Response to anti-IL17 therapy in inflammatory disease is not strongly impacted by genetic background.

Response to the anti-IL17 monoclonal antibody secukinumab is heterogeneous, and not all participants respond to treatment. Understanding whether this heterogeneity is driven by genetic variation is a key aim of pharmacogenetics and could influence precision medicine approaches in inflammatory diseases. Using changes in disease activity scores across 5,218 genotyped individuals from 19 clinical trials across four indications (psoriatic arthritis, psoriasis, ankylosing spondylitis, and rheumatoid arthritis), we tested whether genetics predicted response to secukinumab. We did not find any evidence of association between treatment response and common variants, imputed HLA alleles, polygenic risk scores of disease susceptibility, or cross-disease components of shared genetic risk. This suggests that anti-IL17 therapy is equally effective regardless of an individual's genetic background, a finding that has important implications for future genetic studies of biological therapy response in inflammatory diseases.

DCMD: Distance-based classification using mixture distributions on microbiome data.

Current advances in next-generation sequencing techniques have allowed researchers to conduct comprehensive research on the microbiome and human diseases, with recent studies identifying associations between the human microbiome and health outcomes for a number of chronic conditions. However, microbiome data structure, characterized by sparsity and skewness, presents challenges to building effective classifiers. To address this, we present an innovative approach for distance-based classification using mixture distributions (DCMD). The method aims to improve classification performance using microbiome community data, where the predictors are composed of sparse and heterogeneous count data. This approach models the inherent uncertainty in sparse counts by estimating a mixture distribution for the sample data and representing each observation as a distribution, conditional on observed counts and the estimated mixture, which are then used as inputs for distance-based classification. The method is implemented into a k-means classification and k-nearest neighbours framework. We develop two distance metrics that produce optimal results. The performance of the model is assessed using simulated and human microbiome study data, with results compared against a number of existing machine learning and distance-based classification approaches. The proposed method is competitive when compared to the other machine learning approaches, and shows a clear improvement over commonly used distance-based classifiers, underscoring the importance of modelling sparsity for achieving optimal results. The range of applicability and robustness make the proposed method a viable alternative for classification using sparse microbiome count data. The source code is available at https://github.com/kshestop/DCMD for academic use.

Investigating Molecular Signatures Underlying Trapeziometacarpal Osteoarthritis Through the Evaluation of Systemic Cytokine Expression.

Purpose: Non-operative management of trapeziometacarpal osteoarthritis (TMOA) demonstrates only short-term symptomatic alleviation, and no approved disease modifying drugs exist to treat this condition. A key issue in these patients is that radiographic disease severity can be discordant with patient reported pain, illustrating the need to identify molecular mediators of disease. This study characterizes the biochemical profile of TMOA patients to elucidate molecular mechanisms driving TMOA progression. Methods: Plasma from patients with symptomatic TMOA undergoing surgical (n=39) or non-surgical management (n=44) with 1-year post-surgical follow-up were compared using a targeted panel of 27 cytokines. Radiographic (Eaton-Littler), anthropometric, longitudinal pain (VAS, TASD, quick DASH) and functional (key pinch, grip strength) data were used to evaluate relationships between structure, pain, and systemic cytokine expression. Principal Component Analysis was used to identify clusters of patients. Results: Patients undergoing surgery had greater BMI as well as higher baseline quick DASH, TASD scores. Systemically, these patients could only be distinguished by differing levels of Interleukin-7 (IL-7), with an adjusted odds ratio of 0.22 for surgery for those with increased levels of this cytokine. Interestingly, PCA analysis of all patients (regardless of surgical status) identified a subset of patients with an "inflammatory" phenotype, as defined by a unique molecular signature consisting of thirteen cytokines. Conclusion: Overall, this study demonstrated that circulating cytokines are capable of distinguishing TMOA disease severity, and identified IL-7 as a target capable of differentiating disease severity with higher levels associated with a decreased likelihood of TMOA needing surgical intervention. It also identified a cluster of patients who segregate based on a molecular signature of select cytokines.

MicroRNA-34a-5p Promotes Joint Destruction During Osteoarthritis.

