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Abstract IA10: Inhibition of the Hippo pathway by AMPK-family kinases

2020· article· en· W3047601981 on OpenAlexaff
Liliana Attisano

Bibliographic record

VenueMolecular Cancer Research · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHippo pathway signaling and YAP/TAZ
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHippo signaling pathwayKinaseAMPKPhosphorylationCancer researchProtein-Serine-Threonine KinasesBiologyCancerCell biologySignal transductionProtein kinase AGenetics

Abstract

fetched live from OpenAlex

Abstract Inactivation of the Hippo pathway is a common feature in numerous cancers, yet mutations in pathway components are relatively rare. To uncover novel Hippo pathway regulators, we conducted multidimensional high-throughput screens. These efforts uncovered two AMPK family kinases, MARK4 and NUAK2, as negative regulators of the Hippo pathway. MARK kinases, including MARK3 and MARK4, phosphorylate both SAV1 and MST1/2 and inhibit MST1/2-dependent activation of LATS. Moreover, we showed that DLG5 acts as a scaffold to promote MARK-mediated phosphorylation of MST. In contrast to MARKs, NUAK2 interacts with and phosphorylates LATS. Interestingly, NUAK2 is induced by YAP/TAZ in cooperation with AP-1 and this is required for robust YAP/TAZ signaling. Inhibition or loss of NUAK2 reduces the growth of cultured cancer cells and mammary tumors in mice. In human patient samples, NUAK2 expression is elevated in aggressive, high-grade bladder cancer and strongly correlates with a YAP/TAZ gene signature. Thus, we identified a positive feed-forward loop in the Hippo pathway that establishes a key role for NUAK2 in enforcing the tumor-promoting activities of YAP/TAZ. Citation Format: Liliana Attisano. Inhibition of the Hippo pathway by AMPK-family kinases [abstract]. In: Proceedings of the AACR Special Conference on the Hippo Pathway: Signaling, Cancer, and Beyond; 2019 May 8-11; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2020;18(8_Suppl):Abstract nr IA10.

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 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 score0.027
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

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

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 head0.051
GPT teacher head0.327
Teacher spread0.276 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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