Towards a quantitative understanding of tumor evolution

定量了解肿瘤进化

• 批准号: 10642835
• 负责人: Raul Rabadan
• 金额: $ 76.54万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10642835
• 关键词: Address; Architecture; Biological; Cells; Complex; Development; Drug resistance; Evolution; Exposure to; Failure; Future; Genomics; Glioblastoma; Goals; Heterogeneity; Immune system; Malignant Neoplasms; Minor; Pattern; Phase; Play; Population; Process; Research; Resistance; Role; Sampling; Structure; System; Therapeutic; Work; driving force; novel; novel therapeutic intervention; pressure; single cell technology; tumor; tumor progression

ABSTRACT Cancers are dynamic biological entities whose clonal architecture can change under strong selection pressures, such as exposure to therapy. As tumors progress through different stages, they coevolve with stromal populations, which in aggregate constitutes a significant challenge in assessing the potential value of new therapeutic strategies. Recent discoveries by us and other groups have provided a glimpse of the complexity of the clonal architecture of many tumors, their dynamics under therapy, and their interactions with the immune system. In most tumors, several sub-clonal populations simultaneously co-exist, and initially minor clones play a dominant role in subsequent phases of the tumor’s evolution. As clonal and stromal heterogeneity emerge as driving forces underlying cancer progression and therapeutic failure, there is a critical need for uncovering the quantitative fundamental principles underlying the evolution of tumors and their dynamic interaction with their microenvironment. My recent work has shown that tumor evolution does not proceed in a stochastic fashion but through a highly structured process, and that future dominant subclones can both be identified and targeted. The quantitative approaches developed by my group in the last few years are particularly tailored to elucidate the evolutionary patterns of clonal systems under strong selection. The central hypothesis of this proposal is that (1) tumor and stroma coevolve in an orchestrated fashion, (2) seeding clones can be identified through genomic and single cell longitudinal sampling, (3) these clones can be targeted, and (4) in order to characterize these clones we need to develop new quantitative approaches. The overarching goal of the present proposal is to uncover the mechanisms by which small tumor and stromal populations coevolve and drive tumor progression and the emergence of drug resistance, using glioblastoma as a paradigm. Quantitative approaches and fundamental principles of tumor evolution derived from this research will then be applied to other tumor types.

摘要 癌症是动态的生物实体，其克隆结构在强大的选择压力下会发生变化， 比如接受治疗。随着肿瘤的发展经历不同的阶段，它们与间质共同进化。 人口总数构成了评估新的潜在价值的重大挑战 治疗策略。我们和其他组织最近的发现让我们得以一窥 许多肿瘤的克隆构筑，它们在治疗中的动态，以及它们与免疫的相互作用 系统。在大多数肿瘤中，几个亚克隆群体同时共存，最初的小克隆扮演着 在肿瘤演化的后续阶段起主导作用。随着克隆和基质的异质性出现为 癌症进展和治疗失败的驱动力，迫切需要揭示 肿瘤演化的量化基本原理及其与肿瘤的动态相互作用 微环境。我最近的工作表明，肿瘤的进化不是以随机的方式进行的，而是 通过一个高度结构化的过程，未来的主导亚克隆可以被识别和定位。这个 我的团队在过去几年中开发的量化方法特别适合于阐明 强选择条件下克隆系统的进化格局。这一提议的中心假设是：(1) 肿瘤和间质以一种有组织的方式共同进化，(2)种子克隆可以通过基因组和 单细胞纵向采样，(3)这些克隆可以作为靶点，以及(4)为了表征这些克隆 我们需要开发新的量化方法。本提案的总体目标是揭示 小肿瘤和间质群体共同进化和推动肿瘤进展的机制 出现耐药，以胶质母细胞瘤为范式。量化方法和基本原理 这项研究得出的肿瘤进化原理将应用于其他类型的肿瘤。