DETECTION OF CHROMOSOME INTERACTIONS

Field of the nvention

The invention relates to detecting chromosome interactions. Background of the invention

Health care costs are spiralling and so there is a need to treat people more effectively using existing drugs.

Summary of the Invention

The inventors have investigated the use of epigenetic chromosome interactions as the basis of or for use in conjunction with companion diagnostics, and in particular in the detection of epigenetic states to determine responsiveness to therapy (e.g. pharmaceutical therapy), predisposition to disease / conditions, and/or monitoring residual disease. The inventors' work shows the role played by epigenetic interactions in a diverse set of conditions and provides methods for identifying the relevant chromosomal interactions. The invention includes a method of identifying relevant chromosomal interactions based on looking at the chromosome interactions present in subgroups of individuals. The invention also includes using the identified chromosome interactions as the basis for companion diagnostic tests.

Accordingly a first aspect of the invention provides a method of determining the epigenetic chromosome interactions which are relevant to a companion diagnostic that distinguishes between subgroups, comprising contacting a first set of nucleic acids from the subgroups with a second set of nucleic acids representing an index population of chromosome interactions, and allowing complementary sequences to hybridise, wherein the nucleic acids in the first and second sets of nucleic acids represent a ligated product comprising sequences from both of the chromosome regions that have come together in the epigenetic chromosome interaction, and wherein the pattern of hybridisation between the first and second set of nucleic acids allows a determination of which epigenetic chromosome interactions are specific to subgroups in the population, wherein the subgroups differ in a characteristic relevant to a companion diagnostic. Preferably, in the first aspect (and/or any other aspect) of the invention: - the subgroups are subgroups in a or the animal (e.g. mammal or nematode worm) population, most preferably subgroups in a or the human population; and/or

- the first set of nucleic acids is from at least 8 individuals; and/or

- the first set of nucleic acids is from at least 4 individuals from a first subgroup and at least 4 individuals from a second subgroup which is preferably non-overlapping with the first subgroup, and/or

- the second set of nucleic acids represents an unselected group of chromosome interactions; and/or

- the second set of nucleic acids is bound to an array at defined locations; and/or

- the second set of nucleic acids represents chromosome interactions in least 100 different genes or loci; and/or

- the second set of nucleic acids comprises at least 1000 different nucleic acids representing at least 1000 different epigenetic chromosome interactions; and/or

- the first set of nucleic acids and the second set of nucleic acids comprise nucleic acid sequences of length 10 to 100 nucleotide bases; and/or

- the method is carried out to determine which locus or gene is relevant to said characteristic relevant to a companion diagnostic;

and/or

- the subgroups differ in respect to:

(i) responding to a specific treatment and/or prophylaxis (in particular to a specific pharmaceutical treatment and/or prophylaxis), and/or

(ii) predisposition to a specific condition, and/or

(iii) the presence of residual disease which may lead to relapse;

and/or

- the first set of nucleic acids is generated in a method comprising the steps of:

(i) in vitro cross-linking of chromosome regions which have come together in a chromosome interaction;

(ii) subjecting said cross-linked DNA to restriction digestion cleavage with an enzyme; and

(iii) ligating said cross-linked cleaved DMA ends to form the first set of nucleic acids (in particular comprising iigated DNA);

and/or

- said characteristic relevant to a companion diagnostic is:

(i) responsiveness to methotrexate (or to another rheumatoid arthritis drug) in rheumatoid arthritis patients, and/or

(ii) responsiveness to therapy for acute myeloid leukaemia, and/or

(iii) likelihood of relapse in melanoma. Preferably, in the first and/or other aspects of the invention, the feature the nucleic acids in the first and second sets of nucleic acids represent a ligated product comprising sequences from both of the chromosome regions that have come together in the epigenetic chromosome interaction ..." can comprise or be: "... the nucleic acids in the first and second sets of nucleic acids are in the form of a ligated product(s) (preferably a ligated nucleic acid(s), more preferably ligated DNA) comprising sequences from both of the chromosome regions that have come together in the epigenetic chromosome interaction". A second aspect of the invention provides a companion diagnostic assay method which selects a subgroup having a characteristic relevant to treatment and/or prophylaxis (in particular pharmaceutical treatment and/or prophylaxis), which method comprises:

(a) typing a locus which has been identified by the above method as having an epigenetic interaction characteristic to the subgroup, and/or

(b) detecting the presence or absence of at least 5 epigenetic chromosome interactions, preferably at at least 5 different loci, which are characteristic for: i. responding to a specific treatment and/or prophylaxis (in particular a specific pharmaceutical treatment and/or prophylaxis), and/or ii. predisposition to a specific condition, and/or

iii. the presence of residual disease which may lead to relapse.

Preferably, in the second aspect (and/or any other aspect) of the invention: - the method comprises step (a) as defined in the second aspect of the invention, wherein:

(1) said locus is a gene, and/or

(2) a single nucleotide polymorphism (SNP) is typed, and/or

(3) a microRNA (miRNA) is expressed from the locus, and/or

(4) a non-coding RNA (ncRNA) is expressed from the locus, and/or

(5) the locus expresses a nucleic acid sequence encoding at least 10 contiguous amino acid residues, and/or

(6) the locus expresses a regulating element, and/or

(7) said typing comprises sequencing or determining the level of expression from the locus; and/or

- the method comprises step (b) as defined in the second aspect of the invention, wherein:

- 5 to 500, preferably 20 to 300, more preferably 50 to 100, epigenetic chromosome interactions are typed, preferably at at least 5 different loci; and/or

- the presence or absence of 5 to 500, preferably 20 to 300, more preferably 50 to 100, epigenetic chromosome interactions, preferably at at least 5 different loci, are detected, Other preferred or particular features of or in the second aspect (and/or any other aspect) of the invention include the following:

The companion diagnostic assay method of the second aspect of the invention can particularly be used to detect the presence of any of the specific conditions or characteristics mentioned herein.

Preferably, the companion diagnostic method of the second aspect of the invention is used to detect: - responsiveness to methotrexate (or another rheumatoid arthritis drug) in rheumatoid arthritis patients,

- responsiveness to therapy for acute myeloid leukaemia (AML) patients,

- likelihood of relapse in melanoma,

- likelihood of developing prostate cancer and/ or aggressive prostate cancer, and/or

- likelihood of developing and/or having a predisposition to a neurodegenerative disease or condition, preferably a dementia such as Alzheimer's disease, mild cognitive impairment, vascular dementia, dementia with Lewy bodies, frontotemporai dementia, or more preferably Alzheimer's disease, most preferably beta-amyloid aggregate induced Alzheimer's disease, and/or

- a comparison(s) between dementia patients (preferably Alzheimer's disease patients, more preferably Alzheimer's disease patients with beta-amyloid aggregates) and cognitively-impaired patients without Alzheimer's disease, in particular with respect to memory and/or cognition; in all cases preferably in a human.

Preferably, in the second aspect and in all other aspects of the invention, the disease or condition (in particular in a human) comprises:

- an inflammatory disease, preferably rheumatoid arthritis; in particular in a human;

- a blood cancer, preferably acute myeloid leukaemia (AM L); in particular in a human; - a solid cancer / solid tumour, preferably melanoma, prostate cancer and/ or aggressive prostate cancer; in particular in a human; and/or

- a neurodegenerative disease or condition, preferably a dementia such as Alzheimer's disease, mild cognitive impairment, vascular dementia, dementia with Lewy bodies, frontotemporal dementia, or more preferably Alzheimer's disease such as beta-amyloid aggregate induced Alzheimer's disease; in particular in a human, and/or

- responsiveness to immunotherapy, such as antibody therapy, preferably anti-PDl therapy.

In one embodiment responsiveness to therapy, preferably anti-PDl therapy, is detected in any of the following cancers, preferably of the stage or class which is indicated and/or preferably with other indicated characteristics such as viral infection:

- DLBCL_ABC: Diffuse large B-cell lymphoma subtype activated B-cells

- DLBCL 3BC: Diffuse large B-celi lymphoma subtype germinal center B-ceiis

- HCC: hepatocellular carcinoma

- HCQ_HEPB: hepatocellular carcinoma with hepatitis B virus

- HCC_HEPC: hepatocellular carcinoma with hepatitis C virus

- HEPB+R: Hepatitis B in remission

- Pca_Class3: Prostate cancer stage 3

- Pca_Class2: Prostate cancer stage 2

- Pca_Classl: Prostate cancer stage 1

- BrCa_Stg4: Breast cancer stage 4

- BrCa_Stg3B: Breast cancer stage 3B

- BrCa_Stg2A: Breast cancer stage 2A

- BrCa_Stg2B: Breast cancer stage 2B

- BrCa_StglA: Breast cancer stage 1A

- BrCa_Stgl: Breast cancer stage 1.

The condition or characteristic may be:

- responsiveness to !FN-B (IFN-beta) treatment in multiple sclerosis patients, and/or

- predisposition to reiapsing-remitting multiple sclerosis, and/or

- likelihood of primary progressive multiple sclerosis, and/or

- predisposition to amyotrophic lateral sclerosis (ALS) disease state (in particular in humans), and/or

- predisposition to fast progressing amyotrophic lateral sclerosis (ALS) disease state, and/or

- predisposition to aggressive type 2 diabetes disease state, and/or - predisposition to type 2 diabetes disease state, and/or

- predisposition to a pre-type 2 diabetes state, and/or

- predisposition to type 1 diabetes disease state, and/or

- predisposition to systemic lupus erythematosus (SLE) disease state, and/or

- predisposition to ulcerative colitis disease state, and/or

- likelihood of relapse of colorectal cancer for ulcerative colitis patients, and/or

- likelihood of malignant peripheral nerve sheath tumours for neurofibromatosis patients, and/or

- likelihood of developing prostate cancer and/or aggressive prostate cancer.

A third aspect of the present invention provides a therapeutic agent (in particular a pharmaceutical therapeutic agent) for use in the treatment and/or prophylaxis of a condition in an individual (in particular in a human individual), wherein said individual has been identified as being in need of said therapeutic agent by the method of the second aspect of the invention. The third aspect of the invention also provides the use of a therapeutic agent (e.g. pharmaceutical therapeutic agent) in the manufacture of a medicament (in particular a pharmaceutical composition comprising the therapeutic agent) for use in the treatment and/or prophylaxis of a condition in an individual (in particular in a human individual), wherein said individual has been identified as being in need of said therapeutic agent by the method of the second aspect of the invention. The third aspect of the present invention also provides a method of treatment and/or prophylaxis of a condition in an individual (in particular in a human individual and/or an individual in need thereof), comprising administering a therapeutic agent (e.g. pharmaceutical therapeutic agent and/or an effective amount of a therapeutic agent) to the individual, wherein said individual has been identified as being in need of said therapeutic agent by the method of the second aspect of the invention.

Preferably, in the third aspect (and/or other aspects) of the invention, the therapeutic agent (in particular pharmaceutical therapeutic agent) comprises:

- a pharmaceutically active agent (e.g. compound or biologic / biological agent such as a protein or antibody) suitable for use in the treatment and/or prophylaxis of an inflammatory disease; in particular in a human;

- preferably a pharmaceutically active agent (e.g. compound or biologic / biological agent such as a protein or antibody) suitable for use in the treatment and/or prophylaxis of rheumatoid arthritis (RA); in particular in a human; wherein preferably the pharmaceutically active agent comprises methotrexate; hydroxychloroquine; sulfasalazine; leflunomide; a TNF-aipha (tumor necrosis factor alpha) inhibitor, in particular a monoclonal antibody TNF-alpha inhibitor such as infliximab, adalimumab, certoiizumab pegol or golimumab, or a circulating receptor fusion protein TNF-alpha inhibitor such as etanercept; or a T eel! costimuiation inhibitor such as abatacept; or an interieukin 1 (IL-1) inhibitor such as anakinra; or a monoclonal antibody against B ceils such as rituximab or tociiizumab; or

- a pharmaceutically active agent (e.g. compound or biologic / biological agent such as a protein or antibody) suitable for use in the treatment and/or prophylaxis of a blood cancer, preferably acute myeloid leukaemia (AMU; in particular in a human; or

- a pharmaceutically active agent (e.g. compound or biologic / biological agent such as a protein or antibody) suitable for use in the treatment and/or prophyiaxis of a solid cancer / solid tumour, preferably melanoma, prostate cancer and/ or aggressive prostate cancer; in particular in a human; or

- a pharmaceutically active agent (e.g. compound or biologic / biological agent such as a protein or antibody) suitable for use in the treatment and/or prophylaxis of a neurodegenerative disease or condition, preferably a dementia such as Alzheimer's disease, mild cognitive impairment, vascular dementia, dementia with Lewy bodies, frontotemporal dementia, or more preferably Alzheimer's disease such as beta-amyloid aggregate induced Alzheimer's disease; in particular in a human.

A fourth aspect of the invention provides a method of identifying an agent which is capable of changing the disease state of an individual from a first state to a second state comprising determining whether a candidate agent is capable of changing the chromosomal interactions from those corresponding with the first state to chromosomal interactions which correspond to the second state, wherein preferably the first and second state correspond to presence or absence of:

(i) responsiveness to a specific treatment and/or prophylaxis (in particular a specific pharmaceutical treatment and/or prophylaxis), and/or

(ii) predisposition to a specific condition, and/or

(iii) a residual disease which may lead to relapse.

A fifth aspect of the invention provides a method of determining the effect of a drug comprising detecting the change in epigenetic chromosome interactions caused by the drug, wherein said effect is preferably the mechanism of action of the drug or are the pharmacodynamics properties of the drug, and wherein said the chromosome interactions are preferably specific to:

(i) responsiveness to a specific treatment and/or prophylaxis (in particular to a specific pharmaceutical treatment and/or prophylaxis), and/or

(ii) predisposition to a specific condition, and/or

(iii) a residual disease which may lead to relapse.

Additionally or alternatively, according to a preferred embodiment of all aspects of the present invention, the present invention does not relate to a method of determining responsiveness to a specific therapy (in particular a specific pharmaceutical therapy) for rheumatoid arthritis in a subject (e.g. a mammalian such as human subject), comprising detecting the presence or absence of 5 or more (in particular 7 or more, or 10 or more, or 15 or more, or 20 or more) chromosomal interactions; wherein said chromosomal interactions are in particular at 5 or more (for example 5) different loci; and/or wherein said detecting in particular comprises determining for each interaction whether or not the regions of a chromosome which are part of the interaction have been brought together.

Brief Description of the Drawings

Figure 1 is a figure comprising pie-charts and graphs relating to: Chromosome Conformation Signature EpiSwitch™ Markers discriminate MTX responders (R) from non-responders (M R). A discovery cohort of responder (R) and non-responder (N R) RA patients were selected based on DAS28 (Disease Activity Score of 28 joints) EULAR (The European League Against Rheumatism) response criteria (see methods). (A) Pie charts show the clinical interpretation of CDAI scores for both R and N R patients at baseline and 6 months. (B) CDAI scores of R and N R patients at baseline and 6 months. (C) EpiSwitch™ array analysis of peripheral blood mononuclear cells taken at diagnosis from R and N R, and healthy controls (HC) identified 922 statistically significant stratifying marker candidates. Further analysis revealed that 420 were specific for N R, 210 to R and 159 to HC. Pie charts show the proportion in relation to the 13,322 conditional chromosome conformations screened. All markers showed adjusted p<0,2. (D) Hierarchical clustering using Manhattan distance measure with complete linkage agglomeration is shown by the heatmaps. Marker selection using binary pattering across the 3 groups (R, NR and HC) initially reduced the 922 EpiSwitch™ Markers to 65 and then the top 30 markers.

Figure 2 is a figure comprising pie-charts and graphs relating to: Refinement and validation of the Chromosome Conformation Signature EpiSwitch™ Markers. The validation cohort of responder (R) and non-responder (NR) RA patients were selected based on DAS28 (Disease Activity Score of 28 joints) EULAR (The European League Against Rheumatism) response criteria (see methods). (A) Pie charts show the clinical interpretation of CDAI scores for both R and NR patients at baseline and 6 months. (B) CDAI scores of R and NR patients at baseline and 6 months. ****P<0.0001 by Kruskal- Waliis test with Dunn's multiple comparison post-test (C) Correlation plot of the classifying 5 EpiSwitch™ markers. The red box indicates the markers that define NR whilst the orange box indicated markers that define R. (D) Principle Component Analysis (PCA) for a 60 patient cohort based on their binary scores for the classifying 5 EpiSwitch™ markers.

Figure 3 is a figure comprising graphs relating to: Prognostic stratification and model validation for response to methotrexate (MTX) treatment. (A) Representative examples of 5 selected Receiver Operating Characteristics (ROC) curves from 150 randomisations of the data using the 5 CCS marker logistic regression classifiers. (B) Factor Analysis for responder (R) and non-responder (NR) RA patients vs healthy controls (HC) using EpiSwitch™ CCS markers selected for discerning MTX responders from MTX non-responders.

Figure 4 is a Schematic diagram of the 3C extraction process. 3C means chromatin conformation capture, or chromosome conformation capture.

Figure 5 is a Scheme illustrating the Design for Discovery and Validation of Epigenetic Stratifying Biomarker Signature for DMARDS Naive ERA patients, who were confirmed within 6 months of MTX treatment as responders (N) or non-responders (NR). Epigenetic stratification was based on conditional chromosome confirmations screened and monitored by EpiSwitch™ Array and PCR (polymerase chain reaction) platforms. Disease specific epigenetic nature of the identified biomarkers was confirmed by stratification against healthy controls (HC). Validation was performed on 60 RA patients (30 responders and 30 non-responders) and 30 HC.

Detailed Description of the Invention

The invention has several different aspects: - a method of identifying chromosome interactions (in particular epigenetic) relevant to a companion diagnostic, in particular that distinguishes between subgroups;

a companion diagnostic method;

a therapeutic agent for use in treatment and/or prophylaxis of an individual (e.g. treatment and/or prophylaxis of a condition in an individual, e.g. human individual), wherein said individual has been identified as being in need of the therapeutic agent in particular by the companion diagnostic method of the invention; a method of screening for (identifying) an agent, in particular a therapeutic agent, which is capable of changing the disease state in or of an individual, comprising determining whether a candidate agent is capable of changing chromosomal interactions, in particular chromosomal interactions relevant to or associated with the disease state;

- a method of determining the effect of a drug comprising detecting the change in epigenetic chromosome interactions caused by the drug.

Epigenetic interactions

As used herein, the term 'epigenetic' interactions typically refers to interactions between distal regions of a locus on a chromosome, said interactions being dynamic and altering, forming or breaking depending upon the status of the region of the chromosome. in particular methods of the invention, chromosome interactions are detected by first generating a ligated nucleic acid that comprises sequence(s) from both regions of the chromosomes that are part of the chromosome interactions. In such methods the regions can be cross-linked by any suitable means. In a preferred embodiment, the interactions are cross-linked using formaldehyde, but may also be cross-linked by any aldehyde, or D-Biotinoyl-e- aminoeaproic aeid-N-hydroxysuccinimide ester or Digoxigenin-3-0-methyicarbonyl- e-aminocaproic acid-M-hydroxysuccinimide ester. Paraformaldehyde can cross link DNA chains which are 4 Angstroms apart.

The chromosome interaction may reflect the status of the region of the chromosome, for example, if it is being transcribed or repressed in response to change of the physiological conditions. Chromosome interactions which are specific to subgroups as defined herein have been found to be stable, thus providing a reliable means of measuring the differences between the two subgroups. in addition, chromosome interactions specific to a disease condition will normally occur early in the disease process, for example compared to other epigenetic markers such as methylation or changes to binding of histone proteins. Thus the companion diagnostic method of the invention is able to detect early stages of a disease state. This allows early treatment which may as a consequence be more effective. Another advantage of the invention is that no prior knowledge is needed about which loci are relevant for identification of relevant chromosome interactions. Furthermore there is little variation in the relevant chromosome interactions between individuals within the same subgroup. Detecting chromosome interactions is highly informative with up to 50 different possible interactions per gene, and so methods of the invention can interrogate 500,000 different interactions.

Location and Causes of Epigenetic interactions Epigenetic chromosomal interactions may overlap and include the regions of chromosomes shown to encode relevant or undescribed genes, but equally may be in intergenic regions. It should further be noted that the inventors have discovered that epigenetic interactions in all regions are equally important in determining the status of the chromosomal locus. These interactions are not necessarily in the coding region of a particular gene located at the locus and may be in intergenic regions.

The chromosome interactions which are detected in the invention could be caused by changes to the underlying DNA sequence, by environmental factors, DNA methylation, non-coding antisense RNA transcripts, non-mutagenic carcinogens, histone modifications, chromatin remodelling and specific local DMA interactions. The changes which lead to the chromosome interactions may be caused by changes to the underlying nucleic acid sequence, which themselves do not directly affect a gene product or the mode of gene expression. Such changes may be for example, SMP's within and/or outside of the genes, gene fusions and/or deletions of intergenic DNA, microRNA, and non-coding RMA. For example, it is known that roughly 20% SNPs are in non-coding regions, and therefore the method as described is also informative in non-coding situation, in one embodiment the regions of the chromosome which come together to form the interaction are less than 5 kb, 3 kb, 1 kb, 500 base pairs or 200 base pairs apart.

The chromosome interaction which is detected in the companion diagnostic method is preferably one which is within any of the genes mentioned in the Tables herein. However it may also be upstream or downstream of the genes, for example up to 50,000, 30,000, 20,000, 10,000 or 5000 bases upstream or downstream from the gene or from the coding sequence.

The chromosome interaction which is detected may or may not be one which occurs between a gene (including coding sequence) and its regulatory region, such as a promoter. The chromosome interaction which is typed may or may not be one which is inherited, for example an inherited imprinted characteristic of a gene region.

Types of Clinical Situation

The specific case of RA (Rheumatoid Arthritis) illustrates the general principles. There are currently no tests that clinicians can use a priori to determine if patients will respond to methotrexate (MTX) when the patients are first given the drug. Since a significant number (about 30%) of patients do not respond to MTX, being able to predict whether a patient is a responder or non-responder will increase the chances of successfully treating RA, as well as saving time and money. This is one example of how the inventors visualise the present invention to be used. More broadly speaking, the aim of the present invention is to permit detection and monitoring of disease. In more detail, this technology allows stratification based on biomarkers for specific phenotypes relating to medical conditions, i.e. by recognising a particular chromosome confirmation signature and/or a change in that particular signature.

The methods of the invention are preferably used in the context of specific characteristics relating to disease, such as responsiveness to treatments and/or prophylaxes, identification of the most effective therapy/drug, monitoring the course of disease, identifying predisposition to disease, and/or identifying the presence of residual disease and/or the likelihood of relapse. Therefore the methods may or may not be used for diagnosis of the presence of a specific condition. The methods of the invention can be used to type loci where the mechanisms of disease are unknown, unclear or complex. Detection of chromosome interactions provides an efficient way of following changes at the different levels of regulation, some of which are complex. For example in some cases around 37,000 non-coding RISIAs can be activated by a single impulse. Subgroups and Personalised Treatment

As used herein, a "subgroup" preferably refers to a population subgroup (a subgroup in a population), more preferably a subgroup in a or the population of a particular animal such as a particular mammal (e.g. human, non-human primate, or rodent e.g. mouse or rat) or a particular nematode worm (e.g. C. e!egans). Most preferably, a "subgroup" refers to a subgroup in a or the human population. Particular populations, e.g. human populations, of interest include: the human population overall, a or the human population suffering from a specific condition / disease (in particular inflammatory disease e.g. RA, blood cancer eg AML, solid cancer eg melanoma or prostate cancer (PC), or neurodegenerative disease / condition e.g. Alzheimer's disease (AD)), the human healthy population (healthy controls), the human population which is healthy in the sense of not suffering from the specific condition / disease of interest or of study (eg RA, AML, melanoma, PC or AD), the human population (e.g. either healthy and/or with a specific condition / disease e.g. RA, A L, melanoma, PC or AD) who are responders to a particular drug / therapy, or the human population (e.g. either healthy and/or with a specific condition / disease e.g. RA, AML, melanoma, PC or AD) who are non-responders to a particular drug / therapy. The invention relates to detecting and treating particular subgroups in a population, preferably in a or the human population. Within such subgroups the characteristics discussed herein (such as responsiveness to treatment and/or prophylaxis; in particular responsiveness to a specific treatment and/or prophylaxis of one or more conditions or diseases, and/or responsiveness to a specific medicine or therapeutically active substance / therapeutic agent, in particular in the treatment and/or prophylaxis of one or more conditions or diseases) will be present or absent. Epigenetic interaction differences on a chromosome are, generally speaking, structural differences which exist at a genomic level. The inventors have discovered that these differ between subsets (for example two, or two or more subsets) in a given population. Identifying these differences will allow physicians to categorize their patients as a part of one subset of the population as described in the method. The invention therefore provides physicians with a method of personalizing medicine for the patient based on their epigenetic chromosome interactions, and provide an alternative more effective treatment and/or prophylaxis regime. in another embodiment, threshold levels for determining to what extent a subject is defined as belonging to one subgroup and not to a or the other subgroup of the population are applied. In one preferable embodiment wherein the subgroups comprise responders versus non-responders of a therapy for the treatment and/or prophylaxis of a particular disease, said threshold may be measured by change in DAS28 (Disease Activity Score of 28 joints) score, in particular for rheumatoid arthritis. In one embodiment, a score above 1.2 units indicates a subject falls into the responder subgroup, whilst a score below 1.2 units indicates a subject is defined as a non-responder.

Typically a subgroup will be at least 10%, at least 30%, at least 50% or at least 80% of the general population. Generating Ligated Nucieic Acids

Certain embodiments of the invention utilise ligated nucieic acids, in particular ligated DNA. These comprise sequences from both of the regions that come together in a chromosome interaction and therefore provide information about the interaction. The EpiSwitch™ method, described herein, uses generation of such ligated nucieic acids to detect chromosome interactions. One such method, in particular one particular method of detecting chromosome interactions and/or one particular method of determining epigenetic chromosome interactions and/or one particular method of generating ligated nucieic acids (e.g. DNA), comprises the steps of:

(i) in vitro crosslinking of said epigenetic chromosomal interactions present at the chromosomal locus;

(ii) optionally isolating the cross-linked DNA from said chromosomal locus; (iii) subjecting said cross-linked DNA to restriction digestion with an enzyme that cuts it at least once (in particular an enzyme that cuts at least once within said chromosomal locus);

(iv) ligating said cross-linked cleaved DMA ends (in particular to form DNA loops); and

(v) identifying the presence of said ligated DNA and/or said DNA loops, in particular using techniques such as PCR (polymerase chain reaction), to identify the presence of a specific chromosomal interaction.

