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Record W4283579922 · doi:10.3389/fonc.2022.905398

The Androgen Receptor and Its Crosstalk With the Src Kinase During Castrate-Resistant Prostate Cancer Progression

2022· review· en· W4283579922 on OpenAlexaff
Lin Gao, Bo Han, Xuesen Dong

Bibliographic record

VenueFrontiers in Oncology · 2022
Typereview
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProstate cancerProto-oncogene tyrosine-protein kinase SrcAndrogen receptorCancer researchProstateMedicineTyrosine kinaseMetastasisAndrogenReceptor tyrosine kinaseInternal medicineOncologyCancerReceptorHormone

Abstract

fetched live from OpenAlex

While the androgen receptor (AR) signalling is the mainstay therapeutic target for metastatic prostate cancers, these tumours will inevitably develop therapy resistance to AR pathway inhibitors suggesting that prostate tumour cells possess the capability to develop mechanisms to bypass their dependency on androgens and/or AR to survive and progress. In many studies, protein kinases such as Src are reported to promote prostate tumour progression. Specifically, the pro-oncogene tyrosine Src kinase regulates prostate cancer cell proliferation, adhesion, invasion, and metastasis. Not only can Src be activated under androgen depletion, low androgen, and supraphysiological androgen conditions, but also through crosstalk with other oncogenic pathways. Reciprocal activations between Src and AR proteins had also been reported. These findings rationalize Src inhibitors to be used to treat castrate-resistant prostate tumours. Although several Src inhibitors had advanced to clinical trials, the failure to observe patient benefits from these studies suggests that further evaluation of the roles of Src in prostate tumours is required. Here, we summarize the interplay between Src and AR signalling during castrate-resistant prostate cancer progression to provide insights on possible approaches to treat prostate cancer patients.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.386
Teacher spread0.347 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations10
Published2022
Admission routes1
Has abstractyes

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