Abstract A50: Mutational analysis of a mouse model of second malignant neoplasms
Notice bibliographique
Résumé
Abstract Purpose: Second malignant neoplasms (SMNs) are therapy-induced malignancies and severe late complications that develop in pediatric cancer survivors. Ionizing radiation (IR) is a known mutagen and can cause SMNs. The mutational landscape of fractionated IR-induced tumorigenesis is not well-characterized on a genome level and but may reveal biological mechanisms that specifically contribute the development of SMNs. To study the influence of clinically relevant radiation delivery and germline mutations in a tumor suppressor gene, we previously developed mouse models of SMNs by delivering focal, fractionated irradiation to wildtype and Nf1 mutant mice. Irradiated mice developed diverse malignancies replicating the sarcomas and carcinomas observed as SMNs in pediatric cancer survivors. The goal of this study is to characterize the mutational profile of tumors induced by ionizing radiation that models clinical radiotherapy and to determine whether germline Nf1 mutations independently influence the mutational landscape. Materials/Methods: Whole exome sequencing was performed on 25 IR-induced malignancies arising in wildtype and Nf1 mutant mice. Indexed paired-end libraries were prepared using the Agilent SureSelectXT Mouse All Exon kit and sequencing was performed using Illumina HiSeq2000 technology (Illumina, San Diego, CA, USA). Alignments and somatic variants were identified using established procedures. Each exome was sequenced to a minimum of 5 Gb. Results: 6,623 somatic mutations were identified, of which 4,633 were non-synonymous. Tumors had an average mutation rate of 265 total SNVs/sample. Most nucleotide substitutions were C -> T or G-> A transitions. We analyzed the immediately flanking sequence context for each somatic variant using non-negative matrix factorization, and extracted 3 stable and distinctive mutational signatures. These signatures are present in multiple types of radiation-induced histologies, are uniquely distinguishable from mutational signatures of other well-recognized mutagens such as UV, and are similarly present in tumors arising in wildtype mice as well as Nf1 mutant mice, suggesting that the IR mutational signature persists in genetic backgrounds either resistant or susceptible to IR-induced tumorigenesis. We compared copy number alterations between tumors from wildtype and Nf1 mutant mice, and found significant differences between genetic backgrounds. Conclusions: IR-induced malignancies possess distinguishable and unique mutational signatures characterized by base substitutions occurring in very specific and unique sequence contexts. This analysis suggests that IR and genetic background influence the mutational landscape of tumors in very specific and discrete ways that are distinguishable from other cancer-promoting processes. SMNs from pediatric cancer survivors may harbor distinctive mutational motifs, and continued studies are needed to examine this. This data also suggest that the mutational landscape may differ between malignancies from different germline mutations or genetic backgrounds in pediatric cancer survivors. From a clinical standpoint, defining distinct mutational mechanisms in SMNs may improve the ability to predict which pediatric cancer survivors are at greatest risk for SMN formation as well as enable the development of strategies to mitigate and manage this risk. Citation Format: Amy Sherborne, Philip Davidson, Katharine Yu, Alice Nakamura, Mamunur Rashid, Jean Nakamura. Mutational analysis of a mouse model of second malignant neoplasms. [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 A50.
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) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 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 | 0,001 | 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 ».