The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Molecular signatures of antitumor neoantigen-reactive T cells from metastatic human cancers
Open accessAbstract
The accurate identification of antitumor T cell receptors (TCRs) represents a major challenge for the engineering of cell-based cancer immunotherapies. By mapping 55 neoantigen-specific TCR clonotypes (NeoTCRs) from 10 metastatic human tumors to their single-cell transcriptomes, we identified signatures of CD8 + and CD4 + neoantigen-reactive tumor-infiltrating lymphocytes (TILs). Neoantigen-specific TILs exhibited tumor-specific expansion with dysfunctional phenotypes, distinct from blood-emigrant bystanders and regulatory TILs. Prospective prediction and testing of 73 NeoTCR signature–derived clonotypes demonstrated that half of the tested TCRs recognized tumor antigens or autologous tumors. NeoTCR signatures identified TCRs that target driver neoantigens and nonmutated viral or tumor-associated antigens, suggesting a common metastatic TIL exhaustion program. NeoTCR signatures delineate the landscape of TILs across metastatic tumors, enabling successful TCR prediction based purely on TIL transcriptomic states for use in cancer immunotherapy.
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Cites
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
Siegel, R. L., Miller, K. D., & Jemal, A. (2020) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21590 · Cited by 20,444
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
Near-optimal probabilistic RNA-seq quantification
Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016) · Nature Biotechnology · doi:10.1038/nbt.3519 · Cited by 9,723
Scenic: single-cell regulatory network inference and clustering.
Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4463 · Cited by 6,520
Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016) · Science · doi:10.1126/science.aad0501 · Cited by 4,944
Phenotypic Analysis of Antigen-Specific T Lymphocytes
Altman, J. D., Moss, P. A. H., Goulder, P. J. R., Barouch, D. H., McHeyzer-Williams, M. G., Bell, J. I., McMichael, A. J., & Davis, M. M. (1996) · Science · doi:10.1126/science.274.5284.94 · Cited by 3,544
Prospective Derivation of a Living Organoid Biobank of Colorectal Cancer Patients
van de Wetering, M., Francies, H. E., Francis, J. M., Bounova, G., Iorio, F., Pronk, A., van Houdt, W., van Gorp, J., Taylor-Weiner, A., Kester, L., et al. (2015) · Cell · doi:10.1016/j.cell.2015.03.053 · Cited by 2,408
Tertiary lymphoid structures improve immunotherapy and survival in melanoma
Cabrita, R., Lauss, M., Sanna, A., Donia, M., Skaarup Larsen, M., Mitra, S., Johansson, I., Phung, B., Harbst, K., Vallon-Christersson, J., et al. (2020) · Nature · doi:10.1038/s41586-019-1914-8 · Cited by 2,374
Molecular Signature of CD8+ T Cell Exhaustion during Chronic Viral Infection
Wherry, E. J., Ha, S.-J., Kaech, S. M., Haining, W. N., Sarkar, S., Kalia, V., Subramaniam, S., Blattman, J. N., Barber, D. L., & Ahmed, R. (2007) · Immunity · doi:10.1016/j.immuni.2007.09.006 · Cited by 2,103
Cited by
Clinical implications of T cell exhaustion for cancer immunotherapy
Chow, A., Perica, K., Klebanoff, C. A., & Wolchok, J. D. (2022) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-022-00689-z · Cited by 1,044
Neoantigens: promising targets for cancer therapy
Xie, N., Shen, G., Gao, W., Huang, Z., Huang, C., & Fu, L. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-01270-x · Cited by 1,020
Weber, J. S., Carlino, M. S., Khattak, A., Meniawy, T., Ansstas, G., Taylor, M. H., Kim, K. B., McKean, M., Long, G. V., Sullivan, R. J., et al. (2024) · The Lancet · doi:10.1016/s0140-6736(23)02268-7 · Cited by 916
Liu, B., Zhang, Y., Wang, D., Hu, X., & Zhang, Z. (2022) · Nature Cancer · doi:10.1038/s43018-022-00433-7 · Cited by 309
Wang, A. Z., Mashimo, B. L., Schaettler, M. O., Sherpa, N. D., Leavitt, L. A., Livingstone, A. J., Khan, S. M., Li, M., Anzaldua-Campos, M. I., Bradley, J. D., et al. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-0913 · Cited by 73
Borràs, D. M., Verbandt, S., Ausserhofer, M., Sturm, G., Lim, J., Verge, G. A., Vanmeerbeek, I., Laureano, R. S., Govaerts, J., Sprooten, J., et al. (2023) · Cell Discovery · doi:10.1038/s41421-023-00605-4 · Cited by 67
“Hence, we analyzed a previously published neoantigen-reactive TCR-activation signature for CD8 + T cells (NeoTCR8) 78 .”
