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Record W3118040066 · doi:10.3324/haematol.2020.270595

Molecular and phenotypic diversity of <I>CBL</I>-mutated juvenile myelomonocytic leukemia

2020· article· en· W3118040066 on OpenAlexafffund
Anna Hecht, Julia A. Meyer, Astrid Behnert, Eric B. Wong, Farid Chehab, Adam B. Olshen, Aaron Hechmer, Catherine Aftandilian, Rukhmi Bhat, Sung Won Choi, Satheesh Chonat, Jason E. Farrar, Mark Fluchel, Haydar Frangoul, Jennifer Han, E. Anders Kolb, Dennis John Kuo, Margaret L. MacMillan, Luke Maese, Kelly W. Maloney, Aru Narendran, Benjamin Oshrine, Kirk R. Schultz, Maria Luisa Sulis, David Van Mater, Sarah K. Tasian, Wolf-Karsten Hofmann, Mignon L. Loh, Elliot Stieglitz

Bibliographic record

VenueHaematologica · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsBC Children's HospitalAlberta Children's Hospital
FundersNational Cancer InstituteNational Heart, Lung, and Blood InstituteYork UniversityEuropean Hematology Association
KeywordsJuvenile myelomonocytic leukemiaPhenotypeCancer researchJuvenileBiologyGeneticsMolecular biologyGeneHaematopoiesis

Abstract

fetched live from OpenAlex

Mutations in the gene CBL were first identified in adults with various myeloid malignancies. Some patients with juvenile myelomonocytic leukemia (JMML) were also noted to harbor mutations in CBL, but were found to have generally less aggressive disease courses compared to other forms of Ras pathway-mutant JMML. Importantly, and in contrast to most reports in adults, the majority of CBL mutations in JMML patients are germline with acquired uniparental disomy occurring in affected marrow cells. Here, we systematically studied a large cohort of 33 JMML patients with CBL mutations and found this disease to be highly diverse in presentation and overall outcome. Moreover, we discovered somatically-acquired CBL mutations in 15% of pediatric patients who presented with more aggressive disease. Neither clinical features nor methylation profiling were able to distinguish somatic CBL patients from germline CBL patients, highlighting the need for germline testing. Overall, we demonstrate that disease courses are quite heterogeneous even among germline CBL patients. Prospective clinical trials are warranted to find ideal treatment strategies for this diverse cohort of patients.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.200
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.029
GPT teacher head0.262
Teacher spread0.233 · 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.

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".

Quick stats

Citations47
Published2020
Admission routes2
Has abstractyes

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