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Record W3094935533

Exploring the potential of CDK7 inhibition to permanently arrest cancer cells

2020· dissertation· en· W3094935533 on OpenAlexaboutno aff
Gemma A. Wilson

Bibliographic record

VenueUCL Discovery (University College London) · 2020
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsCyclin-dependent kinase 7PI3K/AKT/mTOR pathwayCyclin-dependent kinaseSenescenceCarcinogenesisCancer cellCancer researchCell growthApoptosisCell cycle checkpointCell cycleCancerCell biologyKinaseBiologyChemistrySignal transductionProtein kinase ABiochemistryGeneticsCyclin-dependent kinase 2
DOInot available

Abstract

fetched live from OpenAlex

Despite the clinical success of some targeted therapies in treating cancer, the response to them is often temporary, due to treatment resistance developing. Therefore, researchers must continue to look for novel ways to treat cancer. A number of groups are developing CDK7 inhibitors as anti-cancer drugs. CDK7 is a protein that has two major roles in cells - regulation of RNA polymerase II-mediated transcription and cell cycle progression as the CDK activating kinase. Both of these cellular processes are often deregulated during oncogenesis. Our collaborators at Imperial College London have developed a novel CDK7 inhibitor, ICEC0942, that is currently in Phase I/II clinical trials. ICEC0942 has been shown to inhibit the proliferation of cancer cell lines, but the mechanism by which it does this has not been previously determined. The work presented in this thesis aimed to understand this better. To establish its mechanism of action, we studied the effects of ICEC0942 on non- transformed RPE1 cells. Our data shows that ICEC0942 induces a permanent cell cycle arrest, with the cells displaying phenotypic characteristics of senescence. This contrasts to a more well-studied CDK7 inhibitor THZ1, which inhibits cell proliferation by inducing apoptosis. The data from a chemogenetic screen with ICEC0942, carried out by collaborators at the University of Montreal, revealed that activation of mTOR signalling was positively correlated with ICEC0942 efficacy. Further experiments confirmed these results by showing that inhibition of the mTOR signalling pathway partially rescued characteristics of senescence induced by ICEC0942 treatment. This was demonstrated in RPE1 cells and MCF7 cells, a breast cancer cell line. From this we hypothesise that ICEC0942 induces senescence by uncoupling cell division and cell growth, and that mTOR signalling plays an important role in this. Our future work will focus on how to use this insight to guide the clinical use of ICEC0942.

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 score0.002
Threshold uncertainty score0.008

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.203
Teacher spread0.192 · 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
Published2020
Admission routes1
Has abstractyes

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