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Record W3096617937 · doi:10.1182/blood-2020-137700

<i>Nfkbiz</i> 3′ UTR Mutations Confer Selective Growth Advantage and Affect Drug Response in Diffuse Large B-Cell Lymphoma

2020· article· en· W3096617937 on OpenAlexaff
Sarah E. Arthur, Nicole Thomas, Christopher Rushton, Jeffrey Tang, Miguel Alcaide, Shannon Healy, Adèle Telenius, Anja Mottok, David W. Scott, Christian Steidl, Ryan D. Morin

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsCanada's Michael Smith Genome Sciences CentreSpinal Cord Injury BCSimon Fraser University
Fundersnot available
KeywordsUntranslated regionBiologyDiffuse large B-cell lymphomaThree prime untranslated regionMutationGeneGeneticsCRISPRCancer researchMolecular biologyMessenger RNA

Abstract

fetched live from OpenAlex

Introduction: The activated B-cell-like (ABC) molecular subgroup of diffuse large B-cell lymphoma (DLBCL) is characterized by activation of NF-κB signaling and increased mortality. Although recurrent mutations affecting genes such as MYD88, CD79A/B and TNFAIP3 contribute to this phenomenon in some cases, there remain tumors with no known genetic basis for NF-κB-activation. Previously, amplification (AMP) of the NFKBIZ locus has been reported in 10% of ABC DLBCLs and it was demonstrated that this contributes to activation of NF-κB signaling. We recently described a novel pattern of non-coding mutations affecting the 3′ untranslated region (UTR) of NFKBIZ resulting in an overall mutation rate of ~30% (UTR or AMP) in ABC DLBCL. NFKBIZ mutations are mutually exclusive with MYD88 mutations, suggesting they may also act as driver mutations. The NFKBIZ protein interacts with NF-κB transcription factors and is thought to regulate canonical NF-κB signaling. We hypothesized that NFKBIZ 3′ UTR mutations affect post-transcriptional regulation of the mRNA by disrupting the conserved secondary structure of the UTR. The binding of regulatory proteins is abrogated by the structural changes induced by mutations, which consequently lead to stabilization of the mRNA. This causes an accumulation of protein and may be a novel mechanism to promote cell growth and survival in ABC DLBCL. Methods: NFKBIZ 3′ UTR mutations were introduced into the WSU-DLCL2 DLBCL cell line using the CRISPR-Cas9 system, producing eight different CRISPR-mutant lines. Custom droplet digital PCR assays and western blotting were used to asses mRNA and protein levels, respectively. Competitive growth assays with wild-type (WT) and CRISPR-mutant lines were performed to assess whether UTR mutations provide a growth advantage in vitro. A similar study was performed in vivo by engrafting a mix of WT and mutant cells into NSG mice. We separately compared gene expression profiles (generated by RNA-Seq) of the parental cell line and a subset of CRISPR-mutant lines. Genes up- and down-regulated by NFKBIZ 3' UTR mutations were identified and analyzed for pathway enrichment. Finally, the IC50 of drugs relevant to DLBCL was determined by WST-1 assays after drug treatment on WT and mutant lines. Results: Introduction of NFKBIZ mutations into DLBCL cells confirmed that UTR mutations lead to varying degrees of increased NFKBIZ mRNA and protein levels. NFKBIZ UTR deletions afforded DLBCL cells a selective growth advantage over WT both in vitro and in vivo. In an assay containing all mutants and WT, mutants with the highest NFKBIZ expression had the largest advantage, suggesting NFKBIZ expression drives this growth advantage. In assays comparing individual mutants to WT, each mutant out-competed WT over time despite varying degrees of NFKBIZ expression, suggesting that all of these mutations act as drivers. Analysis of differentially expressed genes revealed some known NF-κB targets as well as overlap with multiple targets of MYD88, which supports our hypothesis that NFKBIZ and MYD88 regulate a common set of genes. We also discovered potential novel targets of NFKBIZ, including CD274 (PD-L1) and the src kinase HCK. Western blot confirmed that HCK protein is highly expressed in NFKBIZ CRISPR-mutant lines. HCK is a potentially relevant therapeutic target as it has been shown to be overexpressed in multiple cancer types and has been associated with poor overall survival. Expression of PD-L1 in NFKBIZ mutant cases could suggest that immunotherapies may be useful in patients with these mutations, as immunotherapies have had limited success in DLBCL, this may be a way to select patients likely to respond. Mutant cell lines had significantly higher IC50 compared to WT for the drugs Ibrutinib, Idelalisib and Masitinib, but not Bortezomib, suggesting that NKFBIZ UTR mutations confer resistance to drugs specifically targeting the NF-κB pathway. Conclusions: This work directly establishes a role for NFKBIZ amplifications and 3′ UTR mutations in driving ABC DLBCL through NF-κB signaling. We demonstrate that these mutations can cause over-expression of NFKBIZ and provide a selective growth advantage to tumor cells. We also identified novel targets of NFKBIZ including HCK and PD-L1, both of which have implications as therapeutic targets in this subset of DLBCLs. In addition, we found that these mutant lines were more resistant to some targeted lymphoma drugs. Disclosures Scott: NIH: Consultancy, Other: Co-inventor on a patent related to the MCL35 assay filed at the National Institutes of Health, United States of America.; Roche/Genentech: Research Funding; Janssen: Consultancy, Research Funding; Abbvie: Consultancy; AstraZeneca: Consultancy; Celgene: Consultancy; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoString, Research Funding. Steidl:Seattle Genetics: Consultancy; AbbVie: Consultancy; Bayer: Consultancy; Curis Inc: Consultancy; Roche: Consultancy; Bristol-Myers Squibb: Research Funding; Juno Therapeutics: Consultancy. Morin:Celgene: Consultancy.

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 score0.005

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 head0.008
GPT teacher head0.239
Teacher spread0.231 · 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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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