SPP1 + macrophages cause exhaustion of tumor-specific T cells in liver metastases
Trehan, R., Huang, P., Zhu, X. B., Wang, X., Soliman, M., Strepay, D., Nur, A., Kedei, N., Arhin, M., Ghabra, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59529-0 · Cited by 74
Chen, X., Zhao, J., Yue, S., Li, Z., Duan, X., Lin, Y., Yang, Y., He, J., Gao, L., Pan, Z., et al. (2024) · Nature Cancer · doi:10.1038/s43018-024-00760-x · Cited by 68
Self-citationTCR catch bonds nonlinearly control CD8 cooperation to shape T cell specificity
Qin, R., Zhang, Y., Shi, J., Wu, P., An, C., Li, Z., Liu, N., Wan, Z., Hua, T., Li, X., et al. (2025) · Cell Research · doi:10.1038/s41422-025-01077-9 · Cited by 35
Reinstein, Z. Z., Zhang, Y., Ospina, O. E., Nichols, M. D., Chu, V. A., Pulido, A. D. M., Prieto, K., Nguyen, J. V., Yin, R., Moran Segura, C., et al. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-0798 · Cited by 33
Self-citationLutz, R., Poos, A. M., Solé-Boldo, L., John, L., Wagner, J., Prokoph, N., Baertsch, M. A., Vonficht, D., Palit, S., Brobeil, A., et al. (2025) · Science Immunology · doi:10.1126/sciimmunol.adp6667 · Cited by 25
“…by using recently published transcriptional signatures to determine the level of T cell exhaustion (28), T cell neoantigen-reactivity (27), NK-cell exhaustion (23), NK-cell cytotoxicity (24), extracellular matrix production (Gene ontology geneset) and M2-like macrophages (25, 69).”
He, S., Tan, Y., Ye, Q., M Gubin, M., Dede, M., Rafei, H., Peng, W., Rezvani, K., Mohanty, V., & Chen, K. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-025-00060-4 · Cited by 3
Metadata
- Authors
- Lowery, F. J., Krishna, S., Yossef, R., Parikh, N. B., Chatani, P. D., Zacharakis, N., Parkhurst, M. R., Levin, N., Sindiri, S., Sachs, A., Hitscherich, K. J., Yu, Z., Vale, N. R., Lu, Y.-C., Zheng, Z., Jia, L., Gartner, J. J., Hill, V. K., Copeland, A. R., Nah, S. K., Masi, R. V., Gasmi, B., Kivitz, S., Paria, B. C., Florentin, M., Kim, S. P., Hanada, K.-I., Li, Y. F., Ngo, L. T., Ray, S., Shindorf, M. L., Levi, S. T., Shepherd, R., Toy, C., Parikh, A. Y., Prickett, T. D., Kelly, M. C., Beyer, R., Goff, S. L., Yang, J. C., Robbins, P. F., & Rosenberg, S. A.
- Venue
- Science
- Published
- 2022-02-25
- Volume
- 375
- Issue
- 6583
- Pages
- 877-884
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W4210297691
- Cited by
- 447
- References
- 27
- FWCI
- 33.26
- Type
- journal article
- Language
- en
- Open access
- green
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