OBJECTIVE: MicroRNA-34a-5p (miR-34a-5p) expression is elevated in the synovial fluid of patients with late-stage knee osteoarthritis (OA); however, its exact role and therapeutic potential in OA remain to be fully elucidated. This study was undertaken to examine the role of miR-34a-5p in OA pathogenesis. METHODS: Expression of miR-34a-5p was determined in joint tissues and human plasma (n = 71). Experiments using miR-34a-5p mimic or antisense oligonucleotide (ASO) treatment were performed in human OA chondrocytes, fibroblast-like synoviocytes (FLS) (n = 7-9), and mouse OA models, including destabilization of the medial meniscus (DMM; n = 22) and the accelerated, more severe model of mice fed a high-fat diet and subjected to DMM (n = 11). Wild-type (WT) mice (n = 9) and miR-34a-knockout (KO) mice (n = 11) were subjected to DMM. Results were expressed as the mean ± SEM and analyzed by t-test or analysis of variance, with appropriate post hoc tests. P values less than 0.05 were considered significant. RNA sequencing was performed on WT and KO mouse chondrocytes. RESULTS: Expression of miR-34a-5p was significantly increased in the plasma, cartilage, and synovium of patients with late-stage OA and in the cartilage and synovium of mice subjected to DMM. Plasma miR-34a-5p expression was significantly increased in obese patients with late-stage OA, and in the plasma and knee joints of mice fed a high-fat diet. In human OA chondrocytes and FLS, miR-34a-5p mimic increased key OA pathology markers, while miR-34a-5p ASO improved cellular gene expression. Intraarticular miR-34a-5p mimic injection induced an OA-like phenotype. Conversely, miR-34a-5p ASO injection imparted cartilage-protective effects in the DMM and high-fat diet/DMM models. The miR-34a-KO mice exhibited protection against DMM-induced cartilage damage. RNA sequencing of WT and KO chondrocytes revealed a putative miR-34a-5p signaling network. CONCLUSION: Our findings provide comprehensive evidence of the role and therapeutic potential of miR-34a-5p in OA.

Effect of autotaxin inhibition in a surgically-induced mouse model of osteoarthritis.

Objective: Osteoarthritis (OA) is a degenerative joint disease with no approved disease modifying therapy. The enzyme autotaxin (ATX) converts lysophoshatidylcholine analogues to lysophosphatidic acid. Systemic inhibition of ATX reduces pain in animal models of OA; however, OA disease-modifying effects associated with ATX inhibition remain unknown. Here, we sought to determine whether local (knee joint) injection of an ATX inhibitor attenuates surgically-induced OA in mice. Methods: ATX expression was evaluated in human knee OA cartilage. Ten-week-old mice were subjected to surgically-induced OA. ATX inhibitor (PF-8380, 2.5ng/joint) was injected intra-articularly either at three and five weeks post-surgery or at two, four, six and eight weeks post-surgery and knee joints were evaluated by histopathology and immunohistochemistry to study the expression of catabolic and cell death markers. mRNA sequencing of human OA chondrocytes treated with/without ATX inhibitor was performed to identify differentially expressed transcripts, followed by pathway enrichment analysis. Results: ATX expression was elevated in severely degenerated cartilage compared to less degenerated human OA cartilage. In surgically-induced OA mice, intra-articular injection of ATX inhibitor at three and five weeks post-surgery partially protected knee joints from cartilage degeneration. Interestingly, earlier and more frequent ATX inhibitor injections did not confer significant protection. Immunohistochemical analysis showed decreased expression of catabolic and apoptotic markers with two ATX inhibitor injections. mRNA sequencing followed by pathway analysis identified pathways related to cholesterol analogue metabolism and cell-cycle that could be modulated by ATX inhibition in human OA chondrocytes. Conclusion: Local delivery of ATX inhibitor partially attenuates surgically-induced OA in mice.

Bone Marrow Mesenchymal Stromal Cell Treatment in Patients with Osteoarthritis Results in Overall Improvement in Pain and Symptoms and Reduces Synovial Inflammation.

