There is a pressing need in rheumatoid arthritis (RA) to identify patients who will not respond to first-line disease-modifying anti-rheumatic drugs (DMARD). We explored whether differences in genomic architecture represented by a chromosome conformation signature (CCS) in blood taken from early RA patients before methotrexate (MTX) treatment could assist in identifying non-response to DMARD and, whether there is an association between such a signature and RA specific expression quantitative trait loci (eQTL). We looked for the presence of a CCS in blood from early RA patients commencing MTX using chromosome conformation capture by EpiSwitch™. Using blood samples from MTX responders, non-responders and healthy controls, a custom designed biomarker discovery array was refined to a 5-marker CCS that could discriminate between responders and non-responders to MTX. We cross-validated the predictive power of the CCS by generating 150 randomized groups of 59 early RA patients (30 responders and 29 non-responders) before MTX treatment. The CCS was validated using a blinded, independent cohort of 19 early RA patients (9 responders and 10 non-responders). Last, the loci of the CCS markers were mapped to RA-specific eQTL. We identified a 5-marker CCS that could identify, at baseline, responders and non-responders to MTX. The CCS consisted of binary chromosome conformations in the genomic regions of IFNAR1, IL-21R, IL-23, CXCL13 and IL-17A. When tested on a cohort of 59 RA patients, the CCS provided a negative predictive value of 90.0% for MTX response. When tested on a blinded independent validation cohort of 19 early RA patients, the signature demonstrated a true negative response rate of 86 and a 90% sensitivity for detection of non-responders to MTX. Only conformations in responders mapped to RA-specific eQTL. Here we demonstrate that detection of a CCS in blood in early RA is able to predict inadequate response to MTX with a high degree of accuracy. Our results provide a proof of principle that a priori stratification of response to MTX is possible, offering a mechanism to provide alternative treatments for non-responders to MTX earlier in the course of the disease.

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).

Background: Diffuse large B-cell lymphoma (DLBCL) is a heterogenous blood cancer, but can be broadly classified into two main subtypes, germinal center B-cell-like (GCB) and activated B-cell-like (ABC). GCB and ABC subtypes have very different clinical courses, with ABC having a much worse survival prognosis. It has been observed that patients with different subtypes also respond differently to therapeutic intervention, in fact, some have argued that ABC and GCB can be thought of as separate diseases altogether. Due to this variability in response to therapy, having an assay to determine DLBCL subtypes has important implications in guiding the clinical approach to the use of existing therapies, as well as in the development of new drugs. The current gold standard assay for subtyping DLBCL uses gene expression profiling on formalin fixed, paraffin embedded (FFPE) tissue to determine the “cell of origin” and thus disease subtype. However, this approach has some significant clinical limitations in that it 1) requires a biopsy 2) requires a complex, expensive and time-consuming analytical approach and 3) does not classify all DLBCL patients. Methods: Here, we took an epigenomic approach and developed a blood-based chromosome conformation signature (CCS) for identifying DLBCL subtypes. An iterative approach using clinical samples from 118 DLBCL patients was taken to define a panel of six markers (DLBCL-CCS) to subtype the disease. The performance of the DLBCL-CCS was then compared to conventional gene expression profiling (GEX) from FFPE tissue. Results: The DLBCL-CCS was accurate in classifying ABC and GCB in samples of known status, providing an identical call in 100% (60/60) samples in the discovery cohort used to develop the classifier. Also, in the assessment cohort the DLBCL-CCS was able to make a DLBCL subtype call in 100% (58/58) of samples with intermediate subtypes (Type III) as defined by GEX analysis. Most importantly, when these patients were followed longitudinally throughout the course of their disease, the EpiSwitch™ associated calls tracked better with the known patterns of survival rates for ABC and GCB subtypes. Conclusion: This proof-of-concept study provides an initial indication that a simple, accurate, cost-effective and clinically adoptable blood-based diagnostic for identifying DLBCL subtypes is possible.

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 0.93 and NPV of 0.95 when tested on the independent combined 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.

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.