The development of a rapid patient-derived xenograft model to predict chemotherapeutic drug sensitivity/resistance in malignant glial tumors
Bibliographic record
Abstract
BACKGROUND: High-grade gliomas (HGG) are aggressive brain tumors associated with short median patient survival and limited response to therapies, driving the need to develop tools to improve patient outcomes. Patient-derived xenograft (PDX) models, such as mouse PDX, have emerged as potential Avatar platforms for personalized oncology approaches, but the difficulty for some human grafts to grow successfully and the long time required for mice to develop tumors preclude their use for HGG. METHODS: We used a rapid and efficient ex-ovo chicken embryo chorioallantoic membrane (CAM) culture system to evaluate the efficacy of oncologic drug options for HGG patients. RESULTS: Implantation of fresh glioma tissue fragments from 59 of 60 patients, that include difficult-to-grow IDH-mutated samples, successfully established CAM tumor xenografts within 7 days, with a tumor take rate of 98.3%. These xenografts faithfully recapitulate the histological and molecular characteristics of the primary tumor, and the ability of individual fragments to form tumors was predictive of poor patient prognosis. Treatment of drug-sensitive or drug-resistant xenografts indicates that the CAM-glioma assay enables testing tumor sensitivity to temozolomide and carboplatin at doses consistent with those administered to patients. In a proof-of-concept study involving 14 HGG patients, we observed a correlation of 100% between the CAM xenograft response to temozolomide or carboplatin and the clinical response of patients. CONCLUSION: The CAM-glioma model is a fast and reliable assay that has the potential to serve as a complementary model to drug discovery and a real-time Avatar platform to predict the best treatment for HGG patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".