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Record W3158198742 · doi:10.21037/tlcr-21-303

Targeting histone deacetylase enhances the therapeutic effect of Erastin-induced ferroptosis in EGFR-activating mutant lung adenocarcinoma

2021· article· en· W3158198742 on OpenAlexaff
Tuo Zhang, Beibei Sun, Chenxi Zhong, Ke Xu, Zhexin Wang, Paul Hofman, Tatsuya Nagano, Antoine Legras, Daniel Breadner, Biagio Ricciuti, Duilio Divisi, Ralph A. Schmid, Ren‐Wang Peng, Haitang Yang, Feng Yao

Bibliographic record

VenueTranslational Lung Cancer Research · 2021
Typearticle
Languageen
FieldMedicine
TopicFerroptosis and cancer prognosis
Canadian institutionsLondon Health Sciences Centre
FundersNational Natural Science Foundation of ChinaShanghai Chest HospitalMassachusetts Department of Agricultural Resources
KeywordsVorinostatCancer researchHistone deacetylaseLung cancerEpidermal growth factor receptorMedicineCancerBiologyHistoneOncologyGeneInternal medicine

Abstract

fetched live from OpenAlex

Background: Intrinsic or acquired resistance to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) is common, thus strategies for the management of EGFR-TKIs resistance are urgently required. Ferroptosis is a recently discovered form of cell death that has been implicated in tumorigenesis and resistance treatment. Accumulating evidence suggests that ferroptosis can be therapeutically exploited for the treatment of solid tumors; however, whether ferroptosis can be targeted to treat EGFR mutant lung cancer and/or overcome the resistance to EGFR-TKIs is still unknown.Methods: The effect of ferroptosis inducers on a panel of EGFR mutant lung cancer cell lines, including those with EGFR-TKI intrinsic and acquired (generated by long-term exposure to the third-generation EGFR-TKI osimertinib), was determined using cytotoxicity assays. Further, drug candidates to enhance the effect of ferroptosis inducers were screened through implementing WGCNA (weighted gene co-expression network analysis) and CMAP (connectivity map) analysis. Flow cytometry-based apoptosis and lipid hydroperoxides measurement were used to evaluate the cell fates after treatment.Results: Compared with EGFR-TKI-sensitive cells, those with intrinsic or acquired resistance to EGFR-TKI display high sensitivity to ferroptosis inducers. In addition, Vorinostat, a clinically used inhibitor targeting histone deacetylase, can robustly enhance the efficacy of ferroptosis inducers, leading to a dramatic increase of hydroperoxides in EGFR mutant lung cancer cells with intrinsic or acquired resistance to EGFR-TKI. Mechanistically, Vorinostat promotes ferroptosis via xCT downregulation.Conclusions: Ferroptosis-inducing therapy shows promise in EGFR-activating mutant lung cancer cells that display intrinsic or acquired resistance to EGFR-TKI. Histone deacetylase inhibitor (HDACi) Vorinostat can further promote ferroptosis by inhibiting xCT expression.

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 score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.057
GPT teacher head0.420
Teacher spread0.363 · 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

Citations88
Published2021
Admission routes1
Has abstractyes

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