scholarly journals Comparison of continuous measures across diagnostic PD-L1 assays in non-small cell lung cancer using automated image analysis

2019 ◽  
Vol 33 (3) ◽  
pp. 380-390 ◽  
Author(s):  
Moritz Widmaier ◽  
Tobias Wiestler ◽  
Jill Walker ◽  
Craig Barker ◽  
Marietta L. Scott ◽  
...  

Abstract Tumor programmed cell death ligand-1 (PD-L1) expression is a key biomarker to identify patients with non-small cell lung cancer who may have an enhanced response to anti-programmed cell death-1 (PD-1)/PD-L1 treatment. Such treatments are used in conjunction with PD-L1 diagnostic immunohistochemistry assays. We developed a computer-aided automated image analysis with customized PD-L1 scoring algorithm that was evaluated via correlation with manual pathologist scores and used to determine comparability across PD-L1 immunohistochemistry assays. The image analysis scoring algorithm was developed to quantify the percentage of PD-L1 positive tumor cells on scans of whole-slide images of archival tumor samples from commercially available non-small cell lung cancer cases, stained with four immunohistochemistry PD-L1 assays (Ventana SP263 and SP142 and Dako 22C3 and 28-8). The scans were co-registered and tumor and exclusion annotations aligned to ensure that analysis of each case was restricted to comparable tissue areas. Reference pathologist scores were available from previous studies. F1, a statistical measure of precision and recall, and overall percentage agreement scores were used to assess concordance between pathologist and image analysis scores and between immunohistochemistry assays. In total, 471 PD-L1-evalulable samples were amenable to image analysis scoring. Image analysis and pathologist scores were highly concordant, with F1 scores ranging from 0.8 to 0.9 across varying matched PD-L1 cutoffs. Based on F1 and overall percentage agreement scores (both manual and image analysis scoring), the Ventana SP263 and Dako 28-8 and 22C3 assays were concordant across a broad range of cutoffs; however, the Ventana SP142 assay showed very different characteristics. In summary, a novel automated image analysis scoring algorithm was developed that was highly correlated with pathologist scores. The algorithm permitted quantitative comparison of existing PD-L1 diagnostic assays, confirming previous findings that indicate a high concordance between the Ventana SP263 and Dako 22C3 and 28-8 PD-L1 immunohistochemistry assays.

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Boju Pan ◽  
Yuxin Kang ◽  
Yan Jin ◽  
Lin Yang ◽  
Yushuang Zheng ◽  
...  

Abstract Introduction Programmed cell death ligand-1 (PD-L1) expression is a promising biomarker for identifying treatment related to non-small cell lung cancer (NSCLC). Automated image analysis served as an aided PD-L1 scoring tool for pathologists to reduce inter- and intrareader variability. We developed a novel automated tumor proportion scoring (TPS) algorithm, and evaluated the concordance of this image analysis algorithm with pathologist scores. Methods We included 230 NSCLC samples prepared and stained using the PD-L1(SP263) and PD-L1(22C3) antibodies separately. The scoring algorithm was based on regional segmentation and cellular detection. We used 30 PD-L1(SP263) slides for algorithm training and validation. Results Overall, 192 SP263 samples and 117 22C3 samples were amenable to image analysis scoring. Automated image analysis and pathologist scores were highly concordant [intraclass correlation coefficient (ICC) = 0.873 and 0.737]. Concordances at moderate and high cutoff values were better than at low cutoff values significantly. For SP263 and 22C3, the concordances in squamous cell carcinomas were better than adenocarcinomas (SP263 ICC = 0.884 vs 0.783; 22C3 ICC = 0.782 vs 0.500). In addition, our automated immune cell proportion scoring (IPS) scores achieved high positive correlation with the pathologists TPS scores. Conclusions The novel automated image analysis scoring algorithm permitted quantitative comparison with existing PD-L1 diagnostic assays and demonstrated effectiveness by combining cellular and regional information for image algorithm training. Meanwhile, the fact that concordances vary in different subtypes of NSCLC samples, which should be considered in algorithm development.


2018 ◽  
Vol 25 ◽  
pp. 94 ◽  
Author(s):  
A. Pabani ◽  
C.A. Butts

For patients with advanced non-small-cell lung cancer (nsclc) lacking a targetable molecular driver, the mainstay of treatment has been cytotoxic chemotherapy. The survival benefit of chemotherapy in this setting is modest and comes with the potential for significant toxicity. The introduction of immunotherapeutic agents targeting the programmed cell death 1 protein (PD-1) and the programmed cell death ligand 1 (PD-L1) has drastically changed the treatment paradigms for these patients. Three agents—atezolizumab, nivolumab, and pembrolizumab—have been shown to be superior to chemotherapy in the second-line setting. For patients with tumours strongly expressing PD-L1, pembrolizumab has been associated with improved outcomes in the first-line setting.Demonstration of the significant benefits of immunotherapy in nsclc has focused attention on new questions. Combination checkpoint regimens, with acceptable toxicity and potentially enhanced efficacy, have been developed, as have combinations of immunotherapy with chemotherapy. In this review, we focus on the published trials that have changed the treatment landscape in advanced nsclc and on the ongoing clinical trials that offer hope to further improve outcomes for patients with advanced nsclc.


