A single-cell analysis of breast cancer cell lines to study tumour heterogeneity and drug response

Abstract

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.

Drug combination prioritization for cancer treatment using single-cell RNA-seq based transfer learning

“…We downloaded 35, 276 transcriptomes from 32 ATCC-authenticated breast cancer cell lines that had been indi-vidualized using DROP-seq and used them to generate a reference embedding as described in the methods section (22). After compressing the transcriptional data, Symphony generated a uniform manifold approximation and projection (UMAP) using the top 50 PCA components that discriminates the 32 clusters of cells in the low-dimensional space with a minimal overlap ( < 1%, Figure 2A), which is better to differenciate cell lines than the projec-tion reported by the dataset’s original authors (21, 26). This indicates that the logarithm of the counts per million log (CPM + 1) normalization provides a greater ability to distinguish between breast cancer cell lines than the gene frequency–inverse cell frequency (GF-ICF) normalization method (35).…”

“…It maps cells into the reference without recomputing, maintaining the structure of the reference atlas. Given that all of the reference cell lines employed in this study originated from the same project (21). The atlas was constructed using a constant covariate, which enabled Symphony to recognize transcriptional differences across cell lines and cluster the cells appropriately.…”

“…The transcriptionally profile of 35, 276 individual cells from 32 breast cancer cell lines reported by Gambardella, G., et al “A single-cell atlas of breast cancer cell lines to study tumour heterogeneity and drug response . ” bioRxiv (2021) (21). Accessed through: DOI, record 10.6084/m9.figshare.15022698…”

“…Recently, Gambardella and collaborators released a single-cell atlas of breast cancer cell lines (21). The atlas reports single-cell RNA-seq data for 35, 276 individual cells from 32 breast cancer cell lines individualized using DROP-seq (22).…”

Computational estimation of quality and clinical relevance of cancer cell lines

“…Second, single‐cell studies could enable a more fine‐grained mapping of the relationship of the subclonal populations and cell states in heterogeneous tumour populations to representative in vitro models, effectively delineating each tumour as a mixture of in vitro models. For example, Gambardella et al ( 2022 ) created a single‐cell atlas of 32 breast CCLs and showed that the single‐cell transcriptional profile from a single patient could be mapped into the in vitro model atlas to assign a CCL model to each patient's cells. Strikingly, the tumours were found to be highly heterogeneous as none was mapped into a single CCL and they were overall represented by a mixture of models.…”