CADD-35. THE DEVELOPMENT OF PERSONALIZED CAM AVATAR MODEL TO PREDICT CHEMOTHERAPEUTIC DRUG SENSITIVITY/RESISTANCE OF GLIOMAS
Bibliographic record
Abstract
Malignant glial tumors are associated with a poor prognosis, presenting a short median patient survival and a very limited response to therapies. Although the first line therapy is standardized, there exists no consensus as to which second line treatment modality is better. We thus sought to demonstrate the feasibility of transforming our newly established expertise into personalized treatments for glioma patients by developing an advanced in vivo Avatar model developed from patient derived tumors. The typical medical Avatar system entails implantation of patient tumor samples in immunodeficient mice for subsequent test in drug efficacy. As the generation of mouse Avatars is a slow and costly approach, many cancer patients are set to have a significant disease progression before the results from the mouse model become available. We recently developed a rapid and cost-effective, pre-clinical model that is well suited for precision medicine – the ex-ovo chicken embryo ChorioAllantoic Membrane (CAM) assay. We will present data indicating that tumor growth occurs very rapidly in ex ovo CAMs, with measurable tumors obtained within a few days, as opposed to several weeks in mice. Even though tumor sizes are smaller in the CAM than in mice, the engrafting rate is higher and tumor sizes are more uniform. Implantation of glioma tissue fragments from 25 patients led to the successful establishment of CAM xenograft tumors which faithfully recapitulate the histology of the primary tumor for the majority of patients. Furthermore, we observed a significant inhibition of tumor growth in CAMs treated with first and second line chemotherapeutic drugs. This next-generation Avatar model has the potential to become an asset for personalized medicine in gliomas treatment.
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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) | 0.001 | 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 | 0.001 | 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".