One particularly preferable method of detecting, determining and/or monitoring chromosome interactions and/or epigenetic changes, involving inter alia the above-mentioned steps of crosslinking, restriction digestion, ligating, and identifying, is disclosed in WO 2009/147386 Al (Oxford Biodynamics Ltd), the entire disclosure of which (in particular claims 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or 21 of which) are incorporated herein by reference as though fully set forth, Claim 1 of WO 2009/147386 Al, which can be used in those methods of the present invention which involve a ligated product(s) and/or a ligated nucleic acid(s), discloses a method of monitoring epigenetic changes comprising monitoring changes in conditional long range chromosomal interactions at at least one chromosomal locus where the spectrum of long range interaction is associated with a specific physiological condition, said method comprising the steps of:-

(i) in vitro crosslinking of said long range chromosomal interactions present at the at least one chromosomal locus;

(ii) isolating the cross linked DNA from said chromosomal locus;

(iii) subjecting said cross linked DNA to restriction digestion with an enzyme that cuts at least once within the at least one chromosomal locus;

(iv) ligating said cross linked cleaved DNA ends to form DNA loops; and

(v) identifying the presence of said DNA loops; wherein the presence of DNA loops indicates the presence of a specific long range chromosomal interaction.

PCR (polymerase chain reaction) may be used to detect or identify the iigated nucleic acid, for example the size of the PCR product produced may be indicative of the specific chromosome interaction which is present, and may therefore be used to identify the status of the locus. The skilled person will be aware of numerous restriction enzymes which can be used to cut the DNA within the chromosomal locus of interest, it will be apparent that the particular enzyme used will depend upon the locus studied and the sequence of the DNA located therein. A non-limiting example of a restriction enzyme which can be used to cut the DNA as described in the present invention is Taq I polymerase.

Embodiments such as EpiSwitch™ Technology

The EpiSwitch™ Technology relates to the use of microarray EpiSwitch™ marker data in the detection of epigenetic chromosome conformation signatures specific for phenotypes. The present inventors describe herein how the EpiSwitch™ Array Platform has been used for discovery of chromosome signature pool of potential biomarkers specific for particular disadvantageous phenotypes subgroups versus healthy controls. The inventors also provide examples of validated use and translation of chromosome conformation signatures from microarray into PCR platform with examples of several markers specific between subgroups from the cohorts tested on the array.

Embodiments such as EpiSwitch™ which utilise ligated nucleic acids in the manner described herein (for identifying relevant chromosome interactions and in companion diagnostic methods) have several advantages. They have a low level of stochastic noise, for example because the nucleic acid sequences from the first set of nucleic acids of the present invention either hybridise or fail to hybridise with the second set of nucleic acids. This provides a binary result permitting a relatively simple way to measure a complex mechanism at the epigenetic level. EpiSwitch™ technology also has fast processing time and low cost, in one embodiment the processing time is 3 hours to 6 hours.

Samples and Sample Treatment

The sample will contain DNA from the individual. It will normally contain cells. In one embodiment a sample is obtained by minimally invasive means, and may for example be blood. DNA may be extracted and cut up with standard restriction enzymes. This can pre-determine which chromosome conformations are retained and will be detected with the EpiSwitch™ platforms. In one embodiment wherein the sample is a blood sample previously obtained from the patient, the described method is advantageous because the procedure is minimally invasive. Due to the synchronisation of chromosome interactions between tissues and blood, including horizontal transfer, a blood sample can be used to detect the chromosome interactions in tissues, such as tissues relevant to disease. For certain conditions, such as cancer, genetic noise due to mutations can affect the chromosome interaction 'signal' in the relevant tissues and therefore using blood is advantageous. Properties of Nucleic acids of the invention

The disclosure herein mentions first and second nucleic acids, in addition the nucleic acids are used in the companion diagnostic method and in other embodiments to detect the presence or absence of chromosome interactions (for example by binding to iigated nucleic acids generated from samples). The nucleic acids of the invention typically comprise two portions each comprising sequence from one of the two regions of the chromosome which come together in the chromosome interaction. Typically each portion is at least 8, 10, 15, 20, 30 or 40 nucleotides in length. Preferred nucleic acids comprise sequence from any of the genes mentioned in the tables, in particular where the nucleic acid is used in an embodiments relevant to the condition relevant for that table. Preferred nucleic acids comprise the specific probe sequences mentioned in the tables for specific conditions or fragments or homoiogues of such sequences. Preferably the nucleic acids are DNA. It is understood that where a specific sequence is provided the invention may use the complementary as required in the particular embodiment. The Second Set of Nucleic Acids - the 'index' Sequences

The second set of nucleic acid sequences has the function of being an index, and is essentially a set of nuclei acid sequences which are suitable for identifying subgroup specific sequence. They can represent the ^' background' chromosomal interactions and might be selected in some way or be unseiected. They are in general a subset of all possible chromosomal interactions. The second set of nucleic acids may be derived by any suitable method. They can be derived computationally or they may be based on chromosome interaction in individuals. They typically represent a larger population group than the first set of nucleic acids. In one particular embodiment, the second set of nucleic acids represents all possible epigenetic chromosomal interactions in a specific set of genes. In another particular embodiment, the second set of nucleic acids represents a large proportion of all possible epigenetic chromosomal interactions present in a population described herein. In one particular embodiment, the second set of nucleic acids represent at least 50% or at least 80% of epigenetic chromosomal interactions in at least 20, 50, 100 or 500 genes.

The second set of nucleic acids typically represents at least 100 possible epigenetic chromosome interactions which modify, regulate or in any way mediate a disease state/ phenotype in population. The second set of nucleic acids may represent chromosome interactions that affect a diseases state in a species, for example comprising nucleic acids sequences which encode cytokines, kinases, or regulators associated with any disease state, predisposition to a disease or a disease phenotype. The second set of nucleic acids comprises sequences representing epigenetic interactions relevant and not relevant to the companion diagnostic method. in one particular embodiment the second set of nucleic acids derive at least partially from a naturally occurring sequences in a population, and are typically obtained by in siiico methods. Said nucleic acids may further comprise single or multiple mutations in comparison to a corresponding portion of nucleic acids present in the naturally occurring nucleic acids. Mutations include deletions, substitutions and/ or additions of one or more nucleotide base pairs. In one particular embodiment, the second set of nucleic acids may comprise sequence representing a homologue and/or orthologue with at least 70% sequence identity to the corresponding portion of nucleic acids present in the naturally occurring species. In another particular embodiment, at least 80% sequence identity or at least 90% sequence identity to the corresponding portion of nucleic acids present in the naturally occurring species is provided.

Properties of the Second Set of Nucleic Acids in one particular embodiment, there are at least 100 different nucleic acid sequences in the second set of nucleic acids, preferably at least 1000, 2000 or 5000 different nucleic acids sequences, with up to 100,000, 1,000,000 or 10,000,000 different nucleic acid sequences. A typical number would be 100 to 1,000,000, such as 1,000 to 100,000 different nucleic acids sequences. All or at least 90% or at least 50% or these would correspond to different chromosomal interactions.

In one particular embodiment, the second set of nucleic acids represent chromosome interactions in at least 20 different loci or genes, preferably at least 40 different loci or genes, and more preferably at least 100, at least 500, at least 1000 or at least 5000 different loci or genes, such as 100 to 10,000 different loci or genes.

The lengths of the second set of nucleic acids are suitable for them to specifically hybridise according to Watson Crick base pairing to the first set of nucleic acids to allow identification of chromosome interactions specific to subgroups. Typically the second set of nucleic acids will comprise two portions corresponding in sequence to the two chromosome regions which come together in the chromosome interaction. The second set of nucleic acids typically comprise nucleic acid sequences which are at least 10, preferably 20, and preferably still 30 bases (nucleotides) in length. In another embodiment, the nucleic acid sequences may be at the most 500, preferably at most 100, and preferably still at most 50 base pairs in length. In a preferred embodiment, the second set of nucleic acids comprise nucleic acid sequences of between 17 and 25 base pairs. In one embodiment at least 100, 80% or 50% of the second set of nucleic acid sequences have lengths as described above. Preferably the different nucleic acids do not have any overlapping sequences, for example at least 100%, 90%, 80% or 50% of the nucleic acids do not have the same sequence over at least 5 contiguous nucleotides.

Given that the second set of nucleic acids acts as an 'index' then the same set of second nucleic acids may be used with different sets of first nucleic acids which represent subgroups for different characteristics, i.e. the second set of nucleic acids may represent a 'universal' collection of nucleic acids which can be used to identify chromosome interactions relevant to different disease characteristics.

The first set of nucleic acids The first set of nucleic acids are normally from individuals known to be in two or more distinct subgroups defined by presence or absence of a characteristic relevant to a companion diagnostic, such as any such characteristic mentioned herein. The first nucleic acids may have any of the characteristics and properties of the second set of nucleic acids mentioned herein. The first set of nucleic acids is normaliy derived from a sample from the individual which has undergone treatment and processing as described herein, particularly the EpiSwitch ¹ cross-linking and cleaving steps. Typically the first set of nucleic acids represent ail or at least 80?^ or 50% of the chromosome interactions present in the samples taken from the individuals.

Typically, the first set of nucleic acids represents a smaller population of chromosome interactions across the loci or genes represented by the second set of nucleic acids in comparison to the chromosome interactions represented by second set of nucleic acids, i.e. the second set of nucleic acids is representing a background or index set of interactions in a defined set of loci or genes.

Library of Nucleic Acids

The nucleic acids described herein may be in the form of a library which comprises at least 200, at least 500, at least 1000, at least 5000 or at least 10000 different nucleic acids from the second set of nucleic acids. The invention provides a particular library of nucleic acids which typically comprises at least 200 different nucleic acids. The library of nucleic acids may have any of the characteristics or properties of the second set of nucleic acids mentioned herein. The library may be in the form of nucleic acids bound to an array. Hybridisation

The invention requires a means for allowing wholly or partially complementary nucleic acid sequences from the first set of nucleic acids and the second set of nucleic acids to hybridise, in one embodiment ail of the first set of nucleic acids is contacted with ail of the second set of nucleic acids in a single assay, i.e. in a single hybridisation step. However any suitable assay can be used.

Labelled Nucleic Acids and Pattern of Hybridisation

The nucleic acids mentioned herein may be labelled, preferably using an independent label such as a fiuorophore (fluorescent molecule) or radioactive label which assists detection of successful hybridisation. Certain labels can be detected under UV light. The pattern of hybridisation, for example on an array described herein, represents differences in epigenetic chromosome interactions between the two subgroups, and thus provides a method of comparing epigenetic chromosome interactions and determination of which epigenetic chromosome interactions are specific to a subgroup in the population of the present invention.

The term 'pattern of hybridisation' broadly covers the presence and absence of hybridisation between the first and second set of nucleic acids, i.e. which specific nucleic acids from the first set hybridise to which specific nucleic acids from the second set, and so it is not limited to any particular assay or technique, or the need to have a surface or array on which a 'pattern' can be detected.

Companion Diagnostic Method

The invention provides a companion diagnostic method based on information provided by chromosome interactions. Two distinct companion diagnostic methods are provided which identify whether an individual has a particular characteristic relevant to a companion diagnostic. One method is based on typing a locus in any suitable way and the other is based on detecting the presence or absence of chromosome interactions. The characteristic may be any one of the characteristics mentioned herein relating to a condition. The companion diagnostic method can be carried out at more than one time point, for example where monitoring of an individual is required.

Companion Diagnostic Method Based on Typing a Locus

The method of the invention which identified chromosome interactions that are specific to subgroups can be used to identify a locus, which may be a gene that can be typed as the basis of companion diagnostic test. Many different gene-re!ated effects can lead to the same chromosome interaction occurring. In this embodiment any characteristic of the iocus may be typed, such as presence of a polymorphism in the Iocus or in an expressed nucleic acid or protein, the level of expression from the Iocus, the physical structure of the Iocus or the chromosome interactions present in the Iocus. In one particular embodiment the iocus may be any of the genes mentioned herein in the tables, in particular in Tables 1, 3, 5, 6c, 6E, 18a, 18b, 18c, 18d, 18e, 18f, 22, 23, 24 or 25 (in particular Tables 1, 3 and/or 5), or any property of a Iocus which is in the vicinity of a chromosome interaction found to be linked to the relevant condition.

Companion Diagnostic Method Based on Detecting Chromosome interactions The invention provides a companion diagnostic method which comprises detecting the presence or absence of chromosome interactions, typically 5 to 20 or 5 to 500 such interactions, preferably 20 to 300 or 50 to 100 interactions, in order to determine the presence or absence of a characteristic in an individual. Preferably the chromosome interactions are those in any of the genes mentioned herein. In one particular embodiment the chromosome interactions which are typed are those represented by the nucleic acids disclosed in the tables herein, in particular in Tables 6b, 6D, 18b, 18e, 18f, 22, 23, 24 or 25 herein, for example when the method is for the purpose of determining the presence or absence of characteristics defined in those tables.

Specific Conditions

The companion diagnostic method can be used to detect the presence of any of the specific conditions or characteristics mentioned herein. The companion diagnostic method can be used to detect responsiveness to methotrexate (or another rheumatoid arthritis drug) in rheumatoid arthritis patients, responsiveness to therapy for acute myeloid leukaemia (AML) patients, likelihood of relapse in melanoma, likelihood of developing prostate cancer and/ or aggressive prostate cancer, and/or likelihood of developing beta-amyloid aggregate induced Alzheimer's disease. in one embodiment the method of the invention detects responsiveness to immunotherapy, such as antibody therapy. Preferably the responsiveness to antibody therapy of cancer is detected, for example in immunotherapy using anti-PD-1 or anti-PD-Ll or a combined anti-PD-l/anti-PD-Ll therapy. Preferably the cancer is melanoma, breast cancer, prostate cancer, acute myeloid leukaemia (AML), diffuse large B-ceil lymphoma (DLBCL), pancreatic cancer, thyroid cancer, nasal cancer, liver cancer or lung cancer. In such embodiments detection of chromosome interactions in STAT5B and/or IL15 are preferred, such as described in the Examples. The work in the Examples is consistent with the fact that response to immunotherapy is a feature of the immune system epigenetic set up rather than cancer identity. ['Anti-PD-1' is an antibody or antibody derivative or fragment that binds specifically to PD-1 (programmed ceil death protein 1). 'Anti-PD-Ll' is an antibody or antibody derivative or fragment that binds specifically to PD-L1 protein which is a ligand of PD-1.] The method(s) and/or companion diagnostic method of the invention can be used to: responsiveness to IFN-B (IFN-beta) treatment in multiple sclerosis patients (in particular in humans), and/or

predisposition to reiapsing-remitting multiple sclerosis (in particular in humans), and/or likelihood of primary progressive multiple sclerosis (in particular in humans), and/or - predisposition to amyotrophic lateral sclerosis (ALS) disease state (in particular in humans), and/or,

predisposition to fast progressing amyotrophic lateral sclerosis (ALS) disease state (in particular in humans), and/or

predisposition to aggressive type 2 diabetes disease state (in particular in humans), and/or - predisposition to type 2 diabetes disease state (in particular in humans), and/or

predisposition to a pre-type 2 diabetes state (in particular in humans), and/or

predisposition to type 1 diabetes disease state (in particular in humans), and/or

predisposition to systemic lupus erythematosus (SLE) disease state (in particular in humans), and/or

- predisposition to ulcerative colitis disease state (in particular in humans), and/or

likelihood of relapse of colorectal cancer for ulcerative colitis patients (in particular in humans), and/or

likelihood of malignant peripheral nerve sheath tumours for neurofibromatosis patients (in particular in humans), and/or

- likelihood of developing prostate cancer and/or aggressive prostate cancer (in particular in humans), and/or

likelihood of developing and/or predisposition to a neurodegenerative disease or condition, preferably a dementia such as Alzheimer's disease, mild cognitive impairment, vascular dementia, dementia with Lewy bodies, frontotemporai dementia, or more preferably Alzheimer's disease, most preferably beta-amyloid aggregate induced Alzheimer's disease; in particular in a human; and/or

a comparison between dementia patients (preferably Alzheimer's disease patients, more preferably Alzheimer's disease patients with beta-amyloid aggregates) and cognitively- impaired patients without Alzheimer's disease, in particular with respect to memory and/or cognition; in particular in humans.

Preferably the presence or absence of any of the chromosome interactions within any of the relevant genes mentioned in the tables are detected. For example in at least 1, 3, 10, 20, 50 of the genes mentioned in any one of the tables. Preferably the presence or absence of chromosome interactions represented by the probes sequences in the Tables is determined in the method. For example at least 1, 3, 10, 20, 50, or 100 of the relevant chromosome interactions from any one of the tables. These numbers of genes or chromosome interactions can be used in any of the different embodiments mentioned herein. The individual Tested Using the Companion Diagnostic Method

The individual to be tested may or may not have any symptoms of any disease condition or characteristic mentioned herein. The individual may be at risk of any such condition or characteristic. The individual may have recovered or be in the process of recovering from the condition or characteristic. The individual is preferably a mammal, such as a non-human primate, human or rodent. The individual may be male or female. The individual may be 30 years old or older. The individual may be 29 years oid or younger.

Screening Method

The invention provides a method of identifying an agent which is capable of changing the disease state of an individual from a first state to a second state comprising determining whether a candidate agent is capable of changing the chromosomal interactions from those corresponding with the first state to chromosomal interactions which correspond to the second state, wherein preferably the first and second state correspond to presence or absence of: responsiveness to a specific treatment and/or prophylaxis, and/or

predisposition to a specific condition, and/or

- a residua! disease which may lead to relapse. in one embodiment the method determines whether a candidate agent is capable of changing any chromosomal interaction mentioned herein.

The method may be carried out in vitro (inside or outside a cell) or in vivo (upon a non-human organism). In one embodiment the method is carried out on a ceil, ceil culture, ceil extract, tissue, organ or organism, such as one which comprises the relevant chromosome interaetion(s). The cell is The method is typically carried out by contacting (or administering) the candidate agent with the gene, cell, cell culture, cell extract, tissue, organ or organism.

Suitable candidate substances which tested in the above screening methods include antibody agents (for example, monoclonal and polyclonal antibodies, single chain antibodies, chimeric antibodies and CDR-grafted antibodies). Furthermore, combinatorial libraries, defined chemical identities, peptide and peptide mimetics, oligonucleotides and natural agent libraries, such as display libraries (e.g. phage display libraries) may also be tested. The candidate substances may be chemical compounds, which are typically derived from synthesis around small molecules which may have any of the properties of the agent mentioned herein.

Preferred Loci, Genes and Chromosome interactions

For all aspects of the invention preferred loci, genes and chromosome interactions are mentioned in the tables. For all aspects of the invention preferred loci, genes and chromosome interactions are provided in the tables. Typically the methods chromosome interactions are detected from at least 1, 3, 10, 20, 30 or 50 of the relevant genes listed in the table. Preferably the presence or absence of at least 1, 3, 10, 20, 30 or 50 of the relevant specific chromosome interactions represented by the probe sequences in any one table is detected.

The loci may be upstream or downstream of any of the genes mentioned herein, for example 50 kb upstream or 20 kb downstream. in one embodiment for each condition the presence or absence of at least 1, 3, 5, 10, 20 of the relevant specific chromosome interactions represented by the top range of p-va!ues or adjusted p-vaiues shown in Table 48 are detected. In another embodiment for each condition the presence or absence of at least 1, 3, 5, 10, 20, 30 or 50 of the relevant specific chromosome interactions represented by the mid range of p-values or adjusted p-values shown in Table 48 are detected, in yet another embodiment for each condition the presence or absence of at least 1, 3, 5, 10, 20, 30 or 50 of the relevant specific chromosome interactions represented by the bottom range of p-values or adjusted p-values shown in Table 48 are detected. In another embodiment for each condition the presence or absence of at least 1, 2, 3, 5 or 10 of the relevant specific chromosome interactions from each of the top, mid and bottom ranges of p-vaiues or adjusted ρ-values shown in Table 48 are detected, i.e. at least 3, 6, 9, 18 or 30 in total.

Particular combinations of chromosome interactions can be detected (i.e. determining the presence of absence of), which typically represent all of the interactions disclosed in a table herein or a selection from a table. As mentioned herein particular numbers of interactions can be selected from individual tables. In one embodiment at least 10%, 20%, 30%, 50%, 70% or 90% of the interactions disclosed in any table, or disclosed in relation to any condition, are detected.

The interactions which are detected may correspond to presence or absence of a particular characteristic, for example as defined herein, such as in any table herein, if a combination of interactions are detected then they may ail correspond with presence of the characteristic or they may all correspond to absence of the characteristic. In one embodiment the combination of interactions which is detected corresponds to at least 2, 5 or 10 interactions which relate to presence of the characteristic and at least 2, 5 or 10 other interactions that relate to absence of the characteristic.

The probe shown in table 49 may be part of or combined with any of the selections mentioned herein, particularly for conditions relating to cancer, and responsiveness to therapy, such as anti-PDl therapy.

Embodiments Concerning Genetic Modifications in certain embodiments the methods of the invention can be carried out to detect chromosome interactions relevant to or impacted by a genetic modification, i.e. the subgroups may differ in respect to the genetic modification. Clearly the modification might be of entire (non-human) organisms or parts of organisms, such as cells, in the method of determining which chromosomal interactions are relevant to a biological system state the first set of nucleic acids may be from at least two subgroups, one of which has a defined genetic modification and one which does not have the genetic modification, and the method may determine which chromosomal interactions are relevant to, and/or affected by, the genetic modification. The modification may be achieved by any suitable means, including CRISPR technology.

The invention includes a method of determining whether a genetic modification to the sequence at a first locus of a genome affects other loci of the genome comprising detecting chromosome signatures at one or more other loci after the genetic modification is made, wherein preferably the genetic modification changes system characteristics, wherein said system is preferably the metabolic system, the immune system, the endocrine system, the digestive system, integumentary system, the skeletal system, the muscular system, the lymphatic system, the respiratory system, the nervous system, or the reproductive system. Said detecting chromosome signatures optionally comprises detecting the presence or absence of 5 or more (e.g. 5) different chromosomal interactions, preferably at 5 or more (e.g. 5) different loci, preferably as defined in any of the Tables. Preferably the chromosomal signatures or interactions are identified by any suitable method mentioned herein. In one embodiment the genetic modification is achieved by a method comprising introducing into a cell (a) two or more RNA-guided endonudeases or nucleic acid encoding two or more RNA-guided endonucleases and (b) two or more guiding RNAs or DNA encoding two or more guiding RNAs, wherein each guiding RNA guides one of the RNA-guided endonucleases to a targeted site in the chromosomal sequence and the R A-guided endonuclease cleaves at least one strand of the chromosomal sequence at the targeted site, in another embodiment the modification is achieved by a method of altering expression of at least one gene product comprising introducing into a eukaryotic cell containing and expressing a DNA molecule having a target sequence and encoding the gene product an engineered, non-naturally occurring Clustered Regularly interspaced Short Palindromic Repeats fCR!SPR)— CRISPR associated (Cas) (CRISPR-Cas) system comprising one or more vectors comprising:

a) a first regulatory element operable in a eukaryotic ceil operably linked to at least one nucleotide sequence encoding a CRISPR-Cas system guide RNA that hybridizes with the target sequence, and b) a second regulatory element operable in a eukaryotic cell operably linked to a nucleotide sequence encoding a Type-li Cas9 protein, wherein components (a) and (b) are located on same or different vectors of the system, whereby the guide RNA targets the target sequence and the Cas9 protein cleaves the DNA molecule, whereby expression of the at least one gene product is altered; and, wherein the Cas9 protein and the guide RNA do not naturally occur together, wherein preferably each RNA-guided endonuclease is derived from a Cas9 protein and comprises at least two nuclease domains, and optionally wherein one of the nuclease domains of each of the two RNA-guided endonucleases is modified such that each RNA- guided endonuclease cleaves one strand of a double-stranded sequence, and wherein the two RNA- guided endonucleases together introduce a double-stranded break in the chromosomal sequence that is repaired by a DNA repair process such that the chromosomal sequence is modified.

Typically the modification comprised a deletion, insertion or substitution of at least 5, 20, 50, 100 or 1000 bases, preferably up 10,000 or 1000,000 bases,

The modification may be at any of the loci mentioned herein, for example in any of the regions or genes mentioned in any of the tables. The chromosomal interactions which are detected at other (non-modified) loci may also be in any of the loci mentioned herein, for example in any of the regions or genes mentioned in any of the tables. Embodiments relating to genetic modifications many be performed on any organism, including eukaryotes, chordates, mammals, plants, agricultural animals and plants, and non-human organisms.

Methods and Uses of the invention

The method of the invention can be described in different ways. It can be described as a method of making a ligated nucleic acid comprising (i) in vitro cross-linking of chromosome regions which have come together in a chromosome interaction; (ii) subjecting said cross-linked DNA to cutting or restriction digestion cleavage; and (iii) ligating said cross-linked cleaved DNA ends to form a ligated nucleic acid, wherein detection of the ligated nucleic acid may be used to determine the chromosome state at a locus, and wherein preferably : - the locus may be any of the loci, regions or genes mentioned herein,

- and/or wherein the chromosomal interaction may be any of the chromosome interactions mentioned herein or corresponding to any of the probes disclosed in the tables, and/or

- wherein the ligated product may have or comprise (i) sequence which is the same as or homologous to any of the probe sequences disclosed herein; or (ii) sequence which is complementary to (ii). The method of the invention can be described as a method for detecting chromosome states which represent different subgroups in a population comprising determining whether a chromosome interaction is present or absent within a defined region of the genome, wherein preferably:

the subgroup is defined by presence or absence of a characteristic mentioned herein, and/or the chromosome state may be at any locus, region or gene mentioned herein; and/or - the chromosome interaction may be any of those mentioned herein or corresponding to any of the probes or primer pairs disclosed herein.

The invention includes detecting chromosome interactions at any locus, gene or regions mentioned herein. The invention includes use of the nucleic acids and probes (or primers) mentioned herein to detect chromosome interactions, for example use of at least 10, 50, 100 or 500 such nucleic acids or probes to detect chromosome interactions in at least 10, 20, 100 or 500 different loci or genes.

Tables Provided Herein

Tables herein either show probe (Episwitch™ marker) data or gene data representing chromosome interactions present in a condition (the first mentioned group) and absent in a control group, typically but not necessarily healthy individuals (the second mentioned group). The probe sequences show sequence which can be used to detect a ligated product generated from both sites of gene regions that have come together in chromosome interactions, i.e. the probe wii! comprise sequence which is complementary to sequence in the ligated product. The first two sets of Start-End positions show probe positions, and the second two sets of Start-End positions show the relevant 4kb region. The following information is provided in the probe data table: - HyperG_Stats: p-value for the probability of finding that number of significant EpiSwitch™ markers in the locus based on the parameters of hypergeometric enrichment

Probe Count Total: Total number of EpiSwitch™ Conformations tested at the locus

Probe Count Sig: Number of EpiSwitch™ Conformations found to be statistical significant at the locus

- FDR HyperG: Multi-test (False Discovery Rate) corrected hypergeometric p-value

Percent Sig: Percentage of significant EpiSwitch™ markers relative the number of markers tested at the locus

logFC: logarithm base 2 of Epigenetic Ratio (FC)

AveExpr: average log2-expression for the probe over ail arrays and channels

- T: moderated t-statistic

p-value: raw p-value

adj. p-value: adjusted p-value or q-vaiue

B - B-statistic (lods or B) is the log-odds that that gene is differentially expressed.

FC - non-log Fold Change

- FC_1 - non-log Fold Change centred around zero

LS - Binary value this relates to FC_1 values. FC_1 value below -1.1 it is set to -1 and if the FC_1 value is above 1.1 it is set to 1. Between those values the value is 0

The gene table data shows genes where a relevant chromosome interaction has been found to occur. The ρ-value in the loci table is the same as the HyperG_j>tats (p-value for the probability of finding that number of significant EpiSwitch™ markers in the locus based on the parameters of hypergeometric enrichment).

