The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
NIH Image to ImageJ: 25 years of image analysis
Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012) · Nature Methods · doi:10.1038/nmeth.2089 · Cited by 64,638
PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer
Hartkopf, A. D., Taran, F., Wallwiener, M., Walter, C. B., Krämer, B., Grischke, E., & Brucker, S. Y. (2016) · Breast Care · doi:10.1159/000453569 · Cited by 30,989
Adverse Renal Effects of Immune Checkpoint Inhibitors: A Narrative Review
Wanchoo, R., Karam, S., Uppal, N. N., Barta, V. S., Deray, G., Devoe, C., Launay‐Vacher, V., Jhaveri, K. D., & on behalf of Cancer and Kidney International Network Workgroup on Immune Checkpoint Inhibitors (2017) · American Journal of Nephrology · doi:10.1159/000455014 · Cited by 15,320
Chromatin modifications and their function
Kouzarides, T. (2007) · Cell · doi:10.1016/j.cell.2007.02.005 · Cited by 10,883 · cited 1×
“Since H3K9ac was shown to be correlated with open chromatin and H3K27me3 with closed chromatin (Kouzarides, 2007), these global changes in their ratios may indicate that cells on the tumor border are more transcriptionally active than those in the tumor center.”
Combined Nivolumab and Ipilimumab or Monotherapy in Untreated Melanoma
Larkin, J., Chiarion-Sileni, V., Gonzalez, R., Grob, J. J., Cowey, C. L., Lao, C. D., Schadendorf, D., Dummer, R., Smylie, M., Rutkowski, P., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1504030 · Cited by 8,157
Cancer immunotherapy using checkpoint blockade
Ribas, A., & Wolchok, J. D. (2018) · Science · doi:10.1126/science.aar4060 · Cited by 6,791
Myeloid-derived-suppressor cells as regulators of the immune system
Gabrilovich, D. I., & Nagaraj, S. (2009) · Nature Reviews Immunology · doi:10.1038/nri2506 · Cited by 6,753
PD-1 blockade induces responses by inhibiting adaptive immune resistance
Tumeh, P. C., Harview, C. L., Yearley, J. H., Shintaku, I. P., Taylor, E. J. M., Robert, L., Chmielowski, B., Spasic, M., Henry, G., Ciobanu, V., et al. (2014) · Nature · doi:10.1038/nature13954 · Cited by 6,609
Elements of cancer immunity and the cancer–immune set point
Chen, D. S., & Mellman, I. (2017) · Nature · doi:10.1038/nature21349 · Cited by 5,352
Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients
Herbst, R. S., Soria, J.-C., Kowanetz, M., Fine, G. D., Hamid, O., Gordon, M. S., Sosman, J. A., McDermott, D. F., Powderly, J. D., Gettinger, S. N., et al. (2014) · Nature · doi:10.1038/nature14011 · Cited by 5,231
Flagged first, then by citations
Research works printed but not linked: 1
- Haralick, 1973, Textural Features for Image Classification. IEEE Trans. Syst. Man. Cybern, SMC, 3, 610
Cited by
Sorted by citations
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Zhang, Y., & Zhang, Z. (2020) · Cellular and Molecular Immunology · doi:10.1038/s41423-020-0488-6 · Cited by 2,906
Concepts of extracellular matrix remodelling in tumour progression and metastasis
Winkler, J., Abisoye-Ogunniyan, A., Metcalf, K. J., & Werb, Z. (2020) · Nature Communications · doi:10.1038/s41467-020-18794-x · Cited by 2,296
Tumor microenvironment as a therapeutic target in cancer
Xiao, Y. Q., & Yu, D. (2020) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2020.107753 · Cited by 2,026
Exploring tissue architecture using spatial transcriptomics
Rao, A., Barkley, D., França, G. S., & Yanai, I. (2021) · Nature · doi:10.1038/s41586-021-03634-9 · Cited by 1,700
Microglia Biology: One Century of Evolving Concepts
Prinz, M., Jung, S., & Priller, J. (2019) · Cell · doi:10.1016/j.cell.2019.08.053 · Cited by 1,525
Garrido-Castro, A. C., Lin, N. U., & Polyak, K. (2019) · Cancer Discovery · doi:10.1158/2159-8290.cd-18-1177 · Cited by 1,451
Treatment landscape of triple-negative breast cancer — expanded options, evolving needs
Bianchini, G., Angelis, C. D., Licata, L., & Gianni, L. (2021) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-021-00565-2 · Cited by 1,450
Deep learning for cellular image analysis
Moen, E., Bannon, D., Kudo, T., Graf, W., Covert, M., & Van Valen, D. (2019) · Nature Methods · doi:10.1038/s41592-019-0403-1 · Cited by 1,225
Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma
Ji, A. L., Rubin, A. J., Thrane, K., Jiang, S., Reynolds, D. L., Meyers, R. M., Guo, M. G., George, B. M., Mollbrink, A., Bergenstråhle, J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.05.039 · Cited by 1,117
“Recent advances, however, enable simultaneous capture of the locations of dozens of cell types within the TME, which is critical for understanding tumor-stroma crosstalk (Angelo et al., 2014; Keren et al., 2018; Salmén et al., 2018a; Thrane et al., 2018).”
