Akademik Veri Yönetim Sistemi
Cancer Immunology, Immunotherapy, cilt.72, sa.12, ss.4065-4075, 2023 (SCI-Expanded)
Targeting PD-1/PD-L1 has shown substantial therapeutic response and unprecedented long-term durable responses in the clinic. However, several challenges persist, encompassing the prediction of treatment effectiveness and patient responses, the emergence of treatment resistance, and the necessity for additional biomarkers. Consequently, we comprehensively explored the often-overlooked isoforms of crucial immunotherapy players, leveraging transcriptomic analysis, structural modeling, and immunohistochemistry (IHC) data. Our investigation has led to the identification of an alternatively spliced isoform of PD-L1 that lacks exon 3 (PD-L1∆3) and the IgV domain required to interact with PD-1. PD-L1∆3 is expressed more than the canonical isoform in a subset of breast cancers and other TCGA tumors. Using the deep learning-based protein modeling tool AlphaFold2, we show the lack of a possible interaction between PD-L1∆3 and PD-1. In addition, we present data on the expression of an additional ligand for PD-1, PD-L2. PD-L2 expression is widespread and positively correlates with PD-L1 levels in breast and other tumors. We report enriched epithelial-mesenchymal transition (EMT) signature in high PD-L2 transcript expressing (PD-L2 > PD-L1) tumors in all breast cancer subtypes, highlighting potential crosstalk between EMT and immune evasion. Notably, the estrogen gene signature is downregulated in ER + breast tumors with high PD-L2. The data on PD-L2 IHC positivity but PD-L1 negativity in breast tumors, together with our results on PD-L1∆3, highlight the need to utilize PD-L2 and PD-L1 isoform-specific antibodies for staining patient tissue sections to offer a more precise prediction of the outcomes of PD-1/PD-L1 immunotherapy.