Abstract A40: Integration of high-throughput drug screening on patient-derived organdies into pediatric precision medicine programs: The future is now!
Bibliographic record
Abstract
Abstract Background/Objectives: The most promising option to improve outcomes for pediatric cancer patients with relapsed or refractory disease is through mutation-based targeted therapeutic strategies. However, only 50% of these tumors harbor actionable events. Therefore, compound screening on organoids grown from tumor tissue might reveal additional treatment options. Design/Methods: The Dutch Childhood Oncology Group-individualized Therapies (DCOG-iTHER) clinical trial in collaboration with INFORM consists of whole-exome sequencing (WES), low-coverage whole-genome sequencing (WGS), RNA profiling using RNAseq and Affymetrix microarrays, and a methylation array. Data are analyzed using the Dutch R2 bioinformatic platform. In addition, tumor-derived organoids are cultured profiled through the same pipeline. Semi-high-throughput screening of a 170-compound library is performed, and effect on cell viability using the CellTiter-Glo® 3D cell viability assay is assessed. Results: To date, 133 patients with relapsed/refractory pediatric malignancies have been enrolled and analysis has been completed in 80 study subjects. In 47 patients, a total of 128 molecular targets were identified and 29 patients harbored an actionable event with priority score ≥ moderate (46%). In 14% a matched targeted therapy was initiated, and one-third of these patients achieved complete remission. In addition, tumor organoids have been cultured of neuroblastoma tumors and molecular profiling was performed to confirm they reflect the patient’s tumor biology. High-throughput drug screen confirmed existing molecular targets such as ALK and c-kit and in addition revealed increased compound sensitivity to drugs not previously found through sequencing. Conclusions: Our proof of concept of patient-specific drug testing on tumor organoids within our pediatric precision medicine program might reveal additional treatment options for children who currently have none. Clinical benefit will be evaluated in our upcoming clinical trial iTHER 2.0. Citation Format: Karin Langenberg, Emmy Dolman, Jan Molenaar. Integration of high-throughput drug screening on patient-derived organdies into pediatric precision medicine programs: The future is now! [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 A40.
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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 | 0.004 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".