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Record W1994358878 · doi:10.1158/1538-7445.am2014-3087

Abstract 3087: Whole genome sequencing of rhabdoid tumors of the kidney

2014· article· en· W1994358878 on OpenAlexaff
Hye-Jung E. Chun, Kelsey Zhu, Jenny Q. Qian, Karen Mungall, Yussanne Ma, Yongjun Zhao, Andrew J. Mungall, Richard A. Moore, Jacquie Schein, Daniela S. Gerhard, Elizabeth J. Perlman, Marco A. Marra

Bibliographic record

VenueCancer Research · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChromatin Remodeling and Cancer
Canadian institutionsCanada's Michael Smith Genome Sciences CentreBC Cancer AgencyUniversity of British Columbia
Fundersnot available
KeywordsEpigeneticsBiologyGeneticsSMARCB1ChromatinGenomeCarcinogenesisEpigenomicsGeneDNA methylationChromatin remodelingCancer researchComputational biologyGene expression

Abstract

fetched live from OpenAlex

Abstract Rhabdoid tumors (RT) of the kidney (RTK) are aggressive pediatric solid tumors that predominantly affect infants. There is no effective chemotherapy and the overall 4-year survival rate is 23%. RT has a characteristic loss of SMARCB1 function, found in >90% of the patients. SMARCB1 is a conserved core subunit of the SWI/SNF chromatin-remodeling complex, which in turn is responsible for proper chromatin assembly and dynamic regulation of gene expression. Previous studies showed a remarkable paucity of mutations in coding regions of genomes and a highly penetrant cancer susceptibility in a conditional knockout mouse model. These findings support the interpretation that SMARCB1 is a tumor suppressor whose inactivation is the primary driver in RT and that RT follows a tumorigenesis model in which cancer is driven by aberrant epigenetic regulation and gene expression instead of accumulation of somatic mutations. Characterizing interplays of mutations, gene expression and epigenetic regulation will be important in understanding RT development and biology. To achieve this goal, we will comprehensively characterize genetic and epigenetic aberrations in RT using HiSeq sequencing technology. Our research efforts include profiling whole genome, whole transcriptome, promoter methylation and histone modification in 40 primary RTK samples. Here, we report preliminary results from whole genome analyses. Using an amplification-free library construction method, we sequenced whole genomes of 40 RTK and matched normal cases to an average haploid coverage of 39.4X. The RTK genomes were mostly diploid, but we found 35 loci that are either recurrently focally amplified or deleted using GISTIC 2.0 at FDR ≤0.05. Using the Trans-ABySS de novo short-read assembler, we assembled the RT cases’ whole genomes and identified a total of 19 genes that were recurrently rearranged in 8 out of 40 cases. Eleven of the genes were either known tumor suppressors (e.g. CABIN1, BCR) or associated with developmental or neurodegenerative diseases (e.g. UPB1, SPECC1L). The genome-wide single nucleotide variant and indel analysis showed an average somatic mutation rate at 0.37 per Mb in RTK, comparable to the previous finding of 0.19 per Mb in AT/RT. Approximately 99% of the somatic mutations occurred in non-genic regions. SMARCB1 had homozygous loss of function in 83% of cases by somatic homozygous deletion, or heterozygous deletion or truncating point mutations followed by loss of heterozygosity. The remaining cases appeared to have at least 1 copy of the gene unaffected and analyses are ongoing to investigate the inactivation mechanism in these cases. Citation Format: Hye-Jung E. Chun, Kelsey Zhu, Jenny Q. Qian, Karen L. Mungall, Yussanne Ma, Yong-Jun Zhao, Andrew J. Mungall, Richard A. Moore, Jacquie E. Schein, Daniela S. Gerhard, Elizabeth J. Perlman, Marco A. Marra. Whole genome sequencing of rhabdoid tumors of the kidney. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3087. doi:10.1158/1538-7445.AM2014-3087

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.352
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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