The DOI matches its Crossref record; 79 of the 79 references it cites were found in a registry or an index.
Single-cell RNA sequencing reveals the effects of chemotherapy on human pancreatic adenocarcinoma and its tumor microenvironment
Open accessAbstract
The tumor microenvironment (TME) in pancreatic ductal adenocarcinoma (PDAC) is a complex ecosystem that drives tumor progression; however, in-depth single cell characterization of the PDAC TME and its role in response to therapy is lacking. Here, we perform single-cell RNA sequencing on freshly collected human PDAC samples either before or after chemotherapy. Overall, we find a heterogeneous mixture of basal and classical cancer cell subtypes, along with distinct cancer-associated fibroblast and macrophage subpopulations. Strikingly, classical and basal-like cancer cells exhibit similar transcriptional responses to chemotherapy and do not demonstrate a shift towards a basal-like transcriptional program among treated samples. We observe decreased ligand-receptor interactions in treated samples, particularly between TIGIT on CD8 + T cells and its receptor on cancer cells, and identify TIGIT as the major inhibitory checkpoint molecule of CD8 + T cells. Our results suggest that chemotherapy profoundly impacts the PDAC TME and may promote resistance to immunotherapy.
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Hallmarks of Cancer: The Next Generation
Hanahan, D., & Weinberg, R. A. (2011) · Cell · doi:10.1016/j.cell.2011.02.013 · Cited by 66,590
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
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927
Siegel, R. L., Miller, K. D., Fuchs, H. E., & Jemal, A. (2022) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21708 · Cited by 17,196
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
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353
Dimensionality reduction for visualizing single-cell data using UMAP
Becht, E., McInnes, L., Healy, J., Dutertre, C.-A., Kwok, I. W. H., Ng, L. G., Ginhoux, F., & Newell, E. W. (2019) · Nature Biotechnology · doi:10.1038/nbt.4314 · Cited by 5,358
TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data
Colaprico, A., Silva, T. C., Olsen, C., Garofano, L., Cava, C., Garolini, D., Sabedot, T. S., Malta, T. M., Pagnotta, S. M., Castiglioni, I., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1507 · Cited by 4,183
Genomic analyses identify molecular subtypes of pancreatic cancer
Bailey, P., Chang, D. K., Nones, K., Johns, A. L., Patch, A.-M., Gingras, M.-C., Miller, D. K., Christ, A. N., Bruxner, T. J. C., Quinn, M. C., et al. (2016) · Nature · doi:10.1038/nature16965 · Cited by 3,621 · cited 1×
“We also assessed the bulk RNA-derived sub-type classi fi cations of Collisson, Bailey and Puleo 26 – 28 .”
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Concurrent loss of the Y chromosome in cancer and T cells impacts outcome
Chen, X., Shen, Y., Choi, S., Abdel-Hafiz, H. A., Basu, M., Hoelzen, L., Tufano, M., Kailasam Mani, S. K., Ranjpour, M., Zhu, J., et al. (2025) · Nature · doi:10.1038/s41586-025-09071-2 · Cited by 50
Metadata
- Authors
- Werba, G., Weissinger, D., Kawaler, E. A., Zhao, E., Kalfakakou, D., Dhara, S., Wang, L., Lim, H. B., Oh, G., Jing, X., Beri, N., Khanna, L., Gonda, T., Oberstein, P., Hajdu, C., Loomis, C., Heguy, A., Sherman, M. H., Lund, A. W., Welling, T. H., Dolgalev, I., Tsirigos, A., & Simeone, D. M.
- Venue
- Nature Communications
- Published
- 2023-02-13
- Volume
- 14
- Issue
- 1
- Pages
- 797-797
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W4320491309
- Cited by
- 283
- References
- 79
- FWCI
- 30.33
- Type
- journal article
- Language
- en
- Open access
- gold
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- Werba, G., Weissinger, D., Kawaler, E. A., Zhao, E., Kalfakakou, D., Dhara, S., Wang, L., Lim, H. B., Oh, G., Jing, X., Beri, N., Khanna, L., Gonda, T., Oberstein, P., Hajdu, C., Loomis, C., Heguy, A., Sherman, M. H., Lund, A. W., . . . Simeone, D. M. (2023). Single-cell RNA sequencing reveals the effects of chemotherapy on human pancreatic adenocarcinoma and its tumor microenvironment. Nature Communications. https://doi.org/10.1038/s41467-023-36296-4
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