Breast cancer is one of the most common malignant tumors in women worldwide, and its extensive clinical heterogeneity poses major challenges for diagnosis and treatment. Breast cancer comprises multiple subtypes, while normal human breast tissue contains three types of epithelial cells: luminal hormone-responsive cells (LumHR), luminal secretory cells (LumSec), and basal–myoepithelial cells (MyoEpi). Direct evidence has long been lacking to determine which normal epithelial cell type gives rise to each breast cancer subtype.
In a study published in Cell, a research team led by Prof. WANG Kaile from the Center for Excellence in Molecular Cell Science (Shanghai Institute of Biochemistry and Cell Biology) of the Chinese Academy of Sciences, and Prof. Nicholas Navin from MD Anderson Cancer center unveiled a comprehensive single-cell map of breast cancer progression. By developing a high-throughput single-cell genome and transcriptome co-sequencing method, wellDR-seq, enabling profiling the DNA and RNA of over 30,000 single cells from 12 breast cancer cases.
This groundbreaking work not only directly identified and characterized the epithelial cell-of-origin of tumor ancestral subclones of ER-positive breast cancer, but also, for the first time at the single-cell level, quantitatively described the complex dosage-regulation relationship between copy number variations (DNA) and gene expression (RNA). These findings provide new insights for early diagnosis, prognosis assessment, and precision treatment of breast cancer.
Correctly identifying the origins of different breast cancer subtypes, as well as the genes driving subsequent tumor proliferation and evolution, is key to cure and prevent the disease. However, because ancestral clones exist at very low frequencies and harbor few mutations, only high-throughput, high–genomic-resolution single-cell genome and transcriptome co-sequencing technologies can enable the identification of these populations while simultaneously capturing their phenotypes. Yet, this has remained a formidable technical challenge.
wellDR-seq achieved simultaneous whole genome and transcriptome profiling of thousands of single cells with high genomics resolution by integrating dual-index barcoding, optimized biochemistry with nanoliter-scale microwell chip. By applying wellDR-seq to 33,646 single cells from 12 patients with ER-positive breast cancer, the researchers made several significant discoveries：
1）Identified the epithelial cell-of-origin of ER+ breast cancer cells: The study successfully identified tumor ancestral subclones in four patients. These early cancer subclones, which carried minimal CNAs, later acquired more CNAs and underwent massive expansion, eventually forming the bulk of the tumor. Importantly, using scRNA-seq data of wellDR-seq, the researchers traced all these ancestral cells back to the Luminal HR (hormone-responsive) cell lineage, suggesting that this specific cell type is the likely origin of ER+ breast cancer.
2）CNAs in Non-Cancerous Cells: The study also uncovered sporadic CNAs in non-cancerous cells, including both epithelial and stromal populations. Among epithelial cells, all CNA-bearing cells belonged to the luminal lineage (LumSec and LumHR), with alterations happened on autosomes. In contrast, CNA-positive stromal cells were identified in fibroblasts, endothelial cells, and pericytes, with most alterations occurring on chromosome X. These findings suggest that luminal epithelial cells may possess a greater propensity to undergo malignant transformation.
3）Decoding Gene Dosage Effect: This study further, for the first time, systematically quantified the impact of copy number alterations (CNAs) on gene expression at the single-cell level. The researchers found that at the chromosomal segment level, 56% of CNAs were positively correlated with changes in gene expression. Among these CNAs, increases in expression levels were nearly linearly correlated with increases in copy number.
Unexpectedly, by comparing differentially expressed genes among tumor subclones, the authors discovered that the majority of these genes (69%) were located in regions without copy number changes, rather than in regions affected by CNAs.
Moreover, at the single-gene level, substantial variation was observed in the relationship between copy number and expression changes. These genes were classified into two categories: “Dosage-sensitive” genes and “Dosage-insensitive” genes.
In ER+ breast cancers, the study found the breast cancer genes such as PGR, AURKA, and RB1, were dosage sensitive genes, while some other genes such as PIK3CA, BRCA1, and TP53, whose expression levels were independent of copy number changes and remained stable despite CNA events. This discovery provides a new perspective for understanding the genetics and functional biology of tumors, helping explain why certain gene mutations or copy number alterations strongly drive tumor progression, while others do not.
In summary, this study not only developed a high-throughput single-cell genome and transcriptome co-sequencing technology, but also systematically elucidated the origins of breast cancer and quantitatively characterized gene dosage effects. wellDR-seq is a versatile method that can be broadly applied to investigate the origins and evolution of various tumors, as well as the interactions between genotype and phenotype. Beyond cancer research, wellDR-seq holds great potential for wide-ranging applications across biology and biomedicine, including prenatal genetic testing, DNA replication, developmental biology, neuroscience, normal tissue mosaicism, aging, and microbiology.
Reference: https://www.cell.com/cell/abstract/S0092-8674(25)00926-2
Appendix：
