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Record W4313569637 · doi:10.3390/biom13010111

Nuclear Export in Non-Hodgkin Lymphoma and Implications for Targeted XPO1 Inhibitors

2023· review· en· W4313569637 on OpenAlexaff
Kyla L. Trkulja, Farheen Manji, John Kuruvilla, Rob C. Laister

Bibliographic record

VenueBiomolecules · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoCanada Research ChairsUniversity Health Network
Fundersnot available
KeywordsLymphomaNuclear export signalCancer researchCarcinogenesisClinical trialSuppressorTranscription factorCancerTranscription (linguistics)MedicineBiologyRNABioinformaticsImmunologyInternal medicineGeneGenetics

Abstract

fetched live from OpenAlex

Exportin-1 (XPO1) is a key player in the nuclear export pathway and is overexpressed in almost all cancers. This is especially relevant for non-Hodgkin lymphoma (NHL), where high XPO1 expression is associated with poor prognosis due to its oncogenic role in exporting proteins and RNA that are involved in cancer progression and treatment resistance. Here, we discuss the proteins and RNA transcripts that have been identified as XPO1 cargo in NHL lymphoma including tumour suppressors, immune modulators, and transcription factors, and their implications for oncogenesis. We then highlight the research to date on XPO1 inhibitors such as selinexor and other selective inhibitors of nuclear export (SINEs), which are used to treat some cases of non-Hodgkin lymphoma. In vitro, in vivo, and clinical studies investigating the anti-cancer effects of SINEs from bench to bedside, both as a single agent and in combination, are also reported. Finally, we discuss the limitations of the current research landscape and future directions to better understand and improve the clinical utility of SINE compounds in NHL.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.037
GPT teacher head0.352
Teacher spread0.315 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations11
Published2023
Admission routes1
Has abstractyes

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