Project description:Abnormal function of genes is at the root of most cancers, but heritable cancer syndromes account for a very small minority of all tumors in humans and domestic animals. The majority of cancers are “sporadic,” that is, they are not heritable in the strictest sense. Instead, sporadic cancers occur due to interactions of unknown intrinsic (heritable) and environmental factors that lead to malignant transformation and uncontrolled growth. Identification of heritable risk factors in sporadic human cancers is difficult because individual genetic backgrounds are very heterogeneous. To this end, individual genetic backgrounds of purebred dogs are more homogeneous, and dog breeds show different predilection to develop specific cancers. Here, we used genomic screens based on gene expression profiling to identify sets of genes that may contribute to the development of canine hemangiosarcoma, a relatively common endothelial sarcoma. Specific genes in a single breed (Golden Retrievers) are modulated by (or with) heritable risk traits, showing functional features that appear to modulate tumor behavior. Our results suggest these methods are suitable to identify genes that will enhance our understanding of how these cancers happen, as well as possible treatment targets that will improve outcomes of both human and canine cancer patients. Keywords: Hemangiosarcoma, microarray, heritability, GSEA, canine 10 samples were analysed. 6 Golden Retrievers with hemangiosarcoma, 3 non-Golden Retrievers with hemangiosarcoma, and 1 mixed breed Golden Retriever with hemangiosarcoma. The experiment was designed to find genes associated with breed and hemangiosarcoma to asses genetic make-up on disease susceptibility and/or progression
Project description:Comparative oncology is a developing research discipline that is being used to assist our understanding of human neoplastic diseases. Companion canines are a preferred animal oncology model due to spontaneous tumor development and similarity to human disease at the pathophysiological level. We use a paired RNA sequencing (RNA-Seq)/microarray analysis of a set of four normal canine lymph nodes and ten canine lymphoma fine needle aspirates to identify technical biases and variation between the technologies and convergence on biological disease pathways. Surrogate Variable Analysis (SVA) provides a formal multivariate analysis of the combined RNA-Seq/microarray data set. Applying SVA to the data allows us to decompose variation into contributions associated with transcript abundance, differences between the technology, and latent variation within each technology. A substantial and highly statistically significant component of the variation reflects transcript abundance, and RNA-Seq proved more sensitive for detection of transcripts expressed at low levels. Latent random variation among RNA-Seq samples is also distinct in character from that impacting microarray samples. In particular, we observed variation between RNA-Seq samples that reflects transcript GC content. Platform-independent variable decomposition without a priori knowledge of the sources of variation using SVA represents a generalizable method for accomplishing cross-platform data analysis. We identified genes differentially expressed between normal lymph nodes of disease free dogs and a subset of the diseased dogs diagnosed with B-cell lymphoma using each technology. There is statistically significant overlap between the RNA-Seq and microarray sets of differentially expressed genes. Analysis of overlapping genes in the context of biological systems suggests elevated expression and activity of PI3K signaling in B-cell lymphoma biopsies compared with normal biopsies, consistent with literature describing successful use of drugs targeting this pathway in lymphomas.
Project description:Comparative oncology is a developing research discipline that is being used to assist our understanding of human neoplastic diseases. Companion canines are a preferred animal oncology model due to spontaneous tumor development and similarity to human disease at the pathophysiological level. We use a paired RNA sequencing (RNA-Seq)/microarray analysis of a set of four normal canine lymph nodes and ten canine lymphoma fine needle aspirates to identify technical biases and variation between the technologies and convergence on biological disease pathways. Surrogate Variable Analysis (SVA) provides a formal multivariate analysis of the combined RNA-Seq/microarray data set. Applying SVA to the data allows us to decompose variation into contributions associated with transcript abundance, differences between the technology, and latent variation within each technology. A substantial and highly statistically significant component of the variation reflects transcript abundance, and RNA-Seq proved more sensitive for detection of transcripts expressed at low levels. Latent random variation among RNA-Seq samples is also distinct in character from that impacting microarray samples. In particular, we observed variation between RNA-Seq samples that reflects transcript GC content. Platform-independent variable decomposition without a priori knowledge of the sources of variation using SVA represents a generalizable method for accomplishing cross-platform data analysis. We identified genes differentially expressed between normal lymph nodes of disease free dogs and a subset of the diseased dogs diagnosed with B-cell lymphoma using each technology. There is statistically significant overlap between the RNA-Seq and microarray sets of differentially expressed genes. Analysis of overlapping genes in the context of biological systems suggests elevated expression and activity of PI3K signaling in B-cell lymphoma biopsies compared with normal biopsies, consistent with literature describing successful use of drugs targeting this pathway in lymphomas. RNA was extracted from 10 lymphoma fine needle aspirates attained from companion canines. 4 normal lymph node samples were obtained from a Beagle breeding colony at Pfizer, including two samples that were taken from the same dog but different lymph nodes. This Series represents the Affymetrix gene expression only, not RNA-Seq referenced above. RNA-Seq data have been submitted to SRA as SRA059558.