Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Accurate detection of tumor-specific gene fusions reveals strongly immunogenic personal neo-antigens

Nature Biotechnology volume 40, pages 1276–1284 (2022)Cite this article

Subjects

Abstract

Cancer-associated gene fusions are a potential source for highly immunogenic neoantigens, but the lack of computational tools for accurate, sensitive identification of personal gene fusions has limited their targeting in personalized cancer immunotherapy. Here we present EasyFuse, a machine learning computational pipeline for detecting cancer-specific gene fusions in transcriptome data obtained from human cancer samples. EasyFuse predicts personal gene fusions with high precision and sensitivity, outperforming previously described tools. By testing immunogenicity with autologous blood lymphocytes from patients with cancer, we detected pre-established CD4+ and CD8+ T cell responses for 10 of 21 (48%) and for 1 of 30 (3%) identified gene fusions, respectively. The high frequency of T cell responses detected in patients with cancer supports the relevance of individual gene fusions as neoantigens that might be targeted in personalized immunotherapies, especially for tumors with low mutation burden.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Highly diverse GF prediction with different tools.
Fig. 2: Recurrent GFs are enriched for cis-near fusions in normal tissue.
Fig. 3: EasyFuse improves sensitivity in detecting tumor-specific GFs.
Fig. 4: Machine learning contributes to highly specific prediction of GFs in FFPE tumor samples.
Fig. 5: Predicted GFs encode immunogenic neo-antigens eliciting CD4+ and CD8+ T cell responses.

Similar content being viewed by others

Data availability

Sequence data from this study have been deposited in the Sequence Read Archive (SRA accession, used in Figs. 1, 2 and 3; NCBI BioProject ID PRJNA764684, used in Fig. 2) or the European Genome-phenome Archive (EGA accession EGAS00001004877, used in Figs. 4 and 5). Previously published sequencing data (immunogenicity cohort, Fig. 5, samples 10–14) are available in the EGA (EGA accession EGAD00001004455). Previously sequenced cell line data used in Fig. 1 are available at SRA accession PRJNA543964. Raw predicted GFs for all samples are available on Figshare (https://doi.org/10.6084/m9.figshare.19087049). Source data are provided with this paper.

Code availability

The source code and documentation of EasyFuse is available at GitHub (https://github.com/TRON-Bioinformatics/easyfuse).

References

  1. Gao, Q. et al. Driver fusions and their implications in the development and treatment of human cancers. Cell Rep. 23, 227–238 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Shtivelman, E., Lifshitz, B., Gale, R. P. & Canaani, E. Fused transcript of abl and bcr genes in chronic myelogenous leukaemia. Nature 315, 550–554 (1985).

    Article  CAS  PubMed  Google Scholar 

  3. Amatu, A., Sartore-Bianchi, A. & Siena, S. NTRK gene fusions as novel targets of cancer therapy across multiple tumour types. ESMO Open 1, e000023 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Sahin, U. & Türeci, Ö. Personalized vaccines for cancer immunotherapy. Science 359, 1355–1360 (2018).

    Article  CAS  PubMed  Google Scholar 

  5. Carreno, B. M. et al. Cancer immunotherapy. A dendritic cell vaccine increases the breadth and diversity of melanoma neoantigen-specific T cells. Science 348, 803–808 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Garraway, L. A. & Lander, E. S. Lessons from the cancer genome. Cell 153, 17–37 (2013).

    Article  CAS  PubMed  Google Scholar 

  8. Chang, M. T. et al. Identifying recurrent mutations in cancer reveals widespread lineage diversity and mutational specificity. Nat. Biotechnol. 34, 155–163 (2016).

    Article  CAS  PubMed  Google Scholar 

  9. Bosch, G. J., Joosten, A. M., Kessler, J. H., Melief, C. J. & Leeksma, O. C. Recognition of BCR-ABL positive leukemic blasts by human CD4+ T cells elicited by primary in vitro immunization with a BCR-ABL breakpoint peptide. Blood 88, 3522–3527 (1996).

    Article  CAS  PubMed  Google Scholar 

  10. Yang, W. et al. Immunogenic neoantigens derived from gene fusions stimulate T cell responses. Nat. Med. 25, 767–775 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Haas, B. J. et al. Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods. Genome Biol. 20, 213 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Kosugi, S. et al. Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing. Genome Biol. 20, 117 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Zhou, J. X. et al. Identification of KANSARL as the first cancer predisposition fusion gene specific to the population of European ancestry origin. Oncotarget 8, 50594–50607 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Pintarelli, G. et al. Read-through transcripts in normal human lung parenchyma are down-regulated in lung adenocarcinoma. Oncotarget 7, 27889–27898 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Babiceanu, M. et al. Recurrent chimeric fusion RNAs in non-cancer tissues and cells. Nucleic Acids Res. 44, 2859–2872 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Sorn, P., Hohlsträter, C., Löwer, M., Sahin, U. & Weber, D. ArtiFuse—computational validation of fusion gene detection tools without relying on simulated reads. Bioinformatics 36, 373–379 (2019).