2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A39-A39
Author(s):  
Roberto Gianani ◽  
Will Paces ◽  
Elliott Ergon ◽  
Kristin Shotts ◽  
Vitria Adisetiyo ◽  
...  

BackgroundDetermination of programmed death-ligand 1 (PD-L1) level in tumor by immunohistochemistry (IHC) is widely used to predict response to check point inhibitor therapy. In particular, the Dako PD-L1 (22C3) antibody is a common companion diagnostic to the monoclonal antibody drug Keytruda® (pembrolizumab) in non-small cell lung cancer (NSCLC).1 However, for the practicing pathologist, interpretation of the PD-L1 (22C3) assay is cumbersome and time consuming. Manual pathologist scoring also suffers from poor intra- and inter-pathologist precision, particularly around the cut-off point.2 In this clinical validation study, we developed an image analysis (IA) based solution to accurately and precisely score digital images obtained from PD-L1 stained NSCLC tissues for making clinical enrollment decisions.Methods10 NSCLC tissue samples were purchased from a qualified vendor and IHC stained for PD-L1; 4 of these samples had serial sections stained on two separate days. Stained slides were scanned at 20X magnification and analyzed using Flagship Biosciences’ IA solutions that quantify PD-L1 expression and separate tumor and stromal compartments. Resulting image markups of cell detection and PD-L1 expression were reviewed by an MD pathologist for acceptance. PD-L1 staining was evaluated by digital IA in the sample’s tumor compartment for Total Proportion Score (TPS,%). Assay specificity was defined by ≥ 90% of the tissue cohort exhibiting appropriate cell recognition (≥ 90% cells correctly recognized as determined by the pathologist), with ≤ 10% false positive rate for staining classification. Sensitivity was defined by ≥ 90% of the cohort exhibiting appropriate cell identification (≥ 90% cells correctly identified), with ≤ 10% false negative rate for staining classification. Accuracy was defined by the combination of sensitivity and specificity and precision was defined by concordance of the binned TPS (<1%, ≥ 1%, ≥ 50%) in ≥ 80% of the samples stained on multiple days.ResultsThe preliminary results show that IA can yield high analytical sensitivity, specificity, accuracy, and precision in the determination of the PD-L1 score. 100% of the tissue cohort met criteria for analytical specificity, sensitivity, and accuracy and 100% of the samples stained on multiple days met the precision criteria.ConclusionsThis data demonstrates the feasibility of an IA approach as applied to PD-L1 (22C3) scoring. Ongoing experiments include application of the developed 22C3 algorithm on a separate cohort of 20 NSCLC samples to determine the correlation of digital scoring and scoring obtained by three pathologists. Additionally, we will evaluate the precision obtained by digital scoring in relation to the intra- and inter-pathologist concordance.ReferencesIncorvaia L, Fanale D, Badalamenti G, et al. Programmed death ligand 1 (PD-L1) as a predictive biomarker for pembrolizumab therapy in patients with advanced non-small-cell lung cancer (NSCLC). Adv Ther 2019;36:2600–2617.Rimm DL, Han G, Taube JM, et al. A prospective, multi-institutional, pathologist-based assessment of 4 immunohistochemistry assays for PD-L1 expression in non–small cell lung cancer. JAMA Oncol 2017;3:1051–1058.


2018 ◽  
Vol 127 (1) ◽  
pp. 52-61 ◽  
Author(s):  
Enrico Munari ◽  
Giuseppe Zamboni ◽  
Giorgia Sighele ◽  
Marcella Marconi ◽  
Marco Sommaggio ◽  
...  

2021 ◽  
Vol 12 ◽  
Author(s):  
Qin Zhang ◽  
Liansha Tang ◽  
Yuwen Zhou ◽  
Wenbo He ◽  
Weimin Li

Immunotherapy that includes programmed cell death-1 (PD-1), programmed cell death- ligand 1 (PD-L1) and cytotoxic T lymphocyte antigen 4 (CTLA-4) inhibitors has revolutionized the therapeutic strategy in multiple malignancies. Although it has achieved significant breakthrough in advanced non-small cell lung cancer patients, immune-related adverse events (irAEs) including checkpoint inhibitor pneumonitis (CIP), are widely reported. As the particularly worrisome and potentially lethal form of irAEs, CIP should be attached more importance. Especially in non-small cell lung cancer (NSCLC) patients, the features of CIP may be more complicated on account of the overlapping respiratory signs compromised by primary tumor following immunotherapy. Herein, we included the previous relevant reports and comprehensively summarized the characteristics, diagnosis, and management of CIP. We also discussed the future direction of optimal steroid therapeutic schedule for patients with CIP in NSCLC based on the current evidence.


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