Abstract A1-03: Mutational analysis of ionizing radiation-induced neoplasms
Notice bibliographique
Résumé
Abstract Introduction: Ionizing radiation is a known mutagen and can cause cancers in different contexts, for example second malignant neoplasms (SMNs). SMNs are therapy-induced malignancies and severe late complications that develop in cancer survivors, particularly survivors of pediatric cancers who have received radiotherapy. The mutational landscape of ionizing radiation-induced tumorigenesis is not well-characterized on a genome level. Because ionizing radiation produces DNA damage that differs from damage produced by other genotoxins (UV for example), the mutational landscape of ionizing-radiation induced neoplasms may differ from the mutational landscape induced by other mutagens. Defining the mutational landscape of malignancies induced by ionizing radiation may reveal biological mechanisms that specifically contribute to this process. This insight has clinical implications, particularly with regard to understanding the pathogenesis of SMNs. We previously developed mouse models of SMNs by delivering focal, fractionated irradiation to wildtype and Nf1 mutant mice, and established that the Nf1+/- genetic background is sensitized to radiation-induced tumorigenesis. Diverse malignancies recapitulating clinical SMNs arose in irradiated wildtype and Nf1 mutant mice. The goal of this study is to characterize the mutational profile of tumors induced by ionizing radiation that models radiotherapy delivered to patients and is typically responsible for SMNs. Experimental Procedures: Whole exome sequencing was performed on 25 ionizing radiation-induced malignancies generated from our mouse models (sarcomas, carcinomas and hematopoietic malignancies) and a germline control. Malignancies from Nf1 mutant and wildtype mice were sequenced. Indexed paired-end libraries were prepared using the Agilent SureSelectXT Mouse All Exon kit covering all Ensembl genes and miRNAs (50Mb), Sequencing was performed using Illumina HiSeq2000 technology. Alignments were processed and variants were called according to standard practices. Exome sequencing of a 129/Sv-C57BL/6 heterozygote was used as a normal control and only somatic variants were considered for analysis. Each exome was sequenced to a minimum of 5 Gb. Results: In the entire cohort, 7566 somatic mutations were identified, of which 5187 were non-synonymous. Tumors had an average of 200 mutations (range, 31-594). Most nucleotide substitutions were C -> T transitions. In addition to examining somatic variants comprised of nucleotide substitutions, we analyzed immediately flanking sequence context for each somatic variant using an approach developed at the Wellcome Trust Sanger Institute. We applied non-negative matrix factorization methods to exome data and extracted 3 stable and distinctive mutational signatures. These signatures are characterized by unique, context-dependent, patterns of base substitutions, and were present in neoplasms arising from wildtype or Nf1+/- genetic backgrounds. Pathway analysis and functional validation of recurrently mutated genes are underway. Conclusions: Ionizing radiation-induced malignancies display a unique mutational landscape that is shared among different tumor histologies. Three distinct signatures were identified and these are not genotype-dependent (wildtype vs. Nf1 mutant). The enrichment of specific substitutions in each of the signatures implicates discrete mechanisms of DNA repair and operational enzymes. These mechanisms and recurrently mutated genes are being functionally tested in tumor cell lines we established from our mouse models. Ionizing radiation-induced malignancies possess a mutational landscape that is distinguishable from those associated with other common mutagens. Citation Format: Amy L. Sherborne, Philip R. Davidson, Katharine Yu, Alice O. Nakamura, Jean L. Nakamura. Mutational analysis of ionizing radiation-induced neoplasms. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-03.
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 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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) | 0,005 | 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 ».