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.

  • Letter
  • Published:

GENOMICS AND GENE THERAPY

Real life evaluation of AlphaMissense predictions in hematological malignancies

Leukemia volume 38, pages 420–423 (2024)Cite this article

Subjects

Abstract

High-throughput sequencing plays a pivotal role in hematological malignancy diagnostics, but interpreting missense mutations remains challenging. In this study, we used the newly available AlphaMissense database to assess the efficacy of machine learning to predict missense mutation effects and its impact to improve our ability to interpret them. Based on the analysis of 2073 variants from 686 patients analyzed for clinical purpose, we confirmed the very high accuracy of AlphaMissense predictions in a large real-life data set of missense mutations (AUC of ROC curve 0.95), and provided a comprehensive analysis of the discrepancies between AlphaMissense predictions and state of the art clinical interpretation.

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: Performances of AlphaMissense predictions in clinically anotated variants.
Fig. 2: Pie chart representation of the missense variants discordant between AphaMissense predictions and clinical interpretation.

Similar content being viewed by others

References

  1. Arber DA, Orazi A, Hasserjian RP, Borowitz MJ, Calvo KR, Kvasnicka H-M, et al. International Consensus Classification of Myeloid Neoplasms and Acute Leukemias: integrating morphologic, clinical, and genomic data. Blood. 2022;140:1200–28.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Campo E, Jaffe ES, Cook JR, Quintanilla-Martinez L, Swerdlow SH, Anderson KC, et al. The International Consensus Classification of Mature Lymphoid Neoplasms: a report from the Clinical Advisory Committee. Blood. 2022;140:1229–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Khoury JD, Solary E, Abla O, Akkari Y, Alaggio R, Apperley JF, et al. The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Myeloid and Histiocytic/Dendritic Neoplasms. Leukemia. 2022;36:1703–19.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Alaggio R, Amador C, Anagnostopoulos I, Attygalle AD, Araujo IBDO, Berti E, et al. The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Lymphoid Neoplasms. Leukemia. 2022;36:1720–48.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Guillermin Y, Lopez J, Chabane K, Hayette S, Bardel C, Salles G, et al. What Does This Mutation Mean? The Tools and Pitfalls of Variant Interpretation in Lymphoid Malignancies. Int J Mol Sci. 2018;19:1251.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Cheng J, Novati G, Pan J, Bycroft C, Žemgulytė A, Applebaum T, et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science. 2023;381:eadg7492.

    Article  CAS  PubMed  Google Scholar 

  7. Huet S, Paubelle E, Lours C, Grange B, Courtois L, Chabane K, et al. Validation of the prognostic value of the knowledge bank approach to determine AML prognosis in real life. Blood. 2018;132:865–7.

    Article  CAS  PubMed  Google Scholar 

  8. Alcazer V. StatAid: An R package with a graphical user interface for data analysis. JOSS. 2020;5:2630.

    Article  ADS  Google Scholar 

  9. Ng PK-S, Li J, Jeong KJ, Shao S, Chen H, Tsang YH, et al. Systematic functional annotation of somatic mutations in cancer. Cancer Cell. 2018;33:450–462.e10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2:401–4.

    Article  PubMed  Google Scholar 

  11. Liu X, Huang Q, Chen L, Zhang H, Schonbrunn E, Chen J. Tumor-derived CK1α mutations enhance MDMX inhibition of p53. Oncogene. 2020;39:176–86.

    Article  CAS  PubMed  Google Scholar 

  12. Döhner H, Wei AH, Appelbaum FR, Craddock C, DiNardo CD, Dombret H, et al. Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN. Blood. 2022;140:1345–77.

    Article  PubMed  Google Scholar 

  13. Duncavage EJ, Schroeder MC, O’Laughlin M, Wilson R, MacMillan S, Bohannon A, et al. Genome sequencing as an alternative to cytogenetic analysis in myeloid cancers. N. Engl J Med. 2021;384:924–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This work was not funded by a specific grant.

Author information

Authors and Affiliations

  1. Hospices Civils de Lyon, Hôpital Lyon Sud, Service d’hématologie biologique, Pierre Bénite, France

    Kaddour Chabane, Carole Charlot, Sarah Huet, Sandrine Hayette & Pierre Sujobert

  2. Hospices Civils de Lyon, Plateforme de séquençage NGS, cellule bioinformatique, Bron, France

    Thomas Simonet

  3. Centre international de recherche en infectiologie, INSERM U1111 - CNRS UMR5308, Université Lyon 1, Equipe Lymphoma Immunobiology, Pierre Bénite, France

    David Armisen, Sarah Huet, Vincent Alcazer & Pierre Sujobert

  4. INSERM U1245, Centre Henri Becquerel, Rouen, France

    Pierre-Julien Viailly

  5. Institut Carnot CALYM, PariSanté Campus, Paris, France

    Guillaume Codet de Boisse

  6. Hospices Civils de Lyon, Hôpital Lyon Sud, Service d’hématologie clinique, Pierre Bénite, France

    Vincent Alcazer

Authors
  1. Kaddour Chabane
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carole Charlot
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dan Gugenheim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Thomas Simonet
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. David Armisen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Pierre-Julien Viailly
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Guillaume Codet de Boisse
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Sarah Huet
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Sandrine Hayette
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Vincent Alcazer
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Pierre Sujobert
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

KC, CC, TS, SHu and SHa performed HTS analysis, DG, VA and DA analysed data, PJV and GCDB designed the web application to make data accessible, PS designed the research, analysed data and wrote the paper.

Corresponding author

Correspondence to Pierre Sujobert.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chabane, K., Charlot, C., Gugenheim, D. et al. Real life evaluation of AlphaMissense predictions in hematological malignancies. Leukemia 38, 420–423 (2024). https://doi.org/10.1038/s41375-023-02116-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41375-023-02116-3

This article is cited by

Search

Advanced search

Quick links