The probes are designed to be 30bp away from the Taql site, in case of PCR, PCR primers are also designed to detect ligated product but their locations from the Taql site vary.

Probe locations: Start 1 - 30 bases upstream of Taql site on fragment 1 End 1 - Taql restriction site on fragment 1 Start 2 - Taql restriction site on fragment 2 End 2 - 30 bases downstream of Taql site on fragment 2

4kb Sequence Location: Start 1 - 4000 bases upstream of Taql site on fragment 1 End 1 - Taql restriction site on fragment 1 Start 2 - Taql restriction site on fragment 2 End 2 - 4000 bases downstream of Taql site on fragment 2 The following information is also provided in the tables:

GL NET - procedures for fitting the entire lasso or elastic-net regularization. Lambda set to 0.5 (elastic-net)

- GL NET_1 - lambda set to 1 (lasso)

Fishers P-value - Exact Fishers Test P-value

- Coef - Logistic Regression Coefficient, if you raise e (e ^A X) to power of the coefficient you get the odds ratio for the variable

S.E. - Standard Error

Waid - Waid Equation Statistic. Wald statistics are part of a Wald test that the maximum likelihood estimate of a model coefficient is equal to 0. The test assumes that the difference between the maximum likelihood estimate and 0 is asymptotically normally distributed

Pr(> I Z I ) - P-value for the marker within the logistic model. Values below <0.05 are statistically significant and should be used in the logistic model.

Preferred Embodiments for Sample Preparation and Chromosome interaction Detection

Methods of preparing samples and detecting chromosome conformations are described herein. Optimised (non-conventional) versions of these methods can be used, for example as described in this section.

Typically the sample will contain at least 2 xlO ⁵ cells. The sample may contain up to 5 xlO ⁵ cells, in one embodiment, the sample will contain 2 xlO ⁵ to 5.5 xlO ⁵ cells

Crosslinking of epigenetic chromosomal interactions present at the chromosomal locus is described herein. This may be performed before cell lysis takes place. Cell lysis may be performed for 3 to 7 minutes, such as 4 to 6 or about 5 minutes. In some embodiments, cell lysis is performed for at least 5 minutes and for less than 10 minutes.

Digesting DNA with a restriction enzyme is described herein. Typically, DISIA restriction is performed at about 55°C to about 70°C, such as for about 65°C, for a period of about 10 to 30 minutes, such as about 20 minutes.

Preferably a frequent cutter restriction enzyme is used which results in fragments of ligated DNA with an average fragment size up to 4000 base pair. Optionally the restriction enzyme results in fragments of ligated DNA have an average fragment size of about 200 to 300 base pairs, such as about 256 base pairs. In one embodiment, the typical fragment size is from 200 base pairs to 4,000 base pairs, such as 400 to 2,000 or 500 to 1,000 base pairs. in one embodiment of the EpiSwitch method a DNA precipitation step is not performed between the DNA restriction digest step and the DNA ligation step. DNA ligation is described herein. Typically the DNA ligation is performed for 5 to 30 minutes, such as about 10 minutes.

The protein in the sample may be digested enzymatically, for example using a proteinase, optionally Proteinase K. The protein may be enzymatically digested for a period of about 30 minutes to 1 hour, for example for about 45 minutes, in one embodiment after digestion of the protein, for example Proteinase K digestion, there is no cross-link reversal or phenol DNA extraction step. in one embodiment PCR detection is capable of detecting a single copy of the iigated nucleic acid, preferably with a binary read-out for presence/absence of the iigated nucleic acid.

Homologues

Homologues of polynucleotide / nucleic acid (e.g. DNA) sequences are referred to herein. Such homologues typically have at least 70% homology, preferably at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98% or at least 99% homology, for example over a region of at least 10, 15, 20, 30, 100 or more contiguous nucleotides, or across the portion of the nucleic acid which is from the region of the chromosome involved in the chromosome interaction. The homology may be calculated on the basis of nucleotide identity (sometimes referred to as "hard homology").

Therefore, in a particular embodiment, homologues of polynucleotide / nucleic acid (e.g. DNA) sequences are referred to herein by reference to % sequence identity. Typically such homologues have at least 70% sequence identity, preferably at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98% or at least 99% sequence identity, for example over a region of at least 10, 15, 20, 30, 100 or more contiguous nucleotides, or across the portion of the nucleic acid which is from the region of the chromosome involved in the chromosome interaction.

For example the UWGCG Package provides the BESTFIT program which can be used to calculate homology and/or % sequence identity (for example used on its default settings) (Devereux et al (1984) Nucleic Acids Research 12, p387-395). The PILEUP and BLAST algorithms can be used to calculate homology and/or % sequence identity and/or line up sequences (such as identifying equivalent or corresponding sequences (typically on their default settings), for example as described in Altschul S. F. (1993) J Moi Evoi 36:290-300; Aitschul, S, F et ai (1990) J Mo I Biol 215:403-10. Software for performing BLAST analyses is publicly available through the National Center for Biotechnology Information. This algorithm involves first identifying high scoring sequence pair (HSPs) by identifying short words of length W in the query sequence that either match or satisfy some positive-valued threshold score T when aligned with a word of the same length in a database sequence, T is referred to as the neighbourhood word score threshold (Altschul et al, supra). These initial neighbourhood word hits act as seeds for initiating searches to find HSPs containing them. The word hits are extended in both directions along each sequence for as far as the cumulative alignment score can be increased. Extensions for the word hits in each direction are halted when: the cumulative alignment score falls off by the quantity X from its maximum achieved value; the cumulative score goes to zero or below, due to the accumulation of one or more negative-scoring residue alignments; or the end of either sequence is reached. The BLAST algorithm parameters W5 T and X determine the sensitivity and speed of the alignment. The BLAST program uses as defaults a word length (VV) of 11 , the BLOSUM62 scoring matrix (see Henikoff and Henikoff (1992) Proc. Natl. Acad. Sci. USA 89: 10915- 10919) alignments (B) oi 50, expectation (E) of 10, =5, N=4, and a comparison of both strands.

The BLAST algorithm performs a statistical analysis of the similarity between two sequences; see e.g., Kariin and Altschul (1993) Proc. Natl. Acad. Sci. USA 90: 5873-5787. One measure of similarity provided by the BLAST algorithm is the smallest sum probability (P(N)), which provides an indication of the probability by which a match between two polynucleotide sequences would occur by chance. For example, a sequence is considered similar to another sequence if the smallest sum probability in comparison of the first sequence to the second sequence is less than about 1, preferably less than about 0.1, more preferably less than about 0.01, and most preferably less than about 0.001.

The homologous sequence typically differs by 1, 2, 3, 4 or more bases, such as less than 10, 15 or 20 bases (which may be substitutions, deletions or insertions of nucleotides). These changes may be measured across any of the regions mentioned above in relation to calculating homology and/or % sequence identity.

Arrays

The second set of nucleic acids may be bound to an array, and in one embodiment there are at least 15,000, 45,000, 100,000 or 250,000 different second nucleic acids bound to the array, which preferably represent at least 300, 900, 2000 or 5000 loci. In one embodiment one, or more, or ail of the different populations of second nucleic acids are bound to more than one distinct region of the array, in effect repeated on the array allowing for error detection. The array be based on an Agilent SurePrint G3 Custom CGH microarray platform. Detection of binding of first nucleic acids to the array may be performed by a dual colour system.

Therapeutic Agents Therapeutic agents are mentioned herein. The invention provides such agents for use in preventing or treating the relevant condition. This may comprise administering to an individual in need a therapeutically effective amount of the agent. The invention provides use of the agent in the manufacture of a medicament to prevent or treat the disease. The methods of the invention may be used to select an individual for treatment. The methods of the invention, and in particular the companion diagnostic assay method, may include a treatment step where a person identified by the method may then be administered with an agent that prevents or treats the relevant condition.

The formulation of the agent will depend upon the nature of the agent. The agent will be provided in the form of a pharmaceutical composition containing the agent and a pharmaceutically acceptable carrier or diluent. Suitable carriers and diluents include isotonic saline solutions, for example phosphate-buffered saline. Typical oral dosage compositions include tablets, capsules, liquid solutions and liquid suspensions. The agent may be formulated for parenteral, intravenous, intramuscular, subcutaneous, transdermal or oral administration.

The dose of agent may be determined according to various parameters, especially according to the substance used; the age, weight and condition of the individual to be treated; the route of administration; and the required regimen. A physician will be able to determine the required route of administration and dosage for any particular agent. A suitable dose may however be from 0.1 to 100 mg/kg body weight such as 1 to 40 mg/kg body weight, for example, to be taken from 1 to 3 times daily.

Forms of the Substance Mentioned Herein

Any of the substances, such as nucleic acids or therapeutic agents, mentioned herein may be in purified or isolated form. The may be in a form which is different from that found in nature, for example they may be present in combination with other substance with which they do not occur in nature. The nucleic acids (including portions of sequences defined herein) may have sequences which are different to those found in nature, for example having at least 1, 2, 3, 4 or more nucleotide changes in the sequence as described in the section on homology. The nucleic acids may have heterologous sequence at the 5' or 3' end. The nucleic acids may be chemically different from those found in nature, for example they may be modified in some way, but preferably are still capable of Watson-Crick base pairing. Where appropriate the nucleic acids will be provided in double stranded or single stranded form. The invention provides ail the of specific nucleic acid sequences mentioned herein in single or double stranded form, and thus includes the complementary strand to any sequence which is disclosed.

The invention also provides a kit for carrying out any method of the invention, including detection of a chromosomal interaction associated with a particular subgroup. Such a kit can include a specific binding agent capable of detecting the relevant chromosomal interaction, such as agents capable of detecting a ligated nucleic acid generated by processes of the invention. Preferred agents present in the kit include probes capable of hybridising to the ligated nucleic acid or primer pairs, for example as described herein, capable of amplifying the ligated nucleic acid in a PGR reaction.

The invention also provides a device that is capable of detecting the relevant chromosome interactions. The device preferably comprises any specific binding agents, probe or primer pair capable of detecting the chromosome interaction, such as any such agent, probe or primer pair described herein.

Preferred therapeutic agents for use in the invention for specific stated condition A. Predisposition to e!apsing-Remitting Multiple Sclerosis (RRMS)

s Drugs used to treat the condition: oDisease modifying therapies (DMT): s Injectable medications

o Avonex (interferon beta-la)

o Betaseron (interferon beta-lb)

o Copaxone (glatiramer acetate)

o Extavia (interferon beta-lb)

o Giatopa (glatiramer acetate)

o Plegridy (peginterferon beta-la)

o Rebif (interferon beta-la)

s Oral medications

o Aubagio (teriflunomide)

o Gilenya (fingoiimod)

o Tecfidera (dimethyl fumarate)

a Infused medications

o Lemtrada (alemtuzumab)

o Novantrone (mitoxantrone)

o Tysabri (natalizumab) o Managing relapses:

• High-dose intravenous Solu-Medrol ^® (methy!prednisolone)

• High-dose oral Deltasone ^® (prednisone)

β H. P. Acthar Gel (ACTH) o Steriods:

* Methylprednisolone

B, Likelihood of Primary Progressive Multiple Sclerosis (PPMS) ygs used to treat the condition: o Steroids

o Immunosuppressive therapies such as total lymphoid radiation, cyclosporine, methotrexate, 2-chlorodeoxyadenosine, cyclophosphamide, mitoxantrone, azathioprine, interferon, steroids, and immune globulin,

o Copaxone

o Ocrelizumab (Genetech). C. Predisposition to fast progressing amyotrophic lateral sclerosis (ALS) disease state

1 ⁸ Drugs used to treat the condition: o Riluzole

o Baclofen.

D. Predisposition to type 2 diabetes disease state

⁸ Drugs used to treat the condition:

o Metformin

o Sulphonylureas such as:

^B glibenclamide

B glidazide

B glimepiride

B glipizide

B gliquidone

o Glitazones (thiazolidinediones, TZDs)

o Gliptins (DPP-4 inhibitors) such as:

s Linagliptin

s Saxagliptin

s Sitagliptin

s Vildagliptin

o GLP-1 agonists such as:

s Exenatide

s Liraglutide

o Acarbose

o Nateglinide and Repaglinide

o Insulin treatment. E. Predisposition to type 1 diabetes disease state s Drugs used to treat the condition: o Lantus subcutaneous

o Lantus Solostar subcutaneous

o Levemir subcutaneous

o Novoiog Flexpen subcutaneous

o Novoiog subcutaneous

o Humaiog subcutaneous

o IMovolog Mix 70-30 FlexPen subcutaneous o SymiinPen GO subcutaneous

o Humaiog KwikPen subcutaneous

o SymiinPen 120 subcutaneous

o Novoiin R injection

o Toujeo SoloStar subcutaneous

o Apidra subcutaneous

o Humaiog Mix 75-25 subcutaneous

o Humulin 70/30 subcutaneous

o Humaiog Mix 75-25 KwikPen subcutaneous o IMovoiin N subcutaneous

o Humulin R injection

o Novolin 70/30 subcutaneous

o insulin detemir subcutaneous

_Q Levemir FiexTouch subcutaneous

_Q Humulin N subcutaneous

o insulin glargine subcutaneous

o Apidra SoloStar subcutaneous

o insulin iispro subcutaneous

o insulin regular human injection

o insulin regular human inhalation

o Humaiog Mix 50-50 KwikPen subcutaneous

Q insulin aspart subcutaneous

Q Novoiog Mix 70-30 subcutaneous

Q Humaiog Mix 50-50 subcutaneous

o Afrezza inhalation

Q insulin NPH human recomb subcutaneous

Q insulin NPH and regular human subcutaneous

Q insulin aspart protamine-insulin aspart subcutaneous

Q Humulin 70/30 KwikPen subcutaneous

Q Humulin N KwikPen subcutaneous

Q Tresiba FiexTouch U-100 subcutaneous

Q Tresiba FiexTouch U-200 subcutaneous

o insulin iispro protamine and Iispro subcutaneous o pramiintide subcutaneous

Q insulin gluiisine subcutaneous

Q Novoiog PenFili subcutaneous

Q insulin degludec subcutaneous F. Predisposition to systemic lupus erythematosus (SLE) disease state

Drugs used to treat the condition:

Non-steroidal anti-inflammatory drugs (NSAIDS): ibuprofen, naproxen and diclofenac.

Hydroxychloroquine

Corticosteriods

Immunosuppressants: azathioprine, methotrexate, mycophenolate mofetil and cyclophosphamide.

Rituximab

Belimumab.

Corticosteroids: prednisone, cortisone and hydrocortisone

NSAIDs: indomethacin (indocin), nabumetone (Relafen), and celecoxib (Celebrex)

Anti-inflammatories: aspirin and acetaminophen (Tylenol)

Disease-Modifying Anti-Rheumatic Drugs (DMARDs): hydroxychloroquine (Plaqenil cyclosporine (Gengraf, Neoral, Sandimmune), and azathioprine (Azasan, Imuran). Antimalarials: chloroquine (Aralen) and hydroxychloroquine (Plaquenil).

BLyS-specific inhibitors or Monoclonal Antibodies (MAbS): Belimumab (Beniysta). immunosuppressive Agents/Immune Modulators: azathioprine (Imuran), methotrexate (Rheumatrex), and cyclophosphamide (Cytoxan).

Anticoagulants: low-dose aspirin, heparin (Caiciparine, Liquaemin), and warfarin (Coumadin).

G. Predisposition to ulcerative colitis disease state

⁸ Drugs used to treat the condition:

o Anti-inflammatory drugs: Aminosalicylates - sulfasalazine (Azulfidine), as well as certain 5-aminosalicyiates, including mesaiamine (Asacol, Lialda, Rowasa, Canasa, others), balsaiazide (Colazal) and olsalazine (Dipentum) and Corticosteroids - prednisone and hydrocortisone.

o immune system supressors: azathioprine (Azasan, Imuran), mercaptopurine (Purinethol, Purixam), cyclosporine (Gengraf, Neoral, Sandimmune), infliximab (Remicade), adalimumab (Humira), golimumab (Simponi) and vedoiizumab (Entyvio). o Other medications to manage specific symptoms of ulcerative colitis:

8 Antibiotics

⁸ Anti-diarrheai medication

8 Pain relievers

8 iron supplements. H. Likelihood of relapse of colorectal cancer for ulcerative colitis patients

* Drugs used to treat the condition:

Aminosalicylates

UC steroids

Azathioprine i. Likelihood of malignant peripheral nerve sheath tumours for neurofibromatosis patients

^B Treatment

Treatments for PNST include surgery, radiotherapy and chemotherapy.

Likelihood of developing prostate cancer and/or aggressive prostate cancer a Drugs used to treat the condition:

^B luteinising hormone-releasing hormone (LHRH) agonists

B anti-androgen treatment

^B combined LHRH and anti-androgen treatment

o Steroids

o Other medical treatments:

B Abiraterone

B Enzalutamide

^B docetaxel (Taxotere ^® )

^B carboplatin or cisplatin chemotherapy . Alzheimer's disease:

* Drugs used to treat the condition:

° Donepezil

°Rivastigmine

°Galantamine

* Memantine

Publications

The contents of ail publications mentioned herein are incorporated by reference into the present specification and may be used to further define the features relevant to the invention.

Specific Embodiments

The EpiSwitch™ platform technology detects epigenetic regulatory signatures of regulatory changes between normal and abnormal conditions at loci. The EpiSwitch™ platform identifies and monitors the fundamental epigenetic level of gene regulation associated with regulatory high order structures of human chromosomes also known as chromosome conformation signatures. Chromosome signatures are a distinct primary step in a cascade of gene deregulation. They are high order biomarkers with a unique set of advantages against biomarker platforms that utilize late epigenetic and gene expression biomarkers, such as DNA methylation and RNA profiling.

EpiSwitch™ Array Assay The custom EpiSwitch™ array-screening platforms come in 4 densities of, 15K, 45K, 100K, and 250K unique chromosome conformations, each chimeric fragment is repeated on the arrays 4 times, making the effective densities 60K, 180K, 400K and 1 Million respectively.

Custom Designed EpiSwitch™ Arrays

The 15K EpiSwitch™ array can screen the whole genome including around 300 loci interrogated with the EpiSwitch™ Biomarker discovery technology. The EpiSwitch™ array is built on the Agilent SurePrint G3 Custom CGH microarray platform; this technology offers 4 densities, 60K, 180K, 400K and 1 M illion probes. The density per array is reduced to 15K, 45K, 100K and 250K as each EpiSwitch™ probe is presented as a quadruplicate, thus allowing for statistical evaluation of the reproducibility. The average number of potential EpiSwitch™ markers interrogated per genetic loci is 50; as such the numbers of loci that can be investigated are 300, 900, 2000, and 5000.

EpiSwitch™ Custom Array Pipeline The EpiSwitch™ array is a dual colour system with one set of samples, after EpiSwitch™ library generation, labelled in Cy5 and the other of sample (controls) to be compared/ analyzed labelled in Cy3. The arrays are scanned using the Agilent SureScan Scanner and the resultant features extracted using the Agilent Feature Extraction software. The data is then processed using the EpiSwitch™ array processing scripts in R. The arrays are processed using standard dual colour packages in Bioconductor in R: Limma *. The normalisation of the arrays is done using the normalised within Arrays function in Limma * and this is done to the on chip Agilent positive controls and EpiSwitch™ positive controls. The data is filtered based on the Agilent Flag calls, the Agilent control probes are removed and the technical replicate probes are averaged, in order for them to be analysed using Limma *. The probes are modelled based on their difference between the 2 scenarios being compared and then corrected by using False Discovery Rate. Probes with Coefficient of Variation (CV) <=30?^ that are <=-l.l or =>1.1 and pass the p<=0.1 FDR p-value are used for further screening. To reduce the probe set further Multiple Factor Analysis is performed using the FactorMineR package in R.

* Mote: LIM MA is Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments. Limma is a R package for the analysis of gene expression data arising from microarray or RIMA-Seq. The pool of probes is initially selected based on adjusted p-value, FC and CV <30% (arbitrary cut off point) parameters for final picking. Further analyses and the final list are drawn based only on the first two parameters (adj p-value; FC). Examples

The invention is illustrated by the following non-limiting Examples.

Statistical Pipeline

EpiSwitch™ screening arrays are processed using the EpiSwitch™ Analytical Package in R in order to select high value EpiSwitch™ markers for translation on to the EpiSwitch™ PCR platform.

Step 1

Probes are selected based on their corrected p-value (False Discovery Rate, FDR), which is the product of a modified linear regression model. Probes below p-value

<= 0.1 are selected and then further reduced by their Epigenetic ratio (ER), probes ER have to be <=- 1.1 or =>1.1 in order to be selected for further analysis. The last filter is a coefficient of variation (CV), probes have to be below <=0.3.

Step 2 The top 40 markers from the statistical lists are selected based on their ER for selection as markers for PCR translation. The top 20 markers with the highest negative ER load and the top 20 markers with the highest positive ER load form the list.

Step 3

The resultant markers from step 1, the statistically significant probes form the bases of enrichment analysis using hypergeometric enrichment (HE). This analysis enables marker reduction from the significant probe list, and along with the markers from step 2 forms the list of probes translated on to the EpiSwitch"" ¹ PCR platform.

The statistical probes are processed by HE to determine which genetic locations have an enrichment of statistically significant probes, indicating which genetic locations are hubs of epigenetic difference. The most significant enriched loci based on a corrected p-value are selected for probe list generation. Genetic locations below p-value of 0.3 or 0.2 are selected. The statistical probes mapping to these genetic locations, with the markers from step2, form the high value markers for EpiSwitch™ PCR translation. Array design and processing

Array Design

1. Genetic ioci are processed using the SI! software (currently v3.2) to:

a. Pull out the sequence of the genome at these specific genetic loci (gene sequence with 50kb upstream and 20kb downstream)

b. Define the probability that a sequence within this region is involved in CC's c. Cut the sequence using a specific RE

d. Determine which restriction fragments are likely to interact in a certain orientation e. Rank the likelihood of different CC's interacting together.

2. Determine array size and therefore number of probe positions available (x)

3. Pull out x/4 interactions.

4. For each interaction define sequence of 30bp to restriction site from part 1 and 30bp to restriction site of part 2. Check those regions aren't repeats, if so exclude and take next interaction down on the list. Join both 30bp to define probe.

5. Create list of x/4 probes plus defined control probes and replicate 4 times to create list to be created on array

6. Upload list of probes onto Agilent Sure design website for custom CGH array.

7. Use probe group to design Agilent custom CGH array.

Array Processing

1. Process samples using EpiSwitch™ SOP for template production.

2. Clean up with ethanol precipitation by array processing laboratory.

3. Process samples as per Agilent SureTag complete DNA labelling kit - Agilent Oligonucleotide Array-based CGH for Genomic DNA Analysis Enzymatic labelling for Blood, Ceils or Tissues

4. Scan using Agilent C Scanner using Agilent feature extraction software.

Example 1: A method of determining the chromosome interactions which are relevant to a companion diagnostic that distinguishes between non-responders and responders of methotrexate for the treatment of Rheumatoid Arthritis.

Source: Glasgow Scottish Educational Research Association (SERA) cohort.

introduction to and Brief Summary of Example 1

Stable epigenetic profiles of individual patients modulate sensitivity of signalling pathways, regulate gene expression, influence the paths of disease development, and can render ineffective the regulatory controls responsible for effective action of the drug and response to treatment. Here we analysed epigenetic profiles of rheumatoid arthritis (RA) patients in order to evaluate its role in defining the non-responders to Methotrexate (MTX) treatment.

Reliable clinical prediction of response to first-line disease modifying anti-rheumatic drugs (DMARDs, usually methotrexate (MTX)) in rheumatoid arthritis is not currently possible. Currently the ability to determine response to first line DMARDs (in particular, methotrexate (MTX)) is dependent on empiric clinical measures after the therapy. in early rheumatoid arthritis (ERA), it has not been possible to predict response to first line DMARDs (in particular, methotrexate (MTX)) and as such treatment decisions rely primarily on clinical algorithms. The capacity to classify drug naive patients into those that will not respond to first line DMARDs would be an invaluable tool for patient stratification. Here we report that ch ro m oso m e co nfo rm atio na l sig natu res ( h ig h ly i nfo rmative a nd sta b l e e pige netic modifications that have not previously been described in RA) in blood leukocytes of early RA patients can predict non- responsiveness to MTX treatment. Methods:

Peripheral blood mononuclear cells (PBMCs) were obtained from DMARD naive ERA patients recruited in the Scottish early rheumatoid arthritis (SERA) inception cohort. Inclusion in this study was based on diagnosis of RA (fulfilling the 2010 ACR/EULAR Criteria) with moderate to high disease activity (DAS28 > 3.2) and subsequent monotherapy with methotrexate (MTX). DAS28 = Disease Activity Score of 28 joints. EULAR = The European League Against Rheumatism. ACR = American College of Rheumatology. MTX responsiveness was defined at 6 months using the following criteria: Responders - DAS28 remission (DAS28 <2.6) or a good response (DAS28 improvement of >1.2 and DAS28 3.2). Non- responders - no improvement in DAS28 (<0.6). Initial analysis of chromosome conformational signatures (CCS) in 4 MTX responders, 4 MTX non-responders and 4 healthy controls was undertaken using an EpiSwitch™ array containing 13,322 unique probes covering 309 RA-reiated genetic loci. Differentiating CCS were defined by LIMMA * linear modeling, subsequent binary filtering and cluster analysis. A validation cohort of 30 TX responders and 30 non-responders were screened for the differentiating CCS using the EpiSwitch ^{| M} PCR platform. The differentiating signature was further refined using binary scores and logistical regression modeling, and the accuracy and robustness of the model determined by ROC analysis **.

* Mote: LIMMA is Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments. Limma is a R package for the analysis of gene expression data arising from microarray or RNA-Seq.

** Note: ROC means Receiver Operating Characteristic and refers to ROC curves. An ROC curve is a graphical plot that illustrates the performance of a binary classifier system as its discrimination threshold is varied. The curve is created by plotting the true positive rate against the false positive rate at various threshold settings. CCS EpiSwitch™ array analysis identified a 30-marker stratifying profile differentiating responder and non-responder ERA patients. Subsequent evaluation of this signature in our validation cohort refined this to a 5-marker CCS signature that was able to discriminate responders and non-responders. Prediction modeling provided a probability score for responders and non-responders, ranging from 0.0098 to 0.99 (0 = responder, 1 = non-responder). There was a true positive rate of 92% (95% confidence interval [95% Ci] 75-99%) for responders and a true negative rate of 93% (95% Ci 76-99%) for non-responders. Importantly, ROC analysis to validate this stratification model demonstrated that the signature had a predictive power of sensitivity at 92% for MR to MTX.

