Abstract A50: Mutational analysis of a mouse model of second malignant neoplasms
Bibliographic record
Abstract
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.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".