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A single-cell atlas of the tumor and immune ecosystem of human breast cancer

Wagner et al. Cell (2019).
A subset of ER-negative tumors responsive to anti-PD-1 can be identified by mass cytometry based on unique features of tumor composition.
A subset of ER-negative tumors responsive to anti-PD-1 can be identified by mass cytometry based on unique features of tumor composition.

Despite recent studies showing many subtypes of breast cancer exist, patients are currently divided into treatment groups based solely on the expression of only four tumor markers: ER, PR, HER2, and Ki-67. However, the patient’s prognosis and response to therapies within these subtypes are still highly variable.


To understand the various breast cancer ecosystems with finer granularity, Wagner et al. used mass cytometry to profile 26 million cells from 144 tumor samples spanning all breast cancer stages and subtypes, 46 adjacent non-tumoral tissue samples, and four tissue samples from healthy individuals undergoing breast reduction surgery. The expression of 73 proteins was evaluated using two tumor and immune cell-centric antibody panels across all 194 samples.


According to other reports, ER- breast cancer subtypes react better to immune checkpoint blockade than ER+ subtypes. Interestingly, this study revealed that a subset of ER+ tumors were linked to PD-L1+ tumor-associated macrophage and exhausted T cell phenotypes similar to ER- tumors, suggesting that neoadjuvant or early adjuvant anti-PD-1 and anti-PD-L1 therapy could help some ER+ patients. Additionally, the glycoprotein CD38, an immune checkpoint molecule, was identified as a marker of CD4 and CD8 T cell exhaustion in breast cancer.


This comprehensive study shows how mass cytometry can be used to study the complex interaction between tumor and immune cells within the local microenvironment. Mass cytometry identified patterns within the tumor which could be associated with ICB response, independent of traditional tumor subtype and grade classification. Mapping the entire cancer ecosystem could be considered when designing immunotherapies and selecting patients for clinical trials to improve response rates within cancer subtypes.

Reference citation: Wagner J, Rapsomaniki MA, Chevrier S, Anzeneder T, Langwieder C, Dykgers A, Rees M, Ramaswamy A, Muenst S, Soysal SD, Jacobs A, Windhager J, Silina K, van den Broek M, Dedes KJ, Rodríguez Martínez M, Weber WP, Bodenmiller B. A Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer. Cell. 2019 May 16;177(5):1330-1345.e18. doi: 10.1016/j.cell.2019.03.005.

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Illustration. In the center is a grey mass labeled "tumor" under a magnifying glass. Inside the tumor are three grey cells all with "zzz" above them. Outside the magnifying glass (and therefore outside the tumor) in the four corners are different depictions. Top left shows three red cells and is labeled "novel T cell clones". Top right shows a lymph node with red T cell zones and the words "LN trafficking required". Bottom right shows two red T cells in a blood vessel labeled "LAG-3 and CD8+" with the words "peripheral biomarkers". Bottom left shows a spleen with the same grey, sleeping T cells. There's a red lightning bolt and the words "impaired systemic immune response in cancer".

Peripheral blood analysis is key to developing effective cancer immunotherapies

Although most research focuses on tumor-killing immune cells at the site of the tumor, new research increasingly demonstrates the value of studying circulating immune cells in peripheral blood to identify new therapeutic targets to enhance tumor-killing immune responses. We highlight recent publications with peripheral blood biomarkers and discuss recommendations for how to incorporate peripheral blood immune profiling into your drug development pipeline.