BackgroundTertiary lymphoid structures in cancer
Schumacher, T. N., & Thommen, D. S. (2022) · Science · doi:10.1126/science.abf9419 · Cited by 1,170
Multiplex digital spatial profiling of proteins and RNA in fixed tissue
Merritt, C. R., Ong, G. T., Church, S. E., Barker, K., Danaher, P., Geiss, G., Hoang, M., Jung, J., Liang, Y., McKay-Fleisch, J., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0472-9 · Cited by 1,072
Schürch, C. M., Bhate, S. S., Barlow, G. L., Phillips, D. J., Noti, L., Zlobec, I., Chu, P., Black, S., Demeter, J., McIlwain, D. R., et al. (2020) · Cell · doi:10.1016/j.cell.2020.07.005 · Cited by 1,084
Targeting inflammation in atherosclerosis — from experimental insights to the clinic
Soehnlein, O., & Libby, P. (2021) · Nature Reviews Drug Discovery · doi:10.1038/s41573-021-00198-1 · Cited by 1,085
Intratumor Heterogeneity: The Rosetta Stone of Therapy Resistance
Marusyk, A., Janiszewska, M., & Polyak, K. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.03.007 · Cited by 1,009
Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)
Cossarizza, A., Chang, H., Radbruch, A., Acs, A., Adam, D., Adam‐Klages, S., Agace, W. W., Aghaeepour, N., Akdiş, M., Allez, M., et al. (2019) · European Journal of Immunology · doi:10.1002/eji.201970107 · Cited by 1,007
Greenwald, N. F., Miller, G., Moen, E., Kong, A., Kagel, A., Dougherty, T., Fullaway, C. C., McIntosh, B. J., Leow, K. X., Schwartz, M. S., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01094-0 · Cited by 953
Giotto: a toolbox for integrative analysis and visualization of spatial expression data
Dries, R., Zhu, Q., Dong, R., Eng, C.-H. L., Li, H., Liu, K., Fu, Y., Zhao, T., Sarkar, A., Bao, F., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02286-2 · Cited by 966
The single-cell pathology landscape of breast cancer
Jackson, H. W., Fischer, J. R., Zanotelli, V. R., Ali, H. R., Mechera, R., Soysal, S. D., Moch, H., Muenst, S. E., Varga, Z., Weber, W. P., et al. (2020) · Nature · doi:10.1038/s41586-019-1876-x · Cited by 892
Triple negative breast cancer: Pitfalls and progress
Zagami, P., & Carey, L. (2022) · npj Breast Cancer · doi:10.1038/s41523-022-00468-0 · Cited by 954
A Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer
Wagner, J., Rapsomaniki, M., Chevrier, S., Anzeneder, T., Langwieder, C., Dykgers, A., Rees, M., Ramaswamy, A., Muenst, S. E., Soysal, S. D., et al. (2019) · Cell · doi:10.1016/j.cell.2019.03.005 · Cited by 807
Deciphering breast cancer: from biology to the clinic
Nolan, E., Lindeman, G. J., & Visvader, J. E. (2023) · Cell · doi:10.1016/j.cell.2023.01.040 · Cited by 792
CODEX multiplexed tissue imaging with DNA-conjugated antibodies
Black, S. A., Phillips, D. J., Hickey, J. W., Kennedy‐Darling, J., Venkataraaman, V. G., Samusik, N., Goltsev, Y., Schürch, C. M., & Nolan, G. P. (2021) · Nature Protocols · doi:10.1038/s41596-021-00556-8 · Cited by 671
Improving cancer immunotherapy through nanotechnology
Goldberg, M. S. (2019) · Nature reviews. Cancer · doi:10.1038/s41568-019-0186-9 · Cited by 628
Spatial omics and multiplexed imaging to explore cancer biology
Lewis, S. M., Asselin-Labat, M.-L., Nguyen, Q. H., Berthelet, J., Tan, X., Wimmer, V. C., Mérino, D., Rogers, K. L., & Naik, S. H. (2021) · Nature Methods · doi:10.1038/s41592-021-01203-6 · Cited by 623
