Chromosome conformation signatures define predictive markers of inadequate response to methotrexate in early rheumatoid arthritis

On difficulties to define prognostic factors for clinical practice in rheumatoid arthritis

In rheumatoid arthritis (RA), the identification of prognostic factors (PF) capable of predicting disease outcome, response to treatment or success of dose reduction is an important issue, as these factors are intended to serve as a basis for decision-making. The task is complex from the outset, as the definition of disease prognosis or therapeutic prognosis is not uniquevocal. The heterogeneity of the definitions used partly explains the failure to identify PF that can be applied at an individual level. But other factors also contribute. First, the scope of the disease studied is too broad, including nosologically different entities. Second, potential PF are only measured at a single point of time, whereas changes over a period of time should be taken into account to a greater extent, not forgetting the potential impact of the treatment received during this period. Beyond these limiting factors, one of the main obstacles to the identification of PF is probably the fact that the phase of the disease is not sufficiently taken into account. Predicting the disease outcome when it is well established is a more complex challenge than when it is just beginning, as many factors are likely to interfere. The same applies to therapeutic PF, which should be determined according to disease duration. Difficulties also arise from the approaches used, which are often restricted to a single field of interest whereas they should be much more integrative and call on new large-scale data analysis tools with a view to precision medicine.In RA, prognosis can be defined at two levels: disease outcome, including joint damage and risk of extra-articular manifestations and/or complications, and treatment outcome, including response to therapy, risk of adverse effects and drug-free remission.

Role of IFN-α in Rheumatoid Arthritis

PURPOSE OF REVIEW

Type 1 interferons (IFN-I) are of increasing interest across a wide range of autoimmune rheumatic diseases. Historically, research into their role in rheumatoid arthritis (RA) has been relatively neglected, but recent work continues to highlight a potential contribution to RA pathophysiology.

RECENT FINDINGS

We emphasise the importance of disease stage when examining IFN-I in RA and provide an overview on how IFN-I may have a direct role on a variety of relevant cellular functions. We explore how clinical trajectory may be influenced by increased IFN-I signalling, and also, the limitations of scores composed of interferon response genes. Relevant environmental triggers and inheritable RA genetic risk relating to IFN-I signalling are explored with emphasis on intriguing data potentially linking IFN-I exposure, epigenetic changes, and disease relevant processes. Whilst these data cumulatively illustrate a likely role for IFN-I in RA, they also highlight the knowledge gaps, particularly in populations at risk for RA, and suggest directions for future research to both better understand IFN-I biology and inform targeted therapeutic strategies.

Development and Validation of Blood-Based Predictive Biomarkers for Response to PD-1/PD-L1 Checkpoint Inhibitors: Evidence of a Universal Systemic Core of 3D Immunogenetic Profiling across Multiple Oncological Indications

BACKGROUND

Unprecedented advantages in cancer treatment with immune checkpoint inhibitors (ICIs) remain limited to only a subset of patients. Systemic analyses of the regulatory 3D genome architecture linked to individual epigenetic and immunogenetic controls associated with tumour immune evasion mechanisms and immune checkpoint pathways reveal a highly prevalent molecular profile predictive of response to PD-1/PD-L1 ICIs. A clinical blood test based on a set of eight (8) 3D genomic biomarkers has been developed and validated on the basis of an observational trial to predict response to ICI therapy.

METHODS

The predictive eight biomarker set is derived from prospective observational clinical trials, representing 280 treatments with Pembrolizumab, Atezolizumab, Durvalumab, Nivolumab, and Avelumab in a broad range of indications: melanoma, lung, hepatocellular, renal, breast, bladder, colon, head and neck, bone, brain, lymphoma, prostate, vulvar, and cervical cancers.

RESULTS

The 3D genomic eight biomarker panel for response to immune checkpoint therapy achieved a high accuracy of 85%, sensitivity of 93%, and specificity of 82%.

