McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Canada; Ludmer Centre for NeuroInformatics and Mental Health, Montreal, Canada.
Biospective Inc., Montreal, Canada.
Neuroimage. 2018 Oct 1;179:40-50. doi: 10.1016/j.neuroimage.2018.06.028. Epub 2018 Jun 14.
Personalized Medicine (PM) seeks to assist the patients according to their specific treatment needs and potential intervention responses. However, in the neurological context, this approach is limited by crucial methodological challenges, such as the requirement for an understanding of the causal disease mechanisms and the inability to predict the brain's response to therapeutic interventions. Here, we introduce and validate the concept of the personalized Therapeutic Intervention Fingerprint (pTIF), which predicts the effectiveness of potential interventions for controlling a patient's disease evolution. Each subject's pTIF can be inferred from multimodal longitudinal imaging (e.g. amyloid-β, metabolic and tau PET; vascular, functional and structural MRI). We studied an aging population (N = 331) comprising cognitively normal and neurodegenerative patients, longitudinally scanned using six different neuroimaging modalities. We found that the resulting pTIF vastly outperforms cognitive and clinical evaluations on predicting individual variability in gene expression (GE) profiles. Furthermore, after regrouping the patients according to their predicted primary single-target interventions, we observed that these pTIF-based subgroups present distinctively altered molecular pathway signatures, supporting the across-population identification of dissimilar pathological stages, in active correspondence with different therapeutic needs. The results further evidence the imprecision of using broad clinical categories for understanding individual molecular alterations and selecting appropriate therapeutic needs. To our knowledge, this is the first study highlighting the direct link between multifactorial brain dynamics, predicted treatment responses, and molecular alterations at the patient level. Inspired by the principles of PM, the proposed pTIF framework is a promising step towards biomarker-driven assisted therapeutic interventions, with additional important implications for selective enrollment of patients in clinical trials.
个性化医学（PM）旨在根据患者的特定治疗需求和潜在干预反应来为其提供帮助。然而，在神经科学领域，这种方法受到了关键性的方法论挑战的限制，例如需要了解因果性疾病机制以及无法预测大脑对治疗干预的反应。在这里，我们引入并验证了个性化治疗干预指纹（pTIF）的概念，该概念可以预测潜在干预措施控制患者疾病进展的效果。每个患者的 pTIF 可以从多模态纵向成像（例如，β-淀粉样蛋白、代谢和 tau PET；血管、功能和结构 MRI）中推断出来。我们研究了一个老龄化人群（N=331），包括认知正常和神经退行性疾病患者，使用六种不同的神经影像学模式进行了纵向扫描。我们发现，由此产生的 pTIF 在预测个体基因表达（GE）谱的变异性方面，大大优于认知和临床评估。此外，根据他们预测的主要单一靶向干预措施对患者进行分组后，我们观察到这些基于 pTIF 的亚组呈现出明显改变的分子途径特征，支持在人群中识别不同的病理阶段，与不同的治疗需求密切相关。结果进一步证明了使用广泛的临床类别来理解个体分子改变和选择适当治疗需求的不准确性。据我们所知，这是第一项强调多因素大脑动力学、预测治疗反应和个体患者分子改变之间直接联系的研究。受 PM 原则的启发，所提出的 pTIF 框架是朝着基于生物标志物的辅助治疗干预迈出的有前途的一步，对临床试验中患者的选择性招募具有额外的重要意义。
