Chromosome marker test

Abstract

A method for analysing chromosome interactions relating to canine cancer wherein the chromosome interactions relate to either a class of cancer or a specific cancer. The invention allows use of a liquid biopsy method.

Description

[0001] CHROMOSOME MARKER TEST Field of the InventionThe invention relates to assessing canine health by a liquid biopsy method.Background of the Invention The dog has been selectively bred over millennia for various behaviours, sensory capabilities, and physical attributes. Dog breeds vary widely in shape, size, and colour. They perform many roles for humans, such as hunting, herding, pulling loads, protection, assisting police and the military, companionship, therapy, and aiding disabled people. However selective breeding of dogs has also led to them being susceptible to particular disease conditions, such as cancer. Cancer is normally diagnosed based on various protein or cell markers or by imaging techniques. It is standard procedure to obtain biopsy samples from the site of the suspected tumour. Summary of the InventionThe invention allows determination of the cancer status of a canine using a systemic blood-based profileof a chromosome conformation signature (3D genomic) state. This avoids the need for biopsy profilingbased on biopsy sampling from the site of the tumour, i.e. the invention provides non-invasive blood-based readouts. In particular, the invention allows blood-based detection and distinction betweenimportant classes of cancer, such as sarcoma and melanoma, which is difficult to do. The new approachof the invention to liquid biopsy results in a test of high sensitivity and specificity.The inventors have also investigated the role of chromosome interactions in canines and the relationship of these interactions to cancer. This work went beyond a simple understanding of how any one specific chromosome interaction leads to cancer, and instead investigated how chromosome interactions relate to both classes of cancers and also to specific individual cancers. The work identified chromosome interactions which could be typed together to arrive at a high performance test.The work allows class of the cancer to be identified and / or the specific cancer to be identified. In thetest improved accuracy can be achieved by typing both (i) chromosome interactions which are specific for a class of cancer as well as (ii) chromosome interactions which are specific for an individual cancer. The work of the inventors thus identifies chromosome conformation signatures that are relevant tocancer. The work elucidates the role of this modality (the 3D architecture of chromosomes) in cancerallows a ‘readout’ of the chromosome interactions present or absent in cancer. The work thus providesa diagnostic test for cancer in canines. Accordingly, the invention provides a method of detecting a chromosome state associated with cancer in a canine comprising: (a) detecting the presence or absence of at least 5 chromosome interactions as shown in any of tables 2, 3 or 9 to determine the class of cancer to be a lymphoma or sarcoma, or to determine the specific cancer to be malignant melanoma, and(b) in the case where the at least 5 chromosome interactions detected in (a) are from table 2 thenadditionally detecting the presence or absence of at least 5 chromosome interactions as shown in any oftable 4 or 5 to determine the specific cancer to be a diffuse large B cell lymphoma or T-zone lymphoma,and(c) in the case where the at least 5 chromosome interactions detected in (a) are from table 3 thenadditionally detecting the presence or absence of at least 5 chromosome interactions as shown in any of tables 6, 7 or 8 to determine the specific cancer to be hemangiosarcoma, histiocytic sarcoma or osteosarcoma. The invention also provides a method of detecting a systemic blood-based profile of a chromosome conformation signature associated with cancer in a canine comprising:(a) detecting the presence or absence of at least 5 chromosome interactions as shown in any of tables 2,3 or 9 to determine the class of cancer to be a lymphoma or sarcoma, or to determine the specific cancer to be malignant melanoma, and(b) in the case where the at least 5 chromosome interactions detected in (a) are from table 2 thenadditionally detecting the presence or absence of at least 5 chromosome interactions as shown in any oftable 4 or 5 to determine the specific cancer to be a diffuse large B cell lymphoma or T-zone lymphoma,and (c) in the case where the at least 5 chromosome interactions detected in (a) are from table 3 then additionally detecting the presence or absence of at least 5 chromosome interactions as shown in any of tables 6, 7 or 8 to determine the specific cancer to be hemangiosarcoma, histiocytic sarcoma or osteosarcoma, wherein said detecting in (a), (b) or (c) is in a blood sample from the canine. Brief Description of the DrawingsFigure 1 shows performance of the EpiSwitch array-based biomarker classifier for calling presence oflymphoma as a class, based on common systemic markers shared by DLBCL and T-Zone lymphoma across 1 million data point profiles of the 3D genomic architecture. Confusion matrix and test performance statistics for the 37-marker classifier on the 35 canines in the test cohort.Figure 2 shows performance of the EpiSwitch array-based biomarker classifier for calling presence ofsarcomas as a class, based on common systemic markers shared by all three sarcomas across 1 million data point profiles of the 3D genomic architecture. Confusion matrix and test performance statistics for the 100-marker classifier on the 40 canines in the test cohort.Figure 3 shows performance of the EpiSwitch array-based biomarker classifier for calling presence ofDLBCL in the validation cohort. Confusion matrix and test performance statistics for the multi-choice classifier against healthy controls (A) and the full validation cohort of 56 samples, including 2 lymphomas, threes sarcomas and melanoma.Figure 4 shows performance of the EpiSwitch array-based biomarker classifier for calling presence of T-Zone Lymphoma in the validation cohort. Confusion matrix and test performance statistics for the multi-choice classifier against healthy controls (A) and the full validation cohort of 56 samples, including 2 lymphomas, threes sarcomas and melanoma.Figure 5 shows performance of the EpiSwitch array-based biomarker classifier for calling presence ofHemangiosarcoma in the validation cohort. Confusion matrix and test performance statistics for the multi-choice classifier against healthy controls (A) and the full validation cohort of 56 samples, including 2 lymphomas, threes sarcomas and melanoma.Figure 6 shows performance of the EpiSwitch array-based biomarker classifier for calling presence ofHistiocytic Sarcoma in the validation cohort. Confusion matrix and test performance statistics for the multi-choice classifier against healthy controls (A) and the full validation cohort of 56 samples, including 2 lymphomas, threes sarcomas and melanoma.Figure 7 shows performance of the EpiSwitch array-based biomarker classifier for calling presence ofOsteosarcoma in the validation cohort. Confusion matrix and test performance statistics for the multi- choice classifier against healthy controls (A) and the full validation cohort of 56 samples, including 2 lymphomas, threes sarcomas and melanoma.Figure 8 shows performance of the EpiSwitch array-based biomarker classifier for calling presence ofMelanoma in the validation cohort. Confusion matrix and test performance statistics for the multi- choice classifier against just healthy controls (A) and the full cohort of 56 samples, including 2 lymphomas, threes sarcomas and melanoma.Figure 9 shows the 3C / EpiSwitch method for detecting the presence or absence of chromosomeinteractions. Description of the TablesTable 1 – list of the samples used in the inventors’ workTable 2 – data and probes for the lymphoma classTable 3 – data and probes for the sarcoma classTable 4 – data and probes for DLBCLTable 5 – data and probes for T-Zone lymphomaTable 6 – data and probes for hemangiosarcomaTable 7 – data and probes for histiocytic sarcomaTable 8 – data and probes for osteosarcomaTable 9 – data and probes for malignant melanoma (which is a specific cancer that stratifies as aseparate class from lymphoma and sarcoma)Tables 10 – dog breeds in the USTable 11 – dog breeds in the UKTable 12 - shows mapping of the top 100 systemic 3D genomic common sarcoma markers to biologicalpathways. This includes analysis of the top 3D genomic markers common between hemangiosarcoma, histiocytic sarcoma and osteosarcoma. Detailed Description of the Invention Terms Used Herein Any aspect of the invention may be carried out on a liquid biopsy sample, preferably a blood sample. The method of the invention may be referred to as the ‘process’ of the invention herein. The chromosome interactions which are typed may be referred to as ‘markers’, ‘CCS’, ‘chromosomeconformation signature’, ‘epigenetic interaction’ or ‘EpiSwitch markers’ herein.The word ‘type’ will be interpreted as per the context, but will usually refer to detection of whether aspecific chromosome interaction is present or absent. The typing will generally be by physicaldetermination of whether the chromosome interaction is present.The chromosome interactions which are typed in the method of the invention are defined in tables 2 to9. They are defined by means of the probe sequences which detect the ligated product made by anEpiSwitch method (see Figure 9). They are also defined by the chromosome position numbers of theinteraction which are within the probe name. The probe sequences and the chromosome positionnumbers represent separate exact ways of defining any specific chromosome interaction. Therefore anyspecific chromosome interaction can be defined in any of the following 3 ways:- the chromosome interaction that is shown in table ‘X’- the chromosome interaction represented by a ‘specific probe sequence’- the chromosome interaction which is formed by chromosome regions ‘X to X’ and ‘X to X’ comingtogether.The term ‘group’ herein relates to a specific set of individuals which either do or don’t have a cancerdefined by the relevant class of cancer or by the identity of the specific cancer. The term ‘cancer status’ refers to the presence or absence of the relevant cancer. The method of the invention preferably results in a diagnosis of a specific cancer. The method can result in diagnosis of a class of cancer. The method can result in the obtaining of information relating to the cancer which is present in the canine, for example information which can assist diagnosis. Thus in one aspect the invention provides a method of diagnosing a cancer, for example a class of cancer or specific cancer, such as defined herein. The chromosome interactions which are relevant to melanoma which are disclosed herein are specific to malignant melanoma. These stratify as a separate class, but are defined as a ‘specific cancer’ herein.The Chromosome Interactions Relevant to the InventionThe chromosome interactions which are typed in the invention are typically interactions between distalregions of a chromosome, said interactions being a stable readout of the state of the region of thechromosome. That state will reflect the cancer status of the canine. The chromosome interaction may, for example, reflect if it is being transcribed or repressed.Chromosome interactions which are specific to ‘groups’ as defined herein have been found to be stable,thus providing