We have identified a highly informative systemic epigenetic state in the peripheral blood of DMARD naive ERA patients that has the power to stratify patients at the time of diagnosis. The capacity to differentiate patients a priori into non-responders, using a blood-based clinical test, would be an invaluable clinical tool; paving the way towards stratified medicine and justifying more aggressive treatment regimes in ERA clinics. Detailed Version of Example 1

The capacity to differentiate patients a priori into responders (R) and non-responders (NR) would be an invaluable tool for patient stratification leading to earlier introduction of effective treatment. We have used the EpiSwitch™ biomarker discovery platform to identify Chromosome Conformation Signatures (CCS) in blood-derived leukocytes, which are indicative of disease state and MTX responsiveness. Thereby we identified an epigenetic signature contained in the CXCL13, IFNAR1, IL- 17 A, IL-21R and IL-23 loci that provide the first prognostic molecular signature that enables the stratification of treatment naive early RA (ERA) patients into TX R and NR. Importantly, this stratification model had a predictive power of sensitivity at 92% for NR to MTX. This epigenetic RA biomarker signature can distinguish between ERA and healthy controls (HC). This combinatorial, predictive peripheral blood signature can support earlier introduction of more aggressive therapeutics in the clinic, paving the way towards personalized medicine in RA.

RA is a chronic autoimmune disease affecting up to 1% of the global population. Pathogenesis is multifactorial and characterized by primarily immune host gene loci interacting with environmental factors, particularly smoking and other pulmonary stimuli. The exposure of a genetically susceptible individual to such environmental factors suggests an epigenetic context for disease onset and progression. Recent studies of chromatin markers (e.g. methyiation status of the genome) provide the first evidence of epigenetic differences associated with RA. However, to date neither genetic associations, nor epigenetic changes, have provided a validated predictive marker for response to a given therapy. Moreover, clinical presentation only weakly predicts the efficacy and toxicity of conventional DMARDs. MTX ⁸ , the commonest first-choice medication recommended by EULAR (The European League Against Rheumatism) and ACR (American College of Rheumatology) management guidelines, delivers clinically meaningful response rates ranging from 50 to 65% after 6 months of treatment. Such responses, and especially the rather smaller proportion that exhibits high hurdle responses, cannot currently be predicted in an individual patient. This begets a 'trial and error' based approach to therapeutic regimen choice (mono or combinatorial therapeutics). The ability to predict drug responsiveness in an individual patient would be an invaluable clinical tool, given that response to first-line treatment is the most significant predictor of long-term outcome.

Herein we focused on epigenetic profiling of DMARD-narve, ERA patients from the Scottish Early Rheumatoid Arthritis (SERA) inception cohort in order to ascertain if there is a stable blood-based epigenetic profile that indicates NR to MTX treatment and thus enables a priori identification and stratification of such patients to an alternate therapeutic. The source Epigenetic modulation can strongly influence cellular activation and transcriptionai profiles. Conceivably, the mode of action for a drug could be affected by epigeneticaily modified loci. We have focused on CCS, also known as long- range chromatin interactions, because they reflect highly informative and stable high-order epigenetic status which have significant implications for transcriptionai regulation. They also offer significant advantages ¹⁵ and early functional links to phenotypic differences ¹⁶ , and have been reported as informative biomarkers candidates in oncology and other disease areas.

We used early RA (ERA) patients provided by the Scottish early rheumatoid arthritis (SERA) inception cohort. Demographic, clinical and immunological factors were obtained at diagnosis and 6 months. Inclusion in this study was based on a diagnosis of RA (fulfilling the 2010 ACR/EULAR Criteria) with moderate to high disease activity (DAS28 > 3.2) and subsequent monotherapy with MTX. Responders were defined as patients who upon receiving MTX achieved DAS28 remission (DAS28 <2.6) or a good response (DAS28 improvement of >1.2 and DAS28 <3.2) at 6 months. Non-responders were defined as patients who upon receiving MTX had no improvement in DAS28 (<0.6) at 6 months. Blood samples for epigenetic analysis were collected at diagnosis. ( DAS28 = Disease Activity Score of 28 joints.)

We used a binary epigenetic biomarker profiling by analysing over 13,322 chromosome conformation signatures (CCS) (13,322 unique probes) across 309 genetic loci functionally linked to RA. CCS, as a highly informative class of epigenetic biomarkers (1), were read, monitored and evaluated on EpiSwitch™ platform which has been already successfully utilized in blood based stratifications of Mayo Clinic cohort with early melanoma (2) and is currently used for predictive stratification of responses to immunothera pies with PD-1/PD-L1.

Identified epigenetic profiles of naive RA patients were subject to statistical analysis using GraphPad Prism, WEKA and R Statistical language. By using EpiSwitch™ platform and extended cohort of 90 clinical samples we have identified a pool of over 922 epigenetic lead biomarkers, statistically significant for responders, non-responders, RA patients and healthy controls.

To identify a pre-treatment circulating CCS status in ERA patients, 123 genetic loci (Table 1) associated with RA pathogenesis were selected and annotated with chromosome conformations interactions predicted using the EpiSwitch™ in siiico prediction package. The EpiSwitch™ in silico prediction generated 13,322 high-confidence CCS marker candidates (Table 1). These candidates were used to generate a bespoke discovery EpiSwitch™ array (Figure 5) to screen peripheral blood mononuclear cells isolated at the time of diagnosis (DMARD-naive) from 4 MTX responders (R) and 4 MTX NR, all clinically defined after 6 months therapy (Figure 1A, B and Table 2), and 4 healthy controls (HC). To identify the CCS that differentiated R, NR and HC, a LI A * linear model of the normalized epigenetic load was employed. A total of 922 statistically significant stratifying markers (significance assessed on the basis of adjusted ρ value and EpiSwitch™ Ratio) were identified. Of the 922 lead markers, 420 were associated with NR, 210 with R and 159 with HC (Fig. 1C). Binary filtering and cluster analysis was applied to the EpiSwitch™ markers to assess the significance of CCS identified. A stepwise hierarchical clustering approach (using Manhattan distance measure with complete linkage agglomeration and taking into account R vs NR, HC vs R & HC vs NR) reduced the number of significant markers from 922 to 65 and finally resulted in a 30-marker stratifying profile (Figure ID and Table 3). * Note: LIMMA is Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments, Limma is a R package for the analysis of gene expression data arising from microarray or RNA-Seq.

To refine and validate the CCS signature, the 30 identified markers were screened in a second ERA patient cohort of R and NR (Figure 2A, B and Table 4) in a stepwise approach, using the EpiSwitch™ PCR platform (Figure 5). In the first instance, the entire 30 CCS markers were run in 12 ERA patients (6 R and 6 NR). The best differentiating CCS markers were identified by applying a Chi-squared test for independence with Yate's continuity correction on the binary scores, revealing a 12-marker CCS profile (Table 5). These 12 CCS markers were run on an additional 12 ERA patients (6 R and 6 NR) and the data combined with the previous 12 ERA, Combining the 24 patient samples (12 R and 12 NR) a logistic regression Model in the WEKA classification platform (using 5-fold cross validation to score the discerning power of each marker) was built and run 10 times by random data re-sampling of the initial data set to generate 10 different start points for model generation. The markers with the highest average scores were selected, thus reducing the profile to the 10 best discerning CCS markers (Table 5). The 10 CCS markers were used to probe a further 36 ERA samples (18 R and 18 NR). Combining all data (30 R and 30 NR), and using the same logistical regression and score calculation analysis, revealed a 5 CCS marker signature (IFNAR1, 1L-21R, !L-23, IL-17A and CXCL13) that distinguished MTX R from NR (Figure 2C, Table 5). CCS in the CXCL13 and IL-17A loci were associated with non-responders whilst CCS in the IFNAR1, IL-23 and IL-21R loci were associated with responders. This was an intriguing profile given the centra! role postulated for the IL-17 axis in human autoimmunity.

Importantly, the composition of the stratifying signature identifies the location of chromosomal conformations that potentially control genetic locations of primary importance for determining MTX response. Principal component analysis (PCA) of the binary scores for the classifying 5 EpiSwitch™ CCS markers provided clear separation of ERA patients based on their MTX response (Figure 2D). The model provided a prediction probability score for responders and non-responders, ranging from 0.0098 to 0.99 (0 = responder, 1 = non-responder). The cut-off values were set at <0.30 for responders and >0.70 for non-responders. The score of <0.30 had a true positive rate of 92% (95% confidence interval [95% CI] 75-99%) whilst a score of >0.70 had a true negative response rate of 93% (95% CI 76- 99%). The number of observed and predicted patients per response category (R or MR to TX) is shown in Table 6. With the EpiSwitch™ CCS marker model, 53 patients (88%) were classified as either responder or non-responder. Table 6, Observed and predicted number of R and NR to MTX monotherapy at 6 months using the EpiSwitch™ CCS mode!

Predicted response

Observed response , Undefined Responder

responder

Non-responder 25 3 2

Responder 2 4 24

Notes to Table 6: Cut off levels were chosen based on the probability of response to MTX of (approximately) >0.70 for MR and <0.3 for R. NR and R were defined as described in the methods. in order to test the 'accuracy' and 'robustness of performance' of the logistic classifying model that determined the 5 EpiSwitch™ CSS markers, 150 ROC ** curves (with unique start points) were generated by random data re-sampling of the R and NR data (Figure 3A). This resulted in the data being split into training (66%, equivalent to 6000 known class samples) and test (34%, equivalent to 3000 unknown class samples) groups; importantly the same split is never seen in the data for cross validation. The average discriminative ability (AUC) of the model was 89.9% (95% Ci 87-100%), with an average sensitivity (adjusted for response prevalence) for R of 92% and an average specificity for R of 84%. To determine the predictive capability of the model, the average model accuracy statistics were adjusted for population R/NR to MTX using Bayes prevalence theorem ²¹ . Using a 55% MTX response rate, the positive predictive value (PPV) was 90.3?^ whilst the negative predictive value (NPV) was 86.5?^. If the response rate was adjusted to 60%, this decreased the PPV to 87% whilst increasing the NPV to 89%.

** Note: ROC means Receiver Operating Characteristic and refers to ROC curves. An ROC curve is a graphical plot that illustrates the performance of a binary classifier system as its discrimination threshold is varied. The curve is created by plotting the true positive rate against the false positive rate at various threshold settings.

As an independent evaluation of the discerning powers of the selected 5 EpiSwitch™ CCS markers, factor analysis of mixed data (FAMD) incorporating 30 HC was performed. This illustrated that the signature not only has the power to differentiate between MTX R and NR but also retains sufficient disease-specific features to differentiate between healthy individuals and RA patients (Figure 3B).

Example 1 - Table 6D and 6E - Stratifying between RA-MTX responders and non-responders

Table 6D, and continuation Table 6E. presented hereinafter after Tables 6b+6c from Example 1A, show inter alia a list of about 54 DMA probes (SOmers) and their DMA sequences. These probes represent some of the probes used in Example 1. Without being bound, most of the probes illustrated in Table 6D+6E are thought likely to be significant to / useful in stratifying between RA-MTX responders and RA-MTX non-responders. The shown probes were investigated further by PCR. P Value = Probability value; adj. = adjusted.

Example 1 - Conclusion

In conclusion, our study of the epigenetic profile classification of DMARD naive ERA patients on the basis of prospective clinical assessment for R/NR has identified a consistent epigenetic signature, which discriminates an epigenetic state that is conducive and non-conducive to MTX response. This is to our knowledge, the first example of a stable and selectively differentiating blood based epigenetic biomarker in early RA patients that appears disease related (versus healthy controls) and that can predict non-responsiveness to first-iine MTX therapy. This model offers direct and practical benefits with a validated classifier based on 5 conditional CCS and their detection by the industrial ISO-13485 EpiSwitcb™ platform, which has the potential to be routinely available in the near future within clinical practice. Importantly, by adopting this predictive signature it should be possible to stratify MTX naive ERA patients into R and NR cohorts. This offers the potential to accelerate patient progression through the currently approved treatment strategy for ERA seeking earlier use of effective therapeutics, hence leading to a 'personalised' treatment regime. Furthermore, it is conceivable that alternative CCS signatures are present in RA patients (and patients with other autoimmune diseases) that could be used to justify fast-tracked biological treatment regimes in the clinic. This would have far reaching socio-economic implications, providing more cost effective and robust therapeutic approaches.

Example 1 - Material and Methods Example 1 - RA patient population

ERA patients in this study are part of the Scottish early rheumatoid arthritis (SERA) inception cohort. Demographic, clinical and immunological factors were obtained at diagnosis and 6 months (Table 1). Inclusion in the inception cohort was based on clinical diagnosis of undifferentiated polyarthritis or RA (>1 swollen joint) at a secondary care rheumatology unit in Scotland. Exclusion criteria were previous or current D ARD/bioiogica! therapy and/or established alternative diagnosis (i.e. psoriatic arthritis, reactive arthritis). Inclusion in this study was based on a diagnosis of RA (fulfilled the 2010 ACR/EULAR criteria for RA) with moderate to high disease activity (DAS28 > 3.2) and subsequent monotherapy with !ViTX. [DAS28 = Disease Activity Score of 28 joints. EULAR = European League Against Rheumatism. ACR = American College of Rheumatology.] Responders were defined as patients who upon receiving MIX achieved DAS28 remission (DAS28 <2.6) or a good response (DAS28 improvement of >1.2 and DAS28 <3.2) at 6 months. Non-responders were defined as patients who upon receiving MIX had no improvement in DAS28 (<0.6) at 6 months. Blood samples were collected at diagnosis (Baseline) in EDTA tubes and centrifuged to generate a buffy layer containing PBMCs, which was harvested and stored at -80°C. Local ethics committees approved the study protocol and all patients gave informed consent before enrolment into the study.

Example 1 - EpiSwitch™ processing, array and PCR detection. Probe design and locations for Ep iS witch™ assays Pattern recognition methodology was used to analyse human genome data in relation to the transcriptional units in the human genome. The proprietary EpiSwitch™ pattern recognition software ^{18, 20} provides a probabilistic score that a region is involved in chromatin interaction. Sequences from 123 gene loci were downloaded and processed to generate a list of the 13,322 most probable chromosomal interactions. 60mer probes were designed to interrogate these potential interactions and uploaded as a custom array to the Agilent SureDesign website. Sequence-specific oligonucleotides were designed using Primer3 ²³ , at the chosen sites for screening potential markers by nested PCR. Oligonucleotides were tested for specificity using oligonucleotide specific BLAST.

Example 1 - Chromatin Conformation signature analysis from patient PBMCs

Template preparation: Chromatin from 50 μΙ of each PB C sample was extracted using the EpiSwitch™ assay following the manufacturer's instructions (Oxford BioDynamics Ltd). Briefly, the higher order structures are fixed with formaldehyde, the chromatin extracted, digested with Taql, dilution and ligation in conditions to maximize intramolecular ligation, and subsequent proteinase K treatment. EpiSwitch™ microarray: EpiSwitch™ microarray hybridization was performed using the custom Agilent 8x60k array using the Agilent system, following the manufacturer's instructions (Agilent). Each array contains 55088 probes spots, representing 13,322 potential chromosomal interactions predicted by the EpiSwitch™ pattern recognition software quadruplicated, plus EpiSwitch™ and Agilent controls. Briefly, 1 g of EpiSwitch™ template was labelled using the Agilent SureTag labelling kit. Processing of labelled DNA was performed. Array analysis was performed immediately after washing using the Agilent scanner and software, in order to compare all the experiments the data was background corrected and normalized. Since each spot in the array is present in quadruplicate, the median of the four spots of each probe in the array was calculated and its log2 transformed value was used for further analysis. The coefficient of variation and p-value was calculated for each probe replicate. EpiSwitch™ PCR detection: Oligonucleotides were tested on template to confirm that each primer set was working correctly. To accommodate for technical and replicate variations, each sample was processed four times. Ail the extracts from these four replicates were pooled and the final nested PCR was performed on each sample. This procedure permitted the detection of limited copy-number templates with higher accuracy. Ail PCR amplified samples were visualised by electrophoresis in the LabChip* GX from Perkin Elmer, using the LabChip DNA IK VersionZ kit (Perkin Elmer) and internal DNA marker was loaded on the DNA chip according to the manufacturer's protocol using fluorescent dyes. Fluorescence was detected by laser and electropherogram read-outs translated into a simulated band on gel picture using the instrument software. The threshold we set for a band to be deemed positive was 30 fluorescence units and above.

Examp!e 1 - Statistical Methods and Packages.

GraphPad Prism and SPSS were used for ail statistical analyses of clinical data. The chi-square test and Fisher's exact test (for categorical variables), the t-test for independent samples (for continuous normally distributed variables), and the Mann-Whitney U test (for continuous variables without normal distribution) were used to identify differences. The level of statistical significance was set at 0.05, and all tests were 2-sided. R (and appropriate packages) were used for evaluation of EpiSwitch™ data. This included Stats package for Chi-square test and GLM (iogit), ROCR package for ROC curves from WEKA odds probabilities, gpiot & stats package in R for Heatmaps. FactorM iner package was used for PCA and Factor plots. Weka was used for Attribute Reduction, data randomisation and resampling, Logistic Model Classifier, AUC calculations and model accuracy calculations.

Example 1 - Table 1. Selected genes for EpiSwitch™ Array

j IM umber of identified

GENE Description Comments EpiSwitch™ sites

CHUK Conserved helix-loop-helix ubiquitous kinase NFKB 92

CIITA Class l!, major histocompatibility complex, transactivator NLR pathway 80

CLEC12A C-type lectin domain family 12, member A Other 52

CLEC16A C-type lectin domain family 16, member A Other 108

C0L2A1 Collagen, type !!, alpha 1 Coliagens 100

CTLA4 Cytotoxic T-lymphocyte-associated protein 4 RA SNP association 68

CX3CL1 Chemokine (C-X3-C motif) iigand 1 I Cytokines & Chenokines 92

CXCL12 Chemokine (C-X-C motif) iigand 12 I Cytokines & Chemokines j SO

CXCL13 Chemokine (C-X-C motif) iigand 13 [ Cytokines & Chemokines 80

CXCL8 Chemokine (C-X-C motif) iigand 8 I Cytokines & Chemokines j 48

Cytokines & Chemokines

CXCR3 Chemokine (C-X-C motif) receptor 3 receptors 72

j Cytokines & Chemokines

CXCR4 Chemokine (C-X-C motif) receptor 4 [ receptors 56

DHFR Dihydrofolate reductase MTX related genes j 72

ESR1 Oestrogen receptor 1 FL.S MTX responsive genes I 140

FCGR2A Fc fragment of IgG, low affinity Ma, receptor (CD32) RA SNP association 100

FCGR3B Fc fragment of IgG, low affinity lllb, receptor (CD16b) RA SNP association 192

FCRL3 Fc receptor-like 3 Other 68

FPGS Folylpolyg!utamate synthase MTX related genes 56

HTR2A 5-hydroxytryptamine (serotonin) receptor 2A, G protein-coupled I Other 80

ICAM 1 Intercellular adhesion molecule 1 FLS MIX responsive genes j Ϊ32 ^"

ICOS Inducible T-celi co-stimulator j RA SNP association I 200

Example 1 - Table 1. Selected genes for EpiSwitch™ Array

j IM umber of identified

GENE Description Comments EpiSwitch™ sites

I Cytokines & Chemokines

IFNAR1 interferon (alpha, beta and ome ¾a) receptor 1 receptors 80

IFNg Interferon, gamma I Cytokines & Chemokines 52

IKBKB inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase beta I NFKB 128

IL-10 Interleuk in 10 I Cytokines & Chemokines j 48

IL-15 Interleuk in 15 Cytokines & Chemokines 76

IL-17A Interleuk in 17A Cytokines & Chemokines 32

IL-18 Interleuk in 18 Cytokines & Chemokines 64

IL-la Interleuk in 1 alpha Cytokines & Chemokines 196

IL-2 Interleuk in 2 Cytokines & Chemokines 44

[ Cytokines & Chemokines

IL-21R Interleuk in 21 receptor [ receptors 60

IL-23 Interleuk in 23 I Cytokines & Chemokines j 56

j Cytokines & Chemokines

IL-23R Interleuk in 23 receptor receptors 104

[ Cytokines & Chemokines

IL-2RA Interleuk in 2 receptor, alpha [ receptors 100

j Cytokines & Chemokines

IL-2RB Interleuk in 2 receptor, beta receptors 1 72

IL-32 Interleuk in 32 Cytokines & Chemokines 44

IL-4 Interleuk in 4 Cytokines & Chemokines 32

j Cytokines & Chemokines

IL-4R Interleuk in 4 receptor receptors 76

IL-6 Interleuk in 6 Cytokines & Chemokines 48

Cytokines & Chemokines

IL-6ST Interleuk in 6 signal transducer (g pl30, oncostatin M receptor) j receptors 1 72

Example 1 - Table 1. Selected genes for EpiSwitch™ Array

j IM umber of identified

GENE Description Comments EpiSwitch™ sites

NFKBiB Nuclear factor of kappa light polypeptide gene enhancer in B-cells inr libitor, beta NFKB 120

NF BIA Nuclear factor of kappa light polypeptide gene enhancer in B-cells inr libitor, alpha NFKB 88

NLRP1 NLR family, pyrin domain containing 1 NLR pathway 108

NLRP3 NLR family, pyrin domain containing 3 NLR pathway 128

PADI4 Peptidyi arginine deiminase, type IV RA SNP association 168

PRDM1 PR domain containing 1, with ZNF domain I RA SNP association 120

P CQ, Protein kinase C, theta RA SNP association 216

PR CZ Protein kinase C, zeta Other j 184

PSTPiPl Proline-serine-threonine phosphatase interacting protein 1 [ Cytoskeletal 96

Prostaglandin-endoperoxide synthase 2 (prostaglandin G/H synthase and

PTGS2 cyclooxygenase) __ s gj ajjing 52

PTPN22 Protein tyrosine phosphatase, non-receptor type 22 RA SNP association i 196 ^~

PXK PX domain containing serine/threonine kinase RA SNP association 296

RBPJ Recombination signal binding protein for immunoglobulin kappa J rej »ion RA SNP association 296

REL V-rel reticuloendotheliosis viral oncogene homolog A NFKB 92

RFC-1 Replication factor C (activator 1) 1, 145kDa I MTX related genes

RGMB RGM domain family, member B [ FLS MTX responsive genes 80

RUNX1 Runt-related transcription factor 1 RA SNP association j 212.

SH2B3 SH2B adaptor protein 3 RA SNP association 124

SH T Serine hydroxymethyltransferase 1 (soluble) MTX related genes j 68

SLC19A1 Solute carrier family 19 (folate transporter), member 1 MTX related genes 76

SPRED2 Sprouty-related, EVH1 domain containing 2 RA SNP association 336

STAT4 Signal transducer and activator of transcription 4 1 Signalling j 128

Example 1 - Table 1. Selected genes for EpiSwitch™ Array

j IM umber of identified

GENE Description Comments EpiSwitch™ sites

SU 01 S T3 suppressor of mif two 3 homolog 1 SUMO 132

TAGAP T-cell activation RhoGTPase activating protein RA SNP association 92

TLR1 Toll -like receptor 1 TLR pathway 204

TLR2 To II -like receptor 2 TLR pathway 52

TLR4 Toll -like receptor 4 TLR pathway 52

TNF Tumour necrosis factor Cytokines & Chemokines 68

TNFAIP3 Tumour necrosis factor, alpha-induced protein 3 I A SNP association 180

1 Cytokines 8 Chonokinos j

TNFRSF11B Tumour necrosis factor receptor superfamily, member lib receptors 80

j Cytokines & Chemokines

TNFRSF13C Tumour necrosis factor receptor superfamily, member 13C I receptors 52

TNFRSF14 Tumour necrosis factor receptor superfamily, member 14 [ RA SNP association 112

j Cytokines & Chemokines j

TNFRSF17 Tumour necrosis factor receptor superfamily, member 17 receptors 44

j Cytokines & Chemokines

TNFRSF1A Tumour necrosis factor receptor superfamily, member 1A j receptors 72

j Cytokines & Chemokines

TNFRSF1B Tumour necrosis factor receptor superfamily, member IB receptors 72

TNFSF11 Tumour necrosis factor (!igand) superfamily, member 11 Cytokines & Chemokines 52

TNFSF13 Tumour necrosis factor (iigand) superfamily, member 13 Cytokines & Chemokines 48

TRAF1 TNF receptor-associated factor 1 RA SNP association 120

TRAF6 TNF receptor-associated factor 6 RA SNP association 72

TYMS Thymidylate synthetase MTX related genes 48

WISP3 WNT1 inducible Signalling pathway protein 3 j Signalling 1 88

Example 1 - Table 2. Patient Characteristics - Discovery Cohort

The Fisher exact unconditional test is used to assess differences in proportions between the two groups. To examine differences in continuous variables between the two groups, the independent samples t-test or the Mann-Whitney U-test (depending on distribution of data) is used. ⁱ n=3

Example 1 - Table 3. 65 Selected genes from EpiSwitch™ Array analysis

Example 1 - Table 4. Patient characteristics - Validation Cohort

The Fisher exact unconditional test is used to assess differences in proportions between the two groups. To examine differences in continuous variables between the two groups, we used the independent samples t-test or the Mann-Whitney U-test (depending on distribution of data).

⁵ One patient "other" (non-white, non-South East Asian, non-Indian Sub-Continent, Non-Afro-Caribbean), one patient did not give an answer.

s ⁱ n= 2.5 in responders for BMI

^€ Baseline - n = 29 non-R, n= 30 R; 6m - n = 30 non-R, n= 29

* Baseline - n = 26 non-R, n= 29 R; 6m - n == 21 non-R, n= 29

5 Baseline - n = 19 non-R, n= 23 R; 6m - n = 19 non- R, n= 22

' ^■ Baseline - n = 13 non-R, n= 23 R

°° Baseline - n = 26 non-R, n= 29 R

¹¹ Baseline - n = 29 non-R, n= 27 R; 6m■■ n = 28 non-R, n= 25

Example 1 - Table 5. 12 Selected genes from EpiSwitch'"' PCR

Example 1 - Table 6. Observed and predicted number of R and R to MTX monotherapy at 6 months using the EpiSwitch™ CCS model.

Predicted response

Non- Observed response Undefined Responder

responder

Non-responder 25 3 2

Responder 2 4 24

Notes to Table 6: Cut off levels were chosen based on the probability of response to MTX of (approximately) >0.70 for NR and <0.3 for R. NR and R were defined as described in the methods.

Example 1A -- ^■ RA analysis: MTX responders vs non-responders: Work subsequent to Example 1

Following on after Example 1, in Example 1A, a biostatisticai hypergeometric analysis was carried out, using the "Statistical Pipeline" method(s) at the beginning of the Examples section in the present specification, to generate further refined DNA probes stratifying between MTX responders vs MTX non- responders, for RA patients on MTX monotherapy.

Example 1A Results: Table 6b (and continuation part Table 6c) hereinafter discloses Probe and Loci data for RA-MTX - DNA probes stratifying between responders (R) and non-responders (NR). B = B-statistic (!ods or B), which is the log-odds that that gene is differentially expressed. FC is the non-log Fold Chang FC_1 is the non-log Fold Change centred around zero, it is seen that Table 6b+6c includes the sequences of 25 refined preferable DNA probes (60mers) for identifying MTX responders (MTX-R), and of 24 refined preferable DNA probes (60mers) for identifying MTX responders (MTX-NR), from the

hypergeometric analysis.

Example 1A - Table 6b, Probe and Loci data for RA-MTX - probes stratifying between responders and non-responders.

