Candiota Ana Paula, Arús Carles
Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, 08193 Barcelona, Spain.
Departament de Bioquímica i Biologia Molecular, Unitat de Bioquímica de Biociències, Edifici Cs, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, 08193 Barcelona, Spain.
Metabolites. 2022 Mar 14;12(3):243. doi: 10.3390/metabo12030243.
This hypothesis proposal addresses three major questions: (1) Why do we need imaging biomarkers for assessing the efficacy of immune system participation in glioblastoma therapy response? (2) Why are they not available yet? and (3) How can we produce them? We summarize the literature data supporting the claim that the immune system is behind the efficacy of most successful glioblastoma therapies but, unfortunately, there are no current short-term imaging biomarkers of its activity. We also discuss how using an immunocompetent murine model of glioblastoma, allowing the cure of mice and the generation of immune memory, provides a suitable framework for glioblastoma therapy response biomarker studies. Both magnetic resonance imaging and magnetic resonance-based metabolomic data (i.e., magnetic resonance spectroscopic imaging) can provide non-invasive assessments of such a system. A predictor based in nosological images, generated from magnetic resonance spectroscopic imaging analyses and their oscillatory patterns, should be translational to clinics. We also review hurdles that may explain why such an oscillatory biomarker was not reported in previous imaging glioblastoma work. Single shot explorations that neglect short-term oscillatory behavior derived from immune system attack on tumors may mislead actual response extent detection. Finally, we consider improvements required to properly predict immune system-mediated early response (1-2 weeks) to therapy. The sensible use of improved biomarkers may enable translatable evidence-based therapeutic protocols, with the possibility of extending preclinical results to human patients.
（1）为何我们需要成像生物标志物来评估免疫系统参与胶质母细胞瘤治疗反应的疗效？（2）为何目前尚无此类标志物？以及（3）我们如何生成它们？我们总结了文献数据，这些数据支持以下观点：免疫系统是大多数成功的胶质母细胞瘤治疗疗效的背后因素，但不幸的是，目前尚无其活性的短期成像生物标志物。我们还讨论了使用具有免疫活性的胶质母细胞瘤小鼠模型，该模型可使小鼠治愈并产生免疫记忆，为胶质母细胞瘤治疗反应生物标志物研究提供了合适的框架。磁共振成像和基于磁共振的代谢组学数据（即磁共振波谱成像）均可提供对该系统的非侵入性评估。基于磁共振波谱成像分析及其振荡模式生成的疾病分类图像预测指标应可转化至临床。我们还回顾了一些障碍，这些障碍或许可以解释为何此前在胶质母细胞瘤成像研究中未报告此类振荡生物标志物。忽略免疫系统对肿瘤攻击产生的短期振荡行为的单次探索可能会误导对实际反应程度的检测。最后，我们考虑了为准确预测免疫系统介导的早期（1 - 2周）治疗反应所需的改进措施。合理使用改进后的生物标志物可能会促成可转化的循证治疗方案，并有可能将临床前结果推广至人类患者。