a reliable means of measuring the differences between groups (for example the presenceof a specific cancer). Chromosome interactions specific to a group will normally be present in the early stages of a disease process, for example compared to other epigenetic markers such as methylation or changes to binding of histone proteins. Thus the process of the invention is able to provide valuable information about thecancer at an early stage. This allows early intervention (for example treatment) which as a consequencewill be more effective and also allows early choices to be made of the type of treatment which isappropriate for the patient, and which treatments should not be used. Chromosome interactions also reflect the current state of the individual and therefore can be used to assess changes to disease status. Furthermore there is little variation in the relevant chromosome interactions between individuals within the same group. The chromosome interactions which are detected in the invention could be impacted 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 DNA interactions. However it must be borne in mind that chromosome interactions as defined herein are a regulatory modality in their own right and do not have a one to one correspondence with any genetic marker (DNA sequence change) or any other epigenetic marker.The changes which lead to the chromosome interactions may be impacted by changes to the underlyingnucleic acid sequence which themselves do not directly affect a gene product or the mode of geneexpression. Such changes may be for example, SNPs within and / or outside of the genes, gene fusions and / or deletions of intergenic DNA, microRNA, and non-coding RNA. For example, it is known that roughly 20% of SNPs are in non-coding regions, and therefore the process as described is also informative in non-coding situation. Typically 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 on the same chromosome. The Chromosome Interactions Which are Typed The invention provides a method in which chromosome interactions are typed corresponding to (i) a class of cancer of cancer and / or (ii) an individual specific cancer. The class of cancer is preferablylymphoma or sarcoma. The individual specific cancer is preferably melanoma (i.e. malignant melanoma herein), diffuse large B cell lymphoma, T-zone lymphoma, hemangiosarcoma, histiocytic sarcoma or osteosarcoma. Typically at least 3, 5, 10, 20, 25 or 30 chromosome interactions are typed from Table 2 or at least all ofthe chromosome interactions of Table 2 are typed.Typically at least 5, 10, 20, 30, 40, 50 or 80 chromosome interactions are typed from Table 3 or at leastall of the chromosome interactions of Table 3 are typed. Typically at least 3, 5, 8, 10, 12 or 15 chromosome interactions are typed from Table 4 or at least all of the chromosome interactions of Table 4 are typed. Typically at least 2, 3, 4 or 5 chromosome interactions are typed from Table 5 or at least all of the chromosome interactions of Table 5 are typed. Typically at least 2, 3, 4, 5 or 8 chromosome interactions are typed from Table 6 or at least all of the chromosome interactions of Table 6 are typed. Typically at least 3, 5, 8, 10, 12 or 14 chromosome interactions are typed from Table 7 or at least all of the chromosome interactions of Table 7 are typed. Typically at least 3, 5, 8, 10 or 12 chromosome interactions are typed from Table 8 or at least all of the chromosome interactions of Table 8 are typed. Typically at least 2, 3, 4 or 5 chromosome interactions are typed from Table 9 or at least all of the chromosome interactions of Table 9 are typed.In the method chromosome interactions relating to 1, 2 or 3 classes of cancer may be typed. At least 10,20, 30, 40, 50 or 60 chromosome interactions may typed selecting from all of tables 2 to 9.In the method at least the first 10 chromosome interactions (i.e. at the top of the table) may be typedfrom any of tables 2, 3, 4 or 7.In the method at least the first 20 chromosome interactions (i.e. at the top of the table) may be typedfrom any of tables 2 or 3.The chromosome interactions may be typed in any order in time, and for example chromosome interactions relating to a specific cancer may be typed before typing chromosome interactions relating to the associated class. The typing of chromosome interactions may be done in a way in which the results of earlier chromosome interaction typings impact which further chromosome interaction typings are performed, for example in some form of ‘decision tree’. Preferably when chromosome interactions are typed for a class of cancer then chromosome interactions are also typed for at least one specific cancer within that class. The method may comprise: (a) detecting the presence or absence of chromosome interactions as shown in any of tables 2, 3 or 9 todetermine the class of cancer to be a lymphoma or sarcoma or determining the specific cancer to bemalignant melanoma, and (b) in the case where there are chromosome interactions detected in (a) from table 2 then additionally detecting the presence or absence of chromosome interactions as shown in any of table 4 or 5 to determine the specific cancer to be a diffuse large B cell lymphoma or T-zone lymphoma, and(c) in the case where there are chromosome interactions detected in (a) from table 3 then additionallydetecting the presence or absence of at least 5 chromosome interactions as shown in any of tables 6, 7 or 8 to determine the specific cancer to be a hemangiosarcoma, histiocytic sarcoma or osteosarcoma.A chromosome interaction specific for a class of cancer is shared across the class by individual specificcancers within the class, and not shared within other classes of cancer. A chromosome interactionspecific for an individual cancer is not present within other specific cancers within the class. Preferablyin the method there is a first step in which chromosome interaction common within a class are typed asdifferentiator against other classes, and as a second step chromosome interactions unique to a specificcancer within the class are typed as differentiator of an individual cancer against others within the class.The Process of the InventionThe process of the invention comprises a typing system for detecting chromosome interactions relatingto cancer status. Any suitable typing method can be used, for example a method in which the proximityof the chromosomes in the interaction is detected and / or in which a marker that reflects chromosomeinteraction status is detected. The typing method may be performed using the EpiSwitch™ systemmentioned herein, which for example may be carried out by a method comprising the following steps(for example on DNA and / or a sample from the subject):(i) cross-linking regions of chromosome which have come together in a chromosome interaction; (ii) optionally isolating the cross-linked DNA;(iii) subjecting the cross-linked DNA to cleavage; and(iv) ligating the nucleic acids present in the cross-linked entity to derive ligated nucleic acids withsequence from both the regions which formed a chromosomal interaction. Detection of this ligated nucleic acid allows determination of the presence or absence of a particular chromosome interaction. The ligated nucleic acid therefore acts as a marker for the presence of thechromosome interaction. Preferably the ligated nucleic acid is detected by PCR or a probe basedmethod, including a qPCR method.In the method the chromosomes can be cross-linked by any suitable means, for example by a cross- linking agent, which is typically a chemical compound. In a preferred aspect, the interactions are cross- linked using formaldehyde, but may also be cross-linked by any aldehyde, or D-Biotinoyl-e- aminocaproic acid-N-hydroxysuccinimide ester or Digoxigenin-3-O-methylcarbonyl-e-aminocaproic acid- N-hydroxysuccinimide ester. Para-formaldehyde can cross link DNA chains which are 4 Angstroms apart. Preferably the chromosome interactions are on the same chromosome. Typically the chromosomeinteractions are 2 to 10 Angstroms apart.The cross-linking is preferably in vitro. The cleaving is preferably by restriction digestion with an enzyme,such as TaqI. The ligating may form DNA loops.Where PCR (polymerase chain reaction) is used to detect or identify the ligated nucleic acid, 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. Where a probe is used to detect or identify the ligated nucleic acid, this is generally by Watson-Crickbased base-pairing between the probe and ligated nucleic acid. When detection is performed using aprobe, typically sequence from both regions of the probe (i.e. from both sites of the chromosome interaction) could be detected. In preferred aspects probes are used in the process which comprise or consist of the same or complementary sequence to a probe shown in any table. Probe sequences as shown in any table herein may be used, for example the probe sequences shown intables 2 to 9 (corresponding to the chromosome interaction which is being detected). Homologues ofsuch probe sequences may also be used, which can have at least 70% identity to the original sequence. Typing according to the process of the invention may be carried out at multiple time points, for example to monitor the progression of the disease. This may be at one or more defined time points, for example at at least 1, 2, 5, 8 or 10 different time points. The durations between at least 1, 2, 5 or 8 of the time points may be at least 5, 10, 20, 50, 80 or 100 days. Typically there are 3 time points at least 50 days apart.The Individual to Tested and / or TreatedThe individual who is tested in the method of the invention is a canine (or dog), preferably of any breedshown in table 1, 10 or 11.The individual that is tested in the process of the invention may have been selected in some way, forexample based on a risk factor, symptom or physical characteristic. The individual may have beenselected based on having a symptom of cancer and / or or being in the early stages of cancer.The individual may be susceptible to any cancer mentioned herein and / or may be in need of any therapy mentioned in. The individual may be receiving any therapy mentioned herein. In particular, theindividual may have, or be suspected of having, cancer, for example any specific cancer mentionedherein. The Data in the Tables Provided HereinTables 2 to 9 show specific chromosome interaction markers which can be used to detect cancer status,for example the class of cancer or the specific cancer. Their presence or absence can be used in such adetection (i.e. they are ‘disseminating’ markers).The markers are defined using probe sequences (which detect a ligated product as defined herein).The following information is provided in the probe data table:- logFC: logarithm base 2 of Epigenetic Ratio (FC)- AveExpr: average log2-expression for the probe over all arrays and channels- T: moderated t-statistic- p-value: raw p-value- adj. p-value: adjusted p-value or q-value- B-statistic (lods or B) is the log-odds that that gene is differentially expressed- FC: (class1 / class2), average computed fold change- RP / RSum – Rank sum.The FC indicates prevalence of marker in each comparison, 2 means twice over average test, 1.5 means1.5 over the average test, etc., and so FC indicates the weight of a marker to phenotype / group. The FCvalue can be used to give an indication of how many markers are needed for a highly effective test. The probes are designed to be 30bp away from the Taq1 site. In case of PCR, PCR primers are typicallydesigned to detect ligated product