Identification of novel genetic mutations for the treatment prognostication of canine lymphoma
Bibliographic record
Abstract
Canine lymphoma, a phenotypically and genetically heterogeneous disease, represents a significant proportion of canine cancers. We present a large-scale study of 238 dogs with lymphoma to better understand the genetic landscape of canine lymphoma, as well as the relationship to clinical outcomes. Using a targeted next-generation sequencing panel comprising 308 genes, we screened somatic and germline mutations in matched tumor and normal samples. Our findings revealed key associations between genetic alterations and lymphoma subtypes, with certain somatic variants linked to significant differences in response to common chemotherapy regimens. Recurrent mutations in genes such as KMT2C, KMT2D, NOTCH2, TRAF3, CCND1, ARID1A, CREBBP, and TP53 were observed, with TRAF3 mutations standing out for their significant association with prolonged progression-free survival and overall survival in B-cell lymphomas. In contrast, mutations in PIK3CD and CREBBP were associated with inferior outcomes in T-cell lymphomas, highlighting the immunophenotype-specific impact of genetic alterations on treatment responses. These findings support the integration of comprehensive genomic profiling in planning treatment strategies and optimizing clinical outcomes in canine 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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".