Department of Biological Sciences, Middle East Technical University (METU), Dumlupinar Blv No:1 Universiteler Mah, Cankaya, 06800, Ankara, Türkiye.
AQUARIUS/NPG Genetic Diseases Evaluation Center, Kucukbakkalkoy Mah. Kayisdagi Cad. 137/6 Atasehir, Istanbul, Türkiye.
Cancer Immunol Immunother. 2023 Dec;72(12):4065-4075. doi: 10.1007/s00262-023-03543-y. Epub 2023 Sep 28.
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.
靶向 PD-1/PD-L1 在临床上显示出了显著的治疗反应和前所未有的长期持久反应。然而,仍存在一些挑战,包括治疗效果和患者反应的预测、治疗耐药性的出现以及额外生物标志物的需求。因此,我们综合利用转录组分析、结构建模和免疫组化(IHC)数据,全面探索了关键免疫治疗靶点经常被忽视的异构体。我们的研究确定了一种缺少与 PD-1 相互作用所需的外显子 3 和 IgV 结构域的 PD-L1 剪接异构体(PD-L1∆3)。在一部分乳腺癌和其他 TCGA 肿瘤中,PD-L1∆3 的表达量高于经典异构体。我们使用基于深度学习的蛋白质建模工具 AlphaFold2 展示了 PD-L1∆3 与 PD-1 之间缺乏可能的相互作用。此外,我们还提供了 PD-1 的另一个配体 PD-L2 表达的数据。PD-L2 的表达广泛,与乳腺癌和其他肿瘤中 PD-L1 水平呈正相关。我们报告了在所有乳腺癌亚型中,PD-L2 高表达(PD-L2>PD-L1)的肿瘤中富集上皮-间充质转化(EMT)特征,突出了 EMT 与免疫逃逸之间的潜在相互作用。值得注意的是,高 PD-L2 表达的 ER+乳腺癌肿瘤中雌激素基因特征下调。乳腺癌中 PD-L2 IHC 阳性但 PD-L1 阴性的数据,以及我们关于 PD-L1∆3 的结果,强调了需要使用 PD-L2 和 PD-L1 异构体特异性抗体来对患者组织切片进行染色,以更准确地预测 PD-1/PD-L1 免疫治疗的结果。