Comparison of Biomarker Modalities for Predicting Response to PD-1/PD-L1 Checkpoint Blockade
Lu, S., Stein, J. E., Rimm, D. L., Wang, D. W., Bell, M. A., Johnson, D. B., Sosman, J. A., Schalper, K. A., Anders, R. A., Wang, H., et al. (2019) · JAMA Oncology · doi:10.1001/jamaoncol.2019.1549 · Cited by 632
Bressan, D., Battistoni, G., & Hannon, G. J. (2023) · Science · doi:10.1126/science.abq4964 · Cited by 622
Spatial profiling technologies illuminate the tumor microenvironment
Elhanani, O., Ben-Uri, R., & Keren, L. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.01.010 · Cited by 546
Fu, T., Dai, L.-J., Wu, S.-Y., Xiao, Y., Ma, D., Jiang, Y.-Z., & Shao, Z.-M. (2021) · Journal of Hematology & Oncology · doi:10.1186/s13045-021-01103-4 · Cited by 480
Resistance Mechanisms to Anti-PD Cancer Immunotherapy
Vesely, M. D., Zhang, T., & Chen, L. (2022) · Annual Review of Immunology · doi:10.1146/annurev-immunol-070621-030155 · Cited by 468
Spatially distinct tumor immune microenvironments stratify triple-negative breast cancers
Gruosso, T., Gigoux, M., Manem, V. S., Bertos, N., Zuo, D., Perlitch, I., Saleh, S. M. I., Zhao, H., Souleimanova, M., Johnson, R. M., et al. (2019) · Journal of Clinical Investigation · doi:10.1172/jci96313 · Cited by 452
The emerging landscape of spatial profiling technologies
Moffitt, J. R., Lundberg, E., & Heyn, H. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00515-3 · Cited by 419
Chan, J. M., Quintanal-Villalonga, À., Gao, V. R., Xie, Y., Allaj, V., Chaudhary, O., Masilionis, I., Egger, J. V., Chow, A. T., Walle, T., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.09.008 · Cited by 464
MIBI-TOF: A multiplexed imaging platform relates cellular phenotypes and tissue structure
Keren, L., Bosse, M. M., Thompson, S., Risom, T., Vijayaragavan, K., McCaffrey, E., Marquez, D. M., Angoshtari, R., Greenwald, N. F., Fienberg, H. G., et al. (2019) · Science Advances · doi:10.1126/sciadv.aax5851 · Cited by 426
Tumor Microenvironment Dynamics in Clear-Cell Renal Cell Carcinoma
Vuong, L. H., Kotecha, R. R., Voss, M. H., & Hakimi, A. A. (2019) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-0499 · Cited by 443
Expansion sequencing: Spatially precise in situ transcriptomics in intact biological systems
Alon, S., Goodwin, D., Sinha, A., Wassie, A. T., Chen, F., Daugharthy, E. R., Bando, Y., Kajita, A., Xue, A. G., Marrett, K., et al. (2021) · Science · doi:10.1126/science.aax2656 · Cited by 421
Cell-cell communication: New insights and clinical implications
Su, J., Song, Y., Zhu, Z., Huang, X., Fan, J., Qiao, J., & Mao, F. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01888-z · Cited by 433
Single-cell spatial immune landscapes of primary and metastatic brain tumours
Karimi, E., Yu, M. W., Maritan, S. M., Perus, L. J. M., Rezanejad, M., Sorin, M., Dankner, M., Fallah, P., Doré, S., Zuo, D., et al. (2023) · Nature · doi:10.1038/s41586-022-05680-3 · Cited by 401
Risom, T., Glass, D. R., Averbukh, I., Liu, C. C., Baranski, A., Kagel, A., McCaffrey, E., Greenwald, N. F., Rivero‐Gutiérrez, B., Strand, S. H., et al. (2022) · Cell · doi:10.1016/j.cell.2021.12.023 · Cited by 373
Towards targeting the breast cancer immune microenvironment
Harris, M. A., Savas, P. S., Virassamy, B., O’Malley, M. M. R., Kay, J., Mueller, S. N., Mackay, L. K., Salgado, R., & Loi, S. (2024) · Nature reviews. Cancer · doi:10.1038/s41568-024-00714-6 · Cited by 409
