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Record W4206562439

Precision Targeting of Mutant PI3Kα in Cancer by Selective Degradation

2022· letter· en· W4206562439 on OpenAlexafffund
Bart Vanhaesebroeck, John E. Burke, Ralitsa R. Madsen

Bibliographic record

VenueUCL Discovery (University College London) · 2022
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsUniversity of VictoriaUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCancer Research UKMichael Smith Health Research BCCIHR Skin Research Training CentreCancer Research Society
KeywordsMutantPI3K/AKT/mTOR pathwayReceptor tyrosine kinaseProtein subunitP110αKinaseCancer researchCancerTyrosine kinaseBreast cancerBiologyChemistryCell biologyBiochemistrySignal transductionGeneticsGene
DOInot available

Abstract

fetched live from OpenAlex

PIK3CA, which encodes the p110α catalytic subunit of PI3Kα, is one of the most frequently genetically activated kinases in solid tumors. In this issue of Cancer Discovery, Song and colleagues report that the related PI3Kα inhibitors taselisib and inavolisib trigger receptor tyrosine kinase (RTK)-dependent degradation of the mutant p110α protein in breast cancer cells that are positive for HER2 RTK, limiting feedback-mediated drug resistance and potentially widening the therapeutic index of PI3Kα inhibition.See related article by Song et al., p. 204.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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 designNot applicable
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

Citations20
Published2022
Admission routes2
Has abstractyes

Explore more

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