精准医学的表型图谱:在个性化水平上评估治疗反应和耐药性的一个有前途的系统生物学工具。

Frontiers in network physiology Pub Date : 2023-10-25 eCollection Date: 2023-01-01 DOI:10.3389/fnetp.2023.1256104
Sayantan Bhattacharyya, Shafqat F Ehsan, Loukia G Karacosta
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引用次数: 0

摘要

从这个角度来看,我们讨论了肿瘤异质性和治疗耐药性如何需要关注更个性化的方法,从而促使向精准医学的转变。在向个性化医疗转变的核心,组学驱动的系统生物学成为一股驱动力,因为它利用了高通量技术和新型生物信息学工具。这些能够创建基于系统的地图,提供单个肿瘤功能可塑性的全面视图。我们重点介绍了创新的PHENOSTAMP程序,该程序利用高维数据构建视觉上直观且用户友好的地图。该图谱的创建是为了概括癌细胞中复杂的过渡状态,如上皮-间充质转化(EMT)和间充质-上皮转化(MET),提供了一种直观的方法来了解与EMT相关的单细胞表型相关的疾病进展和单细胞治疗反应。最重要的是,PHENOSTAMP作为参考图谱,允许研究人员和临床医生一次评估一个临床标本的表型异质性,为构建个性化医疗的表型图谱奠定基础。这一观点认为,这种动态预测地图也可以促进个性化癌症治疗的发展。它们有可能改变我们对癌症生物学的理解,为未来根据每个患者独特的分子和细胞肿瘤特征量身定制治疗奠定基础。随着我们对癌症知识的扩展,这些地图可以不断地改进,确保它们仍然是精确肿瘤学的有价值的工具。
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Phenotypic maps for precision medicine: a promising systems biology tool for assessing therapy response and resistance at a personalized level.

In this perspective we discuss how tumor heterogeneity and therapy resistance necessitate a focus on more personalized approaches, prompting a shift toward precision medicine. At the heart of the shift towards personalized medicine, omics-driven systems biology becomes a driving force as it leverages high-throughput technologies and novel bioinformatics tools. These enable the creation of systems-based maps, providing a comprehensive view of individual tumor's functional plasticity. We highlight the innovative PHENOSTAMP program, which leverages high-dimensional data to construct a visually intuitive and user-friendly map. This map was created to encapsulate complex transitional states in cancer cells, such as Epithelial-Mesenchymal Transition (EMT) and Mesenchymal-Epithelial Transition (MET), offering a visually intuitive way to understand disease progression and therapeutic responses at single-cell resolution in relation to EMT-related single-cell phenotypes. Most importantly, PHENOSTAMP functions as a reference map, which allows researchers and clinicians to assess one clinical specimen at a time in relation to their phenotypic heterogeneity, setting the foundation on constructing phenotypic maps for personalized medicine. This perspective argues that such dynamic predictive maps could also catalyze the development of personalized cancer treatment. They hold the potential to transform our understanding of cancer biology, providing a foundation for a future where therapy is tailored to each patient's unique molecular and cellular tumor profile. As our knowledge of cancer expands, these maps can be continually refined, ensuring they remain a valuable tool in precision oncology.

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