Spatial predictors of immunotherapy response in triple-negative breast cancer
Wang, X. Q., Danenberg, E., Huang, C., Egle, D., Callari, M., Bermejo, B., Dugo, M., Zamagni, C., Thill, M., Anton, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06498-3 · Cited by 375
Atlas of clinically distinct cell states and ecosystems across human solid tumors
Luca, B. A., Steen, C. B., Matusiak, M., Azizi, A., Varma, S., Zhu, C., Przybył, J., Espín-Pérez, A., Diehn, M., Alizadeh, A. A., et al. (2021) · Cell · doi:10.1016/j.cell.2021.09.014 · Cited by 359
Spatial components of molecular tissue biology
Palla, G., Fischer, D. S., Regev, A., & Theis, F. J. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01182-1 · Cited by 318
Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images
Caicedo, J. C., Roth, J., Goodman, A., Becker, T., Karhohs, K. W., Broisin, M., Molnár, C., McQuin, C., Singh, S., Theis, F. J., et al. (2019) · Cytometry Part A · doi:10.1002/cyto.a.23863 · Cited by 332
Quiescent cancer cells resist T cell attack by forming an immunosuppressive niche
Baldominos, P., Barbera-Mourelle, A., Barreiro, O., Huang, Y., Wight, A. E., Cho, J.-W., Zhao, X., Estivill, G., Adam, I., Sanchez, X., et al. (2022) · Cell · doi:10.1016/j.cell.2022.03.033 · Cited by 318
Single-cell metabolic profiling of human cytotoxic T cells
Hartmann, F. J., Mrdjen, D., McCaffrey, E., Glass, D. R., Greenwald, N. F., Bharadwaj, A., Khair, Z., Verberk, S. G. S., Baranski, A., Baskar, R., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0651-8 · Cited by 315
The immunoregulatory landscape of human tuberculosis granulomas
McCaffrey, E., Donato, M., Keren, L., Chen, Z., Delmastro, A. C., Fitzpatrick, M. B., Gupta, S., Greenwald, N. F., Baranski, A., Graf, W., et al. (2022) · Nature Immunology · doi:10.1038/s41590-021-01121-x · Cited by 334
Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer
Ali, H. R., Jackson, H. W., Zanotelli, V. R., Danenberg, E., Fischer, J. R., Bardwell, H. A., Provenzano, E., CRUK IMAXT Grand Challenge Team, Ali, H. R., Sa’d, M. A., et al. (2020) · Nature Cancer · doi:10.1038/s43018-020-0026-6 · Cited by 313
Metabolic adaptation of lymphocytes in immunity and disease
Chapman, N. M., & Chi, H. (2022) · Immunity · doi:10.1016/j.immuni.2021.12.012 · Cited by 317
Taylor, M. J., Lukowski, J. K., & Anderton, C. (2021) · Journal of the American Society for Mass Spectrometry · doi:10.1021/jasms.0c00439 · Cited by 306
An end-to-end workflow for multiplexed image processing and analysis
Windhager, J., Zanotelli, V. R., Schulz, D., Meyer, L., Daniel, M., Bodenmiller, B., & Eling, N. (2023) · Nature Protocols · doi:10.1038/s41596-023-00881-0 · Cited by 301
Genetic and non-genetic clonal diversity in cancer evolution
Black, J. R., & McGranahan, N. (2021) · Nature reviews. Cancer · doi:10.1038/s41568-021-00336-2 · Cited by 298
Taube, J. M., Aktürk, G., Angelo, M., Engle, E. L., Gnjatic, S., Greenbaum, S., Greenwald, N. F., Hedvat, C. V., Hollmann, T. J., Juco, J. W., et al. (2020) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2019-000155 · Cited by 300
Geospatial immune variability illuminates differential evolution of lung adenocarcinoma
AbdulJabbar, K., Raza, S. E. A., Rosenthal, R., Jamal‐Hanjani, M., Veeriah, S., Akarca, A. U., Lund, T., Moore, D. A., Salgado, R., Bakir, M. A., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0900-x · Cited by 287
