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Record W4415669274 · doi:10.1016/j.htct.2025.104548

GERMLINE PREDISPOSITION TO MYELOID MALIGNANCIES: A COMPREHENSIVE ANALYSIS OF CEBPA, DDX41 AND RUNX1

2025· article· en· W4415669274 on OpenAlexaff
D Kusma-Wosniaki, B Nichele-Kusma, Harjot Kaur, E Batista-Mendes, M Nóbrega-Aoki, Dalila Lucíola Zanette, JM Capo-Chichi

Bibliographic record

VenueHematology Transfusion and Cell Therapy · 2025
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsCEBPARUNX1GermlineMyeloidMyeloid leukemiaGermline mutationETV6MutationGene

Abstract

fetched live from OpenAlex

Myeloid malignancies such as acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS) are typically sporadic, but germline predisposition involving genes like CEBPA, DDX41, and RUNX1 is increasingly recognized. However, the interplay between germline and somatic events in these genes remains incompletely understood. To investigate the prevalence, co-variant patterns, and clonality of pathogenic and likely pathogenic variants in CEBPA, DDX41, and RUNX1 in patients with myeloid malignancies. We analyzed 2,437 patients diagnosed with AML, MDS, MDS/AML, or myeloproliferative neoplasms (MPN). DNA from peripheral blood or bone marrow was sequenced using targeted NGS, covering 63 genes. Variants were processed using validated bioinformatic pipelines and classified per CAP/AMP/ASCO guidelines. Among the cohort, AMP/ASCO Tier-1 and Tier-2 variants were most frequently detected in RUNX1 (9.97%, n = 243), followed by CEBPA (3.94%, n = 96) and DDX41 (2.63%, n = 64). RUNX1 mutations were commonly associated with co-occurring variants in ASXL1 and SRSF2, particularly in cases with double RUNX1 hits (37.5%). VAF distribution in double-mutated RUNX1 (RUNX1 highest hit: minimum: 10.9, maximum: 47.2, median: 38.5) with variants in ASXL1 (ASXL1 highest hit: min.: 4.3, max.: 49.5, median: 32.6 and/or SRSF2 (min.: 37.5, max.: 49.5, median: 46.1) were frequently consistent suggestive of a clonal process. CEBPA alterations with bi-allelic CEBPA variants (bZIP domain in-frame variant + an N-terminal loss-of-function variant). Co-occurring variants in GATA2 and WT1 were enriched in bi-allelic cases (60%) and were often seen at similar VAF (CEBPA bi-allelic highest hit: min.: 6, max.: 59.2, median: 41.9, GATA2 highest hit: min.: 5, max.: 50, median: 42,6; WT1 highest hit: min.: 5, max.: 94.1, median: 46.8), supporting a shared clonal origin and suggesting a distinct molecular signature potentially driving leukemogenesis. DDX41 variants showed a bimodal VAF distribution in double-mutated cases, with clusters likely suggestive of acquired (min.: 3, max.: 47, median: 6) or inherited (min.: 7, max.: 93, median: 49) variants. Unlike CEBPA and RUNX1, DDX41 variants were not associated with consistent co-variant patterns, suggestive of a different pathobiology. Of note biallelic disruptive DDX41 variants have been associated with hematologic malignancies with unique AML/MDS like features. Our findings reveal distinct mutational patterns in CEBPA, DDX41, and RUNX1, genes linked to hereditary hematologic cancers. Bi-allelic CEBPA mutations, formed a molecularly coherent subgroup frequently co-mutated with GATA2 and WT1 at similar VAFs, suggesting a shared clonal origin. Similarly, RUNX1 variants often co-occurred with ASXL1 and SRSF2 mutations, also showing consistent VAFs, though RUNX1 mutations were distributed across the gene. In contrast, DDX41 cases lacked co-variant patterns but exhibited a bimodal VAF distribution, with higher VAFs (>35%) suggestive of germline variants and lower VAFs (<20%) likely representing secondary somatic events. This study highlights the distinct biological and clinical profiles of germline and somatic variants in CEBPA, DDX41, and RUNX1, underscoring the need for further research into germline predisposition and its role in the pathogenesis of myeloid neoplasms.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.014
GPT teacher head0.296
Teacher spread0.282 · 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 designBench or experimental
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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Citations0
Published2025
Admission routes1
Has abstractyes

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