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PF576 NONSENSE‐MEDIATED MRNA DECAY IS A THERAPEUTIC TARGET IN MULTIPLE MYELOMA

2019· article· en· W2952817796 on OpenAlexaff
Alexander C. Leeksma, I. A. M. Derks, Brett Garrick, Aldo Jongejan, Martino Colombo, Timon A. Bloedjes, Torsten Trowe, Jim Leisten, Matthew Groza, Rama Krishna Narla, Ruth J. F. Loos, Michael Kersten, Deborah S. Mortensen, Perry D. Moerland, Jej Guikema, Arnon P. Kater, Eric Eldering, Ellen Filvaroff

Bibliographic record

VenueHemaSphere · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsNonsense-mediated decayGene knockdownRNA splicingCell cultureBiologyMolecular biologyCancer cellTranslation (biology)Cancer researchCancerMessenger RNACell biologyChemistryGeneRNABiochemistryGenetics

Abstract

fetched live from OpenAlex

Background: Nonsense‐mediated mRNA decay (NMD) is a translation‐coupled cellular quality control system that degrades mRNAs containing premature termination codons (PTCs) and regulates the expression of 5–10% of normal mRNAs. NMD prevents translation of misfolded proteins, thereby halting the potential activation of the unfolded protein response (UPR). Cancer cells display increased alternative splicing events and splicing factors are frequently mutated in cancer. This suggests that NMD, and its link with ER‐stress, might be therapeutically targetable in cancer. This notion was investigated, based on preliminary data showing that CC‐115, a known inhibitor of mTOR kinase (TORK) and DNA‐PK, also inhibits a key player in NMD activation. Aims: To explore the application of CC‐115 as a specific inhibitor of NMD in cancer. Methods: A comparative screen in 141 cancer cell lines was performed with CC‐115 versus specific inhibitors of TORK and DNA‐PK, using inhibition of proliferation (Cell‐Titer Glow) and cell death (apoptosis) as read‐out. Additional targets of CC‐115 were determined by ActivX KiNativ analysis, and confirmed by knockdown and additional kinase inhibitors. Dependence of CC‐115 sensitivity on specific genes was determined by CRISPR/Cas9 technology in various multiple myeloma (MM) cell lines. RNA sequencing was used for identification of potential targets in 3 sensitive and 3 resistant cell lines and confirmed by qPCR. Activity in vivo was tested in xenograft tumors. Results: ActivX KiNativ analysis revealed NMD inhibition as an additional target of CC‐115. NMD inhibition with CC‐115 resulted in a decreased phosphorylation of UPF1 and a dose dependent increase of NMD sensitive transcripts. CC‐115 activated the PERK branch of the UPR, as evidenced by increased mRNA expression of ATF4 , ATF3 and CHOP, while HSPA5 and sXBP1 were not affected. Cell death analyses among various B cell lines showed that MM cell lines were in general highly sensitive to CC‐115. Activity of CC‐115 correlated strongly with cell death by the known ER‐stress inducer, thapsigargin. Cell death by CC‐115 occurred via the mitochondrial pathway of apoptosis, as it depended on caspase activity and the presence of Bax‐Bak. RNA sequencing indicated that CC‐115‐sensitive cell lines had a lower baseline expression of cytoskeleton and vesicle transport genes and a higher expression of genes involved in SMAD and BMP signaling. Gene set enrichment analysis revealed an overrepresentation of MYC downstream targets, oxidative phosphorylation and UPR genes in CC‐115 sensitive vs. resistant cell lines, and confirmed the inhibitory effects of CC‐115 on NMD. Lastly, antitumor effects of CC‐115 were superior to specific TORK inhibition in xenograft mice, without general toxicity. Summary/Conclusion: Hematologic tumors with high protein production are highly sensitive to CC‐115, a clinically exploitable inhibitor of NMD. This might be particularly interesting in hematologic malignancies such as multiple myeloma, that depend on NMD to avoid excessive protein stress.

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 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 score0.076
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

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 head0.013
GPT teacher head0.255
Teacher spread0.242 · 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.

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
Published2019
Admission routes1
Has abstractyes

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