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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

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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 4

Reproducible prognostic spatial metrics in breast cancer cohorts.

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Reproducible prognostic spatial metrics in breast cancer cohorts.
(A) Ex...
(A) Example tissue colored by number of CD3 T cell neighbors of each cell. Tumor in orange. (B and C) Recurrence-free survival (RFS) of patients who are ER+ stratified by stromal neighbors of epithelial (B) or overall survival (OS) of patients with TNBC stratified by immune neighbors of immune (C) in the discovery (left) and validation (right) cohorts. (D) Multivariable CPH modeling of (left) RFS of patients who are ER+ for stromal neighbors of epithelial or (right) OS of patients TNBC for immune neighbors of immune. (E and G) RFS of patients with TNBC stratified by macrophage neighbors of tumor (E) or B cell neighbors of T cells (G) in the discovery (left) and validation (right) cohorts. (F and H) Multivariable CPH modeling of RFS of patients with TNBC for macrophage neighbors of tumor (F) or CD20 B cell neighbors of CD3 T cells (H). (I) TNBC OS stratified by tumor-immune mixing score in MIBI (top) and validation cohorts (i.e., CycIF and IMC; bottom). (I) TNBC OS stratified by occupancy AUC of T (left) or B lymphocytes (right). (K) Multivariable CPH modeling of T (left) and B lymphocyte (right) occupancy AUC. (L) TNBC OS stratified by fractal dimension slope difference for T (top) or B lymphocytes (bottom). (M) Top: Representative tissue showing nuclei (blue) and PD-1 (red). Scale bar: 130 μm. Bottom: Voronoi tessellation of tissue; all cells (blue), PD-1+ cells (red), and interactions (black line). (N) OS of CycIF TNBC patients stratified by PD-1 interactions. Bottom: CPH modeling of PD-1 interaction metric. (A–N) Kaplan-Meier P values from log-rank test; validation cohort FDR corrected with the Benjamini-Hochberg method. CPH modeling P values for spatial variable given in panel titles; HR estimates are marked by boxes, and data are shown as 95% CI. (A–H) Neighbors are within a 40 μm radius. (J–L) Includes lymphocytes within 20 μm of tumor.

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