Comparison of PD-L1 immunohistochemistry assays for non-small-cell lung cancer
<bold>Objective:</bold> Recently, PD-1/PD-L1 inhibitors have demonstrated efficacy in patients with advanced NSCLC. For each immune check-point inhibitor, a separate immunohistochemistry PD-L1 assay has been developed. The PD-L1 22C3 pharmDx assay is an FDA-approved diagnostic assay for pembrolizumab use. The Ventana SP263 assay can be used to inform treatment decisions involving nivolumab and pembrolizumab in NSCLC. However, some studies have shown discordant results between the two assays. This study aimed was to compare PD-L1 expression using these two assays. <bold>Methods:</bold> For this retrospective study, total of 172 samples from consecutive cases of NSCLC were tested using the two PD-L1 assays between June 2017 and July 2023. <bold>Results:</bold> Total 172 patients (122 males, 55 females) with a mean age of 70.4 years were included. 97 patients (56.4%) had adenocarcinoma histology, 73 patients (42.4%) had squamous cell carcinoma, and 2 had adenosquamous cell carcinoma histology. In a total of 108 samples (62.8%), the two PD-L1 assays values matched perfectly. 64 cases exhibited discordant results between the two assays. Of these discordant cases, 6 cases exhibited different values between the two assays and were assigned different categories. Of the 18 cases were similar values between the two assays but were assigned different categories as the values were near the cut-off point and the rest cases were similar values and assigned same category. The agreement rates of the two assays were 85-96% at various cut-offs. There was no significant difference between 22C3 and SP263 in tumor proportion score. <bold>Conclusions:</bold> This study has shown that interassay concordance of tumor cell staining is generally high between the two assays.
Authors
- Goohyeon Hong
- Youn Seup Kim
Institutions
- Dankook University Hospital (KR)
Publication Details
- Journal
- Lung Cancer
- Published
- 2024-09-14
- DOI
- https://doi.org/10.1183/13993003.congress-2024.pa4225
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00