Comparative analysis of patient-derived organoids and patient-derived xenografts as avatar models for predicting response to anti-cancer therapy
Bibliographic record
Abstract
Patient-derived xenografts (PDX) and patient-derived organoids (PDO) are widely used to model cancer and predict treatment response in matched patients. However, their predictive accuracy has not been systematically studied nor compared. We conducted a systematic review and meta-analysis of studies using PDX or PDO from solid tumors treated with identical anti-cancer agents as the matched patient, identifying 411 patient-model pairs (267 PDX, 144 PDO). Overall concordance in treatment response between patients and matched models was 70%, with no significant differences between PDX and PDO. Sensitivity, specificity, and positive and negative predictive value were also comparable. Patients whose matched PDO responded to therapy had prolonged progression-free survival. For PDX, this association held only when analyses were restricted to patient-model pairs with low risk of bias after applying a bias assessment metric. Together, these findings suggest that in some contexts, PDO perform similarly to PDX in predicting matched-patient response while potentially offering lower financial and ethical burdens. Given that both platforms have distinct strengths and weaknesses, they continue to serve complementary roles in translational cancer research. Additional prospective studies will be required before definitive recommendations can be made.
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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 | 0.039 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".