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Abstract LB-223: Whole genome sequencing of rhabdomyosarcoma germline cohort identifies low frequency of pathogenic mutations

2017· article· en· W2739562807 on OpenAlexaff
Nicholas Light, Philip J. Lupo, Javed Khan, Joshua D. Schiffman, Douglas S. Hawkins, Adam Shlien, David Malkin

Bibliographic record

VenueCancer Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsGeneticsFrameshift mutationBiologyMUTYHGermline mutationGermlineCancer1000 Genomes ProjectNonsynonymous substitutionMSH6MutationSingle-nucleotide polymorphismGeneGenomeGenotype

Abstract

fetched live from OpenAlex

Abstract Introduction: Rhabdomyosarcoma (RMS) is among the most frequently occurring tumors in Li-Fraumeni syndrome patients and is often observed in patients with a strong family history of cancer. Recent studies suggest that at least 10% of children with cancer harbor an underlying pathogenic germline mutation, although the frequency in children with RMS is not well characterized. In this study we sought to determine the rate of likely pathogenic germline mutations in rhabdomyosarcoma patients, unselected for family history. Methods: Of 275 RMS patients enrolled on one clinical trial (COG ARST0531), whole-genome sequencing was performed on blood-derived DNA of an initial set of 50 using the Illumina HiseqX to an average sequencing depth of 44.9X [34.2-58.4]. Single nucleotide variants (SNVs) and Indels were called and filtered to select only those occurring in exons or splicing regions. Variants were further filtered to include only those with a frequency of <0.5% in the ExAC database and lying in one of 99 genes identified as a cancer predisposition gene in the COSMIC Cancer Gene census. Nonsynonymous SNVs were required to be predicted as deleterious by one or more of Polyphen, mutation assessor or sift. Results: Across the 50 initial samples, a total of 290 variants ([0-17], median of 5) passed the described filters. Notably, no point mutations were identified in TP53 in the initial 50 samples. Two samples contained variants annotated as pathogenic in ClinVar - a frameshift deletion in CHEK2 and a rare SNP in MUTYH. Also notable was a potentially pathogenic frameshift deletion in MSH6 that lacked previous annotation. Conclusions: Our preliminary analysis suggests a lower incidence of causative TP53 mutations in RMS than previously suggested. Whole genome sequencing of the remaining samples in the cohort along with analysis of copy number and structural variants is ongoing. Statistical analysis of the entire cohort of 275 patient samples will allow for the most complete characterization of the germline genomic landscape of rhabdomyosarcoma to date. This information will better inform clinical surveillance and management parameters for RMS patients and families. Citation Format: Nicholas Light, Philip Lupo, Javed Khan, Joshua Schiffman, Douglas Hawkins, Adam Shlien, David Malkin. Whole genome sequencing of rhabdomyosarcoma germline cohort identifies low frequency of pathogenic mutations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-223. doi:10.1158/1538-7445.AM2017-LB-223

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.045
GPT teacher head0.368
Teacher spread0.323 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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