Abstract PR004: Next generation pediatric precision oncology: Functional profiling of patient-derived viable tumor material to link genotype and phenotype
Bibliographic record
Abstract
Abstract Background Pediatric precision medicine programs iTHER (The Netherlands), INFORM (international), and ZERO (Australia), report actionable molecular drug targets in 70-86% of children with cancer. This paradigm-changing approach has led to clinical benefit in selected groups. However, no relevant molecular targets are identified in subsets of patients e.g., malignant rhabdoid tumors or ependymoma, and clinical impact for individual patients remains hard to predict. This is the rationale underpinning our aim to integrate functional approaches into therapeutic decision making. Here, we report on drug sensitivity screening integrated with matched molecular profiles from the global collaboration between iTHER, INFORM and ZERO. Methods Functional profiling was performed on patient-derived viable material from high-risk, relapsed or refractory pediatric tumors after short- or long-term culture, and/or in vivo expansion. Long-term cultured and in vivo expanded samples were authenticated using short tandem repeat analysis and validated using single nucleotide polymorphism array, immunohistochemistry and/or flow cytometry. Samples were exposed to clinically relevant drug libraries and drug efficacy parameters including half-maximal inhibitory concentration (IC50), area under the dose-response curve value (AUC), and drug sensitivity score (DSS) were obtained. Tumor molecular profiles were established using whole-genome sequencing or whole exome sequencing, RNA sequencing and/or methylation profiling. All data were integrated, after which clustering analysis and gene set enrichment analysis were used to identify drug sensitivity patterns. Results Drug sensitivity screening was performed for 270 (iTHER=71; INFORM=109; ZERO=90) solid tumors, 105 (iTHER=19 ; INFORM=45; ZERO=41) CNS tumors, and 23 (ZERO) hematological malignancies. Results were collected in the R2 platform (http://r2platform.com), which incorporates dedicated visualization and analysis tools. As a result, a powerful reference set is available, reflecting both known pharmacologic vulnerabilities, such as sensitivity of NTRK-fusion positive samples to NTRK inhibition, and novel vulnerabilities including sensitivity of PIK3R1 mutated brain tumors to MEK inhibition. Additionally, tumor-type specific drug sensitivities were discovered, including sensitivity to MEK inhibitors in Wilms tumors and high-grade and diffuse midline gliomas without Ras-MAPK pathway alterations. Moreover, in vitro non-responsiveness of heavily pre-treated samples to chemotherapy was confirmed, which potentially could avoid ineffective treatments. Clinical follow-up of a subset of patients confirmed correlation with in vitro drug sensitivity. Conclusions Our data support complementary functional profiling to omics-guided precision medicine by strengthening molecular results; identifying new treatment options; and avoiding ineffective treatments. Global collaboration and data sharing is essential as childhood cancer remains rare, and innovative approaches are urgently needed to improve outcome for future patients. Citation Format: Eleonora J. Looze, Jie Mao, Heike Peterziel, Arjan Boltjes, Bianca Koopmans, Jan Koster, Marcel Kool, Max M. Van Noesel, Olaf Witt, Jan J. Molenaar, Ina Oehme, M. Emmy M. Dolman, Karin P.S. Langenberg. Next generation pediatric precision oncology: Functional profiling of patient-derived viable tumor material to link genotype and phenotype [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr PR004.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".