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Abstract NT-117: INHIBITION OF PARG, SENSITIZES OVARIAN CANCER CELLS TO PARP INHIBITORS AND DNA DAMAGING AGENTS

2019· article· en· W3041199890 on OpenAlexaff
Emad Matanes, Tahira Baloch, David Octeau, Roy Kessous, Liron Kogan, Ido Laskov, Walter H. Gotlieb, Amber Yasmeen

Bibliographic record

VenueClinical Cancer Research · 2019
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsJewish General Hospital
Fundersnot available
KeywordsPoly ADP ribose polymeraseSynthetic lethalityDNA repairCancer researchBiologyMolecular biologyPARP inhibitorCisplatinDNA damageBlotOvarian cancerCell cultureCancerChemistryDNAPolymeraseBiochemistryGeneticsGeneChemotherapy

Abstract

fetched live from OpenAlex

Abstract BACKGROUND: Poly(ADP-ribose) glycohydrolase (PARG) is responsible poly(ADP-ribose) (PAR) catabolism, which is synthesized by poly(ADP-ribose) polymerases (PARPs) at the site of DNA single-strand breaks (SSB). Faulty PAR formation or disintegration inhibits SSB repair. PARP inhibitors (PARPi) exploit the synthetic lethality between SSB repair and homologous recombination (HR) double strand break (DSB) repair. The most effective PARPi traps PARP at the SSB site, causing a double strand break during DNA synthesis that the cell cannot repair. The trapping of PARP can also be achieved through PARG inhibition. Due to the high rate of HR defects in epithelial ovarian cancer, the aim of this study is to evaluate the effect of a PARG inhibitor (PARGi) on epithelial ovarian cancer cell lines, alone and in combination with PARPi or cisplatin. METHODS: PARG protein levels were assessed in 74, unselected, snap-frozen, tumors kept in our bio-bank by western blotting and immuno-histochemistry (IHC), as well as in the TCGA database. PARG was knocked down in two cell lines resistant to PARP inhibition, OVCAR3 and SKOV3. The knockdowns were subjected to cell migration and cell proliferation assays, as well as single cell analysis. BRCA1 mutated and BRCA1 wild-type cell lines were exposed to clinically relevant doses of PARGi, PARPi, or cisplatin, separately or in various combination. Therapeutic efficacy was assessed using colony formation assay. Western Blotting was used to detect the levels of PARG and PARP proteins in the cell lines. RESULTS: PARG mRNA was expressed in 30% of tumors in TCGA. PARG protein was also detected in 30% of the biobank tumors analyzed. PARG knockdowns exhibited reduced rates of cellular proliferation and significant G2/M cell cycle arrest. In PARG over expressed ovarian cancer cell lines, PARG inhibitor similarly reduced cell migration (OVCAR3 and SKOV3: 75% reduction, SNU-251: 48% reduction). CONCLUSION: PARG inhibition appears to be a viable, complementary strategy to PARP inhibition in HR-deficient cancers. The effect of BRCA1 on the efficacy of PARG inhibition, as well as various treatment combinations, are currently under investigation. Citation Format: Emad Matanes, Tahira Baloch , David Octeau, Roy Kessous, Liron Kogan, Ido Laskov, Walter Gotlieb, Amber Yasmeen. INHIBITION OF PARG, SENSITIZES OVARIAN CANCER CELLS TO PARP INHIBITORS AND DNA DAMAGING AGENTS [abstract]. In: Proceedings of the 12th Biennial Ovarian Cancer Research Symposium; Sep 13-15, 2018; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2019;25(22 Suppl):Abstract nr NT-117.

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 score0.003
Threshold uncertainty score0.009

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)0.0030.001

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 head0.184
GPT teacher head0.512
Teacher spread0.328 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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