Pharmacogenomic Assessment of Genes Implicated in Thiopurine Metabolism and Toxicity in a UK Cohort of Pediatric Patients With Inflammatory Bowel Disease.

BACKGROUND: Thiopurine drugs are effective treatment options in inflammatory bowel disease and other conditions but discontinued in some patients due to toxicity. METHODS: We investigated thiopurine-induced toxicity in a pediatric inflammatory bowel disease cohort by utilizing exome sequencing data across a panel of 46 genes, including TPMT and NUDT15. RESULTS: The cohort included 487 patients with a median age of 13.1 years. Of the 396 patients exposed to thiopurines, myelosuppression was observed in 11%, gastroenterological intolerance in 11%, hepatotoxicity in 4.5%, pancreatitis in 1.8%, and "other" adverse effects in 2.8%. TPMT (thiopurine S-methyltransferase) enzyme activity was normal in 87.4%, intermediate 12.3%, and deficient in 0.2%; 26% of patients with intermediate activity developed toxicity to thiopurines. Routinely genotyped TPMT alleles associated with defective enzyme activity were identified in 28 (7%) patients: TPMT*3A in 4.5%, *3B in 1%, and *3C in 1.5%. Of these, only 6 (21%) patients developed toxic responses. Three rare TPMT alleles (*3D, *39, and *40) not assessed on routine genotyping were identified in 3 patients, who all developed toxic responses. The missense variant p.R139C (NUDT15*3 allele) was identified in 4 patients (azathioprine 1.6 mg/kg/d), but only 1 developed toxicity. One patient with an in-frame deletion variant p.G13del in NUDT15 developed myelosuppression at low doses. Per-gene deleteriousness score GenePy identified a significant association for toxicity in the AOX1 and DHFR genes. CONCLUSIONS: A significant association for toxicity was observed in the AOX1 and DHFR genes in individuals negative for the TPMT and NUDT15 variants. Patients harboring the NUDT15*3 allele, which is associated with myelosuppression, did not show an increased risk of toxicity.

This study reports thiopurine-induced toxicity in pediatric patients with inflammatory bowel disease. The findings are presented in the context of genetic variations, focusing on genes implicated in thiopurine drug metabolism, thereby contributing to the missing pharmacogenomic association in patients developing toxicity.

Modelling human genetic disorders in Xenopus tropicalis.

Recent progress in human disease genetics is leading to rapid advances in understanding pathobiological mechanisms. However, the sheer number of risk-conveying genetic variants being identified demands in vivo model systems that are amenable to functional analyses at scale. Here we provide a practical guide for using the diploid frog species Xenopus tropicalis to study many genes and variants to uncover conserved mechanisms of pathobiology relevant to human disease. We discuss key considerations in modelling human genetic disorders: genetic architecture, conservation, phenotyping strategy and rigour, as well as more complex topics, such as penetrance, expressivity, sex differences and current challenges in the field. As the patient-driven gene discovery field expands significantly, the cost-effective, rapid and higher throughput nature of Xenopus make it an essential member of the model organism armamentarium for understanding gene function in development and in relation to disease.

A gene pathogenicity tool "GenePy" identifies missed biallelic diagnoses in the 100,000 Genomes Project.

PURPOSE: The 100,000 Genomes Project diagnosed a quarter of affected participants, but 26% of diagnoses were not on the applied gene panel(s); with many being de novo variants. Assessing biallelic variants without a gene panel is more challenging. METHODS: We sought to identify missed biallelic diagnoses using GenePy, which incorporates allele frequency, zygosity, and a user-defined deleterious metric, generating an aggregate GenePy score per gene, per participant. We calculated GenePy scores for 2862 recessive disease genes in 78,216 100,000 Genomes Project participants. For each gene, we ranked participant GenePy scores and scrutinized affected participants without a diagnosis, whose scores ranked among the top 5 for each gene. In cases which participant phenotypes overlapped with the disease gene of interest, we extracted rare variants and applied phase, ClinVar, and ACMG classification. RESULTS: 3184 affected individuals without a molecular diagnosis had a top-5-ranked GenePy score and 682 of 3184 (21%) had phenotypes overlapping with a top-ranking gene. In 122 of 669 (18%) phenotype-matched cases (excluding 13 withdrawn participants), we identified a putative missed diagnosis (2.2% of all undiagnosed participants). A further 334 of 669 (50%) cases have a possible missed diagnosis but require functional validation. CONCLUSION: Applying GenePy at scale has identified 456 potential diagnoses, demonstrating the value of novel diagnostic strategies.

