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Abstract B026: Screening non-small cell lung cancer patient-derived organoids with epigenetic probes reveals PRMT5 as an oncogenic target

2024· article· en· W4399504454 on OpenAlexaffabout
Khadija Jafarova, Quan Li, Nikolina Radulovich, Geoffrey Liu, Ming‐Sound Tsao

Bibliographic record

VenueMolecular Cancer Therapeutics · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related gene regulation
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsOrganoidCancer researchMatrigelEpigeneticsBiologyLung cancerCancerProtein arginine methyltransferase 5Targeted therapyMedicineBioinformaticsMethyltransferasePathologyCell biologyGeneMethylationGeneticsAngiogenesis

Abstract

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Abstract Lung cancer is the leading cause of cancer-related mortality. Detection of drug actionable genetic alterations has made the standard systemic therapy to a more personalized one including targeted therapy and immune checkpoint blockade. Despite the initial response, majority of patients develop resistance to treatment. Therefore, understanding the mechanisms of resistance as well as discovering novel cancer targets is necessary. Resistance to therapies can be associated with an existing clone of tumor cells with genetic alteration, or the emergence of cells with epigenetic changes overcoming the treatment-induced stress. Therefore, investigating the epigenetic aberrations that occur in tumor cells may reveal novel targetable markers that can be of great clinical significance. Methods: Organoid models established from non-small cell lung cancer (NSCLC) patient surgical resections and pleural effusion were dissociated into single cells and plated in matrigel-coated 384-well plate. A panel of 32 NSCLC organoid models were treated with a library of 41 epigenetic probes (1uM) and a DMSO control over a period of 8 days. Cell titre glo assay was performed to measure cell survival. Results: Arginine methyl transferase protein 5 (PRMT5) inhibited the cell growth of large proportion of the NSCLC organoid models. LLY-283 showed the most significant effect, with 50% of all organoid models (16/32) treated with LLY283 showing ≥50% reduction in cell viability. Further testing with multiple concentrations of several PRMT5 inhibitors revealed models with differential responses to PRMT5 inhibition. To investigate the differential gene expression changes induced by PRMT5 inhibition, we performed RNA-sequencing on three PRMT5 inhibitor sensitive and three resistant organoid models. Ongoing analyses aim at identifying differentially expressed genes in sensitive and resistant models in response to PRMT5 inhibition, and pathway enrichment analysis to determine the differential mechanisms of response to PRMT5 inhibition. Conclusion: Epigenetic drug screening on a large cohort of NSCLC organoid models reveals PRMT5 as potential novel therapeutic target in NSCLC. Further correlative analysis with transcriptomic changes induced by the PRMT5 inhibitors may reveal biomarkers of response. Citation Format: Khadija Jafarova, Quan Li, Nikolina Radulovich, Geoffrey Liu, Ming S Tsao. Screening non-small cell lung cancer patient-derived organoids with epigenetic probes reveals PRMT5 as an oncogenic target [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr B026.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.069
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.263
Teacher spread0.253 · 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".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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