Genetic inactivation of TRAF3 in canine and human B-cell lymphoma
Bibliographic record
Abstract
Non-Hodgkin lymphomas (NHLs) are the most common cancer to affect pet dogs. In contrast to the many genes whose mutation contributes to lymphomagenesis in humans, relatively little is known about the acquired genetic alterations that lead to canine B-cell lymphomas (cBCLs). We performed a survey of 84 canine NHL tumors to identify genes affected by somatic point mutations. We found mutations affecting TRAF3, which encodes a negative regulator of nuclear factor (NF)-κB, to be a common feature of cBCLs, with mutations observed in 44% of tumors including a combination of somatic and rare germ-line variants. Overall, 30% of the tumors contained ≥1 somatic TRAF3 mutation. The majority of mutations are predicted to cause loss of TRAF3 protein including those impacting reading frame and splicing. To determine whether TRAF3 loss might be relevant to human NHL, we also analyzed 148 human diffuse large B-cell lymphoma (DLBCL) tumors and identified loss of TRAF3 as a common event, affecting ∼9% of DLBCLs, and reduced expression of TRAF3 among deleted cases. This study implicates mutations affecting NF-κB activity as a novel genetic commonality between human and canine NHLs and supports the potential utility of cBCLs with mutated TRAF3 as a model of the more aggressive activated B-cell subgroup of DLBCL.
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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 | 0.000 |
| 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) | 0.001 | 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".