Towards a quantitative understanding of tumor evolution

定量了解肿瘤进化

• 批准号: 10454356
• 负责人: Raul Rabadan
• 金额: $ 95.26万
• 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/10454356
• 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，其克隆结构可以在强大的选择压力下改变， 例如接受治疗。随着肿瘤进展的不同阶段，它们与基质细胞共同进化， 这些问题总体上构成了评估新技术的潜在价值的重大挑战， 治疗策略我们和其他研究小组最近的发现， 许多肿瘤的克隆结构，治疗下的动态，以及它们与免疫系统的相互作用 系统在大多数肿瘤中，几个亚克隆群体同时共存，最初较小的克隆起作用， 在肿瘤演变的后续阶段起主导作用。随着克隆和基质异质性的出现， 癌症进展和治疗失败的潜在驱动力，迫切需要揭示 定量的基本原则，肿瘤的演变及其动态相互作用， 微环境我最近的研究表明，肿瘤的演变并不是随机的， 通过一个高度结构化的过程，未来的优势亚克隆可以识别和瞄准。的 我的小组在过去几年中开发的定量方法特别适合于阐明 强选择下克隆系统的进化模式。该建议的核心假设是：（1） 肿瘤和间质以协调的方式共同进化，（2）可以通过基因组和 单细胞纵向取样，（3）这些克隆可以被靶向，和（4）为了表征这些克隆 我们需要制定新的量化方法。本提案的首要目标是揭示 小肿瘤和间质群体共同进化并驱动肿瘤进展的机制， 耐药性的出现，使用胶质母细胞瘤作为范例。定量方法和基本原则 从这项研究中得出的肿瘤进化原理将应用于其他肿瘤类型。