Abstract A002-PR011: Precision Diagnosis in High-Risk Pediatric Oncology through Integrated Whole Genome, Transcriptome, and Epigenome Profiling
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".