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Record W3181382552 · doi:10.1158/1538-7445.am2021-1045

Abstract 1045: Combined targeting of actin/tropomyosin and microtubules underlies a potential treatment strategy of epithelial ovarian cancer with cell-cycle dependent synergy

2021· article· en· W3181382552 on OpenAlexaff
Xing Xu, Yao Wang, Nicole S. Bryce, Katrina Tang, Nicola S. Meagher, Eun Young Kang, Linda E. Kelemen, Martin Köbel, Susan J. Ramus, Michael Friedländer, Caroline E. Ford, Edna C. Hardeman, Peter W. Gunning

Bibliographic record

VenueCancer Research · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsVinorelbineOvarian cancerCancer researchPaclitaxelBiologyMicrotubuleCancerMedicineInternal medicinePathologyOncologyCell biologyChemotherapyCisplatin

Abstract

fetched live from OpenAlex

Abstract Anti-microtubule agents are widely used to treat ovarian cancers in the first line and recurrent setting either in combination with platinum or as single agents. However, the majority of patients will experience a recurrence, and most will die with drug resistant disease. We investigated co-targeting the actin cytoskeleton in combination with anti-microtubule agents to increase efficacy of treatment in epithelial ovarian cancers and potentially overcome resistance mechanisms. We examined the presence of actin/tropomyosin 3.1 (Tpm3.1) filaments in a large cohort of clinical specimens from patients with epithelial ovarian cancer of all histotypes using immunohistochemistry. Combinatorial effects of an anti-Tpm3.1 compound (ATM) with both vinorelbine and paclitaxel were evaluated in three ovarian cancer cell lines using cell viability and apoptosis assays. The mechanisms of synergy of both combinations were established using live-cell imaging, fluorescent microscopy, and pathway analysis. We found that Tpm3.1 is abundant and overexpressed in 97% of ovarian cancers examined (558 of 577) representing all histotypes of epithelial ovarian cancer. High levels of Tpm3.1 were also present in all sites sampled and similar at primary diagnosis and at recurrence. ATM displayed both single agent activity as well as synergy with both anti-microtubule drugs to reduce cell viability in all ovarian cancer cell lines tested, including one with platinum resistance. Only vinorelbine, however, synergised with ATM in the induction of apoptosis. Vinorelbine-induced mitotic arrest was significantly prolonged by ATM with elevated activity of the spindle assembly checkpoint, leading to almost one third of total cells dying in mitosis. In contrast, ATM showed minor impact on paclitaxel-induced mitotic defects. Both combinations resulted in a substantial increase in cells arrested in the subsequent G1 phase with a large decrease of both cyclin D1 and E1 as compared to single agents. Upregulation of p21Cip and p27Kip were associated with both combinations. In summary, targeting Tpm3.1-associated actin filaments in combination with anti-microtubule drugs is a promising treatment strategy that should be tested in clinical trials and is potentially applicable to all histotypes of ovarian cancer. Citation Format: Xing Xu, Yao Wang, Nicole Bryce, Katrina Tang, Nicola S. Meagher, Eun Young Kang, Linda E. Kelemen, Martin Köbel, Susan J. Ramus, Michael Friedlander, Caroline E. Ford, Edna C. Hardeman, Peter W. Gunning. Combined targeting of actin/tropomyosin and microtubules underlies a potential treatment strategy of epithelial ovarian cancer with cell-cycle dependent synergy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1045.

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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.309
Teacher spread0.290 · 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
Published2021
Admission routes1
Has abstractyes

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