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SF3B1 Mutations Are Associated with Favorable Cytogenetics in Patients with Myelodysplastic Syndromes with Ring Sideroblasts

2012· article· en· W2553030279 on OpenAlexaffabout
Vincent‐Philippe Lavallée, David Faucher, Bruno Lamontagne, Josée Hébert, Lambert Busque

Bibliographic record

VenueBlood · 2012
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsMyelodysplastic syndromesInternational Prognostic Scoring SystemGene mutationMedicineCytogeneticsDysplasiaInternal medicineExonMutationOncologyGeneticsPathologyBiologyBone marrowChromosomeGene

Abstract

fetched live from OpenAlex

Abstract Abstract 3823 Background: Somatic mutations of splicing factor 3B, subunit 1 (SF3B1) are prevalent in myelodysplastic syndromes (MDS) with ring sideroblasts (RS). Little is known about the clinical phenotype associated with these mutations. Initial studies have reported an excellent event-free survival (EFS) in patients with SF3B1 mutations but subsequent reports did not confirm that observation. Therefore, its clinical significance remains uncertain. Our objective was to determine if the SF3B1 mutational status predicts distinctive morphological, cytogenetic or clinical characteristics in patients with MDS with RS. Methods and patients: Twenty-seven patients diagnosed with MDS and ≥ 15% RS in bone marrow aspiration between 2000 and 2011 at our institution and other hospitals affiliated to the Quebec Leukemia Cell Bank (BCLQ) and with available material for genetic analyses were included in this study. Morphology of samples was revised. After DNA extraction, polymerase chain reaction was performed to amplify SF3B1 mutational hotspots in exons 13 to 15. Amplicons were sequenced by the Sanger method. Clinical and laboratory data were collected retrospectively from hospital medical records and BCLQ database. Results: Point mutations of SF3B1 were found in 48% (13/27) with the following distribution: refractory anemia with RS (RARS): 7/10, refractory cytopenia with multilineage dysplasia (RCMD): 5/10 and refractory anemia with excess blasts (RAEB): 1/7. All mutations were heterozygous nonsense mutations and K700E exon 15 mutations accounted for 85% (11/13) of mutations. The remaining mutations were located in exon 14 at position 666. In the entire cohort, SF3B1 mutations were associated with an older median age at diagnosis (69 vs 60 years, p= 0.007). They were associated with favorable cytogenetics whereas unmutated SF3B1 was associated with unfavorable cytogenetics (8 cases vs 0 with favorable cytogenetics and 0 cases vs 11 with unfavorable cytogenetics for mutated and wild type SF3B1 respectively, p < 0.0001). Median overall survival (OS) was similar in the 2 groups (not reached vs 27 months, p=0.7) but median EFS was higher in patients with mutated SF3B1 (not reached vs 5 months, p=0.02). To evaluate the influence of higher bone marrow blasts on outcomes, analyses were restricted to the RARS and RCMD-RS categories. In that subgroup, SF3B1 mutations were correlated with higher median platelet counts at diagnosis (201 vs 70 × 109/L, p=0,04). In addition, SF3B1 mutations remained associated with favorable cytogenetics, and unmutated SF3B1 with unfavorable cytogenetics (p=0.004). Moreover, SF3B1 mutations were more frequently detected within favorable or intermediate-1 categories of the International Prognostic Scoring System (IPSS) compared to wild type SF3B1 (p=0.04). However, in patients with < 5% bone marrow blasts, EFS was similar between mutated and unmutated SF3B1 (not reached vs 46 months, p =0.4). Conclusion: In this series, SF3B1 mutations are frequent in MDS with RS and they are strongly associated with favorable cytogenetics. They are mostly detected among low-risk groups of the World Health Organisation classification (RARS and RCMD-RS), but they are not independently associated with an increased overall or event-free survival. Disclosures: No relevant conflicts of interest to declare.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.240
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), 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
Published2012
Admission routes2
Has abstractyes

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