Next-Generation Pathology
A new stratification of breast cancer patients based on spatial and cellular resolution is emerging. The question is, could imaging mass cytometry (IMC)—21st-century microscopy—provide the deep resolution needed for researchers to better understand disease outcomes? And could it support the shift toward personalized medicine?
Current single-cell phenotyping methods can characterize and quantify protein expression in large numbers of live cells with high sensitivity across multiple parameters. Immunohistochemistry (IHC) traditionally detects surface proteins in diseased tissue using antibodies, but it has limitations. Specifically, adding new parameters can be challenging because designing high-dimensional panels is complex. Mass cytometry, in contrast, allows simultaneous detection of markers using metal isotopes as barcodes, though it requires cell destruction in the process. Mass cytometry is ideal for assessing complex tissues with heterogeneous cell populations, answering fundamental questions about cell types, appearances, interactions, and roles within contexts such as development and disease. With larger panels and a broader set of markers, researchers can study entire systems rather than focusing on specific cell typesr, a major drawback of conventional mass cytometry is the loss of spatial information about cell-to-cell interactions within tissue, as the process requires cells to be in suspension. We can measure structural information with imaging or single-cell information with mass cytometry, but often spatial data is essential for answering biological questions. For example, immune cells must physically interact with cancer cells, making it important to observe these interactions and understand immune cell distributions and characteristics. IMC, combined with computational techniques, now allows the parallel measurement of spatial and phenotypic features, offering insights into tumor biology and complexity . Howeves a trade-off: while IMC provides spatial data, higher parameter counts can reduce throughput. With knowledge of key predictive markers for therapy response, we can pinpoint therapeutic approaches likely to benefit patients .
For the past denmiller and colleagues have developed experimental and computational methods to study tumor tissue at the single-cell level . Six years ago, Giesen etduced a high-resolution IMC approach that combines immunohistochemistry and mass cytometry, capable of measuring up to 100 markers at a cellular resolution of 1 µm. They applied this technique to breast tumor samples, which are stratified into luminal A/B, basal, and triple-negative subtypes based on molecular characteristics . Using conventional pathological s, clinicians can predict patient prognosis and evaluate tissue and cell morphology. However, within clinical subtypes, responses to therapy can vary widely, suggesting the need for better stratification methods .
In a study using single-cell RNA and DNA sequencinhers discovered common signatures and intratumoral heterogeneity in carcinoma cells across breast cancer subtypes . This heterogeneity, along with insights into the tumor microt, could lead to improved prognostic and therapeutic strategies. For instance, identifying immune cells that infiltrate tumors and understanding their pathways can reveal new treatment targets .
Bodenmiller’s team analyzed 21 breast cancer samples using IMC, reveroups within clinical subtypes defined by traditional histology and demonstrating how the tumor microenvironment influences cancer cells . As a follow-up, Wagner et al. analyzed millions of cells from 144 breast tumorng tumor-immune cell interactions that were linked to poor prognosis and immunosuppression, especially in high-grade ER+ tumors . These findings highlighted why breast cancer clinical trials involving checkpoint inhibi often fail, unlike trials for melanoma.
Recently, Jackson et al. analyzed breast tumor tissue from 352 patients, covering all molecular subtypes and grades, identifying more than 800,000 cells, which they segmented into tumor and stromal regions. Using an unsupervised clustering approach called Phonograph, they grouped the cells into 27 metaclusters, representing immune, stromal, and epithelial cells, as well as various tumor cell phenotypes. They found that immune cells often separated from the tumor mass, although rare cell populations with low hormone receptor expression infiltrated the tumor-stroma interface .
Neighborhood analysis revealed unique interactions between cell populations, with either homotypited heterotypic interactions, including immune cells interacting with fibroblasts and endothelial cells around blood vessels. These metaclusters helped predict patient outcomes more accurately than traditional methods, identifying subpopulations that could distinguish between primary and relapse samples . This analysis revealed that interactions at the tumor-stromal interface correlated with tumor grade, as scored by convstology techniques.
Notably, the researchers introduced 18 new subgroups for breast tumor stratification based on single-cell spatial and phenotypic features. This new stratification was supported by the discovery of multiple cell populations in every subtype, showing the limitations of traditional tissue classification. The new stratification scheme correlated more closely with clinical outcomes than existing classifications. For example, while luminal A tumors generally have a good prognosis, those with CK7 expression were associated with poorer outcomes. Similarly, certain triple-negative breast cancer (TNBC) subgroups lacking luminal markers had poor outcomes due to high heterogeneity, whereas others with apoptotic markers and P53 expression had better prognoses .
IMC provides a new layer of spatial data that can differentiate patient outcomes based on cellular communities rather than soletype or cellular clusters. This spatial data highlighted the role of the tumor microenvironment, with stromal features contributing less to clinical subtype identification . The study’s findings, validated in a separate cohort of 400,000 cells, confirmed the reproducibility and potential applicability of this aoss different cancers. IMC could provide insights into complex cancer ecosystems, enabling a more comprehensive view of cancer biology across large patient cohorts .
In sum, Bodenmiller and colleagues’ research suggests that IMC could redefine single-cell pathology, enhancing prognosis beyond existing classifica approach may improve understanding of how features within the human ecosystem predict disease progression and patient outcomes, potentially reshaping cancer diagnostics. The impact of these findings is likely to extend beyond breast cancer, paving the way for similar studies across various tumor types .
References
- Giesen, C., et al. “Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry.” Nat. Methods 11 (202.
- Malhotra, G. K., et al. “Histological, molecular and functional subtypes of breast cancers.” Cancer Biol. Ther. 10 (2010): 955-960.
- Chung, W., et al. “Single-cell RNA-seq enables comprehensive tumor and immune cell profiling in primary breast cancer.” Nat. Commun. 8 (2017).
- Nik-Zainal, S., et al. “The life history of 21 breast cancers.” Cell 149 (2012): 994-1007.
- Wagner, J., et al. “A Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer.” Cell 177 (2019): 1330-1345.e18.
- Jackson, H. W., et al. “The single-cell pathology landscape of breast cancer.” Nature (2020). doi:10.1038/s41586-019-1876-x.
- Levine, J. H., et al. “Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis.” Cell (2015). doi:10.1016/j.cell.2015.05.047.
- Bombonati, A., and Sgroi, D. C. “The molecular pathology of breast cancer progression.” J. Pathol. 223 (2011): 307-317.