CONCLUSIONS

This study demonstrates that a 3D genomic approach can be used to develop a predictive clinical assay for response to PD-1/PD-L1 checkpoint inhibition in cancer patients.

Circulating Chromosome Conformation Signatures Significantly Enhance PSA Positive Predicting Value and Overall Accuracy for Prostate Cancer Detection

BACKGROUND

Prostate cancer (PCa) has a high lifetime prevalence (one out of six men), but currently there is no widely accepted screening programme. Widely used prostate specific antigen (PSA) test at cut-off of 3.0 ng/mL does not have sufficient accuracy for detection of any prostate cancer, resulting in numerous unnecessary prostate biopsies in men with benign disease and false reassurance in some men with PCa. We have recently identified circulating chromosome conformation signatures (CCSs, Episwitch^® PCa test) allowing PCa detection and risk stratification in line with standards of clinical PCa staging. The purpose of this study was to determine whether combining the Episwitch PCa test with the PSA test will increase its diagnostic accuracy.

METHODS

n = 109 whole blood samples of men enrolled in the PROSTAGRAM screening pilot study and n = 38 samples of patients with established PCa diagnosis and cancer-negative controls from Imperial College NHS Trust were used. Samples were tested for PSA, and the presence of CCSs in the loci encoding for of DAPK1, HSD3B2, SRD5A3, MMP1, and miRNA98 associated with high-risk PCa identified in our previous work.

RESULTS

PSA > 3 ng/mL alone showed a low positive predicted value (PPV) of 0.14 and a high negative predicted value (NPV) of 0.93. EpiSwitch alone showed a PPV of 0.91 and a NPV of 0.32. Combining PSA and Episwitch tests has significantly increased the PPV to 0.81 although reducing the NPV to 0.78. Furthermore, integrating PSA, as a continuous variable (rather than a dichotomised 3 ng/mL cut-off), with EpiSwitch in a new multivariant stratification model, Prostate Screening EpiSwitch (PSE) test, has yielded a remarkable combined PPV of 0.92 and NPV of 0.94 when tested on the independent prospective cohort.

CONCLUSIONS

Our results demonstrate that combining the standard PSA readout with circulating chromosome conformations (PSE test) allows for significantly enhanced PSA PPV and overall accuracy for PCa detection. The PSE test is accurate, rapid, minimally invasive, and inexpensive, suggesting significant screening diagnostic potential to minimise unnecessary referrals for expensive and invasive MRI and/or biopsy testing. Further extended prospective blinded validation of the new combined signature in a screening cohort with low cancer prevalence would be the recommended step for PSE adoption in PCa screening.

Chromosome conformation capture approaches to investigate 3D genome architecture in Ankylosing Spondylitis

Ankylosing Spondylitis (AS) is a chronic inflammatory arthritis of the spine exhibiting a strong genetic background. The mechanistic and functional understanding of the AS-associated genomic loci, identified with Genome Wide Association Studies (GWAS), remains challenging. Chromosome conformation capture (3C) and derivatives are recent techniques which are of great help in elucidating the spatial genome organization and of enormous support in uncover a mechanistic explanation for disease-associated genetic variants. The perturbation of three-dimensional (3D) genome hierarchy may lead to a plethora of human diseases, including rheumatological disorders. Here we illustrate the latest approaches and related findings on the field of genome organization, highlighting how the instability of 3D genome conformation may be among the causes of rheumatological disease phenotypes. We suggest a new perspective on the inclusive potential of a 3C approach to inform GWAS results in rheumatic diseases. 3D genome organization may ultimately lead to a more precise and comprehensive functional interpretation of AS association, which is the starting point for emerging and more specific therapies.