Patients with late-stage Kellgren-Lawrence knee osteoarthritis received a single intra-articular injection of 1, 10, or 50 million bone marrow mesenchymal stromal cells (BM-MSCs) in a phase I/IIa trial to assess safety and efficacy using a broad toolset of analytical methods. Besides safety, outcomes included patient-reported outcome measures (PROMs): Knee Injury and Osteoarthritis Outcome Score (KOOS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC); contrast-enhanced magnetic resonance imaging (MRI) for cartilage morphology (Whole Organ MRI Scores [WORMS]), collagen content (T2 scores), and synovitis; and inflammation and cartilage turnover biomarkers, all over 12 months. BM-MSCs were characterized by a panel of anti-inflammatory markers to predict clinical efficacy. There were no serious adverse events, although four patients had minor, transient adverse events. There were significant overall improvements in KOOS pain, symptoms, quality of life, and WOMAC stiffness relative to baseline; the 50 million dose achieved clinically relevant improvements across most PROMs. WORMS and T2 scores did not change relative to baseline. However, cartilage catabolic biomarkers and MRI synovitis were significantly lower at higher doses. Pro-inflammatory monocytes/macrophages and interleukin 12 levels decreased in the synovial fluid after MSC injection. The panel of BM-MSC anti-inflammatory markers was strongly predictive of PROMs over 12 months. Autologous BM-MSCs are safe and result in significant improvements in PROMs at 12 months. Our analytical tools provide important insights into BM-MSC dosing and BM-MSC reduction of synovial inflammation and cartilage degradation and provide a highly predictive donor selection criterion that will be critical in translating MSC therapy for osteoarthritis. Stem Cells Translational Medicine 2019;8:746&757.

Analyzing differences between microbiome communities using mixture distributions.

In this paper, we present a method to assess differences between microbiome communities that effectively models sparse count data and accounts for presence-absence bias frequently encountered when zeros are present. We assume that the observed data for each operational taxonomic unit is Poisson generated with the rate for each sample originating from an underlying rate distribution. We propose to model this distribution using a mixture model, specifying the components based on the posterior rate distribution of a count and estimating the optimal weights using a least squares objective function. The distribution incorporates varying resolutions of samples, a point mass for differentiating structural and nonstructural zeros, and a truncation point mass to account for high values that are too sparse to model. As mixture component specification is not always straightforward, a method to estimate a joint model from several mixture distributions using minimum distances of bootstrap iterates is proposed. Once the population rate distribution is approximated, we obtain sample-specific distributions by conditioning on the observed operational taxonomic unit count, resolution, and estimated mixture distribution and then use these to estimate pairwise distances for a permutation test. The method gives an accurate estimate of the true proportion of zeros for presence-absence, effectively models the distribution of low counts using the mixture distribution, and achieves good power for detecting differences in a variety of scenarios. The method is tested using a simulation study and applied to two microbiome datasets. In each case, the results are compared with a number of existing methods.

A classification modeling approach for determining metabolite signatures in osteoarthritis.

Multiple factors can help predict knee osteoarthritis (OA) patients from healthy individuals, including age, sex, and BMI, and possibly metabolite levels. Using plasma from individuals with primary OA undergoing total knee replacement and healthy volunteers, we measured lysophosphatidylcholine (lysoPC) and phosphatidylcholine (PC) analogues by metabolomics. Populations were stratified on demographic factors and lysoPC and PC analogue signatures were determined by univariate receiver-operator curve (AUC) analysis. Using signatures, multivariate classification modeling was performed using various algorithms to select the most consistent method as measured by AUC differences between resampled training and test sets. Lists of metabolites indicative of OA [AUC > 0.5] were identified for each stratum. The signature from males age > 50 years old encompassed the majority of identified metabolites, suggesting lysoPCs and PCs are dominant indicators of OA in older males. Principal component regression with logistic regression was the most consistent multivariate classification algorithm tested. Using this algorithm, classification of older males had fair power to classify OA patients from healthy individuals. Thus, individual levels of lysoPC and PC analogues may be indicative of individuals with OA in older populations, particularly males. Our metabolite signature modeling method is likely to increase classification power in validation cohorts.

FUT2 genotype and secretory status are not associated with fecal microbial composition and inferred function in healthy subjects.

