TY - JOUR
T1 - Highly multiplexed imaging reveals prognostic immune and stromal spatial biomarkers in breast cancer
AU - Eng, Jennifer R.
AU - Bucher, Elmar
AU - Hu, Zhi
AU - Walker, Cameron R.
AU - Risom, Tyler
AU - Angelo, Michael
AU - Gonzalez-Ericsson, Paula
AU - Sanders, Melinda E.
AU - Chakravarthy, A. Bapsi
AU - Pietenpol, Jennifer A.
AU - Gibbs, Summer L.
AU - Sears, Rosalie C.
AU - Chin, Koei
N1 - Publisher Copyright:
: © 2025, Eng et al.
PY - 2025/2/10
Y1 - 2025/2/10
N2 - 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.
AB - 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.
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U2 - 10.1172/jci.insight.176749
DO - 10.1172/jci.insight.176749
M3 - Article
C2 - 39808504
AN - SCOPUS:85217826238
SN - 2379-3708
VL - 10
JO - JCI Insight
JF - JCI Insight
IS - 3
M1 - e176749
ER -