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
Items 301–400 of 1,000
Fusco, L., Gazzi, A., Shuck, C. E., Orecchioni, M., Alberti, D., D'Almeida, S. M., Rinchai, D., Ahmed, E. I., Elhanani, O., Rauner, M., et al. (2022) · Advanced Materials · doi:10.1002/adma.202205154 · Cited by 40
SOTIP is a versatile method for microenvironment modeling with spatial omics data
Yuan, Z., Li, Y., Shi, M., Yang, F., Gao, J. T., Yao, J., & Zhang, M. Q. (2022) · Nature Communications · doi:10.1038/s41467-022-34867-5 · Cited by 40
Corredor, G., Bharadwaj, S., Pathak, T. B., Viswanathan, V. S., Toro, P., & Madabhushi, A. (2023) · Clinical Breast Cancer · doi:10.1016/j.clbc.2023.06.004 · Cited by 41
Erler, P., Kurćon, T., Cho, H., Skinner, J. N., Dixon, C., Grudman, S., Rozlan, S., Dessez, E., Mumford, B., Jo, S., et al. (2024) · Science Advances · doi:10.1126/sciadv.adn9857 · Cited by 41
Shi, W., Zhang, Z., Xu, X., Tian, Y., Feng, L., Huang, X., Du, Y., & Li, Z. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1649468 · Cited by 45
Merritt, C. R., Ong, G. T., Church, S., Barker, K., Geiss, G. K., Hoang, M. L., Jung, J., Liang, Y., McKay-Fleisch, J., Nguyen, K., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/559021 · Cited by 39
Multiplexed Ion Beam Imaging: Insights into Pathobiology
Liu, C. C., McCaffrey, E., Greenwald, N. F., Soon, E., Risom, T., Vijayaragavan, K., Oliveria, J. P., Mrdjen, D., Bosse, M. M., Tebaykin, D., et al. (2021) · Annual Review of Pathology Mechanisms of Disease · doi:10.1146/annurev-pathmechdis-030321-091459 · Cited by 39
Mi, H., Sivagnanam, S., Ho, W. J., Zhang, S., Bergman, D. R., Deshpande, A. M., Baras, A. S., Jaffee, E. M., Coussens, L. M., Fertig, E. J., et al. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae421 · Cited by 38
Zhu, B., Chen, P., Aminu, M., Lǐ, J., Fujimoto, J., Tian, Y., Hong, L., Chen, H., Hu, X. Y., Li, C., et al. (2025) · Cancer Cell · doi:10.1016/j.ccell.2025.04.003 · Cited by 41
Spatial epitope barcoding reveals clonal tumor patch behaviors
Rovira‐Clavé, X., Drainas, A. P., Jiang, S., Bai, Y., Baron, M., Zhu, B., Dallas, A. E., Lee, M. C., Chu, T. P., Holzem, A. M. E., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.09.014 · Cited by 40
Li, Y., Duan, Z., Pan, D., Ren, L. F., Gu, L., Li, X., Xu, G., Zhu, H., Zhang, H., Gu, Z., et al. (2022) · Advanced Materials · doi:10.1002/adma.202210161 · Cited by 38
Fischer, J. R., Jackson, H. W., de Souza, N., Varga, Z., Schraml, P., Moch, H., & Bodenmiller, B. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.100977 · Cited by 37
Centofanti, E., Wang, C., Iyer, S., Krichevsky, O., Oyler‐Yaniv, A., & Oyler, J. E. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2304190120 · Cited by 35
A computational pipeline for spatial mechano-transcriptomics
Hallou, A., He, R., Simons, B. D., & Dumitrascu, B. (2025) · Nature Methods · doi:10.1038/s41592-025-02618-1 · Cited by 43
Quantifying in situ adaptive immune cell cognate interactions in humans
Liarski, V. M., Sibley, A., van Panhuys, N., Ai, J., Chang, A., Kennedy, D. E., Merolle, M. I., Germain, R. N., Giger, M. L., & Clark, M. R. (2019) · Nature Immunology · doi:10.1038/s41590-019-0315-3 · Cited by 37
Techniques for Studying Decoding of Single Cell Dynamics
Jeknić, S., Kudo, T., & Covert, M. W. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.00755 · Cited by 40
