Abstract A002-PR011: Precision Diagnosis in High-Risk Pediatric Oncology through Integrated Whole Genome, Transcriptome, and Epigenome Profiling
Notice bibliographique
Résumé
Abstract Introduction: The ZERO Childhood Cancer Program is Australia’s national precision medicine initiative for children with cancer. Through rapid integration of tumor and germline whole genome sequencing (WGS), RNA-seq, and methylome analysis, ZERO aims to determine the precise molecular diagnosis for every child and inform precision-guided treatment. We present updated findings from 764 consecutively enrolled high-risk patients, highlighting the diagnostic utility of multi-omics profiling and its unique insights into pediatric cancer biology. Methods: Tumor and germline WGS, RNA-seq, methylome and microbial analysis were performed using in-house Graphene, Carbonite, Methyliser and Micrite platforms. CNS tumors and sarcomas were classified using the MNP methylation classifier. Leukemias were classified using RNA-seq classifiers: LSC17, ALLSorts, and in-house tools. Integrated data were reviewed via the ZeroDash platform, with molecular reports issued within 4 weeks and therapeutic recommendations within 8 weeks following national tumor board discussion. Results: We identified genetic driver alterations in 95% of cases, including 912 SNVs/indels, 738 copy number variants, and 401 structural variants (SVs). Canonical drivers such as EWSR1 and PAX3 fusions in sarcomas, and TP53 and H3-3A mutations in CNS tumors were among the most frequently altered genes. Strikingly, 29% of reportable SVs were intragenic, disrupting gene structure & typically missed by clinical testing. RNA-seq validated >90% of these, revealing novel isoforms, aberrant splicing, or loss of expression. Among 78 complex SVs, including genome shattering events, multi-hop rearrangements, and paralogous gene fusions, RNA-seq resolved their transcriptional consequences, supporting assessment of pathogenicity and identified 10 missed by WGS. The MNP classifier provided a matching diagnosis in 93% of CNS tumors and 82% of sarcomas, resolving diagnostically ambiguous cases. RNA-based classifiers refined subtype classification in >75% of leukemias. In AML, stratification using LSC17 scores identifies an ultra-high-risk group with a hazard ratio of 0.18 compared to the least adverse group, indicating they are five times more likely to die. Among the 40 tumors with no reportable findings, 30 had low (<20%) tumor purity. Interestingly, 13% of these cases showed evidence of Epstein-Barr virus (EBV) infection, suggesting a possible viral etiology in a subset of undiagnosed tumors. Retrospective review of the 10 high purity samples identified the driver in 9 leaving only a germ cell tumour without a known driver. Conclusions: Multi-omics profiling provides a detailed molecular view of high-risk childhood cancers, with each platform offering complementary insights. WGS captures genomic alterations, RNA-seq detects all mutation types and informs pathogenicity, and methylome analysis supports classification. Together, these approaches maximize diagnostic yield, refine risk stratification, and guide treatment. This strategy has enabled ZERO’s expansion to all children with cancer across Australia. Citation Format: Chelsea Mayoh, Pamela Ajuyah, Ann Altekoester, Frank Alvaro, Paulette Barahona, Noemi Fuentes-Bolanos, Susan Corley, Lujing Cui, Ben Curran, Kimberly Dias, David D Eisenstat, Paul G Ekert, Raylene Endersby, Christian Fares, Jamie Fletcher, Emmy DG Fleuren, Andrew Gifford, Nicholas G Gottardo, Jordan Hansford, Sophie Jessop, Sam El-Kamand, Seong-Lin Khaw, Rishi S Kotecha, Loretta MS Lau, Angela Lin, Richard B Lock, Neevika Manoharan, Glenn M Marshall, Marion Mateos, Geoffrey McCowage, Andrew S Moore, Sumanth Nagabushan, Wayne Nicholls, Natacha Omer, Luciana Dalla Pozza, Peter Priestley, Dong-Anh Khuong Quang, Megan Rumford, Akanksha Senapati, Charles Shale, Ashleigh Sullivan, Patricia Sullivan, Kamile Taouk, Toby N Trahair, Marie Wong-Erasmus, Paul J Wood, David S Ziegler, Vanessa Tyrrell, Michelle Haber, Mark J Cowley. Precision Diagnosis in High-Risk Pediatric Oncology through Integrated Whole Genome, Transcriptome, and Epigenome Profiling [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Discovery and Innovation in Pediatric Cancer— From Biology to Breakthrough Therapies; 2025 Sep 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_2):Abstract nr A002-PR011.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».