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.

抽象的 癌症是动态的生物学实体，其克隆建筑可以在强大的选择压力下改变， 例如接触治疗。随着肿瘤在不同阶段的发展，它们会与基质相结合 人口总共构成了评估新的潜在价值的重大挑战 治疗策略。我们和其他群体的最新发现瞥见了 许多肿瘤的克隆结构，正在接受治疗的动力学及其与免疫的相互作用 系统。在大多数肿瘤中，几个亚克隆种群仅共存，最初较小的克隆播放 在肿瘤进化的随后阶段中的主要作用。随着克隆和基质异质性的出现 癌症进展和治疗衰竭的驱动力，迫切需要发现 肿瘤演变的基本原理及其与其动态相互作用的基本原理 微环境。我最近的工作表明，肿瘤进化不是以随机方式进行的，而是 通过一个高度结构化的过程，可以识别和针对未来的主要子克隆。这 我小组在过去几年中开发的定量方法特别量身定制，以阐明 克隆系统在强烈选择下的进化模式。该提议的中心假设是（1） 肿瘤和基质以精心策划的方式相结合，（2）播种克隆可以通过基因组和 单细胞纵向采样，（3）这些克隆可以靶向，（4）为了表征这些克隆 我们需要开发新的定量方法。本提案的总体目标是揭露 小肿瘤和基质种群共同进化并驱动肿瘤进展的机制 使用胶质母细胞瘤作为范式的耐药性出现。定量方法和基本 然后将这项研究得出的肿瘤进化原理应用于其他肿瘤类型。