NEW YORK – A single-cell imaging mass cytometry analysis has uncovered additional breast cancer subgroups that are associated with clinical outcomes.
Using Fluidigm's Hyperion Imaging System, researchers from the University of Zurich and elsewhere quantified 35 protein biomarkers within tumor tissues from more than 350 breast cancer patients to generate high-dimensional images. The researchers zoomed in on single cells within these images to deeply profile tumors' phenotypes and organization. As they reported Monday in Nature, they uncovered more than a dozen common cellular subtypes, in addition to single-cell pathology groups associated with clinical outcomes.
"This created an unprecedented view of a tumor's cellular landscape and the surrounding tissue, which enabled us to determine whether more complex biomarkers exist for clinical outcome," Jana Fischer, co-first author of the study and a researcher at the University of Zurich, said in a statement.
Fluidigm's Hyperion Imaging System combines immunohistochemical staining with mass-spectrometry-based detection to generate images. For their analysis, the researchers designed a breast cancer-specific imaging mass cytometry panel of 35 antibodies, including ones aimed at detecting established targets like estrogen receptor, progesterone receptor, and HER2, but also the proliferation marker Ki-67 and cell lineage markers.
In all, they generated 720 high-dimensional pathology images from tumor tissue from 352 breast cancer patients for whom survival data was known. The researchers reported that their images were comparable to those generated via immunohistochemistry or immunofluorescence approaches.
By quantifying the expression of these markers, the researchers could gauge the spatial features of these single cells and place them into phenotype clusters, of which they identified 14 main ones.
Additionally, they uncovered 18 single-cell pathology subgroups. These subgroups, they noted, differed from the classical clinical subtypes and were associated with distinct clinical outcomes. For instance, single-cell pathology subgroup 1 (SCP1) was associated with patients with a promising prognosis, while SCP3 was linked to a poorer prognosis.
When they compared clinically defined subtypes and single-cell pathology subgroups, the researchers found the single-cell-based approach was better able to predict the overall survival of a patient.
They replicated their analysis on a set of tumor samples from 73 patients and likewise uncovered the same cellular metaclusters and single-cell pathology subgroups. They noted, though, a difference in the proportion of the subgroups present, which they attributed to differences in the patient-selection strategies for the two cohorts.
"This landmark study is the first to demonstrate the potential clinical value of highly multiplexed Imaging Mass Cytometry to identify breast cancer subtypes that correlate with clinical outcomes," Chris Linthwaite, president and CEO of Fluidigm, said in a statement. "By shedding new light with single-cell spatial images and data about the features of the tumor microenvironment, we believe this study will further increase adoption of IMC in translational and clinical research to deliver better predictive and personalized approaches to cancer care in the future."
The Zurich team is now examining whether tumors with certain molecular profiles can be targeted by particular drugs.
"By improving our ability to describe cellular features and categories as well as our ability to precisely identify patients that have high- or low-risk breast cancer, we're opening up new possibilities for precision medicine," senior author Bernd Bodenmiller, a professor of quantitative biology at Zurich, said in a statement.