Real life evaluation of AlphaMissense predictions in hematological malignancies.
Journal
Leukemia
ISSN: 1476-5551
Titre abrégé: Leukemia
Pays: England
ID NLM: 8704895
Informations de publication
Date de publication:
22 Dec 2023
22 Dec 2023
Historique:
received:
25
10
2023
accepted:
07
12
2023
revised:
05
12
2023
medline:
23
12
2023
pubmed:
23
12
2023
entrez:
22
12
2023
Statut:
aheadofprint
Résumé
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.
Identifiants
pubmed: 38135759
doi: 10.1038/s41375-023-02116-3
pii: 10.1038/s41375-023-02116-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Nature Limited.
Références
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.
doi: 10.1182/blood.2022015850
pubmed: 35767897
pmcid: 9479031
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.
doi: 10.1182/blood.2022015851
pubmed: 35653592
pmcid: 9479027
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.
doi: 10.1038/s41375-022-01613-1
pubmed: 35732831
pmcid: 9252913
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.
doi: 10.1038/s41375-022-01620-2
pubmed: 35732829
pmcid: 9214472
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.
doi: 10.3390/ijms19041251
pubmed: 29677173
pmcid: 5979354
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.
doi: 10.1126/science.adg7492
pubmed: 37733863
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.
doi: 10.1182/blood-2018-03-840348
pubmed: 29866812
Alcazer V. StatAid: An R package with a graphical user interface for data analysis. JOSS. 2020;5:2630.
doi: 10.21105/joss.02630
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.
doi: 10.1016/j.ccell.2018.01.021
pubmed: 29533785
pmcid: 5926201
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.
doi: 10.1158/2159-8290.CD-12-0095
pubmed: 22588877
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.
doi: 10.1038/s41388-019-0979-z
pubmed: 31462704
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.
doi: 10.1182/blood.2022016867
pubmed: 35797463
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.
doi: 10.1056/NEJMoa2024534
pubmed: 33704937
pmcid: 8130455