Metabolic Alterations in Canine Mammary Tumors
Bibliographic record
Abstract
Canine mammary tumors (CMTs) are among the most common diseases in female dogs and share similarities with human breast cancer, which makes these animals a model for comparative oncology studies. In these tumors, metabolic reprogramming is known as a hallmark of carcinogenesis whereby cells undergo adjustments to meet the high bioenergetic and biosynthetic demands of rapidly proliferating cells. However, such alterations are also vulnerabilities that may serve as a therapeutic strategy, which has mostly been tested in human clinical trials but is poorly explored in CMTs. In this dedicated review, we compiled the metabolic changes described for CMTs, emphasizing the metabolism of carbohydrates, amino acids, lipids, and mitochondrial functions. We observed key factors associated with the presence and aggressiveness of CMTs, such as an increase in glucose uptake followed by enhanced anaerobic glycolysis via the upregulation of glycolytic enzymes, changes in glutamine catabolism due to the overexpression of glutaminases, increased fatty acid oxidation, and distinct effects depending on lipid saturation, in addition to mitochondrial DNA, which is a hotspot for mutations. Therefore, more attention should be paid to this topic given that targeting metabolic fragilities could improve the outcome of CMTs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".