Abstract PR01: Three-hit model of Wilms’ tumor formation reveals immunogenic transcriptional subtypes
Notice bibliographique
Résumé
Abstract Wilms’ tumor is the most common kidney cancer in children. Despite advances in care, children with metastatic, anaplastic, or relapsed disease still fare poorly. Recent studies have uncovered novel types of driver mutations, including in microRNA processing genes such as DROSHA. However, there are no ways to rationally guide therapy based on mutation, as the mechanisms by which they cause cancer remain poorly defined. Thus, here we investigated how specific types of driver mutations affect gene and protein expression. To understand how these mutations drive Wilms’ tumor formation, we first recategorized known mutations and copy number changes using a new classification schema. We found four mutation classes to be mutually exclusive with each other: microRNA processing, MYCN-activating, chromatin remodeling, and RNA splicing. These mutations were not mutually exclusive with common mutations in kidney development genes or loss-of-heterozygosity/imprinting (LOH/LOI) of chr11p15. We propose that this mutational pattern implies a “three-hit” model, whereby 11p15 LOH/LOI and mutations impairing kidney development predispose to but are often not sufficient for Wilms’ tumor formation. A third mutation then transforms the transcriptome via microRNA processing, MYCN, chromatin remodeling, or splicing. To study how these “third hits” affect gene expression, we next performed gene set enrichment analysis. As expected, we found that microRNA impairment leads to overexpression of microRNA target genes, and MYCN activation drives MYC target genes. Interestingly, we also found that loss of microRNA processing correlated with expression of oxidative phosphorylation genes, which may reveal a metabolic dependency in these tumors. In addition, mutations affecting splicing led to high levels of interferon-stimulated genes. Thus, the abnormal RNA species generated by altered splicing appear to trigger the innate immune response that normally responds to viral RNA. Finally, we measured how these mutations affect protein levels using reverse-phase protein arrays. Strikingly, Wilms’ tumors with either anaplastic histology or microRNA processing mutations express high levels of immune-related markers such as PD-1, PD-L1, phospho-Stat3, and phospho-NF-kB. In summary, many Wilms’ tumors develop a total of three types of mutations: 11p15 LOH/LOI, kidney development impairment, and a third hit that reshapes the transcriptome in an oncogenic fashion. These “third hits” may affect microRNA processing, MYCN activity, chromatin remodeling, or RNA splicing, and each type of mutation has distinct effects on gene expression. In particular, mutations in microRNA processing cause aberrant overexpression of microRNA target genes, leading to metabolic reprogramming and increased immunogenicity. As a result, some Wilms’ tumor mutations have widespread effects on the transcriptome that may be susceptible to immune checkpoint blockade. This abstract is also being presented as Poster B05. Citation Format: Kenneth S. Chen, Kavita Desai, James F. Amatruda. Three-hit model of Wilms’ tumor formation reveals immunogenic transcriptional subtypes [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 PR01.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 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) | 0,003 | 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 ».