Abstract IA27: Patient-derived organoids in pediatric cancer research
Bibliographic record
Abstract
Abstract Recent advances in in vitro culture technologies, such as adult stem cell-derived organoids, have opened up new avenues for the development of novel, more physiologic human cancer models. Such preclinical models are essential for efficient translation of basic cancer research into novel treatment regimens. We succeeded in growing organoids from a range of pediatric solid tumors, including Wilms’ tumors, renal cell carcinomas, and different types of rhabdoid tumors (i.e., AT/RT, MRT). Tumor organoids retain many characteristics of parental tumor tissue. For instance, Wilms’ tumor organoids retain the cellular heterogeneity of tumors, as they are composed of an intricate network of different cell types. Moreover, we demonstrate that tumor organoids are amenable to gene editing and high-throughput drug screens. In conclusion, our pediatric cancer organoids capture disease and tissue heterogeneity and provide a platform for basic cancer research, drug screening, and personalized medicine. Citation Format: Camilla Calandrini, Frans Schutgens, Rurika Oka, Thanasis Margaritis, Tito Candelli, Luka Mathijsen, Carola Ammerlaan, Ravian van Ineveld, Sepideh Derakhshan, Lars Custers, Philip Lijnzaad, Harry Begthel, Hinri Kerstens, Maarten Rookmaker, Marianne Verhaar, Patrick Kemmeren, Ronald de Krijger, Kathy Pritchard-Jones, Anne Rios, Marry van den Heuvel-Eibrink, Frank Holstege, Ruben van Boxtel, Hans Clevers, Jarno Drost. Patient-derived organoids in pediatric cancer research [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr IA27.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".