Inverse Mapping of Spatial-Temporal Molecular Heterogeneity from Imaging Phenotype

从成像表型逆映射时空分子异质性

• 批准号: 1903135
• 负责人: Jing Li
• 金额: $ 80万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1903135&HistoricalAwards=false
• 关键词: Inverse; Mapping; Spatial; Temporal; Molecular

A significant challenge in treating some aggressive cancers and neurological diseases is the lack of understanding regarding the spatial-temporal molecular heterogeneity of the diseased tissue/organ. Molecular characteristics and interaction vary significantly across different spatial sub-units of the tissue/organ. The spatial pattern also changes over time as the disease progresses. The capability of mapping out the spatial-temporal patterns of molecular biomarkers is instrumental for elucidating the biological underpinning of disease formation, progression, and treatment resistance. This mapping, however, is extremely challenging, because it would require dense sampling of the diseased tissue/organ of a living person by highly invasive biopsy, which is infeasible. In reality, only a few samples can be taken, leaving a vast amount of unknown blank regions. On the other hand, recent years have witnessed the rapid advances of biomedical imaging technologies, which create structural and functional images of various modalities. Multimodality images can be taken non-invasively and for the entire diseased tissue/organ; importantly, they provide a complementary phenotypic presentation of underlying molecular characteristics. This creates an unprecedented opportunity to generate inverse-estimates of the underlying spatial-temporal molecular characteristics from the images to fill in the "blank regions." Once achieved, such inverse-estimates would help decipher the complex biological system of the diseased tissue/organ and inform new effective treatments with unparalleled precision adapted to spatial-temporal molecular heterogeneity. The objective of this project is to develop a suite of new statistical models for inverse mapping/estimation of the spatial-temporal heterogeneity of molecular biomarkers from multimodality image phenotype. The investigators propose a novel modeling framework that integrates data-driven and biological-principle-driven mechanistic models, and meanwhile fuses global-scale image data and sparsely-sampled local biopsy measurements. This framework embraces modeling approaches to characterize both spatial heterogeneity and temporal dynamics of the disease. Furthermore, to account for patient similarity and specificity, the investigators propose a robust transfer learning model for integrating each patient?s data with information selectively transferred from other patients to avoid the negative transfer. Also, the project tackles joint modeling of a biomarker panel for characterizing spatial-temporal biomarker interaction. The proposed models will be validated in two applications: glioblastoma and Alzheimer's Disease. This project is expected to generate significant insight for unraveling the complex biological systems underlying these diseases and provide the groundwork for new treatment intervention. Additionally, the proposed modeling framework integrates statistical and bio-mechanistic models, which bridges two traditionally separate research fields together. The research team is committed to educating the next generation statisticians and biomedical researchers with cross-disciplinary skills, recruiting minority and women students, and disseminating research results in both statistical and bio/biomedical communities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

治疗一些侵袭性癌症和神经系统疾病的一个重大挑战是缺乏对患病组织/器官的时空分子异质性的了解。组织/器官的不同空间亚单位的分子特征和相互作用存在显着差异。随着疾病的进展，空间模式也会随着时间而变化。绘制分子生物标志物时空模式的能力有助于阐明疾病形成、进展和治疗耐药性的生物学基础。然而，这种绘图极具挑战性，因为它需要通过高侵入性活检对活人的患病组织/器官进行密集采样，这是不可行的。实际上，只能采集少量样本，留下大量未知的空白区域。另一方面，近年来生物医学成像技术的快速发展，产生了各种模式的结构和功能图像。可以非侵入性地拍摄整个病变组织/器官的多模态图像；重要的是，它们提供了潜在分子特征的互补表型呈现。这创造了前所未有的机会，可以从图像中生成潜在时空分子特征的逆估计，以填充“空白区域”。一旦实现，这种逆估计将有助于破译患病组织/器官的复杂生物系统，并以无与伦比的精度适应时空分子异质性，为新的有效治疗提供信息。 该项目的目标是开发一套新的统计模型，用于根据多模态图像表型对分子生物标志物的时空异质性进行逆映射/估计。研究人员提出了一种新颖的建模框架，该框架集成了数据驱动和生物原理驱动的机制模型，同时融合了全球规模的图像数据和稀疏采样的局部活检测量结果。该框架采用建模方法来表征疾病的空间异质性和时间动态。此外，为了考虑患者的相似性和特异性，研究人员提出了一种强大的迁移学习模型，用于将每个患者的数据与有选择地从其他患者传输的信息整合起来，以避免负迁移。此外，该项目还解决了生物标记物组的联合建模问题，以表征时空生物标记物相互作用。所提出的模型将在两种应用中得到验证：胶质母细胞瘤和阿尔茨海默病。该项目预计将为揭示这些疾病背后的复杂生物系统提供重要的见解，并为新的治疗干预提供基础。此外，所提出的建模框架集成了统计和生物力学模型，将两个传统上独立的研究领域连接在一起。研究团队致力于培养具有跨学科技能的下一代统计学家和生物医学研究人员，招募少数族裔和女性学生，并在统计和生物/生物医学界传播研究成果。该奖项反映了 NSF 的法定使命，并被认为是值得的通过使用基金会的智力优势和更广泛的影响审查标准进行评估来获得支持。