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Record W2032955575 · doi:10.1158/1538-7445.am2011-5334

Abstract 5334: Genome-wide copy number analysis reveals two novel loci for susceptibility to sporadic osteosarcoma

2011· article· en· W2032955575 on OpenAlexaff
Rinnat M. Porat, Ivan Pašić, Adam Shlien, Nalan Golgoz, Irene L. Andrulis, Jay S. Wunder, David Malkin

Bibliographic record

VenueCancer Research · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsMount Sinai HospitalSickKids Foundation
Fundersnot available
KeywordsCopy-number variationCopy number analysisSNP arrayBiologyGeneticsOsteosarcomaGene dosageCancerGermlineSNPMalignancySingle-nucleotide polymorphismGenomeGeneCancer researchGenotype

Abstract

fetched live from OpenAlex

Abstract Copy-number variants (CNVs) frequently overlap genomic regions that contain genes associated with cancer. In addition, copy number alterations (CNAs) often coincide with sites of CNVs and may arise through progression of CNVs. Osteosarcoma (OS), the most common human bone malignancy, can arise in familial or sporadic forms. While germline mutations of tumor suppressor genes have been implicated in some hereditary forms of OS, little is known about genetic changes that contribute to the etiology of sporadic OS. Using high-resolutation SNP/CNV arrays, we have observed a high global CNV frequency in patients with sporadic osteosarcoma. We hypothesized that these genomic alterations are linked to susceptibility to OS. Here we investigate whether CNAs in OS tumor DNA reflect extensive constitutional CNVs in OS patients and whether OS tumor CNAs arise by progression of constitutional CNVs in these patients. Paired samples from blood and pre-therapy tumor biopsies of 44 OS patients were hybridized to Affymetrix GW 6.0 arrays and analyzed using Partek Genomics Suite and Nexus Copy Number™ software. Analysis focused on CNVs which fulfilled the following criteria: 1) absent from normal controls, 2) recurrent, 3) previously unreported, 4) mapped within or nearby known genes and 5) overlapped with paired tumour CNA. Regions identified in this analysis were validated as candidate regions by quantitative PCR. Two candidate CNVs have been identified: a copy-number loss at 1q43 and a copy-number gain at 2p11.2. The copy number loss at 1q43 was present in DNA of 43% of the blood and 40% of tumor samples. 38% of the 1q43 deletions in the tumors were de novo events. This CNV is upstream of the centrosomal protein 170kDa gene (CEP170) which has been shown to maintain microtubule organization and cell morphology. As microtubule function is critical for proper chromosome segregation during cell division, copy-number change-driven dysregulation of CEP170 might be related to the characteristic genomic instability in OS. We are currently exploring this possibility via expression profiling. The copy-number gain at 2p11.2 was identified in 45% of the OS blood derived DNA and in 65% of those, the gain was also seen in matched tumor DNA. This CNV is in proximity to FLJ40330, a non-coding RNA, and to ribose 5-phosphate isomerase (RPI). A custom CGH array platform is currently being used to further characterize these loci on paired blood and tumor DNA samples from an additional cohort of OS patients. These findings support the premise that excessive constitutional CNV is a hallmark of sporadic OS and may open new avenues for the discovery of novel specific CNVs and genes implicated in susceptibility to OS which may be of diagnostic and/or prognostic relevance. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 5334. doi:10.1158/1538-7445.AM2011-5334

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.084
GPT teacher head0.395
Teacher spread0.310 · 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".

Quick stats

Citations1
Published2011
Admission routes1
Has abstractyes

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