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Record W2000211955 · doi:10.1158/1538-7445.am10-2536

Abstract 2536: Therapeutic testing of a novel PKC inhibitor GAP-107B8 on ovarian cancer cells

2010· article· en· W2000211955 on OpenAlexaffabout
Fu Yan, Isabella Steffensen, Kenneth Garson, Barbara C. Vanderhyden

Bibliographic record

VenueCancer Research · 2010
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsMicropharma (Canada)Ottawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsCancer researchOvarian cancerCell growthCancerCell cultureOncogeneBiologyKinaseProtein kinase CCellCancer cellCell cycleMedicineInternal medicineCell biologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Background: Ovarian cancer is the most fatal gynaecologic disease in the western world. In 2009 in North America, an estimated 23,896 women will develop ovarian cancer and an estimated 15,24050 women will die from this disease. Current treatments are limited to surgery or chemotherapy, but the disease often recurs. Thus, the development of novel cancer therapeutics remains important. The protein kinase C (PKC) family of serine/threonine kinases is involved in cellular proliferation, differentiation, apoptosis and cell polarity. One PKC isoform, PKC iota, has recently been identified as a human oncogene and has been shown to be overexpressed in serous epithelial ovarian cancers and is thus a potential therapeutic target for ovarian cancer. Objective: We have tested a novel PKC inhibitor GAP-107B8 (PharmaGap Inc., Ottawa) in vitro on a panel of nine ovarian cancer cell lines to determine its potential to inhibit cell proliferation, proliferation in soft agar, and migration. Methods: Nine ovarian cancer cell lines were treated with three different concentrations of GAP-107B8 and then screened using high throughput assays to measure the proliferation of cells in adherent and anchorage independent (soft agar) cultures. The ability of cells to migrate in the presence of GAP-107B8 was also determined. Results: We observed significant reduction in cell proliferation in 6 of 9 ovarian cancer cell lines tested, including two cell lines resistant to the standard chemotherapy. GAP-107B8 inhibited cell proliferation by 30% to 79% compared with untreated cells, with more than 50% inhibition in 4 of 7 cell lines. Treatment with GAP-107B caused a reduction in growth in soft agar in 7 of the 9 cell lines tested in vitro. GAP-107B8 inhibited growth in soft agar by 50% to 94% compared with untreated cells, with 80% or greater inhibition in 6 of 7 cell lines. Finally, 5 of 8 cell lines tested showed significant inhibition of mobility following treatment with GAP-107B8. There was 50% or greater inhibition in all 5 cell lines compared with untreated cells. Conclusion: The novel PKC inhibitor GAP-107B8 displays good efficacy in vitro in suppressing several cancer cell characteristics in a variety of ovarian cancer cell lines. Further experiments are underway to investigate the therapeutic potential of GAP-107B8 in xenograft models of ovarian cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2536.

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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.194
GPT teacher head0.436
Teacher spread0.242 · 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
Published2010
Admission routes2
Has abstractyes

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