MétaCan
Menu
Back to cohort
Record W3186993723 · doi:10.1182/blood.2021011787

BTK inhibition sensitizes acute lymphoblastic leukemia to asparaginase by suppressing the amino acid response pathway

2021· article· en· W3186993723 on OpenAlexaff
Miriam Butler, Dorette S. van Ingen Schenau, Jiangyan Yu, Silvia Jenni, Maria Pamela Dobay, Rico Hagelaar, Britt M. T. Vervoort, Trisha Tee, Fieke W. Hoff, Jules P.P. Meijerink, Steven M. Kornblau, Beat Bornhäuser, Jean‐Pierre Bourquin, Roland P. Kuiper, Laurens T. van der Meer, Frank N. van Leeuwen

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsPediatric Oncology Group
FundersRadboud Universitair Medisch CentrumSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsIbrutinibBruton's tyrosine kinaseAsparaginaseCancer researchPharmacologyMedicineTyrosine kinaseLeukemiaLymphoblastic LeukemiaImmunologyInternal medicineChronic lymphocytic leukemiaReceptor

Abstract

fetched live from OpenAlex

Asparaginase (ASNase) therapy has been a mainstay of acute lymphoblastic leukemia (ALL) protocols for decades and shows promise in the treatment of a variety of other cancers. To improve the efficacy of ASNase treatment, we used a CRISPR/Cas9-based screen to identify actionable signaling intermediates that improve the response to ASNase. Both genetic inactivation of Bruton's tyrosine kinase (BTK) and pharmacological inhibition by the BTK inhibitor ibrutinib strongly synergize with ASNase by inhibiting the amino acid response pathway, a mechanism involving c-Myc-mediated suppression of GCN2 activity. This synthetic lethal interaction was observed in 90% of patient-derived xenografts, regardless of the genomic subtype. Moreover, ibrutinib substantially improved ASNase treatment response in a murine PDX model. Hence, ibrutinib may be used to enhance the clinical efficacy of ASNase in ALL. This trial was registered at www.clinicaltrials.gov as # NCT02884453.

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 score0.020
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.012
GPT teacher head0.266
Teacher spread0.254 · 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

Citations24
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueBloodSame topicChronic Lymphocytic Leukemia ResearchFrench-language works237,207