Cited by 1,183Cancer Immunotherapy and BiomarkersSingle-cell and spatial transcriptomicsBreast Cancer Treatment Studies

A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging

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Keren, Leeat, Bosse, Marc, Marquez, Diana, Angoshtari, Roshan, Jain, Samir, Varma, Sushama, Yang, Soo-Ryum, Kurian, Allison +4 more·2018·Cell

Abstract

The immune system is critical in modulating cancer progression, but knowledge of immune composition, phenotype, and interactions with tumor is limited. We used multiplexed ion beam imaging by time-of-flight (MIBI-TOF) to simultaneously quantify in situ expression of 36 proteins covering identity, function, and immune regulation at sub-cellular resolution in 41 triple-negative breast cancer patients. Multi-step processing, including deep-learning-based segmentation, revealed variability in the composition of tumor-immune populations across individuals, reconciled by overall immune infiltration and enriched co-occurrence of immune subpopulations and checkpoint expression. Spatial enrichment analysis showed immune mixed and compartmentalized tumors, coinciding with expression of PD1, PD-L1, and IDO in a cell-type- and location-specific manner. Ordered immune structures along the tumor-immune border were associated with compartmentalization and linked to survival. These data demonstrate organization in the tumor-immune microenvironment that is structured in cellular composition, spatial arrangement, and regulatory-protein expression and provide a framework to apply multiplexed imaging to immune oncology.

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Crossref · 10.1016/j.cell.2018.08.039

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  • Nuclear segmentation was performed with DeepCell using in-house training and validation MIBI imaging datasets from a variety of human clinical tissue types to ensure reproducibility.

    “Nuclear segmentation was performed with DeepCell (Van Valen et al., 2016) (https://github.com/vanvalenlab/deepcell-tf) as previously described (Keren et al., 2018), using in-house training and validation MIBI imaging datasets from a variety of human clinical tissue types to ensure reproducibility.”

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  1. Haralick RM, Shanmugam K, and Dinstein I (1973). Textural Features for Image Classification. IEEE Trans. Syst. Man. Cybern SMC-3, 610– 621.
  2. Molecular Subtypes: Implications for Neoadjuvant Chemotherapy Selection. PLoS One 11, e0157368. PMC4911051 27310713

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Authors
Keren, L., Bosse, M., Marquez, D., Angoshtari, R., Jain, S., Varma, S., Yang, S.-R., Kurian, A., Van Valen, D., West, R., Bendall, S. C., & Angelo, M.
Affiliations
  • Stanford University (US)
  • Stanford Medicine (US)
Corresponding author
  • Bendall, Sean C.
  • Angelo, Michael
Venue
Cell
Publisher
Elsevier BV
Published
2018-09
Volume
174
Issue
6
Pages
1373-1387.e19
ISSN
0092-8674 (ISSN-L)
OpenAlex
W2890156321
Cited by
1,183
References
54
Citations by year
2026: 222 · 2025: 147 · 2024: 141 · 2023: 164 · 2022: 153 · 2021: 167 · 2020: 105 · 2019: 74 · 2018: 9
FWCI
33.83
Type
journal article
Language
en
Open access
Locations
  • Cell (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • CaltechAUTHORS (California Institute of Technology) (submittedVersion)
Funders
  • Damon Runyon Cancer Research Foundation (DRG-2292-17, DRG-2017-09)
  • EMBO Long-Term fellowship (ALTF 1128-2016)
  • NIH F32 Postdoctoral Fellowship
  • Burroughs Wellcome PDEP award
  • The Allen Discovery Center at Stanford University
  • Crowdflower A.I.
  • NIH (5R01CA18390402, 1DP2OD022550-01, 1-R00-GM104148-01, 5U19AI116484-02, U19 AI104209, 1-DP5-OD019822, 1R01AG056287-01, 1R01AG057915-01, 1U24CA224309-01)
  • Bill and Melinda Gates Foundation
  • Stanford Cancer Institute (NSF ECCS-1542152)
Topics
  • Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
Keywords
triple-negative breast cancer, tumor immune microenvironment, MIBI, immune checkpoint molecules, PD-1, PD-L1, IDO, immune cell infiltration, patient survival
MeSH terms
Machine Learning, Lymphocyte Activation Gene 3 Protein, Female, Humans, Lymphocytes, Mass Spectrometry, Antigens, CD, Cluster Analysis, Principal Component Analysis, Indoleamine-Pyrrole 2,3,-Dioxygenase, Kaplan-Meier Estimate, Tumor Microenvironment, B7-H1 Antigen, Programmed Cell Death 1 Receptor, Spatial Analysis, Triple Negative Breast Neoplasms

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