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.

  • Research Highlight
  • Published:

A machine learning approach to predict response to immunotherapy in type 1 diabetes

Cellular & Molecular Immunology volume 18, pages 515–517 (2021)Cite this article

Subjects

Type 1 diabetes (T1D) is an autoimmune disease mediated by self-reactive T cells that destroy insulin-producing β cells of the pancreatic islets.1 The disease is a complex multifactorial disorder resulting from a poorly defined interaction between genetic and environmental factors. Approximately 40–50% of the disease risk is linked to genes, with the remaining risk arising from environmental factors. HLA genes account for approximately one-half of T1D risk, and the residual risk may be ascribed to more than 40 single-nucleotide polymorphisms (SNPs) in genes outside the HLA region. Among confirmed non-HLA susceptibility genes, the best-characterized polymorphisms are related to INS, PTPN22, IL2RA, IFIH1, and CTLA4.2 Islet-specific autoantibodies are a hallmark of T1D and currently the best predictive biomarker of disease progression.3 However, the mechanism leading to the loss of humoral tolerance in T1D is not fully understood, with the exception of a few studies showing diminished B cell central tolerance in patients with T1D.4 B cells acquire their antibody-secreting function after interaction with a specific subset of CD4 T cells in specialized structures within the B cell follicles of secondary lymphoid organs, known as germinal centers. The CD4 T-cell subset that provides the help necessary for B cells to differentiate into memory B cells and high-affinity antibody-producing plasma cells is known as follicular helper T (TFH) cells.5

Based on recent understanding of disease pathogenesis, the complex interplay between a multitude of factors (environmental factors, genome, microbiome, and immune alterations) leading to abnormal T and B cell activation and destruction of pancreatic β cells varies between individual cases of T1D. Accordingly, T1D should therefore not be considered a single autoimmune disorder but rather a spectrum of pathologies related to different mechanisms, including lack of central and peripheral tolerance, defective immune regulation, abnormal autoimmune activation and altered tissue homeostasis, ultimately resulting in β cell autoimmunity.1 This concept may have important therapeutic implications, and the failure of clinical trials for T1D may be related to the high degree of heterogeneity in disease pathogenesis. In fact, most of the interventional trials involving immunotherapies in T1D patients, such as the humanized anti-CD3 antibody (teplizumab) and blocking molecules for the costimulatory CD28/CD80 (abatacept) or LFA-3/CD2 (alefacept) pathways, failed to reach their primary endpoint for the tested T1D population while demonstrating good therapeutic response only in a subset of subjects.6,7,8 To date, stratification of T1D patients has been based on age and disease stage, but subgroups of T1D may also be distinguished based on different immunopathogenic mechanisms, i.e., the involvement of a single T/B cell subset and/or immune molecular pathway, leading to β cell autoimmunity. Overall, stratification of T1D patients based on disease stage or underlying pathogenic mechanisms may be instrumental for designing better clinical trials, resulting in more significant treatment outcomes.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

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

Fig. 1

References

  1. Edner, N. M. et al. Follicular helper T cell profiles predict response to costimulation blockade in type 1 diabetes. Nat. Immunol. 21, 1244–1255 (2020).

    Article  CAS  Google Scholar 

  2. Todd, J. A. Etiology of type 1 diabetes. Immunity 32, 457–467 (2010).

    Article  CAS  Google Scholar 

  3. Fousteri, G., Ippolito, E., Ahmed, R. & Hamad, A. R. A. Beta-cell specific autoantibodies: are they just an indicator of type 1 diabetes? Curr. Diabet. Rev. 13, 322–329 (2017).

    Article  CAS  Google Scholar 

  4. Chamberlain, N. et al. Rituximab does not reset defective early B cell tolerance checkpoints. J. Clin. Invest. 126, 282–287 (2016).

    Article  Google Scholar 

  5. Crotty, S. T follicular helper cell biology: a decade of discovery and diseases. Immunity 50, 1132–1148 (2019).

    Article  CAS  Google Scholar 

  6. Sherry, N. et al. Teplizumab for treatment of type 1 diabetes (Protege study): 1-year results from a randomised, placebo-controlled trial. Lancet 378, 487–497 (2011).

    Article  CAS  Google Scholar 

  7. Keymeulen, B. et al. Four-year metabolic outcome of a randomised controlled CD3-antibody trial in recent-onset type 1 diabetic patients depends on their age and baseline residual beta cell mass. Diabetologia 53, 614–623 (2010).

    Article  CAS  Google Scholar 

  8. Orban, T. et al. Co-stimulation modulation with abatacept in patients with recent-onset type 1 diabetes: a randomised, double-blind, placebo-controlled trial. Lancet 378, 412–419 (2011).

    Article  CAS  Google Scholar 

  9. Zhao, J. et al. Immune and genomic correlates of response to anti-PD-1 immunotherapy in glioblastoma. Nat. Med. 25, 462–469 (2019).

    Article  Google Scholar 

  10. Rachid, O., Osman, A., Abdi, R. & Haik, Y. CTLA4-Ig (abatacept): a promising investigational drug for use in type 1 diabetes. Expert Opin. Investig. Drugs 29, 221–236 (2020).

    Article  CAS  Google Scholar 

  11. Ferreira, R. C. et al. IL-21 production by CD4+ effector T cells and frequency of circulating follicular helper T cells are increased in type 1 diabetes patients. Diabetologia 58, 781–790 (2015).

    Article  CAS  Google Scholar 

  12. Vecchione, A. et al. Reduced PD-1 expression on circulating follicular and conventional FOXP3(+) Treg cells in children with new onset type 1 diabetes and autoantibody-positive at-risk children. Clin. Immunol. 211, 108319 (2020).

    Article  CAS  Google Scholar 

  13. McAdam, A. J. et al. Mouse inducible costimulatory molecule (ICOS) expression is enhanced by CD28 costimulation and regulates differentiation of CD4+ T cells. J. Immunol. 165, 5035–5040 (2000).

    Article  CAS  Google Scholar 

  14. Xu, H. et al. Follicular T-helper cell recruitment governed by bystander B cells and ICOS-driven motility. Nature 496, 523–527 (2013).

    Article  CAS  Google Scholar 

  15. Linsley, P. S., Greenbaum, C. J., Speake, C., Long, S. A. & Dufort, M. J. B lymphocyte alterations accompany abatacept resistance in new-onset type 1 diabetes. JCI Insight 4, e126136 (2019).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Regulation of Adaptive immunity Unit, Transplantation and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Georgia Fousteri

  2. Experimental Diabetes Unit, Division of Immunology, Transplantation and Infectious Diseases, Diabetes Research Institute (DRI), IRCCS San Raffaele Scientific Institute, Milan, Italy

    Ely Montee Rodrigues, Gian Maria Giamporcaro & Marika Falcone

Authors
  1. Georgia Fousteri
    View author publications

    Search author on:PubMed Google Scholar

  2. Ely Montee Rodrigues
    View author publications

    Search author on:PubMed Google Scholar

  3. Gian Maria Giamporcaro
    View author publications

    Search author on:PubMed Google Scholar

  4. Marika Falcone
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Marika Falcone.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fousteri, G., Rodrigues, E.M., Giamporcaro, G.M. et al. A machine learning approach to predict response to immunotherapy in type 1 diabetes. Cell Mol Immunol 18, 515–517 (2021). https://doi.org/10.1038/s41423-020-00594-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41423-020-00594-4

Search

Advanced search

Quick links