but their locations from the Taq1 site vary. Probe locations:Start 1 - 30 bases upstream of TaqI site on fragment 1End 1 - TaqI restriction site on fragment 1Start 2 - TaqI restriction site on fragment 2End 2 - 30 bases downstream of TaqI site on fragment 2The Approach Taken to Identify Markers and Panels of Markers The invention described herein relates to chromosome conformation profile and 3D architecture as a regulatory modality in its own right, closely linked to the phenotype. The discovery of biomarkers was based on annotations through pattern recognition and screening on representative cohorts of clinical samples representing the differences in phenotypes. We annotated and screened significant parts of thegenome, across coding and non-coding parts and over large sways of non-coding 5’ and 3’ of knowngenes for identification of statistically disseminating consistent conditional disseminating chromosome conformations, which for example anchor in the non-coding sites within (intronic) or outside of open reading frames.Selection of the best markers was driven by statistical data and p values for the marker leads. Selectedand validated chromosome conformations within the signature are disseminating stratifying entities in their own right, irrespective of the expression profiles of the genes used in the reference. Further work may be done on relevant regulatory modalities, such as SNPs at the anchoring sites, changes in gene transcription profiles, changes at the level of H3K27ac. The question of clinical phenotype differences and their stratification is looked at from the basis offundamental biology and epigenetic controls over phenotype - including for example from theframework of network of regulation. As such, to assist stratification, one can capture changes in the network and it is preferably done through signatures of several biomarkers, for example through following a machine learning algorithm for marker reduction which includes evaluating the optimalnumber of markers to stratify the testing cohort with minimal noise. This may end with 3-20 markers.Selection of markers for panels may be done by cross-validation statistical performance (and not for example by the functional relevance of the neighbouring genes, used for the reference name). A panel of markers (with names of adjacent genes) is a product of clustered selection from the screening across significant parts of the genome, in non-biased way analysing statistical disseminating powers over 14,000-60,000 annotated EpiSwitch sites across significant parts of the genome. It should not beperceived as a tailored capture of a chromosome conformation on the gene of know functional value forthe question of stratification. The total number of sites for chromosome interaction in a canine are 1.13million, and so the potential number of combinations is 1.13 million to the power 1.13 million. Theapproach that we have followed nevertheless allows the identifying of the relevant chromosome interactions. The specific markers that are provided by this application have passed selection, being statistically(significantly) associated with the condition or subgroup. This is what the data in the relevant tabledemonstrates. Each marker can be seen as representing an event of biological epigenetic as part of network deregulation that is manifested in the relevant condition. In practical terms it means that these markers are prevalent across groups of patients when compared to controls. On average, as an example,an individual marker may typically be present in 80% of the relevant responder group and in 10% ofcontrols, and therefore the results of the testing by the method of the invention is straightforward to interpret and essentially amounts to a ‘binary readout’. Simple addition of all markers would not directly represent the network interrelationships between some of the deregulations. This is where the standard multivariate biomarker analysis GLMNET (Rpackage) can be brought in. GLMNET package helps to identify interdependence between some of themarkers, that reflect their joint role in achieving deregulations leading to disease phenotype. Modellingand then testing markers with highest GLMNET scores offers not only identify the minimal number ofmarkers that accurately identifies the patient cohort, but also the minimal number that offers the least false positive results in the control group of patients, due to background statistical noise of low prevalence in the control group. Typically a group (combination) of selected markers (such as 3 to 11) offers the best balance between both sensitivity and specificity of detection, emerging in the context of multivariate analysis from individual properties of all the selected statistical significant markers for the condition. The tables herein show the reference names for the array probes for array analysis that overlaps the juncture between the long range interaction sites, the chromosome number and the start and end oftwo chromosomal fragments that come into juxtaposition.Samples and Sample Treatment The process of the invention will normally be carried out on a sample. The sample may be obtained at a defined time point, for example at any time point defined herein. The sample will normally contain DNA from the individual. It will normally contain cells. In one aspect a sample is obtained by minimally invasive means, and may for example be a blood sample. DNA may be extracted and cut up with a standard restriction enzyme. This can pre-determine which chromosome conformations are retained and will be detected with the EpiSwitchTMplatforms. 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. The sample may be a liquid biopsy sample, preferably a blood sample. The liquid biopsy sample is preferably not from the site of the suspected tumour. Preferred Aspects for Sample Preparation and Chromosome Interaction DetectionMethods of preparing samples and detecting chromosome conformations are described herein.Optimised (non-conventional) versions of these processes can be used, for example as described in this section. Typically the sample will contain at least 2 x105cells. The sample may contain up to 5 x105cells. In one aspect, the sample will contain 2 x105to 5.5 x105cells. 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 aspects, 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, DNA restriction is performed at about 55oC to about 70oC, such as for about 65oC, 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 aspect, 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 aspect of the EpiSwitch process a DNA precipitation step is not performed between the DNArestriction 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 aspect after digestion of the protein, for example Proteinase K digestion, there is no cross-link reversal or phenol DNA extraction step. In one aspect PCR detection is capable of detecting a single copy of the ligated nucleic acid, preferably with a binary read-out for presence / absence of the ligated nucleic acid.Figure 9 shows a preferred process of detecting chromosome interactions.Processes and Uses of the InventionThe process of the invention can be described in different ways. It can be described as a process ofmaking one or more ligated nucleic acids comprising (i) in vitro cross-linking of chromosome regionswhich 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 one ormore ligated nucleic acids, wherein optionally detection of the ligated nucleic acid may be used todetermine the chromosome state at a locus. HomologuesHomologues of polynucleotide / nucleic acid (e.g. DNA) sequences are referred to herein. Suchhomologues typically have at least 70% homology, preferably at least 80%, at least 85%, at least 90%, atleast 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 aspect, homologues of polynucleotide / nucleic acid (e.g. DNA) sequences are referred to herein by reference to percentage 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. The homologues may have at least 70% sequence identity, preferably at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least98% or at least 99% sequence identity across the entire probe, primer or primer pair.For example the UWGCG Package provides the BESTFIT program which can be used to calculatehomology 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 Evol 36:290-300; Altschul, S, F et al (1990) J MoI 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 isreferred 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 (W) of 11, the BLOSUM62 scoring matrix (see Henikoff and Henikoff (1992) Proc. Natl. Acad. Sci. USA 89: 10915-10919) alignments (B) of 50, expectation (E) of 10, M=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., Karlin 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 % percentage sequence identity. Homology of a ‘pair of primers’ can be calculated, for example, by considering the two sequences as a single sequence (as if the two sequences are joined together) for the purpose of then comparing against the another primer pair which again is considered as a single sequence. The Threshold of Detection The markers which are disclosed herein have been found to be ‘disseminating markers’ capable ofdetermining cancer status.In practical terms it means that these markers are prevalent across the relevant cancer group whencompared to controls (as is shown by the FC value, for example). On average, as an example, anindividual marker may typically be present in 80% of the relevant cancer group and in 10% of controls.When testing an individual the result will be a combination of ‘present’ and ‘absent’ chromosomeinteractions for each of the markers shown in tables 2 to 9 allowing determination of the cancer status.Therapeutic Agents and TreatmentsThis section relates to therapies which may be given to individuals based on the results of the testingmethod of the invention. In particular therapies are disclosed for the different types of cancer which aredetected in the method of the invention. The invention includes selection of such therapies and / oradministering of such therapies. The therapy may be a substance, for example a therapeutic molecule,drug or cell. The therapy may be any other treatment regimen, including for example surgery or radiation therapy. The invention provides therapeutic agents for use in preventing or treating any condition mentioned herein. 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 ortreat the condition, for example in individuals tested by the method of the invention. 