Wright, K., Weirather, J. L., Jiang, S., Kao, K. Z., Sigal, Y. M., Giobbie‐Hurder, A., Shipp, M. A., & Rodig, S. J. (2023) · Blood Advances · doi:10.1182/bloodadvances.2023009813 · Cited by 36
T cell-mediated curation and restructuring of tumor tissue coordinates an effective immune response
Hickey, J. W., Haist, M., Horowitz, N. B., Caraccio, C., Tan, Y., Rech, A. J., Baertsch, M., Rovira‐Clavé, X., Zhu, B., Vazquez, G., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113494 · Cited by 39
Vanea, C., Džigurski, J., Rukins, V., Dodi, O., Siigur, S., Salumäe, L., Meir, K. S., Parks, W. T., Hochner‐Celnikier, D., Fraser, A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46986-2 · Cited by 38
Li, Z., Li, Y., Wang, X., & Yang, Q. (2021) · Cancer Cell International · doi:10.1186/s12935-020-01707-9 · Cited by 35
Single-Cell Spatial Proteomic Imaging for Human Neuropathology
Vijayaragavan, K., Cannon, B. J., Tebaykin, D., Bosse, M. M., Baranski, A., Oliveria, J. P., Bukhari, S. A., Mrdjen, D., Corces, M. R., McCaffrey, E., et al. (2022) · Acta Neuropathologica Communications · doi:10.1186/s40478-022-01465-x · Cited by 40
Update on triple‐negative breast cancers – highlighting subtyping update and treatment implication
Tsang, J. Y., & Tse, G. M.-K. (2022) · Histopathology · doi:10.1111/his.14784 · Cited by 36
Fu, X., Sahai, E. A., & Wilkins, A. C. (2023) · The Journal of Pathology · doi:10.1002/path.6153 · Cited by 37
Li, W., Yang, F., Wang, F., Rong, Y., Liu, L., Wu, B., Zhang, H., & Yao, J. (2024) · Nature Methods · doi:10.1038/s41592-024-02214-9 · Cited by 39
Unveiling spatial complexity in solid tumor immune microenvironments through multiplexed imaging
Scheuermann, S., Kristmann, B., Engelmann, F., Nuernbergk, A., Scheuermann, D., Koloseus, M., Abed, T., Solaß, W., & Seitz, C. M. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1383932 · Cited by 36
Syrnioti, A., Petousis, S., Newman, L. A., Margioula‐Siarkou, C., Papamitsou, T., Dinas, K. D., & Κoletsa, T. (2024) · Cancers · doi:10.3390/cancers16112094 · Cited by 34
Pentimalli, T. M., Schallenberg, S., León-Periñán, D., Legnini, I., Theurillat, I., Thomas, G., Boltengagen, A., Fritzsche, S., Nimo, J., Ruff, L., et al. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101261 · Cited by 36
Mitophagy: A key regulator of radiotherapy resistance in the tumor immune microenvironment
Xia, J., Jin, J., Dai, S., Fan, H., Chen, K., Li, J., Luo, F., & Peng, X. (2025) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2025.101385 · Cited by 37
Ouyang, P., Zhang, J., He, X., Yang, C., Zeng, D., & Xu, D. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1661545 · Cited by 44
Kurebayashi, Y., Kubota, N., & Sakamoto, M. (2020) · Hepatology Research · doi:10.1111/hepr.13539 · Cited by 35
Imaging Mass Spectrometry Reveals Tumor Metabolic Heterogeneity
Zhang, Y., Guillermier, C., Raedt, T. D., Cox, A. G., Maertens, O., Yimlamai, D., Lun, M., Whitney, A., Maas, R. L., Goessling, W., et al. (2020) · iScience · doi:10.1016/j.isci.2020.101355 · Cited by 33
SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery
Hu‐Lieskovan, S., Bhaumik, S., Dhodapkar, K. M., Grivel, J., Gupta, S. V., Hanks, B. A., Janetzki, S., Kleen, T. O., Koguchi, Y., Lund, A. W., et al. (2020) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2020-000705 · Cited by 35
Active surveillance characterizes human intratumoral T cell exhaustion
