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Record W2563324575 · doi:10.1158/1538-7445.am2015-4994

Abstract 4994: RET isoforms differentially contribute to focal adhesion formation

2015· article· en· W2563324575 on OpenAlexaff
Piriya Yoganathan, Eric Lian, Lois M. Mulligan

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldMedicine
TopicNeuroendocrine Tumor Research Advances
Canadian institutionsQueen's University
Fundersnot available
KeywordsPaxillinFocal adhesionCell biologyBiologyReceptor tyrosine kinasePTK2AutophosphorylationCancer researchTyrosine kinaseSignal transductionKinaseProtein kinase AProtein kinase C

Abstract

fetched live from OpenAlex

Abstract RET is a receptor tyrosine kinase crucial for the development of the kidney, and some neuroendocrine tissues. RET activation by its ligand, glial cell-line derived neurotrophic factor (GDNF), results in autophosphorylation of several key tyrosine kinase residues in the intracellular region, which allow binding of adaptor and signalling proteins, and consequently activation of various cellular processes, including survival, proliferation and migration. RET has two major protein isoforms, called RET9 and RET51 after the number of unique amino acids at the C-terminus, generated by alternative splicing. Although the RET isoforms are highly conserved across species, and both isoforms are normally co-expressed, they have major molecular and functional differences, including different transforming abilities, and trafficking properties. RET is implicated in several human diseases. Gain-of-function RET mutations result in the cancer syndrome multiple endocrine neoplasia type 2, that affects neuroendocrine tissues, somatic RET rearrangements are found in papillary thyroid carcinoma, and lung adenocarcinoma, and aberrant wild-type RET activation occurs in many cancers, including breast and pancreatic cancer. Focal adhesion protein complexes are involved in interactions of cells with the extracellular matrix, and are crucial for controlling cancer-related processes, including cell migration and invasion. The contributions of RET isoforms to the processes of focal adhesion formation and dynamics have not yet been investigated. To explore the roles of RET isoforms in focal adhesion formation, we used total internal resonance fluorescence (TIRF) microscopy to assess localization of various focal adhesion proteins, including paxillin, vinculin and zyxin in response to GDNF in a cell-based model system. Our results suggest that RET51 promotes more early (ie. paxillin) and late (ie. zyxin) stage focal adhesion formation than does RET9. Additionally, we demonstrate that the kinase dead form of RET51 results in a decrease of the number of focal adhesions compared to RET51 wild-type, suggesting that the increase in focal adhesion formation in the presence of RET51 is phosphorylation-dependent. Inhibition of SRC and FAK led to a reduction in focal adhesion formation, indicating that these pathways play roles in this process. Ongoing studies are investigating the focal adhesion dynamics of the RET isoforms and further characterizing the molecular differences underlying the ability of RET51 to induce greater focal adhesion formation than RET9. Citation Format: Piriya Yoganathan, Eric Lian, Lois M. Mulligan. RET isoforms differentially contribute to focal adhesion formation. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4994. doi:10.1158/1538-7445.AM2015-4994

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 score0.004
Threshold uncertainty score0.014

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.135
GPT teacher head0.457
Teacher spread0.322 · 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
Published2015
Admission routes1
Has abstractyes

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