Biodesix, Inc, 2970 Wilderness Place, Boulder, CO 80301, USA.
Int J Mol Sci. 2020 Jan 28;21(3):838. doi: 10.3390/ijms21030838.
The remarkable success of immune checkpoint inhibitors (ICIs) has given hope of cure for some patients with advanced cancer; however, the fraction of responding patients is 15-35%, depending on tumor type, and the proportion of durable responses is even smaller. Identification of biomarkers with strong predictive potential remains a priority. Until now most of the efforts were focused on biomarkers associated with the assumed mechanism of action of ICIs, such as levels of expression of programmed death-ligand 1 (PD-L1) and mutation load in tumor tissue, as a proxy of immunogenicity; however, their performance is unsatisfactory. Several assays designed to capture the complexity of the disease by measuring the immune response in tumor microenvironment show promise but still need validation in independent studies. The circulating proteome contains an additional layer of information characterizing tumor-host interactions that can be integrated into multivariate tests using modern machine learning techniques. Here we describe several validated serum-based proteomic tests and their utility in the context of ICIs. We discuss test performances, demonstrate their independence from currently used biomarkers, and discuss various aspects of associated biological mechanisms. We propose that serum-based multivariate proteomic tests add a missing piece to the puzzle of predicting benefit from ICIs.
免疫检查点抑制剂 (ICIs) 的显著成功为一些晚期癌症患者带来了治愈的希望；然而，根据肿瘤类型的不同，应答患者的比例为 15-35%，持久应答的比例更小。具有强大预测潜力的生物标志物的鉴定仍然是当务之急。到目前为止，大多数研究都集中在与 ICI 假定作用机制相关的生物标志物上，例如程序性死亡配体 1 (PD-L1) 的表达水平和肿瘤组织中的突变负荷，作为免疫原性的替代物；然而，它们的性能并不令人满意。一些旨在通过测量肿瘤微环境中的免疫反应来捕捉疾病复杂性的检测方法显示出了希望，但仍需要在独立研究中进行验证。循环蛋白质组包含另外一层描述肿瘤-宿主相互作用的信息，可以使用现代机器学习技术将其整合到多变量测试中。在这里，我们描述了几种经过验证的基于血清的蛋白质组学检测方法及其在 ICI 背景下的应用。我们讨论了检测性能，证明了它们与当前使用的生物标志物的独立性，并讨论了相关生物学机制的各个方面。我们提出，基于血清的多变量蛋白质组学检测为预测 ICI 获益增加了缺失的一环。