    Google Scholar 

  17. Asmann, Y. W. et al. A novel bioinformatics pipeline for identification and characterization of fusion transcripts in breast cancer and normal cell lines. Nucleic Acids Res. 39, e100 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Edgren, H. et al. Identification of fusion genes in breast cancer by paired-end RNA-sequencing. Genome Biol. 12, R6 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kangaspeska, S. et al. Reanalysis of RNA-sequencing data reveals several additional fusion genes with multiple isoforms. PLoS ONE 7, e48745 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Maher, C. A. et al. Transcriptome sequencing to detect gene fusions in cancer. Nature 458, 97–101 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Sakarya, O. et al. RNA-seq mapping and detection of gene fusions with a suffix array algorithm. PLoS Comput. Biol. 8, e1002464 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Nicorici, D. et al. FusionCatcher—a tool for finding somatic fusion genes in paired-end RNA-sequencing data. Preprint at https://www.biorxiv.org/content/10.1101/011650v1 (2014).

  23. Wang, K. et al. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res. 38, e178 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Jia, W. et al. SOAPfuse: an algorithm for identifying fusion transcripts from paired-end RNA-seq data. Genome Biol. 14, R12 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Okonechnikov, K. et al. InFusion: advancing discovery of fusion genes and chimeric transcripts from deep RNA-sequencing data. PLoS ONE 11, e0167417 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Uhrig, S. et al. Accurate and efficient detection of gene fusions from RNA sequencing data. Genome Res. 31, 448–460 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  27. ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).

  28. Leng, Q., Tarbe, M., Long, Q. & Wang, F. Pre-existing heterologous T-cell immunity and neoantigen immunogenicity. Clin. Transl. Immunology 9, e01111 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Heyer, E. E. et al. Diagnosis of fusion genes using targeted RNA sequencing. Nat. Commun. 10, 1388 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Creason, A. et al. A community challenge to evaluate RNA-seq, fusion detection, and isoform quantification methods for cancer discovery. Cell Syst 12, 827–838 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Buzyn, A. et al. Peptides derived from the whole sequence of BCR-ABL bind to several class I molecules allowing specific induction of human cytotoxic T lymphocytes. Eur. J. Immunol. 27, 2066–2072 (1997).

    Article  CAS  PubMed  Google Scholar 

  32. Gambacorti-Passerini, C. et al. Human CD4 lymphocytes specifically recognize a peptide representing the fusion region of the hybrid protein pml/RARa present in acute promyelocytic leukemia cells. Blood 81, 1369–1375 (1993).

    Article  CAS  PubMed  Google Scholar 

  33. Makita, M. et al. Leukemia-associated fusion proteins, dek-can and bcr-abl, represent immunogenic HLA-DR-restricted epitopes recognized by fusion peptide-specific CD4+ T lymphocytes. Leukemia 16, 2400–2407 (2002).

    Article  CAS  PubMed  Google Scholar 

  34. Sato, Y. et al. Detection and induction of CTLs specific for SYT-SSX-derived peptides in HLA-A24+ patients with synovial sarcoma. J. Immunol.169, 1611–1618 (2002).

    Article  CAS  PubMed  Google Scholar 

  35. van den Broeke, L. T., Pendleton, C. D., Mackall, C., Helman, L. J. & Berzofsky, J. A. Identification and epitope enhancement of a PAX-FKHR fusion protein breakpoint epitope in alveolar rhabdomyosarcoma cells created by a tumorigenic chromosomal translocation inducing CTL capable of lysing human tumors. Cancer Res. 66, 1818–1823 (2006).

    Article  PubMed  CAS  Google Scholar 

  36. Sahin, U. et al. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 547, 222–226 (2017).

    Article  CAS  PubMed  Google Scholar 

  37. Richman, L. P., Vonderheide, R. H. & Rech, A. J. Neoantigen dissimilarity to the self-proteome predicts immunogenicity and response to immune checkpoint blockade. Cell Syst. 9, 375–382 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Bjerregaard, A.-M. et al. An analysis of natural T cell responses to predicted tumor neoepitopes. Front. Immunol. 8, 1566 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Strønen, E. et al. Targeting of cancer neoantigens with donor-derived T cell receptor repertoires. Science 352, 1337–1341 (2016).

    Article  PubMed  CAS  Google Scholar 

  40. Balachandran, V. P. et al. Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer. Nature 551, 512–516 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Bessell, C. A. et al. Commensal bacteria stimulate antitumor responses via T cell cross-reactivity. JCI Insight 5, e135597 (2020).

