Recurrent Oncogenic Mutations in CCND3 in Aggressive Lymphomas
Bibliographic record
Abstract
Abstract Abstract 435 The recent development of high throughput mRNA sequencing technologies has allowed major advances in the characterization and quantification of transcriptomes in human cancer. These advances include gene mutation detection, gene fusion discovery, analysis of alternative splicing events, and unbiased gene expression profiling. To comprehensively discover pathogenic sequence variants in lymphomas, we used Illumina technology to sequence mRNA of 206 lymphoma biopsies and cell lines, including 64 diffuse large B-cell lymphomas (DLBCL) of the activated B-cell-like (ABC) subtype, 71 DLBCL of the germinal center (GCB) subtype, and 41 Burkitt's lymphomas (BL). This effort uncovered a large number of mutations that were verified by Sanger sequencing. Using this methodology, we discovered somatic mutations in CCND3, encoding Cyclin D3, in 38 % of BLs, 14 % of ABC DLBCLs, and 10 % of GCB DLBCLs. These mutations stabilized the Cyclin D3 protein by altering a phosphorylation motif at Thr283 that is required for proteasomal degradation. Knockdown of CCND3 by RNA interference confirmed the oncogenic addiction to this gene in the mutated cell lines. In parallel, an RNA interference screen revealed that the cell lines that are dependent on CCND3 are also reliant on cyclin-dependent kinase 6 (CDK6), which together with Cyclin D3 regulates the G1/S transition of the cell cycle. Ectopic expression of the mutant CCND3 isoforms accelerated lymphoma cell line proliferation thereby demonstrating the oncogenic potential of these mutations. Remarkably, treatment of the CCND3/CDK6 addicted cell lines with a small molecule inhibitor of CDK 4/6 caused G1 arrest followed by apoptosis suggesting novel therapeutic strategies for these aggressive lymphomas. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".