Genomic analysis across 53 canine cancer types reveals novel mutations and high clinical actionability potential.

A genomic understanding of the oncogenic processes and individual variability of human cancer has steadily fueled improvement in patient outcomes over the past 20 years. Mutations within tumour tissues are routinely assessed through clinical genomic diagnostic assays by academic and commercial laboratories to facilitate diagnosis, prognosis and effective treatment stratification. The application of genomics has unveiled a wealth of mutation-based biomarkers in canine cancers, suggesting that the transformative principles that have revolutionized human cancer medicine can be brought to bear in veterinary oncology. To advance clinical genomics and genomics-guided medicine in canine oncology, we have developed and validated a canine cancer next-generation sequencing gene panel for the identification of multiple mutation types in clinical specimens. With this panel, we examined the genomic landscapes of 828 tumours from 813 dogs, spanning 53 cancer types. We identified 7856 alterations, encompassing copy number variants, single nucleotide variants, indels and internal tandem duplications. Additionally, we evaluated the clinical utility of these alterations by incorporating a biomarker framework from comprehensive curation of primary canine literature and inferences from human cancer genomic biomarker literature and clinical diagnostics. Remarkably, nearly 90% of the cases exhibited mutations with diagnostic, prognostic or therapeutic implications. Our work represents a thorough assessment of genomic landscapes in a large cohort of canine cancers, the first of its kind for its comprehensive inclusion of multiple mutation types and structured annotation of biomarkers, demonstrating the clinical potential of leveraging mutation-based biomarkers in veterinary oncology.

The Arg59Trp variant in ANGPTL8 (betatrophin) is associated with total and HDL-cholesterol in American Indians and Mexican Americans and differentially affects cleavage of ANGPTL3.

We previously identified a locus linked to total cholesterol (TC) concentration in Pima Indians on chromosome 19p. To characterize this locus, we genotyped >2000 SNPs in 1838 Pimas and assessed association with log(TC). We observed evidence for association with log(TC) with rs2278426 (3.5% decrease/copy of the T allele; P=5.045×10(-6)) in the ANGPTL8 (angiopoietin-like 8) gene. We replicated this association in 2413 participants of the San Antonio Mexican American Family Study (SAMAFS: 2.0% decrease per copy of the T allele; P=0.005842). In a meta-analysis of the combined data, we found the strongest estimated effect with rs2278426 (P=2.563×10(-7)). The variant T allele at rs2278426 predicts an Arg59Trp substitution and has previously been associated with LDL-C and HDL-C. In Pimas and SAMAFS participants, the T allele of rs2278426 was associated with reduced HDL-C levels (P=0.000741 and 0.00002, respectively), and the combined estimated effect for the two cohorts was -3.8% (P=8.526×10(-8)). ANGPTL8 transcript and protein levels increased in response to both glucose and insulin. The variant allele was associated with increased levels of cleaved ANGPTL3. We conclude that individuals with the variant allele may have lower TC and HDL-C levels due to increased activation of ANGPTL3 by ANGPTL8.

Whole blood gene expression profiles in insulin resistant Latinos with the metabolic syndrome.

Although insulin resistance in skeletal muscle is well-characterized, the role of circulating whole blood in the metabolic syndrome phenotype is not well understood. We set out to test the hypothesis that genes involved in inflammation, insulin signaling and mitochondrial function would be altered in expression in the whole blood of individuals with metabolic syndrome. We further wanted to examine whether similar relationships that we have found previously in skeletal muscle exist in peripheral whole blood cells. All subjects (n=184) were Latino descent from the Arizona Insulin Resistance registry. Subjects were classified based on the metabolic syndrome phenotype according to the National Cholesterol Education Program's Adult Treatment Panel III. Of the 184 Latino subjects in the study, 74 were classified with the metabolic syndrome and 110 were without. Whole blood gene expression profiling was performed using the Agilent 4x44K Whole Human Genome Microarray. Whole blood microarray analysis identified 1,432 probes that were altered in expression ≥ 1.2 fold and P<0.05 after Benjamini-Hochberg in the metabolic syndrome subjects. KEGG pathway analysis revealed significant enrichment for pathways including ribosome, oxidative phosphorylation and MAPK signaling (all Benjamini-Hochberg P<0.05). Whole blood mRNA expression changes observed in the microarray data were confirmed by quantitative RT-PCR. Transcription factor binding motif enrichment analysis revealed E2F1, ELK1, NF-kappaB, STAT1 and STAT3 significantly enriched after Bonferroni correction (all P<0.05). The results of the present study demonstrate that whole blood is a useful tissue for studying the metabolic syndrome and its underlying insulin resistance although the relationship between blood and skeletal muscle differs.

Single nucleotide polymorphism genotyping using BeadChip microarrays.

The genotyping of single nucleotide polymorphisms (SNPs) has successfully contributed to the study of complex diseases more than any other technology to date. Genome-wide association studies (GWAS) using 10,000s to >1,000,000 SNPs have identified 1000s of statistically significant SNPs pertaining to 17 different human disease and trait categories. Post-GWAS fine-mapping studies using 10,000s to 100,000s SNPs on a microarray have narrowed the region of interest for many of these GWAS findings; in addition, independent signals within the original GWAS region have been identified. Focused content, SNP-based microarrays such as the human exome, for example, have too been used successfully to identify novel disease associations. Success has come to studies where 100s to 10,000s (mostly) to >100,000 samples were genotyped. For the time being, SNP-based microarrays remain cost-effective especially when studying large numbers of samples compared to other "genotyping" technologies including next generation sequencing. In this unit, protocols for manual (LIMS-free), semi-manual, and automated processing of BeadChip microarrays are presented. Lower throughput studies will find value in the manual and semi-manual protocols, while all types of studies--low-, medium-, and high-throughput--will find value in the semi-manual and automated protocols.