Unified tumor growth mechanisms from multimodel inference and dataset integration.
Unified tumor growth mechanisms from multimodel inference and dataset integration.
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DOI：
10.1371/journal.pcbi.1011215
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发表时间：
2023-07
影响因子：
4.3
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中科院分区：
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生物过程的机制模型可以解释观察到的现象，并预测对扰动的反应。数学模型通常使用专家知识和非正式推理来构建，以生成对给定观察的机械解释。虽然这种方法适用于具有丰富数据和完善原则的简单系统，但定量生物学通常面临缺乏关于过程的数据和知识的问题，因此识别和验证系统行为背后的所有可能的机制假设具有挑战性。为了克服这些限制，我们引入了贝叶斯多模型推理（Bayes-MMI）方法，该方法量化了机械假设如何解释给定的实验数据集，同时，每个数据集如何告知给定的模型假设，从而使假设空间探索在可用数据的背景下。我们展示了这种方法来探讨小细胞肺癌（SCLC）肿瘤生长机制中的异质性，谱系可塑性和细胞-细胞相互作用的问题。我们整合了三个数据集，每个数据集都对SCLC中的肿瘤生长机制做出了不同的解释，应用贝叶斯-MMI，发现数据支持模型预测肿瘤进化是由高谱系可塑性促进的，而不是通过扩大罕见的干细胞样群体。此外，模型预测，在存在与SCLC-N或SCLC-A2亚型相关的细胞的情况下，从SCLC-A亚型通过中间体向SCLC-Y亚型的转变被减速。总之，这些预测为观察到的SCLC生长的并列结果提供了一个可检验的假设，并为肿瘤治疗耐药性提供了一个机制解释。为了建立数学模型，研究人员需要了解并结合感兴趣的系统中存在的生物学关系。然而，如果不知道生物系统组成部分之间的确切关系，如何构建模型？建立一个单一的模型可能包括虚假的关系或排除重要的关系。因此，模型选择方法使我们能够建立多个模型假设，将生物特征的各种组合及其之间的关系。每个生物特征代表一个不同的假设，可以通过模型拟合实验数据来研究。在这项工作中，我们的目标是改进的信息理论框架的模型选择，将贝叶斯元素。我们将我们的方法应用于小细胞肺癌（SCLC），使用多个数据集，以解决关于细胞间相互作用，表型转变和肿瘤组成的假设。除了贝叶斯推理，我们可以在模型选择中加入对这些假设是否可能或不可能的评估，甚至可以评估数据是否能够评估假设。我们的分析发现，SCLC可能具有高度可塑性，细胞能够轻松转换表型身份。这些预测可以帮助解释为什么SCLC是一种难以治疗的疾病，并为进一步的实验提供基础。
Mechanistic models of biological processes can explain observed phenomena and predict responses to a perturbation. A mathematical model is typically constructed using expert knowledge and informal reasoning to generate a mechanistic explanation for a given observation. Although this approach works well for simple systems with abundant data and well-established principles, quantitative biology is often faced with a dearth of both data and knowledge about a process, thus making it challenging to identify and validate all possible mechanistic hypothesis underlying a system behavior. To overcome these limitations, we introduce a Bayesian multimodel inference (Bayes-MMI) methodology, which quantifies how mechanistic hypotheses can explain a given experimental datasets, and concurrently, how each dataset informs a given model hypothesis, thus enabling hypothesis space exploration in the context of available data. We demonstrate this approach to probe standing questions about heterogeneity, lineage plasticity, and cell-cell interactions in tumor growth mechanisms of small cell lung cancer (SCLC). We integrate three datasets that each formulated different explanations for tumor growth mechanisms in SCLC, apply Bayes-MMI and find that the data supports model predictions for tumor evolution promoted by high lineage plasticity, rather than through expanding rare stem-like populations. In addition, the models predict that in the presence of cells associated with the SCLC-N or SCLC-A2 subtypes, the transition from the SCLC-A subtype to the SCLC-Y subtype through an intermediate is decelerated. Together, these predictions provide a testable hypothesis for observed juxtaposed results in SCLC growth and a mechanistic interpretation for tumor treatment resistance. To make a mathematical model, an investigator needs to know and incorporate biological relationships present in the system of interest. However, if the exact relationships between components of a biological system are not known, how can a model be constructed? Building a single model may include spurious relationships or exclude important ones. Therefore, model selection methods enable us to build multiple model hypotheses, incorporating various combinations of biological features and the relationships between them. Each biological feature represents a distinct hypothesis, which can be investigated via model fitting to experimental data. In this work, we aim to improve upon the information theoretic framework of model selection by incorporating Bayesian elements. We apply our approach to small cell lung cancer (SCLC), using multiple datasets, to address hypotheses about cell-cell interactions, phenotypic transitions, and tumor makeup across experimental model systems. Incorporating Bayesian inference, we can add into model selection an assessment of whether these hypotheses are likely or unlikely, or even whether the data enables assessment of a hypothesis at all. Our analysis finds that SCLC is likely highly plastic, with cells able to transition phenotypic identities easily. These predictions could help explain why SCLC is such a difficult disease to treat and provide the basis for further experiments.
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