NFKBIZ3′ UTR Mutations Confer Selective Growth Advantage and Activate Genes with Therapeutic Implications in Diffuse Large B-Cell Lymphoma
Bibliographic record
Abstract
Introduction: The activated B-cell-like (ABC) subtype of diffuse large B-cell lymphoma (DLBCL) is characterized by activation of NF-κB signaling and increased mortality. Recurrent mutations affecting genes such as MYD88, CD79A/B and TNFAIP3 have been shown to be involved in activation of the NF-κB pathway in ABC DLBCL; however there still remain cases with no known genetic basis for this pathway activation suggesting that our understanding of the drivers of ABC DLBCL remains incomplete. Previously, NFKBIZ was shown to be amplified in ~10% of ABC DLBCLs and to contribute to activation of NF-κB signaling. We have recently described a novel pattern of mutations affecting the 3′ UTR of NFKBIZ resulting in an overall mutation rate of 34% for this gene (UTR mutations or amplifications) in ABC DLBCL. These NFKBIZ UTR mutations are mutually exclusive with MYD88 mutations, thus suggesting they may also lead to activation of NF-κB signaling. NFKBIZ encodes the IκB-ζ protein, which interacts with NF-κB transcription factors and is thought to regulate canonical NF-κB signaling. We hypothesized that NFKBIZ UTR mutations affect the normally rapid degradation of mRNA by disrupting secondary structures recognized by RNA-binding proteins such as ribonucleases. The resulting elevated mRNA levels would in turn lead to accumulation of IκB-ζ protein as a novel mechanism to promote cell growth and survival in ABC DLBCL. Methods: NFKBIZ 3′ UTR mutations were introduced into a DLBCL cell line (WSU-DLCL2) using the CRISPR-Cas9 system. NFKBIZ mRNA and protein levels were evaluated using custom designed droplet digital PCR assays and western blot analyses. Cells were stimulated with LPS to induce NFKBIZ expression and mRNA and protein levels were measured in wild-type (WT) and CRISPR-mutant lines to compare rates of mRNA decay and protein expression. A competitive growth assay with WT and CRISPR-mutant lines was performed to assess whether UTR mutations provide a growth advantage in culture (in vitro) and in mouse xenografts (in vivo). The composition of the pool of WT and mutant lines was determined by comparing WT and mutant DNA sequence proportions. RNA-sequencing was then performed on WT and a subset of CRISPR-mutant cell lines to identify genes up-regulated by IκB-ζ in mutant lines. Findings from these models were compared to the effects of NFKBIZ over-expression in patient tissues. Results: Introduction of NFKBIZ mutations into a DLBCL cell line confirmed that UTR deletions lead to increased mRNA and protein levels. LPS stimulation showed prolonged mRNA elevation in mutant lines, consistent with our model wherein these mutations disrupt post-transcriptional regulatory mechanisms. NFKBIZ UTR deletions gave DLBCL cells a selective growth advantage over WT both in vitro (cell culture) and in vivo (xenograft mouse model). RNA-sequencing of mutant and WT lines revealed possible transcriptional targets of IκB-ζ including some NF-κB targets and genes commonly over-expressed in ABC DLBCL (BATF, MAML2, and TNFRSF13B). HCK, a gene known to be activated by MYD88 was also upregulated in NFKBIZ mutant lines. This is consistent with our hypothesis that mutations in MYD88 and the NFKBIZ UTR are mutually exclusive because they activate similar pathways. HCK is also a target of ibrutinib, suggesting the potential utility of ibrutinib in these patients. Novel targets of IκB-ζ were also discovered through this analysis including CD274, the gene encoding PD-L1. This could be a novel mechanism for DLBCL tumours to express PD-L1 and therefore suggest that these tumours may be susceptible to anti-PD1/PD-L1 immunotherapies. Conclusions: This work highlights the role of NFKBIZ and 3′ UTR mutations in driving ABC DLBCL. We demonstrate that these mutations can lead to over-expression of NFKBIZ and provide a selective growth advantage to cells both in vitro and in vivo. In addition, we described multiple targets of IκB-ζ that may have implications in treatment susceptibility and/or resistance in ABC DLBCL. These findings contribute to a better understanding of the genetic basis of DLBCL, which is necessary to guide personalized therapeutic strategies. Disclosures Scott: Roche/Genentech: Research Funding; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoSting [Institution], Research Funding; Janssen: Consultancy, Research Funding; Celgene: Consultancy. Steidl:Bristol-Myers Squibb: Research Funding; Nanostring: Patents & Royalties: Filed patent on behalf of BC Cancer; Juno Therapeutics: Consultancy; Tioma: Research Funding; Bayer: Consultancy; Roche: Consultancy; Seattle Genetics: Consultancy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".