Multi-omics evaluation of relapsed pediatric cancers: What information do these sequential analyses yield?
Notice bibliographique
Résumé
10047 Background: While cure rates for children with cancer have significantly improved, relapses remain a challenge, requiring deeper understanding to address them. Nowadays, genomic analyses are widely used at diagnosis and in relapse settings, becoming a standard-of-care in pediatric. The aim of this study is to describe the genomic evolution of relapsed pediatric tumors in search of clonal selection and pathway identification. We also want to assess the clinical value of these new data obtained in relapsed tumors. Methods: This is a retrospective analysis from canadian pediatric oncology precision medicine projects. We selected patients aged < 30 years with sequencing data available at diagnosis and relapse. Clinical and genomic data were collected, and each patient was paired with a non-relapsed patient. The incidences of genomic alterations were compared in the two populations and for each patient. For patients who relapsed, patient-adjusted longitudinal mixed models assessed differentially expressed genes at relapse vs diagnosis. Gene set enrichment analyses were performed, using GLMMSeq results, to study metabolic pathways that undergo significant dysregulation over time (p < 0.05). Electronic surveys were sent to the treating physicians of relapsed patients. Results: A total of 45 patients with 1 or more relapses were compared with 44 patients without relapse. Longitudinal analysis was performed on 35 relapsed patients. Our population has a median age of 10 y.o., a majority had leukemia (47 %) or sarcoma (31%). Among relapsed patients, the mutational burden at diagnosis was 0.82 mut/MB and 1.21 mut/MB at first relapse, compared with 0.47 mut/MB in non-relapsed patients (p = 0.02). 33% of relapsed patients had or acquired a TP53 alteration, compared with 16% without relapse (p = 0.084). MAPK pathway alterations were more prevalent among relapsed patients (p = 0.004). Longitudinal analyses showed enrichment in MAPK pathway at relapse. Other pathways were also significantly enriched at relapse (Wnt, TP53, TNFa, TGFb), while immune pathways (immunoglobulin/lymphocyte complex and activation) were downregulated. Although, 45% of clinicians considered that genomic analysis at relapse was useful, only 18% actually integrated the genomic data into clinical decisions due to better options available than targeted therapies. Indeed, when targeted therapies were used as proposed, it was mostly for a 2 nd or 3 rd relapse and for sarcoma. Conclusions: This study describes the evolution of the genomic landscape, showing an enrichment in mutation and pathways, and increased mutational burden in relapsed pediatric tumors. Longitudinal differential analyses brought more information about genomic evolution than the usual genomic reports sent to clinicians. Overall, the analyses performed are useful for clinicians, and a small subset of patients benefited from this information to guide therapies.
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 | 0,008 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».