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Record W2922092416 · doi:10.1093/jcag/gwz006.276

A277 DNA-PK SUSTAINS AUTOPHAGY AND PANCREATIC CANCER CELL GROWTH

2019· article· en· W2922092416 on OpenAlexaffabout
Ramdas Chatterjee, Benoît Marchand, Marie-France Bossanyi, Marie‐Josée Boucher

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsAutophagyDNA damagePancreatic cancerCancer researchClonogenic assayBiologyDNA repairGemcitabineDNA-PKcsProgrammed cell deathCancer cellCell growthCellCell biologyCancerDNAApoptosisGenetics

Abstract

fetched live from OpenAlex

Pancreatic cancer (PDAC) is currently the 4th leading cause of cancer related deaths in Canada. The life expectancy with metastatic PDAC is 3–6 months. Malignant pancreatic tumours are one of the most intrinsically resistant tumours that can withstand majority of the currently available treatment modalities. The tumours are showing resistance to Gemcitabine, the most commonly prescribed drug. Gemcitabine is known to induce DNA damage that, if not repaired, lead to cell death. Of note, it was observed that PDAC tissues display higher expression of DNA-PKcs, the catalytic subunit of DNA-PK involved in DNA repair, suggesting that DNA-PKcs and/or DNA-PK could provide a growth advantage to PDAC cells. Still, the role of DNA-PK in pancreatic pathophysiology remains elusive. Studies in the recent years have validated the high-basal levels of autophagy in PDAC cells and proven its importance in sustaining cell growth. It is noteworthy that agents inducing DNA damage have been correlated with autophagy modulation suggesting a potential link between autophagy-DNA damage and/or DNA repair mechanisms. However, the underlined mechanisms have yet to be identified. The overall objective was to interrogate a potential role for DNA-PK, increased in PDAC tissues, in regulating autophagy and growth of PDAC cells. The PDAC cell line MiaPaCa-2 was used. The specific inhibitor NU7441 was used to block DNA-PK activity. Cell counting and clonogenic assays were performed to assess cell growth. Autophagic flux was measured by co-treatment with the autophagy inhibitor Bafilomycin A1. 1) We observed a dose- and time-dependent decrease in cell number in DNA-PK inhibited cells as compared to control cells. 2) This coincides with the reduced colony forming ability of PDAC cells when treated with NU7441. 3) The impact on cell growth correlated with induction of cell apoptosis as measured by PARP and caspase-7 cleavage. 4) Given the reported role of autophagy in sustaining PDAC cell growth, we assessed the impact of DNA-PK inhibition on autophagy. Immunoblotting and immunofluorescence studies revealed that NU7441 treatment triggers increased expression of the autophagy receptor p62/SQSTM1 as well as lipidation of LC3B indicative of autophagy modulation. 5) Autophagy flux measurement supported blockade of autophagy upon DNA-PK inhibition. Taken together, our results demonstrate that interference with DNA-PK activity is an attractive avenue to abrogate PDAC cell growth. Our observations support a novel role for DNA-PK in regulating autophagy. Given that PDAC tissues were shown to display high expression levels of DNA-PKcs, it is tempting to speculate that DNA-PK could be involved in maintaining high basal levels of autophagy as observed in PDAC cells. NSERC, CRS

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 score0.005

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)0.0020.001

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 head0.003
GPT teacher head0.192
Teacher spread0.190 · 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
Published2019
Admission routes2
Has abstractyes

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