Prediction of tumor-reactive T cell receptors from scRNA-seq data for personalized T cell therapy.
Journal
Nature biotechnology
ISSN: 1546-1696
Titre abrégé: Nat Biotechnol
Pays: United States
ID NLM: 9604648
Informations de publication
Date de publication:
07 Mar 2024
07 Mar 2024
Historique:
received:
13
06
2023
accepted:
01
02
2024
medline:
8
3
2024
pubmed:
8
3
2024
entrez:
7
3
2024
Statut:
aheadofprint
Résumé
The identification of patient-derived, tumor-reactive T cell receptors (TCRs) as a basis for personalized transgenic T cell therapies remains a time- and cost-intensive endeavor. Current approaches to identify tumor-reactive TCRs analyze tumor mutations to predict T cell activating (neo)antigens and use these to either enrich tumor infiltrating lymphocyte (TIL) cultures or validate individual TCRs for transgenic autologous therapies. Here we combined high-throughput TCR cloning and reactivity validation to train predicTCR, a machine learning classifier that identifies individual tumor-reactive TILs in an antigen-agnostic manner based on single-TIL RNA sequencing. PredicTCR identifies tumor-reactive TCRs in TILs from diverse cancers better than previous gene set enrichment-based approaches, increasing specificity and sensitivity (geometric mean) from 0.38 to 0.74. By predicting tumor-reactive TCRs in a matter of days, TCR clonotypes can be prioritized to accelerate the manufacture of personalized T cell therapies.
Identifiants
pubmed: 38454173
doi: 10.1038/s41587-024-02161-y
pii: 10.1038/s41587-024-02161-y
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : 404521405
Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : 259332240
Informations de copyright
© 2024. The Author(s).
Références
Rohaan, M. W. et al. Tumor-infiltrating lymphocyte therapy or ipilimumab in advanced melanoma. N. Engl. J. Med. 387, 2113–2125 (2022).
pubmed: 36477031
doi: 10.1056/NEJMoa2210233
Monberg, T. J., Borch, T. H., Svane, I. M. & Donia, M. TIL therapy: facts and hopes. Clin. Cancer Res. 29, 3275–3283 (2023).
pubmed: 37058256
doi: 10.1158/1078-0432.CCR-22-2428
Cohen, C. J. et al. Isolation of neoantigen-specific T cells from tumor and peripheral lymphocytes. J. Clin. Invest. 125, 3981–3991 (2015).
pubmed: 26389673
doi: 10.1172/JCI82416
pmcid: 4607110
Crompton, J. G., Sukumar, M. & Restifo, N. P. Uncoupling T cell expansion from effector differentiation in cell-based immunotherapy. Immunol. Rev. 257, 264–276 (2014).
pubmed: 24329803
doi: 10.1111/imr.12135
pmcid: 3915736
Poschke, I. C. et al. The outcome of ex vivo TIL expansion is highly influenced by spatial heterogeneity of the tumor T cell repertoire and differences in intrinsic in vitro growth capacity between T cell clones. Clin. Cancer Res. 26, 4289–4301 (2020).
pubmed: 32303540
doi: 10.1158/1078-0432.CCR-19-3845
Zacharakis, N. et al. Immune recognition of somatic mutations leading to complete durable regression in metastatic breast cancer. Nat. Med. 24, 724–730 (2018).
pubmed: 29867227
doi: 10.1038/s41591-018-0040-8
pmcid: 6348479
Wang, B. et al. Generation of hypoimmunogenic T cells from genetically engineered allogeneic human induced pluripotent stem cells. Nat. Biomed. Eng. 5, 429–440 (2021).
pubmed: 34002062
doi: 10.1038/s41551-021-00730-z
Simoni, Y. et al. Bystander CD8
pubmed: 29769722
doi: 10.1038/s41586-018-0130-2
Hundal, J. et al. PVACtools: a computational toolkit to identify and visualize cancer neoantigens. Cancer Immunol. Res. 8, 409–420 (2020).
pubmed: 31907209
doi: 10.1158/2326-6066.CIR-19-0401
pmcid: 7056579
Shah, N. M. et al. Pan-cancer analysis identifies tumor-specific antigens derived from transposable elements. Nat. Genet. 55, 631–639 (2023).
pubmed: 36973455
doi: 10.1038/s41588-023-01349-3
Bartok, O. et al. Anti-tumour immunity induces aberrant peptide presentation in melanoma. Nature 590, 332–337 (2021).
pubmed: 33328638
doi: 10.1038/s41586-020-03054-1
Kacen, A. et al. Post-translational modifications reshape the antigenic landscape of the MHC I immunopeptidome in tumors. Nat. Biotechnol. 41, 239–251 (2023).
pubmed: 36203013
doi: 10.1038/s41587-022-01464-2
Ouspenskaia, T. et al. Unannotated proteins expand the MHC-I-restricted immunopeptidome in cancer. Nat. Biotechnol. 40, 209–217 (2022).