You, R., Artichoker, J., Fries, A. C., Edwards, A. W., Combes, A. J., Reeder, G. C., Samad, B., & Krummel, M. F. (2021) · Journal of Clinical Investigation · doi:10.1172/jci144353 · Cited by 35
Wang, X., Chang, S., Wang, T., Wu, R., Huang, Z., Sun, J., Liu, J., Yu, Y., & Mao, Y. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.857289 · Cited by 34
Single-cell high-dimensional imaging mass cytometry: one step beyond in oncology
Glasson, Y., Chépeaux, L.-A., Dumé, A., Lafont, V., Faget, J., Bonnefoy, N., & Michaud, H. (2023) · Seminars in Immunopathology · doi:10.1007/s00281-022-00978-w · Cited by 34
Wu, S., Pan, R., Lu, J., Wu, X., Xie, J., Tang, H., & Li, X. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.896927 · Cited by 34
Molecular Heterogeneity and Evolution in Breast Cancer
Caswell-Jin, J. L., Lorenz, C., & Curtis, C. N. (2020) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-060220-014137 · Cited by 32
Clinical and research applications of multiplexed immunohistochemistry and in situ hybridization
McGinnis, L. M., Ibarra-Lopez, V., Rost, S., & Ziai, J. M. (2021) · The Journal of Pathology · doi:10.1002/path.5663 · Cited by 32
DeepCell Kiosk: Scaling deep learning-enabled cellular image analysis with KubernetesPreprint
Bannon, D., Moen, E., Schwartz, M. S., Borba, E., Kudo, T., Greenwald, N. F., Vijayakumar, V., Chang, B., Pao, E., Osterman, E., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/505032 · Cited by 31
Hwang, W. L., Jagadeesh, K. A., Guo, J. A., Hoffman, H. I., Yadollahpour, P., Mohan, R. R., Drokhlyansky, E., Wittenberghe, N. V., Ashenberg, O., Farhi, S. L., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.25.267336 · Cited by 35
Patel, A. G., Ashenberg, O., Collins, N. B., Segerstolpe, Å., Jiang, S., Slyper, M., Huang, X., Caraccio, C., Jin, H., Sheppard, H., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.07.574538 · Cited by 39
Summers, H. D., Wills, J. W., & Rees, P. C. T. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100348 · Cited by 32
Multiplexed Imaging Mass Cytometry Analysis Characterizes the Vascular Niche in Pancreatic Cancer
Sussman, J., Kim, N., Kemp, S. B., Traum, D., Katsuda, T., Kahn, B., Xu, J., Kim, I., Eskandarian, C., Delman, D., et al. (2024) · Cancer Research · doi:10.1158/0008-5472.can-23-2352 · Cited by 32
Spatial architecture of development and disease
Lázár, E., & Lundeberg, J. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00892-5 · Cited by 38
Overview of Tissue Imaging Methods
Patel, S. S., & Rodig, S. J. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9773-2_21 · Cited by 32
Rutland, J. W., Gill, C. M., Loewenstern, J., Arib, H., Pain, M., Umphlett, M., Kinoshita, Y., McBride, R. B., Bederson, J. B., Donovan, M. J., et al. (2020) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-020-02671-z · Cited by 31
Dissecting Tumor-Immune Microenvironment in Breast Cancer at a Spatial and Multiplex Resolution
Tzoras, E., Zerdes, I., Tsiknakis, N., Manikis, G. C., Mezheyeuski, A., Bergh, J. C. S., Matikas, A., & Foukakis, T. (2022) · Cancers · doi:10.3390/cancers14081999 · Cited by 30
Du, M., Lei, M., Han, Y., Zhao, D., Zhang, X., Yang, Y.-Y., & Liu, R. (2018) · OncoTargets and Therapy · doi:10.2147/ott.s187221 · Cited by 31
Mistry, A. M., Greenplate, A. R., Ihrie, R. A., & Irish, J. M. (2018) · FEBS Journal · doi:10.1111/febs.14730 · Cited by 32
