Highly multiplexed imaging reveals prognostic immune and stromal spatial biomarkers in breast cancer
Jennifer R. Eng, Elmar Bucher, Zhi Hu, Cameron R. Walker, Tyler Risom, Michael Angelo, Paula Gonzalez-Ericsson, Melinda E. Sanders, A. Bapsi Chakravarthy, Jennifer A. Pietenpol, Summer L. Gibbs, Rosalie C. Sears, Koei Chin
Jennifer R. Eng, Elmar Bucher, Zhi Hu, Cameron R. Walker, Tyler Risom, Michael Angelo, Paula Gonzalez-Ericsson, Melinda E. Sanders, A. Bapsi Chakravarthy, Jennifer A. Pietenpol, Summer L. Gibbs, Rosalie C. Sears, Koei Chin
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Research Article Immunology Oncology

Highly multiplexed imaging reveals prognostic immune and stromal spatial biomarkers in breast cancer

Abstract

Spatial profiling of tissues promises to elucidate tumor-microenvironment interactions and generate prognostic and predictive biomarkers. We analyzed single-cell spatial data from 3 multiplex imaging technologies: cyclic immunofluorescence (CycIF) data we generated from 102 patients with breast cancer with clinical follow-up as well as publicly available mass cytometry and multiplex ion-beam imaging datasets. Similar single-cell phenotyping results across imaging platforms enabled combined analysis of epithelial phenotypes to delineate prognostic subtypes among patients who are estrogen-receptor+ (ER+). We utilized discovery and validation cohorts to identify biomarkers with prognostic value. Increased lymphocyte infiltration was independently associated with longer survival in triple-negative (TN) and high-proliferation ER+ breast tumors. An assessment of 10 spatial analysis methods revealed robust spatial biomarkers. In ER+ disease, quiescent stromal cells close to tumor were abundant in tumors with good prognoses, while tumor cell neighborhoods containing mixed fibroblast phenotypes were enriched in poor-prognosis tumors. In TN disease, macrophage/tumor and B/T lymphocyte neighbors were enriched, and lymphocytes were dispersed in good-prognosis tumors, while tumor cell neighborhoods containing vimentin+ fibroblasts were enriched in poor-prognosis tumors. In conclusion, we generated comparable single-cell spatial proteomic data from several clinical cohorts to enable prognostic spatial biomarker identification and validation.

Authors

Jennifer R. Eng, Elmar Bucher, Zhi Hu, Cameron R. Walker, Tyler Risom, Michael Angelo, Paula Gonzalez-Ericsson, Melinda E. Sanders, A. Bapsi Chakravarthy, Jennifer A. Pietenpol, Summer L. Gibbs, Rosalie C. Sears, Koei Chin

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Figure 1

Concordant cell phenotypes in multiplex imaging data from different platforms.

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Concordant cell phenotypes in multiplex imaging data from different plat...
(A) Three multiplex imaging datasets from breast cancer tissue microarrays were processed through single-cell segmentation and feature extraction using the mplexable pipeline. The single-cell datasets were separately clustered using the unsupervised Leiden algorithm resulting in cell types that were annotated with similar names across platforms. We generated a suite of spatial statistics for each tissue, and the resulting cellular and spatial features were used for discovery and validation of prognostic cell abundance and spatial biomarkers across datasets. CycIF, cyclic immunofluorescence; IMC, imaging mass cytometry; MIBI, multiplex ion beam imaging. Asterisk indicates a new dataset, and cross indicates public data. (B) Overlap of markers (left) and annotated cell types (right) in each multiplex imaging dataset. (C) Representative images from the 3 multiplex imaging platforms showing epithelial (orange), immune (red), and fibroblast (green) markers. Scale bar: 100 μm. Total of 413 patient tissues imaged. (D) Cell lineage types showing cell location and lineages: epithelial (orange), immune (red), fibroblast (green), endothelial (blue), and other stromal (purple). CycIF, top; IMC, middle; MIBI, bottom. (E) The correlation between platforms of the fraction of each cell lineage per total cells per subtype, per platform, using unsupervised clustering and annotation to determine lineage. No. Pts., number of patients. (F) Correlation between cell types on adjacent sections of a TMA stained with MIBI and CycIF. n = 9 tissues. (E and F) Pearson’s correlation r (2-sided) between platforms and P value shown in panel title.