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 an 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.The invention provides an anti-cancer agent, preferably selected from any of the anti-cancer agentsmentioned herein, for use in a method of treating an individual identified as having cancer (or a cancerassociated chromosome state), optionally said method comprising:- identifying whether an individual has a chromosome state associated with cancer by the method of theinvention, and- administering to any individual identified as having a chromosome state associated with cancer ananti-cancer agent. Canine lymphomas are typically treated with chemotherapy agents, most commonly using the CHOP protocol involving the chemotherapy agents cyclophosphamide, hydroxydaunorubicin (doxorubicin), vincristine, prednisone. The following chemotherapy agents can also be used individually or in combination; doxorubicin, prednisone, L-asparaginase (L-spar), CCNU (Lomustine), mitoxantrone, and Tanovea (rabacfosadine). Chemotherapy can be performed in conjunction with half-body radiation therapy or bone-marrow transplants. Hemangiosarcoma in dogs is most commonly treated by surgical removal of the tumour. Chemotherapy using hydroxydaunorubicin (doxorubicin) and radiation therapy can also be used for treatment either in combination with surgery or for palliative care. Treatment for canine histiocytic sarcoma if localised is typically performed by excision or amputation of the skin, bone or joint which can then be followed by chemotherapy using Lomustine (CCNU), Doxorubicin or Zoledronate (bisphosphonate). For dogs with disseminated histiocytic sarcoma, chemotherapy is the primary treatment as well as radiation therapy. Radiation therapy and prednisone can be used, typically for palliative treatment. Treatment of canine osteosarcoma often involves surgical removal of the affected bone of limb. This can be done in conjunction with chemotherapy, using platinum-based agents (cisplatin or carboplatin) or doxorubicin, or radiation therapy. Bisphosphate drugs (Zoledronate) can be used to inhibit bone destruction and decrease pain. For canine melanoma surgery to remove tumours is the most common option. Radiation therapy can be used to shrink tumours before surgery. Chemotherapy is not commonly used, however carboplatin based chemotherapy can be used for oral cases of melanoma after operation. Oncept xenogeneic DNA vaccination immunotherapy is a new treatment option. Lymphoma (Diffuse large B cell lymphoma (DLBCL) and T-Zone lymphoma) Most common cancer in Golden retrievers. Treatment is generally palliative, but treatment is used for dogs to improve survival time and quality of life.- CHOP chemotherapy protocol- cyclophosphamide, hydroxydaunorubicin (doxorubicin), vincristine,prednisone is most common. Doxorubicin and prednisone can be used in monotherapy.- Other chemotherapy treatments can be used in combination or as single use, including L-asparaginase(L-spar), CCNU (Lomustine), mitoxantrone, and Tanovea (rabacfosadine).- Chemotherapy can be performed in conjunction with half-body radiation or bone-marrow transplant.- Verdinexor recently FDA approved for treating canine cancer.Hemangiosarcoma- Surgical removal of tumour is the most common treatment.- Chemotherapy using hydroxydaunorubicin (doxorubicin) alone or with other drugs.- Radiation therapy.Histiocytic sarcoma Most common in dog breeds; Flat-coated retrievers, Miniature schnauzers, Rottweilers, Labrador retrievers, Pembroke Welsh corgis and Golden retrievers.- Surgery for cases where the tumours are localised on the skin, bone or joint which can then befollowed by chemotherapy.- Chemotherapy using Lomustine (CCNU), Doxorubicin or Zoledronate (bisphosphonate) or combinationsof these drugs can be used for disseminated subtypes.- Radiation therapy, often for palliative treatment.- Prednisone can be used for palliative treatment.Osteosarcoma- Surgical removal of diseased bone.- Standard is chemotherapy using platinum-based chemotherapy agents (cisplatin or carboplatin) ordoxorubicin.- Bisphosphate drugs (Zoledronate) to inhibit bone destruction and decrease pain.- Stereotactic radiation therapy (more invasive) and high-dose radiation therapy.Melanoma (Malignant Melanoma)Mostly presents as oral melanoma and is more common in cocker spaniels, chow chows, Scottish terriers, poodles, golden retrievers and dachshunds.- Surgery to remove tumours is the most common option.- Radiation therapy can be used to shrink tumours before surgery.- Chemotherapy is not commonly used.- Carboplatin based chemotherapy for oral cases of melanoma after operation.- Oncept® Xenogeneic DNA vaccination immunotherapy used in conjunction with surgery for oralmelanoma. Labelled Nucleic Acids and Pattern of Hybridisation The nucleic acids mentioned herein may be labelled, preferably using an independent label such as a fluorophore (fluorescent molecule) or radioactive label which assists detection of successful hybridisation. Certain labels can be detected under UV light. 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. They 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 all of the 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 provides a kit for carrying out any process of the invention, including detection of a chromosomal interaction relating to prognosis. 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 ofamplifying the ligated nucleic acid in a PCR reaction. Preferred agents include any of the specific primersand probes disclosed herein and / or homologues of such primers and probes. The invention 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. Detection Process In one aspect quantitative detection of the ligated sequence which is relevant to a chromosome interaction is carried out using a probe which is detectable upon activation during a PCR reaction, wherein said ligated sequence comprises sequences from two chromosome regions that come togetherin an epigenetic chromosome interaction, wherein said process comprises contacting the ligatedsequence with the probe during a PCR reaction, and detecting the extent of activation of the probe, andwherein said probe binds the ligation site. The process typically allows particular interactions to bedetected in a MIQE compliant manner using a dual labelled fluorescent hydrolysis probe. The probe is generally labelled with a detectable label which has an inactive and active state, so that it is only detected when activated. The extent of activation will be related to the extent of template (ligation product) present in the PCR reaction. Detection may be carried out during all or some of the PCR, for example for at least 50% or 80% of the cycles of the PCR. The probe can comprise a fluorophore covalently attached to one end of the oligonucleotide, and a quencher attached to the other end of the nucleotide, so that the fluorescence of the fluorophore is quenched by the quencher. In one aspect the fluorophore is attached to the 5’end of the oligonucleotide, and the quencher is covalently attached to the 3’ end of the oligonucleotide.Fluorophores that can be used in the process of the invention include FAM, TET, JOE, Yakima Yellow,HEX, Cyanine3, ATTO 550, TAMRA, ROX, Texas Red, Cyanine 3.5, LC610, LC 640, ATTO 647N, Cyanine 5, Cyanine 5.5 and ATTO 680. Quenchers that can be used with the appropriate fluorophore include TAM, BHQ1, DAB, Eclip, BHQ2 and BBQ650, optionally wherein said fluorophore is selected from HEX, Texas Red and FAM. Preferred combinations of fluorophore and quencher include FAM with BHQ1 and Texas Red with BHQ2. Use of the Probe in a qPCR Assay Hydrolysis probes of the invention are typically temperature gradient optimised with concentration matched negative controls. Preferably single-step PCR reactions are optimized. More preferably a standard curve is calculated. An advantage of using a specific probe that binds across the junction of the ligated sequence is that specificity for the ligated sequence can be achieved without using a nested PCR approach. The processes described herein allow accurate and precise quantification of low copy number targets. The target ligated sequence can be purified, for example gel-purified, prior to temperature gradient optimization. The target ligated sequence can be sequenced. Preferably PCR reactions are performed using about 10ng, or 5 to 15 ng, or 10 to 20ng, or 10 to 50ng, or 10 to 200ng template DNA. Forward and reverse primers are designed such that one primer binds to the sequence of one of the chromosome regions represented in the ligated DNA sequence, and the other primer binds to other chromosome region represented in the ligated DNA sequence, for example, by being complementary to the sequence. Choice of Ligated DNA TargetThe invention includes selecting primers and a probe for use in a PCR process as defined hereincomprising selecting primers based on their ability to bind and amplify the ligated sequence and selecting the probe sequence based properties of the target sequence to which it will bind, in particular the curvature of the target sequence. Probes are typically designed / chosen to bind to ligated sequences which are juxtaposed restriction fragments spanning the restriction site. In one aspect of the invention, the predicted curvature of possible ligated sequences relevant to a particular chromosome interaction is calculated, for example using a specific algorithm referenced herein. The curvature can be expressed as degrees per helical turn, e.g.10.5° per helical turn. Ligated sequences are selected for targeting where the ligated sequence has a curvature propensity peak score of at least 5° per helical turn, typically at least 10°, 15° or 20° per helical turn, for example 5° to 20° per helical turn. Preferably the curvature propensity score per helical turn iscalculated for at least 20, 50, 100, 200 or 400 bases, such as for 20 to 400 bases upstream and / ordownstream of the ligation site. Thus in one aspect the target sequence in the ligated product has any of these levels of curvature. Target sequences can also be chosen based on lowest thermodynamic structure free energy.Disclosure in Publications and Priority ApplicationsThe contents of all publications mentioned herein are incorporated by reference into the presentspecification and may be used to further define the features relevant to the invention. The contents ofall priority applications are incorporated by reference into the present specification and may be used to define the features relevant to the invention. Techniques Used to Identify the Specific Relevant Chromosome InteractionsThe EpiSwitch™ platform technology detects epigenetic regulatory signatures of regulatory changesbetween normal and abnormal conditions at loci. The EpiSwitch™ platform identifies and monitors thefundamental 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 uniqueset of advantages against biomarker platforms that utilize late epigenetic and gene expressionbiomarkers, such as DNA methylation and RNA profiling.EpiSwitch™ Array AssayThe custom EpiSwitch™ array-screening platforms come in 4 densities of, 15K, 45K, 100K, and 250Kunique 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™ ArraysThe 15K EpiSwitch™ array can screen the whole genome including around 300 loci interrogated with theEpiSwitch™ Biomarker discovery technology. The EpiSwitch™ array is built on the Agilent SurePrint G3Custom CGH microarray platform; this technology offers 4 densities, 60K, 180K, 400K and 1 millionprobes. The density per array is reduced to 15K, 45K, 100K and 250K as each EpiSwitch™ probe ispresented as a quadruplicate, thus allowing for statistical evaluation of the reproducibility. The averagenumber of potential EpiSwitch™ markers interrogated per genetic loci is 50, as such the numbers of locithat can be investigated are 300, 900, 2000, and 5000.EpiSwitch™ Custom Array PipelineThe EpiSwitch™ array is a dual colour system with one set of samples, after EpiSwitch™ librarygeneration, 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 usingthe Agilent Feature Extraction software. The data is then processed using the EpiSwitch™ arrayprocessing 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 normalisedWithinArrays function inLimma* and this is done to the on chip Agilent positive controls and EpiSwitch™ positive controls. Thedata 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 <=-1.1 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. * Note: LIMMA is Linear Models and Empirical Bayes Processes for Assessing Differential Expression in Microarray Experiments. Limma is an R package for the analysis of gene expression data arising from microarray or RNA-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). Statistical PipelineEpiSwitch™ screening arrays are processed using the EpiSwitch™ Analytical Package in R in order toselect 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 tothe EpiSwitch™ 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 thesegenetic locations, with the markers from step 2, form the high value markers for EpiSwitch™ PCRtranslation. Array Design and Processing Array Design Genetic loci are processed using the SII software (currently v3.2) to:- Pull out the sequence of the genome at these specific genetic loci (gene sequence with 50kb upstreamand 20kb downstream)- Define the probability that a sequence within this region is involved in CCs- Cut the sequence using a specific RE- Determine which restriction fragments are likely to interact in a certain orientation- Rank the likelihood of different CCs interacting together.