Erickson Andrew, Figiel Sandy, Rajakumar Timothy, Rao Srinivasa, Yin Wencheng, Doultsinos Dimitrios, Magnussen Anette, Singh Reema, Poulose Ninu, Bryant Richard J, Cussenot Olivier, Hamdy Freddie C, Woodcock Dan, Mills Ian G, Lamb Alastair D
Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom.
Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland.
PLoS One. 2025 Jan 3;20(1):e0316475. doi: 10.1371/journal.pone.0316475. eCollection 2025.
Epithelial cancers are typically heterogeneous with primary prostate cancer being a typical example of histological and genomic variation. Prior studies of primary prostate cancer tumour genetics revealed extensive inter and intra-patient genomic tumour heterogeneity. Recent advances in machine learning have enabled the inference of ground-truth genomic single-nucleotide and copy number variant status from transcript data. While these inferred SNV and CNV states can be used to resolve clonal phylogenies, however, it is still unknown how faithfully transcript-based tumour phylogenies reconstruct ground truth DNA-based tumour phylogenies. We sought to study the accuracy of inferred-transcript to recapitulate DNA-based tumour phylogenies. We first performed in-silico comparisons of inferred and directly resolved SNV and CNV status, from single cancer cells, from three different cell lines. We found that inferred SNV phylogenies accurately recapitulate DNA phylogenies (entanglement = 0.097). We observed similar results in iCNV and CNV based phylogenies (entanglement = 0.11). Analysis of published prostate cancer DNA phylogenies and inferred CNV, SNV and transcript based phylogenies demonstrated phylogenetic concordance. Finally, a comparison of pseudo-bulked spatial transcriptomic data to adjacent sections with WGS data also demonstrated recapitulation of ground truth (entanglement = 0.35). These results suggest that transcript-based inferred phylogenies recapitulate conventional genomic phylogenies. Further work will need to be done to increase accuracy, genomic, and spatial resolution.
上皮癌通常具有异质性，原发性前列腺癌是组织学和基因组变异的典型例子。先前对原发性前列腺癌肿瘤遗传学的研究揭示了患者间和患者内广泛的基因组肿瘤异质性。机器学习的最新进展使得能够从转录数据推断真实的基因组单核苷酸和拷贝数变异状态。虽然这些推断的单核苷酸变异（SNV）和拷贝数变异（CNV）状态可用于解析克隆系统发育，但基于转录本的肿瘤系统发育如何忠实地重建基于DNA的肿瘤系统发育仍然未知。我们试图研究推断转录本以概括基于DNA的肿瘤系统发育的准确性。我们首先对来自三种不同细胞系的单个癌细胞的推断和直接解析的SNV和CNV状态进行了计算机模拟比较。我们发现推断的SNV系统发育准确地概括了DNA系统发育（纠缠度 = 0.097）。我们在基于iCNV和CNV的系统发育中观察到了类似的结果（纠缠度 = 0.11）。对已发表的前列腺癌DNA系统发育以及推断的基于CNV、SNV和转录本的系统发育的分析表明了系统发育的一致性。最后，将伪批量空间转录组数据与相邻切片的全基因组测序（WGS）数据进行比较，也证明了对真实情况的概括（纠缠度 = 0.35）。这些结果表明基于转录本的推断系统发育概括了传统的基因组系统发育。需要进一步开展工作以提高准确性、基因组和空间分辨率。