HNRNPA2B1 myopathy presenting in a family with an early onset oculopharyngeal muscular dystrophy-like phenotype.

Genetic variation at HNRNPA2B1 is associated with inclusion body myopathy, Paget's disease and paediatric onset oculopharyngeal muscular dystrophy. We present a pedigree where a mother and two daughters presented with adolescent to early-adulthood onset of symptoms reminiscent of oculopharyngeal muscular dystrophy or chronic progressive external ophthalmoplegia, with a later limb-girdle pattern of weakness. Creatine Kinase was ∼1000 U/L. Myoimaging identified fatty replacement of sartorius, adductors longus and magnus, biceps femoris, semitendinosus and gastrocnemii. Muscle biopsies showed a variation of fibre size, occasional rimmed vacuoles and increased internalised myonuclei. Cases were heterozygous for a frameshift variant at HNRNPA2B1, consistent with a dominant and fully-penetrant mode of inheritance. Genetic variation at HNRNPA2B1 should be considered in adults with an oculopharyngeal muscular dystrophy-like or chronic progressive external ophthalmoplegia-like myopathy where initial testing fails to identify a cause.

A Panel-Agnostic Strategy 'HiPPo' Improves Diagnostic Efficiency in the UK Genomic Medicine Service.

Genome sequencing is available as a clinical test in the UK through the Genomic Medicine Service (GMS). The GMS analytical strategy predominantly filters genome data on preselected gene panels. Whilst this reduces variants requiring assessment by reporting laboratories, pathogenic variants outside applied panels may be missed, and variants in genes without established disease-gene relationships are largely ignored. This study compares the analysis of a research exome to a GMS clinical genome for the same patients. For the research exome, we applied a panel-agnostic approach filtering for variants with High Pathogenic Potential (HiPPo) using ClinVar, allele frequency, and in silico prediction tools. We then restricted HiPPo variants to Gene Curation Coalition (GenCC) disease genes. These results were compared with the GMS genome panel-based approach. Twenty-four participants from eight families underwent parallel research exome and GMS genome sequencing. Exome HiPPo analysis identified a similar number of variants as the GMS panel-based approach. GMS genome analysis returned two pathogenic variants and one de novo variant. Exome HiPPo analysis returned the same variants plus an additional pathogenic variant and three further de novo variants in novel genes, where case series are underway. When HiPPo was restricted to GenCC disease genes, statistically fewer variants required assessment to identify more pathogenic variants than reported by the GMS, giving a diagnostic rate per variant assessed of 20% for HiPPo versus 3% for the GMS. With UK plans to sequence 5 million genomes, strategies are needed to optimise genome analysis beyond gene panels whilst minimising the burden of variants requiring clinical assessment.

CCPlotR: an R package for the visualization of cell-cell interactions.

Summary: We present CCPlotR-an R package that generates visualizations of cell-cell interactions. CCPlotR is designed to work with the output of tools that predict cell-cell interactions from single-cell gene expression data and requires only a table of predicted interactions as input. The package can generate a comprehensive set of publication-ready figures such as heatmaps, dotplots, circos plots and network diagrams, providing a useful resource for researchers working on cell-cell interactions. Availability and implementation: CCPlotR is available to download and install from GitHub (https://github.com/Sarah145/CCPlotR) and comes with a toy dataset to demonstrate the different functions. Support for users will be provided via the GitHub issues tracker (https://github.com/Sarah145/CCPlotR/issues).

A Role for Genetic Modifiers in Tubulointerstitial Kidney Diseases.

With the increased availability of genomic sequencing technologies, the molecular bases for kidney diseases such as nephronophthisis and mitochondrially inherited and autosomal-dominant tubulointerstitial kidney diseases (ADTKD) has become increasingly apparent. These tubulointerstitial kidney diseases (TKD) are monogenic diseases of the tubulointerstitium and result in interstitial fibrosis and tubular atrophy (IF/TA). However, monogenic inheritance alone does not adequately explain the highly variable onset of kidney failure and extra-renal manifestations. Phenotypes vary considerably between individuals harbouring the same pathogenic variant in the same putative monogenic gene, even within families sharing common environmental factors. While the extreme end of the disease spectrum may have dramatic syndromic manifestations typically diagnosed in childhood, many patients present a more subtle phenotype with little to differentiate them from many other common forms of non-proteinuric chronic kidney disease (CKD). This review summarises the expanding repertoire of genes underpinning TKD and their known phenotypic manifestations. Furthermore, we collate the growing evidence for a role of modifier genes and discuss the extent to which these data bridge the historical gap between apparently rare monogenic TKD and polygenic non-proteinuric CKD (excluding polycystic kidney disease).

