Abstract A1-03: Mutational analysis of ionizing radiation-induced neoplasms
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".