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Random Mutagenesis Reveals Residues of JAK2 Critical in Evading Inhibition by Tyrosine Kinase Inhibitors

2008· article· en· W2569817649 on OpenAlexaff
Michael R. Marit, Manprit Chohan, Natasha Matthew, Kai Huang, Dwayne L. Barber

Bibliographic record

VenueBlood · 2008
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsOntario Institute for Cancer Research
Fundersnot available
KeywordsBiologyJanus kinase 2Myeloid leukemiaMutationTyrosine kinaseMutagenesisPolycythemia veraProtein kinase domainPoint mutationCancer researchMolecular biologyMutantGeneticsKinaseSignal transductionGeneImmunology

Abstract

fetched live from OpenAlex

Abstract Mutation and activation of JAK2 is a common event in Myeloproliferative Disease as JAK2 V617F and related deletion mutations are observed in Polycythemia Vera, Essential Thrombocythemia and Primary Myelofibrosis. In addition, the TEL-JAK2 chromosomal translocation is a rare event in Acute Myeloid Leukemia and Chronic Myelomonocytic Leukemia. We reasoned that activated alleles of JAK2 would develop resistance to JAK inhibitors in a clinical setting, similar to the development of Imatinib resistance in BCR-ABL-mediated Chronic Myeloid Leukemia. The objective of this study was to develop a random mutagenesis screen to isolate mutations of JAK2 resistant to tyrosine kinase inhibitors. We selected JAK Inhibitor-1 for this study, since the crystal structure of this inhibitor bound to the JAK2 JH1 kinase domain has been reported and would allow for mapping of confirmed mutations. TEL-JAK2(5–12) and JAK2 V617F were subcloned into retroviral expression vectors and random libraries of mutations were generated by transformation into XL-1 Red strain of E. coli, a strain defective in pathways of DNA repair. High titer retroviral supernatants were generated and used to transduce Ba/F3 (for TEL-JAK2) or Ba/F3-EPO-R (for JAK2 V617F). Control experiments were performed with “wild type” versions of each JAK2 allele. Inhibitor-resistant clones were identified and DNA sequencing was performed to identify JAK2 JH1 kinase domain mutations that confer resistance to inhibitor. We have restricted the analysis of JAK inhibitor-resistant mutants to those that map within the kinase domain of JAK2 for purposes of this study. We have confirmed that E864K, V881A, N909K, G935R, R975G confer resistance to JAK inhibitor-1 in growth assays. In addition, M929I (analogous to BCR-ABL T315I) mediates resistance to JAK inhibitor-1, relative to wild-type activated JAK2 alleles. All mutations result in increased phosphorylation of STAT5, Akt and Erk in the presence of inhibitor. We are currently testing the catalytic activity of each mutant to determine whether JAK2 kinase domain mutations have similar enzymatic activity or whether mutation also affects catalysis. We have mapped each mutation within the JAK2 JH1 crystal structure and models for how each mutant affects inhibitor binding will be presented. Importantly, many of these residues are highly conserved in other JAK tyrosine kinases and within BCR-ABL. We are extending these observations by testing clinically relevant inhibitors in our screen. At the conclusion of this study we will be able to identify common residues critical for resistance by JAK2 inhibitors and unique residues that are inhibitor-specific. Random mutagenesis screening offers an excellent strategy to identify JAK2 residues that may be relevant in the clinic and also serve in enhancing our knowledge regarding JAK kinase activation and regulation.

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 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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.275
Teacher spread0.257 · 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 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

Citations1
Published2008
Admission routes1
Has abstractyes

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