Abstract A036 The Stratified Medicine Paediatrics programme for cancers of childhood: Cell free DNA and serial tumour sequencing identifies subtype specific evolution and epigenetic states
Bibliographic record
Abstract
Abstract Stratified Medicine Paediatrics (SMPaeds) is the UK precision medicine programme offering rapid turnaround sequencing with clinical reporting for patients with paediatric-type tumours at the time of relapse or disease progression. A defined aim of SMPaeds was to identify patients likely to respond to molecularly targeted therapies, and to discover genomic and epigenetic determinants of cancer relapse. Alongside the clinical programme, an archival tissue sample and a temporally matched plasma sample was obtained. Here we report profiling results from a large cohort of matched diagnostic-relapse tumour tissue (n=280) and matched tumour and cell free DNA (cfDNA) (n=402) pairs from patients with relapsed and progressive solid tumours of childhood. Serial tumour sequencing identified putative drivers of relapse - with alterations in CHEK2 and epigenetic drivers being a common feature across the whole cohort. Tissue and cfDNA sequencing results were concordant, with a wider spectrum of mutant alleles and higher degree of intra-tumour heterogeneity captured by the latter, if sufficient circulating tumour-derived DNA was present. In patients with neuroblastoma, who had the highest levels of circulating tumour derived DNA there was a high frequency of cfDNA unique mutations in genes known to be important in neuroblastoma biology, including TP53 and RAS pathway genes associated with ultra high-risk disease. Finally, in this heterogenous cohort, we asked if cfDNA analysis could be used to infer epigenetic characteristics relating to tissue of origin and cancer-specific expression signatures. Using low-coverage whole genome sequencing (lcWGS), we identified cfDNA fragmentation patterns by analysing differences in sequencing coverage between open and closed chromatin, then combined this with a database of binding sites for 425 transcription factors. This approach clearly identified cancer-specific chromatin accessibility, with specific transcription factor associated clusters relating to cell of origin identified in neuroblastoma, rhabdomyosarcoma, non-Hodgkin’s lymphoma and hepatoblastoma. Increased accessibility in cfDNA for experimentally verified core regulatory circuit (CRC) transcription factor binding sites was identified, including MYOD1 and MYOG in rhabdomyosarcoma and ASCL1, HAND2, ISL1, MYCN and TBX2 in neuroblastoma. In Wilms tumour, only one transcription factor was identified as highly specific: SIX2 - a known poor prognostic biomarker and blastemal subtype associated transcription factor. This demonstrates the potential of low-cost clinical cfDNA sequencing in multiple applications including supporting diagnosis, providing prognostic information, and understanding how therapies alter epigenetic programming. In summary, this study leverages a large and well-annotated genomic dataset of aggressive childhood malignancies, identifies genomic and epigenetic drivers of childhood cancer relapse, and highlights the power and practicality of cfDNA analysis to capture both intra-tumoral heterogeneity and the epigenetic state of cancer cells. Citation Format: Sally L. George, Claire Lynn, Reda Stankunaite, Debbie Hughes, Jane Chalker, Saira Waqar Ahmed, Minou Oostveen, Paula Proszek, Lina Yuan, Ridwan Shaikh, Sabri Jamal, Jennifer Tall, Janet Shipley, Deborah Tweddle, Lynley Marshall, Chris Jones, Susanne Gatz, Aditi Vedi, Paola Angelini, John Anderson, George Cresswell, Trevor A. Graham, Bissan Al-Lazikani, Pamela Kearns, J. Ciaran Hutchinson, Darren Hargrave, Thomas Jacques, Michael Hubank, Andrea Sottoriva, Louis Chesler. The Stratified Medicine Paediatrics programme for cancers of childhood: Cell free DNA and serial tumour sequencing identifies subtype specific evolution and epigenetic states [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 A036.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".