Abstract LB-307: Translational and mechanistic implications of osteosarcoma genomics: A TARGET report
Notice bibliographique
Résumé
Abstract Osteosarcoma (OS) is the most common malignant bone tumor of children and young adults. Although the optimization of combination chemotherapy has led to significantly improved prognosis, survival remains poor for patients with recurrent tumor or metastatic disease at diagnosis. The international TARGET (Therapeutically Applicable Research to Generate Effective Therapy) OS project team collected 285 clinically annotated samples (age <40 years) for integrative genomics analyses. Eighty-nine cases, the discovery set, were characterized with paired tumor and normal DNA sequencing as well as profiling of transcriptomes, DNA methylation, and miRNA. An additional 196 validation cases were used for copy number analysis and targeted DNA sequencing guided by results from the discovery set. The most frequent recurrent somatic mutations cause loss of function in the tumor suppressors TP53, RB1, CDKN2A, PTEN, and NF1 as well as the chromatin remodeler ATRX. Structural rearrangements are an important mechanism of inactivation for all of these genes. In the discovery set, every case carried at least one mutation of a cell cycle regulator, with TP53 somatically altered in 87/89 cases. Analysis of TP53 mutations suggests that these are often truncal. OS genomes are strikingly complex and contain copy number aberrations (CNA) arising from aneuploidy and extensive structural rearrangements. Transcriptome analysis revealed profound remodeling of gene expression with statistically significant correlation of CNA and gene expression of 3340 genes (FDR<0.001). Regions of high copy number amplification include known therapeutically relevant cancer drivers (e.g. PDGFRA, MYC, CDK4, MDM2, IGF1R, and CCNE1). In contrast, activating mutations of signal transduction genes were rare. With few exceptions (e.g. MYC amplification), mutations or CNA of individual genes were not predictive of outcome. The pattern of significant co-occurrence or mutual exclusion of frequent mutations and amplifications allowed partitioning of 240 cases into four distinct genomic groups. One of these, characterized by relatively simple genomes lacking high copy number amplification, had a more favorable outcome. Unsupervised transcriptome analysis by NMF separated the discovery set into two groups with significantly different outcomes. A pathway-based approach identified a high-risk bone differentiation signature comprised of genes highly correlated with the expression of the osteogenic transcription factors. NMF analysis of DNA methylation and miRNA data also partitioned the discovery set into two groups with the methylation groups significantly correlated with clinical outcome. Our results provide a detailed picture of the genomic complexity and heterogeneity of these tumors that suggests a model wherein most OS are initiated by TP53 loss in a proliferative cell of the osteoblast lineage. TP53 deficiency leads directly to impairment of cell cycle control, DNA repair, and terminal differentiation, a state permissive for the development of secondary CNA and epigenetic changes. The TARGET OS data are publicly available (phs000218) and of immediate relevance to future investigations of the molecular mechanisms driving osteosarcoma. Findings suggest a path forward to improved assessment of risk for individual patients and support a precision medicine approach to future clinical trial development. Citation Format: Paul S. Meltzer, Sean Davis, Jack Zhu, Yonghong Wang, Sven Bilke, Joshua Waterfall, Robert Walker, Marbin Pineda, Yuan Jiang, Sharon Savage, Lisa Mirabello, Tsz-Kwong Man, Aaron Taylor, Monika J. Sun, Jay Wunder, Irene Andrulis, Nalan Gokgoz, Shintaro Iwata, Miki Ohira, Mark Krailo, Don Barkauskas, Lisa Teot, Timothy Triche, Silvia de Toledo, Antonio S. Petrilli, Jaime M. Guidry Auvil, Richard Gorlick, Malcolm A. Smith, Daniela Gerhard, Ching C. Lau. Translational and mechanistic implications of osteosarcoma genomics: A TARGET report [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-307.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».