Longitudinal Multi-Omic Data Integration for the Study of Evolutionary Dynamics and Awakening in Hormone Dependent Breast Cancer.

用于激素依赖性乳腺癌进化动力学和觉醒研究的纵向多组学数据整合。

• 批准号: 2749481
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2749481
• 关键词: Longitudinal; Multi; Omic; Data; Integration

Relapse continues to be a major problem in oestrogen receptor positive (ER) breast cancer. Risk of relapse continues for at least 15 years after endocrine treatment discontinuation, and the recurrence rate remains stable across time. This points towards a subpopulation of cancer cells entering dormancy, acquiring resistance, and then stochastically awakening, leading to relapse. Genomic, transcriptomic and epigenetic profiles at bulk, along with single cell expression and lineage tracing profiles, are available in house from MCF7 and T47D cell lines undergoing dormancy-awakening dynamics in response to endocrine therapy. Previous analyses of this data have highlighted the importance of studying the awakening of dormant cells, and longitudinal multi-omic data integration could unveil new insights into the mechanisms driving this process. Our first aim will be to apply and benchmark established statistical machine learning approaches, including dimensionality reduction, semi-supervised data integration and network analysis. For our second aim, we will develop novel statistical methods for data integration building on the work of the Evangelou Group. In particular, we will consider the addition of a temporal dimension to data integration approaches, an area in multi omic data integration that is currently under-explored. This exploration of temporal data will let us address our third and main aim, understanding the biological and molecular processes underlying the evolution of cancer cells from dormancy to awakening and test hypotheses such as epigenetic erosion.

复发仍然是雌激素受体阳性（ER）乳腺癌的主要问题。停止内分泌治疗后，复发风险至少持续15年，复发率随时间保持稳定。这表明癌细胞亚群进入休眠状态，获得抗性，然后随机觉醒，导致复发。基因组学、转录组学和表观遗传图谱，以及单细胞表达和谱系追踪图谱，都可以从MCF7和T47D细胞系中获得，这些细胞系在内分泌治疗下经历休眠-觉醒动力学。先前对这些数据的分析强调了研究休眠细胞觉醒的重要性，而纵向多组学数据整合可以揭示驱动这一过程的机制的新见解。我们的第一个目标是应用和测试已建立的统计机器学习方法，包括降维、半监督数据集成和网络分析。对于我们的第二个目标，我们将在Evangelou小组的工作基础上开发新的数据集成统计方法。特别是，我们将考虑在数据集成方法中添加时间维度，这是目前尚未充分探索的多组数据集成领域。这种对时间数据的探索将使我们解决我们的第三个也是主要目标，即了解癌细胞从休眠到觉醒的进化背后的生物学和分子过程，并测试诸如表观遗传侵蚀等假设。