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
Notice bibliographique
Résumé
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.
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) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 | 0,000 |
| 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 ».