Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland.
SIB Swiss Institute of Bioinformatics, Basel 4058, Switzerland.
Bioinformatics. 2022 Oct 14;38(20):4713-4719. doi: 10.1093/bioinformatics/btac577.
Tumours evolve as heterogeneous populations of cells, which may be distinguished by different genomic aberrations. The resulting intra-tumour heterogeneity plays an important role in cancer patient relapse and treatment failure, so that obtaining a clear understanding of each patient's tumour composition and evolutionary history is key for personalized therapies. Single-cell sequencing (SCS) now provides the possibility to resolve tumour heterogeneity at the highest resolution of individual tumour cells, but brings with it challenges related to the particular noise profiles of the sequencing protocols as well as the complexity of the underlying evolutionary process.
By modelling the noise processes and allowing mutations to be lost or to reoccur during tumour evolution, we present a method to jointly call mutations in each cell, reconstruct the phylogenetic relationship between cells, and determine the locations of mutational losses and recurrences. Our Bayesian approach allows us to accurately call mutations as well as to quantify our certainty in such predictions. We show the advantages of allowing mutational loss or recurrence with simulated data and present its application to tumour SCS data.
SCIΦN is available at https://github.com/cbg-ethz/SCIPhIN.
Supplementary data are available at Bioinformatics online.
肿瘤作为细胞的异质群体而进化，这些细胞可能因不同的基因组异常而有所区别。由此产生的肿瘤内异质性在癌症患者的复发和治疗失败中起着重要作用，因此，清晰了解每个患者肿瘤的组成和进化史是个性化治疗的关键。单细胞测序（SCS）现在提供了以单个肿瘤细胞的最高分辨率解析肿瘤异质性的可能性，但也带来了与测序方案特定噪声谱以及潜在进化过程复杂性相关的挑战。
通过对噪声过程进行建模，并允许在肿瘤进化过程中丢失或重新出现突变，我们提出了一种在每个细胞中共同调用突变、重建细胞间系统发育关系以及确定突变丢失和重现位置的方法。我们的贝叶斯方法允许我们准确地调用突变，并量化我们在这些预测中的确定性。我们展示了允许突变丢失或重现的模拟数据的优势，并介绍了其在肿瘤 SCS 数据中的应用。
SCIΦN 可在 https://github.com/cbg-ethz/SCIPhIN 上获得。
补充数据可在“Bioinformatics”在线获得。
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