Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave - Icahn (East) Building, Floor 14, Room 14-20E, New York, NY, 10029, USA.
Pacific Biosciences, CA, Menlo Park, USA.
Cell Oncol (Dordr). 2023 Jun;46(3):603-628. doi: 10.1007/s13402-022-00765-7. Epub 2023 Jan 4.
Breast Cancer (BC) is the most diagnosed cancer in women; however, through significant research, relative survival rates have significantly improved. Despite progress, there remains a gap in our understanding of BC subtypes and personalized treatments. This manuscript characterized cellular heterogeneity in BC cell lines through scRNAseq to resolve variability in subtyping, disease modeling potential, and therapeutic targeting predictions.
We generated a Breast Cancer Single-Cell Cell Line Atlas (BSCLA) to help inform future BC research. We sequenced over 36,195 cells composed of 13 cell lines spanning the spectrum of clinical BC subtypes and leveraged publicly available data comprising 39,214 cells from 26 primary tumors.
Unsupervised clustering identified 49 subpopulations within the cell line dataset. We resolve ambiguity in subtype annotation comparing expression of Estrogen Receptor, Progesterone Receptor, and Human Epidermal Growth Factor Receptor 2 genes. Gene correlations with disease subtype highlighted S100A7 and MUCL1 overexpression in HER2 + cells as possible cell motility and localization drivers. We also present genes driving populational drifts to generate novel gene vectors characterizing each subpopulation. A global Cancer Stem Cell (CSC) scoring vector was used to identify stemness potential for subpopulations and model multi-potency. Finally, we overlay the BSCLA dataset with FDA-approved targets to identify to predict the efficacy of subpopulation-specific therapies.
The BSCLA defines the heterogeneity within BC cell lines, enhancing our overall understanding of BC cellular diversity to guide future BC research, including model cell line selection, unintended sample source effects, stemness factors between cell lines, and cell type-specific treatment response.
乳腺癌（BC）是女性最常见的癌症；然而，通过大量研究，相对存活率显著提高。尽管取得了进展，但我们对 BC 亚型和个性化治疗的理解仍存在差距。本文通过 scRNAseq 对 BC 细胞系进行了细胞异质性分析，以解决亚分型、疾病建模潜力和治疗靶点预测中的可变性问题。
我们生成了乳腺癌单细胞细胞系图谱（BSCLA），以帮助指导未来的 BC 研究。我们对 13 种细胞系中的超过 36195 个细胞进行了测序，这些细胞涵盖了临床 BC 亚型的广泛范围，并利用了包含 26 个原发性肿瘤的 39214 个细胞的公开数据。
无监督聚类在细胞系数据集中确定了 49 个亚群。我们通过比较雌激素受体、孕激素受体和人类表皮生长因子受体 2 基因的表达，解决了亚型注释的歧义。与疾病亚型相关的基因突出了 HER2+细胞中 S100A7 和 MUCL1 的过表达，这可能是细胞迁移和定位的驱动因素。我们还提出了驱动种群漂移的基因，以生成新的基因向量来表征每个亚群。使用全局癌症干细胞（CSC）评分向量来识别亚群的干性潜力并模拟多潜能性。最后，我们将 BSCLA 数据集与 FDA 批准的靶标进行叠加，以预测亚群特异性治疗的疗效。
BSCLA 定义了 BC 细胞系内的异质性，增强了我们对 BC 细胞多样性的整体理解，以指导未来的 BC 研究，包括模型细胞系选择、意外样本来源效应、细胞系之间的干性因素以及特定于细胞类型的治疗反应。
