Identification of novel genetic mutations for the treatment prognostication of canine lymphoma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 it