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Record W2116344367 · doi:10.1517/14728222.2010.531014

Paving the way for targeting RSK in cancer

2010· article· en· W2116344367 on OpenAlexaff
Yves Roméo, Philippe P. Roux

Bibliographic record

VenueExpert Opinion on Therapeutic Targets · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and Cancer
Fundersnot available
KeywordsKinaseRibosomal s6 kinaseMAPK/ERK pathwayBiologyCell biologyMotilityCancer cellCancerSignal transductionPhosphorylationCell growthProtein kinase Ap38 mitogen-activated protein kinasesCancer researchPI3K/AKT/mTOR pathwayBiochemistryGeneticsP70-S6 Kinase 1

Abstract

fetched live from OpenAlex

The 90 kDa ribosomal S6 kinase (RSK) family is a group of highly conserved Ser/Thr kinases that promote cell proliferation, growth, motility and survival. Deregulated RSK expression or activity has been associated with several human diseases, including cancer. RSK lies downstream of the Ras/mitogen-activated protein kinase (MAPK) signalling pathway and is directly phosphorylated by the extracellular signal-regulated kinases 1 and 2 (ERK1/2). Significant advances in the field of RSK signalling have occurred in the past few years, unravelling novel RSK cellular substrates and biological functions as well as new RSK regulatory mechanisms. Together, these findings suggest that RSK may be a promising therapeutic target for the treatment of cancer, particularly those characterized by oncogenic mutations in components of the Ras signalling pathway. This article briefly describes our current knowledge on the impact of RSK on cell growth and proliferation, as well as RSK-dependent mechanisms associated with tumourigenesis. The potential of targeting RSK in cancer is discussed in light of available data on the biological functions of each RSK family members. Targeting RSK with small molecule inhibitors appears to be a promising path for cancer therapy, but several considerations need to be evaluated and will be discussed in detail.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.440

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 head0.027
GPT teacher head0.319
Teacher spread0.292 · 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

Citations67
Published2010
Admission routes1
Has abstractyes

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