Abstract 2404: Change in expression of microRNAs following limb amputation in canine osteosarcoma patients predicts survival time after chemotherapy
Bibliographic record
Abstract
Abstract Osteosarcoma (OS) is the most common primary bone tumour in dogs and humans. There are an estimated 8, 000 new cases of canine OS per year in the United States compared to only 1, 000 in humans. The most common primary location for both species is the appendicular skeleton, with metastases forming mostly in the lungs and bones. Appendicular OS is not only more common in dogs, but also more aggressive, making dogs a valuable spontaneously metastasizing animal model of aggressive human OS. Median survival after amputation and adjuvant chemotherapy in dogs is 1 year, less than 30% survive 2 years, and an estimated 90% of dogs develop metastases after therapy. In contrast, humans have a 76% 5-year survival rate and 15-25% will develop metastases. Predicting clinical outcomes after treatment is difficult in both species, particularly for those without clinically detectable metastatic disease at diagnosis. MicroRNAs (miRNAs) are small, non-coding RNA that control gene expression and are often dysregulated in cancer. MiRNAs share entirely or nearly identical sequences between dogs and humans and are present in blood, making them good potential biomarkers. Recently, we identified multi-miRNA models which could reliably predict survival using pre-amputation canine OS plasma samples. Tumour cells can release miRNAs into the blood via active and passive mechanisms; therefore, with the primary tumour removed after amputation, miRNA expression levels in the blood should be altered. We hypothesize these changes in miRNA expression can be used to prognosticate canine OS patients better. MiRNA expression was measured using real-time quantitative PCR in matched pre- and post-amputation plasma samples from canine OS patients (n=25) at the Ontario Veterinary College. Change in expression was calculated for each miRNA as [(post-amputation) - (pre-amputation)] for each matched pair of samples. For each dog, survival was measured as overall survival and disease-free interval (DFI). Overall survival is the time from diagnosis to death and DFI is the time from diagnosis to clinically detectable metastasis. Dogs were censored if they died from non-OS events, were lost during follow-up, or were still alive. Prognostic ability was evaluated for individual miRNAs by identifying an optimal expression cutoff point using the survminer package in R and visualized using Kaplan-Meier curves. Decision trees were grown using the rpart package in R to evaluate whether multi-miRNA models offered improved prognostic ability compared to single miRNAs. We found that a model combining miRNA-144 and miRNA-23a, for both overall survival and DFI metrics, was able to identify 3 distinct groups which reflected survival time worse, on par, and better than the median survival time reported in literature. These models offer a simple prognostic test that may have clinical applications for veterinary and human OS patients. Citation Format: Heather Treleaven, Michael Edson, Latasha Ludwig, Alicia Viloria-Petit, R. Darren Wood, R. Ayesha Ali, Geoffrey A. Wood. Change in expression of microRNAs following limb amputation in canine osteosarcoma patients predicts survival time after chemotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2404.
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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 | 0.000 | 0.000 |
| 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".