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Record W4401728325 · doi:10.1097/cad.0000000000001658

Screening of a kinase inhibitor library identified novel targetable kinase pathways in triple-negative breast cancer

2024· article· en· W4401728325 on OpenAlexafffund
Caroline H. Rinderle, C. Baker, C Lagarde, Khoa Nguyen, Sara Al‐Ghadban, Margarite D. Matossian, Van T. Hoang, Elizabeth C. Martin, Bridgette M. Collins‐Burow, Simak Ali, David H. Drewry, Matthew E. Burow, Bruce A. Bunnell

Bibliographic record

VenueAnti-Cancer Drugs · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsStructural Genomics Consortium
FundersOntario Genomics InstituteNational Cancer InstituteEuropean Federation of Pharmaceutical Industries and AssociationsMerck KGaAOntario GenomicsGenome CanadaMcGill UniversityGenentechBayerTulane UniversityPfizerBristol-Myers Squibb
KeywordsKinomeCyclin-dependent kinase 4Triple-negative breast cancerCancer researchKinaseCyclin-dependent kinaseCyclin-dependent kinase 7BiologyCyclin-dependent kinase 2Cyclin-dependent kinase 9CancerCell biologyCell cycleBreast cancerProtein kinase AGenetics

Abstract

fetched live from OpenAlex

Triple-negative breast cancer (TNBC) is a highly invasive breast cancer subtype that is challenging to treat due to inherent heterogeneity and absence of estrogen, progesterone, and human epidermal growth factor 2 receptors. Kinase signaling networks drive cancer growth and development, and kinase inhibitors are promising anti-cancer strategies in diverse cancer subtypes. Kinase inhibitor screens are an efficient, valuable means of identifying compounds that suppress cancer cell growth in vitro, facilitating the identification of kinase vulnerabilities to target therapeutically. The Kinase Chemogenomic Set is a well-annotated library of 187 kinase inhibitor compounds that indexes 215 kinases of the 518 in the known human kinome representing various kinase networks and signaling pathways, several of which are understudied. Our screen revealed 14 kinase inhibitor compounds effectively inhibited TNBC cell growth and proliferation. Upon further testing, three compounds, THZ531, THZ1, and PFE-PKIS 29, had the most significant and consistent effects across a range of TNBC cell lines. These cyclin-dependent kinase (CDK)12/CDK13, CDK7, and phosphoinositide 3-kinase inhibitors, respectively, decreased metabolic activity in TNBC cell lines and promote a gene expression profile consistent with the reversal of the epithelial-to-mesenchymal transition, indicating these kinase networks potentially mediate metastatic behavior. These data identified novel kinase targets and kinase signaling pathways that drive metastasis in TNBC.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentalhigh
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentalhigh
models agreeAgreement compares identical category sets and study designs across arms.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.041
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.267
Teacher spread0.249 · 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

Labeled directly by 2 models reading the full record.

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

Citations1
Published2024
Admission routes2
Has abstractyes

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