    Article  PubMed Central  Google Scholar 

  42. Nelson, R. W. et al. T cell receptor cross-reactivity between similar foreign and self peptides influences naive cell population size and autoimmunity. Immunity 42, 95–107 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Robinson, D. R. et al. Functionally recurrent rearrangements of the MAST kinase and Notch gene families in breast cancer. Nat. Med. 17, 1646–1651 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  45. Haas, B. J. et al. STAR-Fusion: fast and accurate fusion transcript detection from RNA-seq. Preprint at https://www.biorxiv.org/content/10.1101/120295v1 (2017).

  46. Kent, W. J. BLAT—the BLAST-like alignment tool. Genome Res. 12, 656–664 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Untergasser, A. et al. Primer3—new capabilities and interfaces. Nucleic Acids Res. 40, e115 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Koressaar, T. & Remm, M. Enhancements and modifications of primer design program Primer3. Bioinformatics 23, 1289–1291 (2007).

    Article  CAS  PubMed  Google Scholar 

  49. Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).

    Google Scholar 

  50. Boegel, S. et al. HLA typing from RNA-seq sequence reads. Genome Med. 4, 102 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Jurtz, V. et al. NetMHCpan-4.0: improved peptide–MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data. J. Immunol. 199, 3360–3368 (2017).

    Article  CAS  PubMed  Google Scholar 

  52. Jensen, K. K. et al. Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology 154, 394–406 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Holtkamp, S. et al. Modification of antigen-encoding RNA increases stability, translational efficacy, and T-cell stimulatory capacity of dendritic cells. Blood 108, 4009–4017 (2006).

    Article  CAS  PubMed  Google Scholar 

  54. Dauer, M. et al. Mature dendritic cells derived from human monocytes within 48 hours: a novel strategy for dendritic cell differentiation from blood precursors. J. Immunol. 170, 4069–4076 (2003).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank the multicentre phase I study NCT02035956 and the RB_T002 research program (DRKS-ID: DRKS00011790) patients, from whom analyzed samples were obtained, and we thank the involved study site teams for their support and collaboration. We thank K. Chu and C. Büchner for proofreading and assistance with the manuscript. We thank O. Akilli-Oeztuerk for support with biosampling, R. Siek for support with installation of tools and setup on our servers and C. Ritzel for testing of EasyFuse pipeline and Docker image. Furthermore, we thank S. G. Maurici for technical support with RT–qPCR analysis and A. Henrich, S. Burchard and P. D. Dinh for technical support with the RNA sequencing. This work was supported by a European Research Council Advanced Grant to U.S. (ERC-AdG 789256).

Author information

Author notes

  1. These authors contributed equally: D. Weber, J. Ibn-Salem.

Authors and Affiliations

  1. TRON – Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz, Germany

    David Weber, Jonas Ibn-Salem, Patrick Sorn, Martin Suchan, Christoph Holtsträter, Franziska Lang, Barbara Schrörs, Martin Löwer & Ugur Sahin

  2. BioNTech SE, Mainz, Germany

    Urs Lahrmann, Isabel Vogler, Kathrin Schmoldt & Ugur Sahin

  3. Johannes Gutenberg University Mainz, Mainz, Germany

    Ugur Sahin

Authors
  1. David Weber
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jonas Ibn-Salem
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Patrick Sorn
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Martin Suchan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Christoph Holtsträter
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Urs Lahrmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Isabel Vogler
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kathrin Schmoldt
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Franziska Lang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Barbara Schrörs
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Martin Löwer
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Ugur Sahin
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

U.S. conceptualized the work and strategy. D.W., J.I., M.S. and I.V. planned and analyzed experiments. K.S. and M.S. performed the experiments. D.W., J.I., P.S., C.H., U.L., B.S., F.L. and M.L. performed data analysis. D.W., J.I. and U.S. interpreted data and wrote the manuscript.

Corresponding author

Correspondence to Ugur Sahin.

Ethics declarations

Competing interests

U.S. is a board member and employee of BioNTech SE. U.L., K.S. and I.V. are employees of BioNTech SE. U.S. is chief executive officer and stock owner of BioNTech SE. U.S., K.S. and I.V. have securities in BioNTech SE. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Biotechnology thanks Daniel Wells, Malachi Griffith and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–10 and associated legends

Reporting Summary

Supplementary Table

Supplementary Tables 1–8 and associated legends

Source data

Source Data Fig. 5e

Unprocessed raw image compilation of ELISpot plates. The example shown in 5e is presented in columns 10/11 for CD4.

Source Data Fig. 5e

Unprocessed raw image compilation of ELISpot plates. The example shown in 5e is presented in columns 10/11 for CD8.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Weber, D., Ibn-Salem, J., Sorn, P. et al. Accurate detection of tumor-specific gene fusions reveals strongly immunogenic personal neo-antigens. Nat Biotechnol 40, 1276–1284 (2022). https://doi.org/10.1038/s41587-022-01247-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41587-022-01247-9

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer