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Record W2740611655 · doi:10.1158/1538-7445.am2017-216

Abstract 216: Tracking expression, post-translational modifications and interactions of EGF signalling proteins in A431 cells with antibody microarrays

2017· article· en· W2740611655 on OpenAlexaff
Lambert Yue, Steven Pelech

Bibliographic record

VenueCancer Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Biosensing Techniques and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProtein microarrayAntibody microarrayPhosphorylationA431 cellsBiologyAntibodyEpidermal growth factorDNA microarrayKinaseProtein Array AnalysisProteomicsCell biologyMolecular biologyProtein phosphorylationBiotinEpidermal growth factor receptorLysisProtein kinase ABiochemistryCellCell cycleGene expressionOncogeneReceptorGeneImmunology

Abstract

fetched live from OpenAlex

Abstract Elucidation of epidermal growth factor (EGF) signalling pathways in cancer cells has helped to define the mechanisms for their neoplastic transformation and potential drug targets for therapeutic intervention. Antibody microarrays are promising tools to evaluate alterations in the levels and phosphorylation status of hundreds of proteins of interest with only microgram amounts of crude cell and tissue lysate protein. However, interpretations of the results from traditional antibody microarray approaches have been hampered by the problems associated with sample preparation and protein detection, even when reliable antibodies are deployed in these arrays. The Kinex™ KAM-900P antibody microarray permitted semi-quantitative measurements of the expressions, post-translational modifications and interactions of proteins with 100 µg or less of lysate proteins. These microarrays utilize approximately 878 different pan- and phosphosite-specific antibodies for tracking protein kinases, phosphatases and other low abundance regulatory proteins. Multiple detection protocols were developed with the KAM-900P slides to enable high depth profiling of protein levels, phosphorylation and protein-protein interactions in A431 cells in response to EGF treatment. One method (KAM) involved the capture of in vitro biotin-labeled proteins, followed by their detection with a secondary dye-labeled anti-biotin antibody. False positive signals from associated proteins in complexes with the targets were reduced by chemical cleavage with NTCB prior to their capture on the array, and this also produced more uniformity of the dye signals for protein targets despite vast differences in their sizes. Transient changes in protein phosphorylation in EGF treated cells that were typically lost when processed by conventional methods were better preserved by chemical cleavage right at time of sample homogenization. Biotin-labeling and subsequent detection of the protein on the array with a dye-labeled secondary antibody further reduced non-specific background signals, allowed for a greater dynamic range of detection, and enhanced discrimination of subtle changes. In conjunction with other detection protocols, such as the usage of dye-labeled reporter antibodies for generic protein-tyrosine phosphorylation in sandwich antibody microarrays (SAM format) or generic protein phosphorylation with nanoparticles such as pAMIGO (PAM format), it is also feasible to monitor changes in general post-translational modifications of target proteins or their specific association with other adapter, scaffolding and chaperone proteins of interest for which antibody probes are available. Citation Format: Lambert Yue, Steven Pelech. Tracking expression, post-translational modifications and interactions of EGF signalling proteins in A431 cells with antibody microarrays [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 216. doi:10.1158/1538-7445.AM2017-216

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.013
Threshold uncertainty score0.277

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.058
GPT teacher head0.419
Teacher spread0.361 · 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 teacher head, 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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