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Record W6961245326 · doi:10.15017/1866274

Rasを介したがん細胞の生存および浸潤におけるDOCK1の重要性

2017· dissertation· en· W6961245326 on OpenAlexfundno aff

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2017
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceFonds de Recherche du Québec - SantéCenter for Clinical and Translational ResearchJapan Agency for Medical Research and Development
KeywordsInvadopodiaCancer cellCancerMetastasisIn vivoSynthetic lethalityGuanine nucleotide exchange factorCell growth

Abstract

fetched live from OpenAlex

Oncogenic Ras plays a key role in cancer initiation but also contributes to malignant phenotypes by stimulating nutrient uptake and promoting invasive migration. Because these latter cellular responses require Rac-mediated remodeling of the actin cytoskeleton, we hypothesized that molecules involved in Rac activation may be valuable targets for cancer therapy. We report that genetic inactivation of the Rac-specific guanine nucleotide exchange factor DOCK1 ablates both macropinocytosis-dependent nutrient uptake and cellular invasion in Ras-transformed cells. By screening chemical libraries, we have identified 1-(2-(3'-(trifluoromethyl)-[1,1'-biphenyl]-4-yl)-2-oxoethyl)-5-pyrrolidinylsulfonyl-2(1H)-pyridone (TBOPP) as a selective inhibitor of DOCK1. TBOPP dampened DOCK1-mediated invasion, macropinocytosis, and survival under the condition of glutamine deprivation without impairing the biological functions of the closely related DOCK2 and DOCK5 proteins. Furthermore, TBOPP treatment suppressed cancer metastasis and growth in vivo in mice. Our results demonstrate that selective pharmacological inhibition of DOCK1 could be a therapeutic approach to target cancer cell survival and invasion.

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 score0.012
Threshold uncertainty score0.039

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)0.0120.004

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 head0.022
GPT teacher head0.263
Teacher spread0.240 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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