Login / Signup

Programmed death ligand-1 (PD-L1) immunohistochemical assessment using the QR1 clone in muscle-invasive urothelial carcinomas: a comparison with reference clones 22C3 and SP263.

Andrada LoghinAdela Nechifor-BoilăAngela BordaIoan Alin Nechifor-BoilăSeptimiu Toader VoidazanMyriam Decaussin-Petrucci
Published in: Virchows Archiv : an international journal of pathology (2021)
Programmed death ligand-1 (PD-L1) immunohistochemical (IHC) status is used to predict which patients with metastatic urothelial carcinoma (UC) will respond to immunotherapy. We aimed to compare QR1(Quartett), 22C3 (Dako), and SP263 (Ventana) detection of PD-L1 expression in muscle-invasive UCs and determine the best scoring algorithm for assessment of PD-L1 expression when using the QR1 clone. Our study included 69 UCs. For SP263 and 22C3, PD-L1-positive tumor cell (TC) and/or immune cell (IC) percentages (TC%/IC%) and the Combined Positive Score (CPS) were assessed, respectively (positivity cut-offs of ≥ 25% and ≥ 10). For QR1, both interpretation systems were evaluated. The concordances between assays were calculated. PD-L1 IHC staining characteristics were comparable between QR1, 22C3, and SP263 in both conventional and variant histology UCs. We demonstrated strong or very strong correlations between clones; the strongest correlation for TCs was between QR1 and SP263 (r = 0.92; p = 0.001) and for ICs was between QR1 and 22C3 (r = 0.85; p = 0.001). Our comparative analysis of the scoring algorithms revealed very good concordances among the three assays (range 0.791-0.878); the highest concordance was between QR1 and SP263 when CPS was used as the scoring algorithm for QR1 (0.878; p < 0.001). Our study is the first to demonstrate that the QR1 clone can be used to evaluate PD-L1 status in UCs, with a very good agreement rate with the reference clones. QR1 appeared to be more similar to the SP263 clone. With regard to the scoring algorithm, when evaluating PD-L1 expression using QR1 clone, CPS performed better compared with the TC%/IC% score.
Keyphrases
  • machine learning
  • deep learning
  • stem cells
  • skeletal muscle
  • high throughput
  • cell therapy
  • sensitive detection
  • clinical evaluation