Heritability analysis of the microbiota has demonstrated the importance of host genotype in defining the human microbiota. The alpha (1,2)-fucosyltransferase 2 encoded by FUT2 is involved in the formation of the H antigen and the SNP, rs601338 is associated with ABO histo-blood group antigen secretion in the intestinal mucosa. Previous studies have provided non replicated results for the association of this polymorphism with the composition and inferred function of intestinal microbiota. We aimed to assess this relationship in a large cohort of 1,190 healthy individuals. Genotyping was performed using the HumanCoreEXOME chip, microbial composition was addressed by 16S rRNA gene sequencing. Firmicutes, Bacteroidetes, and Actinobacteria were the dominant phyla in this cohort. Although we have sufficient power to detect significant associations of FUT2 genotype/ inferred phenotype with the microbiota, our data demonstrate that FUT2 genotype and secretor status is not associated with microbial alpha diversity, microbial composition or inferred microbial function after correction for multiple testing. Thus, FUT2 genotype and inferred phenotype are not associated with human fecal microbial composition and imputed function.

Germline INDELs and CNVs in a cohort of colorectal cancer patients: their characteristics, associations with relapse-free survival time, and potential time-varying effects on the risk of relapse.

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 early-relapse markers in colorectal cancer.

Association of host genome with intestinal microbial composition in a large healthy cohort.

Intestinal microbiota is known to be important in health and disease. Its composition is influenced by both environmental and host factors. Few large-scale studies have evaluated the association between host genetic variation and the composition of microbiota. We recruited a cohort of 1,561 healthy individuals, of whom 270 belong in 123 families, and found that almost one-third of fecal bacterial taxa were heritable. In addition, we identified 58 SNPs associated with the relative abundance of 33 taxa in 1,098 discovery subjects. Among these, four loci were replicated in a second cohort of 463 subjects: rs62171178 (nearest gene UBR3) associated with Rikenellaceae, rs1394174 (CNTN6) associated with Faecalibacterium, rs59846192 (DMRTB1) associated with Lachnospira, and rs28473221 (SALL3) associated with Eubacterium. After correction for multiple testing, 6 of the 58 associations remained significant, one of which replicated. These results identify associations between specific genetic variants and the gut microbiome.

No associations of a set of SNPs in the Vascular Endothelial Growth Factor (VEGF) and Matrix Metalloproteinase (MMP) genes with survival of colorectal cancer patients.

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.

A model for estimating ovarian cancer risk: application for preventive oophorectomy.

OBJECTIVE: It is important to identify women in the population who have a high risk of ovarian cancer and who might benefit from prophylactic bilateral salpingo-oophorectomy. The probability that a woman will develop ovarian cancer depends on her current age, her reproductive history and her genetic status. METHODS: We simulated the distribution of ovarian cancer risk for the 2011 Ontario female population. We generated (at random) individual risks of ovarian cancer to age 80 for 6,301,340 women, based on the published risk factors, mutation frequencies and population age-specific incidence rates (SEER database). Risk factors included parity, breastfeeding, oral contraceptives, tubal ligation and family history. Genetic factors included 11 single nucleotide polymorphisms (SNPs) and BRCA1/2 mutations. RESULTS: Of the 6,301,340 women simulated as the general population of Ontario, the (complete) model predicts that 65,805 women (1.0%) will develop ovarian cancer by age 80. There were 46,069 women (0.7%) with a risk of ovarian cancer above 5%. BRCA1/2 mutation carriers accounted for 67.4% of the women at greater than 5% risk (31,028 women). Among ovarian cancer patients at greater than 5% risk, a BRCA1/2 mutation was present in 89.2%. In contrast, SNPs contribute to a very small proportion of the ovarian cancer patients who were at greater than 5% risk. CONCLUSIONS: Approximately 12.9% of all ovarian cancers in Ontario occur in the 0.7% of women in the general population who have a lifetime ovarian cancer risk in excess of 5%, the majority of whom carry a mutation in BRCA1 or BRCA2.

A Survival Association Study of 102 Polymorphisms Previously Associated with Survival Outcomes in Colorectal Cancer.

