MétaCan
Menu
← Back to cohort
Record W2330598133 · doi:10.1158/1538-7445.am2012-1243

Abstract 1243: RET and integrin crosstalk provides functional plasticity

2012· article· en· W2330598133 on OpenAlexaff
Jessica Cockburn, Anirudh Goel, Simona Wagner, Lois M. Mulligan

Bibliographic record

VenueCancer Research · 2012
Typearticle
Languageen
FieldMedicine
TopicHER2/EGFR in Cancer Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsCell biologyIntegrinGlial cell line-derived neurotrophic factorProto-oncogene tyrosine-protein kinase SrcCancer researchReceptor tyrosine kinaseBiologyCell adhesionChemistryNeurotrophic factorsSignal transductionCellReceptorGenetics

Abstract

fetched live from OpenAlex

Abstract The RET receptor tyrosine kinase is an important mediator of cell growth, proliferation, and differentiation. RET becomes activated upon formation of a complex with the glial cell-line derived neurotrophic factor (GDNF) ligand and GDNF-family receptor (GFRa), which leads to activation of multiple downstream signalling pathways, including PI3K/AKT, MEK/ERK, STAT3, and SRC. Constitutive activation of RET, through translocations or missense mutations, leads to papillary thyroid carcinoma (PTC) and medullary thyroid carcinoma (MTC), respectively. Also, RET expression has been detected in pancreatic and breast cancers, and has been linked to increased metastatic potential. In order to clarify the underlying role of RET in tumour progression, we examined the relationship between RET and two integrin subunits, β1 (ITGB1) and β3 (ITGB3). Integrin proteins are important for cell attachment to the extracellular matrix and focal adhesion (FA) formation. Functionally, integrins are important for cell-adhesion and migration as they provide traction and leverage for cell movement. Previously, we have shown that RET is able to increase cell-migration and adhesion, and that both ITGB1 and ITGB3 are important for these processes. Here, we show that RET-mediated cell-migration is persistent over 24 hours, and that patterns of cell-adhesion fluctuate over this time, predictably representing different forces needed to move across the microenvironment. Cell-adhesion is increased upon 1 hour of GDNF treatment, but is lost between 3-12 hours of GDNF treatment. Interestingly, we observed that ITGB1 activation downstream of RET, detected by co-immunoprecipitation with paxillin, is transient, and lasts for 1 hour. Conversely, ITGB3 activation downstream of RET, detected using an active heterodimer-specific antibody, is sustained between 1-12 hours of GDNF treatment. These results demonstrate unique functional roles for ITGB1 and ITGB3 downstream of RET. We also examined the importance of signalling pathways downstream of RET for integrin activation, and found that, in a 2D collagen environment, PI3K/AKT and MEK/ERK are important for RET-mediated FA formation. However, in a 3D collagen environment where cells formed spheroids, PI3K/AKT and STAT3 are important for cell-outgrowth, downstream of RET activation. This likely represents different cellular responses needed to overcome environment-specific obstacles. Finally, we showed evidence for a relationship between RET, ITGB1 and ITGB3 in tumour progression using a cohort of various clinically annotated thyroid cancer samples. Ultimately, we have shown a role for in activation of two integrin subunits, ITGB1 and ITGB3, downstream of RET and how each of these proteins may contribute to metastatic events, particularly those involved in thyroid cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1243. doi:1538-7445.AM2012-1243

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 score0.006
Threshold uncertainty score0.020

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)0.0060.001

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 head0.256
GPT teacher head0.492
Teacher spread0.235 · 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
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueCancer Research→Same topicHER2/EGFR in Cancer Research→French-language works237,207→