemotherapy in First-line Treatment of Metastatic Non–Small Cell Lung Cancer. JAMA Oncology 2020;6(5) :661–674
Gustavson M, et al. Novel approach to HER2 quantification: digital pathology coupled with AI-based image and data analysis delivers objective and quantitative HER2 expression analysis for enrichment of responders to trastuzumab deruxtecan (T-DXd; DS-8201), specifically in HER2-low patients. Cancer Res 2021, 81 (4_Supplement): PD6–01.
Schmidt G, et al, Computational pathology delivers objective and quantitative PD-L1 expression analysis for enrichment of responders to durvalumab in non-small cell lung cancer (NSCLC), J Immunother Cancer 2021;9(Suppl 2):A1–A1054
Meinecke L, et al., Presence of TLS and combined high densities of PD-L1+ macrophages & CD8+ T cells predict long-term overall survival for patients with advanced NSCLC treated with durvalumab. Cancer Res 2022 82 (12_Supplement): 1235.
Brieu N, et al. Stain Isolation-based Guidance for Improved Stain Translation, Medical Imaging with Deep Learning (MIDL) 2022, https://arxiv.org/abs/2207.00431
Statistics from Altmetric.com
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.