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Single-Cell Spatial Connectivity Analysis Offers ‘Unprecedented’ Information in Breast Cancer


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A next-generation technology that focuses on protein expression at the single-cell level has yielded a wealth of information about “spatial connectivity” between cancer cells and their microenvironment. It also provided information on the benefit of adding the immune checkpoint inhibitor atezolizumab to chemotherapy as neoadjuvant treatment in the NeoTRIPaPDL1 trial in triple-negative breast cancer, investigators reported at the 2021 San Antonio Breast Cancer Symposium.1

“We are experiencing a revolution in the technologies available for characterizing the molecular complexity of tumors. Among them, imaging mass cytometry allows us to collect unprecedented information about the heterogeneity of tumors and their surrounding microenvironment…. It is feasible in a large, randomized trial and provides a comprehensive overview of triple-negative breast cancer heterogeneity at a single-cell level with spatial resolution,” said Giampaolo Bianchini, MD, Head of the Breast Cancer Group in the Department of Medical Oncology at IRCCS Ospedale San Raffaele, Milan, Italy.


“We are experiencing a revolution in the technologies available for characterizing the molecular complexity of tumors.”
— Giampaolo Bianchini, MD

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In the analysis of the NeoTRIPaPDL1 samples, he reported: “Spatial epithelial cell-to-tumor microenvironment interactions outperform cell phenotype density in predicting the differential response to immunotherapy.”

Possibilities of Imaging Mass Cytometry

Through imaging mass cytometry, it is possible to simultaneously analyze more than 40 markers in a single tissue section and identify the set of proteins present on individual cells while accounting for their precise location within the tissue. This technique combines the principles of flow cytometry, which analyzes single cells or particles as they flow past single or multiple lasers, and mass spectrometry, which identifies the molecules present in a sample by accurately measuring their mass.

In contrast, “bulk protein analysis or immune-related gene-expression signatures do not consider the cell compartment of expression, and this may explain why they provide more limited predictive information,” he said.

“The predictive information we obtained through imaging mass cytometry complemented what can be derived with commonly used immune biomarkers such as PD-L1 expression or the amount of stromal tumor-infiltrating lymphocytes. In addition, we found that several immune-related gene-expression signatures that capture immune cell types and function were less informative than the corresponding biomarkers assessed by imaging mass cytometry,” he said.

Application in Neoadjuvant Triple-Negative Breast Cancer

Biomarkers are needed to help identify patients who will benefit the most from the addition of immunotherapy and those who will do well with chemotherapy alone. With this aim, Dr. Bianchini and his group performed imaging mass cytometry analysis in the context of the phase III NeoTRIPaPDL1 trial. NeoTRIPaPDL1 evaluated the benefit of adding atezolizumab to a chemotherapy regimen as neoadjuvant therapy in patients with early high-risk and locally advanced triple-negative breast cancer.

The researchers used single-cell analysis to determine the predictive value of the different phenotypes present within the tumor and the tumor microenvironment and the relevance of the cell-cell interactions. As they pointed out, physical interactions among cells are required for both immune activation and tumor cell killing, so information on the spatial organization of the tumor tissue is critical when studying the response to immunotherapy.

The analysis involved 43 proteins expressed on more than 1 million single cells identified in the study’s pretreatment biopsy samples of 243 patients. For each sample, three high-dimensional images that encompassed the tumor, tumor-stroma interface, and adjacent stroma were generated. Researchers investigated the association between protein expression on cells in the tumor and microenvironment, cell phenotypes, and spatial tissue organization and the occurrence of pathologic complete response rate at surgery.

By allowing for a precise identification of the different cell phenotypes, including cell type and functional state, this approach revealed the potential predictive role of the density of certain cell populations in NeoTRIPaPLD1. High density of antigen-presenting cells with high expression of PD-L1 and the immunosuppressive molecule IDO and of epithelial cells with high expression of the CD56 neuroendocrine marker were associated with higher rates of pathologic complete response in patients who received atezolizumab plus chemotherapy but not in patients who received chemotherapy alone.

Pathologic complete response rates were also more likely to be seen in patients with a high degree of spatial connectivity between epithelial cells and specific cells in the tumor microenvironment. For instance, CD8-positive T cells with granzyme B or PD-1 expression and features of exhaustion correlated with a significant increase in pathologic complete response after atezolizumab, whereas lower expression of these markers was associated with similar rates between the atezolizumab arm and the chemotherapy-alone arm. This association was independent of PD-L1 expression and the presence of stromal tumor-infiltrating lymphocytes.

A Complex Technology

“Our results demonstrated that spatial data on the interactions among specific cells in the tumor microenvironment might be very informative about the benefit provided by an immune checkpoint inhibitor such as atezolizumab in addition to chemotherapy,” Dr. Bianchini commented. “This type of information can only be provided by technologies that allow us to simultaneously characterize the single cells and their spatial localization with precision.”

This approach also confirmed the extreme heterogeneity of triple-negative breast cancer, both in terms of tumor cell composition and in the amount, type, and functional state of the cells present in the microenvironment.

KEY POINTS

  • Single-cell spatial analysis by a sophisticated assay called imaging mass cytometry is able to show connections between cancer cells and their tumor microenvironment.
  • Analysis of this “spatial connectivity” has shown that the interactions between epithelial cells and the tumor microenvironment outperform cell phenotype density in predicting the differential response to immunotherapy.
  • The analysis was performed on samples from the NeoTRIPaPLD1 trial, which evaluated neoadjuvant therapy with atezolizumab plus chemotherapy.
  • At this point, the technology is potentially useful only in large randomized clinical trials and not in the clinic.

“The predictive information we obtained through imaging mass cytometry complemented what can be derived with commonly used immune biomarkers such as PD-L1 expression or the amount of stromal tumor-infiltrating lymphocytes. In addition, we found that several immune-related gene-expression signatures that capture immune cell types and function were less informative than the corresponding biomarkers assessed by the new technology,” Dr. Bianchini said.

Because of its complexity, imaging mass cytometry is currently applicable only to samples collected in large clinical trials, he said, where it is “paving the way for its broad implementation in cancer research to aid precision immunology.” He said the challenge is how to make this complex technology clinically useful. “This is still something that can be done by only a few very experienced groups,” he said. “There is lots of work to be done.” 

DISCLOSURE: Dr. Bianchini has received honoraria from Amgen, AstraZeneca, Chugai, Daiichi Sankyo, Eisai, Exact Sciences, Gilead, Eli Lilly, MSD, Novartis, Pfizer, Roche, Sanofi, and Seagen.

REFERENCE

1. Bianchini G, Wang XQ, Danenberg E, et al: Single-cell spatial analysis by imaging mass cytometry and immunotherapy response in triple-negative breast cancer in the NeoTRIPaPDL1 trial. 2021 San Antonio Breast Cancer Symposium. Abstract GS1-00. Presented December 7, 2021.


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