Dissecting cellular crosstalk by sequencing physically interacting cells
Giladi, A., Cohen, M., Medaglia, C., Baran, Y., Li, B., Zada, M., Bost, P., Blecher‐Gonen, R., Salame, T., Mayer, J. U., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0442-2 · Cited by 276
Breast tumor microenvironment structures are associated with genomic features and clinical outcome
Danenberg, E., Bardwell, H. A., Zanotelli, V. R., Provenzano, E., Chin, S., Rueda, O. M., Green, A. R., Rakha, E. A., Aparicio, S. A. J. R., Ellis, I. O., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01041-y · Cited by 273
GM-CSF: A Double-Edged Sword in Cancer Immunotherapy
Kumar, A., Khani, A. T., Ortiz, A. S., & Swaminathan, S. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.901277 · Cited by 287
Liao, M., Yao, D., Wu, L., Luo, C., Wang, Z., Zhang, J., & Liu, B. (2023) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2023.12.003 · Cited by 299
Bairi, K. E., Haynes, H. R., Blackley, E. F., Fineberg, S., Shear, J., Turner, S., de Freitas, J. R., Sur, D. G., Amendola, L. C., Gharib, M., et al. (2021) · npj Breast Cancer · doi:10.1038/s41523-021-00346-1 · Cited by 285
Hammerl, D., Martens, J. W. M., Timmermans, M., Smid, M., Trapman-Jansen, A. M., Foekens, R., Isaeva, O. I., Voorwerk, L., Balcioglu, H. E., Wijers, R., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25962-0 · Cited by 262
Host tissue determinants of tumour immunity
Salmon, H., Remark, R., Gnjatic, S., & Mérad, M. (2019) · Nature reviews. Cancer · doi:10.1038/s41568-019-0125-9 · Cited by 263
Big data in basic and translational cancer research
Jiang, P., Sinha, S., Aldape, K., Hannenhalli, S., Sahinalp, C., & Ruppin, E. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00502-0 · Cited by 254
MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging
Schapiro, D., Sokolov, A., Yapp, C., Chen, Y., Muhlich, J. L., Hess, J. M., Creason, A., Nirmal, A. J., Baker, G. J., Nariya, M. K., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01308-y · Cited by 244
An Integrated Multi-omic Single-Cell Atlas of Human B Cell Identity
Glass, D. R., Tsai, A. G., Oliveria, J. P., Hartmann, F. J., Kimmey, S. C., Calderon, A. A., Borges, L., Glass, M. C., Wagar, L. E., Davis, M. M., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.06.013 · Cited by 232
Stoltzfus, C. R., Filipek, J., Gern, B. H., Olin, B. E., Leal, J. M., Wu, Y., Lyons‐Cohen, M. R., Huang, J. Y., Paz-Stoltzfus, C. L., Plumlee, C. R., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107523 · Cited by 234
Macrophage Biology and Mechanisms of Immune Suppression in Breast Cancer
Mehta, A. K., Kadel, S., Townsend, M. G., Oliwa, M., & Guerriero, J. L. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.643771 · Cited by 237
Intratumoral Heterogeneity: More Than Just Mutations
Hinohara, K., & Polyák, K. (2019) · Trends in Cell Biology · doi:10.1016/j.tcb.2019.03.003 · Cited by 241
Tietscher, S., Wagner, J., Anzeneder, T., Langwieder, C., Rees, M., Sobottka, B., de Souza, N., & Bodenmiller, B. (2023) · Nature Communications · doi:10.1038/s41467-022-35238-w · Cited by 233
Miller, B. F., Huang, F., Atta, L., Sahoo, A., & Fan, J. (2022) · Nature Communications · doi:10.1038/s41467-022-30033-z · Cited by 217