- Determine array size and therefore number of probe positions available (x)- Pull out x / 4 interactions.- For each interaction define sequence of 30bp to restriction site from part 1 and 30bp to restriction siteof part 2. Check those regions are not repeats, if so exclude and take next interaction down on the list.Join both 30bp to define probe.- Create list of x / 4 probes plus defined control probes and replicate 4 times to create list to be createdon array- Upload list of probes onto Agilent Sure design website for custom CGH array.- Use probe group to design Agilent custom CGH array.Array Processing- Process samples using EpiSwitch™ Standard Operating Procedure (SOP) for template production.- Clean up with ethanol precipitation by array processing laboratory.- Process samples as per Agilent SureTag complete DNA labelling kit - Agilent Oligonucleotide Array-based CGH for Genomic DNA Analysis Enzymatic labelling for Blood, Cells or Tissues- Scan using Agilent C Scanner using Agilent feature extraction software.EpiSwitchTMbiomarker signatures demonstrate high robustness, sensitivity and specificity in the stratification of complex disease phenotypes. This technology takes advantage of the latest breakthroughs in the science of epigenetics, monitoring and evaluation of chromosome conformationsignatures as a highly informative class of epigenetic biomarkers. Current research methods deployed inacademic environment require from 3 to 7 days for biochemical processing of cellular material in order to detect CCSs. Those procedures have limited sensitivity, and reproducibility; and furthermore, do nothave the benefit of the targeted insight provided by the EpiSwitch™ Analytical Package at the designstage.EpiSwitch™ Array in silico marker identificationCCS sites across the genome are directly evaluated by the EpiSwitch™ Array on clinical samples fromtesting cohorts for identification of all relevant stratifying lead biomarkers. The EpiSwitch™ Arrayplatform is used for marker identification due to its high-throughput capacity, and its ability to screen large numbers of loci rapidly. The array used was the Agilent custom-CGH array, which allows markersidentified through the in silico software to be interrogated.EpiSwitch™ PCRPotential markers identified by EpiSwitch™ Array are then validated either by EpiSwitch™ PCR or DNAsequencers (i.e. Roche 454, Nanopore MinION, etc.). The top PCR markers which are statistically significant and display the best reproducibility are selected for further reduction into the finalEpiSwitch™ Signature Set, and validated on an independent cohort of samples. EpiSwitch™ PCR can beperformed by a trained technician following a standardised operating procedure protocol established. All protocols and manufacture of reagents are performed under ISO 13485 and 9001 accreditation toensure the quality of the work and the ability to transfer the protocols. EpiSwitch™ PCR and EpiSwitch™Array biomarker platforms are compatible with analysis of both whole blood and cell lines. The tests aresensitive enough to detect abnormalities in very low copy numbers using small volumes of blood. Use of a Classifier The method of the invention may include analysis of the chromosome interactions identified in the individual, for example using a classifier, which may increase performance, such as sensitivity or specificity. The classifier is typically one that has been ‘trained’ on samples from the population andsuch training may assist the classifier to detect any responder group mentioned herein.The invention is illustrated by the following: Examples Veterinary oncology has a critical need for an accurate, specific and sensitive preferably non-invasive (blood) biomarker assay to assess multiple canine oncological indications early to better inform the therapeutic intervention. Extended from clinical applications in human oncology, here we report on a novel 3D genomics approach to identify systemic biomarkers for canine DLBCL, T-Zone Lymphoma, Hemangiosarcoma, Histiocytic Sarcoma, Osteosarcoma and Melanoma, in a single assay format that encompasses multiple classes and phenotypes of cancer. In the validation of the independent test cohort the 3D whole-genome profiling in blood demonstrated high sensitivity and specificity for lymphomas and sarcomas as a class, with accuracy >80%; and high sensitivity and specificity for individual indication, with accuracy >89%. This study demonstrates a 3D genomic approach can has been used to develop a non-invasive, blood-based test for multiple choice diagnosis of canine oncological indications. The modular EpiSwitch® Specific Canine Blood (EpiSwitch SCB) test allows veterinary specialists to diagnose the disease, make more informed treatment decisions, better utilize alternative effective treatments, minimize or avoid unnecessarily toxicity, and efficiently manage costs and resources. The lifetime risk of cancer and associated mortality rates in dogs have been estimated to be approximately 30% but vary remarkably by breed: around 50% of Irish Water Spaniels and Flat-CoatedRetrievers will succumb to cancer, while less susceptible breeds will have a mortality rate closer to 15-20%. For greater context, non-neoplastic conditions, including those arising from traumatic, infectious, metabolic, inflammatory, degenerative, toxic, congenital, and vascular processes, individually account for about 10% of adult dog deaths. When accounting for age, sex, and weight, the lifetime incidence of cancer in purebred and mixed-breed dogs is reported to be comparable; however, purebred dogs are found to be diagnosed at considerably younger ages though the age of these purebred diagnoses is shown to vary widely. Taken together, a study cohort comprised of 3,400 dogs with a cancer diagnosis had a median age of 8.8 years and found that females and neutered dogs were diagnosed at substantially later ages. Malignant lymphomas are one of the most common cancers in dogs, with an estimated incidence of 25 per 100,000. While they may occur at any age, they are predominately diagnosed in older dogs. Of these, the most frequently encountered subtype is diffuse large B-cell lymphoma (DLBCL), accounting for 40% of all canine lymphomas and approximately 70% of canine B-cell lymphomas. Marginal zone lymphoma (MZL) is considerably rarer but the expansion of immunophenotyping and molecular clonality assessments available to veterinary pathologists are making greater scrutiny possible 10. Indolent lymphomas make up approximately 29% of all canine lymphomas. T-zone lymphoma (TZL) is the most frequently diagnosed indolent lymphoma in dogs. Estimations speaking to TZL incidence specifically vary widely, with estimations ranging from 15.5 and 62%. Histopathology and immunohistochemistry (IHC) remain the clinical gold standard for diagnosing and classifying lymphomas. Approximately 7% of all malignant tumours in dogs are melanocytic tumors. Oral melanocytic tumours make up 30-40% of all canine oral neoplasms. Regardless of their location, malignant melanocytic neoplasms are diagnosed via cytology and / or histopathology. The sensitivity and specificity of this diagnostic method are excellent for pigmented melanocytic neoplasms but can drop significantly with diagnostic attempts of amelanotic melanocytic neoplasms. If the diagnosis of amelanotic melanocytic neoplasms remains evasive with microscopic evaluation, IHC for melanocyte-specific markers is required for definitive confirmation. Sarcomas comprise approximately 10-15% of malignant tumors in dogs. Of these, bone is the primary anatomic location of origin in 20% of cases, while the remaining 80% are found in soft tissue. Hemangiosarcoma (HSA) is among the more commonly occurring soft tissue sarcomas with an incidence of 2.8-5.0% of all diagnosed cancers, with primary sites of occurrence in the spleen, heart, liver, and skin. Diagnosis of cutaneous HSA is performed via analysis of histopathology from incisional or excisional biopsies and may require IHC for definitive diagnosis owing to its heterogeneous makeup. Splenic and liver HSA typically requires abdominal ultrasonography and three-view chest radiography. Definitive diagnosis requires histopathological examination and differential IHC is recommended. Cardiac HSAdiagnosis is made by fluid cytology but may not always be achieved due to the low cellular exfoliationand elevated hemodilution. Histiocytic sarcoma represents approximately 4% of all canine cancers, originating from antigen- presenting cells with primary sites in the lymph nodes, kidneys, liver, and central nervous system. Cutaneous histiocytic sarcoma can be diagnosed with relative ease by cytopathologic examination but as with all subtypes of histiocytic sarcoma, IHC staining is required to provide a definitive diagnosis. Within non-soft tissue sarcomas, osteosarcoma is the most common bone cancer in dogs, occurring in the appendicular (~85%) and axial skeleton. Definitive diagnosis of osteosarcoma requires histological observation of osteoid production by malignant osteoblast cells. Securing a definitive diagnosis and classification of cancer in dogs falls on a spectrum of varying degrees of difficulty as location, subtypes, complicating local conditions, and specific biopsy requirements are factored in. the difficulties include the invasive nature of tissue biopsies requiring sedation or anesthesia, the economic costs associated with the process, and the specialized technical training required to achieve an accurate and definitive classification. Among the most widely employed alternatives is fine needle aspiration alone or in conjunction with immunocytochemistry, flow cytometry, and / or polymerase chain reaction (PCR) for antigen receptor rearrangement (PARR). Liquid biopsies may aid in the diagnosis and subtyping of canine lymphoma, in addition to facilitating early disease detection, minimal residual disease monitoring, and the capacity to guide therapy. The invention includes a liquid biopsy biomarker modality focused on the changes in 3-dimensional genomic architecture (3D genomics) in peripheral blood cells to the establish presence of the cancers in dogs described above, as well as report on cancer indications in training. Of central importance are 3D chromosomal conformations signatures (CCSs) derived from combinations of long range DNA contacts that establish a regulatory fragmentation of the active genome, linking together genetic risks and epigenetic oversight of genome regulation. By determining long-range chromosome