Shared and distinct genetic features in human and canine B-cell lymphomas
Bibliographic record
Abstract
ABSTRACT Animal models of human cancers are an important tool for the development and preclinical evaluation of therapeutics. Canine B-cell lymphoma (cBCL) is an appealing model for human mature B-cell neoplasms due to the high sequence similarity in cancer genes to humans and inactive telomerase in adult tissues. We performed targeted sequencing on 86 canine patients from the Canine Comparative Oncology Genomic Consortium, with 61 confirmed as B-cell lymphomas. We confirmed a high frequency of mutations in TRAF3 (45%) and FBXW7 (20%) as has been reported by our group and others. We also note a higher frequency of DDX3X (20%) and MYC (13%) mutations in our canine cohort. We compared the pattern and incidence of mutations in cBCL to human diffuse large B-cell lymphoma (hDLBCL) and human Burkitt lymphoma (hBL). Canine MYC mutations displayed a focal pattern with 80% of mutations affecting the conserved phosphodegron sequence in MYC box 1, which are known to stabilize MYC protein. We also note that MYC and FBXW7 mutations do not co-occur in our cBCL cohort, leading to the hypothesis that these mutations represent alternative approaches to stabilize MYC in canine lymphoma. We observed striking differences in the pattern of DDX3X mutations in canine lymphoma as compared to hBL and uncovered a sex-specific pattern of DDX3X mutations in hBL that is not consistent with those identified in canine lymphomas. In sum, we describe key differences between cBCL and human mature B-cell lymphomas which may indicate differences in the biology of these cancers. This should be considered in future studies of cBCL as a model of human lymphomas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".