McKinley, E. T., Shao, J., Ellis, S. T., Heiser, C. N., Roland, J. T., Macedonia, M. C., Vega, P. N., Shin, S., Coffey, R. J., & Lau, K. S. (2022) · Cytometry Part A · doi:10.1002/cyto.a.24541 · Cited by 31
A review on deep learning applications in highly multiplexed tissue imaging data analysis
Zidane, M., Makky, A., Bruhns, M., Rochwarger, A., Babaei, S., Claassen, M., & Schürch, C. M. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1159381 · Cited by 32
Deep topographic proteomics of a human brain tumour
Davis, S. A., Scott, C., Oetjen, J., Charles, P. D., Kessler, B. M., Ansorge, O., & Fischer, R. (2023) · Nature Communications · doi:10.1038/s41467-023-43520-8 · Cited by 32
Blise, K. E., Sivagnanam, S., Betts, C. B., Betre, K., Kirchberger, N. C., Tate, B. J., Furth, E. E., Costa, A. D., Nowak, J. A., Wolpin, B. M., et al. (2024) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-23-0873 · Cited by 31
A High-Dimensional Window into the Micro-Environment of Triple Negative Breast Cancer
Nederlof, I., Horlings, H. M., Curtis, C. N., & Kok, M. (2021) · Cancers · doi:10.3390/cancers13020316 · Cited by 33
Chakiryan, N. H., Kimmel, G. J., Kim, Y., Hajiran, A. J., Aydın, A. M., Zemp, L. W., Katende, E. N., Nguyen, J. V., Lopez-Blanco, N., Chahoud, J., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0245415 · Cited by 30
Labrie, M., Li, A., Creason, A., Betts, C. B., Keck, J. M., Johnson, B., Sivagnanam, S., Boniface, C. T., Ma, H., Blucher, A., et al. (2021) · npj Precision Oncology · doi:10.1038/s41698-021-00232-w · Cited by 31
Plutynski, A. (2021) · European Journal for Philosophy of Science · doi:10.1007/s13194-021-00371-8 · Cited by 31
McGuigan, A. J., Coleman, H. G., McCain, R. S., Kelly, P. J., Johnston, D. I., Taylor, M., & Turkington, R. C. (2021) · The Journal of Pathology Clinical Research · doi:10.1002/cjp2.192 · Cited by 33
Research advances and new challenges in overcoming triple-negative breast cancer
Zong, Y., & Pegram, M. D. (2021) · Cancer Drug Resistance · doi:10.20517/cdr.2021.04 · Cited by 29
Toward reproducible, scalable, and robust data analysis across multiplex tissue imaging platforms
Burlingame, E. A., Eng, J. R., Thibault, G., Chin, K., Gray, J. W., & Chang, Y. H. (2021) · Cell Reports Methods · doi:10.1016/j.crmeth.2021.100053 · Cited by 29
Zhang, S., Gong, C., Ruiz-Martínez, Á., Wang, H., Davis-Marcisak, E. F., Deshpande, A. M., Popel, A. S., & Fertig, E. J. (2021) · ImmunoInformatics · doi:10.1016/j.immuno.2021.100002 · Cited by 30
Lazic, D., Kromp, F., Rifatbegovic, F., Repiščák, P., Kirr, M., Mívalt, F., Halbritter, F., Bernkopf, M., Bileck, A., Ussowicz, M., et al. (2021) · Cancers · doi:10.3390/cancers13174311 · Cited by 32
Andreou, C., Plakas, K., Berisha, N., Gigoux, M., Rosch, L. E., Mirsafavi, R., Oseledchyk, A., Pal, S., Zamarin, D., Merghoub, T., et al. (2022) · Nanoscale Horizons · doi:10.1039/d2nh00331g · Cited by 28
Shen, Y., Zhu, Q., Xiao, M., Yin, L., Feng, W., Feng, J., He, J., Li, P., Chen, X., Ding, W., et al. (2022) · Cell Death and Disease · doi:10.1038/s41419-022-05185-x · Cited by 28
Zhang, D., Ni, Y., Wang, Y., Feng, J., Zhuang, N., Li, J., Liu, L., Shen, W., Zheng, J., Zheng, W., et al. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04336-8 · Cited by 28
Li, L., Jiang, D., Liu, H., Guo, C., Zhao, R., Zhang, Q., Xu, C., Qin, Z., Feng, J., Liu, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37221-5 · Cited by 32
