Cell Adaptive Fitness and Cancer Evolutionary Dynamics.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Cancer Informatics Pub Date : 2023-01-01 DOI:10.1177/11769351231154679
Youcef Derbal
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Abstract

Genome instability of cancer cells translates into increased entropy and lower information processing capacity, leading to metabolic reprograming toward higher energy states, presumed to be aligned with a cancer growth imperative. Dubbed as the cell adaptive fitness, the proposition postulates that the coupling between cell signaling and metabolism constrains cancer evolutionary dynamics along trajectories privileged by the maintenance of metabolic sufficiency for survival. In particular, the conjecture postulates that clonal expansion becomes restricted when genetic alterations induce a sufficiently high level of disorder, that is, high entropy, in the regulatory signaling network, abrogating as a result the ability of cancer cells to successfully replicate, leading to a stage of clonal stagnation. The proposition is analyzed in the context of an in-silico model of tumor evolutionary dynamics to illustrate how cell-inherent adaptive fitness may predictably constrain clonal evolution of tumors, which would have significant implications for the design of adaptive cancer therapies.

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细胞适应适应度和癌症进化动力学。
癌细胞基因组的不稳定性转化为熵的增加和信息处理能力的降低,导致代谢重编程向更高的能量状态发展,据推测这与癌症生长的必要性是一致的。这一命题被称为细胞适应性适应度,它假设细胞信号传导和代谢之间的耦合限制了癌症沿着维持生存所需的代谢充足性的轨迹进化动力学。特别是,该猜想假设,当基因改变在调节信号网络中引起足够高水平的紊乱(即高熵)时,克隆扩展受到限制,从而取消癌细胞成功复制的能力,导致克隆停滞阶段。该命题在肿瘤进化动力学的计算机模型的背景下进行了分析,以说明细胞固有的适应适应度如何可预测地约束肿瘤的克隆进化,这将对适应性癌症治疗的设计具有重要意义。
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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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