Example 1A - Table 6c. Probe And Loci data for RA- TX

Probe Location 4 kb Sequence Location

Ch

FC FC_1 Chr Startl Endl Start2 End2 r Startl Endl Start2 End2

0.577409

7 -1.7318725 12 69702274 69702303 69759619 69759648 12 69702274 69706273 69759619 69763618

0.605266

9 -1.6521636 7 22743265 22743294 22801876 22801905 7 22739295 22743294 22797906 22801905

0.656750

7 -1.5226477 7 22743265 j 22743294 22769055 22769084 7 22739295 22743294 22769055 22773054

0.662477

5 -1.5094851 7 22743265 ] 22743294 22757576 22757605 7 22739295 22743294 22757576 22761575

0.662880

4 -1.5085678 1 67644699 67644728 67729398 67729427 1 67640729 67644728 67725428 67729427

0.685058

8 -1.4597286 12 69702274 69702303 69805129 69805158 12 69702274 69706273 69805129 69809128

0.686815

3 -1.4559955 1 67644699 6764472S 67672222 67672251 1 67640729 67644728 67672222 67676221

0.689005

3 -1.4513676 1 67673763 67673792 67752422 67752451 1 67669793 67673792 67748452 67752451

0.694339

8 -1.4402171 7 22743265 22743294 22766800 22766829 7 22739295 22743294 22762830 22766829

0.696301

9 -1.4361587 4 123383001 123383030 123399247 123399276 4 123379031 123383030 123399247 123403246

0.700803

6 -1.4269334 7 22743265 22743294 22765456 22765485 7 22739295 22743294 22765456 22769455

0.713259

3 -1.4020146 7 22718635 22718664 22743265 22743294 7 22718635 22722634 22739295 22743294

0.714170

5 -1.4002258 12 48397660 | 48397689 48423816 48423845 12 48397660 48401659 48423816 48427815

Example 1A - Table 6c. Probe And Loci data for RA- TX fcontinued)

Probe location 4 kb Sequence location

Ch

FC FC_1 Chr Startl 1 Endl Start2 End2 r Startl Endl j Start2 End2

0.715620

4 -1.397389 17 32738857 32738886 32777305 32777334 17 32738857 32742856 32777305 32781304

0.718372

1 -1.3920362 1 67644699 67644728 67673763 67673792 1 6764072.9 67644728 67669793 67673792

0.718940

8 -1.390935 12 69702274 69702303 69766052 69766081 12 69702274 69706273 69762082 69766081

0.722487 -1.384108 12 48397660 48397689 48412400 48412429 12 48397660 48401659 48412400 48416399

0.725445

8 -1.3784627 12 69702274 69702303 69806507 69806536 12 69702274 69706273 69802537 69806536

0.737411

9 -1.3560941 7 22743265 22743294 22773903 22773932 7 22739295 22743294 22769933 22773932

0.737475

8 -1.3559748 19 55449063 55449092 55486679 55486708 19 55449063 55453062 55482709 55486708

0.738555 -1.3539954 17 32622187 32622216 32745745 32745774 17 32618217 3262.2216 32745745 32749744

0.741563

9 ^■ 1.3485014 13 43129388 43129417 43181041 43181070 13 43125418 43129417 43181041 43185040

1 ¹⁰ 104130466 104130495 104156468 104156497 10 104126496 104130495 104152498 104156497

0.743043

1 -1.3458169 1 67614064 67614093 67644699 67644728 1 67614064 67618063 67640729 67644728

0.743227

3 -1.3454835 7 22743265 22743294 22798802 22798831 7 22739295 22743294 22798802 22802801

1.655335 1.5553354

5 7 1 2460436 2460465 2486982 2487011 1 2456466 2460465 2486982 2490981

1.432101 1.4321012

2 1 10 6391740 6391769 6577853 6577882 10 6391740 6395739 6577853 6581852

1.417976 1.4179762

3 6 10 6520005 j 6520034 6577853 6577882 10 6516035 6520034 j 6577853 6581852

Example 1A - Table 6c. Probe And Loci data for RA-MTX {continued!

Probe location 4 kb Sequence location

Ch

FC FC_1 Chr Startl 1 Endl Start2 End2 r Startl Endl Start2 End2

1.415001 1.4150016

7 5 10 6427823 6427852 6577853 6577882 10 6427823 6431822 6577853 6581852

1.375539 1.3755396

5 4 18 74845065 74845094 74866978 74867007 18 74845065 74849064 74863008 74867007

1.3660090

1.366009 4 10 6470268 6470297 6577853 6577882 10 6466298 6470297 6577853 6581852

1.361195 1.3611955

5 3 20 44704386 44704415 44720665 44720694 j 20 44700416 44704415 44716695 44720694

1.340800 1.3408009

9 2 17 32551069 32551098 32617664 32617693 17 32551069 32555068 32617664 32621663

1.335081 1.3350815

5 3 1 2486982. 2487011 2540813 2540842 1 2486982 2490981 2536843 2540842

1.319143 1.3191430

1 7 12 66647072 66647101 66696510 66696539 12 66647072 66651071 66696510 66700509

1.318344 1.3183444

4 1 1 2476023 2476052 2486982 2487011 1 2472053 2476052 2486982 2490981

1.316485 1.3164851

1 2 12 66663907 66663936 66696510 66696539 12 66663907 66667906 66696510 66700509

1.305692

5 1.3056925 10 6556987 6557016 65778.53 6577882 10 6556987 6560986 6577853 6581852

1.287652

9 1.2876529 12 6268999 6269028 6304632 6304661 12 6268999 6272998 6300662 6304661

1.277785 1.2777852

3 7 17 32.617664 32617693 32708031 32708060 17 32617664 32621663 32704061 32708060

1.277347

4 1.2773474 10 6442502 I 6442531 6577853 6577882 10 6442502 6446501 6577853 6581852

Example 1A - Table 6cc. Continuation of Tables 6b and 6c (RA-MTX)

o

Example 1 - Table 6D - Stratifying between RA-MTX responders and non-responders

Table 6D Probe sequence

I Probes NR_R_P.Value NR_R_ac!j. P.Vai 60 mer

TNFRSF14_Site4_Sitel_F 0.001232118 0.079419805 TGATCACTGTTTCCTATGAGGATACAGCTCGAGGGGCAGGGGGCGGTCCTGGGCCAGGCG

TNFRSF14_Site4_Sitel_RR 0.002061691 0.082014717 AACCTGGAGAACGCCAAGCGCTTCGCCATCGAGGGGCAGGGGGCGGTCCTGGGCCAGGCG

TNFRSFlA_Site2_Site5_FR 0.004469941 0.093849223 CTACCTTTGTGGCACTTGGTACAGCAAATCGACGGGCCCCGTGAGGCGGGGGCGGGACCC

TN FRSFlA Sitel SiteS FR 0.005468033 0.09532964 CATCAATTATAACTCACCTTACAGATCATCGACG GGCCCCGTG AGGCG GGG GCGG GACCC

TNFRSF 1 _. Si te4 _. Si te8_F R 0.005244102 0.094393734 TGATCACTGTTTCCTATGAGGATACAGCTCGAAGATTAGGTAAAGGTGGGGACGCGGAGA UMX: Silo? Site.? RR 0.001313112 0.079419805 GAAAGGTAATTGCCCCCAATATTTATTTTCGAAACAGATCGGGCGGCTCGGGTTACACAC

TNFRSF 14_Sitel_Si te8_R F 0.003725772 0.090200643 TTCTCCACAGCCGGCCGGTCCTTGGCAGTCGAGGGGCAGGGGGCGGTCCTGGGCCAGGCG

18_74845064_74846657_74? 364995 _{, .} 74867007 _.. RF 0.001604249 0.079419805 CGTGTCCCAATTTCTAGTGCACTGTGAACTCGACCTCGCGGGAGGGGTGCCAGGCCGCAT

PRKCZ_Site8_Site6_FR 1.26726E-05 0.079228864 CCTCTCTTCTAAAAGGTCTCAACATCACTCGACTGGAGAGCCCGGGGCCTCGCGCCGCTT

RUNXl_Site5_Site2_RR 0.000540863 0.079228864 GTTTCCCCTTGATGCTCAGAGAAAGGCCTCGAAACAGATCGGGCGGCTCGGGTTACACAC

PRKCQ_Site7_Site4_FR 0.003958472 0.090816122 CATAATGCATGTGCATGAAAACTAATCTTCGATCTATGAGGAAATGCCCCCAGCCTCCCA

18_74756101_74757557_74E ί 45064 _. 74846657 _.. RR 0.003489147 0.089578901 AGATGTGTAAGTCACCAGGGAGTGCATTCGCGACCTCGCGGGAGGGGTGCCAGGCCGCAT

PRKCQ, SitelO _Site4 _FR 0.004639159 0.093849223 GTAATGGTGCCATCATAGCTCAAGCTCCTCGATCTATGAGGAAATGCCCCCAGCCTCCCA

PRKCQ_SitelO_Site4_RR 0.007812066 0.108064059 AATACAAAGGATGGTATATTTTGCATATTCGATCTATGAGGAAATGCCCCCAGCCTCCCA

PR CZ SiteS Site9 FR 0.000560117 0.079228864 CCTCTCTTCTAAA.AG GTCTCAA CATCACTCG ATG G TG CG GG AG GTG G CCGG CAG G GTTG G THFDl SiteS Sitel RF 0.000404338 0.079228864 ATAATTCTTCCTG G CACATAATAAGTATTCGA ATCG GG CG G GTTCCG G CGTGG GTTTCAG

1 NFAT_Site6_Sitel_FF 0.000514351 0.079228864 TCTAAAGGGATTTCCACTATATGTAGATTCGAGGGGCGTGTGCGCGCGTGGCGGGGCCCG

1 Table 6D Probe sequence

Probes NR_R_P.Value NR_R_adj. P.Val 60 mer

PRKCQ_Sitell_Site4_RR 0.006796573 0.102494645 AACTTATGATTCTAATCTTGAATGTCTGTCGATCTATGAGGAAATGCCCCCAGCCTCCCA

TNFRSFlA_Site5_Site6_FF 0.011987094 0.126537326 GAGGTGGGCAGATCACGGGGTCAGGGTATCGAGGCCCATCACTGGCGGGGAGACGGGAGG

18 7484506 _74846657_ 74864266 74864995 RF 0.008686097 0.111746517 ACTG AATATG AAAAAAAATGTAAAAATTATCG ACCTCG CGGGAGGG GTGCCAG G CCG CAT

PRKCQ_Site7_Site4_RR 0.011239245 0.123381356 GATTTTATAGCAAATTTACAAAAATGAGTCGATCTATGAGGAAATGCCCCCAGCCTCCCA

PRKCZ_Site5_Site9_RR 0.002885944 0.086622849 ACCAAGAGTTGG ACCCCC 1 I I I 1 G ATGTTCG ATGGTG CGGGAGGTGGCCGG CAG G GTTGG

MAL_Site4_Site2_FR 0.000818457 0.079228864 TATATTGCTATCTACTAGCAAAGGATAATCGAAGAGGTTCAGGGCGGTGCCCGCGGCGCT PRKCQ_Site9_Site4_RR 0.003669785 0.090200643 ATCAGTAAGCTGGTCAGCTACCCATGAATCGATCTATGAGGAAATGCCCCCAGCCTCCCA

1 TNFRSF14_Site3_Site8_FR 0.000995361 0.079228864 TGAAAACAGTTCATCCTGAGTTTCAGTCTCGAAGATTAGGTAAAGGTGGGGACGCGGAGA

•J\

IFNARl_Site2_Site4_RR 0.004801376 0.093849223 GTGCAGAGCGAGAGCGGGGCAGAGGCGGTCGAAACTGGGAGAATTCATCTGAAATGATTA

IL-21R_Site5_Site2_RR 0.034533931 0.199109911 GAGGCAGGCAGATCATGAGGTCAGGAGTTCGAGCCCTGGACCCCAGGCCAGCTAATGAGG

19_10326358_10327821_ 10368389 _. 10370560 _. _RR 0.000174676 0.079228864 GCTCACTGCAACCTCCACCTCCCAGGTTCGCGAACCTCCTGATAACTTCAGCATTAACAG

19_55449062_55451429_ 55484960 _. _55486708_ _RF 7.78E-05 0.079228864 AGGGTCTTGCTATGTTGCCCAGGCTGGCCTCGAGATCAGCCTGGGCAACACGGTGAAAAC

TLRl_Site4_Site7_FR 0.000969535 0.079228864 TGTAATATAAGCATAGCTCACTGCAGCCTCGAAGCATTTGTACGACATTCTCATCTTCTT

IRF5_Site8_Site2_FF 0.000148986 0.079228864 ACAGAGGAGCGAGGCCCGATCCTTACTTTCGAACTCCTGACCTCGTGATCTGCCCACCTC

SPRED2 _. Site4 _. Site8 _. _RF 0.018236449 0.149371667 GGGTTTCACCATGTTAGCCAGGATGGTCTCGATCTCCTGACCTCATGATCCGCCTGCCTC

!KBKB SiteS SiteS FR 0.013123191 0.130076121 GCATTTCACCATGTTGGTGAGGCTGGTCTCGAAGAGTTCACACGTGTCCAAATTTGGTGG

TLRl_Site9_Site2_FF 0.002914123 0.086622849 CTGGGATCACAGGCATGTGCCACCATGCTCGACAAGAATAGTCTCCTTGTTTCTGAACAT

CD28_Sitel_Site9_RR 0.003257956 0.088621062 GTATTTCTGGTTCTAGATCCTTGAGGAATCGAGCAGAAGGAGTCTCTCCCTGAGGCCACC i 12_10289678_10290500_ 10350455 _. 10351677 _. RF 0.001491578 0.079419805 CGAGGCGGGCGGATCACGAGGTCAGGAGATCGACCCCCACGTTCTCACCACCTGTTTCTT

1 Table 6D Probe sequence

Probes NR_R_P.Value NR__R_adj. P.Vai 60 mer

CD28_Sitel_Site8_RR 0.007644106 0.107723492 GTATTTCTGGTTCTAGATCCTTGAGGAATCGACCTCCTGGGCTCAACCTATCCTCCCACC

CXCL8_Site2_Site6_RF 0.002891692 0.086622849 GGGTTTCACTGTGTVAGCCAGGATGGTCTCGACCTCCCTGGCT ^' CAAGTGATCTTCCCACC

!L-23R_Site4_Site3_RF 0.001588257 0.079419805 TG CCCTA G A G ATCTG TG G A ACTTTG A A CTCG ATATATG A A A ATAG ΤΠΤΤΤΑ ATT ATA A A

RBPJ_Sitel4_Sitel3_FF 0.010539749 0.118804917 GGTGGGGGAATCACTTGAGGTCAGAAGTTCGAGACCATCCTGGGCAACATGGTAAAACCC

CHUK_Site7_Site2_RF 0.000132328 0.079228864 AATGGCACGATCACGGCTCACTGCAGCCTCGAATGTTACTGACAGTGGACACAGTAAGAA

SH2B3_S!te6_Site5_FF 0.003743845 0.090200643 GAGTTTTGCCATGTTGCCCAGGCTGGTCTCGAGAACAGCCTGGCCAACATGGTGAAACCC

IRAK3_Site7_Site5_FR 0.00056928 0.079228864 AGGTCTCACTATGTTGCCCGGGCTGGTCTCGACGCCGAGGAGCTCTGCAGTGGGGGCGTA

CD28 _Site4 Site2 RF 0.014801185 0.136839161 GGGTTTCACCATGTTGGCGAGGCTGGTCTCGAACTCCTGACCTCAGGTGATCCGCCTGCC

CD28_Site5_Site6_FR 0.007402719 0.106291976 GGTGGGTGGATCACCTGAGGTCAGGAGTTCGACCTAAGGGTGGTCATAATTCTGCTGCTG

19_39424583_39425930_ _. 39445791 39449626 FF 0.001743055 0.079577656 GGGTCTCACAGCCTTCAGAGCTGAGAGCCTAGGCTTCAGTGAGCCATAATCACGCCACTA

!L-la and IL-lb Sitel Site7 RF 0.002815998 0.086622849 CTTTGGGAGGCCAAGGTGAGTGGATTGCTCGACATCTCATTTGATAGGATTAAGTCAACG

!RAK3_Site7_Sitel_FF 0.00166033 0.079419805 AGGTCTCACTATGTTGCCCGGGCTGGTCTCGAACAGCAGCGTGTGCGCCGACAGCGCGCC

C5orf30_Site2_Site8_FR 0.00524841 0.094393734 TCTGTCGCCCAGGTTGGAGTACAGTGGCTCGAGGATGTCCTATTTTGCCACCTTATCTAA

CXCL13_Sitel_Site3_RR 6.56394E-05 0.07922.8864 TTATATCTCCTACCTCCAAGCCTGGCAGTCGATTCCAAAGTGAAGCAAAAAAAAAACTTC

14_55507409_55508411 _{. .} 55583475 _{. .} 55586339 _. RF 0.003368236 0.088703855 AAAGACCCTGTCTCTAAATAAATAGAACATCGAGATCATGCCACTGCACTCCAGCCTGGG

14_914504Q8_91451505_ 91524833 91527062 FF 0.004287708 0.093190996 GGGG ! 1 I 1 I CCATGTTAGTCAGGCTGGTCTAATGGCTCCCTTACCTTGCTGGCTGTGGGC

IL-23_Site4_Site5_FR 0.021765214 0.160960834 AGTGGCATGATCACAGCrCACTGCCACCTCGAAACCAAACCCTGTGAClTCAACACCCAA

1 IL-17A_Site3_Sitel_RR 0.009698852 0.115042065 CCCTCCCTCAACATGCAGGGATTACAATTCGAAGATGGTCTGAAGGAAGCAATTGGGAAA

Example 1 - Table 6E. Stratifying between RA-IViTX responders and non-responders

Example 1 - Table 6E continued. Stratifying between RA-MTX resporsders and non-responders

Example 1 - Table 6E continued. Stratifying between A-IViTX responders and non-responders

Example 2: A method of determining the chromosome interactions relevant to a companion diagnostic as pharmacodynamic biomarker during the inhibition of LSD1 in the treatment of AML (acute myeloid leukemia) Source: institute of Cancer Research UK, Pharmacodynamic Biomarkers

Pharmacodynamic (PD) biomarkers are molecular indicators of drug effect on the target in an organism. A PD biomarker can be used to examine the link between drug regimen, target effect, and bio!ogica! tumour response. Coupling new drug development with focused PD biomarker measurements provides critical data to make informed, early go/no-go decisions, to select rational combinations of targeted agents, and to optimise schedules of combination drug regimens. Use of PD endpoints also enhances the rationality and hypothesis-testing power throughout drug development, from selection of lead compounds in preclinical models to first-in-human trials (National Cancer Institute).

The inventors have discovered that chromosome signatures could be used as pharmacodynam ic biomarkers to monitor response to a number of drugs at time points consistent with phenotypic changes observed.

EpiSwitch™ Markers - Idea! Pharmacodynamic Biomarkers

Work on BET (bromodomain and extra-terminal) inhibitors on V4-11 cell lines has shown that BET inhibition causes the transcriptional repression of key oncogenes BCL2, CDK6, and C-MYC BET inhibitors like LSD1 inhibitors are epigenetic therapies, targeting the acetylated and metbyiation states of histones. As topological changes at loci precede any regulatory changes, the findings at the YC locus with EpiSwitch"" show evidence of regulatory change with LSD1 inhibition. MV4-11 ceil line harbours translocations that express M LL-AF4 and FLT3-ITD whereas THP-1 only expresses M LL-AF9. EpiSwitch™ LSD1 inhibition Biomarker Study for AML (acute myeloid leukemia)

Epigenetic biomarkers identified by EpiSwitchTIVi platform are well suited for delineating epigenetic mechanisms of LSD1 demethyiase and for stratification of different specificities of LSD1 inhibitors within and between cell lines. This work demonstrates that chromosome conformation signatures could be used as mechanism-linked predictive biomarkers in LSD1 inhibition. A standard LSD1 inhibitor is investigated in this study, tranylcypromine (TCP).

EpiSwitch™ LSD1 Pharmacodynamic Biomarker Discovery

The cells were treated with luM of tranylcypromine (TCP). Two AML (acute myeloid leukemia) ceil lines THP-1 and MV4-11 were tested with the above compound. Chromosome signatures identified in the vicinity of MYD88 gene in THP-1 ceils are shown in Table 7. Chromosome signatures identified in the vicinity of MYD88 gene in MV4-11 cells are shown in Table 8. Each number combination, points to individual chromosome interaction. The positions across the gene have been created and selected based on restriction sites and other features of detection and primer efficiency and were then analysed for interactions. The result in tables 7 and 8 represent no signature detection. A signature detection is represented with the number 1. Below are the PCR EpiSwitch™ marker results for the yD88 locus for cell lines THP-1 and MV4-11. FACS analysis was used to sort for the expression of CDllb± cells, as an indicator of differentiation. MyD88 and YC loci were selected on the basis of previously published studies, as key genetic drivers of treatment changes at 72 hrs.

LSD1 inhibitor (TCP) Experiments - Discovery Findings The conformations that change at the later time point (72hrs) relative to the untreated cells show the most consistency between the 2 ceil types. These are the markers above the bold double line shown in the THP-1 data, and highlighted by the shaded cells in the V4-11 data.

LSD1 inhibition removes a long range interaction with 5' upstream to the ORF of MYD88, changing the regulatory landscape for the locus.

LSD1 inhibition Analysis versus Gene Expression Data - Temporal and Structural Correlation of MYC Locus Conformations with Gene Expression (GEX) MYC is the target gene that drives the AML (acute myeloid leukemia) pathology, but at 72hrs treatment, the fold change is too small to be significant for a marker. The changes seen in Table 9 at the MYC locus at 72hrs for GEX data correlates to the conformation changes identified at 72hrs. The negative GEX change at MYC relative to the untreated cells is in keeping with the requirement to perturb MYC proliferation effect. The change is small also in keeping with the tight control elicited on this locus by numerous signal cascades. Unlike GEX data above, the EpiSwitch ^{l M} biomarkers clearly detect changes in chromosome conformation signatures at 72hr treatments correspondent with cells differentiation and their death by apoptosis (phenotypic change).

LSD1 inhibition Analysis versus Gene Expression Data - Temporal and Structural Correlation of MyD88 Locus Conformations with Gene Expression (GEX)

The changes seen at yD88 at 72hrs for the GEX data correlate to the conformation changes identified at 72hrs. The GEX change is positive relative to untreated cells, which is in keeping with the differential seen in these A L (acute myeloid leukemia) cells after treatment with the LSD1 inhibitor.

Only 1.5 fold change observed at 72hr treatment with TCP at YD88 locus identified both by GEX and EpiSwitch™, This level of change is too affected by noise in microarray gene expression analysis. However, epigenetic changes observed for chromosome signatures are clean to follow a binary format of 0 or 1. The data shows distinct pattern of changes. Both MYC and MYD88 are epigenetic drivers that, as shown in the GEX data, may not present with the strong response in gene expression, but can be identified as key epigenetic changes are visible through chromosome signatures. These two genetic drivers define phenotypic changes required for successful therapy treatment. At 72hrs cells differentiate and undergo apoptosis.

Table 7 THP cells - LSD1 Inhibitor (TCP) treated and untreated at 48hrs and 72hrs

Tabie ί I MV4-11 cells - LSDl Inhibitor (TCP) treated and untreated at 48hrs and 72hrs

Tabie 9. !ilumina Human HT-12 V4.0 expression beadchip GEX Data for MYC

Table 10. Treatment with TCP Table 11. Sl!umina Human HT-12 W4.G expression beadchip GEK Data for MYD88

Example 3: A method of determining the chromosome interactions which are relevant to a companion diagnostic for prognosis of meianoma relapse in treated patients (PGR data).

Source: Mayo Clinic metastatic melanoma cohort, USA

A prognostic biomarker predicts the course or outcome (e.g. end, stabilisation or progression) of disease. This study discovers and validates chromosome signatures that could act as prognostic biomarkers for relapse to identify clear epigenetic chromosome conformation differences in monitored meianoma patients, who undergone surgery treatment, for signs of relapse or recovery, and to validate such biomarkers for potential to be prognostic biomarkers for monitoring relapse of meianoma. Here we want to present our example of validated prognostic use of chromosome conformation signatures in application to confirmed melanoma patients who have undergone treatment by the resection of the original growth in order to identify the candidates who are likely to relapse within 2 years of treatment.

224 meianoma patients were treated with surgery to remove their cancer. They were then observed for a period of two years with blood being drawn for analysis at >100 days after the surgery.

EpiSwiich™ Prognostic Biomarker Discovery Chromosome signatures of 44 genes associated with meianoma and the rest of the genome for any disease-specific long range interaction by Next Generation Sequencing NGS were tested. Non-biased assessment of chromosome signatures associated with meianoma through deep sequencing provided initial pool of 2500 candidate markers. Further analysis by EpiSwitch™ platform on expanding sets of blood samples from melanoma patients and patients with non-me!anoma skin cancers (NMSC) as control, reduced the initial pool of candidate markers to 150. With further expansion on sample numbers it has been reduced to 32, as shown in Tabiel3.

Table 13. umber of EpiSwitch™ Markers screened and patients used.

Prognosis of Relapse

Top 15 markers previously identified for stratification of melanoma from non-melanoma skin cancers comprise TBx2 7/15, TYR 1/9, TYR 13/17, TYR 3/11, TYR 3/23, P16 11/19, P16 7/23, P16 9/29, MITF 35/51, ITF 43/61, MITF 49/55, BRAF 5/11, BRAF 27/31, BRAF 21/31, BRAF 13/21, which were taken from a total of 8 genes: TBx2; TYR; BRAF; MiTF; pl6; BRN2; p21; TBx3

3C analysis of melanoma patients' epigenetic profiles revealed 150 chromosome signatures with a potential to be prognostic biomarkers. reduced to three in expanding sets of testing sample cohorts. The three chromosome signatures which show the switches in chromosome conformational signature highly consistent with treatment and 2 year outcome for relapse, and this are the best potential prognostic melanoma markers are: BRAF 5/11, pl6~ll/19 and TYR 13/17. Finally, three chromosome signatures were carried out to the validation stage as prognostic biomarkers.

Table 14. EpiSwitch™ Prognostic signature for patients who relapsed 2 years after treatment (0 = o chromosome conformation detected, 1 = chromosome conformation detected) - Group A

Sample ID BRAF 5/11 P16 11/19 TYR 13/17 Mel_gon _e

AZ2S0439M-2 1 1 1

AZ250439M-1 1 1 1 No

JB220262F-2 1 1 1 No

JS150868F 1 1 1 No

KB200873F-2 1 1 1 No

SW14101951F-1 1 1 1 No

VW250929M-1 1 1 1 No

AC130954F-1 1 1 1 No

AC130954F-2 1 1 1 No

G 271147M-2 1 1 1 No

LW191048F-2 1 1 1 No

LG040535M-2 1 0 1 No

JB220262F-1 1 0 1 No

RH070234F-2 1 0 1 No

RH070234F-4 1 0 1 No Sample ID BRAF 5/11 P16 11/19 TYR 13/17 e!_gone

G 271147 -1 1 0 1 No

LW191048F-1 1 0 1 No

BB08111957F-2 1 1 0 No

RH070234F-1 1 1 0 No

RHQ70234F-1 1 1 0 No

RD200666 -2 1 1 0 No

RD200666 -1 1 1 0 No

KB200873F-1 1 1 0 No

VW250928 -2 1 1 0 No

Table 14 shows that relapse has been observed within two years after the treatment among the above patients. Through completely non-biased analysis of chromosome signatures these disease-specific three markers remained present and unchanged after treatment in majority of patients who relapsed after treatment.

