Localized Osteosarcoma, Metastatic Osteosarcoma, Recurrent Osteosarcoma
Conditions
Brief summary
This research trial studies blood samples from patients with osteosarcoma. Studying the genes found in samples of blood from patients with osteosarcoma may help doctors identify biomarkers related to the disease.
Detailed description
PRIMARY OBJECTIVE: I. Conduct a large-scale candidate gene association study in osteosarcoma (OS) using cases from the national Children's Oncology Group (COG) OS biology study (P9851 and successor study AOST06B1). SECONDARY OBJECTIVES: I. Conduct a genome-wide association study (GWAS) of OS. II. Fine-map genomic regions associated with OS to identify putative functional loci. III. Conduct whole-exome sequencing of germline OS deoxyribonucleic acid (DNA) samples. IV. Investigate the functional implications of promising genetic variants associated with OS. OUTLINE: Blood samples undergo polymorphism analysis of common single-nucleotide polymorphisms and haplotypes to examine genetic variation, gene-gene interactions, and the population structure.
Interventions
OTHERlaboratory biomarker analysis
Correlative studies
Sponsors
National Cancer Institute (NCI)
Children's Oncology Group
Study design
Observational model
CASE_CONTROL
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
* Blood samples collected from clinical trials COG-P9851 and COG-AOST06B1
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Hardy-Weinberg equilibrium on all SNPs | Baseline | Determined on all SNPs by chi-square tests. |
| SNPs associated with OS | Baseline | Logistic regression will be used to estimate odds ratios and 95% confidence intervals for the association between each SNP and OS under co-dominant, dominant and recessive genetic models. Stratified analyses will be conducted to examine sex, tumor subtype and outcome differences. |
| Gene-gene interactions | Baseline | Assessed using a multiplicative model. Haplotypes will be constructed using both Bayesian and expectation-maximization algorithms. Differences between cases and controls will be evaluated with HaploStats which uses haplotype posterior probabilities as weights to update the regression coefficients in an iterative manner. |
| Survival outcomes | Baseline | Kaplan-Meier survival curves will be used to determine outcome relative to genotype. |
| Whole-exome variant loci | Baseline | Annotation and filtering of each whole-exome variant locus will be performed using a custom software pipeline. Variants in \>= 2 OS cases will be validated, and then subsequently replicated in additional OS cases (samples previously received for the GWAS from international collaborators). Variants will also be evaluated for presence in known biologically plausible pathways and genes. |
Countries
United States
Outcome results
None listed