Cell-cell interactome of the hematopoietic niche and its changes in acute myeloid leukemia.

The bone marrow (BM) is a complex microenvironment, coordinating the production of billions of blood cells every day. Despite its essential role and its relevance to hematopoietic diseases, this environment remains poorly characterized. Here we present a high-resolution characterization of the niche in health and acute myeloid leukemia (AML) by establishing a single-cell gene expression database of 339,381 BM cells. We found significant changes in cell type proportions and gene expression in AML, indicating that the entire niche is disrupted. We then predicted interactions between hematopoietic stem and progenitor cells (HSPCs) and other BM cell types, revealing a remarkable expansion of predicted interactions in AML that promote HSPC-cell adhesion, immunosuppression, and cytokine signaling. In particular, predicted interactions involving transforming growth factor ß1 (TGFB1) become widespread, and we show that this can drive AML cell quiescence in vitro. Our results highlight potential mechanisms of enhanced AML-HSPC competitiveness and a skewed microenvironment, fostering AML growth.

A genotype-to-phenotype approach suggests under-reporting of single nucleotide variants in nephrocystin-1 (NPHP1) related disease (UK 100,000 Genomes Project).

Autosomal recessive whole gene deletions of nephrocystin-1 (NPHP1) result in abnormal structure and function of the primary cilia. These deletions can result in a tubulointerstitial kidney disease known as nephronophthisis and retinal (Senior-Løken syndrome) and neurological (Joubert syndrome) diseases. Nephronophthisis is a common cause of end-stage kidney disease (ESKD) in children and up to 1% of adult onset ESKD. Single nucleotide variants (SNVs) and small insertions and deletions (Indels) have been less well characterised. We used a gene pathogenicity scoring system (GenePy) and a genotype-to-phenotype approach on individuals recruited to the UK Genomics England (GEL) 100,000 Genomes Project (100kGP) (n = 78,050). This approach identified all participants with NPHP1-related diseases reported by NHS Genomics Medical Centres and an additional eight participants. Extreme NPHP1 gene scores, often underpinned by clear recessive inheritance, were observed in patients from diverse recruitment categories, including cancer, suggesting the possibility of a more widespread disease than previously appreciated. In total, ten participants had homozygous CNV deletions with eight homozygous or compound heterozygous with SNVs. Our data also reveals strong in-silico evidence that approximately 44% of NPHP1 related disease may be due to SNVs with AlphaFold structural modelling evidence for a significant impact on protein structure. This study suggests historical under-reporting of SNVS in NPHP1 related diseases compared with CNVs.

Supervised Machine Learning Classifies Inflammatory Bowel Disease Patients by Subtype Using Whole Exome Sequencing Data.

BACKGROUND: Inflammatory bowel disease [IBD] is a chronic inflammatory disorder with two main subtypes: Crohn's disease [CD] and ulcerative colitis [UC]. Prompt subtype diagnosis enables the correct treatment to be administered. Using genomic data, we aimed to assess machine learning [ML] to classify patients according to IBD subtype. METHODS: Whole exome sequencing [WES] from paediatric/adult IBD patients was processed using an in-house bioinformatics pipeline. These data were condensed into the per-gene, per-individual genomic burden score, GenePy. Data were split into training and testing datasets [80/20]. Feature selection with a linear support vector classifier, and hyperparameter tuning with Bayesian Optimisation, were performed [training data]. The supervised ML method random forest was utilised to classify patients as CD or UC, using three panels: 1] all available genes; 2] autoimmune genes; 3] 'IBD' genes. ML results were assessed using area under the receiver operating characteristics curve [AUROC], sensitivity, and specificity on the testing dataset. RESULTS: A total of 906 patients were included in analysis [600 CD, 306 UC]. Training data included 488 patients, balanced according to the minority class of UC. The autoimmune gene panel generated the best performing ML model [AUROC = 0.68], outperforming an IBD gene panel [AUROC = 0.61]. NOD2 was the top gene for discriminating CD and UC, regardless of the gene panel used. Lack of variation in genes with high GenePy scores in CD patients was the best classifier of a diagnosis of UC. DISCUSSION: We demonstrate promising classification of patients by subtype using random forest and WES data. Focusing on specific subgroups of patients, with larger datasets, may result in better classification.