Table 15 provides evidence that chromosome signatures change as a result of treatment to reflect more healthy profile. Through completely non-biased analysis of chromosome signatures the same disease- specific three markers have changed and were absent in majorit of patients after treatment, with no signs of relapse for 2 years.

Table 16 shows that the same three prognostic biomarkers show a strong tendency to be absent in healthy population. From all melanoma specific biomarkers identified in initial discovery stage, only these three markers carried prognostic value due to their change after treatment, in that they were different from diagnostic markers.

These results confirm that the three identified chromosome signatures exemplify the evidence for chromosome signatures acting as valid and robust prognostic biomarkers.

Table 15. EpiS itch™ Prognostic Signature for Successful Treatment in Melanoma Patients who did not relapse after 2 years |Q = Mo chromosome conformation detected, 1 = chromosome conformation detected) - Group B

Sample ID BRAF 5/11 P16 11/19 TYR 13/17 Wiel_gone

DG04081968 -2 0 0 0 Yes

DG04081968M-1 0 0 0 Yes

JR08061937F-2 0 0 0 Yes

EM110366F-4 0 0 0 Yes

FS17051942 -2 0 0 0 Yes

GS18081951M-1 0 0 0 Yes

DB24021936M-1 0 0 0 Yes

DB24021936M-2 0 0 0 Yes Sample ID BRAF 5/11 P16 11/19 TYR 13/17 ei_gone

ML23131937 -2 0 0 0 Yes

ML23131937M-1 0 0 0 Yes

D 21Q555 -2 0 0 0 Yes

D 210555M-1 0 0 0 Yes

JS121060F-2 0 0 0 Yes

JH280944M-2 0 0 0 Yes

JH28Q944IV1-1 0 0 0 Yes

RF15091934M-2 0 0 0 Yes

GC23051957 -2 0 0 0 Yes

PA24011941M-2 0 0 0 Yes

PA24011941 -1 0 0 0 Yes

MH12031946M-2 0 0 0 Yes

MH12Q31946M-1 0 0 0 Yes

AC17Q71938M-2 0 0 0 Yes

AC17071938M-1 0 0 0 Yes

TR080147 -2 0 0 0 Yes

Tabie 16. EpiSwitch™ Reference Epigersetic ProfHe in Healthy Controls (HC = Healthy Controls, 0 chromosome conformation detected, 1 - chromosome conformation detected)— Group C

Prognosis for Relapse (Residua! Disease Monitoring in Treated Melanoma Patients)

Cross-validation for the 224 melanoma patients, observed for 2 years after the treatment for a relapse, on the basis of stratification with the three prognostic chromosome signatures from post-operational blood test. Table 17 shows the relevant confusion table. Table ISb.

Classification Result 95% Confidence interval (CI)

Sensitivity 82.1% 70.1%-89.4%

Specificity 87.8% 81.9%-92.1%

PPV 71.0% 59.4%-80.4%

NPV 92.9% 87.7%-96.1%

Table 17. Confusion table Predictive/Pharmacodynamic Biomarkers for Drug Response: anti-PD-l in Metastatic Melanoma Patients (array data)

Melanoma

Malignant melanoma is the least common, but most aggressive form of skin cancer. It occurs in melanocytes, cells responsible for synthesis of the dark pigment melanin. The majority of malignant melanomas are caused by heavy UV exposure from the sun. Most of the new melanoma cases are believed to be linked to behavioural changes towards UV exposure from sunlight and sunbeds. Globally, in 2012, melanoma occurred in 232,000 people and resulted in 55,000 deaths, incidence rates are highest in Australia and New Zealand. The worldwide incidence has been increasing more rapidly amongst men than any other cancer type and has the second fastest incidence increase amongst women over the last decade. The survival rates are very good for individuals with stage 1 and 2 melanomas. However, only 7 - 19% of melanoma patients whose cancer has spread to distant lymph nodes or other parts of the body will live for more than 5 years. Currently, the only way to accurately diagnose meianoma is to perform an excision biopsy on the suspicious mole. The treatment includes surgical removal of the tumour. There is no meianoma screening programme in the UK, but educational programmes have been created to raise awareness of risks and symptoms of melanoma. There is a high demand for screening programmes in countries where melanoma incidence is very high e.g. in Australia. This work concerns biomarkers for diagnosis, prognosis, residual disease monitoring and companion diagnostics for meianoma immunotherapies. Study Background

The major issue with all immunomodulators currently tested in the treatment of cancers is their low response rates, in the case of late melanoma, for anti-PD-l or anti-PD-Ll monoclonal antibodies, the objective response rate is only 30-40%. Such therapy is in strong need of biomarkers predicting responders vs. non-responders. The PD-1 locus is regulated by cytokines epigenetically through resetting of long range chromosome conformation signatures. OBD technology

EpiSwitch ^{| M} platform technology is ideally suited for stratification of PD-1 epigenetic states prior to and in response to immunotherapy. An EpiSwitch ^{l M} array has been designed for analysis of >332 loci implicated in controls and modulation of response to anti-PD-1 treatment in melanoma patients. Methods

Biomarker identification using EpiSwitch™ array analysis:

1. Chromosome conformations for 332 gene locations determined by EpiSwitch™ pattern

recognition.

2. 14,000 EpiSwitch™ markers on PD1 screening array. Samples

Ail patients have been previously treated with chemotherapy and anti-CTLA-4 therapy. Two time points considered pre-treatment (baseline samples) and post-treatment (12 week samples)

Discovery Cohort · 4 responders vs. 4 non-responders at baseline

• 4 responders vs. 4 responders at 12 weeks (Matched)

Hypergeoroetrie Analysis

As the last step of the array data analysis, the hypergeometric analysis was carried out in order to identify regulatory hubs i.e. most densely regulated genes as being potential causative targets and preferred loci for stratification. The data is ranked by the Epigenetic Ratio for R vs R 12W (12W_FC_1), 1 in BL Binary indicates the loop is present in Responders vs Non-Responders, but when Responders baseline are compared to Responders at 12 weeks. The epigenetic ratio indicates that the presence of the loop is more abundant in the 12 week Responder patient samples. This indicates that there has been an expansion of this signature.

Summary

This epigenetic screen of anti-PDl therapy for potential predictive and pharmacodynamic biomarkers provides a wealth of new regulatory knowledge, consistent with prior biological evidence. The work provides a rich pool of predictive and pharmacodynamic/response EpiSwitch™ markers to use in validation analysis. The results show presence of a defined epigenetic profile permissive for anti- PD-1 therapy. The epigenetic profile permissive for anti-PDl therapy is present in naive patients at baseline and is strengthened with treatment over 12 weeks period.

Further information

This work concerns EpiSwitch™ as the basis for a diagnostic test to address the issue of poor melanoma diagnosis by general practitioners. 15 lead EpiSwitch™ biomarkers were screened and identified from an initial set of 86 patient samples representing true clinical setting. The biomarkers were then trained and validated in 2 independent patient cohorts: one from Australia (395 patients) and one from the Mayo Clinic (119 patients): · 119 independently and retrospectively annotated blood samples

• 59 Melanoma Samples

• 60 Controls (20 N SC, 20 Benign Conditions, 20 Healthy Patients) ) 2 Clinic collection in the USA

95% Confidence Interval (CI) Sensitivity 90.0% 79.9%-95.3%

Specificity 78.3% 66.4%-86.9% PPV 88.7% 77.4%-94.7%

MPV 80.6% 69.6%-88.3%

68 EpiSwitch™ Markers identified by statistical processing as predictive biomarkers at baseline for anti- PD-1 therapy. (PD1-R vs NR BL). R is Responded and NR is Non-Responder. 63 EpiSwitch™ Markers identified by statistical processing as response biomarkers for anti-PD-1 therapy. (PD1 R-BL v -12W). 10 Markers are both good candidates for predictive and response markers.

Fisher-Exact test results: top 8 predictive EpiSwitch™ Array Markers validated with the EpiSwitch™ PCR platform on the independent patient cohort (see Table 37). See Table 38 for the discerning markers from the Fisher-Exact analysis for PCR analysis between Responders at Baseline and Responders at 12 weeks. 1 is Conformation Present. 0 is Conformation Absent/ Array: R12_W indicates that the conformation was present in the Responders at 12 weeks.

The STAT5B_17_40403935_40406459_40464294_40468456_FR probe was measured in Responder v Non-Responder at Baseline and the conformation is present in the Responder. In this comparison the marker is in Responders at 12 weeks, this is the case as the concentrating of DNA used to detect the conformation in Responder vs Non Responder is greater than in Responder baseline v Responder at 12 weeks, indicating the Epigenetic Load has increased in the anti-PD-1 responding patients. Markers STAT5B and iL15 are of particular interest and are involved in key personalised medical and regulatory events responsible for the efficacies response to anti-PDl therapies (see tables 39 to 40, 43 to 47).

The following Tables 36a to 36f, 37a, 37b, 38a, and 38b also pertain to Example 3 and are as follows:

Table 36a. Top Probes - Anti PD1 (Melanoma) - responders

Table 36b. Top Probes - Anti PD1 (Melanoma) - responders - probe sequences

Table 36c. Top Probes - Anti PD1 (Melanoma) - Responders - Loci

Table 36d. Top Probes - Anti PD1 (Melanoma) Mon— responders

Table 36e. Top Probes - Anti PD1 (Melanoma) Non— responders

Table 36f. Top Probes - Anti PD1 (Melanoma) Non— responders - probes sequences and loci

Table 37a. Anti-PDl: pharmacodynamic response markers

Table 37b. Anti-PDl: pharmacodynamic response markers

Table 38a. Anti-PDl: pharmacodynamic response markers - No difference in baseline Responders and baseline Non-Responders but show a significant change in 12 week Responder

Table 38b. Probe location - Anti-PDl: pharmacodynamic response markers - No difference in baseline Responders and baseline Non-Responders but shows a significant change in 12 week Responders

indication Examples

Example 4 - Amyotrophic !aterai sclerosis f ALS)

The motor neurone disease Amyotrophic lateral sclerosis (ALS or Lou Gehrig's disease) is a fatal neurodegenerative disease characterised by progressive death of the primary motor neurones in the central nervous system. Symptoms include muscle weakness and muscle wasting, difficulty in swallowing and undertaking everyday tasks. As the disease progresses, the muscles responsible for breathing gradually fail, causing difficulty in breathing, and finally death. ALS has an average prevalence of 2 per 100,000, but is higher in the UK and USA with up to 5 per 100,000. There are estimated to be over 50,000 patients in the USA and 5,000 patients in the UK with the condition. The mortality rate for ALS sufferers is high: the median survival from diagnosis with ALS (i.e. the time when 50% of patients have died) varies in different studies, but in the most reliable (unbiased) population studies it is about 22 months with a range of 18-30 months. With no known cure, treatment of ALS focuses on supportive care. There is only one drug currently approved for treatment, riluzole which provides a modest increase in Iifespan for ALS patients but minimal improvement in symptoms. Despite intensive research into the biological basis of ALS, diagnosis and methods of treatment, as well as monitoring of disease progression remains a challenge. Such prognostic tests would greatly benefit ALS sufferers by allowing sub-stratification of patients according to the biological mediators of clinical heterogeneity, potentially allowing a more precise prognosis and care planning by identifying fast and slow progressors. OBD has been discovering EpiSwitch™ markers to stratify ALS vs. healthy controls, and fast progressing ALS vs. slow progressing ALS, to develop and validate diagnostic, prognostic and predictive EpiSwitch™ biomarkers for ALS.

Source: Northeast Amyotrophic Lateral Sclerosis Consortium (MEALS) - USA.

See Tables 1 , 2, 18, and 42 and hereinafter for ALS Probes - EpiSwitch™ markers to stratify ALS vs. healthy controls. Table 27 shows the gene data for this indication.

Further work was performed to validate the top ALS array markers and identify primers that could study the interactions. Statistical analysis of the array markers informed shortlist selection for PCR based assay development. From the list of the best stratifying ALS array probes, 99 markers were taken to the PCR stage.

Primers were designed using Integrated DNA Technologies (IDT) software (and Primer3web version 4,0.0 software if required) from markers identified from the microarray. Primer testing was carried out on each primer set; each set was tested on a pooled subset of samples to ensure that appropriate primers could study the potential interactions. Presence of an amplified product from PCR was taken to indicate the presence of a ligated product, indicating that a particular chromosome interaction was taking place. If the primer testing was successful then the primer sets were taken through to screening.

The signature set was isolated using a combination of univariate (LIM A package, R language) and multivariate (GLMNET package, R language) statistics and validated using logistic modelling within WEKA (Machine learning algorithms package). The best 10 stratifying PCR markers were selected for validation on 58 individuals (29 x ALS; 29 x Healthy controls - HC) using data from the Northeast Amyotrophic Lateral Sclerosis Consortium (NEALS). These were selected based on their Exact Fisher's P-value. A consistently good marker from ail 3 tests was the EpiSwitch marker in CD36. The first 9 PCR markers shown in Table 41 stratified between ALS and HC with 90% rank discrimination index.

The ALS marker set was analysed against a small independent cohort of samples provided by Oxford University. Even in a small subset of samples stratification of the samples was shown based on the biomarkers. Four markers stratify the subset of 32 (16 ALS, 16 Healthy Control) samples with p-value < 0.3. These core markers are ALS.21,23_2, DNM3.5.7_8, ALS.61,63_4 and NEALS.101.1Q3 _„ 32, in genes EGFR, DIMM3, CD36 and GLYCA 1 respectively. The Fisher-Exact test, GLM ET and Bayesian Logistic modelling marked CLIC4 as a valuable addition to the four core markers.

The sequences of the primers for the PCR markers given in Table 41 are provided in Table 42.

Example 5 - Diabetes meilitus {DM} type li (T2D |

Type 2 diabetes (also known as T2DM ) is the most common form of diabetes. Diabetes may occur through either, the pancreas not producing enough hormone insulin which regulates blood sugar levels, or the body not being able to effectively use the hormone it produces due to reduced insulin sensitivity. Until recently, T2DM has only been diagnosed in adults, but it is now occurring in children and young adults. According to World Health Organisation (WHO), diabetes reached pandemic levels with 346 million sufferers worldwide and its incidence is predicted to double by 2030. In 2004 alone, approximately 3.4 miliion people died as a consequence of diabetes and its complications with the majority of deaths occurring in low- and middle-income countries. The incidence of T2DM is increasing due to an ageing population, changes in lifestyle such as lack of exercise and smoking, as well as diet and obesity. T2DM is not insulin dependent and can be controlled by changes in lifestyle such as diet, exercise and further aided with medication, individuals treated with insulin are at a higher risk of developing severe hypoglycaemia (low blood glucose levels) and thus their medication and blood glucose levels require routine monitoring. Generally, older individuals with established T2D are at a higher risk of cardiovascular disease (CVD) and other complications and thus usually require more treatment than younger adults with a recently- recognised disease. It has been estimated that seven million people in the UK are affected by pre-diabetic conditions, which increase the risk of progressing to T2DM. Such individuals are characterised by raised blood glucose levels, but are usually asymptomatic and thus may be overlooked for many years having a gradual impact on their health. Inventors develop prognostic stratifications for pre-diabetic state and T2DIV1. Presented herein are EpiSwitch™ markers to stratify pre-diabetic state (Pre-T2D ) vs. healthy controls, as well as the discovery of EpiSwitch ^iM markers to stratify T2D vs. healthy control, and prognostic markers to stratify aggressive T2D vs. slow T2D .

Source: Norfolk and Norwich University Hospitals (N NUH), NHS Foundation Trust - Norwich U K See Tables 19a, 19b, 19c and 19d hereinafter for Pre-type 2 diabetes meilitus probes - EpiSwitch™ markers to stratify pre-type 2 diabetes vs. healthy controls. Table 28 shows the gene data.

See also Tables 20a, 20b, 20c, 20d hereinafter for Type 2 diabetes meilitus probes - EpiSwitch™ markers to stratify type 2 diabetes meilitus vs. healthy controls. Table 29 shows the gene data. Example 6 - Diabetes meliitus type i (TIDM)

Diabetes meliitus (DM) type 1 (also known as TIDM; formerly insulin-dependent diabetes or juvenile diabetes) is a form of diabetes that results from the autoimmune destruction of the insulin-producing beta cells in the pancreas. The classical symptoms are polyuria (frequent urination), polydipsia (increased thirst), polyphagia (increased hunger) and weight loss. Although, TIDM accounts for 5% of all diabetes cases, it is one of the most common endocrine and metabolic conditions among children, its cause is unknown, but it is believed that both genetic factors and environmental triggers are involved. Globally, the number of people with TIDM is unknown, although it is estimated that about 80,000 children develop the disease each year. The development of new cases varies by country and region. The United States and northern Europe fall between 8-17 new cases per 100,000 per year. Treatment of diabetes involves lowering blood glucose and the levels of other known risk factors that damage biood vessels. Administration of insulin is essentia! for survival Insulin therapy must be continued indefinitely and does not usually impair normal daily activities. Untreated, diabetes can cause many serious long-term complications such as heart disease, stroke, kidney failure, foot ulcers and damage to the eyes. Acute complications include diabetic ketoacidosis and coma. OBD's diabetes programme is focused on a development of EpiSwitch™ biomarkers for diagnostic and prognostic stratifications of TIDM.

Presented herein are EpiSwitch™ markers to stratify TIDM versus healthy controls.

Source: Caucasian samples collected by Procurement Company Tissue Solutions based in Glasgow (Samples collected in Russia); MEALS consortium controls (USA).

See Tables 21a, 21b, 21c and 21d hereinafter for Type 1 diabetes meliitus (TIDM) probes - EpiSwitch™ markers to stratify TIDM vs. healthy controls. Table 30 shows the gene data.

Example 7 - Ulcerative colitis (UC)

Ulcerative colitis (UC), a chronic inflammatory disease of the gastrointestinal tract, is the most common type of inflammatory disease of the bowel, with an incidence of 10 per 100,000 people annually, and a prevalence of 243 per 100,000. Although, UC can occur in people of any age, it is more likely to develop in people between the ages of 15 and 30 and older than 60. The exact cause of ulcerative colitis is unknown. However, it is believed that an overactive intestinal immune system, famiiy history and environmental factors (e.g. emotional stress) may play a role in causing UC. it is more prevalent in people of Caucasian and Ashkenazi Jewish origin than in other racial and ethnic subgroups. The most common signs and symptoms of this condition are diarrhoea with biood or pus and abdominal discomfort, it can also cause inflammation in joints, spine, skin, eyes, and the liver and its bile ducts. UC diagnosis is carried out through taking family history, physical exam, lab tests and endoscopy of iarge intestine. This lifelong disease is associated with a significant morbidity, and the potential for social and psychological sequelae particularly if poorly controlled. An estimated 30-60% of people with ulcerative colitis will have at least one relapse per year. About 80% of these are mild to moderate and about 20% are severe. Approximately 25% of people with UC will have one or more episodes of acute severe colitis in their lifetime. Of these, 20% will need a surgical removal of all or part of the colon (colectomy) on their first admission and 40% on their next admission. Although mortality rates have improved steadily over the past 30 years, acute severe colitis still has a mortality rate of up to 2%. Mortality is directly influenced by the timing of interventions, including medical therapy and colectomy, Ulcerative colitis has a well-documented association with the development of colorectal cancer, with greatest risk in longstanding and extensive disease. Treatment of relapse may depend on the clinical severity, extent of disease and patient's preference and may include the use of aminosalicylates, corticosteroids or immunomodulators. The resulting wide choice of agents and dosing regimens has produced widespread heterogeneity in management across the UK, and emphasises the importance of comprehensive guidelines to help healthcare professionals provide consistent high quality care.

Presented herein are EpiSwitch™ markers to stratify UC versus healthy controls for a development of disease-specific signatures for UC. Source: Caucasian samples collected by Procurement Company Tissue Solutions based in Glasgow (Samples collected in Russia); NEALS consortium controls (USA).

See Tables 22a, 22b, 22c and 22d hereinafter for Ulcerative colitis (UC) probes - EpiSwitch ^m markers to stratify UC vs. healthy controls. Table 31 shows the gene data.

Example 8 - Systemic lupus erythematosus (SLE)

Systemic lupus erythematosus (SLE), also known as discoid lupus or disseminated lupus erythematosus, is an autoimmune disease which affects the skin, joints, kidneys, brain, and other organs. Although "lupus" includes a number of different diseases, SLE is the most common type of lupus. SLE is a disease with a wide array of clinical manifestations including rash, photosensitivity, oral ulcers, arthritis, inflammation of the lining surrounding the lungs and heart, kidney problems, seizures and psychosis, and blood cell abnormalities. Symptoms can vary and can change over time and are not disease specific which makes diagnosis difficult. It occurs from infancy to old age, with peak occurrence between ages 15 and 40. The reported prevalence of SLE in the population is 20 to 150 cases per 100,000. In women, prevalence rates vary from 164 (white) to 406 (African American) per 100,000. Due to improved detection of mild disease, the incidence nearly tripled in the last 40 years of the 20th century. Estimated incidence rates are 1 to 25 per 100,000 in North America, South America, Europe and Asia. The exact cause of SLE is not known, but several factors have been associated with the disease. People with lupus often have family members with other autoimmune conditions. There may be environmental triggers like ultraviolet rays, certain medications, a virus, physical or emotional stress, and trauma. There is no cure for SLE and the treatment is to ease the symptoms. These will vary depending on expressed symptoms and may include antiinflammatory medications, steroids, corticosteroids and anti-malarial drugs. Survival has been improving, suggesting that more or milder cases are being recognised. OBD has been developing prognostic signatures for SLE. See Tables 23a, 23b, 23c and 23d for SLE probes - EpiSwitch™ markers to stratify SLE vs. healthy controls. Table 32 shows the gene data.

Source: Caucasian samples collected by Procurement Company Tissue Solutions based in Glasgow (Samples collected in USA); NEALS consortium controls.

Example 9 - SViii!tip!e sclerosis f S)

Multiple sclerosis (MS) is an acquired chronic immune-mediated inflammatory condition of the central nervous system (CNS), affecting both the brain and spinal cord. The cause of MS is unknown, it is believed that an abnormal immune response to environmental triggers in people who are genetically predisposed results in immune-mediated acute, and then chronic, inflammation. The initial phase of inflammation is followed by a phase of progressive degeneration of the affected ceils in the nervous system. MS is more common among people in Europe, the United States, Canada, New Zealand, and sections of Australia and less common in Asia and the tropics, it affects approximately 100,000 people in the UK. in the US, the number of people with MS is estimated to be about 400,000, with approximately 10,000 new cases diagnosed every year. People with MS typically develop symptoms between the ages 20 and 40, experiencing visual and sensory disturbances, limb weakness, gait problems, and bladder and bowel symptoms. They may initially have partial recovery, but over time develop progressive disability. Although, there is no cure, there are many options for treating and managing MS. They include drug treatments, exercise and physiotherapy, diet and alternative therapies. MS is a potentially highly disabling disorder with considerable personal, social and economic consequences. People with MS live for many years after diagnosis with significant impact on their ability to work, as well as an adverse and often highly debilitating effect on their quality of life and that of their families. OBD's MS programme involves looking at prognostic stratifications between primary progressive and relapsing-remitting MS. The most common (approx. 90%) pattern of disease is relapsing-remitting MS (MSRR). Most people with this type of MS first experience symptoms in their early 20s. After that, there are periodic attacks (relapses), followed by partial or complete recovery (remissions).The pattern of nerves affected, severity of attacks, degree of recovery, and time between relapses all vary widely from person to person. Eventually, around two-thirds of people with relapsing-remitting MS enter a secondary progressive phase of MS. This occurs when there is a gradual accumulation of disability unrelated to relapses, which become less frequent or stop completely.

Presented herein are EpiSwitch™ monitoring markers to stratify MS patients who are responders to IFiNi- B treatment versus non-responders; EpiSwitch™ markers to stratify MSRR versus healthy controls and EpiSwitch™ markers to stratify MSRR (relapsing remitting type of MS) versus MSPP (primary progressive type of MS).

Source: Caucasian samples collected by procurement company Tissue Solutions, based in Glasgow (Samples collected in MS-RR: Russia; MS IFN-B R vs NR: USA); NEALS consortium controls (USA)

See Tables 24a, b, c and d hereinafter for Relapsing-Remitting Multiple Sclerosis (MSRR) probes - EpiSwitch™ markers to stratify MSRR vs. healthy controls. Table 33 shows the gene data.

See also Tables 25a, 25b, 25c and 25d hereinafter for Multiple Sclerosis (MS) probes - EpiSwitch™ monitoring markers to stratify MS patients who are (B) responders to IFN-B (IFN-beta) treatment vs. (A) non-responders. Table 34 shows the gene data.

Example 1Q - Neurofibromatosis (MF)

In patients with NF1 mutation transformation into malignant state is difficult to predict, as it is governed by epigenetic context of the patient. In NF2 mutants, prognosis of the disease is very reliable and strongly defined by the genetics itself. Presented herein are EpiSwitch ' ^M markers to stratify Malignant Peripheral Nerve Sheath Tumours (MPNSTs) vs. Benign plexiform showing 329 top probes in enriched data.

Source: Belgium - University of Leuven

See Tables 26a and 26b hereinafter for Neurofibromatosis (NF) probes - EpiSwitch™ markers to stratify Benign plexiform vs. Malignant Peripheral Nerve Sheath Tumours (MPNSTs). Table 35 shows the gene data.

