Objective To identify endotypes of osteoarthritis (OA) by a metabolomics analysis. Methods Study participants included hip/knee OA patients and controls. Fasting plasma samples were metabolomically profiled. Common factor analysis and K-means clustering were applied to the metabolomics data to identify the endotypes of OA patients. Logistic regression was utilized to identify the most significant metabolites contributing to the endotypes. Clinical and epidemiological factors were examined in relation to the identified OA endotypes. Results Six hundred and fifteen primary OA patients and 237 controls were included. Among the 186 metabolites measured, 162 passed the quality control analysis. The 615 OA patients were classified in three clusters (A, 66; B, 200; and C, 349). Patients in cluster A had a significantly higher concentration of butyrylcarnitine (C4) than other clusters and controls (all P < 0.0002). Elevated C4 is thought to be related to muscle weakness and wasting. Patients in cluster B had a significantly lower arginine concentration than other clusters and controls (all P < 7.98 × 10−11). Cluster C patients had a significantly lower concentration of lysophosphatidylcholine (with palmitic acid), which is a pro-inflammatory bioactive compound, than other clusters and controls (P < 3.79 × 10−6). Further, cluster A had a higher BMI and prevalence of diabetes than other clusters (all P ≤ 0.0009), and also a higher prevalence of coronary heart disease than cluster C (P = 0.04). Cluster B had a higher prevalence of coronary heart disease than cluster C (P = 0.003) whereas cluster C had a higher prevalence of osteoporosis (P = 0.009). Conclusion Our data suggest three possible clinically actionable endotypes in primary OA: muscle weakness, arginine deficit and low inflammatory OA.

INDELs and CNVs are structural variations that may play roles in cancer susceptibility and patient outcomes. Our objectives were a) to computationally detect and examine the genome-wide INDEL/CNV profiles in a cohort of colorectal cancer patients, and b) to examine the associations of frequent INDELs/CNVs with relapse-free survival time. We also identified unique variants in 13 Familial Colorectal Cancer Type X (FCCX) cases. The study cohort consisted of 495 colorectal cancer patients. QuantiSNP and PennCNV algorithms were utilized to predict the INDELs/CNVs using genome-wide signal intensity data. Duplex PCR was used to validate predictions for 10 variants. Multivariable Cox regression models were used to test the associations of 106 common variants with relapse-free survival time. Score test and the multivariable Cox proportional hazards models with time-varying coefficients were applied to identify the variants with time-varying effects on the relapse-free survival time. A total of 3486 distinct INDELs/CNVs were identified in the patient cohort. The majority of these variants were rare (83%) and deletion variants (81%). The results of the computational predictions and duplex PCR results were highly concordant (93-100%). We identified four promising variants significantly associated with relapse-free survival time (P < 0.05) in the multivariable Cox proportional hazards regression models after adjustment for clinical factors. More importantly, two additional variants were identified to have time-varying effects on the risk of relapse. Finally, 58 rare variants were identified unique to the FCCX cases; none of them were detected in more than one patient. This is one of the first genome-wide analyses that identified the germline INDEL/CNV profiles in colorectal cancer patients. Our analyses identified novel variants and genes that can biologically affect the risk of relapse in colorectal cancer patients. Additionally, for the first time, we identified germline variants that can potentially be earlyrelapse markers in colorectal cancer. Cancer Medicine Open Access 1221

Sequencing exomes/genomes have been successful for identifying recessive genes; however, discovery of dominant genes including deafness genes (DFNA) remains challenging. We report a new DFNA gene, ATP11A, in a Newfoundland family with a variable form of bilateral sensorineural hearing loss (SNHL). Genome-wide SNP genotyping linked SNHL to DFNA33 (LOD = 4.77), a locus on 13q34 previously mapped in a German family with variable SNHL. Whole-genome sequencing identified 51 unremarkable positional variants on 13q34. Continuous clinical ascertainment identified several key recombination events and reduced the disease interval to 769 kb, excluding all but one variant. ATP11A (NC_000013.11: chr13:113534963G>A) is a novel variant predicted to be a cryptic donor splice site. RNA studies verified in silico predictions, revealing the retention of 153 bp of intron in the 3′ UTR of several ATP11A isoforms. Two unresolved families from Israel were subsequently identified with a similar, variable form of SNHL and a novel duplication (NM_032189.3:c.3322_3327+2dupGTCCAGGT) in exon 28 of ATP11A extended exon 28 by 8 bp, leading to a frameshift and premature stop codon (p.Asn1110Valfs43Ter). ATP11A is a type of P4-ATPase that transports (flip) phospholipids from the outer to inner leaflet of cell membranes to maintain asymmetry. Haploinsufficiency of ATP11A, the phospholipid flippase that specially transports phosphatidylserine (PS) and phosphatidylethanolamine (PE), could leave cells with PS/PE at the extracellular side vulnerable to phagocytic degradation. Given that surface PS can be pharmaceutically targeted, hearing loss due to ATP11A could potentially be treated. It is also likely that ATP11A is the gene underlying DFNA33.

We aimed to examine the associations of a genome-wide set of single-nucleotide polymorphisms (SNPs) and 254 copy number variations (CNVs) and/or insertion/deletions (INDELs) with clinical outcomes in colorectal cancer patients (n=505). We also aimed to investigate whether their associations changed (e.g. appeared, diminished) over time. Multivariable Cox proportional hazards and piece-wise Cox regression models were used to examine the associations. The Cancer Genome Atlas (TCGA) datasets were used for replication purposes and to examine the gene expression

In this study, we aimed to investigate the associations of genetic variations within select genes functioning in angiogenesis, lymph‐angiogenesis, and metastasis pathways and the risk of outcome in colorectal cancer patients. We followed a two‐stage analysis: First, 381 polymorphisms from 30 genes (eight Vascular Endothelial Growth Factor (VEGF) and 22 Matrix Metalloproteinase [MMP] genes) were investigated in the discovery cohort (n = 505). Then, 16 polymorphisms with the lowest P‐value in this analysis were investigated in a separate replication cohort (n = 247). Genotypes were obtained using the Illumina® HumanOmni‐1‐Quad (discovery cohort) and Sequenom MassArray® (replication cohort) platforms. The primary outcome measure was overall survival (OS). Kaplan–Meier, univariate and multivariable Cox regression methods were used to test the associations between genotypes and OS. Four SNPs (rs12365082, rs11225389, rs11225388, and rs2846707) had the univariate analysis P < 0.05 in both the discovery and replication cohorts. These SNPs are in linkage disequilibrium with each other to varying extent and are located in the MMP8 and MMP27 genes. In the multivariable analysis adjusting for age, stage, and microsatellite instability status, three of these SNPs (rs12365082, rs11225389, rs11225388) were independent predictors of OS (P < 0.05) in the discovery cohort. However, the same analysis in the replication cohort did not yield statistically significant results. Overall, while the genetic variations in the VEGF and MMP genes are attractive candidates as prognostic markers, our study showed no evidence of associations of a large set of SNPs in these genes and overall survival of colorectal cancer patients in our study.