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Record W2950978058 · doi:10.1002/hon.43_2629

FUNCTIONAL CHARACTERIZATION OF <i>NFKBIZ</i> 3′ UTR MUTATIONS IN DIFFUSE LARGE B‐CELL LYMPHOMA

2019· article· en· W2950978058 on OpenAlexaff
Sarah E. Arthur, Anja Mottok, Razvan Cojocaru, Aixiang Jiang, Bruno M. Grande, Miguel Alcaide, Christopher Rushton, Daisuke Ennishi, Prince Kumar Lat, Jordan Davidson, Kevin Bushell, Timothy E. Audas, Peter J. Unrau, Dipankar Sen, Randy D. Gascoyne, Marco A. Marra, Joseph M. Connors, Gregg B. Morin, David W. Scott, Christian Steidl, Ryan D. Morin

Bibliographic record

VenueHematological Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsCanada's Michael Smith Genome Sciences CentreGenome British ColumbiaSpinal Cord Injury BCSimon Fraser University
Fundersnot available
KeywordsUntranslated regionDiffuse large B-cell lymphomaBiologyThree prime untranslated regionSignal transductionMutationGeneGeneticsCancer researchMolecular biologyMessenger RNA

Abstract

fetched live from OpenAlex

Introduction: The activated B-cell-like (ABC) subtype of diffuse large B-cell lymphoma (DLBCL) is characterized by activation of NF-kB signaling and an increased risk of mortality. Recurrent somatic mutations affecting genes such as MYD88, CD79A/B and TNFAIP3 have been shown to constitutively activate the NF-kB pathway through B-cell receptor signaling in ABC DLBCL; however, there still remain cases with no known genetic basis for this pathway activation (Arthur et al. Nat Com 2018). We recently published a meta-analysis of DLBCL genome and targeted sequencing data identifying non-coding mutations. We described novel mutations affection the 3′ untranslated region (UTR) of NFKBIZ in 18% of ABC DLBCLs. Overall, NFKBIZ is mutated (amplifications and UTR mutations) in 34% of ABC DLBCLs. These NFKBIZ mutations are mutually exclusive with MYD88 mutations, implicating them in activation of the NF-kB signaling pathway. NFKBIZ encodes the IkB-ζ protein, which interacts with NF-kB transcription factors and is thought to regulate canonical NF-kB signaling. We hypothesized that these mutations affect the ability of regulatory mechanisms to target this transcript for degradation through disruption of UTR secondary structures. This leads to enhanced mRNA stability and elevated protein levels and represents a novel mechanism of promoting NF-kB signaling in ABC DLBCL. Methods: NFKBIZ 3′ UTR mutations were introduced in a DLBCL cell line using CRISPR-Cas9. NFKBIZ mRNA and protein levels were evaluated using custom designed droplet digital PCR assays and western blot. RNA-sequencing was performed on mutant and wild-type (WT) cell lines to identify genes up-regulated by IkB-ζ. A competitive growth assay with WT and CRISPR mutant lines was set up to assess whether UTR mutations provide a growth advantage in culture. The pool composition was determined by DNA sequencing and comparison of WT and mutant DNA sequences. Results: Introduction of NFKBIZ mutations into DLBCL cell lines confirmed that UTR deletions lead to increased levels of mRNA and protein. Stimulation with LPS revealed that mRNA levels stay elevated for longer in mutant lines. NFKBIZ UTR deletions also give DLBCL cells a selective growth advantage over WT when grown together in culture. RNA-sequencing of mutant and WT lines revealed possible transcriptional targets of IkB-ζ, including TNFRSF14B, HCK, GNAZ, BATF and CD274. These targets are either involved in activation of NF-kB signaling, associated with decreased survival in other lymphomas or potential new drug targets in NFKBIZ mutant DLBCL. Conclusions: This work highlights the role of NFKBIZ and 3′ UTR mutations in driving ABC DLBCL. We demonstrate that these UTR mutations can lead to over-expression of NFKBIZ and activate potentially novel drug targets in ABC DLBCL. These findings contribute to a better understanding of the genetic basis of DLBCL, which is necessary to guide personalized therapeutic strategies. Keywords: activated B-cell-like (ABC); molecular genetics; NF-kB.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.820

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)0.0010.000

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 head0.019
GPT teacher head0.273
Teacher spread0.254 · 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 designObservational
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

Citations0
Published2019
Admission routes1
Has abstractyes

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