PD-L1 Expression and its Prognostic Value in Metastatic Papillary Renal Cell Carcinoma: Results from a GETUG Multicenter Retrospective Cohort

crossref(2024)

Cited 0|Views17
No score
Abstract
Introduction Papillary renal cell carcinoma (pRCC) is a rare and aggressive cancer with no specifically established therapeutic strategy in the metastatic setting. Combinations of tyrosine kinase and immune checkpoint inhibitors (ICI) are a promising option. We aimed to study the immune landscape of metastatic pRCC, and its interactions with angiogenesis pathways, to search for potential therapeutic targets. Methods The expression of immune markers (PD-L1, PD-1, PD-L2, LAG-3) and angiogenic pathways (CAIX, c-MET), was analyzed by immunohistochemistry on 68 metastatic pRCC retrieved from a retrospective multicenter GETUG cohort.Our primary endpoint was to estimate the prevalence of PD-L1 expression and its prognostic impact in metastatic pRCC. Secondary endpoints included the evaluation of other immune markers (PD-1, PD-L2, and LAG-3) and their association with PD-L1. We also assessed angiogenic markers and their association with PD-L1. Results Overall, 27.9% of tumors were PD-L1 positive. PD-L2 was more frequently expressed (45.6%), PD-1 and LAG-3 were positive in 17.6% and 19.1% respectively. None of these markers was correlated with PD-L1 expression. 66% (45/68) expressed at least one immune marker, and 43% (29/68) were “double-positive”, as they expressed both immune and angiogenic markers. OS was significantly shorter for patients with PD-L1 positive pRCC. A multivariate analysis confirmed a significant association between PD-L1 expression and shorter overall survival (HR=4.0, p=0.01). Conclusion These results reinforce clinical data on the expected benefit of ICI in metastatic pRCC treatment, as PD-L1 expression is a factor of poor prognosis in this multicenter cohort.
More
Translated text
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined