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Record W4385789902 · doi:10.1101/2023.08.11.552992

Synergistic effects of inhibitors targeting PI3K and Aurora Kinase A in preclinical inflammatory breast cancer models

2023· preprint· en· W4385789902 on OpenAlexafffund
Nadia Al Ali, Jacob Kment, Stephanie Young, Andrew W. Craig

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsQueen's University
FundersCanadian Institutes of Health ResearchQueen's University
KeywordsMedicinePI3K/AKT/mTOR pathwayCancer researchBreast cancerCancerPharmacologyInternal medicineSignal transductionBiology

Abstract

fetched live from OpenAlex

Abstract Background Inflammatory breast cancer (IBC) is an aggressive clinical subtype of breast cancer often diagnosed in young women. Lymph node and distant metastases are frequently detected at diagnosis of IBC, and improvements in systemic therapies are needed. For IBC that lack hormone or HER2 expression, no targeted therapies are available. Since the phosphatidyl inositol 3’ kinase (PI3K) pathway is frequently deregulated in IBC, some studies have tested the pan PI3K inhibitor Buparlisib (BKM120). Although the SUM149 IBC cell line was resistant to Buparlisib, a functional genomic screen showed that silencing of Aurora kinase A (AURKA) sensitized cells to killing by Buparlisib. In this study, we tested whether combination treatments of PI3K and AURKA inhibitors act synergistically to kill IBC cells and tumors. Methods SUM149 cells were treated with increasing doses of PI3K inhibitor Buparlisib (BKM120) and AURKA inhibitor Alisertib as monotherapies or combination therapies. Effects on target pathways, cytotoxicity, cell cycle, soft agar colony growth and cell migration were analyzed. The individual and combined treatments were also tested in a mammary orthotopic SUM149 tumor xenograft model to measure effects on tumor growth and metastasis Results The SUM149 IBC cell line treated with Buparlisib showed reduced PI3K/AKT activation but no significant skewing of cell cycle progression. Parallel studies of Alisertib treatment showed that AURKA inhibition led to a significant block in G2/M transition in SUM149 cells. In cytotoxicity assays, Buparlisib and Alisertib combination treatments were highly synergistic compared to monotherapy controls. Evidence of synergy between Buparlisib and Alisertib also extended to soft agar colony growth and wound healing motility in SUM149 cells. The combination of Buparlisib and Alisertib also reduced IBC tumor growth in mammary orthotopic xenograft assays and reduced spontaneous metastases development in lung tissue. Conclusions Although SUM149 IBC cells were relatively resistant to killing by the PI3K inhibitor Buparlisib, our study showed that co-targeting the mitotic kinase AURKA with Alisertib synergized to limit IBC cell growth and motility, as well as IBC tumor growth and metastasis.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.238
Teacher spread0.227 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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