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Record W4389219575 · doi:10.1182/blood-2023-182949

<i>UBA1</i> Mutations Identify a Rare but Distinct Subtype of Myelodysplastic Syndromes

2023· article· en· W4389219575 on OpenAlexaff
Maria Sirenko, Elsa Bernard, Maria Creignou, Dylan Domenico, Andrea Farina, Juan Arango Ossa, Olivier Kosmider, Robert P. Hasserjian, Martin Jädersten, Ulrich Germing, Guillermo Sanz, Arjan A. van de Loosdrecht, Carmelo Gurnari, Matilde Y. Follo, Felicitas Thol, Lurdes Zamora, Andrea Pellagatti, Harold K. Elias, Detlef Haase, Christina Ganster, Lionel Adès, Magnus Tobiasson, Laura Palomo, Matteo Giovanni Della Porta, Kety Huberman, Pierre Fenaux, Monika Beličková, Michael R. Savona, Virginia M. Klimek, Fábio Pires de Souza Santos, Jacqueline Boultwood, Ιoannis Kotsianidis, Valeria Santini, Françesc Solé, Uwe Platzbecker, Michael Heuser, Peter Valent, Carlo Finelli, Maria Teresa Voso, Lee‐Yung Shih, Seishi Ogawa, Michaëla Fontenay, Joop H. Jansen, José Cervera, Benjamin L. Ebert, Rafael Bejar, Peter L. Greenberg, Norbert Gattermann, Luca Malcovati, Mario Cazzola, David B. Beck, Eva Hellström Lindberg, Elli Papaemmanuil

Bibliographic record

VenueBlood · 2023
Typearticle
Languageen
FieldMedicine
TopicOtitis Media and Relapsing Polychondritis
Canadian institutionsHotel Dieu Hospital
Fundersnot available
KeywordsMedicineCohortInternal medicineMyelodysplastic syndromesOncologyPopulationGeneticsBiologyBone marrow

Abstract

fetched live from OpenAlex

Background Mutations in UBA1 are associated with VEXAS (Vacuoles, E1 enzyme, X-linked, Autoinflammatory, Somatic) syndrome, an adult-onset inflammatory disorder (Beck DB et al. NEJM 2020). Approximately 40% of VEXAS patients are also diagnosed with myelodysplastic syndromes (MDS). We previously profiled UBA1 p.M41 mutations in a select subset of the International Working Group for MDS (IWG-PM) cohort (Bernard E et al. NEJM Evid 2022) including 375 male patients lacking disease-defining mutations or established disease classification by WHO guidelines, and identified 28 patients (7%) with UBA1 p.M41T/V/L mutations (Cohort A, Sirenko M et al. Blood 2022). However, the prevalence and characteristics of UBA1 mutations in MDS have not been systematically evaluated in a representative MDS population. Methods We used targeted next-generation sequencing of the full UBA1 locus to profile 2027 diagnostic and treatment-naive MDS samples (Cohort B, 61% male) ascertained through the IWG-PM (Figure 1). Clinical associations of UBA1 mutations were evaluated and clinical history was reviewed for inflammatory conditions when available. Results In Cohort B, 35 UBA1 mutations in 34 (1.7%) patients were identified, of which 20 (0.97%) had likely pathogenic variants and 14 had variants of unknown significance (VUS). Likely pathogenic variants included 13 p.M41T/V/L mutation (variant allele fraction [VAF] range 0.013-0.94), and 7 non-p.M41 mutations where 6/7 had VAF&amp;gt;0.35: 2 with p.S56F (VAF 0.87 and 0.93), 2 p.A478S (VAF 0.72 and 0.80), 2 p.S621C (VAF 0.80 and 0.82) and 1 p.Y55H (VAF 0.025). Three VUS were in female patients. 204 cases were profiled by both assays and of those, all 8 ddPCR positive cases were identified by NGS with concordant VAF (R2=0.999 p&amp;lt;0.0001). Integration of Cohort A and B (n=2,198) yielded 40 patients (all male, median age 72; range 44-89 years) with pathogenic UBA1 variants. The WHO 2016 classification (available for n=38 of 40) was MDS-SLD/MLD (25), MDS-EB1 (4), MDS-U (3), CMML (2), MDS-RS-MLD (1), aCML (1), MDS/MPN-RS-T (1), MDS/MPN-U (1). Patients had a median of 1 myeloid gene mutation in addition to UBA1 (range: 0-4) with most frequent co-occurring events in TET2 (n=12), DNMT3A (n = 10), ASXL1 (n=3), SF3B1 (n=3) and loss of the Y chromosome (n=5). In 8 patients with pathogenic UBA1 &amp;gt; 2% VAF and co-occurring DNMT3A mutations, DNMT3A and UBA1 VAF were correlated (slope = 0.88, r = 0.87, p = 0.0005), suggesting that co-mutation may lead to clonal expansion. Conversely, TET2 co-mutations were either subclonal (n=4) or clonal (n=8) to UBA1. Among patients with pathogenic UBA1 variants, the majority had IPSS-M Very-Low/Low risk (73% 27/37). In contrast, patients with VUS were more likely to have Moderate or High risk (64% 7/11). Partial clinical history was available for 33 cases with pathogenic UBA1 variants. 50% (9/18) had inflammatory-rheumatic disease (IRD) including psoriatic arthritis, relapsing polychondritis, Sweet syndrome, bronchiolitis obliterans with organizing pneumonia, ear chondritis, and rosacea. 3 patients had more than one IRD. 8 were treated for IRD with steroids (n=6) or methotrexate (n=2). 4 patients had MGUS. Other manifestations included vacuoles (4/8), thromboembolic disease (5/16), non-infection fever (5/14), weight loss (4/14), ocular symptoms (5/15), arthralgia (3/16), chondritis (5/15), and other inflammation (6/14). IRD diagnosis usually preceded MDS (average time 0.28 years; range 16.5 years prior to 1.4 years after). Three patients transformed to acute myeloid leukemia (AML). One patient had a low VAF UBA1 p.M41V (0.0002 by ddPCR) and TET2, SF3B1, FLT3, and ASXL2 co-mutations at baseline. The second had UBA1 p.S56F (VAF 0.868) and a Chr 7q deletion. The third had VUS UBA1 p.R869L (VAF 0.222) and IRF1, NFE2 and RRAS co-mutations. Among the other UBA1-mutant patients that died or were censored after 1 year (n=37), none transformed to AML. Conclusion Within the large, representative, diagnostic and well-characterized IWG-PM MDS cohort, we find likely pathogenic UBA1 mutations in 1% of patients, with enrichment in male patients with few or no mutations in myeloid driver genes (7%). UBA1-mutant patients were predominantly IPSS-M low risk with a median of 1 additional mutation, usually in DNMT3A or TET2. UBA1 mutations may define a distinct subset of MDS and its recognition in future guidelines will improve the management of patients with MDS/VEXAS overlap.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.278
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2023
Admission routes1
Has abstractyes

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