Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
Bioinformatics. 2022 Aug 2;38(15):3677-3683. doi: 10.1093/bioinformatics/btac367.
Multi-region sequencing of solid tumors can improve our understanding of intratumor subclonal diversity and the evolutionary history of mutational events. Due to uncertainty in clonal composition and the multitude of possible ancestral relationships between clones, elucidating the most probable relationships from bulk tumor sequencing poses statistical and computational challenges.
We developed a Bayesian hierarchical model called PICTograph to model uncertainty in assigning mutations to subclones, to enable posterior distributions of cancer cell fractions (CCFs) and to visualize the most probable ancestral relationships between subclones. Compared with available methods, PICTograph provided more consistent and accurate estimates of CCFs and improved tree inference over a range of simulated clonal diversity. Application of PICTograph to multi-region whole-exome sequencing of tumors from individuals with pancreatic cancer precursor lesions confirmed known early-occurring mutations and indicated substantial molecular diversity, including 6-12 distinct subclones and intra-sample mixing of subclones. Using ensemble-based visualizations, we highlight highly probable evolutionary relationships recovered in multiple models. PICTograph provides a useful approximation to evolutionary inference from cross-sectional multi-region sequencing, particularly for complex cases.
https://github.com/KarchinLab/pictograph. The data underlying this article will be shared on reasonable request to the corresponding author.
Supplementary data are available at Bioinformatics online.
对实体瘤进行多区域测序可以提高我们对肿瘤内亚克隆多样性和突变事件进化史的理解。由于克隆组成的不确定性以及克隆之间可能存在的多种祖先关系，从大量肿瘤测序中阐明最可能的关系存在统计和计算上的挑战。
我们开发了一种名为 PICTograph 的贝叶斯层次模型，用于对将突变分配给亚克隆的不确定性进行建模，从而能够对癌细胞分数（CCF）进行后验分布，并可视化亚克隆之间最可能的祖先关系。与现有方法相比，PICTograph 提供了更一致和准确的 CCF 估计值，并在一系列模拟的克隆多样性范围内提高了树推断。将 PICTograph 应用于来自具有胰腺癌前体病变的个体的多区域全外显子组测序，证实了已知的早期发生突变，并表明存在大量的分子多样性，包括 6-12 个不同的亚克隆和亚克隆之间的样本内混合。通过基于集合的可视化，我们突出显示了从多个模型中恢复的高度可能的进化关系。PICTograph 为从横截面多区域测序进行进化推断提供了有用的近似值，特别是对于复杂情况。
https://github.com/KarchinLab/pictograph。本文所依据的数据将根据合理请求提供给相应的作者。
补充数据可在生物信息学在线获得。
