Abstract PR14: Harnessing the power of big data to advance pediatric cancer care
Notice bibliographique
Résumé
Abstract DNA and RNA sequencing is increasingly applied in clinical trials to find new therapeutic leads for children with incurable cancers. However, compared to similar studies in adults, these trials have yielded fewer new treatment options, because pediatric and adult malignancies are distinct biologically, and far less data are available on the genomics of pediatric tumors. Initiatives such as the National Cancer Institute's (NCI) Therapeutically Applicable Research to Generate Effective Treatments (TARGET) project, and the Medulloblastoma Advanced Genomics International Consortium (MAGIC) have generated large cohorts through collaboration, however they are limited to specific diseases. In addition, there is no mechanism to integrate these data with genomic data collected in prospective precision medicine trials. The failure to share data has meant that available genomic information is not being utilized to its full potential, resulting in missed therapeutic opportunities. The UC Santa Cruz Treehouse Childhood Cancer Project integrates genomic data generated by pediatric research studies, such as TARGET, MAGIC, the Pediatric Cancer Genome Project, and the Childhood Brain Tumor Tissue Consortium with genomic data generated by clinical trials. Treehouse also makes it possible to compare these data with large adult datasets, including The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), and Stand Up to Cancer (SU2C). Together, these datasets provide access to the genomic information from over 15,000 individual tumors that can be used as context for real-time data interpretation from individual patients in clinical genomics trials. This work presents a case report that illustrates how integrating multiple pediatric and adult gene expression datasets with similar data collected from patients in a prospective clinical trial can provide new clinical leads for children with difficult-to-treat cancers. The data are integrated and analyzed using TumorMap, an unsupervised clustering and visualization approach that has been shown to reveal new clinical insights into adult cancers as part of the TCGA Pan-Cancer effort. The expression of individual genes and their relationship with phenotypic features in the combined cohort can be visualized using the UCSC Xena Bowser. New clinical leads can be recommended for individual patients based on the similarity of their molecular profiles to those of other cancers with available treatment options, as shown in the TumorMap. We propose to extend our case study to a framework of how genomic datasets collected from adult and pediatric patients in research and clinical settings can be used to inform the care of pediatric patients prospectively. This framework will provide new hope for children with difficult to treat cancers so that no therapeutic option is overlooked in the fight to save their lives. Citation Format: Olena Morozova, Yulia Newton, Melissa Cline, Stephen Yip, Arjun Rao, Josh Stuart, Ted Goldstein, Sofie Salama, Rebecca Deyell, S. Rod Rassekh, David Haussler. Harnessing the power of big data to advance pediatric cancer care. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Pediatric Cancer Research: From Mechanisms and Models to Treatment and Survivorship; 2015 Nov 9-12; Fort Lauderdale, FL. Philadelphia (PA): AACR; Cancer Res 2016;76(5 Suppl):Abstract nr PR14.
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 | 0,018 | 0,067 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,002 | 0,009 |
| Intégrité de la recherche | 0,002 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,002 |
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 ».