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A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging
Open accessAbstract
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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“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.”
From Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma (2020) · cites this paper as Keren et al., 2018
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Metadata
- 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
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2018-09
- Volume
- 174
- Issue
- 6
- Pages
- 1373-1387.e19
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC6132072
- 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
- bronze
- doi.org
- Licence
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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