Deciphering and targeting cancer metabolism using executable models

使用可执行模型破译和靶向癌症代谢

• 批准号: MR/S000216/1
• 负责人: Benjamin Hall
• 金额: $ 43.89万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FS000216%2F1
• 关键词: Deciphering; targeting; cancer; metabolism; using

The progression of cancer is driven by mutations and errors in the cell that promote the "hallmarks of cancer". These are a set of distinct behaviours that cells in tumours possess, enabling the survival and growth of the cancer. A longstanding observation in cancer cells is the Warburg effect; the switch away from oxidative phosphorylation to anaerobic glycolysis. Recent evidence has shown that metabolic reprogramming- large scale modification of how the cells process energy- is achieved by mutations in oncogenes including KRAS. This enables cancer cell survival in the tumour. Furthermore mutations in individual metabolic enzymes, such as fumarate hydratase, can affect a transition from epithelial to a mesenchymal morphology. Both the links between individual metabolites and cell behaviour, and the role of the metabolic network in cancer development however remains unclear. Knowledge of both of these are necessary to interpret metabolic changes in cancer and to identify new drug targets that are robust to network effects.Computational modelling offers the opportunity to formalise the relationships between elements in the network and to address this issue. However, conventional approaches based on ordinary differential equations (ODEs) and flux balance analysis (FBA) are not suitable. ODE models require precise physico-chemical parameters that are not available for human metabolism. FBA does not have this requirement but is not capable of modelling the accumulation of metabolites that can occur in response to mutation. I propose to use an "executable" modelling approach to construct models of the metabolic network and link them to cellular behaviour. Executable modelling approaches do not require detailed physical parameters, and can model accumulation events. They have the further advantage that they are amenable to a class of model analysis known as formal verification. Here mathematical proofs are used to offer guarantees of cell properties that are encoded in formal logic. These guarantees can apply over all possible states of the system and so offer a uniquely powerful way to test model correctness. This is particularly useful for highly robust systems such as the metabolic network, but also in taking account of rare events. This latter issue is particularly important when considering how rare events determine the development of cancer. Model building will be guided by the use of machine learning approaches (t-SNE, decision forests) to address publicly available expression data and metabolomic datasets shared with us by AstraZeneca. Finally, these models will be used to make novel predictions about the metabolism can change cell phenotype. The development of these models will allow us to understand how metabolic networks drive cancer progression and help us identify novel oncogenes and drug targets.

癌症的进展是由细胞中的突变和错误驱动的，这些突变和错误促进了“癌症的标志”。这些是肿瘤细胞所具有的一系列独特行为，使癌症能够存活和生长。瓦尔堡效应是癌细胞中长期观察到的现象。从氧化磷酸化转变为无氧糖酵解。最近的证据表明，代谢重编程（细胞处理能量的方式的大规模改变）是通过包括 KRAS 在内的癌基因突变来实现的。这使得癌细胞能够在肿瘤中存活。此外，个体代谢酶（例如富马酸水合酶）的突变可以影响从上皮形态到间质形态的转变。然而，个体代谢物与细胞行为之间的联系以及代谢网络在癌症发展中的作用仍不清楚。了解这两方面的知识对于解释癌症的代谢变化和确定对网络效应具有鲁棒性的新药物靶点是必要的。计算模型提供了将网络中元素之间的关系形式化并解决这个问题的机会。然而，基于常微分方程 (ODE) 和通量平衡分析 (FBA) 的传统方法并不适用。 ODE 模型需要精确的物理化学参数，而人体新陈代谢无法获得这些参数。 FBA 没有此要求，但无法对因突变而发生的代谢物积累进行建模。我建议使用“可执行”建模方法来构建代谢网络模型并将其与细胞行为联系起来。可执行的建模方法不需要详细的物理参数，并且可以对累积事件进行建模。它们还有一个进一步的优点，那就是它们可以进行一类称为形式验证的模型分析。 Here mathematical proofs are used to offer guarantees of cell properties that are encoded in formal logic.这些保证适用于系统的所有可能状态，因此提供了一种独特的强大方法来测试模型的正确性。这对于代谢网络等高度稳健的系统特别有用，而且在考虑罕见事件时也非常有用。在考虑罕见事件如何决定癌症的发展时，后一个问题尤为重要。模型构建将通过使用机器学习方法（t-SNE、决策森林）来指导，以处理阿斯利康与我们共享的公开表达数据和代谢组数据集。最后，这些模型将用于对新陈代谢可以改变细胞表型进行新的预测。这些模型的开发将使我们能够了解代谢网络如何驱动癌症进展，并帮助我们识别新的癌基因和药物靶点。

项目成果

SMAD4 and KCNQ3 alterations are associated with lymph node metastases in oesophageal adenocarcinoma.

SMAD4 和 KCNQ3 的改变与食管腺癌的淋巴结转移相关。

• DOI: 10.1016/j.bbadis.2023.166867
• 发表时间: 2024
• 期刊: Biochimica et biophysica acta. Molecular basis of disease
• 作者: Foley K

Mutant clones in normal epithelium outcompete and eliminate emerging tumours.

• DOI: 10.1038/s41586-021-03965-7
• 发表时间: 2021-10
• 期刊: Nature
• 影响因子: 64.8
• 作者: Colom B;Herms A;Hall MWJ;Dentro SC;King C;Sood RK;Alcolea MP;Piedrafita G;Fernandez-Antoran D;Ong SH;Fowler JC;Mahbubani KT;Saeb-Parsy K;Gerstung M;Hall BA;Jones PH
• 通讯作者: Jones PH

Notch1 mutations drive clonal expansion in normal esophageal epithelium but impair tumor growth.

• DOI: 10.1038/s41588-022-01280-z
• 发表时间: 2023-03
• 期刊: NATURE GENETICS
• 影响因子: 30.8
• 作者: Abby, Emilie;Dentro, Stefan C. C.;Hall, Michael W. J.;Fowler, Joanna C. C.;Ong, Swee Hoe;Sood, Roshan;Herms, Albert;Piedrafita, Gabriel;Abnizova, Irina;Siebel, Christian W. W.;Gerstung, Moritz;Hall, Benjamin A. A.;Jones, Philip H. H.
• 通讯作者: Jones, Philip H. H.

Selection of oncogenic mutant clones in normal human skin varies with body site.

正常人体皮肤中致癌突变克隆的选择因身体部位而异。

• DOI: 10.17863/cam.58979
• 发表时间: 2021
• 作者: Fowler J