pubmed: 34663921
doi: 10.1038/s41587-021-01021-3
Naghavian, R. et al. Microbial peptides activate tumour-infiltrating lymphocytes in glioblastoma. Nature 617, 807–817 (2023).
pubmed: 37198490
doi: 10.1038/s41586-023-06081-w
pmcid: 10208956
Platten, M. et al. A vaccine targeting mutant IDH1 in newly diagnosed glioma. Nature 592, 463–468 (2021).
pubmed: 33762734
doi: 10.1038/s41586-021-03363-z
pmcid: 8046668
Oliveira, G. et al. Phenotype, specificity and avidity of antitumour CD8
pubmed: 34290406
doi: 10.1038/s41586-021-03704-y
pmcid: 9187974
Oliveira, G. et al. Landscape of helper and regulatory antitumour CD4
pubmed: 35508657
doi: 10.1038/s41586-022-04682-5
pmcid: 9815755
Veatch, J. R. et al. Neoantigen-specific CD4
pubmed: 35413271
doi: 10.1016/j.ccell.2022.03.006
pmcid: 9011147
Caushi, J. X. et al. Transcriptional programs of neoantigen-specific TIL in anti-PD-1-treated lung cancers. Nature 596, 126–132 (2021).
Hanada, K. et al. A phenotypic signature that identifies neoantigen-reactive T cells in fresh human lung cancers. Cancer Cell 40, 479–493.e6 (2022).
pubmed: 35452604
doi: 10.1016/j.ccell.2022.03.012
pmcid: 9196205
Zheng, C. et al. Transcriptomic profiles of neoantigen-reactive T cells in human gastrointestinal cancers. Cancer Cell 40, 410–423.e7 (2022).
pubmed: 35413272
doi: 10.1016/j.ccell.2022.03.005
Meng, Z. et al. Transcriptome-based identification of tumor-reactive and bystander CD8
doi: 10.1126/scitranslmed.adh9562
Lowery, F. J. et al. Molecular signatures of antitumor neoantigen-reactive T cells from metastatic human cancers. Science 375, 877–884 (2022).
pubmed: 35113651
doi: 10.1126/science.abl5447
pmcid: 8996692
Joshi, K. et al. Spatial heterogeneity of the T cell receptor repertoire reflects the mutational landscape in lung cancer. Nat. Med. 25, 1549–1559 (2019).
pubmed: 31591606
doi: 10.1038/s41591-019-0592-2
pmcid: 6890490
Hu, Z., Li, Z., Ma, Z. & Curtis, C. Multi-cancer analysis of clonality and the timing of systemic spread in paired primary tumors and metastases. Nat. Genet. 52, 701–708 (2020).
pubmed: 32424352
doi: 10.1038/s41588-020-0628-z
pmcid: 7343625
Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (ACM, 2016).
Lundberg, S. M. & Lee, S.-I. in Advances in Neural Information Processing Systems Vol. 30 (eds Guyon, I. et al.) (Curran Associates, 2017).
Yoshida, M. et al. Local and systemic responses to SARS-CoV-2 infection in children and adults. Nature 602, 321–327 (2022).
pubmed: 34937051
doi: 10.1038/s41586-021-04345-x
Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1100 (2013).
pubmed: 24056875
doi: 10.1038/nmeth.2639
Shugay, M. et al. VDJdb: a curated database of T cell receptor sequences with known antigen specificity. Nucleic Acids Res. 129, 170–177 (2017).
Ahlmann-Eltze, C. & Huber, W. Comparison of transformations for single-cell RNA-seq data. Nat. Methods 20, 665–672 (2023).
pubmed: 37037999
doi: 10.1038/s41592-023-01814-1
pmcid: 10172138
Yu, L., Cao, Y., Yang, J. Y. H. & Yang, P. Benchmarking clustering algorithms on estimating the number of cell types from single-cell RNA-sequencing data. Genome Biol. 23, 49 (2022).
pubmed: 35135612
doi: 10.1186/s13059-022-02622-0
pmcid: 8822786
Wang, Z. et al. Identification and verification of immune subtype-related lncRNAs in clear cell renal cell carcinoma. Front. Oncol. 12, 369–373 (2022).
Song, G. Y. et al. Differential expression profiles and functional analysis of long non-coding RNAs in calcific aortic valve disease. BMC Cardiovasc. Disord. 23, 1–13 (2023).
doi: 10.1186/s12872-023-03311-x
Wu, W. et al. Tissue-specific co-expression of long non-coding and coding RNAs associated with breast cancer. Sci. Rep. 6, 1–13 (2016).
Pogorelyy, M. V. et al. Detecting T cell receptors involved in immune responses from single repertoire snapshots. PLoS Biol. 17, 1–13 (2019).
doi: 10.1371/journal.pbio.3000314
Schmidt, J. et al. Neoantigen-specific CD8 T cells with high structural avidity preferentially reside in and eliminate tumors. Nat. Commun. 14, 3188 (2023).
pubmed: 37280206
doi: 10.1038/s41467-023-38946-z
pmcid: 10244384
Schmid, T. et al. T-FINDER: a highly sensitive, pan-HLA platform for functional T cell receptor and ligand discovery. Sci. Adv. 10, adk3060 (2024).
