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Abstract P5-03-11: Sensitivity to cell cycle inhibitors in taxane resistant breast cancer models

2019· article· en· W2944046125 on OpenAlexaff
Karen J. Taylor, Nicola Lyttle, Linchuan Liao, Cheryl Gourley, David Cameron, JM Bartlett, Melissa Spears

Bibliographic record

VenueCancer Research · 2019
Typearticle
Languageen
FieldMedicine
TopicAdvanced Breast Cancer Therapies
Canadian institutionsUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsTaxaneCyclin-dependent kinase 1PaclitaxelCancer researchSKBR3Cell cycleCell cycle checkpointBiologyBreast cancerCancerMedicineInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background: The use of anthracycline and taxane chemotherapy has improved overall and disease-free survival in breast cancer. However these agents have significant toxicity. In addition, breast cancers can acquire or possess intrinsic chemoresistance. It is imperative to identify patients who will benefit most from adjuvant taxane treatment and those with resistant tumours who could be spared unnecessary toxicity. Methods: A panel of in vitro derived cell lines models of taxane resistance were generated by serial culture in escalating doses of either paclitaxel or docetaxel until resistance was achieved. Taxane resistant cells were characterised by 2D growth, cell cycle and apoptosis analyses. Genomic profiling using the NanoString® platform was performed to identify differentially expressed genes. The identification of kinases which target the chemoresistant models was achieved through a small molecule kinase inhibitor screen. Effects of selected target kinases on cell proliferation, cell cycle, apoptosis and protein expression were assessed. Results: Resistant cell lines exhibited an IC50 at least 40-fold higher than that of the parental cells and displayed cross-resistance to the non-establishing taxane. Cell cycle analysis revealed taxane treatment failed to induce G2/M arrest in the resistant models. A reduced apoptotic response was demonstrated. Genomic profiling identified pathways associated with the cell cycle as being significantly altered. Dinaciclib, a CDK inhibitor of CDK1, CDK2, CDK5 and CDK9, inhibited taxane resistant cell growth with IC50s comparable to the parental lines. Upon exposure to dinaciclib, cell cycle arrest at G2/M was induced and marked apoptosis demonstrated. A reduction in cyclin B1, PLK1 and pRB was observed by western blotting. Table 1:Sensitivity of taxane resistant cell lines models to paclitaxel and docetaxelCell line modelPaclitaxel (μM)Docetaxel (μM)MDA-MB-231 Parent0.004 ± 0.0030.002 ± 0.003MDA-MB-231 PACR0.184 ± 0.030.017 ± 0.02MDA-MB-231 DOCR0.414 ± 0.0470.262 ± 0.058MCF7 Parent0.004 ± 0.00050.005 ± 0.001MCF PACR0.769 ± 0.1050.07 ± 0.02 Table 2:Gene ontology enrichment analysis of biological process terms significantly over-represented in MDA-MB-231 PACR cell line modelGO TermP-valueFDRpositive regulation of transcription from RNA polymerase II promoter1.11E-162.44E-13positive regulation of cell proliferation9.99E-161.10E-12activation of cysteine-type endopeptidase activity involved in apoptotic process1.43E-106.27E-08negative regulation of apoptotic process2.13E-095.83E-07extrinsic apoptotic signaling pathway8.33E-091.62E-06cell cycle arrest8.89E-091.62E-06positive regulation of cell migration2.83E-084.42E-06 Conclusion: In this study we identified candidate resistance-associated pathways which were differentially expressed between in vitro derived taxane resistant cell line models and the sensitive parental line. The CDK inhibitor, dinaciclib, demonstrated potent activity against the taxane resistant cell line models. Clinical validation to ascertain the role of dinaciclib as a novel therapeutic in the treatment of chemorefractory breast cancer is required. Citation Format: Taylor KJ, Lyttle N, Liao L, Gourley C, Cameron DA, Bartlett JM, Spears M. Sensitivity to cell cycle inhibitors in taxane resistant breast cancer models [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P5-03-11.

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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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.044
GPT teacher head0.378
Teacher spread0.334 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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