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Abstract POSTER-THER-1410: Combining small molecule drugs and standard chemotherapy for treatment of granulosa cell tumour cells

2015· article· en· W2562462106 on OpenAlexaff
Powel Crosley, Kate Agopsowicz, Michael Weinfeld, Mary Hitt

Bibliographic record

VenueClinical Cancer Research · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsBrain Tumour Foundation of CanadaUniversity of Alberta
Fundersnot available
KeywordsXIAPCarboplatinApoptosisViability assayProgrammed cell deathCancer cellInhibitor of apoptosisCell cultureIn vitroChemistryCancer researchChemotherapyCellCancerPharmacologyCaspaseBiologyCisplatinMedicineBiochemistryInternal medicine

Abstract

fetched live from OpenAlex

Abstract Evasion of apoptosis is a hallmark of cancer, and direct induction of apoptosis without dependence on signaling upstream of Caspase-3 (CASP3) is an attractive target for cancer therapy. CASP3 sits at the hub of apoptotic pathways and it is a primary target for inhibition by anti-apoptotic proteins like the X-linked inhibitor of apoptosis (XIAP). Granulosa cell tumour (GCT) is a rare form of ovarian cancer, highly lethal in the event of recurrence, and has no standard chemotherapy because it is resistant to most common drugs. A recently discovered small-molecule drug, procaspase activating compound-1 (PAC1), has been shown to effectively cleave procaspase-3 into its active form by removal of an inhibitory zinc ion, facilitating autocleavage of the zymogen and direct induction of apoptosis. Initial in vitro experiments in our lab have shown PAC1 capable of significantly reducing viability of GCT cells (represented by the KGN cell line), and combining PAC1 with a drug inhibiting XIAP further increases the killing effect. In addition, combining the small-molecule XIAP inhibitor with carboplatin, a standard chemotherapy agent, displays significant drug interaction while killing GCT cells, in vitro. Results: GCT cell line KGN was treated with various concentrations of PAC1, for 24 and 48 hour time points, and assessed for cell viability using a metabolic assay. Results showed significant reduction of cell viability (p<0.05) in both time and dose-dependent manners. The dose-response curve for these assays indicate an EC50 of ~10 µM PAC1. High-content screening of PAC1-treated GCT cells produced quantified imagery that suggests treatment with PAC1 is, in fact, inducing activation of CASP3-mediated apoptosis and an assay inhibiting CASP3 displayed reduction in PAC1-induced killing. Combining 10 µM PAC1 with selected concentrations of embelin, a monovalent XIAP-inhibitor, showed strong additive effect in initial experiments, indicating it is an area worthy of further research. Combination of embelin with carboplatin also displayed significant drug interaction as well as single drug effect (p<0.05). This presentation suggests that PAC1 reduces viability of GCT cells in a time and dose-dependent manner, in vitro, and the mechanism of that loss of viability is caspase-mediated apoptosis. Furthermore, combining PAC1 with drugs that inhibit XIAP enhances the killing effect. Combining small molecule drugs that promote apoptosis display strong interaction with carboplatin, a standard chemotherapeutic agent, and may have the potential to allow carboplatin dose reduction. Further research to determine activity in animal models and/or primary human GCT explants is warranted and planned. Citation Format: Powel Crosley, Kate Agopsowicz, Michael Weinfeld, Mary Hitt. Combining small molecule drugs and standard chemotherapy for treatment of granulosa cell tumour cells [abstract]. In: Proceedings of the 10th Biennial Ovarian Cancer Research Symposium; Sep 8-9, 2014; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(16 Suppl):Abstract nr POSTER-THER-1410.

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 score0.008
Threshold uncertainty score0.028

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.139
GPT teacher head0.465
Teacher spread0.325 · 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
Published2015
Admission routes1
Has abstractyes

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