Omics and Multi-Omics Analysis for the Early Identification and Improved Outcome of Patients with Psoriatic Arthritis

The definitive diagnosis and early treatment of many immune-mediated inflammatory diseases (IMIDs) is hindered by variable and overlapping clinical manifestations. Psoriatic arthritis (PsA), which develops in ~30% of people with psoriasis, is a key example. This mixed-pattern IMID is apparent in entheseal and synovial musculoskeletal structures, but a definitive diagnosis often can only be made by clinical experts or when an extensive progressive disease state is apparent. As with other IMIDs, the detection of multimodal molecular biomarkers offers some hope for the early diagnosis of PsA and the initiation of effective management and treatment strategies. However, specific biomarkers are not yet available for PsA. The assessment of new markers by genomic and epigenomic profiling, or the analysis of blood and synovial fluid/tissue samples using proteomics, metabolomics and lipidomics, provides hope that complex molecular biomarker profiles could be developed to diagnose PsA. Importantly, the integration of these markers with high-throughput histology, imaging and standardized clinical assessment data provides an important opportunity to develop molecular profiles that could improve the diagnosis of PsA, predict its occurrence in cohorts of individuals with psoriasis, differentiate PsA from other IMIDs, and improve therapeutic responses. In this review, we consider the technologies that are currently deployed in the EU IMI2 project HIPPOCRATES to define biomarker profiles specific for PsA and discuss the advantages of combining multi-omics data to improve the outcome of PsA patients.

Monocytes acquire prostate cancer specific chromatin conformations upon indirect co-culture with prostate cancer cells

Background

Three-dimensional chromosome loop conformations are powerful regulators of gene expression. These chromosome conformations can be detected both in tumour and in circulating cells and have significant disease biomarker potential. We have recently detected specific chromosome conformations in circulating cells of patients with prostate cancer (PCa) which were similar to ones found in their primary tumours, however, the possibility of horizontal transfer of chromosome conformations was not studied previously.

Methods

Human monocytes (U937) were co-cultured in Boyden chambers through 0.4 uM membrane with or without PC-3 human PCa cells or their conditioned media and a custom DNA microarray for 900,000 chromosomal loops covering all coding loci and non-coding RNA genes was performed on each part of the co-culture system.

Results

We have detected 684 PC-3 cell-specific chromosome conformations across the whole genome that were absent in naïve monocytes but appeared in monocytes co-cultured with PC-3 cells or with PC-3-conditioned media. Comparing PC3-specific conformations to the ones we have previously detected in systemic circulation of high-risk PCa patients revealed 9 positive loops present in both settings.

Conclusions

Our results demonstrate for the first time a proof of concept for horizontal transfer of chromosome conformations without direct cell-cell contact. This carries high clinical relevance as we have previously observed chromatin conformations in circulating cells of patients with melanoma and PCa similar to ones in their primary tumours. These changes can be used as highly specific biomarkers for diagnosis and prognosis. Further studies are required to elucidate the specific mechanism of chromosome conformations transfer and its clinical significance in particular diseases.

How does age determine the development of human immune-mediated arthritis?

Does age substantially affect the emergence of human immune-mediated arthritis? Children do not usually develop immune-mediated articular inflammation during their first year of life. In patients with juvenile idiopathic arthritis, this apparent ‘immune privilege’ disintegrates, and chronic inflammation is associated with variable autoantibody signatures and patterns of disease that resemble adult arthritis phenotypes. Numerous mechanisms might be involved in this shift, including genetic and epigenetic predisposing factors, maturation of the immune system with a progressive modulation of putative tolerogenic controls, parallel development of microbial dysbiosis, accumulation of a pro-inflammatory burden driven by environmental exposures (the exposome) and comorbidity-related drivers. By exploring these mechanisms, we expand the discussion of three (not mutually exclusive) hypotheses on how these factors can contribute to the differences and similarities between the loss of immune tolerance in children and the development of established immune-mediated arthritis in adults. These three hypotheses relate to a critical window in genetics and epigenetics, immune maturation, and the accumulation of burden. The varied manifestation of the underlying mechanisms among individuals is only beginning to be clarified, but the establishment of a framework can facilitate the development of an integrated understanding of the pathogenesis of arthritis across all ages.

