MétaCan
Menu
Back to cohort

Differential impact of Raf-1 kinase activity on tumor cell resistance to paclitaxel and docetaxel

2000· article· en· W2326269621 on OpenAlexaff
Richard A. Britten, Karma Klein

Bibliographic record

VenueAnti-Cancer Drugs · 2000
Typearticle
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsUniversity of AlbertaAlberta Cancer Foundation
Fundersnot available
KeywordsDocetaxelTaxanePaclitaxelCytotoxicityKinaseOvarian cancerCancer researchMedicinePharmacologyOncologyChemotherapyInternal medicineChemistryCancerIn vitroBreast cancerBiochemistry

Abstract

fetched live from OpenAlex

Docetaxel (Taxotere) is becoming increasingly important in the treatment of many tumor sites and is unusually active in tumors that are resistant to the structurally similar taxane, paclitaxel. These data suggest that the processes that confer cellular paclitaxel resistance may have a substantially lower impact upon the cytotoxicity induced by docetaxel. We have recently reported that there is a marked Raf-1 kinase dependency of paclitaxel resistance in human cervical and ovarian tumor cell lines. We therefore characterized the impact that inherent and genetically induced variations in Raf-1 kinase activity have on the docetaxel cytotoxicity in human ovarian and cervical cancer cell lines. Our data suggest that docetaxel cytotoxicity is independent of Raf-1 kinase activity in the cell lines studied and that the lack of cross-resistance between these two taxane compounds may be due to the differential impact that Raf-1 kinase activity has on their cytotoxicity. Should these relationships pertain to the clinical situation, these findings could form the basis for a molecular-based triage of patients to receive docetaxel when response to paclitaxel may be unlikely due to high Raf-1 kinase activity.

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 categoriesInsufficient payload (model declined to judge)
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 score0.388
Threshold uncertainty score0.998

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)0.0030.000

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 head0.013
GPT teacher head0.331
Teacher spread0.318 · 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 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

Citations10
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueAnti-Cancer DrugsSame topicCancer Treatment and PharmacologyFrench-language works237,207