Abstract IA06: New strategies for multidimensional molecular characterization and follow-up of high-risk pediatric cancers
Notice bibliographique
Résumé
Abstract Although 80% of all pediatric patients with cancer can be cured based on modern multimodal therapeutic strategies, at least 20% of these patients will undergo tumor progression or relapse. Understanding the molecular mechanisms underlying tumor progression and resistance to treatment is crucial to the development of new treatment approaches. In high-risk pediatric cancers, somatic DNA alterations such as genomic amplifications, copy number alterations, translocations, or mutations play an important role as molecular diagnostic, prognostic, and predictive biomarkers, which might further define targeted treatment approaches. However, it is now known that in many high-risk pediatric cancers, genetic alterations in a given tumor are not static but undergo modifications, with clonal evolution most likely playing a major role in high-risk tumor progression. Circulating tumor DNA, a fraction of cell-free DNA, can be readily isolated from plasma and now provides an important tool and surrogate for tumor molecular analyses, both at diagnosis and during treatment and follow-up. At diagnosis, a prospective trial, NGSKids (NCT02546453), subsequently enrolled 28 patients with high-risk pediatric cancer (neuroblastoma, rhabdomyosarcoma, Ewing sarcoma, other high-risk cancers) at diagnosis. Whole-exome sequencing (WES) was performed on tumor and germline DNA as well as on cfDNA extracted from plasma at diagnosis, during treatment, and follow-up (2-9 sequential cfDNA samples per patient). Whereas all cfDNA samples obtained at follow-up in patients without evidence of disease revealed no or few tumor cell-specific SNVs, interestingly, cfDNA samples obtained at relapse harbored additional, new relapse-specific SNVs (mean: 10; range 2-42) in all cases, targeting genes such as MAPK and MLL4. Deep sequencing capture techniques enable to develop models of clonal evolution. In nonmetastatic brain tumors, ctDNA isolated from CSF resulted in a higher sensitivity and specificity of detection to tumor cell-specific genetic alterations. A French national program, MICCHADO (NCT03496402), now aims to study clonal evolution based on sequential ctDNA analysis from the time of diagnosis in all high-risk pediatric cancers. At relapse, ctDNA studies can provide complementary information to molecular analyses of tumor samples performed within programs such as MAPPYACTS (NCT02613962). ctDNA also enables to infer expression profiles. Indeed, gene expression levels are reflected by nucleosome positioning, and differences in nucleosome organization at transcription start sites (TSS) lead to differential clipping of fragments upon ctDNA release and distinct nucleosome footprints depending on the expression of a given gene in the originating cells. New technologies are also being explored to analyze epigenetic features. Altogether, the presence of tumor genetic and epigenetic abnormalities in ctDNA can be documented in most patients with high-risk pediatric cancer and frequently suggests spatial and temporal heterogeneity. Sequential studies will further elucidate mechanisms of clonal evolution, tumor progression, and therapy resistance. Thus, sequential studies of ctDNA should now be integrated into the development and optimization of targeted treatment strategies. Citation Format: Gudrun Schleiermacher. New strategies for multidimensional molecular characterization and follow-up of high-risk pediatric cancers [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 IA06.
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 | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».