Impaired expression of metallothioneins contributes to allergen-induced inflammation in patients with atopic dermatitis.

Regulation of cutaneous immunity is severely compromised in inflammatory skin disease. To investigate the molecular crosstalk underpinning tolerance versus inflammation in atopic dermatitis, we utilise a human in vivo allergen challenge study, exposing atopic dermatitis patients to house dust mite. Here we analyse transcriptional programmes at the population and single cell levels in parallel with immunophenotyping of cutaneous immunocytes revealed a distinct dichotomy in atopic dermatitis patient responsiveness to house dust mite challenge. Our study shows that reactivity to house dust mite was associated with high basal levels of TNF-expressing cutaneous Th17 T cells, and documents the presence of hub structures where Langerhans cells and T cells co-localised. Mechanistically, we identify expression of metallothioneins and transcriptional programmes encoding antioxidant defences across all skin cell types, that appear to protect against allergen-induced inflammation. Furthermore, single nucleotide polymorphisms in the MTIX gene are associated with patients who did not react to house dust mite, opening up possibilities for therapeutic interventions modulating metallothionein expression in atopic dermatitis.

A gene pathogenicity tool 'GenePy' identifies missed biallelic diagnoses in the 100,000 Genomes Project.

The 100,000 Genomes Project (100KGP) diagnosed a quarter of recruited affected participants, but 26% of diagnoses were in genes not on the chosen gene panel(s); with many being de novo variants of high impact. However, assessing biallelic variants without a gene panel is challenging, due to the number of variants requiring scrutiny. We sought to identify potential missed biallelic diagnoses independent of the gene panel applied using GenePy - a whole gene pathogenicity metric. GenePy scores all variants called in a given individual, incorporating allele frequency, zygosity, and a user-defined deleterious metric (CADD v1.6 applied herein). GenePy then combines all variant scores for individual genes, generating an aggregate score per gene, per participant. We calculated GenePy scores for 2862 recessive disease genes in 78,216 individuals in 100KGP. For each gene, we ranked participant GenePy scores for that gene, and scrutinised affected individuals without a diagnosis whose scores ranked amongst the top-5 for each gene. We assessed these participants' phenotypes for overlap with the disease gene associated phenotype for which they were highly ranked. Where phenotypes overlapped, we extracted rare variants in the gene of interest and applied phase, ClinVar and ACMG classification looking for putative causal biallelic variants. 3184 affected individuals without a molecular diagnosis had a top-5 ranked GenePy gene score and 682/3184 (21%) had phenotypes overlapping with one of the top-ranking genes. After removing 13 withdrawn participants, in 122/669 (18%) of the phenotype-matched cases, we identified a putative missed diagnosis in a top-ranked gene supported by phasing, ClinVar and ACMG classification. A further 334/669 (50%) of cases have a possible missed diagnosis but require functional validation. Applying GenePy at scale has identified potential diagnoses for 456/3183 (14%) of undiagnosed participants who had a top-5 ranked GenePy score in a recessive disease gene, whilst adding only 1.2 additional variants (per individual) for assessment.

The Pediatric Crohn Disease Morbidity Index (PCD-MI): Development of a Tool to Assess Long-Term Disease Burden Using a Data-Driven Approach.

BACKGROUND/OBJECTIVE: Heterogeneity and chronicity of Crohn disease (CD) make prediction of outcomes difficult. To date, no longitudinal measure can quantify burden over a patient's disease course, preventing assessment and integration into predictive modeling. Here, we aimed to demonstrate the feasibility of constructing a data driven, longitudinal disease burden score. METHODS: Literature was reviewed for tools used in assessment of CD activity. Themes were identified to construct a pediatric CD morbidity index (PCD-MI). Scores were assigned to variables. Data were extracted automatically from the electronic patient records at Southampton Children's Hospital, diagnosed from 2012 to 2019 (inclusive). PCD-MI scores were calculated, adjusted for duration of follow up and assessed for variation (ANOVA) and distribution (Kolmogorov-Smirnov). RESULTS: Nineteen clinical/biological features across five themes were included in the PCD-MI including blood/fecal/radiological/endoscopic results, medication usage, surgery, growth parameters, and extraintestinal manifestations. Maximal score was 100 after accounting for follow-up duration. PCD-MI was assessed in 66 patients, mean age 12.5 years. Following quality filtering, 9528 blood/fecal test results and 1309 growth measures were included. Mean PCD-MI score was 14.95 (range 2.2-32.5); data were normally distributed ( P = 0.2) with 25% of patients having a PCD-MI < 10. There was no difference in the mean PCD-MI when split by year of diagnosis, F -statistic 1.625, P = 0.147. CONCLUSIONS: PCD-MI is a calculatable measure for a cohort of patients diagnosed over an 8-year period, integrating a wide-range of data with potential to determine high or low disease burden. Future iterations of the PCD-MI require refinement of included features, optimized scores, and validation on external cohorts.