interactions, i.e. chromatin loops, at particular loci via chromosome conformation capture (3C) technologies, one can derive a biomarker that provides an instructive view of the regulatory architecture possessed by the 3D genome. In one aspect the novel biomarker EpiSwitch® platform is grounded on 3C and has reduced to practice and meets the regulatory requirements of a clinical biomarker assay. This can utilise high-throughput, high-resolution screening with the EpiSwitch Explorer microarray platform, machine learning algorithms, testing on independent validation cohorts, and a PCR-based format for stratifying biomarker signature. A salient and increasingly appreciated aspect of the regulatory architecture inherent in the 3D genome is that the heritable information in DNA far exceeds the linear genetic and epigenetic histone modifications that academic literature has classically explored. These traditional linear genetic formats and epigenetic histone modifications are in fact “encoded” in 3D genomic folding and architecture, constituting part of the epigenetic memory, reproducing itself with high fidelity through innumerable cell divisions, and are intimately related to cell metabolic and epigenetic states of the cell. More simply,the 3D genome is the storage media of the heritable footprint - genetic, epigenetic, and metabolic - ofthe cellular network regulation. Visualizing in one aspect how a whole blood biopsy could, by utilizing the EpiSwitch microarray platform and machine learning, provide diagnostic and prognostic insights into such extensive pathologies and processes, it is crucial to understand the extent to which individual chromosome conformations could be synchronized in the context of the multicellular organism and all the cross talk between the cells. Exosome traffic fosters the active exchange of epigenetic factors between the cells. These extracellular vesicles, with their cargoes of metabolites, nucleic acids, non-coding RNA, lipids, and peptides transfer horizontally from cells of origin to recipient cells and result in the modulation of immune cells (representing systemic changes), as well as modulation of secondary sites. Together this generates systemic epigenetic synchronization and changes in 3D genomic profiles, detectable by the EpiSwitch microarray platform. Here we have deployed the stratifying capabilities of the whole genome EpiSwitch 3D genomic arrayprofiling based on peripheral blood biopsy to several prevalent canine cancers: lymphomas – DiffuseLarge B Cell Lymphoma and T-Zone Lymphomas; Hemangiosarcoma, Histiocytic sarcoma, Osteosarcoma; and Melanoma. Given the challenge of a multi-choice outcome, we have developed an approach based exclusively on array readouts. The two-step classifier identifies first the strong systemic network signatures for lymphomas, sarcoma sand melanomas, shared by each class, and then identifies individual indication within the class. With batch alignment and internal controls, the performance of the array-based stratifications was then evaluated in validation cohorts for accuracy and specificity. Material and Methods Samples Canine whole blood samples, 150 in total, were imported from the USA. Samples represented Healthy controls and cases of Diffuse Large B Cell Lymphoma, T-Zone Lymphoma, Hemangiosarcoma, Histiocytic sarcoma, Osteosarcoma and melanoma (Table 1). When available, the annotations also include the age and breed of the individual dogs. All samples were profiled on EpiSwitch Canine Whole-genome 3D Explorer Array. Samples were either used in EpiSwitch screening and discovery stage, or in validation evaluation. Those samples were not used in any stages of the classifier model development. Preparation of 3D genomic templates EpiSwitch 3D libraries, chromosome conformation analytes converted to sequence-based tags, were prepared from frozen whole blood samples. Using EpiSwitch protocols following the manufacturer's instructions for EpiSwitch Explorer Array kits (Oxford BioDynamics Plc), samples were processed on the Freedom EVO 200 robotic platform (Tecan Group Ltd). Briefly, aliquots of 50 µl of whole blood were diluted and fixed with an EpiSwitch buffer containing formaldehyde. Density cushion centrifugation was used to purify intact, fixed nuclei. Following a short detergent-based step to permeabilise the nuclei, restriction enzyme digestion and proximity ligation were used to generate the 3D libraries. Samples were centrifuged to pellet the intact nuclei before purification with an adapted protocol from the QIAmp DNA FFPE Tissue kit (Qiagen) Eluting in 1x TE buffer pH7.5.3D libraries were quantified using theQuanti-T™ Picogreen dsDNA Assay kit (Invitrogen) and normalised to 5 ng / ml prior to interrogation byPCR. Array design Custom microarrays were designed using the EpiSwitch pattern recognition algorithm, which operates on Bayesian-modelling and provides a probabilistic score that a region is involved in long-range chromatin interactions. The algorithm was used to annotate the CanFam 3.1 canine genome assembly across ~1.1 million sites with the potential to form long-range chromosome conformations. The most probable interactions were identified and filtered on probabilistic score and proximity to protein, long non-coding RNA, or microRNA coding sequences. Predicted interactions were limited to EpiSwitch sites greater than 10 kb and less than 300 kb apart. Repeat masking and sequence analysis was used to ensure unique marker sequences for each interaction. The EpiSwitch Explorer array (Agilent Technologies, Product Code 087165), containing 60-mer oligonucleotide probes was designed to interrogate potential 3D genomic interactions. In total, 964,631 experimental probes and 2,500 control probes were added to a 1 x 1 M CGH microarray slide design. The experimental probes were placed on the design in singlicate with the controls in groups of 250. The control probes consisted of six different EpiSwitch interactions that are generated during the extraction processes and used for monitoring library quality. A further four external inline control probe designs were added to detect non-human (Arabidopsis thaliana) spike in DNA added during the sample labelling protocol to provide a standard curve and control for labelling. The external spike DNA consists of 400 bp ssDNA fragments from genomic regions of A. thaliana. Array-based comparisons were performed with the modification of only one sample being hybridised to each array slide in the Cy3 channel. Statistical Analysis The cohorts of analyzed samples were normalised by background correction and quantile normalisation, using the EpiSwitch R analytic package, which is built on the Limma Rank Product, tidyverse libraries. The datasets were combined into sample sets by processing batch., Data was corrected for batch effects using ComBat R script. Parametric (Limma R library, Linear Regression) and non-parametric (EpiSwitch RankProd R library) statistical methods were performed to identify 3D genomic changes that demonstrated a difference in abundance between classes. The resulting data from both procedures were further filtered based on p-value and abundance scores (AS). Only 3D genomic markers with p-value <=0.01 and AS -1.2<= or >=1.2 were selected. Both filtered lists from Limma and RankProd analysis were compared and the intersection of the two lists was selected for further processing. Machine learning and modelling All analysis for this study was performed using libraries which are developed for the R Statistical Language (R version 4.2.0). Feature engineering of the EpiSwitch Markers, was performed using Recursive Feature Elimination (RFE) utilising Xgbtree, The XGBoost algorithm model64 was used for final test optimisation. The grid search algorithm was used to optimize the hyper-parameters and learning rate in each iteration. For drawing inferences, we used SHapley Additive exPlanations (SHAP) values that are computed by a game theoretical approach which quantifies the contribution of each feature within a model to the final prediction of an observation. SHAP values were used to reduce the feature space for the cancer specific models. Genomic mapping The 3D genomic markers from the statistically filtered list with the greatest and lowest abundance scores were selected for genome mapping. Mapping was carried out using Bedtools closest function forthe 3 closest protein coding loci – upstream, downstream and within the long-range chromosomeinteraction (Gencode v33). All markers were visualized using the EpiSwitch Analytical Portal. Mapping to STRING database The closest protein coding loci for the chromosome interactions found in this study, where inputted to the STRING DB, utilizing the default settings in order to search STRING. The resultant protein-protein interaction data was exported and then imported and visualised in Cytoscape (v3.10.0). Results. Identification of the top predictive 3D genomic markers common for lymphomas classFollowing the established methodology for EpiSwitch array marker analysis (1 million whole genomecanine 3D array profiles), we evaluated systemic marker leads shared by two types of lymphomas –DLBCL and T-Zone lymphoma. From over 80 million data points across the canine genome, screening of20 healthy controls vs 20 lymphomas (10 with B cell lymphoma and 10 with T cell lymphoma), on thebasis of statistical criteria of p value less then 0.01 and Fold Change on array signal bigger then 1.2, identified 37 strong systemic EpiSwitch biomarkers. Those biomarkers were then used in array-based stratification on a validation cohort of 35 canines, representing 18 healthy controls and 17 of bothlymphomas (10 B cell lymphoma and 7 T cell lymphoma). Stratification calls were made for presence oflymphomas vs healthy controls as a class. Testing of the predictive 3D genomic biomarker panel for lymphomas as a class on the independent sample cohort To access the predictive power of the classifier model, the 37-marker 3D array classifier was validated on an independent test cohort. No samples from that cohort were used in marker selection andbuilding of the model. The EpiSwitch platform readouts for the classifier model were uploaded to theEpiSwitch Analytical Portal for analysis. Veterinary diagnostic assessment for the test cohort included a 18 healthy control samples and a mixture of lymphomas, including 10 DLBCL and 7 T-Zone lymphomas.EpiSwitch classifier model calls based on 37-marker model demonstrated high performance of 83%balanced accuracy and 87% positive predictive value in identifying canines with lymphomas as a class against healthy controls (Figure 1).Identification of the top predictive 3D genomic markers common for sarcomas as a classFollowing the established methodology for EpiSwitch array marker analysis (1 million whole genomecanine 3D array profiles), we also evaluated systemic marker leads shared by three types of sarcomas – Hemangiosarcoma, Histiocytic sarcoma, Osteosarcoma.Blood from 20 control healthy dogs and 30 dogs with sarcomas (10 with Hemangiosarcoma, osteosarcoma and histiocytic sarcoma each) over 50 million data points, on the basis of statistical criteria of p value less then 0.01 and Fold Change on array signalbigger then 1.2.100 array based markers were identified and validated on independent cohort ofsamples containing 18 healthy controls and 22 sarcomas (10 hemangiosarcoma, 10 osteosarcoma, 2 histiocytic sarcomas). Testing of the predictive 3D genomic biomarker panel for sarcomas as a class on the independent sample cohort To access the