In this Review, the authors discuss age-related arthropathy and the similarities and differences between childhood loss of immune tolerance and adult development of immune-mediated arthritis, and develop three hypotheses describing age-related mechanisms that contribute to the onset of arthritis.

The arthritis-free ‘immune privilege’ of early childhood is overridden by multiple mechanisms, progressively and age-dependently, generating recognizable patterns of chronic inflammatory arthritis.

The emergence of arthritis involves interconnected mechanisms related to immune priming, to a situational susceptibility and to the accumulation of an inflammatory burden.

The accumulation of epigenetic drift may contribute to differences across ages.

The exposome is expected to contribute to arthritis emergence in adults as well as in children.

Does persistence to methotrexate treatment in early rheumatoid arthritis have a familial component?

Objectives

To assess whether persistence to treatment with methotrexate (MTX) in early rheumatoid arthritis (RA) is shared among first-degree relatives with RA and to estimate any underlying heritability.

Methods

First-degree relative pairs diagnosed with RA 1999–2018 and starting MTX (in monotherapy) as their first disease-modifying anti-rheumatic drug (DMARD) treatment were identified by linking the Swedish Rheumatology Quality Register to national registers. Short- and long-term persistence to MTX was defined as remaining on treatment at 1 and 3 years, respectively, with no additional DMARDs added. We assessed familial aggregation through relative risks (RR) using log-binomial regression with robust standard errors and estimated heritability using tetrachoric correlations. We also explored the familial aggregation of EULAR treatment response after 3 and 6 months. To mimic the clinical setting, we also tested the association between having a family history of MTX persistence and persistence within the index patient.

Results

Familial persistence was not associated with persistence at 1 (RR=1.02, 95% CI 0.87–1.20), only at 3 (RR=1.41, 95% CI 1.14–1.74) years. Heritability at 1 and 3 years was estimated to be 0.08 (95% CI 0–0.43) and 0.58 (95% CI 0.27–0.89), respectively. No significant associations were found between family history and EULAR response at 3 and 6 months, neither overall nor in the clinical setting analysis.

Conclusions

Our findings imply a familial component, including a possible genetic element, within the long-term persistence to MTX following RA diagnosis. Whether this component is reflective of characteristics of the underlying RA disease or determinants for sustained response to MTX in itself will require further investigation.

Dawn of Precision Medicine in Psoriatic Arthritis

The establishment of precision medicine is considered particularly important in heterogeneous autoimmune diseases (e.g., psoriatic arthritis, systemic lupus erythematosus), which reveal clinical and molecular heterogeneity. The selection of optimal treatment strategies for individual patients may be more important and complex in autoimmune diseases than in other diseases. Two factors are important in precision medicine: patient stratification and use of targeted. When both factors work, patients are likely to have good outcomes. However, research into precision medicine and its practice in systemic autoimmune diseases is lacking. In contrast, the usefulness of peripheral immune cell phenotyping in the evaluation of immunological characteristics and stratification into subgroups of individual patients with systemic autoimmune diseases such as immunoglobulin 4-related disease, systemic lupus erythematosus, and anti-neutrophil cytoplasmic antibody-related vasculitis was reported. Furthermore, the potential of precision medicine using biological disease-modifying antirheumatic drugs based on peripheral immune cell phenotyping was recently demonstrated for psoriatic arthritis in the clinical setting. Precision medicine has not yet been sufficiently investigated in real world clinical settings. However, a dawn of precision medicine has emerged. We should shed further light on precision medicine in PsA and other autoimmune diseases. Here, we first review the usefulness of peripheral immune cell phenotyping in systemic autoimmune diseases and the potential of precision medicine in PsA based on this method.

Radicava/Edaravone Findings in Biomarkers From Amyotrophic Lateral Sclerosis (REFINE-ALS): Protocol and Study Design

Objectives

To identify putative biomarkers that may serve as quantifiable, biological, nonclinical measures of the pharmacodynamic effect of edaravone in amyotrophic lateral sclerosis (ALS) and to report real-world treatment outcomes.

