Abstract A52: Zero Childhood Cancer (ZERO): A comprehensive precision medicine platform for children with high-risk cancer
Bibliographic record
Abstract
Abstract Background/Objectives: The National Zero Childhood Cancer (ZERO) program, the most innovative child cancer research program in Australia, aims to assess the feasibility of a comprehensive precision medicine approach to improve outcomes for patients with high-risk pediatric cancer. Design/Methods: ZERO combines comprehensive molecular profiling analysis (whole-genome sequencing [tumor, germline DNA], deep sequencing of a 386-cancer associated gene panel, whole-transcriptome [RNASeq], DNA methylation profiling [Epic 850K array]) with in vitro high-throughput drug screening (124-compound library, single agent) and patient-derived xenograft (PDX) drug efficacy testing. Results are curated and recommendations made through a national Multidisciplinary Tumor Board (MTB). Recommendations consist of targeted therapy, change of diagnosis, or genetics referral for a germline cancer predisposition gene mutation. Results: The ZERO national trial (PRISM), which opened in September 2017 at all 8 pediatric centers in Australia, has enrolled 213 patients in the first 20 months (36% central nervous system tumors, 29% sarcoma, 15% leukemias/lymphomas, 7% neuroblastoma, 13% other rare or unknown cancers). The unique ZERO testing platform has resulted in at least one recommendation being issued for 70% of patients. 12% of patients have a reportable germline mutation. We have developed an integrated analytical pipeline to interrogate and cross-validate the full range of variants, structural abnormalities, and mutational signatures identified in pediatric cancers, and incorporate the molecular data with in vitro and in vivo drug sensitivity data where possible. The highest yield of reportable variants is derived from the integrated analysis of WGS and RNASeq. The most highly mutated genes/pathways include TP53, MAPK pathway, CDK/cyclin family, and PI3K/mTOR pathway. Mutation signatures and tumor mutation burden assessment support targeted treatment recommendations (e.g., PARP inhibitors or immunotherapy) and contribute to assessment of pathogenicity of some germline variants. Early experience with drug efficacy studies suggests these data may corroborate genomic therapeutic recommendations and may also identify unanticipated drug “vulnerabilities.” Of the first 21 patients who received an MTB-recommended therapy not usually used in the treatment of the respective tumors generally, 33% have a partial or complete response, 24% have stable disease, and 43% have progressive disease. Conclusion: ZERO demonstrates the feasibility of a comprehensive precision medicine platform to identify treatment recommendations in high-risk pediatric cancer patients. ZERO is also partnering nationally and internationally to conduct parallel research studies in immunoprofiling, liquid biopsy, cancer predisposition, proteomics, health economics, health implementation, psychosocial impact of precision medicine, and improving access to molecularly targeted therapeutic clinical trials. Citation Format: Paulette Barahona, Jamie Fletcher, Noemi Fuentes-Bolanos, Marie-Emilie Gauthier, Michelle Haber, Richard B. Lock, Glenn M. Marshall, Chelsea Mayoh, Emily Mould, Sumanth Nagabushan, Murray Norris, Tracey O’Brien, Alexandra Sherstyuk, David Thomas, Toby Trahair, Katherine Tucker, Meera Warby, Marie Wong, David S. Ziegler, Vanessa J. Tyrrell, Paul Ekert, Mark J. Cowley, Loretta Lau, Dong-Anh Khuong Quang, Zero Childhood Cancer Program National Consortium. Zero Childhood Cancer (ZERO): A comprehensive precision medicine platform for children with high-risk cancer [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 A52.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".