[论文解读] A picture guide to cancer progression and monotonic accumulation models: evolutionary assumptions, plausible interpretations, and alternative uses

Ramón Díaz‐Uriarte, Johnston, Iain G.|arXiv (Cornell University)|Dec 11, 2023

Evolution and Genetic Dynamics被引用 9

一句话总结

对不可逆事件积累（如癌症突变）的单调累积模型的全面评述，详细讨论确定性与随机性方法、假设、解释、局限性以及在癌症以外领域的潜在应用。

ABSTRACT

Cancer progression and monotonic accumulation models were developed to discover dependencies in the irreversible acquisition of binary traits from cross-sectional data. They have been used in computational oncology and virology but also in widely different problems such as malaria progression. These methods have been applied to predict future states of the system, identify routes of feature acquisition, and improve patient stratification, and they hold promise for evolutionary-based treatments. New methods continue to be developed. But these methods have shortcomings, which are yet to be systematically critiqued, regarding key evolutionary assumptions and interpretations. After an overview of the available methods, we focus on why inferences might not be about the processes we intend. Using fitness landscapes, we highlight difficulties that arise from bulk sequencing and reciprocal sign epistasis, from conflating lines of descent, path of the maximum, and mutational profiles, and from ambiguous use of the idea of exclusivity. We examine how the previous concerns change when bulk sequencing is explicitly considered, and underline opportunities for addressing dependencies due to frequency-dependent selection. This review identifies major standing issues, and should encourage the use of these methods in other areas with a better alignment between entities and model assumptions.

研究动机与目标

• 解释单调累积模型用于不可逆事件积累的范围与目的。
• 回顾用于从横截面数据推断事件之间依赖关系的确定性与随机性建模方法。
• 讨论常见假设、潜在违规及抽样和大规模测序对推断的影响。
• 强调实际用途、评估考量，以及将这些方法扩展到其他领域的机会。

提出的方法

• 综述确定性依赖模型（Oncogenetic trees、OncoBN、Conjunctive Bayesian Networks、H-ESBCN/PMCE）和随机依赖模型（MHN、HyperTraPS、HyperHMM）。
• 总结输入数据要求（二值横截面矩阵）、输出形式（速率、图、转移概率）以及误差建模考虑因素。
• 比较依赖关系的解释（AND/OR/XOR、互斥性），并讨论不同建模选择对推断的影响。
• 讨论软件生态系统及通过 EvAM-Tools 及相关代码库进行的数据处理。

Figure 1: Cancer progression model with linear dependencies . Left: a linear model of the dependencies in the acquisition of mutations; gaining a mutation in gene B requires having gained a mutation in A; gaining a mutation in gene C requires having gained a mutation in B and, thus, A. The only poss

实验结果

研究问题

• RQ1 构成不可逆事件获得的单调累积模型背后的进化假设是什么？
• RQ2 确定性与随机性依赖结构在横截面数据中的表示、解释与推断有何不同？
• RQ3 在何种抽样与数据处理条件下，这些模型能提供可靠推断，大规模测序和系统发育结构如何影响它们？
• RQ4 这些模型在癌症遗传学之外的实际用途是什么，如何扩展或调整以应用于其他领域？
• RQ5 单调累积模型在方法学发展与评估方面的主要局限与机会是什么？

主要发现

• 存在一系列具有显著不同依赖结构（确定性与随机性）的丰富模型，可对同一数据进行拟合以实现细致的推断。
• 横截面数据被视为在共享约束下的重复进化实验，尽管这一假设可能因异质性与谱系效应而被违反。
• 大规模测序与频率依赖选择带来解释上的挑战，但也为解决依赖关系提供机会。
• 这些模型可用于预测未来状态、识别不同的疾病进展路径、患者分层以及改进生存分析。
• 综述强调在进化假设方面存在系统性批评的空白，并指出将方法扩展到单细胞与系统发育信息数据的机会。
• 软件实现和统一框架（EvAM-Tools）促进方法间的实际应用与比较。

Figure 2: Cancer progression and monotonic accumulation models: main steps . (a) Features of relevance are measured on some subjects (e.g., mutations in genes or presence of malaria symptoms). (b) Data are arranged as a binary matrix of samples by features. For cancer data, each row in (b) is a muta