Methods

This is a prospective, observational, longitudinal, multicenter (up to 40 sites) US study (Clinicaltrials.gov; NCT04259255) with at least 200 patients with ALS who will receive edaravone for 24 weeks (6 cycles; Food and Drug Administration-approved regimen). All participants must either be treatment naive for edaravone or be more than 1 month without receiving any edaravone dose before screening. Biomarker quantification and other assessments will be performed at baseline (before cycle 1) and during cycles 1, 3, and 6. Selected biomarkers of oxidative stress, inflammation, neuronal injury and death, and muscle injury, as well as biomarker discovery panels (EpiSwitch and SOMAscan), will be evaluated and, when feasible, compared with biobanked samples. Clinical efficacy assessments will include the ALS Functional Rating Scale-Revised, King's clinical staging, ALS Assessment Questionnaire-40, Appel ALS Score (Rating Scale), slow vital capacity, hand-held dynamometry and grip strength, and time to specified states of disease progression or death. DNA samples will also be collected for potential genomic evaluation. The predicted rates of progression and survival, and their potential correlations with biomarkers, will be evaluated. Adverse events related to the study will be reported.

Results

The study is estimated to be completed in 2022 with an interim analysis planned.

Conclusions

Findings may help to further the understanding of the pharmacodynamic effect of edaravone, including changes in biomarkers, in response to treatment.

BIOlogical Factors that Limit sustAined Remission in rhEumatoid arthritis (the BIO-FLARE study): protocol for a non-randomised longitudinal cohort study

BACKGROUND

Our knowledge of immune-mediated inflammatory disease (IMID) aetiology and pathogenesis has improved greatly over recent years, however, very little is known of the factors that trigger disease relapses (flares), converting diseases from inactive to active states. Focussing on rheumatoid arthritis (RA), the challenge that we will address is why IMIDs remit and relapse. Extrapolating from pathogenetic factors involved in disease initiation, new episodes of inflammation could be triggered by recurrent systemic immune dysregulation or locally by factors within the joint, either of which could be endorsed by overarching epigenetic factors or changes in systemic or localised metabolism.

METHODS

The BIO-FLARE study is a non-randomised longitudinal cohort study that aims to enrol 150 patients with RA in remission on a stable dose of non-biologic disease-modifying anti-rheumatic drugs (DMARDs), who consent to discontinue treatment. Participants stop their DMARDs at time 0 and are offered an optional ultrasound-guided synovial biopsy. They are studied intensively, with blood sampling and clinical evaluation at weeks 0, 2, 5, 8, 12 and 24. It is anticipated that 50% of participants will have a disease flare, whilst 50% remain in drug-free remission for the study duration (24 weeks). Flaring participants undergo an ultrasound-guided synovial biopsy before reinstatement of previous treatment. Blood samples will be used to investigate immune cell subsets, their activation status and their cytokine profile, autoantibody profiles and epigenetic profiles. Synovial biopsies will be examined to profile cell lineages and subtypes present at flare. Blood, urine and synovium will be examined to determine metabolic profiles. Taking into account all generated data, multivariate statistical techniques will be employed to develop a model to predict impending flare in RA, highlighting therapeutic pathways and informative biomarkers. Despite initial recruitment to time and target, the SARS-CoV-2 pandemic has impacted significantly, and a decision was taken to close recruitment at 118 participants with complete data.

DISCUSSION

This study aims to investigate the pathogenesis of flare in rheumatoid arthritis, which is a significant knowledge gap in our understanding, addressing a major unmet patient need.

TRIAL REGISTRATION

The study was retrospectively registered on 27/06/2019 in the ISRCTN registry 16371380 .

What Is the Persistence to Methotrexate in Rheumatoid Arthritis, and Does Machine Learning Outperform Hypothesis-Based Approaches to Its Prediction?

