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Abstract PO-044: ATG4B loss reduces pancreatic cancer cell viability and enhances the utilization of survival-promoting GABARAP-L2

2020· article· en· W3106260045 on OpenAlexaff
Paalini Sathiyaseelan, Nancy E. Go, Steve E. Kalloger, Daniel J. Renouf, David F. Schaeffer, Sharon M. Gorski

Bibliographic record

VenueCancer Research · 2020
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsVancouver General HospitalBC Cancer Agency
Fundersnot available
KeywordsGene knockdownViability assayAutophagyPancreatic cancerCell cultureCancer researchCancer cellCell growthCancerChemistryBiologyApoptosisMedicineInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Abstract Autophagy is a lysosomal-dependent intracellular recycling pathway that was found to be essential for the survival of some pancreatic ductal adenocarcinoma (PDAC) cells in vitro and in vivo. Hence, there is interest in identifying potential targets within the autophagy pathway for PDAC therapy. The ATG4 cysteine protease family (ATG4A, ATG4B, ATG4C and ATG4D) and their substrates – LC3B, GABARAP, GABARAP-L1 and GABARAP-L2 – are important in the formation of autophagosomes, which are vesicles that encapsulate and transport cargo to lysosomes for degradation. ATG4B, in particular, has garnered attention because of its ability to efficiently recognize and cleave all of the substrates, and several cancer types have shown sensitivity to ATG4B inhibition. To determine the effects of targeting ATG4B in PDAC cells, stable knockdown (KD) of ATG4B was generated using PDAC cell lines, MiaPaCa2 and Panc1. ATG4B KD significantly reduced cell viability under fed conditions (10% serum) compared to the control parental lines. Since autophagy is a stress response mechanism, cell viability was tested under low serum (0.8%) conditions, which showed that the ATG4B KD lines were significantly more sensitive to low serum compared to parental lines. To determine the association between ATG4B and PDAC clinical parameters, 252 PDAC tumors were analyzed for ATG4B expression levels. Unexpectedly, tumors categorized as ATG4B-negative were associated with poor differentiation and worse disease-specific survival of PDAC patients (pLogRank = 0.0006). ATG4B knockout lines (KO) were derived from MiaPaCa2 and Panc1 to study the effects of complete loss of ATG4B KO in PDAC. Under fed conditions, the ATG4BKO lines displayed significantly reduced viability compared to the parental lines. In low serum conditions, however, the ATG4B KO lines displayed similar sensitivity as the parental lines, suggesting the induction of compensatory mechanisms. To determine if the observed differences in viability were associated with changes in ATG4 substrates, the expression and lipidation of LC3B, GABARAP, GABARAP-L1 and GABARAP-L2 were analyzed. While there were no consistent differences for LC3B, GABARAP and GABARAP-L1, both the ATG4B KD and KO lines showed an increase in GABARAP-L2 lipidation under fed and low serum conditions. GABARAP-L2 knockdown resulted in apoptosis of MiaPaCa2 cells regardless of ATG4B status. This finding indicates that GABARAP-L2 is required for the survival of MiaPaca2 cells, and further analyses of GABARAP-L2 in PDAC and normal pancreatic cell lines are in progress. Citation Format: Paalini Sathiyaseelan, Nancy E. Go, Steve E. Kalloger, Daniel J. Renouf, David F. Schaeffer, Sharon M. Gorski. ATG4B loss reduces pancreatic cancer cell viability and enhances the utilization of survival-promoting GABARAP-L2 [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2020 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2020;80(22 Suppl):Abstract nr PO-044.

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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.134
GPT teacher head0.434
Teacher spread0.300 · 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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