Expanded vacuum-stable gels for multiplexed high-resolution spatial histopathology
Bai, Y., Zhu, B., Oliveria, J. P., Cannon, B. J., Feyaerts, D., Bosse, M. M., Vijayaragavan, K., Greenwald, N. F., Phillips, D. J., Schürch, C. M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39616-w · Cited by 29
Sultan, S., Gorris, M. A. J., Martynova, E., van der Woude, L. L., Buytenhuijs, F., van Wilpe, S., Verrijp, K. N., Figdor, C. G., de Vries, I. J. M., & Textor, J. C. (2024) · Biology Methods and Protocols · doi:10.1093/biomethods/bpae094 · Cited by 30
Spatial Dissection of the Immune Landscape of Solid Tumors to Advance Precision Medicine
Mauro, F. D., & Arbore, G. (2024) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-23-0699 · Cited by 28
Kuett, L., Bollhagen, A., Tietscher, S., Sobottka, B., Eling, N., Varga, Z., Moch, H., de Souza, N., & Bodenmiller, B. (2024) · Cancer Research · doi:10.1158/0008-5472.can-24-1211 · Cited by 28
Zhu, Q., Balasubramanian, A., Asirvatham, J. R., Chatterjee, M., Piyarathna, B., Kaur, J. S., Mohamed, N., Wu, L., Wang, S., Pourfarrokh, N., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-61034-3 · Cited by 30
Zanotelli, V. R., Leutenegger, M., Lun, X., Georgi, F., de Souza, N., & Bodenmiller, B. (2020) · Molecular Systems Biology · doi:10.15252/msb.20209798 · Cited by 26
Graeser, M., Feuerhake, F., Gluz, O., Volk, V., Hauptmann, M., Jóźwiak, K., Christgen, M., Küemmel, S., Grischke, E., Forstbauer, H., et al. (2021) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2020-002198 · Cited by 29
Cheng, J., Guo, M., Yang, Y., Liu, Y., Hu, W., Shang, Q., Li, C., Xia, L., Wang, Y., Wang, W., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.848881 · Cited by 27
An immunogenic NSCLC microenvironment is associated with favorable survival in lung adenocarcinoma
Givechian, K. B., Garner, C. P., Benz, S. C., Song, B., Rabizadeh, S., & Soon‐Shiong, P. (2019) · Oncotarget · doi:10.18632/oncotarget.26748 · Cited by 28
Moon, D. W., Park, Y. H., Lee, S. Y., Lim, H., Kwak, S., Kim, M. S., Kim, H., Kim, E., Jung, Y., Hoe, H.-S., et al. (2020) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b21800 · Cited by 28
Goldsborough, T., O’Callaghan, A., Inglis, F., Leplat, L., Filby, A. J., Bilen, H., & Bankhead, P. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.04.611150 · Cited by 27
Gu, T., Xiao, Y.-L., & Shao, Z.-M. (2024) · Precision Medicine and Engineering · doi:10.1016/j.preme.2024.100006 · Cited by 25
Zhang, B., Li, H., Li, H., Xiong, D., Zhang, L., & Sun, Z. (2023) · Cancer Letters · doi:10.1016/j.canlet.2023.216105 · Cited by 27
The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI
Lee, R. Y., Ng, C. W., Rajapakse, M. P., Ang, N., Yeong, J. P.-S., & Lau, M. C. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1172314 · Cited by 28
Spatiotemporal Omics-Refining the landscape of precision medicine
Zhang, J., Yin, J., Heng, Y., Xie, K., Chen, A., Amit, I., Bian, X.-W., & Xu, X. (2022) · Life Medicine · doi:10.1093/lifemedi/lnac053 · Cited by 27
Shi, J., Sun, D., Wu, K., Jiang, Z., Kong, X., Wang, W., Wu, H., & Zheng, Y. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108491 · Cited by 27
Wang, R., Hastings, W. J., Saliba, J. G., Bao, D., Huang, Y., Maity, S., Ahmad, O. M. K., Hu, L., Wang, S., Fan, J., et al. (2024) · ACS Nano · doi:10.1021/acsnano.4c11505 · Cited by 27
SPF: A spatial and functional data analytic approach to cell imaging data