Example 11 - Chromosome interactions Relevant to Anti-PDl Responsiveness in Different Cancers

Table 47 shows the pattern of chromosome interactions present in responders to anti-PDl (unless otherwise stated with NR (non-responder)) in individuals with particular cancers. The terminology used in the tabie is explained below. DLBCL_ABC: Diffuse large B-cei! iymphoma subtype activated B-ceils DLBCL_GBC: Diffuse large B-eell Iymphoma subtype germinal centre B-ceils HCC: hepatocellular carcinoma

HCC HEPB: hepatoceliuiar carcinoma with hepatitis B virus

HCC_HEPC: hepatocellular carcinoma with hepatitis C virus

HEPB+R: Hepatitis B in remission

Pca_Ciass3: Prostate cancer stage 3

Pca_Class2: Prostate cancer stage 2

Pca Ciassl: Prostate cancer stage 1

BrCa_Stg4: Breast cancer stage 4

BrCa_Stg3B: Breast cancer stage 3B

BrCa_Stg2A: Breast cancer stage 2A

BrCa_Stg2B: Breast cancer stage 2B

BrCa_StglA: Breast cancer stage 1A

BrCa_Stgl: Breast cancer stage 1

PD_l_R_Me!anoma: Melanoma responder

PD_l_NR_Melanoma: Melanoma non responder

Table 18a ALS Probes - EpiSwitch™ markers to stratify ALS vs healthy controls

Probe_Count_ Probe_Count__ HyperG_S Percent_S

Probe GeneName Total Sig tats F DR_HyperG ig IogFC

11_923549_925733_976127_979142_FR AP2A2 19 8 0.006668 0 24512 42.11 -0.74197

11_36524913_ 36530925_36605543_36609927_FR RAG1 46 16 0.001656 0 127493 34.78 -0.69372

1__161590754_ _161594100__161627152__161631654_ RR FCGR2B;FCGR3A 106 33 9.75E-05 0 015455 31.13 -0.68658

11_ 36531355 _. _35534043 _. __36605543 _. _3660992.7_RR RAGl. 46 16 0.001655 0 127493 34.78 -0.68331

11_ 36531355. 35534043_36605543_36609927_FR RAGl. 46 16 0.001656 0 127493 34.78 -0.66709

11_36588999_ _36590845_36605543_36609927_FR RAG 2; RAGl 10 4 0.064184 0 80436 140 -0.66598

11_36583119_ _36588432_36605543_36609927_RR RAG 2; RAGl 10 4 0.064184 0 80436 I 40 -0.66346

1_172061602_ _172067357_172083100_172087823_ RF DN 3 1004 200 0.000673 0 069123 19.92 -0.64487

1_171936106_ _171939290__172083100__172087823__ RF DN 3 1004 200 0.000673 0 069123 19.92 -0.63828

1_171811918_ _171813464_172083100_172087823_ RF DN 3 1004 200 0.000673 0 069123 19.92 -0.6224

1_172083100. 172087823_1721 1 85 .17 4127. FF DNM3 1004 200 0.000673 0 069123 19.92 -0.62018

1_171887726. 171889817_172083100_172087823. RF DNM3 1004 200 0.000673 0 069123 19.92 -0.6103

13_111748012JL11752622_111942125_111944243 _RR ARHGEF7 71 20 0.007714 0 24512 j 28.17 -0.59912

1_172083100. .172087823_172212232_172223166_ .FF DN 3 1004 200 0.000673 0 069123 19.92 -0.58901

11_364S9037_ _36490716_36605543_36609927_FR RAGl 46 16 0.001656 0 127493 34.78 -0.56054

16_31228760_ 31230406_31342509_31344379_FR ITGAM 42 12 0.031165 0 564628 28.57 -0.5409

X_153269405_ _153271257_153287046_153289165_ RR I RAKI 3 2 0.070512 0 80436 66.67 -0.51331

13_111748012_111752622_111822569_111834523 _... RR ARHGEF7 71 20 0.007714 0 24512 28.17 -0.50678

1 _„ 172053648_ .172060321_172083100_172087823_ RR DN 3 1004 200 0.000673 0 069123 19.92 -0.49381

6 _„ 112058283_ .112061400_112189648_112191961_ RR FYN 286 61 0.013161 0 344967 I 21.33 -0.49133

11_923549_925733_976127_979142_RR AP2A2 19 8 0.006668 0 24512 42.11 -0.48405

6_111995015_ _111999450_112042041_112045568_ FR FYN 286 61 0.013161 0 344967 21.33 -0.48326

1_198560813_ _198564901_198619228_198622003_ .FF PTPRC 140 33 0.015074 0 344967 23.57 -0.46857

1_198564901_ _198567426_198666515_198673906_ FF PTPRC 140 33 0.015074 0 344967 23.57 -0.46848

19_55146487. 55148774_55168120_55169250_RR L.II..RB4 9 4 0.044033 0 749763 j 44.44 -0.45415

Table 18b. ALS Probes - EpiS steh™ markers to stratify ALS vs. healthy controls

Table 18c. ALS Probes - EpiSwstcb"" ¹ markers to stratify ALS vs. healthy controls

Table 19c. Pre-type 2 DM Probes - EpfSwiteh™ markers to stratify Probe sequence

i Pre-type 2 DM vs. healthy controls

SO mer

: Probe 1

U LU LAAAAAAAAbAbbAbbLLCAbbC 1 LbAbAL i AbAAAAA 1 AbA I i AC Abb i I I b

IGF2_11_2162616_2164979_2210793_2214417_ F

T ^' TTAGCCAAAAAGAAAAAAAGG ^' ETCATTTCGAGAACCAGAGTCAAACTTAGACCCCAGGA

ADCY5_3_123037100_123044621_123133741_1231438 12.. RF

TCCTTTCTTTTTTATTTTrrAAGCTGTTTC

TASP1_20_13265932_13269301_13507251_13521471_ RR

ACCAGCCCTGGGTT ^' CTTAAGGATGGGTGTCGACCCCTGGCTCTGCCT ^' GGGGTCTGGGCTT

TNFRSF1B_1_12241967_12245164_12269283_1227051 8 RR

ACATCTCAGACATGACTTTTGTGTTTCCTCGAGCCTTTTCGGGCAGGCGTCCAGCACGGG

SREBF1_17__17743896__17753157_17777190_17783023 _{, „} RF

CTCAGACrGTATATTCTGTAGCTTCAGTCGAGCTGTITCTTTATATGGTCrCTGCTATC

TSPANS_12J71690383J71707188_71850942_71857145 _RF

ATTATAACATTTATATATCATCTTTTCCTCGAGGTTGCAGTAAGCTGATCATGCCACTAC

CYB5R4_6_84553857_84562119_84611173_84616879_ FF

GACCAAACAGCTGTGGTTTGGCCATCACTCGAGAGAGAGCCTGTGTGAGGAGTGCAGTCA

KCNJ 11_11_17401446_17405499_17445199_17452295 _RF

CTTTTAG CTTTTACTTAGC ATAATTFTCTCG AG AGG GTG GG G CAG G AG AATCTCTTG AAC

PTPR D__9__9058670__9068143_91S6543_9197535_FF

GGAAGGCCGAGGCGGCCAGATCACGAGGTCGAACCTCCTGATAACTTCAGCATTAACAGC

ICAM1_19_10368390_10370561_10406169_10407761_ RF

GACCAAACAGCTGTGGTTTGGCCATCACTCGAGAGAGAGCCTGTGTGAGGAGTGCAGTCA

ABCC8_11_17401446_17405499_17445199_17452295_ RF

GAGTAGGTAAACAAAGCAGTCAGGAAGCTCGAGTCTTTGGTTTTCCCTAGATAATTAATA

CYP2C9_10_96661464_96668745_96741594_96747469 _FR

CTTAGAGCAAAGGCTAGGCTCAGTAATGTCGAGAGAGAGCCTGTGTGAGGAGTGCAGTCA

KCNJ 11_11_17401446_17405499_17419957_17422762 _RF

AGATCAAATCCAGTTTAAGGCTACTCCTTCGATTCATACACCATTCAGGGTATACAATAG

LEP_7_127838673_127843908_127864269_127868140. _RF

1 1 GCGAGCC i CGCAGCt 1 C GGAAGC 1 G 1 GA M i l AAG 1 C 1 A 1 1 i 1 G I 1 AGA I C 1 AAAG j CDKN2A_9_21967880_21969373_22029988_22034038_ RR

ACTGACAGTTTCTTGGGATTCTCCAGACTCGAGAGAGGCTGGTGCGCACCTACCCAGCGG

CACNA1C_12_2099248_2111840_2394923_2398377_FR

CCACTCCCCCAG6CTTACCTGCGAGCCATCGAGGTGGGCCTGGGTTCTCGTGGAGGGAGA

PIK3R3_1_46633134_46639474_46678880_46685388_F \f

CCATCCTGGACGCAGAATGTAGTCCCGTTCGAACAGAGCTGGGAGCTGGGGCCTAGGCTA

j ABCC8_11_17445199_17452295_17545007_17546815_ RF

Table 19c. Pre-type 2 DM Probes - EpfSwiteh™ markers to stratify

Probe sequence

i Pre-type 2 DM vs. healthy controls

SO mer

: Probe 1

I bU I M 1 AAA AAA 1 CAAAbb 1 b 1 AAL 1 ! LbALAbU I CCbbAbbC 1 GLbAbb 1 Cb AA

CDKN2A_9_21967880_21969373_22029988_22034038_ RF

TATGAGGCCCGGTTCCAGCAGAAGCTTCTCGAACAGAGCTGGGAGCTGGGGCCTAGGCTA

KCNJ 11_11_17419957_17422762_17445199_17452295 RR

G6AAGGCCGAGGCGGCCAGATCACGAGGTCGAAAGCGCTCGGATTCAGCCTTCTCCCCGG

ICAM1_19_10341612_10343024_10406169_10407761_ RF

ATGGACAGTAGGCAGGATGAATAAGTGCTCGAGCCTTTTCGGGCAGGCGTCCAGCACGGG

SREBF1_17_17722022_17726360_17743896_17753157. RR

TAACGTCCAAGAAAATTATTGTGACCCGTCGAGAAGTCAGGGAGCGTCTAGGGCTTCTGG

IGF2_11_2162616_2164979_2191728_2194389_FF

TATGAGGCCCGGTTCCAGCAGAAGCTTCTCGAACAGAGCTGGGAGCTGGGGCCTAGGCTA

I ABCC8_11_17419957_17422762_17445199_17452295_ RR

1 ACTGACAGTTTCTTGGGATTCTCCAGACTCGAGGCCTGGAGAAGCCCAGGAGGAGGCGTG CAC N A 1C_12_2D99248_2111840_2221145_2224007_F R

CTCCTCAAAAAAAAGAGGAGGCCCAGGCTCGATCCCAGAGCCGTCCCAGGCCTGGACAGA

I NS_11_2191728_2194389_2210793_2214417_RF

AGGCTGAACirCAAATGTGATAATMCCTCGACTTAATTTTATTACAGCACTAATATAAT

MAPK10_4_87459424_87462716_87493751_87502639 _. FF

GACTTCAACTCACTATGAATAAATAAAATCGAGAGGGTGGGGCAGGAGAATCTCTTGAAC

PTPR D_9_8886566_S895563_91865 3_9197535_FF

I bC I 1 1 1 1 AAA AAA 1 CAAAbb 1 b I AAL 1 1 LbAA 1 1 Abb 1 bbb 1 bbbbb 1 bbbAAA 1 1 bbb

CDKN2A_9_22005914_22007156_22029988_22034038. RF

ATAAGAAACTGAATTTAAATGCTCTCT ITCGATTCATACACCATTCAGGGTATACAATAG

LEP_7_127838673_127843908_127903727_127906543. _RF

AAGGTCTTCAGCTTCACTCCTGAAGCCATCGAGTTCTGTACTTAAGCAAACATTATCCTT

TSPAN8_12_71559221_71564078_71667712_71675824 _RR

CCA I G I I G I AAI A I ! GGA l 1 1 1 l A I CA ! I CGA I A I AG I GG I 1 1 C 1 AGG 1 A ! CA 1 GG 1 AAA

CYB5R4_6_84533887_84541872_84600402_84604101_ RF

GAG I AGG 1 AAACAAAGCAG I CAGGAAGC 1 CGA I CCAG I G I GC I I 1 1 CAC 1 1 CAGACC 1 1 G

CYP2C9_10_96661464_96668745_96755577_96760846 _FF

AA ^" FATC ^" i ^"" i ^" TTCATTT ^" i ^" TTGGTbAAGTC ^" rTCGATGGCTTCAGGAGTGAAGCTGAAGACCTT

TSPAN8_12_71559221_71564078_71675824_71684278 „RF

TGTTCAATCAAAGGAAGGGATAACACTATCGAGGTTGCAGTAAGCTGATCATGCCACTAC

CYB5R4_6_84533887_84541872_84611173_84616879_ FF

GATGTTTATACAAGATTCA ^' n ^' CTTTCCATCGATTCAACATTAATTCATTTTAGACTTCrC

I TAS P 1_20_.13441063_13442565_13507251_13521471_ FR

Tabie 19c. Pre-type 2 DM Probes - EpfSwitch™ markers to stratify Probe sequence

i Pre-type 2 DM vs. healthy controls

SO mer

: Probe 1

TTCCTTGAGGAATCAGTGATCAGGACTCTCGAACAGAGCTGGGAGCTGGGGCCTAGGCTA

KCNJ 11_11_17430922_17433660_17445199_17452295_RR

TAACGTCCAAGAAAAT! ^' ATrGTGACCCGTCGAGAAGTCAGGGAGCGTtTAGGGCTTCTGG

I NS_11_2162616_2164979_2191728_2194389_FF

TAGTACTACCACTGGAAAGCTAGAATATTCGATGCATTAAAATGITCTCGGAAAGAGATA

PTPRD_9_9551379_9564487_9852099_9857206_RR

CAAACCTGTAAT ^' CTAnTTTCTGGAGTCTCGATCCCAGAGCCGTCCCAGGCCTGGACAGA

1GF2 11 2162616 2164979 2191728 2194389 RR

GTTGAGGCTGCAATAAACCGTGATCAAGTCGACACCCATCCTTAAGAACCCAGGGCTGGT

TNFRSF1B_1_12241967_12245164_12274102_12277104_RF

CTCAGACTGTATATTCTCTTAGCTTCAGTCGAGTTCTGTACTTAAGCAAACATTATCCTT

TSPAN8_12_71667712_71675824_71850942_71857145_RF

GGAAATGAGTCTCATGTCTAATTAAATGTCGAAGTTAAGGTTTCTTGGTTCAAGTGGTGT

CACN A1C_12_2D99248_2111840_2200229_2202042_R F

TGAGGTAGGCAGATCACAGGTCAGGAGATCGACCTCCATTACGGAGAGTTTCCTATGTTT

CYP2C9_10_96690028_96694118_96748928_96755577_FR

AAGGTCTTCAGCTTCACTCCTGAAGCCATCGAGCTGTTTCTTTATATGGTCTCTGCTATC

TSPANS_12_71559221_71564078_71690883_71707188_RR

TGGGCTCCTTCAGCCCCACATGCCTGGTTCGAACAGAGCTGGGAGCTGGGGCCTAGGCTA

ABCC8_11_17445199_17452295_17514252_17516772_RF

TTTAGCCAAAAAGAAAAAAAGGTTCATTTCGAGGAATGTTTCCAAGCAATTCTCTCTGCT

ADCY5_3_123098260_123106114_123133741_123143812_RF

CTCCTCAAAAAAAAGAGGAGGCCCAGGCTCGATCCCAGAGCCGTCCCAGGCCTGGACAGA

IGF2_11_2191728_2194389_2210793_2214417_RF

CCC I 1 1 ACCCCAti 1 CCG 1 G 1 GAGCC ! C I 1 CGAGCC I N ! CGGGCAGGCG 1 CCAGCACGGG

SREBF1_17_17722022_17726360_17743896_17753157_FR

GGATTACTTCCATGAGAAGCAATTAAAATCGAACAGAGCTGGGAGCTGGGGCCTAGGCTA

j ABCC8_11_17445199_17452295_17538995_17541116_RF

Table 20a. Type 2 diabetes rneiiitus probes - EpiSwitch™ markers to stratify type 2 diabetes meliitus vs. heaithy controls

Prabe__Count_ Probe__Count_ HyperG_S Perce n

GeneLocus Total Sig tats FDRJHyperG t...Sig IogFC

Probe

ICAM 1 9 5 0.001732 0.070257 55.56 0.454102

ICAM1_19_10368390_10370561_10406169_10407761_ F

SREBF1 19 9 0.000113 0.013705 47.37 0.405312

SREBF1J17_17743895JL7753157_17777190_17783023_RF

CAM KID 115 24 0.002791 0.092599 20.87 0.389359

CA K1D_10_12558950_12568337_12770482_12771684_FR

SLC2A2 5 4 0.000809 0.038824 0.37933

SLC2A2_3_170700264_170710807_170738889_170750047_RF

ICAM 1 9 5 0.001732 0.070257 55.56 0.374366

ICAM1_19_10341512_10343024_10406169_10407761_RF

SREBF1 19 9 0.000113 0.013705 47.37 0.370578

SREBF1_17_17722022_17726360_17743896_17753157_RR

IDE 7 6 1.49E-05 0.004309 85.71 0.335806

IDE_10_94207972_94216393_94322805_94330672_RR

CACNAIC 197 35 0.006212 0.174409 17.77 0.335327

CACNA1C_12_2099248_2111840_2394923_2398377_FR

KCNJ11 22 8 0.002252 0.082207 36.36 0.334267

KCNJ11_11_17401446_17405499_17419957_17422762_RF

SREBF1 19 9 0.000113 0.013705 47.37 0.305866

SREBF1_17_17754197_17760488_17777190_17783023_RF

CACNAIC 197 35 0.006212 0.174409 17.77 0.304928

CACNA1C_12_2099248_2111840_2221145_2224007_FR

CYB5R4 39 10 0.011329 0.217632 25.64 0.304406

CYB5R4_6_84553857_84562119_84611173_84616879_FF

SLC2A2 5 4 0.000809 0.038824 80 0.304351

SLC2A2_3_170700264_170710807_170767515_170774153_RF

KCNJ11 22 8 0.002252 0.082207 36.36 0.30432

KCNJ11_11_17419957_17422762_17452295_17453614_FR

CAM KID 115 24 0.002791 0.092599 20.87 0.29721

CA K1D_10_12425560_12430245_12558950_12568337_RF

KCNJ11 22 8 0.002252 0.082207 36.36 0.294294

KCNJ11 11 17401446 17405499 17445199 174522.95 RF

CAM KID 115 24 0.002791 0.092599 20.87 0.293636

CA 1D_10_12558950_12568337_12609856_12611356_FR

CAM KID 115 24 0.002791 0.092599 20.87 0.288358

CAM K1D_10_12509013_12511923_12558950_12568337_FR

Table 20a. Type 2 diabetes rneiiitus probes - EpiSwitch™ markers to stratify type 2 diabetes meliitus vs. heaithy controls

Prabe__Count_ Probe__Count_ HyperG_S Perce n

GeneLocus Total Sig tats FDRJHyperG t...Sig logFC

Probe

ADCY5 90 18 0.013754 0.230556 20 0.256602

ADCY5_3_123098260_123106114_123133741_123143812_RF

CACNA1C 197 35 0.006212 0.174409 17.77 0.251757

CACNA1C_12_2099248_2111840_2371355_2375397_FF

CACNA1C 197 35 0.006212 0.174409 17.77 0.250871

CACNA1C_12_2099248_2111840_2249555_2251873_ F

CYP2C9 8 5 0.000851 0.038824 j 6 0.248438

CYP2C9_10_96690028_96694118_96755577_96760846_FF

TASP1 172 30 0.013787 0.230556 17.44 0.246916

TASP1_20_13507251_13521471_13641645_13647312_FR

CAM KID 115 24 0.002791 0.092599 20.87 0.246871

CA K1D_10_12392639_12394405_12558950_12568337_FR

AVP 8 4 0.00849 0.18229 50 0.245622

AVP_20_3082527_3084991_3109305_3112452_RR

CYP2C9 8 5 0.000851 0.038824 62.5 0.245603

CYP2C9_10_96661464_96668745_96755577_96760846_FF

ICAM 1 9 5 0.001732 0.070257 55.56 0.245234

ICAM1_19_10341512_10343024_10368390_10370561_RR

LTA 17 6 0.009477 0.192177 35.29 0.242562

LTA_6_31498892_31502771_31523234_31525915_RF

VEGFA 16 6 0.006786 0.176922 37.5 0.241134

VEGFA_6_43711156_43718584_43754116_43756590_RR

CAM KID 115 24 0.002791 0.092599 20.87 0.240511

CA K1D_10_12558950_12568337_12770482_12771684_FF

TAS 1 172 30 0.013787 0.230556 17.44 0.239337

TASP1_20_13279725_13285391_13489615_13507251_RF

SDH B 13 8 2.36E-05 0.004309 51.54 0.238395

SDHB_1_17348194_17353079_17405102_17406505_FF

Table 20d, Type 2 diabetes meilitus probes - E iS itc ™ markers to stratify type 2 diabetes Probe Location 4 kb Sequence Location

vs. healthy controls

Ch

Chr Startl Endl Start2 End2 r Startl Endl Start2 Endl

Probe

ICAM 1_19_10368390_10370561_10406169_104 19 10368391 10368420 10407732 10407761 19 10368391 10372390 10403762 10407761

07761_RF

SREBF1_17_17743896_17753157_17777190_177 17 17743897 17743926 17782994 17783023 17 17743897 17747896 17779024 17783023

83Q23_RF

CAMK1D_10_12558950_12568337_12770482_1 10 12568308 12568337 12770483 12770512 10 12564338 12568337 12770483 12774482

2771684_FR

SLC2A2_3_170700264_170710807_170738889_1 3 170700265 170700294 170750018 170750047 3 170700265 170704264 170746048 17075004

70750047__RF

ICAM 1_19_10341612_10343024_10406169_104 19 10341613 10341642 10407732 10407761 19 10341613 10345612 10403762 10407761

07761_RF

SREBF1_17_17722022_17726360_17743896_177 17 17722023 17722052 17743897 17743926 17 17722023 17726022 17743897 17747896

53157_RR

IDE__10__94207972__94216393__94322805__943306 10 94207973 94208002 94322806 94322835 10 94207973 94211972 94322806 94326805 72_RR

CAC N A1C_12_2099248_2111840_2394923 _„ 2398 12 2111811 2111840 2394924 2394953 12 2107841 2111840 2394924 2398923 377__FR

KCNJ11_11_17401446_17405499_17419957_174 11 17401447 17401476 17422733 17422762 11 17401447 17405446 17418763 17422762

22762 _„ RF

SREBF1_17_17754197_17760488_17777190_177 17 17754198 17754227 17782994 17783023 17 17754198 17758197 17779024 17783023 83023_RF

CAC N A1C_12_2099248_2111840_2221145_2224 12 2111811 2111840 2221146 2221175 12 2107841 2111840 2221146 2225145 0Q7_.FR

CYB5R4_6_84553857_84562119_84611173_846 6 84562090 84562119 84616850 84616879 6 84558120 84562119 84612880 84616879

16879_FF

SLC2A2_3_170700264_170710807_170767515_1 3 170700265 170700294 170774124 170774153 3 170700265 170704264 170770154 17077415

70774153__RF

KCNJ11_11_17419957_17422762_17452295_174 11 17422733 17422762 17452296 17452325 11 17418763 17422762 17452296 17456295

53614_FR

Table 20d, Type 2 diabetes meilitus probes - E iS itc ™ markers to stratify type 2 diabetes Probe Location 4 kb Sequence Location

vs. healthy controls

Ch

Chr Start! Endl Start2 End2 r Startl Endl Start2 Ersd2

Probe

CAMK1D_10_12425560_12430245_12558950_1 10 12425561 12425590 12568308 12568337 10 12425561 12429560 12564338 12568337 2568337_ F

KCNJ11_11_17401446_17405499_17445199_174 11 17401447 17401476 17452266 17452295 11 17401447 17405446 17448296 17452295

52295_RF

CAMK1DJL0JL2558950_12568337_12609856_1 10 12568308 12568337 12609857 12609886 10 12564338 12568337 12609857 12613856

2S11356_FR

CAM Kl D_10_12509013_12511923_12558950_1 10 12511894 12511923 12558951 12558980 10 12507924 12511923 12558951 12562950

2568337__FR

VEGFA_6_43701600_43705478_43718880_4372 6 43701601 43701630 43723754 43723783 6 43701601 43705600 43719784 43723783

3783_ F

ADCY5_3_123037100_123044621_123133741_1 3 123037101 123037130 123143783 123143812 3 123037101 123041100 123139813 12314381

23143812_RF

CAMK1D_10_12558950_12568337_12770482_1 10 12558951 12558980 12770483 12770512 10 12558951 12562950 12770483 12774482

2771684_RR

LTA_6_31498892__31502771_31552034_3155420 6 31502742 31502771 31554173 31554202 6 31498772 31502771 31550203 31554202 2 _„ FF

CYP2C9_10_96661464_96668745_96741594_967 10 96668716 96668745 96741595 96741624 10 96664746 96668745 96741595 96745594

47469 _„ FR

CACNA1C_12_2099248_2111840_2383231_2391 12 2099249 2099278 2383232 2383261 12 2099249 2103248 2383232 2387231 100_RR

SREBF1_17_17722022_17726360_17743896_177 17 17726331 17726360 17743897 17743926 17 17722361 17726360 17743897 17747896

53157_FR

CACNA1C_12_2099248_2111840_2255353_2257 12 2111811 2111840 2257934 2257963 12 2107841 2111840 2253964 2257963

963__FF

CYB5A_18_71929777_71931243_71965803_719 18 71931214 71931243 71970129 71970158 18 71927244 71931243 71966159 71970158

70158_FF

IDE_10_94207972_94216393_94232614_942362 10 94207973 94208002 94236238 94236267 10 94207973 94211972 94232268 94236267 67_RF

Table 20d, Type 2 diabetes meilitus probes - E iS itc ™ markers to stratify type 2 diabetes Probe Location 4 kb Sequence Location

vs. healthy controls

Ch

Chr Startl Endl Start2 End2 r Startl Endl Start2 End!

Probe

TASP1_20J3265932_.13269301_13507251_.1352 20 13265933 13265962 13507252 13507281 20 13265933 13269932 13507252 13511251 1471_ R

SDHB_1_17371319_17376758_17395655_17400 1 17376729 17376758 17395656 17395685 1 17372759 17376758 17395656 17399655

949_FR

CYB5R4_6_84541872_84548862_84611173_846 6 84548833 84548862 84616850 84616879 6 84544863 84548862 84612880 84616879

16879_FF

CAM 1D_10_12584612_12587236_12806730_1 10 12587207 12587236 12814059 12814088 10 12583237 12587236 12810089 12814088

2814088_FF

CYB5R4_6_84533887_84541872_84611173_846 6 84541843 84541872 84616850 84616879 6 84537873 84541872 84612880 84616879

15879_FF

SREBF1_17_17722022_17726360_17754197_177 17 17722023 17722052 17754198 17754227 17 17722023 17726022 17754198 17758197 60488_RR

SREBF1_17_17743896_17753157_17764809_177 17 17753128 17753157 17764810 17764839 17 17749158 17753157 17764810 17768809

67745_FR

SDHBJL_17371319_17376758_17395655_17400 1 17371320 17371349 17395656 17395685 1 17371320 17375319 17395656 17399655 949_RR

ADCY5_3_123098260_123106114_123133741_1 3 123098261 123098290 123143783 123143812 3 123098261 123102260 123139813 12314381

23143812_RF

CACNAIC 12 2099248 2111840_2371355_2375 12 2111811 2111840 2375368 2375397 12 2107841 2111840 2371398 2375397

397_FF

CAC N A1C_12_2099248_2111840_2249555_2251 12 2099249 2099278 2251844 2251873 12 2099249 2103248 2247874 2251873 873_RF

CYP2C9_10_96690028_96694118_96755577_967 10 96694089 96694118 96760817 96760846 10 96690119 96694118 96756847 96760846

60846_FF

TASP1_20_13507251_13521471_13641645_1364 20 13521442 13521471 13641646 13641675 20 13517472 13521471 13641646 13645645 7312_FR

CAMK1D_10_12392639_12394405_12558950_1 10 12394376 12394405 12558951 12558980 10 12390406 12394405 12558951 12562950 2568337_FR

Table 20d, Type 2 diabetes meilitus probes - E iS itc ™ markers to stratify type 2 diabetes Probe Location 4 kb Sequence Location

vs. healthy controls

Ch

Chr Start! Endl Start2 End2 r Startl Endl Start2 End!

