Genome-wide association study identifies two susceptibility loci for osteosarcoma

Zebrafish as a model for human osteosarcoma

For various reasons involving biological comparativeness, expansive technological possibilities, accelerated experimental speed, and competitive costs, zebrafish has become a comprehensive model for cancer research. Hence, zebrafish embryos and full-grown fish have been instrumental for studies of leukemia, melanoma, pancreatic cancer, bone tumors, and other malignancies. Although because of its similarities to human osteogenesis zebrafish appears to be an appealing model to investigate osteosarcoma, only a few osteosarcoma specific studies have been accomplished yet. Here, we review interesting related and unrelated reports of which the findings might be extrapolated to osteosarcoma. More importantly, rational but yet unexplored applications of zebrafish are debated to expand the window of opportunities for future establishment of osteosarcoma models. Accordingly technological advances of zebrafish based cancer research, such as robotic high-throughput multicolor injection systems and advanced imaging methods are discussed. Furthermore, various use of zebrafish embryos for screening drug regimens by combinations of chemotherapy, novel drug deliverers, and immune system modulators are suggested. Concerning the etiology, the high degree of genetic similarity between zebrafish and human cancers indicates that affected regions are evolutionarily conserved. Therefore, zebrafish as a swift model system that allows for the investigation of multiple candidate gene-defects is presented.

Association of ITGA3 gene polymorphisms with susceptibility and clinicopathological characteristics of osteosarcoma

Integrin controls cell adhesion to extracellular matrix and plays an important role in regulating the proliferation and apoptosis of cells. In order to explore the role of ITGA3 gene polymorphisms in the pathogenesis and clinicopathological characteristics of osteosarcoma, we embarked on a study including a group of 118 patients and a group of 126 healthy controls. TaqMan PCR genotyping technology was used to detect the genotypes of ITGA3 gene SNPs (rs2230392, rs2285524 and rs16948627) in the peripheral blood. Then, associations of the SNP (rs2230392, rs2285524 and rs16948627) genotypes with the incidence risk and tumor characteristics of osteosarcoma were evaluated. A significant difference (P = 0.02) in the genotype frequency distribution of rs2230392 was observed between case and control groups. The analysis showed that patients carrying AA genotype had a higher risk of osteosarcoma (OR 2.34, 95 % CI 1.18-4.64) than those with GG genotype. Regarding rs2230392, men carrying AA genotype had a higher risk of osteosarcoma (OR 3.37, 95 % CI 1.25-9.11). Compared with those with GG genotype, patients carrying AA genotype had a twofold increased risk of osteosarcoma metastasis (OR 2.46, 95 % CI 1.09-5.57). Survival analysis showed that for rs2230392, survival time of osteosarcoma patients with three different genotypes was significantly different. Polymorphisms of ITGA3 gene rs2230392 may affect the incidence, metastasis and survival of osteosarcoma, which may clinically become a new target for predicting the risk of osteosarcoma, and have prognostic value.

Genome-wide analyses implicate 33 loci in heritable dog osteosarcoma, including regulatory variants near CDKN2A/B

Background

Canine osteosarcoma is clinically nearly identical to the human disease, but is common and highly heritable, making genetic dissection feasible.

Results

Through genome-wide association analyses in three breeds (greyhounds, Rottweilers, and Irish wolfhounds), we identify 33 inherited risk loci explaining 55% to 85% of phenotype variance in each breed. The greyhound locus exhibiting the strongest association, located 150 kilobases upstream of the genes CDKN2A/B, is also the most rearranged locus in canine osteosarcoma tumors. The top germline candidate variant is found at a >90% frequency in Rottweilers and Irish wolfhounds, and alters an evolutionarily constrained element that we show has strong enhancer activity in human osteosarcoma cells. In all three breeds, osteosarcoma-associated loci and regions of reduced heterozygosity are enriched for genes in pathways connected to bone differentiation and growth. Several pathways, including one of genes regulated by miR124, are also enriched for somatic copy-number changes in tumors.

Conclusions

Mapping a complex cancer in multiple dog breeds reveals a polygenic spectrum of germline risk factors pointing to specific pathways as drivers of disease.