Hemizygous loss of function mutations in CLCN5 causing end-stage kidney disease without Dent disease phenotype.

Dent disease type 1 is suspected in the presence of a complete phenotype of low molecular weight (LMW) proteinuria, hypercalciuria and at least one of the following: nephrocalcinosis, nephrolithiasis, haematuria, hypophosphatemia or chronic kidney disease (CKD). We present two brothers who presented with CKD alone. In the absence of typical clinical features, further assessment of LMW proteinuria and hypercalciuria was not undertaken. Whole-genome sequencing revealed hemizygous loss of function mutations in chloride voltage-gated channel 5 (CLCN5) consistent with Dent disease. Dent disease should, therefore, be considered in patients with an incomplete phenotype, including unexplained CKD alone.

A panel-agnostic strategy 'HiPPo' improves diagnostic efficiency in the UK Genome Medicine Service.

Genome sequencing is now available as a clinical test on the National Health Service (NHS) through the Genome Medicine Service (GMS). The GMS have set out an analytical strategy that predominantly filters genome data on a pre-selected gene panel(s). Whilst this approach reduces the number of variants requiring assessment by reporting laboratories, pathogenic variants outside of the gene panel applied may be missed, and candidate variants in novel genes are largely ignored. This study sought to compare a research exome analysis to an independent clinical genome analysis performed through the NHS for the same group of patients. When analysing the exome data, we applied a panel agnostic approach filtering for variants with High Pathogenic Potential (HiPPo) using ClinVar, allele frequency, and in silico prediction tools. We then compared this gene agnostic analysis to the panel-based approach as applied by the GMS to genome data. Later we restricted HiPPo variants to a panel of the Gene Curation Coalition (GenCC) morbid genes and compared the diagnostic yield with the variants filtered using the GMS strategy. 24 patients from 8 families underwent parallel research exome sequencing and GMS genome sequencing. HiPPo analysis applied to research exome data identified a similar number of variants as the gene panel-based approach applied by the GMS. GMS clinical genome analysis identified and returned 2 pathogenic variants and 3 variants of uncertain significance. HiPPo research exome analysis identified the same variants plus an additional pathogenic variant and a further 3 de novo variants of uncertain significance in novel genes, where case series and functional studies are underway. When HiPPo was restricted to GenCC disease genes (strong or definitive), the same pathogenic variants were identified yet statistically fewer variants required assessment to identify more diagnostic variants than reported by the GMS genome strategy. This gave a diagnostic rate per variant assessed of 20% for HiPPo restricted to GenCC versus 3% for the GMS panel-based approach. With plans to sequence 5 million more NHS patients, strategies are needed to optimise the full potential of genome data beyond gene panels whilst minimising the burden of variants that require clinical assessment.

Targeting de novo loss-of-function variants in constrained disease genes improves diagnostic rates in the 100,000 Genomes Project.

BACKGROUND: Genome sequencing was first offered clinically in the UK through the 100,000 Genomes Project (100KGP). Analysis was restricted to predefined gene panels associated with the patient's phenotype. However, panels rely on clearly characterised phenotypes and risk missing diagnoses outside of the panel(s) applied. We propose a complementary method to rapidly identify pathogenic variants, including those missed by 100KGP methods. METHODS: The Loss-of-function Observed/Expected Upper-bound Fraction (LOEUF) score quantifies gene constraint, with low scores correlated with haploinsufficiency. We applied DeNovoLOEUF, a filtering strategy to sequencing data from 13,949 rare disease trios in the 100KGP, by filtering for rare, de novo, loss-of-function variants in disease genes with a LOEUF score < 0.2. We compared our findings with the corresponding patient's diagnostic reports. RESULTS: 324/332 (98%) of the variants identified using DeNovoLOEUF were diagnostic or partially diagnostic (whereby the variant was responsible for some of the phenotype). We identified 39 diagnoses that were "missed" by 100KGP standard analyses, which are now being returned to patients. CONCLUSION: We have demonstrated a highly specific and rapid method with a 98% positive predictive value that has good concordance with standard analysis, low false-positive rate, and can identify additional diagnoses. Globally, as more patients are being offered genome sequencing, we anticipate that DeNovoLOEUF will rapidly identify new diagnoses and facilitate iterative analyses when new disease genes are discovered.