Several published studies identified associations of a number of polymorphisms with a variety of survival outcomes in colorectal cancer. In this study, we aimed to explore 102 previously reported common genetic polymorphisms and their associations with overall survival (OS) and disease-free survival (DFS) in a colorectal cancer patient cohort from Newfoundland (n = 505). Genotypes were obtained using a genomewide SNP genotyping platform. For each polymorphism, the best possible genetic model was estimated for both overall survival and disease-free survival using a previously published approach. These SNPs were then analyzed under their genetic models by Cox regression method. Correction for multiple comparisons was performed by the False Discovery Rate (FDR) method. Univariate analysis results showed that RRM1-rs12806698, IFNGR1-rs1327474, DDX20-rs197412, and PTGS2-rs5275 polymorphisms were nominally associated with OS or DFS (p < 0.01). In stage-adjusted analysis, the nominal associations of DDX20-rs197412, PTGS2-rs5275, and HSPA5-rs391957 with DFS were detected. However, after FDR correction none of these polymorphisms remained significantly associated with the survival outcomes. We conclude that polymorphisms investigated in this study are not associated with OS or DFS in our colorectal cancer patient cohort.

A genome wide association study on Newfoundland colorectal cancer patients' survival outcomes.

BACKGROUND: In this study we performed genome-wide association studies to identify candidate SNPs that may predict the risk of disease outcome in colorectal cancer. METHODS: Patient cohort consisted of 505 unrelated patients with Caucasian ancestry. Germline DNA samples were genotyped using the Illumina® human Omni-1quad SNP chip. Associations of SNPs with overall and disease free survivals were examined primarily for 431 patients with microsatellite instability-low (MSI-L) or stable (MSS) colorectal tumors using Cox proportional hazards method adjusting for clinical covariates. Bootstrap method was applied for internal validation of results. As exploratory analyses, association analyses for the colon (n = 334) and rectal (n = 171) cancer patients were also performed. RESULTS: As a result, there was no SNP that reached the genomewide significance levels (p < 5x10(-8)) in any of the analyses. A small number of genetic markers (n = 10) showed nominal associations (p <10(-6)) for MSS/MSI-L, colon, or rectal cancer patient groups. These markers were located in two non-coding RNA genes or intergenic regions and none were amino acid substituting polymorphisms. Bootstrap analysis for the MSS/MSI-L cohort data suggested the robustness of the observed nominal associations. CONCLUSIONS: Likely due to small number of patients, our study did not identify an acceptable level of association of SNPs with outcome in MSS/MSI-L, colon, or rectal cancer patients. A number of SNPs with sub-optimal p-values were, however, identified; these loci may be promising and examined in other larger-sized patient cohorts.

Determinants of intestinal permeability in healthy first-degree relatives of individuals with Crohn's disease.

BACKGROUND: The Genetics, Environmental, Microbial Project is a multicenter study assessing etiological factors in Crohn's disease by studying healthy first-degree relatives (FDRs) of individuals affected by Crohn's disease. We aimed to evaluate the contribution of genetic, microbial, and environmental factors to the determination of intestinal permeability in healthy FDRs. METHODS: IP was assessed using the lactulose-mannitol ratio (LacMan ratio). FDRs were genotyped for 167 inflammatory bowel disease-associated single nucleotide polymorphisms. Taxonomic profile of the fecal microbiota was determined by Illumina MiSeq pyrosequencing of 16S ribosomal RNA. The associations of LacMan ratio with demographic factors, inflammatory bowel disease-associated single nucleotide polymorphisms and the fecal microbiota were assessed. RESULTS: One thousand, one hundred ninety-six white FDRs were included [corrected]. Eleven percent of FDRs had an elevated LacMan ratio (≥0.03). A multivariate analysis demonstrated that younger subjects and nonsmokers had higher LacMan ratios, P = 3.62 × 10⁻4 and P = 0.03, respectively. The LacMan ratio was not significantly heritable, H2r, 0.13, P = 0.13. There was no association between any of the 167 inflammatory bowel disease-associated risk variants and LacMan ratio nor was there a correlation between fecal microbial composition and the LacMan ratio. CONCLUSIONS: We did not find LacMan ratio to be significantly heritable suggesting that the contribution of genetic factors to the determination of intestinal permeability in healthy FDRs is modest. Environmental factors, such as smoking, are likely more important determinants. The effect of age on intestinal barrier function has been underappreciated.