Grigoriadis, K. et al. CONIPHER: a computational framework for scalable phylogenetic reconstruction with error correction. Protoc. Exch. 562, 833–845 (2023).
Jokinen, E. et al. TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs. Bioinformatics 39, btac788 (2023).
pubmed: 36477794
doi: 10.1093/bioinformatics/btac788
Campbell, P. J. et al. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).
doi: 10.1038/s41586-020-1969-6
Reisinger, E. et al. OTP: an automatized system for managing and processing NGS data. J. Biotechnol. 261, 53–62 (2017).
pubmed: 28803971
doi: 10.1016/j.jbiotec.2017.08.006
Orenbuch, R. et al. ArcasHLA: high-resolution HLA typing from RNAseq. Bioinformatics 36, 33–40 (2020).
pubmed: 31173059
doi: 10.1093/bioinformatics/btz474
Song, L. et al. TRUST4: immune repertoire reconstruction from bulk and single-cell RNA-seq data. Nat. Methods 18, 627–630 (2021).
pubmed: 33986545
doi: 10.1038/s41592-021-01142-2
pmcid: 9328942
Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).
doi: 10.21105/joss.01686
Cohen, C. J., Zhao, Y., Zheng, Z., Rosenberg, S. A. & Morgan, R. A. Enhanced antitumor activity of murine–human hybrid T cell receptor (TCR) in human lymphocytes is associated with improved pairing and TCR/CD3 stability. Cancer Res. 66, 8878–8886 (2006).
pubmed: 16951205
doi: 10.1158/0008-5472.CAN-06-1450
pmcid: 2147082
Cohen, C. J. et al. Enhanced antitumor activity of T cells engineered to express T cell receptors with a second disulfide bond. Cancer Res. 67, 3898–3903 (2007).
pubmed: 17440104
doi: 10.1158/0008-5472.CAN-06-3986
pmcid: 2147081
Bozza, M. et al. A nonviral, nonintegrating DNA nanovector platform for the safe, rapid, and persistent manufacture of recombinant T cells. Sci. Adv. 7, eabf1333 (2021).
pubmed: 33853779
doi: 10.1126/sciadv.abf1333
pmcid: 8046366
Bocchi, M. et al. Inverted open microwells for cell trapping, cell aggregate formation and parallel recovery of live cells. Lab Chip 12, 3168–3176 (2012).
Schmidt, U., Weigert, M., Broaddus, C. & Myers, G. Lecture Notes in Computer Science Vol. 11071 (Springer, 2018).
Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
pubmed: 28091601
doi: 10.1038/ncomms14049
pmcid: 5241818
R: A Language and Environment for Statistical Computing (R Core Team, 2022).
Young, M. D. & Behjati, S. SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. Gigascience 9, giaa151 (2020).
pubmed: 33367645
doi: 10.1093/gigascience/giaa151
pmcid: 7763177
Hippen, A. A. et al. miQC: an adaptive probabilistic framework for quality control of single-cell RNA-sequencing data. PLoS Comput. Biol. 17, e1009290 (2021).
pubmed: 34428202
doi: 10.1371/journal.pcbi.1009290
pmcid: 8415599
Human T cells from a healthy donor, 1k cells – multi (v2). 10X Genomics https://www.10xgenomics.com/datasets/human-t-cells-from-a-healthy-donor-1-k-cells-multi-v-2-2-standard-5-0-0 (2020).
Szabo, P. A. et al. Single-cell transcriptomics of human T cells reveals tissue and activation signatures in health and disease. Nat. Commun. 10, 4706 (2019).
pubmed: 31624246
doi: 10.1038/s41467-019-12464-3
pmcid: 6797728
Gao, S. et al. Single-cell RNA sequencing coupled to TCR profiling of large granular lymphocyte leukemia T cells. Nat. Commun. 13, 1982 (2022).
pubmed: 35411048
doi: 10.1038/s41467-022-29175-x
pmcid: 9001664
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).
pubmed: 34062119
doi: 10.1016/j.cell.2021.04.048
pmcid: 8238499
Head, T. et al. scikit-optimize/scikit-optimize (v0.9.0). Zenodo https://doi.org/10.5281/zenodo.5565057 (2021).
Andreatta, M. & Carmona, S. J. UCell: robust and scalable single-cell gene signature scoring. Comput. Struct. Biotechnol. J. 19, 3796–3798 (2021).
pubmed: 34285779
doi: 10.1016/j.csbj.2021.06.043
pmcid: 8271111
Berset, M. et al. Expression of Melan-A/MART-1 antigen as a prognostic factor in primary cutaneous melanoma. Int. J. Cancer 95, 73–77 (2001).
pubmed: 11241315
doi: 10.1002/1097-0215(20010120)95:1<73::AID-IJC1013>3.0.CO;2-S