Mapping mammary cell diversity in women at high breast cancer risk

The human breast is far more than a uniform sheet of milk-producing tissue. It is a mosaic of distinct epithelial cell types—contractile basal cells, committed luminal progenitors, and mature luminal cells—that each carry their own molecular identity and, crucially, their own relationship to cancer risk. Yet while decades of transcriptional profiling have mapped the RNA-level landscape of these lineages in exquisite detail, the protein layer, where biology ultimately executes its functions, has remained largely terra incognita. Now, a team at the Princess Margaret Cancer Centre and the University of Toronto has delivered what the researchers describe as the most comprehensive proteomic atlas to date of the high-risk breast, revealing how age, pregnancy, and inherited mutations sculpt the cellular and molecular terrain on which breast cancer may arise.

The study, published in Genome Biology, was led by Rama Khokha and Thomas Kislinger, with postdoctoral fellow Matthew Waas and graduate researcher Bowen Zhang as co-first authors. Rather than relying on the standard arsenal of single-cell RNA sequencing, the team turned to low-input mass spectrometry–based proteomics, a technically demanding approach that measures the actual protein complement of cells. This choice matters because RNA abundance and protein abundance are notoriously imperfect correlates; regulatory processes acting at translation and protein degradation mean that a cell’s transcriptome can only hint at its functional machinery. By reading directly from the proteome, the researchers gained access to the operative biology of mammary epithelial subpopulations in a way that prior studies of high-risk tissue simply could not offer.

The cohort behind the atlas consisted of 22 breast tissue samples donated by women at elevated risk of breast cancer, encompassing a range of germline mutation backgrounds—including carriers of pathogenic variants in cancer-predisposing genes—alongside variation in parity status and age. Tissue was typically obtained during prophylactic risk-reducing surgery, providing a rare window into the pre-malignant breast. From each sample, the researchers isolated epithelial subpopulations, separating basal, luminal progenitor, and mature luminal cells, and then applied optimized low-input proteomic workflows to quantify the proteins within each compartment. In total, they measured 5,555 proteins across the cell types and donors, an achievement made possible by recent advances in sample preparation and mass spectrometry sensitivity that allow deep proteome coverage from materials that would once have been considered too scarce to analyze.

What emerged first from the data was a striking picture of individuality. The extent of variation between donors was marked, spanning the relative proportions of epithelial lineages, the proteomic programs each lineage expressed, and the capacity of isolated cells to form colonies in functional assays. This inter-donor heterogeneity is more than a technical nuisance; it is a biological message. It suggests that the “high-risk breast” is not a single, well-defined entity but a spectrum of tissue states, each shaped by a woman’s unique combination of genetics, reproductive history, and age. For researchers attempting to design controlled studies of breast cancer susceptibility, this variability poses a formidable challenge—one that the authors argue must be explicitly accounted for in future experimental designs.

To bring order to this variability, the team employed multivariable modeling that could disentangle the contributions of individual clinical covariates from the noise of donor-to-donor differences. The analysis revealed that age, parity, and germline mutation status each leave measurable fingerprints on both the global proteomic architecture of the breast epithelium and the lineage-specific activity of key signaling and functional pathways. Some of these responses were conserved across cell types, while others were restricted to particular lineages, indicating that shared risk factors act on the mammary gland in a partially compartmentalized fashion. A molecular insult relevant to cancer initiation may thus manifest differently in a basal cell than in a luminal progenitor, even within the same breast.

Among the covariates, parity—whether a woman has carried a pregnancy to term—produced some of the most notable effects. Pregnancy is known epidemiologically to have complex, biphasic effects on breast cancer risk, and the new data illuminate its biological underpinnings. Women with a history of childbirth showed reduced abundance of basal cells within the epithelial compartment, alongside remodeling of the proteomes of both luminal progenitor and mature luminal populations. Clonogenic capacity—the ability of isolated epithelial cells to proliferate and form colonies in vitro, a proxy for regenerative and, potentially, neoplastic potential—was also altered by parity. Together, these findings suggest that the post-pregnancy breast is not merely a smaller version of the nulliparous gland but a functionally reconfigured tissue whose susceptibility landscape has been substantially redrawn.

The functional dimension of the study extended beyond proteomics into colony-forming assays, which measure how effectively single cells or small populations give rise to expanding colonies. By quantifying clonogenicity across donors and linking it to proteomic profiles, the researchers could connect molecular states to cellular behavior. The results demonstrated that clonogenic capacity varies markedly between individuals and tracks with specific proteomic signatures, effectively associating patterns of protein expression with the functional vigor of epithelial lineages. This linkage between molecular measurement and functional output is what elevates the atlas from a descriptive catalog to a resource with mechanistic implications.

To test whether these clonogenic signatures hold relevance beyond the high-risk breast itself, the team projected them onto large public tumor datasets—the METABRIC collection and The Cancer Genome Atlas (TCGA). The reasoning was straightforward: if protein-based programs observed in healthy high-risk tissue reflect early determinants of susceptibility, they should resonate with the molecular features of established tumors. The analysis confirmed the link, tying the functional programs defined in the study to tumor subtypes and molecular phenotypes in breast cancer patients. This computational bridge from pre-malignant tissue to clinical disease offers a rationale for using proteomic profiles of high-risk breast tissue as potential biomarkers for refined risk stratification.

The implications for prevention are considerable. Current risk models for women with germline mutations rely primarily on genetics, family history, and demographic factors, but they capture only part of the variability in who ultimately develops cancer. A proteomic atlas of this depth suggests that the molecular state of breast epithelium—its composition, its pathway activities, its functional capacity—constitutes an additional, information-rich layer that could be incorporated into risk assessment. For example, understanding how parity reshapes luminal progenitor proteomes could help explain why the protective effect of early childbirth varies among women and may inform interventions designed to mimic or enhance that protection. Similarly, lineage-restricted pathway alterations in mutation carriers could highlight the cell types in which prevention strategies should focus their attention.

The study also represents a methodological milestone. Profiling more than five thousand proteins from low-input samples of sorted epithelial subpopulations demonstrates that proteomics can now operate at a scale and resolution previously reserved for transcriptomics. Combined with functional clonogenic readouts and rigorous multivariable statistics, the platform provides a template for future studies seeking to dissect tissue heterogeneity in other organs and other cancer-susceptibility contexts. The authors note that defining how clinical covariates shape epithelial composition and molecular state clarifies key sources of biological variability and offers a resource for improving mechanistic insight into early events in breast carcinogenesis.

For a field that has long concentrated its attention on the RNA message rather than the protein machinery, the shift in perspective is consequential. Proteins are the effectors of cellular behavior—the enzymes, structural elements, and signaling molecules that actually determine how a cell responds to hormonal cycles, mutational burdens, and regenerative demands. By charting that layer in the breasts of women who face elevated risk, the Toronto team has opened a path toward understanding cancer susceptibility not merely as a matter of inherited DNA, but as a property of living, dynamic tissue whose state can be read, measured, and potentially modified long before disease appears. The atlas now stands as both a benchmark and an invitation: a detailed snapshot of the high-risk breast, and a call to fold molecular and functional tissue states into the next generation of risk prediction and prevention.