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Record W2887555658 · doi:10.1158/1538-7445.am2018-462

Abstract 462: Hyperactivation of ERK/MAPK pathway reduces pancreatic cancer cells' proliferation and tumor progression through phosphoproteome reprogramming

2018· article· en· W2887555658 on OpenAlexaffabout
Marie‐Camille Rowell, Xavier Deschênes‐Simard, Benjamin Le Calvé, Stéphane Lopes‐Paciencia, Ana Fernández Ruiz, Filippos Kottakis, Nabeel Bardeesy, Gerardo Ferbeyre

Bibliographic record

VenueCancer Research · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsMcGill UniversityConcordia UniversityUniversité de Montréal
Fundersnot available
KeywordsMAPK/ERK pathwayCancer researchPancreatic Intraepithelial NeoplasiaPancreatic cancerKinaseCancerBiologyKRASOncogeneTumor progressionRibosome biogenesisMedicineCell cycleCell biologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract This study aims to better understand the loss of tumor -uppression functions of ERK in pancreatic cancer progression. Pancreatic cancer is the 4th leading cause of death by cancer in Canada and USA with a 5-year survival rate below 7%. We have identified a high level of phosphorylation of the MAP kinase ERK in benign pancreatic intraepithelial neoplasms (PanIN), followed by a reduction in phospho-ERK levels in pancreatic ductal adenocarcinoma (PDAC) both in human patient samples and KRas-driven mouse models for pancreatic cancer. Since PanINs are considered senescent lesions, we hypothesized that phospho-ERK levels were reduced in PDAC to bypass oncogene-induced senescence and allow tumor initiation. We propose that reactivating high ERK in PDAC could restore tumor-suppression mechanisms lost during progression. To do so, we use a tamoxifen-inducible constitutive allele of RAF1 kinase, which acts upstream of ERK (ΔRAF1-ERT). In human (PANC-1, KP-4) and murine PDAC cell lines, activated RAF1 slows cell proliferation and induces cellular senescence in vitro in an ERK-dependent and p53/p16-independent manner. Activation of ERK kinases by this system in subcutaneous PDAC xenografts slows tumor growth in nude mice. To characterize key mediators of ERK-dependent tumor suppression, we did phosphoproteomics by mass spectrometry and identified a reprogramming of nuclear vs. cytoplasmic phosphoproteome, involving key ribosome biogenesis factors. Depletion of these nucleolar targets by shRNAs and nucleolar stress inducers such as RNA polymerase I inhibitor CX-5461 recapitulates ERK-dependent tumor suppression. At the same time, we are also investigating autophagy-related processes involved in RAF/MEK signaling blockade in PDAC cells. In summary, we suggest that high levels of p-ERK1/2 in early PanIN stage act as a tumor-suppressor mechanism, which is lost with cancer progression. Our future work aims to better understand the mechanism by which high ERK signaling exerts tumor suppression in order to find downstream effectors that could be used as therapeutic targets in pancreatic cancer. Citation Format: Marie-Camille Rowell, Xavier Deschênes-Simard, Benjamin Le Calvé, Stéphane Lopes-Paciência, Ana Fernandez Ruiz, Filippos Kottakis, Nabeel Bardeesy, Gerardo Ferbeyre. Hyperactivation of ERK/MAPK pathway reduces pancreatic cancer cells' proliferation and tumor progression through phosphoproteome reprogramming [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 462.

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)0.0000.000
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)0.0030.000

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 head0.041
GPT teacher head0.383
Teacher spread0.342 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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