Abstract 6415: Comparison the results from PD-L1 IHC and PD-L1 multiplex immunofluorescence for the prediction of anti-PD-1 immunotherapeutic response
Authors: Sun Hongzhe , Liu Xia , Jiao Tao , Guo Qian , Du Haizhen , Han Shuo , Zhu Lin , Zhang Zhifu , Kong Yan , Li Na 
Published: March 2024

Abstract
PD-L1 immunohistochemistry (IHC) is a popular clinical test before anti PD-1/PD-L1 immunotherapy (IMT). Multiplex immunofluorescence (mIF) exhibits its advantage to evaluate more than one target on one FFPE section to provide the features of multiple target expression and co-expression signals. Here, based on our pathology controlled mIF detection system and AI based analysis data, we compared IHC PD-L1 TPS to mIF PD-L1 TPS, PD-L1 positive rate, and other target positive rates in one mIF panel, to evaluate their correlations. The correlations of IHC PD-L1 TPS, mIF PD-L1 TPS, mIF PD-L1 positive rate and other marker positive rate to anti PD-1 IMT response were further analyzed in this study. Thirty melanoma patient samples in this study have received PD-1 IMT. 13 cases exhibited partial response (PR) and 17 cases were progressive disease (PD). PFS of PR group and PD group was 27.3 and 4.8 months, respectively. Archived patient FFPE sections were examined utilizing mIF of PN 6-Plex Detection Kit (Beijing PhenoVision Bio Co., Itd) targeted CD68, CD3, CD20, S100, PD-L1 (CST, E1L3N) and PD-1. The staining images were scanned, tumor areas were labeled by pathologists and analyzed using PhenoVision mIF AI analysis system trained from Oncotopix Discovery system (Visopharm). Series sections were examined utilizing Dako PD-L1 IHC 22C3 pharmDx and TPS was scored by one clinical pathologist. mIF TPS was calculated after AI data analysis, using PD-L1/S100 co-positive cell number divided by S100 positive cell number. Data mining was performed using log-rank Mantel-Cox test, Cox regression analysis and binary logistic regression model in SPSS. In current study, the correlation between pathology scored TPS (pTPS) and mIF PD-L1 positive rate was observed (Pearson correlation coefficient (PCC)=0.447, p&lt;0.05). The consistency rate between pTPS and mIF TPS was 70% with no statistical significance. Moreover, the correlation between pTPS and mIF PD1/CD3 copositive rate was detected (PCC=0.604, p&lt;0.01). Interestingly, mIF PD-1/CD3 copositive rate predicted PR and PD groups (AUC=0.8, p&lt;0.01). Not pTPS nor mIF PD-L1 rate showed predictions here. Current results indicate that pTPS exhibited correlation to mIF PD-L1 positive rate instead of mIF PD-L1 TPS. CD3/PD-1 copositive rate but not pTPS nor mIF PD-L1 positive rate, shows its potential to predict anti PD-1 IMT response through the pathology controlled mIF detection system and AI based analysis system.
Citation Format: Hongzhe Sun, Xia Liu, Tao Jiao, Qian Guo, Haizhen Du, Shuo Han, Lin Zhu, Zhifu Zhang, Yan Kong, Na Li. Comparison the results from PD-L1 IHC and PD-L1 multiplex immunofluorescence for the prediction of anti-PD-1 immunotherapeutic response [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(6_Suppl):Abstract nr 6415.