NOD2 in Crohn's Disease-Unfinished Business.

Studies of Crohn's disease have consistently implicated NOD2 as the most important gene in disease pathogenesis since first being identified in 2001. Thereafter, genome-wide association, next-generation sequencing and functional analyses have all confirmed a key role for NOD2, but despite this, NOD2 also has significant unresolved complexity. More recent studies have reinvigorated an early hypothesis that NOD2 may be a single-gene cause of disease, and the distinct ileal stricturing phenotype seen with NOD2-related disease presents an opportunity for personalized diagnosis, disease prediction and targeted therapy. The genomics of NOD2 has much that remains unknown, including the role of rare variation, phasing of variants across the haplotype block and the role of variation in the NOD2-regulatory regions. Here, we discuss the evidence and the unmet needs of NOD2 research, based on recently published evidence, and suggest methods that may meet these requirements.

A novel variant in GATM causes idiopathic renal Fanconi syndrome and predicts progression to end-stage kidney disease.

Renal Fanconi syndrome (RFS) is a generalised disorder of the proximal convoluted tubule. Many genes have been associated with RFS including those that cause systemic disorders such as cystinosis, as well as isolated RFS. We discuss the case of a 10-year-old female who presented with leg pain and raised creatinine on a screening blood test. Her mother has RFS and required a kidney transplant in her thirties. Further investigations confirmed RFS in the daughter. Exome sequencing was performed on the affected mother, child, and unaffected father. We identified a novel variant in GATM; c.965G>C p.(Arg322Pro) segregating dominantly in the mother and daughter. We validated our finding with molecular dynamics simulations and demonstrated a dynamic signature that differentiates our variant and two previously identified pathogenic variants in GATM from wildtype. Genetic testing has uncovered a novel pathogenic variant that predicts progression to end stage kidney failure and has important implications for family planning and cascade testing. We recommend that GATM is screened for in children presenting with RFS, in addition to adults, particularly with kidney failure, who may have had previous negative gene testing.

Prediction of Crohn's Disease Stricturing Phenotype Using a NOD2-derived Genomic Biomarker.

BACKGROUND: Crohn's disease (CD) is highly heterogenous and may be complicated by stricturing behavior. Personalized prediction of stricturing will inform management. We aimed to create a stricturing risk stratification model using genomic/clinical data. METHODS: Exome sequencing was performed on CD patients, and phenotype data retrieved. Biallelic variants in NOD2 were identified. NOD2 was converted into a per-patient deleteriousness metric ("GenePy"). Using training data, patients were stratified into risk groups for fibrotic stricturing using NOD2. Findings were validated in a testing data set. Models were modified to include disease location at diagnosis. Cox proportional hazards assessed performance. RESULTS: Six hundred forty-five patients were included (373 children and 272 adults); 48 patients fulfilled criteria for monogenic NOD2-related disease (7.4%), 24 of whom had strictures. NOD2 GenePy scores stratified patients in training data into 2 risk groups. Within testing data, 30 of 161 patients (18.6%) were classified as high-risk based on the NOD2 biomarker, with stricturing in 17 of 30 (56.7%). In the low-risk group, 28 of 131 (21.4%) had stricturing behavior. Cox proportional hazards using the NOD2 risk groups demonstrated a hazard ratio (HR) of 2.092 (P = 2.4 × 10-5), between risk groups. Limiting analysis to patients diagnosed aged < 18-years improved performance (HR-3.164, P = 1 × 10-6). Models were modified to include disease location, such as terminal ileal (TI) disease or not. Inclusion of NOD2 risk groups added significant additional utility to prediction models. High-risk group pediatric patients presenting with TI disease had a HR of 4.89 (P = 2.3 × 10-5) compared with the low-risk group patients without TI disease. CONCLUSIONS: A NOD2 genomic biomarker predicts stricturing risk, with prognostic power improved in pediatric-onset CD. Implementation into a clinical setting can help personalize management.

NOD2 is a well-established risk gene for development of Crohn's disease and stricturing behavior. Here we demonstrate NOD2 can be utilized as a genomic biomarker, stratifying patients into 2 stricturing risk groups. Further refinement using disease location at diagnosis improved risk stratification.