MétaCan
Menu
Back to cohort
Record W4390952294 · doi:10.21203/rs.3.rs-3782509/v1

Acquired resistance to CDK4/6 inhibition is associated with dysregulation of multiple pathways including cyclin D-CDK4/6-RB, EGFR/HER, AKT/mTOR and IFN signaling

2024· preprint· en· W4390952294 on OpenAlexafffund
Melanie Spears, Lauren Bathurst, Linda M. Liao, Megan Hopkins, Cheryl Crozier, Quang M. Trinh, Emanuel F. Petricoin, Jane Bayani, John M.S. Bartlett

Bibliographic record

VenueResearch Square · 2024
Typepreprint
Languageen
FieldMedicine
TopicAdvanced Breast Cancer Therapies
Canadian institutionsUniversity of TorontoOntario Institute for Cancer Research
FundersGenentechGovernment of OntarioAstraZenecaNational Institutes of HealthPfizer
KeywordsProtein kinase BPI3K/AKT/mTOR pathwayCancer researchSignal transductionCell biologyChemistryBiology

Abstract

fetched live from OpenAlex

Abstract While the use of CDK4/6 inhibitors has significantly improved outcomes for patients with ER+/HER2- tumours, understanding the mechanisms responsible for resistance is essential to identify predictive biomarkers and alternative treatment options after tumour progression. To this end, we developed in-vitro models of acquired resistance to palbociclib and abemaciclib using MCF7 and T47D cell lines. Genomic, transcriptomic, and proteomic analyses were used to identify potential actionable molecular alterations in these models. Results show that acquired resistance was associated with dysregulation of multiple signaling pathways, including cyclin D-CDK4/6-RB, EGFR/HER and AKT/mTORC1. Strikingly, acquired resistance across all cell lines was also associated with an upregulated interferon (IFN) response. Expression of an IFN-based gene signature derived from these models was upregulated in breast cancer cell lines and early-stage tumours intrinsically resistant to CDK4/6i, and thus warrants further clinical evaluation as a predictive biomarker of resistance.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.214
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.088
GPT teacher head0.384
Teacher spread0.296 · 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.

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
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueResearch SquareSame topicAdvanced Breast Cancer TherapiesFrench-language works237,207