Association of the vascular endothelial growth factor (VEGF) gene single-nucleotide polymorphisms with osteosarcoma susceptibility in a Chinese population

Osteosarcoma (OS) is the most common bone malignancy worldwide. The vascular endothelial growth factor (VEGF) gene plays an important role in the pathogenesis of OS. The objective of this study aimed to detect the potential association between VEGF genetic polymorphisms and OS susceptibility in Chinese Han population. We recruited 330 OS patients and 342 cancer-free controls in this case-control study. Three single-nucleotide polymorphisms (SNPs) (-634 G > C, +936 C > T, and +1612 G > A) of the VEGF gene were investigated by using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method and confirmed by direct DNA sequencing. Among these SNPs, we found that the genotypes/alleles of +936 C > T were statistically associated with the increased risk of OS (TT versus (vs.) CC: OR = 2.70, 95% CI 1.34-5.45, χ(2) = 8.2271, p = 0.0041; T vs. C: OR = 1.31, 95% CI 1.02-1.68, χ(2) = 4.3861, p = 0.0362). The T allele and TT genotype of +936 C > T could be factors that increase the risk for susceptibility to OS. The results from this study suggest that VEGF genetic variants are potentially related to OS susceptibility in Chinese Han population and might be used as molecular markers for assessing OS susceptibility.

Stem cell systems informatics for advanced clinical biodiagnostics: tracing molecular signatures from bench to bedside

Development of innovative high throughput technologies has enabled a variety of molecular landscapes to be interrogated with an unprecedented degree of detail. Emergence of next generation nucleotide sequencing methods, advanced proteomic techniques, and metabolic profiling approaches continue to produce a wealth of biological data that captures molecular frameworks underlying phenotype. The advent of these novel technologies has significant translational applications, as investigators can now explore molecular underpinnings of developmental states with a high degree of resolution. Application of these leading-edge techniques to patient samples has been successfully used to unmask nuanced molecular details of disease vs healthy tissue, which may provide novel targets for palliative intervention. To enhance such approaches, concomitant development of algorithms to reprogram differentiated cells in order to recapitulate pluripotent capacity offers a distinct advantage to advancing diagnostic methodology. Bioinformatic deconvolution of several “-omic” layers extracted from reprogrammed patient cells, could, in principle, provide a means by which the evolution of individual pathology can be developmentally monitored. Significant logistic challenges face current implementation of this novel paradigm of patient treatment and care, however, several of these limitations have been successfully addressed through continuous development of cutting edge in silico archiving and processing methods. Comprehensive elucidation of genomic, transcriptomic, proteomic, and metabolomic networks that define normal and pathological states, in combination with reprogrammed patient cells are thus poised to become high value resources in modern diagnosis and prognosis of patient disease.

Stem cell systems informatics for advanced clinical biodiagnostics: tracing molecular signatures from bench to bedside

Development of innovative high throughput technologies has enabled a variety of molecular landscapes to be interrogated with an unprecedented degree of detail. Emergence of next generation nucleotide sequencing methods, advanced proteomic techniques, and metabolic profiling approaches continue to produce a wealth of biological data that captures molecular frameworks underlying phenotype. The advent of these novel technologies has significant translational applications, as investigators can now explore molecular underpinnings of developmental states with a high degree of resolution. Application of these leading-edge techniques to patient samples has been successfully used to unmask nuanced molecular details of disease vs healthy tissue, which may provide novel targets for palliative intervention. To enhance such approaches, concomitant development of algorithms to reprogram differentiated cells in order to recapitulate pluripotent capacity offers a distinct advantage to advancing diagnostic methodology. Bioinformatic deconvolution of several "-omic" layers extracted from reprogrammed patient cells, could, in principle, provide a means by which the evolution of individual pathology can be developmentally monitored. Significant logistic challenges face current implementation of this novel paradigm of patient treatment and care, however, several of these limitations have been successfully addressed through continuous development of cutting edge in silico archiving and processing methods. Comprehensive elucidation of genomic, transcriptomic, proteomic, and metabolomic networks that define normal and pathological states, in combination with reprogrammed patient cells are thus poised to become high value resources in modern diagnosis and prognosis of patient disease.