The DOI matches its Crossref record; 107 of the 107 references it cites were found in a registry or an index.
Quiescent cancer cells resist T cell attack by forming an immunosuppressive niche
Open accessAbstract
SUMMARY Immunotherapy is a promising treatment for triple-negative breast cancer (TNBC), but patients relapse, highlighting the need to understand the mechanisms of resistance. We discovered that in primary breast cancer, tumor cells that resist T cell attack are quiescent. Quiescent cancer cells (QCCs) form clusters with reduced immune infiltration. They also display superior tumorigenic capacity and higher expression of chemotherapy resistance and stemness genes. We adapted single-cell RNA-sequencing with precise spatial resolution to profile infiltrating cells inside and outside the QCC niche. This transcriptomic analysis revealed hypoxia-induced programs and identified more exhausted T cells, tumor-protective fibroblasts, and dysfunctional dendritic cells inside clusters of QCCs. This uncovered differential phenotypes in infiltrating cells based on their intra-tumor location. Thus, QCCs constitute immunotherapy-resistant reservoirs by orchestrating a local hypoxic immune-suppressive milieu that blocks T cell function. Eliminating QCCs holds the promise to counteract immunotherapy resistance and prevent disease recurrence in TNBC.
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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Fiji: an open-source platform for biological-image analysis
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2019 · Cited by 66,890
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)
Eisenhauer, E. A., Therasse, P., Bogaerts, J., Schwartz, L. H., Sargent, D., Ford, R., Dancey, J., Arbuck, S., Gwyther, S., Mooney, M., et al. (2009) · European Journal of Cancer · doi:10.1016/j.ejca.2008.10.026 · Cited by 29,534
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424
Integrated analysis of multimodal single-cell data
Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508
Cited by
Gerstberger, S., Jiang, Q., & Ganesh, K. (2023) · Cell · doi:10.1016/j.cell.2023.03.003 · Cited by 913
The hallmarks of cancer immune evasion
Galassi, C., Chan, T. A., Vitale, I., & Galluzzi, L. (2024) · Cancer Cell · doi:10.1016/j.ccell.2024.09.010 · Cited by 565
Cancer drug-tolerant persister cells: from biological questions to clinical opportunities
Russo, M., Chen, M., Mariella, E., Peng, H., Rehman, S. K., Sancho, E., Sogari, A., Toh, T. S., Balaban, N. Q., Batlle, E., et al. (2024) · Nature Reviews Cancer · doi:10.1038/s41568-024-00737-z · Cited by 210
Omics Technologies Improving Breast Cancer Research and Diagnostics
Orsini, A., Diquigiovanni, C., & Bonora, E. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241612690 · Cited by 52
Global analysis of protein turnover dynamics in single cells
Sabatier, P., Lechner, M., Guzmán, U. H., Beusch, C. M., Zeng, X., Wang, L., Izaguirre, F., Seth, A., Gritsenko, O., Rodin, S., et al. (2025) · Cell · doi:10.1016/j.cell.2025.03.002 · Cited by 41
Metadata
- Authors
- Baldominos, P., Barbera-Mourelle, A., Barreiro, O., Huang, Y., Wight, A., Cho, J.-W., Zhao, X., Estivill, G., Adam, I., Sanchez, X., McCarthy, S., Schaller, J., Khan, Z., Ruzo, A., Pastorello, R., Richardson, E. T., Dillon, D., Montero-Llopis, P., Barroso-Sousa, R., Forman, J., Shukla, S. A., Tolaney, S. M., Mittendorf, E. A., von Andrian, U. H., Wucherpfennig, K. W., Hemberg, M., & Agudo, J.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2022-05
- Volume
- 185
- Issue
- 10
- Pages
- 1694-1708.e19
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W4226021421
- Cited by
- 317
- References
- 107
- FWCI
- 24.11
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Baldominos, P., Barbera-Mourelle, A., Barreiro, O., Huang, Y., Wight, A., Cho, J.-W., Zhao, X., Estivill, G., Adam, I., Sanchez, X., McCarthy, S., Schaller, J., Khan, Z., Ruzo, A., Pastorello, R., Richardson, E. T., Dillon, D., Montero-Llopis, P., Barroso-Sousa, R., . . . Agudo, J. (2022). Quiescent cancer cells resist T cell attack by forming an immunosuppressive niche. Cell. https://doi.org/10.1016/j.cell.2022.03.033
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
