Abstract LB-180: The genetic landscape of Wilms tumor
Bibliographic record
Abstract
Abstract Introduction: The National Cancer Institute's Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiative seeks to characterize the genomes of high-risk pediatric tumors to identify therapeutic targets. The High-Risk Renal Tumor TARGET initiative includes the analysis of pre-therapy favorable histology Wilms Tumors (FHWT) that relapsed and tumors with diffuse anaplasia (unfavorable histology; DAWT). These two tumor subsets have survival rates of approximately 50% and 60%, respectively. Experimental procedures: Genomic sequencing (whole genome [WGS] or exome [WXS]), global copy number analysis, and global gene expression analysis were performed on a discovery set of 117 (78 FHWT, 39 DAWT) pre-therapy high-risk WTs treated on National Wilms Tumor Study-5 (NWTS-5). To determine the frequency of recurrent variants, targeted sequencing (Illumina HiSeq2500) was performed on a validation set of pre-therapy tumor DNA from a case-cohort of all FHWT treated on NWTS-5 (531 FHWT) and all available 118 DAWT treated on NWTS-5 (these groups include tumors from the discovery set). Results: WGS and WXS revealed an average of 21.74 ± 22.6 high-quality variants per DAWT (range, 3-131) and 13.8 ± 10.9 per FHWT (range 2-58). Genes previously reported to be recurrently mutated in WT were mutated at the following frequencies in the validation set: WTX (6%), CTNNB1 (15%), WT1 (7.5%), DROSHA (11%), DGCR8 (4.5%), XPO5 (2%), SIX1/2 (7%), and MLLT1 (3%). In addition, mutations were identified in three genes that impact the NMYC pathway, which is known to be involved in renal development. These include MYCN P44L/H (4%), MAX R60Q (2%), and novel mutations in NONO (2%); these mutations were mutually exclusive. Novel mutations in BCOR, a transcriptional corepressor that regulates both gene expression during development and chromatin modification, were found in 3% of validation set tumors. Analysis of global gene expression revealed significant up-regulation of genes associated with kidney development, extracellular matrix organization, and epithelial tube development in BCOR-mutant tumors compared with precursor lesions (5 hyperplastic perilobar nephrogenic rests). TP53 mutations were identified in 48% of DAWTs and 1% of FHWTs. The above data do not include copy number changes, which were recurrently detected in WT1, WTX, NMYC, and TP53. Conclusions: Through the TARGET initiative, we have identified several novel, potential driver mutations that occur in WT and have not been reported in other pediatric tumors. The majority of these genes are known to function in processes critical to early development and/or specifically in renal development. Many of these mutations are accompanied by Wnt activating mutations or 11p15 biallelic expression. However, approximately 50% of WTs lack clear driver mutations. Future studies will need to focus on elucidating epigenetic alterations in these tumors as well as genetic changes outside of protein-coding regions. Citation Format: Samantha L. Gadd, Amy L. Walz, Ariadne HAG Ooms, Vicki Huff, Daniela S. Gerhard, Malcolm A. Smith, Jaime M. Guidry Auvil, Leandro Hermida, Tanja Davidsen, Patee Gesuwan, Daoud Meerzaman, Yussanne Ma, Marco A. Marra, Jeffrey S. Dome, Charles G. Mullighan, David A. Wheeler, Oliver A. Hampton, Julie M. Gastier-Foster, Nicole Ross, Elizabeth J. Perlman. The genetic landscape of Wilms tumor. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-180.
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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 | 0.001 |
| 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) | 0.003 | 0.000 |
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".