Abstract A52: Zero Childhood Cancer (ZERO): A comprehensive precision medicine platform for children with high-risk cancer
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».