Karacosta, L. G., Anchang, B., Ignatiadis, N., Kimmey, S. C., Benson, J. A., Shrager, J. B., Tibshirani, R. J., Bendall, S. C., & Plevritis, S. K. (2019) · Nature Communications · doi:10.1038/s41467-019-13441-6 · Cited by 211
Immune cell topography predicts response to PD-1 blockade in cutaneous T cell lymphoma
Phillips, D. J., Matusiak, M., Rivero‐Gutiérrez, B., Bhate, S. S., Barlow, G. L., Jiang, S., Demeter, J., Smythe, K. S., Pierce, R. H., Fling, S. P., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26974-6 · Cited by 230
Kuett, L., Catena, R., Özcan, A., Plüss, A., Ali, H. R., Sa’d, M. A., Alon, S., Aparicio, S. A. J. R., Battistoni, G., Balasubramanian, S., et al. (2021) · Nature Cancer · doi:10.1038/s43018-021-00301-w · Cited by 219
The Spatial Landscape of Progression and Immunoediting in Primary Melanoma at Single-Cell Resolution
Nirmal, A. J., Maliga, Z., Vallius, T., Quattrochi, B., Chen, A. A., Jacobson, C. A., Pelletier, R. J., Yapp, C., Arias-Camison, R., Chen, Y., et al. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-1357 · Cited by 211
Innate Immunity and Cancer Pathophysiology
Maiorino, L., Daßler‐Plenker, J., Sun, L., & Egeblad, M. (2021) · Annual Review of Pathology Mechanisms of Disease · doi:10.1146/annurev-pathmechdis-032221-115501 · Cited by 233
Asleh, K., Riaz, N., & Nielsen, T. O. (2022) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-022-02476-1 · Cited by 235
CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity
Varrone, M., Tavernari, D., Santamaria‐Martínez, A., Walsh, L. A., & Ciriello, G. (2023) · Nature Genetics · doi:10.1038/s41588-023-01588-4 · Cited by 220
Tumour heterogeneity and the evolutionary trade-offs of cancer
Hausser, J., & Alon, U. S. (2020) · Nature reviews. Cancer · doi:10.1038/s41568-020-0241-6 · Cited by 216
Glial and myeloid heterogeneity in the brain tumour microenvironment
Andersen, B. M., Akl, C. F., Wheeler, M. A., Chiocca, E. A., Reardon, D. A., & Quintana, F. J. (2021) · Nature reviews. Cancer · doi:10.1038/s41568-021-00397-3 · Cited by 216
Chen, Y., Jia, K., Sun, Y. L., Zhang, C., Li, Y., Zhang, L., Chen, Z., Zhang, J., Hu, Y., Yuan, J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32570-z · Cited by 210
If we build it they will come: targeting the immune response to breast cancer
Gatti-Mays, M. E., Balko, J. M., Gameiro, S. R., Bear, H. D., Prabhakaran, S., Fukui, J. A., Disis, M. L., Nanda, R., Gulley, J. L., Kalinsky, K. M., et al. (2019) · npj Breast Cancer · doi:10.1038/s41523-019-0133-7 · Cited by 210
Spatial biology of cancer evolution
Seferbekova, Z., Lomakin, A., Yates, L. R., & Gerstung, M. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00553-x · Cited by 227
Immune Checkpoint Inhibitors in Triple Negative Breast Cancer Treatment: Promising Future Prospects
Thomas, R., Al‐Khadairi, G., & Decock, J. V. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2020.600573 · Cited by 209
Spatial multi-omics analyses of the tumor immune microenvironment
Hsieh, W.-C., Budiarto, B. R., Wang, Y., Lin, C., Gwo, M.-C., So, D. K., Tzeng, Y.-S., & Chen, S.-Y. (2022) · Journal of Biomedical Science · doi:10.1186/s12929-022-00879-y · Cited by 222
Analysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade
Berry, S., Giraldo, N. A., Green, B., Cottrell, T. R., Stein, J. E., Engle, E. L., Xu, H., Ogurtsova, A., Roberts, C. A., Wang, D., et al. (2021) · Science · doi:10.1126/science.aba2609 · Cited by 202
