Unravelling subclonal heterogeneity and aggressive disease states in TNBC through single-cell RNA-seq

Abstract

Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by extensive intratumoral heterogeneity. To investigate the underlying biology, we conducted single-cell RNA-sequencing (scRNA-seq) of >1500 cells from six primary TNBC. Here, we show that intercellular heterogeneity of gene expression programs within each tumor is variable and largely correlates with clonality of inferred genomic copy number changes, suggesting that genotype drives the gene expression phenotype of individual subpopulations. Clustering of gene expression profiles identified distinct subgroups of malignant cells shared by multiple tumors, including a single subpopulation associated with multiple signatures of treatment resistance and metastasis, and characterized functionally by activation of glycosphingolipid metabolism and associated innate immunity pathways. A novel signature defining this subpopulation predicts long-term outcomes for TNBC patients in a large cohort. Collectively, this analysis reveals the functional heterogeneity and its association with genomic evolution in TNBC, and uncovers unanticipated biological principles dictating poor outcomes in this disease.

Fig. 1

Intercellular heterogeneity in TNBC quantified by scRNA-seq. a Workflow showing collection and processing of fresh TNBC primary tumors for generating scRNA-seq data. b Heatmap of the 1189 cells that passed quality control, with columns representing cells and rows representing established gene expression markers for the cell types indicated on the left, clustered separately for each of the six TNBC cases. The upper bar denotes inferred high cycling (pink) and low cycling (gray) cells, as identified by quantifying the expression of a set of relevant genes (see Supplementary Methods). Bottom bar denotes cells collected in the presence/absence of CD45 + cell depletion. c Bar plot depicting the distribution of the 1112 cells assigned to specific cell types, by patient. d Bar plot depicting the high cycling/low cycling distribution of the 1112 cells, by patient. e Proliferation characteristics for two representative TNBC patients, depicted as either the inferred cycling status of single cells (left) or immunohistochemistry staining for Ki67 (right). Scale bars represent 50 µm. A cell is considered high cycling if it has high G1/S or G2/M scores, as identified by quantifying the expression of a set of relevant genes. The two ways of quantifying proliferation show good concordance

Fig. 2

Clustering, genomic CNVs, and correlation maps classify most epithelial cells as malignant. a t-SNE plot of all 1112 classified cells, demonstrating separation of non-epithelial cells by cell type. b t-SNE plot of the 244 non-epithelial cells, demonstrating separation by cell type, and no distinguishable patient effect. c t-SNE plot of the 868 epithelial cells, showing mixed separation by patient, and substantial clustering of cells from different patients, suggesting pronounced intra-tumor heterogeneity. d Inferred CNVs from the single-cell gene expression data. Columns represent individual cells, and rows represent a selected set of genes, arranged according to their genomic coordinates (chromosome number indicated at left). A set of 240 normal mammary epithelial cells is shown on the left for comparison, and epithelial cells from all TNBC cases are shown, clustered separately for each patient. Amplifications (red) or deletions (blue) are inferred by computing, for each gene, a 100-gene moving average expression score, centered at the gene of interest. Prominent subclones defined by shared CNVs in tumors 39 and 81 are indicated by brackets on the top (“clonal”). e WES data for four of the six TNBC cases demonstrates high concordance with the CNV calls inferred from the transcriptomes of single cells (d). Genomic coordinates are arranged as in d from top to bottom, and mean copy number for each region (“CNV mean”) is indicated on a continuous scale, with red representing gain and blue representing loss. Accordingly, scanning from left (d) to right (e) allows for a comparison of inferred CNVs (d) and actual CNVs (e) for the same regions. f Correlation map among the expression profiles of the normal epithelial cells and the TNBC epithelial cells, depicted in the same order from left to right as d. Normal cells, as well as malignant clonal subpopulations defined by shared CNVs for tumors 39 and 81 (indicated as “clonal” at top), are correlated. The remaining non-clonal epithelial populations in all tumors show relatively poor correlation, supporting their identity as malignant cells

Fig. 3

Subpopulations of malignant epithelial cells share common expression profiles. a t-SNE plot of epithelial cells showing the five identified clusters. Patient-specific effects have been excluded through linear regression analysis. b Heatmap depicting the cluster assigned to each cell (top) and the corresponding expression of three normal breast epithelial subtypes signatures: ML (mature luminal), basal, and LP (luminal progenitor). c Average expression of each of the three normal breast epithelial subtype signatures in the epithelial clusters. Clusters 2 and 4 most strongly express the LP signature, while cluster 3 most highly expresses the ML signature. d Heatmap depicting the cluster assigned to each cell (top) and the corresponding expression of the four TNBCtype-4 subtype signatures and the Intrinsic Basal signature. e Average expression of each of the four TNBCtype-4 subtype signatures in the epithelial clusters. Clusters 2 and 4 most strongly express the proliferative Basal-Like 1 signature, while cluster 3 prominently expresses this signature and the luminal AR signature. f Assignment of each TNBC epithelial cell to a single normal breast epithelial subtype signature, depending on the difference between its average expression of the upregulated genes characterizing the signature, and the average expression of the downregulated genes. The plurality of cells in all tumors is LP-like, except for tumor 84, which is predominantly comprised of ML-like cells. g Assignments of each TNBC epithelial cell to a single TNBCtype-4 subtype signature depending on the difference between its average expression of the upregulated genes characterizing the signature, and the average expression of the downregulated genes. Multiple TNBCtype-4 subtypes are expressed among the cells of each tumor

Fig. 4

A cluster 2 subpopulation signature predicts poor patient outcomes and reflects glycosphingolipid and innate immunity pathways. a Heatmap depicting the cluster assigned to each cell (top) and the corresponding expression of three signatures related to aggressive disease behavior: 70-gene prognostic signature (PS), 49-gene metastatic burden signature (MBS), and 354-gene residual tumor signature (RTS) (rows). b Violin plots representing the distribution of expression among cells within the indicated clusters of the three signatures related to aggressive disease behavior. Black squares represent average expression of each signature among the cells of the corresponding cluster. c Kaplan–Meier survival curves for TNBC patients in the METABRIC cohort by expression quartiles of the cluster 2-derived gene signature (left). Higher expression of the signature is significantly associated with worse patient outcome (log-rank test, p = 0.0173). The other three signatures related to aggressive disease behavior are not predictive of survival (right). Separation on quartiles is for visualization purposes. d Heatmap demonstrating expression of genes in the glycosphingolipid metabolism and innate immunity pathways in the epithelial clusters (indicated at top) across all patients. Cluster 2 is significantly enriched for expression of genes in both pathways