single-cell of HCT116 cell lines - Linking proliferation rate to the evolution of single-cell primary and metastatic tumour clones. single-cell of HCT116 cell lines - Linking proliferation rate to the evolution of single-cell primary and metastatic tumour clones

Proliferation is a key phenotypic feature of cancer, with higher rates associated with poorer clinical outcomes. While most tumours have been shown to be composed of several distinct clones, measuring clone proliferation rates has proven to be unfeasible thus far as proliferation and clonal diversity cannot be easily measured for the same set of cells. In this study, we introduce SPRINTER, an algorithm that uses single-cell whole-genome DNA sequencing (scDNA-seq) data to enable the accurate identification of actively replicating cells in both the S and G2 phases of the cell cycle and their assignment to distinct tumour clones, thus providing a proxy to estimate clone-specific proliferation rates. To evaluate SPRINTER’s accuracy, we generated a ground truth dataset of 8,844 diploid and tetraploid cancer cells by coupling scDNA-seq with 5-Ethynyl-2-deoxyuridine (EdU) labelling, providing a more accurate and comprehensive capture of S phase cells than previous datasets. We further generated a longitudinal, primary-metastasis matched dataset of 23,001 cancer cells obtained from 5 samples from the primary tumour and 5 samples from distinct metastases from a patient with non-small cell lung cancer (NSCLC), allowing us to illustrate the impact of SPRINTER’s novel features. We revealed widespread heterogeneity in clone proliferation rates both between and within samples, supported by multiple orthogonal analyses including Ki-67 pathology, nuclei microscopy imaging, and patient clinical imaging, with high proliferation seen in fast-growing metastatic lesions. We demonstrated an association between clones with high proliferation and increased metastatic potential, as well as increased shedding of circulating tumour DNA (ctDNA). We further illustrated SPRINTER’s broad applicability on previous datasets of 42,009 breast cancer cells and 19,905 ovarian cancer cells, revealing an association between high proliferation and increased rates of different genetic variants. In conclusion, SPRINTER infers the proliferation rates of distinct tumour clones from scDNA-seq data, allowing the identification of clones with potentially aggressive phenotypes, such as metastatic potential.

增殖是癌症的关键表型特征，增殖速率越高，患者临床结局越差。尽管多数肿瘤已被证实由多个不同克隆组成，但迄今为止，测量克隆增殖速率仍不可行，因为无法同时对同一组细胞精准测定增殖状态与克隆多样性。本研究提出SPRINTER算法，该算法依托单细胞全基因组DNA测序（single-cell whole-genome DNA sequencing, scDNA-seq）数据，可精准鉴定细胞周期S期与G2期的活跃复制细胞，并将其分配至对应的不同肿瘤克隆，从而为估算克隆特异性增殖速率提供替代指标。为评估SPRINTER的准确性，本研究通过将scDNA-seq与5-乙炔基-2'-脱氧尿苷（5-Ethynyl-2-deoxyuridine, EdU）标记技术相结合，构建了包含8844个二倍体与四倍体癌细胞的金标准数据集，该数据集相较于既往数据集，可更精准、全面地捕获S期细胞。本研究还构建了一套纵向配对的原发-转移癌数据集，包含23001个癌细胞，采集自1例非小细胞肺癌（non-small cell lung cancer, NSCLC）患者的5个原发肿瘤样本与5个不同转移灶样本，以此展示SPRINTER创新功能的应用效果。本研究发现不同样本间及样本内部的克隆增殖速率普遍存在异质性，该结论得到多项正交分析的验证，包括Ki-67病理学检测、细胞核显微成像及患者临床影像学分析，且快速生长的转移灶呈现高增殖状态。本研究证实高增殖克隆与转移潜能增强存在关联，同时与循环肿瘤DNA（circulating tumour DNA, ctDNA）释放量增加相关。本研究还将SPRINTER应用于既往发布的两组数据集（分别包含42009个乳腺癌细胞与19905个卵巢癌细胞），证实高增殖状态与多种遗传变异发生率升高存在关联。综上，SPRINTER可通过scDNA-seq数据推断不同肿瘤克隆的增殖速率，从而鉴定出具有潜在侵袭性表型（如转移潜能）的克隆。