Immune monitoring using mass cytometry and related high-dimensional imaging approaches
Hartmann, F. J., & Bendall, S. C. (2019) · Nature Reviews Rheumatology · doi:10.1038/s41584-019-0338-z · Cited by 208
SM-Omics is an automated platform for high-throughput spatial multi-omics
Vicković, S., Lötstedt, B., Klughammer, J., Mages, S., Segerstolpe, Å., Rozenblatt–Rosen, O., & Regev, A. (2022) · Nature Communications · doi:10.1038/s41467-022-28445-y · Cited by 197
Integration of whole transcriptome spatial profiling with protein markers
Ben‐Chetrit, N., Niu, X., Swett, A. D., Sotelo, J., Jiao, M. S., Stewart, C. M., Potenski, C. J., Mielinis, P., Roelli, P., Stoeckius, M., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01536-3 · Cited by 214
Fountzilas, E., Pearce, T. E., Baysal, M. A., Chakraborty, A., & Tsimberidou, A. M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01471-y · Cited by 233
High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE
Vahid, M. R., Brown, E. L., Steen, C. B., Zhang, W., Jeon, H. S., Kang, M., Gentles, A. J., & Newman, A. M. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01697-9 · Cited by 187
TGF-β suppresses type 2 immunity to cancer
Liu, M., Kuo, F., Capistrano, K. J., Kang, D., Nixon, B. G., Shi, W., Chou, C., Do, M. H., Stamatiades, E. G., Gao, S., et al. (2020) · Nature · doi:10.1038/s41586-020-2836-1 · Cited by 193
A spatially resolved timeline of the human maternal–fetal interface
Greenbaum, S., Averbukh, I., Soon, E., Rizzuto, G. A., Baranski, A., Greenwald, N. F., Kagel, A., Bosse, M. M., Greenwood, E. A., Khair, Z., et al. (2023) · Nature · doi:10.1038/s41586-023-06298-9 · Cited by 209
Wu, J., Gao, F., Wang, C., Qin, M., Han, F., Xu, T., Hu, Z., Long, Y., He, X., Deng, X., et al. (2019) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-019-1310-0 · Cited by 188
High-dimensional cell-level analysis of tissues with Ce3D multiplex volume imaging
Li, W., Germain, R. N., & Gerner, M. Y. (2019) · Nature Protocols · doi:10.1038/s41596-019-0156-4 · Cited by 181
Akhoundova, D., & Rubin, M. A. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.08.011 · Cited by 198
Decoding the tumor microenvironment with spatial technologies
Walsh, L. A., & Quail, D. F. (2023) · Nature Immunology · doi:10.1038/s41590-023-01678-9 · Cited by 199
Tumor-infiltrating lymphocytes: Warriors fight against tumors powerfully
Lin, B., Du, L., Li, H., Zhu, X. M., Cui, L., & Li, X. (2020) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2020.110873 · Cited by 185
Chen, J. H., Nieman, L. T., Spurrell, M., Jorgji, V., Elmelech, L., Richieri, P. M., Xu, K. H., Madhu, R., Parikh, M., Zamora, I., et al. (2024) · Nature Immunology · doi:10.1038/s41590-024-01792-2 · Cited by 204
Mass spectrometry imaging for spatially resolved multi-omics molecular mapping
Zhang, H., Lu, K. H., Ebbini, M., Huang, P., Lu, H., & Li, L. (2024) · npj Imaging · doi:10.1038/s44303-024-00025-3 · Cited by 180
Mass Cytometry Imaging for the Study of Human Diseases—Applications and Data Analysis Strategies
Baharlou, H., Canete, N. P., Cunningham, A. L., Harman, A. N., & Patrick, E. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.02657 · Cited by 180
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