Vu, T., Wrobel, J., Bitler, B. G., Schenk, E. L., Jordan, K. R., & Ghosh, D. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009486 · Cited by 25
Microglia and macrophages in the neuro-glia-vascular unit: From identity to functions
Chen, S., Li, J., Meng, S., He, T., Shi, Z., Wang, C., Wang, Y., Cao, H., Huang, Y., Zhang, Y., et al. (2023) · Neurobiology of Disease · doi:10.1016/j.nbd.2023.106066 · Cited by 24
Li, W., Sang, M., Hao, X., Li, J., Wang, Y., & Shan, B. (2019) · Journal of Cellular Biochemistry · doi:10.1002/jcb.29633 · Cited by 25
Zhou, Z., Jiang, Y., Sun, Z., Zhang, T., Feng, W., Li, G., Li, R., & Xing, L. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.105287 · Cited by 27
Highly multiplexed imaging reveals prognostic immune and stromal spatial biomarkers in breast cancer
Eng, J. R., Bucher, E., Hu, Z., Walker, C. R., Risom, T., Angelo, M., Ericsson, P. I. G., Sanders, M. E., Chakravarthy, A. B., Pietenpol, J. A., et al. (2025) · JCI Insight · doi:10.1172/jci.insight.176749 · Cited by 24
Selective neuronal vulnerability in Alzheimer’s disease
Wang, Z.-T., Zhang, C. M., Wang, Y., Dong, Q., Tan, L., & Yu, J. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101114 · Cited by 24
Jiang, K., Dong, M., Li, C., & Sheng, J. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.557477 · Cited by 23
Davey, M. G., Ryan, É. J., Davey, M. S., Lowery, A., Miller, N., & Kerin, M. J. (2021) · British journal of surgery · doi:10.1093/bjs/znab103 · Cited by 24
Glycan Imaging Mass Spectrometry
Blaschke, C. R. K., McDowell, C. T., Black, A. P., Mehta, A. S., Angel, P. M., & Drake, R. R. (2021) · Clinics in Laboratory Medicine · doi:10.1016/j.cll.2021.03.005 · Cited by 24
Immune Crosstalk Between Lymph Nodes and Breast Carcinomas, With a Focus on B Cells
Alberts, E., Wall, I., Calado, D. P., & Grigoriadis, A. E. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.673051 · Cited by 24
Gawin, M., Kurczyk, A., Niemiec, J. A., Stanek‐Widera, A., Grela-Wojewoda, A., Adamczyk, A., Biskup-Frużyńska, M., Polańska, J., & Widłak, P. (2021) · Cancers · doi:10.3390/cancers13174349 · Cited by 24
Seeing the forest and the tree: TILs and PD-L1 as immune biomarkers
Savas, P. S., Salgado, R., & Loi, S. (2021) · Breast Cancer Research and Treatment · doi:10.1007/s10549-021-06287-4 · Cited by 24
Stachtea, X., Loughrey, M. B., Salvucci, M., Lindner, A. U., Cho, S., McDonough, E., Sood, A., Graf, J. F., Santamaría-Pang, A., Corwin, A. D., et al. (2021) · Modern Pathology · doi:10.1038/s41379-021-00953-0 · Cited by 24
Yang, G., Cai, S., Hu, M., Li, C., Yang, L., Zhang, W., Sun, J., Sun, F., Xing, L., & Sun, X. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-023-04839-4 · Cited by 27
Trigos, A., Pasam, A., Banks, P. D., Wallace, R., Guo, C. W., Keam, S. P., Thorne, H., kConFab, Mitchell, C., Lade, S., et al. (2022) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2021-003744 · Cited by 24
Tan, W., Xie, X., Huang, Z., Chen, L., Tang, W., Zhu, R., Ye, X., Zhang, X., Pan, L., Gao, J. H., et al. (2019) · Artificial Cells Nanomedicine and Biotechnology · doi:10.1080/21691401.2019.1703731 · Cited by 25
The evolving landscape of spatial proteomics technologies in the AI age
Hu, B., Zhu, J., & Zhao, F. (2024) · Fundamental Research · doi:10.1016/j.fmre.2024.11.023 · Cited by 25
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
