Telethon Institute of Genetics and Medicine, Naples, Italy.
University of Naples Federico II, Department of Chemical, Materials and Industrial Engineering, Naples, Italy.
Nat Commun. 2022 Mar 31;13(1):1714. doi: 10.1038/s41467-022-29358-6.
Cancer cells within a tumour have heterogeneous phenotypes and exhibit dynamic plasticity. How to evaluate such heterogeneity and its impact on outcome and drug response is still unclear. Here, we transcriptionally profile 35,276 individual cells from 32 breast cancer cell lines to yield a single cell atlas. We find high degree of heterogeneity in the expression of biomarkers. We then train a deconvolution algorithm on the atlas to determine cell line composition from bulk gene expression profiles of tumour biopsies, thus enabling cell line-based patient stratification. Finally, we link results from large-scale in vitro drug screening in cell lines to the single cell data to computationally predict drug responses starting from single-cell profiles. We find that transcriptional heterogeneity enables cells with differential drug sensitivity to co-exist in the same population. Our work provides a framework to determine tumour heterogeneity in terms of cell line composition and drug response.
肿瘤内的癌细胞具有异质性表型，并表现出动态的可塑性。如何评估这种异质性及其对结果和药物反应的影响尚不清楚。在这里，我们对 32 种乳腺癌细胞系中的 35276 个单个细胞进行转录组谱分析，生成单细胞图谱。我们发现生物标志物的表达存在高度异质性。然后，我们在图谱上训练去卷积算法，以从肿瘤活检的批量基因表达谱中确定细胞系组成，从而能够基于细胞系对患者进行分层。最后，我们将细胞系中大规模体外药物筛选的结果与单细胞数据联系起来，从单细胞图谱开始计算预测药物反应。我们发现转录异质性使具有不同药物敏感性的细胞能够在同一群体中共存。我们的工作为根据细胞系组成和药物反应确定肿瘤异质性提供了一个框架。
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