预测肺癌患者辐射引起的肺损伤 - 挑战和机遇:预测辐射引起的肺损伤。
Predicting radiation-induced lung injury in lung cancer patients - challenges and opportunities: Predicting radiation-induced lung injury.
发表日期:2023 Nov 02
作者:
Merian E Kuipers, Krista C J van Doorn-Wink, Pieter S Hiemstra, Annelies M Slats
来源:
Epigenetics & Chromatin
摘要:
放射引起的肺损伤(RILI)是肺癌放射治疗(RT)中主要的剂量限制性毒性之一。大约 10-20% 的患者表现出不同严重程度的 RILI 体征。 RILI 严重程度如此广泛的原因及其发展机制目前尚不完全清楚。已经确定了许多临床风险因素,可以帮助临床决策。放射治疗技术的进步和严格的危及器官剂量限制的使用有助于减少 RILI。通过结合细胞因子评估、白细胞 γH2AX 检测或表观遗传标记,可以进一步改善对谁有 RILI 风险的预测。一个复杂的因素是缺乏 RILI 的客观定义。可以使用 CT 密度测定、FDG-PET 摄取、肺功能测量变化或呼出气分析等工具来更好地定义和量化 RILI。这有助于寻找新的生物标志物,这些标志物可以通过组学技术、(单细胞)RNA 测序和质谱流式分析以及患者特异性体外细胞培养模型的进展来加速。 RILI 的客观量化与这些新技术相结合,有助于开发未来的生物标志物,以更好地预测哪些患者面临风险并做出个性化治疗决策。版权所有 © 2023。由 Elsevier Inc. 出版。
Radiation-induced lung injury (RILI) is one of the main dose-limiting toxicities in radiotherapy (RT) for lung cancer. Approximately 10-20% of patients show signs of RILI with variable severity. The reason for the wide range of RILI severity and the mechanisms underlying its development are only partially understood. A number of clinical risk factors have been identified and can aid in clinical decision making. Technological advancements in radiotherapy and the use of strict organ-at-risk dose constraints have helped to reduce RILI. Predicting who is at risk for RILI can possibly be further improved with a combination of cytokine assessments, γH2AX-assays in leukocytes, or epigenetic markers. A complicating factor is the lack of an objective definition of RILI. Tools such as CT-densitometry, FDG-PET uptake, changes in lung function measurements or the use of exhaled breath analysis can be implemented to better define and quantify RILI. This can aid in the search for new biomarkers which can be accelerated by omics techniques, (single cell) RNA sequencing and mass cytometry, as well as advances in patient-specific in vitro cell culture models. An objective quantification of RILI combined with these novel techniques can aid in the development of future biomarkers to better predict which patients are at risk and allow personalized treatment decisions.Copyright © 2023. Published by Elsevier Inc.