predictive power of the classifier model, the 100-marker 3D array classifier was validated on an independent test cohort. No samples from that cohort were used in marker selection and building of the model. The EpiSwitch platform readouts for the classifier model were uploaded to the EpiSwitch Analytical Portal for analysis. Veterinary diagnostic assessment for the test cohort included 18 healthy control samples and a mixture of Hemangiosarcoma, Histiocytic sarcoma, Osteosarcoma. EpiSwitch classifier model calls based on 8-marker model demonstrated high performance of 83% balanced accuracy and 83% positive predictive value in identifying canines with sarcomas (Figure 2). Training and Testing Individual Multi-Choice Classifiers Having observed strong systemic signatures shared by the indications representing lymphomas and sarcomas as distinct classes according to systemic EpiSwitch profiling. we have proceeded with two-stepclassifier models pursuing individual indications within each of the groups.For that purpose, our approach was to use once again the development cohort of samples for allindication. A multichoice classifier was developed on the basis of EpiSwitch Array whole genome 3Dprofiling. The development cohort represented 80 million data points with blood profiles from 20 healthy controls, 10 B cell lymphomas, 10 T cell lymphomas, 10 hemangiosarcoma, 10 histiocytic sarcomas, 10 osteosarcomas, 10 melanomas. Top markers positively detected for each indications vs healthy control and other indications were selected based on p value and used for the model classification that also included confirmed classification by class, i.e. lymphomas, or sarcomas, or melanoma. Independent validation of the classification model was made on the cohort containing 18 healthy controls, 10 B cell lymphomas, 7 T cell lymphomas, 10 hemangiosarcoma, 2 histiocytic sarcomas, 10 osteosarcoma, 10 melanoma cases. We have systematically identified top 100 markers from the comparison of each indication to healthy control samples, based on p value and FC, as described earlier. From the total pool we followed only the markers unique for each indication. This has been considered an important filtering step to ensure the high specificity of the multi-choice stratification of individual cancer types. Having ranked the markers, we then pursued only the disease positive markers, i.e. chromosome conformations present for detection in the given indication. Such positive detection markers are associated with negative FC in our data analysis and to we have relied on the development cohorts. As the final step, we have conducted further feature reduction of the markers, based on the SHAP plots that identified markers with highest impact. In this analysis we also have processed samples representing melanoma, as a separate indication and a separate class. We then tested a validation cohort of 56 samples, including 18 healthy controls, with the two-step classification, based first on a class call and then, using indication unique markers, with the individual indication calls. By using validation cohort of 56 mixed indications and healthy controls, the multi-choice calls for DLBCL demonstrated high positive predictive value of 87.5%, with accuracy against healthy controls at 85.7% and accuracy against all the indications included in the validation cohort at 92.8% (Figure 3). Top markers used in the DLBCL individual classifier are listed in the relevant table. The multi-choice calls for T-Zone Lymphoma demonstrated positive predictive value of 71.43%, with accuracy against healthy controls at 84% and accuracy against all the indications included in the validation cohort at 92.8% (Figure 4). Top markers used in the T-Zone Lymphoma individual classifier are listed in the relevant table. The multi-choice calls for Hemangiosarcoma demonstrated high positive predictive with no false positives within the validation cohort of 56 samples, with accuracy against healthy controls at 85.7% and accuracy against all the indications included in the validation cohort at 92.8% (Figure 5). Top markers used in the Hemangiosarcoma individual classifier are listed in the relevant table. The multi-choice calls for Histiocytic Sarcoma was underrepresented in the validation cohort, with only 2 histiocytic sarcoma independent samples available within 56 sample validation cohort. Interesting to note, that the classifier has demonstrated high specificity, with no false positive calls. Also, the one false negative call on a histiocytic sarcoma sample by the individual classifier still was flagged as a sarcoma class sample at the first stage classification. Top markers used in the Histiocytic Sarcoma individual classifier are listed in the relevant table. The multi-choice calls for Osteosarcoma demonstrated high specificity of 80%, positive predictive value of 66.7%, negative predictive value of 66.67%, with accuracy against healthy controls at 78.57% and accuracy against all the indications included in the validation cohort at 89.29% (Figure 7). Top markers used in the Osteosarcoma individual classifier are listed in the relevant table. The multi-choice calls for Melanoma demonstrated high sensitivity of 77.78%, with no false positive calls and accuracy against healthy controls at 92.6% and accuracy against all the indications included in the validation cohort at 96.4% (Figure 8). Top markers used in the Melanoma individual classifier are listed in the relevant table. Case study One of the first case studies for the developed multi-step classifier concerned a Golden Retriever Oliver(Ollie). In February 2020, at the age of 4 years and 3 months Oliver was diagnosed with intranasal,poorly differentiated malignant neoplasm with vascular invasion. The nasal biopsy results indicated a very aggressive tumor as noted by the poor level of differentiation, moderate to high cancer cell activity (mitotic index), and cancer cell invasion into the blood vessels. A first blood sample CANIS080 was collected and processed on EpiSwitch array platform at this point. As a treatment Oliver underwent Stereotactic radiation therapy (SRT) + chemotherapy (Doxorubicin),with reported 1- 1.5 years of survival on average, with a moderate risk of intermittent to chronic rhinitis.Oliver has developed rhinitis linked to the inflammation and remodeling that has occurring at the site of radiation. He was given Prednisone and Clindomycin for the side effects. Following the full cycle of treatment, a second blood sample CANIS193 was collected and processed on EpiSwitch array platform at that point. In October 2023, with no symptoms of any complications, Oliver has undergone a recheck CT scan which confirmed no evidence of regrowth of his right sided nasal mass, and no evidence of spread to his lungs. At the same time CT scan revealed a caudodorsal mediastinal mass, and a retroperitoneal mass. Both were located close to blood vessels, so performing a fine needle aspirate with cytology would have been too risky and dangerous. The mass within his chest was displacing the aorta and compressing the azygous vein. The mass within his abdomen was surrounding the cranial mesenteric artery. A third blood sample CANISOJB was collected and processed at this point. Applying the multi-choice EpiSwitch classifier test, described earlier, to the longitudinal set of three samples, the results were the following:1. First sample collection. A strong call for cancer, but only by the lymphoma general classifier forthe first sample CANIS080 collected after the initial diagnosis: Lymphoma as a class - 0.72103471;Control - 0.27896529, Cut-off >=0.6. None of the two individual lymphomas showed a strong match.2. Second sample collection. Improved call for healthy control by both lymphoma and sarcomageneral classifiers for the second sample CANIS193, collected after the completion of the initial treatment.3. Third sample collection. A strong call for cancer by the sarcoma general classifier for the thirdsample CANISOJB, collected after the second diagnosis in October 2023: Sarcoma as a class -0.72687131, Control - 0.27312869, Cut-off >=0.6;4. Third sample collection. Following up with the individual indications classifiers for CANISOJB,produced a strongest call for Hemangiosarcoma across all indications - 0.83977288.Discussion This work includes an array-based two step classifier. Extensive analysis of the systemic signatures in canine cancers revealed strong signatures by class, with markers shared by all tested lymphomas for one class and distinct strong markers shared by all tested sarcomas for another class. At the level of 3D genomics the systemic signatures, with all its integration of genetic and epigenetic inputs, systemic profiling of cancers does not reveal itself as a universal pan-cancer signature, but as a distinct family of signatures by class, reflecting their distinct mechanisms, shared, in our example, by sarcomas. Thus, firstly, in the context of the multi-choice classifier, the profile of an individual sample undergoes class stratifications against heathy controls. The second stage of classification benefits from the individual stratification models, based on unique selected markers for each individual indication. Today, this approach has been tested on 56 independent samples, representing a mixture of healthy controls, two lymphomas, three sarcomas and melanoma. The results of the first validation demonstrate high efficacy of stratification. Importantly, it also demonstrates high specificity against all other cancer indications. This is an important advantage against genetic mutation approaches in free cancer circulating DNA, which shares similar mutational profile between many cancers, and has poor discriminating power between individual types of cancer. Example of the case study has demonstrated highly informative longitudinal changes captured by the EpiSwitch profiling after the radiotherapy and chemotherapy treatments, as well as after the second diagnosis with unavailable pathology results. The EpiSwitch profiling, apart from delivering robust biomarker modality, also provides invaluable insight into high level integrated network regulation, as reflected in systemic readouts. Similarly, to the analysis of the previous applications in human biology, identified network of 3D genomic canine biomarkers is directly linked to the genomic loci they modulate, providing insights into affected genes and pathways. For example, pathway enrichment for the closest coding regions for the top EpiSwitch sarcoma markers, common across hemangiosarcoma, histiocytic sarcoma and osteosarcoma, identified a number of affected Super Pathways, as listed in table 12. Among those, the Super Pathway for ERK Signalling, reveals 24 genes modulated by common EpiSwitch 3D genomic architecture. This Super Pathway also includes the pathway for Molecular Mechanisms of Cancer, with 19 genes modulated by the EpiSwitch 3D sarcoma markers. Those include ARGEF7, CD4, GAB1, CCN2, HAPLN1, GNB1L, NRG3, COL19A1, COL4A1, COL4A2, WNT5A, BMP1, SRC, CXCR2, CXCR4, VEGFC, VHL, VCAN, FLT3. Interestingly, SRC has a role in solid tumours and Src signalling in sarcomas.WNT has a role in soft tissue sarcomas. Furthermore, we have also analysed the footprints of 3D genomic networks for each of the canine cancer indications. With such data over-imposed against the gene map, we have followed it up to a by building STRING protein interaction networks. showing interactions at the protein that are deduced through 3D genomic architecture, all captured at the systemic level. The STRING