OBJECTIVE

The objectives of this study were to assess the 1-year persistence to methotrexate (MTX) initiated as the first ever conventional synthetic disease-modifying antirheumatic drug in new-onset rheumatoid arthritis (RA) and to investigate the marginal gains and robustness of the results by increasing the number and nature of covariates and by using data-driven, instead of hypothesis-based, methods to predict this persistence.

METHODS

Through the Swedish Rheumatology Quality Register, linked to other data sources, we identified a cohort of 5475 patients with new-onset RA in 2006-2016 who were starting MTX monotherapy as their first disease-modifying antirheumatic drug. Data on phenotype at diagnosis and demographics were combined with increasingly detailed data on medical disease history and medication use in four increasingly complex data sets (48-4162 covariates). We performed manual model building using logistic regression. We also performed five different machine learning (ML) methods and combined the ML results into an ensemble model. We calculated the area under the receiver operating characteristic curve (AUROC) and made calibration plots. We trained on 90% of the data, and tested the models on a holdout data set.

RESULTS

Of the 5475 patients, 3834 (70%) remained on MTX monotherapy 1 year after treatment start. Clinical RA disease activity and baseline characteristics were most strongly associated with the outcome. The best manual model had an AUROC of 0.66 (95% confidence interval [CI] 0.60-0.71). For the ML methods, Lasso regression performed best (AUROC = 0.67; 95% CI 0.62-0.71).

CONCLUSION

Approximately two thirds of patients with early RA who start MTX remain on this therapy 1 year later. Predicting this persistence remains a challenge, whether using hypothesis-based or ML models, and may yet require additional types of data.

Chromatin conformation changes in peripheral blood can detect prostate cancer and stratify disease risk groups

BACKGROUND

Current diagnostic blood tests for prostate cancer (PCa) are unreliable for the early stage disease, resulting in numerous unnecessary prostate biopsies in men with benign disease and false reassurance of negative biopsies in men with PCa. Predicting the risk of PCa is pivotal for making an informed decision on treatment options as the 5-year survival rate in the low-risk group is more than 95% and most men would benefit from surveillance rather than active treatment. Three-dimensional genome architecture and chromosome structures undergo early changes during tumourigenesis both in tumour and in circulating cells and can serve as a disease biomarker.

METHODS

In this prospective study we screened whole blood of newly diagnosed, treatment naïve PCa patients (n = 140) and cancer-free controls (n = 96) for the presence of 14,241 chromosomal loops in the loci of 425 genes.

RESULTS

We have detected specific chromosome conformation changes in the loci of ETS1, MAP3K14, SLC22A3 and CASP2 genes in peripheral blood from PCa patients yielding PCa detection with 80% sensitivity and 80% specificity. Further analysis between PCa risk groups yielded prognostic validation sets consisting of HSD3B2, VEGFC, APAF1, BMP6, ERG, MSR1, MUC1, ACAT1 and DAPK1 genes that achieved 80% sensitivity and 93% specificity stratifying high-risk category 3 vs low risk category 1 and 84% sensitivity and 89% specificity stratifying high risk category 3 vs intermediate risk category 2 disease.

CONCLUSIONS

Our results demonstrate specific chromosome conformations in the blood of PCa patients that allow PCa diagnosis and risk stratification with high sensitivity and specificity.

The Prospective Study of Epigenetic Regulatory Profiles in Sport and Exercise Monitored Through Chromosome Conformation Signatures

The integration of genetic and environmental factors that regulate the gene expression patterns associated with exercise adaptation is mediated by epigenetic mechanisms. The organisation of the human genome within three-dimensional space, known as chromosome conformation, has recently been shown as a dynamic epigenetic regulator of gene expression, facilitating the interaction of distal genomic regions due to tight and regulated packaging of chromosomes in the cell nucleus. Technological advances in the study of chromosome conformation mean a new class of biomarker-the chromosome conformation signature (CCS)-can identify chromosomal interactions across several genomic loci as a collective marker of an epigenomic state. Investigative use of CCSs in biological and medical research shows promise in identifying the likelihood that a disease state is present or absent, as well as an ability to prospectively stratify individuals according to their likely response to medical intervention. The association of CCSs with gene expression patterns suggests that there are likely to be CCSs that respond, or regulate the response, to exercise and related stimuli. The present review provides a contextual background to CCS research and a theoretical framework discussing the potential uses of this novel epigenomic biomarker within sport and exercise science and medicine.

