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10x Genomics Blog

By the time you finish reading this article, 15 people around the world will be diagnosed with breast cancer; the [Breast Cancer Research Foundation](https://www.bcrf.org/breast-cancer-statistics-and-resources/) claims this happens every 14 seconds (1). Every October, people don pink shirts and ribbons in honor of Breast Cancer Awareness Month. But this month is about more than sporting pink paraphernalia—it’s about raising awareness and educating the public about a disease that, [according to the WHO](https://www.who.int/news-room/fact-sheets/detail/breast-cancer), afflicted more than 2.3 million women in 2020 alone (2).

For us, it’s also about the important, basic discoveries scientists are making using single cell and spatial technology to inform new treatments.

Single cell tools are adept at deconstructing the complex cellular puzzle that makes a heterogeneous tumor. Time and time again, single cell RNA sequencing (scRNA-seq) has revealed subsets of cancer cells and their molecular Achilles’ heels that scientists can target to develop better therapies, such as stubborn cancer stem cells in glioblastoma or tumor-infiltrating myeloid cells (3,4). Breast cancer tumors are also heterogeneous bundles of misbehaving cells, but how and when tumors develop is still a black box. This is, in part, due to conflicting and incomplete maps of breast cell lineages.  

In a [study](https://www.science.org/doi/10.1126/sciimmunol.abl9165) published this June in *Developmental Cell*, researchers generated the most detailed breast cell atlas to date, providing a resource for others to identify ways to prevent cancer in people with a high risk of developing breast cancer. The researchers used scRNA-seq, cytometry by time of flight (CyTOF), and cyclic immunofluorescence (CyCIF) to analyze breast tissue from 54 people between ages 19 and 73 to develop a detailed breast cell atlas. They identified several subsets of mammary epithelial cells (MECs) associated with breast cancer risk factors, including age, the cancer-causing mutation *breast cancer gene 1/2* (*BRCA1/2*), and history of pregnancy (5).

### Mapping the mammary gland

The researchers focused on developing detailed maps of MECs because milk-producing mammary glands change a lot over the course of a woman’s life, particularly during puberty, pregnancy, and menopause. The proportion of key cell populations are not only affected by life events, but cancer risk factors such as the aforementioned *BRCA1/2*. An atlas of mammary gland cells could reveal new, targetable cell populations.

To develop a mammary gland cell atlas, the researchers performed scRNA-seq on over 50,000 breast tissue cells from 16 people with and without cancer-causing germline mutations. They used previously characterized transcriptional signatures to identify clusters of epithelial cells specific to milk-producing mammary glands. 

There are three main types of MECs, all of which are critical to mammary gland development: alveolar (AV), hormone sensing (HS), and basal (BA). Each cell type has a distinct function during mammary gland development, but their morphology and function alter with age. The researchers found all three cell types in every patient sample, but the proportion of each cell type was vastly different.

The researchers further explored the cell heterogeneity by identifying subclusters within each MEC lineage. They found several less-characterized subtypes. In particular, they identified a subtype of AV cells, dubbed basal-luminal (BL) cells, with features more often found in cancer cells than normal tissue. Though BL cells were classified as a distinct subset of alveolar cells based on their expression of key AV cell genes and low expression of other MEC lineage genes, they co-expressed intermediate levels of genes from the two other MEC lineages, BA and HS cells. They also expressed genes commonly found in progenitors, including high levels of *ALDH1A3*, indicating these cells were more plastic or progenitor-like than other AV cells. 

### Finding BL cells’ vulnerabilities

CyTOF and CyCIF analysis revealed a striking accumulation of BL cells in tissue from older people independent of germline mutations in oncogenes, such as *BRCA1/2*, or a history of pregnancy. The researchers used data from the [Molecular Taxonomy of Breast Cancer International Consortium (METABRIC](https://www.cbioportal.org/study/summary?id=brca_metabric))—a database of information from over 2,000 human breast tumor samples—to elucidate the connection between BL cells and breast cancer (6, 7). The BL-unique signature was strongly associated with basal-like breast cancers. Using Gene Set Enrichment Analysis, the researchers found that gene sets enriched in basal breast cancer gene sets were highly expressed in BL cells, suggesting that basal-like breast cancer originates from BL cells.

### Cell signaling and BL cell abundance in the mammary gland

The researchers turned to patient-derived mammary gland organoids to understand exactly how MEC composition is regulated and can be therapeutically targeted. They confirmed that all the novel lineages they identified—including BL cells—were present in mammary gland organoids. The researchers altered the composition of the cell media by removing and adding key signaling molecules to determine how it affected MEC composition. 

When the researchers nixed cytokine transforming growth factor beta (TGF-β)—a multifunctional cytokine that plays a role in some cancers—from the media, BL cells exponentially expanded. Removal of noggin, another component of the TGF-β signaling pathway, had the same effect. Further characterization of BL cells and how their cell fate is regulated by the TGF-β signaling pathway could shed light on how breast cancer tumors develop, particularly basal-like breast cancer tumors.

### An atlas for the future

The researchers discovered more unexplored breast cell lineages beyond BL cells. Some HS and BA cell subclusters were associated with cancer-causing mutations in genes, such as *BRCA2* and *human epidermal growth factor receptor 2* (*HER2*), and pregnancy, respectively, further highlighting the depth and breadth they achieved using scRNA-seq. This comprehensive study not only provides a resource for breast cancer researchers to inform new therapeutic targets for treatment and prevention, but also a roadmap to using single cell sequencing to develop detailed cell atlases of other tissues.

*Explore the [study for yourself →](https://www.science.org/doi/10.1126/sciimmunol.abl9165)*

*And learn how 10x Genomics resources can transform your [cancer biology research →](https://www.10xgenomics.com/research-areas/cancer)*

**References:** 

1. https://www.bcrf.org/breast-cancer-statistics-and-resources/
2. https://www.who.int/news-room/fact-sheets/detail/breast-cancer
3. Xie XP, et al. Quiescent human glioblastoma cancer stem cells drive tumor initiation, expansion, and recurrence following chemotherapy. *Dev Cell* 57: 32–46 (2022). doi: 10.1016/j.devcel.2021.12.007
4. Cheng S, et al. A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells. *Cell* 184: 792–809 (2021). doi: 10.1016/j.cell.2021.01.010
5. Grey GK, et al. A human breast atlas integrating single-cell proteomics and transcriptomics. *Dev Cell* 57: 1400–1420 (2022). doi: 10.1016/j.devcel.2022.05.003
6. Curtis C, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. *Nature* 486: 346–352 (2012). doi: 10.1038/nature10983
7. Pereira B, et al. The somatic mutation profiles of 2,433 breast cancers refine their genomic and transcriptomic landscapes. *Nat Commun* 7: 11479 (2016). doi: 10.1038/ncomms11479

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