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Record W2215815605 · doi:10.1084/jem.20150524

Integrative genetic analysis of mouse and human AML identifies cooperating disease alleles

2015· article· en· W2215815605 on OpenAlexaff
Megan Hatlen, Kanika Arora, Vladimir Vacic, Ewa Grabowska, Willey Liao, Bridget Riley‐Gillis, Dayna M. Oschwald, Lan Wang, Jacob E. Joergens, Alan H. Shih, Franck Rapaport, Shengqing Gu, Francesca Voza, Takashi Asai, Benjamin G. Neel, Michael G. Kharas, Mithat Gönen, Ross L. Levine, Stephen D. Nimer

Bibliographic record

VenueThe Journal of Experimental Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
FundersNational Cancer Institute
KeywordsPTPN11Myeloid leukemiaBiologyAlleleChromosomal translocationGeneticsLeukemiaCancer researchMutationGeneKRAS

Abstract

fetched live from OpenAlex

t(8;21) is one of the most frequent chromosomal abnormalities observed in acute myeloid leukemia (AML). However, expression of AML1-ETO is not sufficient to induce transformation in vivo. Consistent with this observation, patients with this translocation harbor additional genetic abnormalities, suggesting a requirement for cooperating mutations. To better define the genetic landscape in AML and distinguish driver from passenger mutations, we compared the mutational profiles of AML1-ETO-driven mouse models of leukemia with the mutational profiles of human AML patients. We identified TET2 and PTPN11 mutations in both mouse and human AML and then demonstrated the ability of Tet2 loss and PTPN11 D61Y to initiate leukemogenesis in concert with expression of AML1-ETO in vivo. This integrative genetic profiling approach allowed us to accurately predict cooperating events in t(8;21)(+) AML in a robust and unbiased manner, while also revealing functional convergence in mouse and human AML.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.321
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.045
GPT teacher head0.376
Teacher spread0.331 · 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".

Quick stats

Citations30
Published2015
Admission routes1
Has abstractyes

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