Unmasking Retinitis Pigmentosa complex cases by a whole genome sequencing algorithm based on open-access tools: hidden recessive inheritance and potential oligogenic variants

Background

Retinitis Pigmentosa (RP) is a clinically and genetically heterogeneous disorder that results in inherited blindness. Despite the large number of genes identified, only ~ 60% of cases receive a genetic diagnosis using targeted-sequencing. The aim of this study was to design a whole genome sequencing (WGS) based approach to increase the diagnostic yield of complex Retinitis Pigmentosa cases.

Methods

WGS was conducted in three family members, belonging to one large apparent autosomal dominant RP family that remained unsolved by previous studies, using Illumina TruSeq library preparation kit and Illumina HiSeq X platform. Variant annotation, filtering and prioritization were performed using a number of open-access tools and public databases. Sanger sequencing of candidate variants was conducted in the extended family members.

Results

We have developed and optimized an algorithm, based on the combination of different open-access tools, for variant prioritization of WGS data which allowed us to reduce significantly the number of likely causative variants pending to be manually assessed and segregated. Following this algorithm, four heterozygous variants in one autosomal recessive gene (USH2A) were identified, segregating in pairs in the affected members. Additionally, two pathogenic alleles in ADGRV1 and PDZD7 could be contributing to the phenotype in one patient.

Conclusions

The optimization of a diagnostic algorithm for WGS data analysis, accompanied by a hypothesis-free approach, have allowed us to unmask the genetic cause of the disease in one large RP family, as well as to reassign its inheritance pattern which implies differences in the clinical management of these cases. These results contribute to increasing the number of cases with apparently dominant inheritance that carry causal mutations in recessive genes, as well as the possible involvement of various genes in the pathogenesis of RP in one patient. Moreover, our WGS-analysis approach, based on open-access tools, can easily be implemented by other researchers and clinicians to improve the diagnostic yield of additional patients with inherited retinal dystrophies.

Adding value to real-world data: the role of biomarkers

Adding biomarker information to real world datasets (e.g. biomarker data collected into disease/drug registries) can enhance mechanistic understanding of intra-patient differences in disease trajectories and differences in important clinical outcomes. Biomarkers can detect pathologies present early in disease potentially paving the way for preventative intervention strategies, which may help patients to avoid disability, poor treatment outcome, disease sequelae and premature mortality. However, adding biomarker data to real world datasets comes with a number of important challenges including sample collection and storage, study design and data analysis and interpretation. In this narrative review we will consider the benefits and challenges of adding biomarker data to real world datasets and discuss how biomarker data have added to our understanding of complex diseases, focusing on rheumatoid arthritis.

Precision medicine in psoriatic arthritis: how should we select targeted therapies?

Psoriatic arthritis (PsA) is a heterogeneous inflammatory arthritis associated with psoriasis. Patients manifest variable presentations with potential involvement of peripheral joints, spine, tendons, skin, and nails. There has been a rapid expansion in targeted treatment options for patients with PsA, but typically less than half of those who receive therapy achieve optimal treatment targets. Many patients respond to second-line or third-line biological therapies, but little evidence exists to guide the choice of therapeutics for each individual. At present, choice of therapy is driven by active clinical disease domains, clinician familiarity with existing treatments, and cost. Here, we review recent data that highlight the potential for personalised, or precision, medicine in PsA and other forms of inflammatory arthritis, noting that this research is still at a preliminary stage. In the future, a combination of detailed immunophenotyping and sophisticated statistical analyses should help to facilitate a personalised medicine approach in PsA, following examples from other clinical areas, such as oncology. This change in approach to the treatment of PsA has the potential to maximise outcomes for patients and to provide optimal therapies without delay.

