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人类癌症的 oncospace。

An oncospace for human cancers.

机构信息

ISEM, CNRS, IRD, EPHE, University of Montpellier, Montpellier, France.

ICREA-Complex Systems Lab, Universitat Pompeu Fabra, Barcelona, Spain.

出版信息

Bioessays. 2023 May;45(5):e2200215. doi: 10.1002/bies.202200215. Epub 2023 Mar 2.

DOI:10.1002/bies.202200215
PMID:36864571
Abstract

Human cancers comprise an heterogeneous array of diseases with different progression patterns and responses to therapy. However, they all develop within a host context that constrains their natural history. Since it occurs across the diversity of organisms, one can conjecture that there is order in the cancer multiverse. Is there a way to capture the broad range of tumor types within a space of the possible? Here we define the oncospace, a coordinate system that integrates the ecological, evolutionary and developmental components of cancer complexity. The spatial position of a tumor results from its departure from the healthy tissue along these three axes, and progression trajectories inform about the components driving malignancy across cancer subtypes. We postulate that the oncospace topology encodes new information regarding tumorigenic pathways, subtype prognosis, and therapeutic opportunities: treatment design could benefit from considering how to nudge tumors toward empty evolutionary dead ends in the oncospace.

摘要

人类癌症包括具有不同进展模式和对治疗反应的异质性疾病。然而,它们都在宿主环境中发展,这种环境限制了它们的自然史。由于它发生在生物体的多样性中,人们可以推测癌症的多元宇宙中有秩序。有没有一种方法可以在可能的范围内捕捉广泛的肿瘤类型?在这里,我们定义了肿瘤空间,这是一个坐标系统,它整合了癌症复杂性的生态、进化和发育成分。肿瘤的空间位置是由其沿着这三个轴偏离健康组织的程度决定的,而进展轨迹则可以提供有关驱动癌症亚型恶性程度的成分的信息。我们假设肿瘤空间拓扑结构编码了关于肿瘤发生途径、亚型预后和治疗机会的新信息:治疗设计可以从考虑如何将肿瘤推向肿瘤空间中的进化死胡同中获益。

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引用本文的文献

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Cancers (Basel). 2024 Oct 17;16(20):3513. doi: 10.3390/cancers16203513.
2
Modeling tumors as complex ecosystems.将肿瘤建模为复杂生态系统。
iScience. 2024 Aug 10;27(9):110699. doi: 10.1016/j.isci.2024.110699. eCollection 2024 Sep 20.
3
Is Cancer Metabolism an Atavism?癌症代谢是一种返祖现象吗?
Cancers (Basel). 2024 Jun 29;16(13):2415. doi: 10.3390/cancers16132415.
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Modeling tumors as species-rich ecological communities.将肿瘤模拟为物种丰富的生态群落。
bioRxiv. 2024 Apr 26:2024.04.22.590504. doi: 10.1101/2024.04.22.590504.
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Ecological network analysis reveals cancer-dependent chaperone-client interaction structure and robustness.生态网络分析揭示了依赖于癌症的伴侣-客户相互作用结构和稳健性。
Nat Commun. 2023 Oct 7;14(1):6277. doi: 10.1038/s41467-023-41906-2.
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Artificial intelligence vs. evolving super-complex tumor intelligence: critical viewpoints.人工智能与不断演变的超级复杂肿瘤智能:批判性观点
Front Artif Intell. 2023 Jul 24;6:1220744. doi: 10.3389/frai.2023.1220744. eCollection 2023.