Probe

AVP_20_3082527_3084991_3109305_3112452_ 20 3082528 3082557 3109306 3109335 20 3082528 3086527 3109306 3113305 R

CYP2C9_10_96661464_96668745_96755577_967 10 96668716 96668745 96760817 96760846 10 96664746 96668745 96756847 96760846

60846 _„ FF

ICAM 1_19_10341612_10343024_10368390_103 19 10341613 10341642 10368391 10368420 19 10341613 10345612 10368391 10372390

70561_RR

LTA__6_31498892_31502771_31523234_3152591 6 31498893 31498922 31525886 31525915 6 31498893 31502892 31521916 31525915 5 _„ RF

VEGFA_6_43711156_43718584_43754116_4375 6 43711157 43711186 43754117 43754146 6 43711157 43715156 43754117 43758116

6590_RR

CAM Kl D_10_12558950_12568337_12770482_1 10 12568308 12568337 12771655 12771684 10 12564338 12568337 12767685 12771684

2771684_FF

TASP1_20_13279725_13285391_13489615_1350 20 13279726 13279755 13507222 13507251 20 13279726 13283725 13503252 13507251 7251_RF

SDHB_1_17348194_17353079_17405102_17406 1 17353050 17353079 17406476 17406505 1 17349080 17353079 17402506 17406505

505__FF

Table 22a. Ulcerative colitis {UC) probes - EpiSwitch™ markers to stratify UC vs. healthy controls

Table 22a. Ulcerative colitis {UC) probes - EpiSwitch™ markers to stratify UC vs. healthy controls

Table 22a. Ulcerative colitis {UC) probes - EpiSwitch™ markers to stratify UC vs. healthy controls

Table 22a. Ulcerative colitis {UC) probes - EpiSwitch™ markers to stratify UC vs. healthy controls

Table 23a. SLE probes - EpiSwitch™ markers to stratify SLE vs. healthy controls

Probe_C Probe_C

ount_To ount_Si HyperG__S P ercent_S r eps;.

GeneLocus tal g tats FDR HyperG i{ Avg_CV !ogFC

Probe

AKT3 329 64 0.046948 0.964009 19.45 4 10.288 -1.1109

1_243635945. _243637780_243655019_243656128_ RR

AKT3 329 64 0.046948 0.964009 19.45 4 12.13 -1.04631

1_243655019. _243656128_243727939_243733240. _RF

AKT3 329 64 0.046948 0.964009 19.45 4 8.518 -1.02107

1 243655019 243656128 243954381 243957141 RF

AKT3 329 64 0.046948 0.964009 19.45 4 9.474 -1.00444

1_243655019_ _243656128_24368Q126_243690814_ _RF

AKT3 329 64 0.046948 0.964009 19.45 4 7.102 -0.95920

1_243655019_ _243656128_243867949_243871515_ RF

A T3 329 64 0.046948 0.964009 19.45 4 4.984 -0.95594

1_243655019_ _243656128_24386Q421_243862288_ _RF

ADCY4 10 4 0.060426 0.976846 40 4 3.707 -0.9213

14_24795078. _24798615_24843066_24844509_RR

AKT3 329 64 0.046948 0.964009 19.45 4 7.955 -0.89981

1_243655019_ _243656128_243816190_243822519_ _RF

ADCY4 10 4 0.060426 0.976846 40 4 2.67 -0.88345

14_24795078_ _24798615_24825321_24828950_RR

AKT3 329 64 0.046948 0.964009 19.45 4 8.951 -0.79777

1_243655019_ _243656128_243938249_243942270_ RF

AP2A2 16 5 0.097194 1 31.25 4 3.706 -0.75088

11 _„ 923549 _„ 925733_976127 _„ 979142 _„ FR

AKT3 329 64 0.046948 0.964009 I Ϊ9.45 4 7.444 -0.720

1__243655019_ _243656128_243864025_243867879_ RF

FCGR2B;FCG

R3A 96 21 0.076199 0.976846 1 21.88 4 3.793 -0.71803

1__161590754_ _161594100_161627152_161631654_ _RR

AKT3 329 64 0.046948 0.964009 1 19.45 j 4 3.009 -0.70897

1__243637780_ _243640834_243655019_243656128_ _RR

ADCY1 30 11 0.004679 0.356305 j 36.67 j 4 7.078 -0.69414

7_45584884_45588878_45736475_45743273_RF

ITGA 28 10 0.008518 0.356305 j 35.7Ϊ j 4 3.617 -0.68591

16_31228760. 31230406_31342509_31344379_FR

Table 23a. SLE probes - EpiSwitch™ markers to stratify SLE vs. healthy controls

Prob ®_c Probe_C

ouni _To ount_Si HyperG__S P ercent_S r eps.

GeneLocus tal g tats FDR HyperG i{ Avg_CV !ogFC

Probe

CBL 19 6 0.068591 0.976846 31.58 4 4.022 -0.55920

11_119059609_119061980_119165298_119170353_RF

AKT3 329 64 0.046948 0.964009 19.45 4 3.629 -0.55854

1_243655019_243656128_243669663_243671724_RR

AKT3 329 64 0.046948 0.964009 19.45 4 4.429 -0.55751

1 243655019 243656128 243816190 243822519 RR

AKT3 329 64 0.046948 0.964009 19.45 4 3.649 -0.55249

1_243655019_243656128_243774056_243776138_RR

ITGB2 7 3 0.085633 0.976846 42.86 4 3.661 -0.54628

21_46345789_46346831_46359648_46362975_FF

CD96 121 31 0.003927 0.356305 25.62 4 3.59 -0.54198

3_111080379_111085861_111238151_111244343_FF

CD96 121 31 0.003927 0.356305 25.62 4 3.619 -0.53848

3_111054275_111073125_111238151_111244343_FF

ADCY2 306 64 0.011305 0.356305 20.92 4 3.602 -0.53219

5_7602410_7603529_7787275_7792598_FF

ARHGEF7 61 15 0.052278 0.976846 24.59 4 3.876 -0.53058

13J.11748012_111752622_111822569_111834523._RR

ADCY5 123 29 0.016881 0.43328 23.58 4 3.106 -0.5220

3_123010454_123013518_123033778_123037100_RF

ADCY2 306 64 0.011305 0.356305 20.92 4 4,127 -0.52044

5_7520707_7525339_7602410_7603529_RF

AKT3 329 64 0.046948 0.964009 I 19.45 4 3.861 -0.51729

1_243655019_243656128_243954381_243957141_RR

ITGB2 7 3 0.085633 0.9768461 42.86 4 5.114 -0.51605

21..46345789..46346831.46359648 _, _46362975_FR

FYN 278 67 0.000218 0.0671961 24.1 4 3.654 -0.48699

6_111988059_111992304_112042041_112045568_FR

ADCY2 306 64 0.011305 0.35630S I 20.92 4 3.405 -0.48575

5_7425481_7432673_7602410_7603529_RF

AKT3 329 64 0.046948 6.964009 I 19.45 4 6.485 -0.479

1_243655019_243656128_243760927_243763803_RF

AP2A2 16 5 0.097194 1 31.25 4 3.228 -0.47712

11_923549_925733_976127_979142_RR

res p nders (A|

Table 25 d Probe Location

Chr Startl Endl Start2 Ertd2 Chr Startl

Probes

A

14 2.4795079 ! 24795108 24843067 ! 24843096 14

09_RR j 2479507

14_24795078_24798615_24843066_248445

14 24795079 24795108 24825322 24825351 1 2479507

14__24795078__24798515_24825321_24828950_RR

11 925704 925733 976128 976157 11 92173

11_923549_925733_976127_979142_FR

Tabie 25 d Probe Location 4 kb Sequence Location

Chr Startl Endl Startl Ersd2 Chr Startl Endl Start2 End2

Probes

11 923550 923579 979113 979142 11 923550 927549 975143 979142

11_923549_925733_976127_979142_RF

16 4067867 4067896 4109380 4109409 16 4063897 4067896 4109380 4113379

16_4065887_4067896_4109379_4115518_FR

1 25048332 25048361 25138049 25138078 1 25048332 25052331 25138049 25142048

1_25048331_25049906_25138048__25141786_ RR

11 1013054 1013083 969416 j 969445 11 1009084 1013083 965446 969445

11_1010876_1013083_964245_969445_FF

B

19 55271507 55271536 55301131 55301160 19 55267537 55271536 55301131. 553051301

19_55265127_55271536__55301130_55304400 _FR

15 45007486 45007515 45023743 45023772 15 45003516 45007515 45023743 45027742

15_45005395_45007515_45023742_45026509 _FR

19 55271507 55271536 55304371 55304400 19 55267537 55271536 55300401 55304400

19 55265127 55271536 55301130 55304400 FF

15 44986847 44986876 45007486 45007515 15 44986847 44990846 45003516 45007515

15 _. 44986846 _. 44994405 _. 45005395 _.. 45007515 ..RF

15 44962D62 44962091 45007486 45007515 15 44962062 44966061 45003516 45007515

15 44962061 44965177 45005395 45007515 RF

17 4709603 4709632 4724774 4724803 17 4709603 4713602 4724774 4728773

17_4709602_4710899_4724773_4727780_RR

15 44994406 44994435 45023743 45023772 15 44994406 44998405 45023743 45027742

15 44994405 44997599 45023742 45026509 ..RR

19 55279923 55279952 55304371 55304400 19 55275953 55279952 55300401 55304400 j

19_55275870_55279952_55301130_55304400_FF

15 44994376 44994405 45007486 45007515 15 44990406 44994405 45003516 45007515

15_44986846_449944O5_45005395_45007515 _FF

1 207739377 207739406 2D7805899 207805928 1 207739377 207743376 207801929 207805928

1_207739376__207741296_207803544_207805928_RF

2 65449443 65449472 65489565 65489594 2 65445473 65449472 65485595 65489594

2_65447148_65449472_65486660_65489594_ FF

1 9722967 9722996 9740103 9740132 1 9722967 9726966 9740103 9744102

1_9722966_9724614_9740102_9742515_RR

Table 26b. Neurofibromatosis (NF) probes - EpiSwttch™ markers to stratify ( A) Benign plexiform vs. (B) Malignant Peripheral Nerve Sheath Tumours fMPNSTs)

Table 27. ALS Gene Data

Description Co-rime tti p-valut!

C-C chemokine receptor activity and protein homodimerization

CC 2 Chemokine (C-C Motif) Receptor 2 0.16237882 activity

CD180 [ CD180 Molecule protein binding 0.16237882

CD274 [ CD274 Molecule protein binding 0.16237882

CD28 [ CD28 Molecule SH3/SH2 adaptor activity and identical protein binding 0.16237882 transmembrane signaling receptor activity and protein

CD3D CD3d Molecule, Delta (CD3-TCR Complex) 0.16237882 heterodimerization activity

receptor signaling complex scaffold activity and protein

CD3g Molecule, Gamma (CD3-TCR Complex)/CD3d Molecule, Delta

CD3G;CD3D heterodimerization activity /transmembrane signaling receptor 0.16237882

(CD3-TCR Complex)

activity and protein heterodimerization activity

CD40J.G [ CD40 Ligand CD40 receptor binding and cytokine activity 0.16237882

ICA 1;ICAM4 | Intercellular Adhesion Molecule 1 and 4 integrin binding and receptor activity 0.16237882

IFITM1;IFITIV12 Interferon Induced Transmembrane Protein 1 and 2 receptor signaling protein activity 0.16237882

IFITM3 Interferon Induced Transmembrane Protein 3 receptor signaling and protein binding activity 0.16237882

IGLC3;IGLC7;IGLC2;

Immunoglobulin Lambda Constant 3/7/2/1/6 antigen binding 0.16237882 1GLC1;IGLC6

Inhibitor Of Kappa Light Polypeptide Gene Enhancer In B-Cells,

1KBKG protein homodimerization activity and signal transducer activity 0.16237882

1 Kinase Gamma

IRS2 Insulin Receptor Substrate 2 phospholipid binding and signal transducer activity 0.16237882

!ntegrin, Alpha 4 (Antigen CD49D, Alpha 4 Subunit Of VLA-4

ITGA4 fibronectin binding 0.16237882

1 Receptor)

ITGAV !ntegrin, Alpha V protein kinase C binding and virus receptor activity 0.16237882

Killer Cell Immunoglobulin-Like Receptor, Two Domains, Short

receptor activity/HLA-B specific inhibitory M HC class 1 receptor

KIR2DS4; IR3DL1 Cytoplasmic Tail, 4/Killer Cell Immunoglobulin-Like Receptor, Three 0.16237882 activity

Domains, Long Cytoplasmic Tail, 1

LILRB2 Leukocyte Immunoglobulin-Like Receptor, Subfamily B (With TM

HC class ! protein binding and receptor activity 0.16237882 1 And ITIM Domains), Member !

PDCD1LG2 Programmed Cell Death 1 Ligand 2 protein binding 0.16237882

Single Immunoglobulin And Toll -Interieukin 1 Receptor (TI R)

protein binding 0.16237882 \ Domain

Description Co-rime tti p-valut! eukin 1 Receptor (T!R) Domain Containing Adaptor

TIRAP T Tol!-lnterl

protein binding, bridging and protein homodimerization activity 0.29840344 [ Protein

TL 10 1 Toll-Like Receptor 10 transmembrane signaling receptor activity 0.29840344

TYRO Protein Tyrosine Kinase Binding Protein/Hematopoietic Cell receptor signaling protein activity and identical protein

TYROBP;HCST 0.29840344

Signal Transducer binding/Hematopaietic Cell Signal Transducer

sequence-specific DNA binding transcription factor

Vav 1 Guanine Nucleotide Exchange Factor/Complement activity and guanyl-nucleotide exchange factor activity/C5L2

VAV1;C3 0.29840344

Component 3 anaphylatoxin chemotactic receptor binding and receptor

binding

Table 28. Pre -Type 2 Diabetes Gene Data

Descriptio Ta le 28 Corrnents p-vaiut!

ArfGAP With RhoGAP Domain, Ankyi in Repeat And PH ARF GTPase activator activity and phosphatidylinositol-3,4,5-trisphosphate

ARAP1 0.09154028

Domain 1 binding

SUCLG2 Succinate-CoA Ligase, GDP-Forrning, Beta Subunit succinate-CoA ligase (GDP-forrning) activity and GTP binding 0.20436256

Table 29, Type 2 Diabetes Gene Data

Table SO. Type 1 Diabetes Gene Data

Table 31. Ulcerative Colitis Gene Data

Table 32, SLE Ge e Data

Table 33, Multiple Sclerosis Relapse Remitting Gene Data

CSFNE Description Tabie 3Ϊ. Otmrnenl-.

PR CQ Protein Kinase C, Theta ubiquitin-protein ligase activity and identical protein binding 1.30E-05

SH3KBP1 j SH3-Domain Kinase Binding Protein 1 SH3 domain binding 0.00108322

PIK3R1 Phosphoinositide-3-Kinase, Regulatory Subunit 1 (Alpha) protein phosphatase binding andl-phosphatidylinositol binding 0.00132825

ADCY2 Adenylate Cyclase 2 (Brain) adenylate cyclase activity and protein heterodimerization activity 0.00228137

PPAPDC1A Phosphatidic Acid Phosphatase Type 2 Domain Containing 1A phosphatidate phosphatase activity 0.00458647

FYN j FYN Oncogene Related To SRC, FGR, YES ion channel binding and identical protein binding 0.0065531

ULBP1 U L16 Binding Protein 1 antigen binding and natural killer cell lectin -like receptor binding 0.00758094

Integrin, Alpha M (Complement Component 3 Receptor 3

heparin binding and heparan sulfate proteoglycan binding 0.00767946 Subumt)

KRAS Kirsten Rat Sarcoma Viral Oncogene Homolog GDP binding and GTP binding 0.011855 chromatin binding and methylated histone residue

RAG2;RAG1 Recombination Activating Gene 2 and 1 binding/ubiquitin-protein ligase activity and protein 0.02258078 homodimerization activity

IRS1 Insulin Receptor Substrate 1 protein kinase C binding and phospholipid binding 0.03623882 phospholipase D activity and NAPE-specific phospholipase D

PLD3 Phospholipase D Family, Member 3 0.03623882 activity

Table 34. Multiple Sclerosis iFN- β Responder Gene Data

Table 35. Neurofibromatosis

4-

Table 36a. Top Probes - Anti PD1 (Melanoma) - responders

Table 36b. Top Probes - Anti PD1 {Melanoma) - responders - probe sequences

Table 36c. Top Probes - ^■■ Anti PP1 (Melanoma) - Responders - Loci

Tabie 36d. Top Probes - Arati PD1 (Melanoma) IMon—responders

Probe_Count Percent

Gene Probe__Count HyperG__ FD Avg„

Probes Locus Total _Sig Stats HyperG | Sig CV gFC

[ PVRL1_11_119523735_119528121_119599998_

I 119609544_RF PVRLl 95 44 3.65E-05 0.003027108 j 46 5.822 0.5342187

! L12B_5 _158737480_158738689_158805563_158807407_FF \ L12B 11 7 0.011584 0.182699883 64 4.1377 0.5312333 YDS8_ 3_38139864_38141788_38192489_38194027_RR MYD88 18 10 0.009424 0.173813506 j 56 11.04 0.5207579

PVRLl J L1_119599998_119609544_119620830_119624585_FR PVRLl 95 44 3.65E-05 0.003027108 46 5.2193 0.5164066

P! 3R3_ 1_46633134_46639474_46678880_46685388_FR PI 3R3 55 24 0.005396 0.137801433 44 4.378 0.4883921

CD6_11. _60744556_60751199_60768894_60771404_RR CD6 62 33 9.60E-06 0.001593482 j 53 3.2567 0.4883341

TREM1__ 6_41229998_41238663_41295986_41297320_RR TRE 1 32 15 0.012077 0.182699883 j 47 5.1013 0.4863315

PVRL1J L1_119508824_119511197_119599998_119609544_RF PVRLl 95 44 3.65E-05 0.003027108 46 5.0377 0.4806483

I L12B_5 _158737480_158738689_158781589_158783887_FF IL12B 11 0.011584 0.182699883

⁷ 64 4.282 0.4760878

! L12A_3 _159657523_159676447_159701524_159705330 _„ FR 9 i_i i2 A. 19 10 0.015159 0.186399228 53 0.9037 0.4739570

PVRL1J L1__119599998__119609544__119640015__119642535_FR PVRLl 95 44 3.65E-05 0.003027108 46 6.073 0.4701489

CD6_11. _60768894_60771404_60785339_60793057_RF CD6 62 33 9.60E-06 0.001593482 53 3.216 0.4688561

I L12B_5 _15873748O_158738689_158805563_158807407_FR IL12B 11 7 0.011584 0.182699883 64 6.2313 0.4647946

PVRLl J L1_119539363J.19541214_119599998_119609544_.RF PVRLl 95 44 3.65E-05 0.003027108 j 46 5.525 0.4607838

I i L12A__3 _159657523_159S76447_159701524_1597Q5330_RR IL12A 19 10 j 0.015159 0.186399228 j 53 3.6993 0.4577863

Probe_ tount Percent

Gene Probe_Count HyperG__ FDR_ Avg_

Probes Locus Total __5ig Stats HyperG Sig CV logFC

PVRL1_ 11_119479071_119480091_119599998_119609544 _RF PVRLl 95 44 3.65E-05 0.003027108 46 3.7273 0.4559861

PS OCA _3_178832360_178841413_178871576_178873671_ .FF P! CA 25 13 0,006619 0.146507027 j 52 2.733 0.4452115

P!K3R3_ .1_46609200_46612260_46633134_46639474_RF PI 3R3 55 24 0.005396 0.137801433 44 10.7787 0.4435134 YD88. _3_38164895_38166955_38192489_38194027_RR MYD88 18 10 0.009424 0.173813506 56 2.8267 0.4397714

PVRL1__ 11_119570787_119575859_119599998_119609544 _RF PVRLl 95 44 3.65E-05 0.003027108 j 46 4.0827 0.4318532

CD6_11 _60744556_60751199_60768894_60771404_FR CD6 62 33 9.60E-06 0.001593482 j 53 5.8293 -0.428730

PI 3R3_ _1_46488955_46494355_46633134_46639474_RF PIK3R3 55 0.005396 0.137801433 44 2.7617 0.4271260

²⁴

PVRLL 11 _„ 119599998 _„ 119609544_119620830_119624585 _„ _FF PVRLl 95 44 3.65E-05 0.003027108 46 8.9813 0.4249957

0.4180861

PiK3R3_ _1_46633134_46639474_46662272_46666981_FR PI 3R3 55 24 0.005396 0.137801433 44 5.5577 22 j Pi OR?,.. _1_46605407_46608166_46633134_46639474_RF PI 3R3 55 24 j 0.005396 0.137801433 j 44 5.008 0.4110502

Tabie 36e. Top Probes - Anti PD1 {Melanoma] Non—responders

Tab!e 36f. Top Probes - Anti PD1 (Melanoma) Mort—responders - probe sequences

Tab!e 36f continued. Top Probes - Anti PD1 (Melanoma) f¾on-~-responders - probe sequences

Table 36g. Top Probes - Anti PD1 {Melanoma) Mon— responders ·- probes sequences and loci

o

Table 38g continued. Top Probes - Anti PDl (Melanoma) Non— responders - probes sequences and loci

I 60 mer Chr Startl Endl Startl End2 Chr Startl Endl Start2 En TCCATAGATTACTTT CAAATCATCC TTCGAAGC

T GGCGGCT GAG GG CCCG GCGCCAAG 17 40406430 40406459 40464295 40464324 17 40402460 40406459 40464295 404682

I GAGT TCAGC GT GC C GCCGGGCGT GAAAGT C GAT TGTTTATGG TT ATC CCCAGTGCCT 15 75047316 75047345 75072258 75072287 15 75043346 75047345 75072258 750762

CACCCTCCCTT CTTCCT GGGCCCTCAGATCGAC C CCCCCCACCCCC A C C G G G C T G GC T G C 19 50158040 50158069 50185426 50185455 19 50158040 50162039 50181456 50185

C CACCCCCGCC CCGGGGGAG CGCCCGGT ^' CGAG ^"

G GC C T GG C AAG AAG AC AGAAG C C G AC 8 42128692 42128721 42138741 42138770 8 42124722 42128721 42138741 421427

T AT GAGT AAT AT AG A T T T CCCCC T T CGAC C Ί C C AG GT CCCC C G C C AC T T C C AC G G C 9 123675825 123675854 12.3702717 123702746 9 123675825 12367982.4 123698747 1237027

1 C AG AA AC TGCT GG TTGGGCTC TACTT TTCGAGG

GCCAGCTCCCC GCACCC CCACCAAGC 11 64023978 64024007 64060064 64060093 11 64023978 64027977 64060064 64064

T T CCCC T G T A G T C A T T T C C T G T G A T T C G G T CACAGCTGTAGTGGGGTGGGGGGTGA 21 45665442 45665471. 45687443 45687472 21 45661472 45665471 45687443 45691 CTT GT TACT GGAATATAC GAA AAAAT C GAT G

1 T GGCGAC C GGC TGT GGGGGT C AC GGA 13 111732623 111732652 111748013 111748042 13 111728653 111732652 111748013 3 111752

Table 37a. Anti-PDl: pharmacodynamic response markers

Table 37b, Anti-PDl: pharmacodynamic response markers

Table 38a. Anii-PDl: pharmacodynamic response markers

No difference in baseline Responders and baseiine Non-Responders but show a significant change in 12 week Responder

Table 38b, Probe location ~ Anti~PDl: pharmacodynamic response markers - Mo difference in baseline Responders and baseline Won- Responders but show a significant change in 12 week Responder

Table 39

Tabie 40

Table 41. Top ALS PCR Markers

Table 42a Primer Sequences of top PGR markers

Table 42b ALS Probes - EpiS itch™ markers to stratify ALS ws. healthy controls

Table 42c ALS Probes - EprS iteh™ markers to stratify ALS vs. healthy controls: Probes

Table 42d

PGR Marker Probe GeneName HyperG_Stats P. alue FC

ALS.61.63_8 7_80060926_ 80068170_80299255_80301429_RF CD36 0.755369 0.00012 1.311151

NEAL5.213.215_8 6_149533510 _149536508_149623404_149626512. _RF TAB 2 0.955552 0.00568 0.915476

NEALS.101.103_3 12_54983093 _54985391_55002281_55007763_RR GLYCAM 1 0.058173 0.000625 0.765425

NEALS.249.251_4 17_73313347 _73315473_73407153_73409693_FR GRB2 0.979786 0.071514 0.946013

NEALS.45.47_8 6 .112058283 _112061400_112189648_112191961_ .RR FYN 0.013161 0.000281 0.71137

ALS.49,51__4 1_198660142 _198665086_198737979_198744955_ RR PTPRC 0.015074 0.000819 1.265845

NEALS.65.67_8 1_172053648 _172060321_172083100_172087823_ .RR DNM3 0.000673 0.000692 0.710146

NEALS.97.99_16 8_42121759_ 42128721_42138740_42142593_FR !KB B 0.046252 0.000338 0.752995

DNM3.5.7_16 1_171936106 _171939290_171988822_171992948_ RF DNM3 0.000673 0.002108 1.354848

CLIC4 1.3_1 1__251068 1_ 25109990_25142389_25144224_RF CLIC4 0.616734 0.012406 1.389081

Table 43

Table 44

Responder Versus IMon Responder

Array Interaction PCR Primer 1 Sequence I PCR Primer 2 Sequence

>I L15_4_142530356_142539177_142656375_142659066_RF TGAGTAACACAAAGCATCTG [ AG TG AC TG G CT ATGTTC C

>MYD88_3_38139864_38141788_38192489_38194027_RR CTGGTGATTTGTGTGACTTTG AGGGAAGATGTGGAGGAG

>HLA-DQB1_6_32607972_32614493_32630138_32632737_RR GTACGACTCCAGCCAAATG G CTGTCTGTTACTAGATTG CAC

>I L12B_5_158737480_158738689_158781589_158783887_FF ACCTTGCAAGAAGCACAG ATG ATACTTCCC A ACTG AC AC

>PVRL1_11_119599998_119609544_119620830_119624585_FR AG GAG CATCCATATCAAGTG CTGCCATGTCTGACTATCC

>PIK3R3_1_46633134_46639474_46678880_46685388_FR CAGTGAAGAAGCCATCATCG GCTTAGAGAAATACACCAGCAG

>CD6_11_60744556_60751199_60768894_60771404_RR ATGGGCAGCATTTCTCAC I AGGGACGATTTATATGACTTGC

Responder Bass ;line Versus 12WKS

Array Interaction PCR Primer 1 Sequence PCR Primer 2 Sequence

>STAT5B_17_40403935_40406459_40464294_40468456_FR GTGCTGGTATGTACCTGTAATC j GAGGGTTGAGAAGCATCTTG

Table 45

Table 46a

Table 46c

Table 46d

Table 47

Table 48. P-values

Table 49 Data for additional anti-PD1 probe