Is prediction of clinical response to methotrexate in individual rheumatoid arthritis patients possible? A systematic literature review

OBJECTIVES

To identify, by a systematic literature review, predictors of clinical response to methotrexate treatment in rheumatoid arthritis patients, which would facilitate personalised treatment.

METHODS

PubMed and Embase databases were searched for original articles. Additionally, congress abstracts of European League Against Rheumatism and American College of Rheumatology annual meetings of the past 2 years were screened. Articles describing predictors of clinical response to methotrexate after 3 to 6 months were included, since this reflects the time span used to determine treatment effectiveness and decide on treatment changes in treat-to-target recommendations.

RESULTS

Thirty articles were included, containing 100 different predictors and 11 predictive models. Nineteen predictors and 2 predictive models were studied in multiple cohorts. Female gender was found to be a predictor of non-response in two studies (odds ratios 0.55 and 0.54), but these findings could not be replicated in two other studies. In two studies, smoking predicted non-response (adjusted odds ratios 0.35 and 0.60), although this was inconsistent over all response criteria assessed. Rheumatoid factor positivity predicted non-response in two studies (adjusted hazard ratio 0.61, adjusted odds ratio 0.4), but this was not found in three other studies. Heterogeneity in studies prohibited further comparison of predictive values between studies. Additionally, a validated epigenetic model was found (area under the curve 0.90 and 0.91).

CONCLUSIONS

No predictors were identified reliably predicting clinical response to methotrexate after 3 to 6 months in the individual patient: clinical predictors were weak. However, a promising epigenetic model was found that needs further validation.

Initial Identification of a Blood-Based Chromosome Conformation Signature for Aiding in the Diagnosis of Amyotrophic Lateral Sclerosis

BACKGROUND

The identification of blood-based biomarkers specific to the diagnosis of amyotrophic lateral sclerosis (ALS) is an active field of academic and clinical research. While inheritance studies have advanced the field, a majority of patients do not have a known genetic link to the disease, making direct sequence-based genetic testing for ALS difficult. The ability to detect biofluid-based epigenetic changes in ALS would expand the relevance of using genomic information for disease diagnosis.

METHODS

Assessing differences in chromosomal conformations (i.e. how they are positioned in 3-dimensions) represents one approach for assessing epigenetic changes. In this study, we used an industrial platform, EpiSwitch™, to compare the genomic architecture of healthy and diseased patient samples (blood and tissue) to discover a chromosomal conformation signature (CCS) with diagnostic potential in ALS. A three-step biomarker selection process yielded a distinct CCS for ALS, comprised of conformation changes in eight genomic loci and detectable in blood.

FINDINGS

We applied the ALS CCS to determine a diagnosis for 74 unblinded patient samples and subsequently conducted a blinded diagnostic study of 16 samples. Sensitivity and specificity for ALS detection in the 74 unblinded patient samples were 83∙33% (CI 51∙59 to 97∙91%) and 76∙92% (46∙19 to 94∙96%), respectively. In the blinded cohort, sensitivity reached 87∙50% (CI 47∙35 to 99∙68%) and specificity was 75∙0% (34∙91 to 96∙81%).

INTERPRETATIONS

The sensitivity and specificity values achieved using the ALS CCS identified and validated in this study provide an indication that the detection of chromosome conformation signatures is a promising approach to disease diagnosis and can potentially augment current strategies for diagnosing ALS. FUND: This research was funded by Oxford BioDynamics and Innovate UK. Work in the Oxford MND Care and Research Centre is supported by grants from the Motor Neurone Disease Association and the Medical Research Council. Additional support was provided by the Northeast ALS Consortium (NEALS).