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EGFR signaling in breast cancer requires licensing from separate membrane nanodomains

2020· article· en· W3016605399 on OpenAlexaff
Michael G. Sugiyama, Gregory D. Fairn, Costin N. Antonescu

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCaveolin-1 and cellular processes
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCancer researchEpidermal growth factor receptorProtein kinase BClathrinCell biologySignal transductionCell signalingEGFR inhibitorsTyrosine kinaseCancerCancer cellCaveolaeChemistryBiologyMedicineCellEndocytosisInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Epidermal growth factor receptor (EGFR) is a receptor tyrosine kinase with numerous roles in maintaining cell physiology; it is upregulated in many cancers, including breast cancer, where it contributes to tumor growth and metastasis. This feature of EGFR signaling in cancer has prompted the development of anti‐EGFR therapies. However, many cancers, while initially responsive to anti‐EGFR therapies, develop resistance to prolonged treatment, which imposes a significant challenge in applying anti‐EGFR therapies to the treatment of difficult to treat cancers such as triple negative breast cancer. Resistance to anti‐EGFR therapy is not a consequence of EGFR mutation, and instead it has been speculated that alterations in EGFR plasma membrane confinement within specialized membrane nanodomains contributes to aberrant signaling in cancer. We previously reported that EGFR activation of downstream Akt signaling requires clathrin nanodomains; the contribution of other nanodomains (flotillins, tetraspanins, caveolae, etc.) to EGFR signaling remains unknown. To probe EGFR signaling in membrane nanodomains we developed tools to track EGFR, EGF‐ligand, and various nanodomains at the single molecule level by dual‐colour, live‐cell TIRF microscopy coupled with MATLAB automated detection and particle analysis. Treatment of breast cancer cell lines with EGFR tyrosine kinase inhibitors (TKI) attenuated EGFR confinement within clathrin nanodomains and subsequent Akt signaling, but only in cell lines sensitive to the cytotoxic effects of the drugs. Interestingly, mobilization of EGFR to clathrin following stimulation with EGF‐ligand required PKC activation and confinement within non‐clathrin nanodomains, a feature that was altered in EGFR TKI‐resistant breast cancer cells. This two nanodomain model of EGFR activation resolves some longstanding, unexplained features of EGFR signaling, such the inability of the majority of EGFR to bind ligand and suggests that EGFR licensing by membrane nanodomains is a critical regulator of EGFR signaling that contributes to therapeutic resistance. Ongoing work will determine the mechanism that links EGFR licensing between membrane nanodomains, EGFR signaling, and drug resistance in breast cancer. Support or Funding Information CIHR PJT‐156355 to GDF and CNA

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 score0.001
Threshold uncertainty score0.004

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)0.0010.001

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 head0.016
GPT teacher head0.248
Teacher spread0.232 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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