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Abstract LB054: A novel chemical proteomics approach toward the identification of kinase targets to sensitize tumour cells to the standard of care drug paclitaxel

2024· article· en· W4393989233 on OpenAlexaff
Alaa Baryyan, Ana Belén Fraga Timiraos, Kurt Dejgaard, Bertrand J. Jean‐Claude

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsMcGill University
Fundersnot available
KeywordsPaclitaxelProteomicsIdentification (biology)Standard of careMedicinePharmacologyDrugComputational biologyCancer researchChemistryCancerBiologyBiochemistryInternal medicine

Abstract

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Abstract Introduction: Chemotherapy, through various signaling pathways changes the kinase activation profiles in tumour cells and it has been shown that blockade of specific kinases led to sensitization of tumour cells to chemotherapeutic drugs. We surmised that, following chemotherapeutic drug treatment, determination of the kinase binding profile of a specific inhibitor using its corresponding chemoprobe may lead to the identification of kinases to be targeted for potentiating the action of the chemotherapeutic drug. The approach we chose to study was to design a chemoprobe carrying a kinase inhibitor warhead to capture and read the kinase binding profiles before and after the chemotherapeutic drug treatment. Our proof-of-concept was made with AB19, a chemoprobe designed with the scaffold of crizotinib, a MET receptor tyrosine kinase inhibitor. The ability of AB19 to bind to MET was validated using KATO-II gastric cancer cells in which MET is amplified. Material and Methods: Gastric cancer cell line KATO-II, prostate cancer cells DU145, PC3 and 22RV1 and normal prostate cells RWPE-1 were used to determine IC50 values for the different drugs alone and in combination with paclitaxel. Cells were lysed 2 h and 24 h after drug treatment. The chemoprobes (control probe or AB19) were subsequently added and kinases captured using magnetic pull down. Proteomics analyses were performed by mass spectrometry. Results: The results showed that AB19 pulled down MET (its primary target) and many other kinases, including LCK, Src, EPHA2, and FAK1 that play an important role in cancer progression. Importantly, the kinase pull-down profiles by the chemoprobe were similar to that reported for free crizotinib, thereby validating the ability of the chemoprobe to simulate the binding of the free drug. In vitro studies with AB19 revealed the following: (a) the kinase pull-down profiles varied with cell lines, with the presence or absence of some important kinases and (b) under conditions where the cells were pretreated with paclitaxel, the kinase profiles between treated and non-treated cells were similar. However, disappearance and appearance of key targets were observed. Based upon the detection of an increase in aurora kinase A (AURKA) and mammalian target of rapamycin (mTOR) spectral counts, we designed 2-drug combinations of specific inhibitors of the latter targets with paclitaxel under equieffective and sequential administrations. Of all the combinations studied, Alisertib (AURKA inhibitor) + paclitaxel was the most synergistic. Conclusion: Taken together, the results suggest that AURKA is a unique target for sensitizing cells to paclitaxel and this novel chemical proteomics approach can be developed as a novel strategy to identify kinase inhibitors capable of significantly sensitizing cancer cells to standard of care drugs. Citation Format: Alaa Omar Baryyan, Ana Belen Fraga Timiraos, Kurt Dejgaard, Bertrand J. Jean-Claude. A novel chemical proteomics approach toward the identification of kinase targets to sensitize tumour cells to the standard of care drug paclitaxel [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB054.

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)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.068
GPT teacher head0.419
Teacher spread0.351 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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