C03 Predictive mechanical tumor markers: solid stress and multiscale viscoelastic data analysis

C03 预测性机械肿瘤标志物：固体应力和多尺度粘弹性数据分析

• 批准号: 530849390
• 负责人: Dr. Jing Guo, Ph.D.
• 金额: --
• 依托单位: Klinik für Radiologie (mit dem Bereich Kinderradiologie) (CCM)
• 依托单位国家: 德国
• 项目类别: Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/530849390?language=en
• 关键词: C03; Predictive; mechanical; tumor; markers

The mechanical traits of cancer include abnormally high solid stress as well as drastic and spatially heterogeneous changes in intrinsic mechanical tissue properties. Whereas solid stress elicits mechanosensory signals promoting tumor progression, mechanical heterogeneity and tissue fluidity are conducive to cell unjamming and metastatic spread. The overall aim of this subproject is to identify predictive fingerprints of mechanical parameter changes that are sensitive to tumor formation and specific to aggressive proliferation and unjamming transitions in tumors. To this end, C03 will first develop the tools to quantify solid stress in vivo, which will ultimately be combined with tissue fluidity (C02) and mechanical heterogeneity (C01) for mechanical profiling of tumors. Solid stress generated by growing tumors forms a biophysical environment in favor of tumor progression that must be analyzed in vivo, as it is not present after resection. Therefore, C03 aims to derive solid stress from in-vivo deformation fields obtained from large stain analysis of high-resolution 3D MRI using trained neuronal networks in combination with in-vivo stiffness maps acquired by tomoelastography. The novel marker of solid stress will support the reconciliation of multiplexed mechanical data, as solid stress is known to alter the apparent stiffness in tumors due to compression stiffening. To address mechanical parameter reconciliation, C03 will compile a library of data from all subprojects that include mechanical parameters from cells, organoids, tissues as well as in-vivo patients. Guided learning approaches that account for solid stress, tissue fluidity and mechanical heterogeneity across length scales will be used to specify best-fitting viscoelastic models and parameter ranges compatible with all mechanical data obtained in this research unit. Modality- and frequency independent mechanical parameters will be combined with clinical, histological and multi-omics data using unsupervised learning in order to identify clusters of high sensitivity, specificity and predictivity with respect to tumor biology of ex vivo and in vivo systems. Overall, our work plan builds on the main hypothesis that the mechanical hallmarks of cancer acquired by a tumor and noninvasively quantified with MRE determine tumor aggressiveness, malignant potential and treatment response. To verify this hypothesis, C03 will focus on (i) solid stress quantification in combination with other cancer-specific mechanical markers such as fluidity and heterogeneity provided by the other subprojects, (ii) learned modality-overarching and tissue-intrinsic constitutive parameters, and (iii) automated assessment of the diagnostic value of multifrequency MRE and quantitative MRI. Collectively, C03 aims to translate the data generated in all A-B-C projects into a roadmap of mechanical profiles which fully reflect the aggressive potential of a tumor and its responsiveness to treatment.

癌症的力学特征包括异常高的固体应力以及固有力学组织特性的剧烈和空间异质性变化。而固体应力引起的机械感觉信号促进肿瘤进展，机械异质性和组织流动性有利于细胞的畅通和转移扩散。这个子项目的总体目标是识别机械参数变化的预测指纹，这些指纹对肿瘤的形成敏感，对肿瘤中的侵袭性增殖和不干扰转变具有特异性。为此，C03将首先开发在体内量化固体应力的工具，最终将与组织流动性(C02)和机械异质性(C01)相结合，用于肿瘤的机械侧写。肿瘤生长产生的固体应力形成了有利于肿瘤进展的生物物理环境，必须在体内进行分析，因为切除后不存在。因此，C03的目标是从高分辨率3D MRI的大应变分析中获得的体内变形场中获得固体应力，该分析使用训练的神经元网络结合通过断层弹性成像获得的体内刚性图。固体应力的新标记物将支持多路力学数据的协调，因为固体应力已知会改变由于压缩僵硬而导致的肿瘤的表观硬度。为了解决机械参数协调问题，C03将汇编来自所有子项目的数据库，其中包括来自细胞、器官、组织以及体内患者的机械参数。将使用考虑固体应力、组织流动性和长度尺度上的力学异质性的指导学习方法来指定与本研究单位获得的所有力学数据相兼容的最佳拟合粘弹性模型和参数范围。与形态和频率无关的机械参数将使用无监督学习与临床、组织学和多组学数据相结合，以识别与体外和体内系统的肿瘤生物学相关的高敏感性、特异性和预测性的簇。总体而言，我们的工作计划建立在主要假设的基础上，即肿瘤获得的癌症的机械特征并通过MRE非侵入性量化决定了肿瘤的侵袭性、恶性潜力和治疗反应。为了验证这一假设，C03将侧重于(I)结合其他子项目提供的其他癌症特异性机械标记物(如流动性和异质性)的固体应力量化，(Ii)学习的总体形态和组织固有的本征参数，以及(Iii)多频率MRE和定量MRI诊断价值的自动评估。总的来说，C03的目标是将所有A-B-C项目中产生的数据转换为充分反映肿瘤侵袭性潜力及其治疗反应性的机械配置路线图。