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

被引:2
作者
Eng, Jennifer R. [1 ,2 ]
Bucher, Elmar [2 ]
Hu, Zhi [2 ]
Walker, Cameron R. [3 ]
Risom, Tyler [4 ]
Angelo, Michael [3 ]
Gonzalez-Ericsson, Paula [5 ]
Sanders, Melinda E. [5 ,6 ]
Chakravarthy, A. Bapsi
Pietenpol, Jennifer A. [5 ,7 ]
Gibbs, Summer L. [2 ]
Sears, Rosalie C. [1 ]
Chin, Koei [8 ]
机构
[1] Oregon Hlth & Sci Univ OHSU, Dept Mol & Med Genet, Portland, OR USA
[2] Oregon Hlth & Sci Univ OHSU, Dept Biomed Engn, Portland, OR USA
[3] Stanford Univ, Sch Med, Dept Pathol, Stanford, CA USA
[4] Genentech Inc, Dept Res Pathol, South San Francisco, CA USA
[5] Vanderbilt Univ, Med Ctr VUMC, Vanderbilt Ingram Canc Ctr, Nashville, TN USA
[6] Vanderbilt Univ Med Ctr VUMC, Dept Pathol Microbiol & Immunol, Nashville, TN USA
[7] Vanderbilt Univ, Dept Biochem, Nashville, TN USA
[8] OHSU, Canc Early Detect Adv Res Ctr, Portland, OR USA
关键词
IMMUNOTHERAPY; ANGIOGENESIS; LANDSCAPE;
D O I
10.1172/jci.insight.176749
中图分类号
R-3 [医学研究方法]; R3 [基础医学];
学科分类号
1001 ;
摘要
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 tumorwere 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 containingvimentin+ 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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页数:21
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共 49 条
[11]  
Janiszewska M, Et al., The impact of tumor epithelial and microenvironmental heterogeneity on treatment responses in HER2+ breast cancer, JCI Insight, 6, 11, (2021)
[12]  
Keren L, Et al., A structure tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging, Cell, 174, 6, pp. 1373-1387, (2018)
[13]  
Ali HR, Et al., Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer, Nat Cancer, 1, 2, pp. 163-175, (2020)
[14]  
Jackson HW, Et al., The single-cell pathology landscape of breast cancer, Nature, 578, 7796, pp. 615-620, (2020)
[15]  
Wortman JC, Et al., Spatial distribution of B cells and lymphocyte clusters as a predictor of triple-negative breast cancer outcome, NPJ Breast Cancer, 7, 1, (2021)
[16]  
Parmar C, Et al., Data analysis strategies in medical imaging, Clin Cancer Res, 24, 15, pp. 3492-3499, (2018)
[17]  
Eng J, Et al., A framework for multiplex imaging optimization and reproducible analysis, Commun Biol, 5, 1, (2022)
[18]  
Patwa A, Et al., Multiplexed imaging analysis of the tumor-immune microenvironment reveals predictors of outcome in triple-negative breast cancer, Commun Biol, 4, 1, (2021)
[19]  
Wolf A, Et al., SCANPY: large-scale single-cell gene expression data analysis, Genome Biol, 19, 1, (2018)
[20]  
Pedregosa F, Et al., Scikit-learn: machine learning in Python, J Mach Learn Res, 12, pp. 2825-2830, (2011)