Abstract A038 Plasma exosome concentrations in healthy canines and dogs with osteosarcoma
Bibliographic record
Abstract
Abstract Canine osteosarcoma is a common disease that shares many clinical and biological features with human osteosarcoma. We have previously demonstrated the potential of using mRNA signatures derived from exosome cargo to detect minimal residual disease in dogs undergoing treatment for osteosarcoma. Our goal is to continue exploring the potential of exosomes in cancer diagnosis and treatment by understanding how exosomes contribute to the biology of osteosarcoma. For this study, we compared exosome concentrations in the plasma of 26 healthy dogs and 29 dogs with various cancers, including 8 dogs with osteosarcoma. Dogs with cancer had higher exosome concentrations than the healthy dogs (p=0.0007), as did the subset of dogs with osteosarcoma. Specifically, the average exosome concentration in the samples from healthy dogs was 5x1011 (+/-2.72x1011) particles/mL, with a normal distribution ranging from 7.22x1010 to 1x1012, whereas the average exosome concentration in the samples from dogs with osteosarcoma was 1.14x1012 (+/-1.41x1012) particles/mL, with a non-parametric distribution ranging from 1.16x1011 to 4.4x1012. Ongoing work includes expanding the data set with archival samples, as well as evaluating potential relationships between exosome concentrations and disease-free survival in dogs with osteosarcoma. In conclusion, the difference in plasma exosome concentrations between healthy dogs and dogs with osteosarcoma, and specifically the heterogeneous distribution of exosomes in the plasma of dogs with osteosarcoma suggests that this could be one feature used in combination with analyses of exosome cargo to aid in prognosis and manage treatment of patients with osteosarcoma. Citation Format: Jaron M. Magstadt, Courtney H. Labé, Meagan Wojtysiak, Kyle DuVal, Ali Khammanivong, Lauren J. Mills, Amber Winter, Caitlin Feiock, Kelly Reid, Mitzi Lewellen, Logan G. Spector, Brenda J. Weigel, Jaime F. Modiano, Kelly M. Makielski. Plasma exosome concentrations in healthy canines and dogs with osteosarcoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr A038.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".