Flow Cytometric Detection of Circulating Osteosarcoma Cells in Dogs
Bibliographic record
Abstract
Abstract Osteosarcoma (OSA) is a malignant tumor of middle‐aged dogs and adolescent humans. The clinical outcome of OSA has not improved over more than three decades, and dogs typically succumb to metastatic disease within 6 months despite tumor resection through limb amputation and adjuvant chemotherapy. Therefore, undetectable tumor cells with potential to form metastases are present at diagnosis. An assay to identify canine immortalized and primary OSA cells through flow cytometric detection of intracellular collagen 1 (Col I) and osteocalcin was optimized, and applied to blood samples from tumor‐bearing dogs for detection of circulating tumor cells (CTCs). Spiking variable number of OSA cells into normal dog blood recovered 50–60% of Col I positive cells with high forward and variable side light scatter. An algorithm to exclude nonviable, doublet, and autofluorescent cells was applied to sequential blood samples from three dogs obtained prior to and after limb amputation, and at approximately, triweekly intervals over 121, 142, and 183 days of chemotherapy, respectively. Dogs had >100 CTC/10 6 leukocytes prior to amputation, variably frequent CTC during chemotherapy, and an increase up to 4,000 CTC/10 6 leukocytes within 4 weeks before overt metastases or death. Sorted CTCs were morphologically similar to direct tumor aspirates and positive for Col I. Although preliminary, findings suggest that CTCs are frequent in canine OSA, more numerous than carcinoma CTC in humans, and that an increase in CTC frequency may herald clinical deterioration. This assay may enable enumeration and isolation of OSA CTC for prognostic and functional studies, respectively. © 2019 International Society for Advancement of Cytometry
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".