protein network for canine DLBCL, for example, shows a key group of affected proteins,such as MYC, BDNF, LRRK2, NRXN1, HDAC2, all consistent with the human DLBCL cases. This was basedon a string network of DLBCL systemic profile, captured through the top 200 EpiSwitch markers. Thestring network for DLBCL shows >550 affected gene products, with the nodes shown for over 10connections. The density of colour corresponded to the number of connections, leading with MYC,BDNF, LRRK2, NRXN1, HDAC2. This work means the top EpiSwitch markers for B cell lymphoma wholegenome 3D profile have been mapped to genes and compared with the known gene product profile of protein network interactions, matching a subnetwork around MYC, BDNF, LRRK2, NRXN1, HDAC2. Thus, the discovered EpiSwitch 3D markers for B cell lymphoma represent control over a defined subnetwork of protein interactions in a cell. The protein network for canine T-Zone Lymphoma, for example, shows a key group of modulatedproteins, such as ESR1, GATA4, CD44, CRBP1, ACTA1, all consistent with the human TLZ cases. This wasbased on a string network of T-Zone Lymphoma systemic profile, captured through the top 200 EpiSwitch markers. The string network for T-Zone Lymphoma shows affected gene products, with the nodes shown for over 10 connections. The density of colour corresponded to the number ofconnections, leading with BMP2, ISL1, APC, HSPA4, FYN, BPTF. This work means the top EpiSwitchmarkers for T cell lymphoma whole genome 3D profile have been mapped to genes and compared with the known gene product profile of protein network interactions, matching a subnetwork around BMP2, ISL1, APC, HSPA4, FYN, BPTF. Thus, the discovered EpiSwitch 3D markers for T cell lymphoma represent control over a defined subnetwork of protein interactions in a cell. The protein network for canine Hemangiosarcoma, for example, shows a key group of modulated proteins, such as MYC, EGFR, POLR2B, PTPRD, NTRK2, RUNX2, all consistent with the humanHemangiosarcoma cases. This was based on a string network of the Hemangiosarcoma systemic profile,captured through the top 200 EpiSwitch markers. The string network for Hemangiosarcoma shows affected gene products, with the nodes shown for over 10 connections. The density of colourcorresponded to the number of connections, leading with MYC, EGFR, POLR2B, PTPRD, NTRK2, RUNX2.This work means the top EpiSwitch markers for Hemangiosarcoma whole genome 3D profile have been mapped to genes and compared with the known gene product profile of protein network interactions, matching a subnetwork around MYC, EGFR, POLR2B, PTPRD, NTRK2, RUNX2. Thus, the discovered EpiSwitch 3D markers for Hemangiosarcoma represent control over a defined subnetwork of protein interactions in a cell. The protein network for canine Histiocytic Sarcoma, for example, shows a key group of modulated proteins, such as EZH2, EPRS, HIST1H4F, CDC6, TOP2A, CUL1, PABPC1, CDH2, NTRK2, all consistent withthe human Histiocytic Sarcoma cases. This was based on a string network of the Histiocytic Sarcomasystemic profile, captured through the top 200 EpiSwitch markers. The string network for Histiocytic Sarcoma shows affected gene products, with the nodes shown for over 10 connections. The density ofcolour corresponded to the number of connections, leading with EZH2, EPRS, HIST1H4F, CDC6, TOP2A,CUL1, PABPC1, CDH2, NTRK2. This work means the top EpiSwitch markers for Histiocytic Sarcoma wholegenome 3D profile have been mapped to genes and compared with the known gene product profile of protein network interactions, matching a subnetwork around EZH2, EPRS, HIST1H4F, CDC6, TOP2A, CUL1, PABPC1, CDH2, NTRK2. Thus, the discovered EpiSwitch 3D markers for Histiocytic Sarcoma represent control over a defined subnetwork of protein interactions in a cell. The protein network for canine Osteosarcoma, for example, shows a key group of modulated proteins, such as EGFR, IL17A, CA10, WASL, SH3GL2, POLR2B, all consistent with the human Osteosarcoma cases. This was based on a string network of the Osteosarcoma systemic profile, captured through the top 200 EpiSwitch markers. The string network for Osteosarcoma shows affected gene products, with the nodesshown for over 10 connections. The density of colour corresponded to the number of connections,leading with EGFR, IL17A, CA10, WASL, SH3GL2, POLR2B. This work means the top EpiSwitch markers forOsteosarcoma whole genome 3D profile have been mapped to genes and compared with the known gene product profile of protein network interactions, matching a subnetwork around EGFR, IL17A, CA10,WASL, SH3GL2, POLR2B. Thus, the discovered EpiSwitch 3D markers for Osteosarcoma represent controlover a defined subnetwork of protein interactions in a cell. The protein network for canine Melanoma, for example, shows a key group of modulated proteins, suchas ESR1, GATA4, CD44, CTBP1, ACTA1, all consistent with the human Melanoma cases. This was basedon a string network of the Melanoma systemic profile, captured through the top 200 EpiSwitch markers. The string network for Melanoma shows >550 affected gene products, with the nodes shown for over 10 connections. The density of colour corresponded to the number of connections, leading with ESR1,GATA4, CD44, CTBP1, ACTA1. This means the top EpiSwitch markers for Malignant Melanoma wholegenome 3D profile have been mapped to genes and compared with the known gene product profile of protein network interactions, matching a subnetwork around ESR1, GATA4, CD44, CTBP1, ACTA1. Thus, the discovered EpiSwitch 3D markers for Malignant Melanoma represent control over a defined subnetwork of protein interactions in a cell. The results constitute the first systemic oncological classifications as an EpiSwitch specific canine blood- based (EpiSwitch SCB) test.The EpiSwitch SCB™ test approach will be a tool for informed decisions on diagnosis, treatment and beststandards of care for our canine companions. Conclusions There is a pressing need to develop better preferably non-invasive (blood) biomarker assays to assess early canine oncological indications in advance of therapeutic intervention. Here we report on a novel 3D genomics approach to identify systemic blood-based markers for canine DLBCL, T-Zone Lymphoma, Hemangiosarcoma, Histiocytic Sarcoma, Osteosarcoma and Melanoma in an assay format that encompasses multiple classes and phenotypes of cancer. As a non-invasive, blood based test, EpiSwitch SCB, promises to assist veterinary specialists in diagnosis of disease and associated treatment decisions, to better utilize alternative effective treatments, minimize or avoid unnecessarily toxicity, and efficiently manage costs and resources. Melanoma is an important case here. In the case of dogs most melanomas are benign. The cases we have defined are metastatic melanoma, which is of primary interest from veterinary side. We are referring to this as a ‘specific cancer’, but it profiles as a class based on data analysis that shows that we see profiles common for lymphoma class and sarcoma class to be completely different from what we see as melanoma. Clinically lymphomas, sarcomas and melanomas are recognized as different major types of cancers. Having established strong systemic markers for the first two clinically defined classes we also see a strong systemic and distinct profile for metastatic melanoma. Further, in humans varioussarcomas at early stages are often misdiagnosed as melanomas. Here, we demonstrate for the first timea very robust systemic diagnosis with high specificity between sarcomas and melanoma. One can say that formally, in canine oncology, the cutaneous melanoma class as defined clinically consists just of benign melanoma (more prevalent) and malignant melanoma. With malignant melanoma being of high utility, we confirm that its strong systemic profiles are distinct from systemic class profiles of lymphomas and sarcomas, and thus from a systemic biomarker point of analysis is also treated as a separate class of its own, with just one major indication (malignant melanoma).Further, the two step approach of discovering and using systemic (e.g. blood based) cancer markerscommon for a class of cancer as the first step, followed by discovering and using systemic (e.g. blood-based) cancer markers specific to individual cancer type within the group against other cancer typeswithin the same group, provided the most accurate and efficient diagnosis. The existing standard approach of trying to find systemic blood-based biomarkers highly specific for an individual type of cancer proves very inaccurate and overlapping with other cancer classes and types in all the complexityof various cancers. Experimental discovery showed that each class of cancers has a very strong anddistinct group of shared markers, different from other classes. However those markers are potentiallynot useful by themselves for distinguishing individual types of cancers within the class. This brings in thesecond step, which looks to the opposite task of finding not common, but distinguishing biomarkers for each individual cancer type within the class only. Such strong individual biomarkers were also discovered as having a strong presence in blood. Together the two steps provided much more accurate diagnosis without a high level of false positives and false negatives. As an example, in human paediatric oncology early diagnosis of melanoma and sarcoma struggles to distinguish between those two indications. In our case we have very clear high accuracy discrimination between canine melanoma and all three types of sarcoma.

[0002] r etus eutN N ?SNS S IN N N N N N I N NaxtM M MFMF F FM MSFM M MSFM MSFM eSSt siasoelegnp gm9.57.6.3 7 26.0.6.8.7.8.1.7.2.8.5.0.8.7.8.1 1 97 11 94 3 61 6 81 4 8 5 3 Aaia1 1 1 1 DSep ytLC LC LC LC LC LC LC LC LC LC LC LC LC LC LC LC LC LC L onB B B B B B B B B B BC eLDLDLDLDLB B B B B B B BDLDLDLDLDL L L L L L L L L Lh D D D D D D D D D D P 2 7 6968 604 7 5 1 4 1el900 8090 090 9 999893058789920 elpSI1 NSI0 NSI1 NSI0 NSI1 1 01 0 00001 0001 NSINSINSINSINSINSINSINSINSINSINSISISISIb m a A A A A A A A A A A A A A A A NA NA NA NA TaSDIC C C C C C C C C C C C C C C C C C C

[0003] NS S S I IN N N N N N N N N N I MF F FSF F FM MSFSFMSFM F M M M M MSFM M 1.7.3.2.7.3 2 01 12.93.318.3.15.7.1.01.5.401 84.9013.70.1 1 1 1 8 7 1 96 1 91 8 9 8 a a a a a a a a a a a a a a a a a a a a mo mo mo mo mo mm mmmmmmmmmmmmm crcrcrc cocococococococococococococococc cLa a arararararararararararararar r r ritaita C BsLosDigosi osi osi osi osi os s s s s s s s sasasasasyioioioioioioioioioioioioioicm oioycm c oocngangangangangangangang g g g g g g g g g g g tananananananananananananasi riatSsi raSmmm mH H ee e emmm mmmmmmmm mmmmmH H H HeHeHeHeHeHeHeHeHeHeHeHeHeHeHeHeH 39 7 65 7 639 0 4932 3941 82 60 8 3 0 52 72 82 92 62 72 03 62 92 72 62 82 0362 52 92 82 1 00 7 7 SINSINSINSINSINSINSINSINSINSISISISISISISISISI3SI3SI3SI1SI1SIA A A A A A N N N N N N N N N N N N N N C C C C C C A C A C A C A C A C A C A C A C A C A C A C A C A C A C A C A C A C

[0004] IFSFSFI N I M M MIFSFI N N ? M M M MIFN M?FSF? M?F9.4.4.4 2300 4 8 5 6.1.3 9.9.0.1 1. .6. . .1 8 1 6 7 9 631 34.1 811 11 40.1 5 cit cicicicicicicic ca a a a a a a a yatatatatatatataitaita c mycmymymymymymymymymmo mo mo mo mo mo mo mo oito sicroiocc oiocc oiocc oiocc oiocc oiocc oocc oocc ooc natsanananananananai r tsi r tsi r tsi r tsi r tsi ritsi ritsi ritsi rleleleleleleleleHSHaSHaSHaSHaSHaSHaSHaSHaSHaSM M M M M M M M 76 68 09 97 98 5 7 0 6 1 05 86847 2 1 1 1 1 1 61 81 81 71 71 61 21 81 61 21 71 41 4 SINSINSINSINSINSINSINSINSISISISISISISISISI1SIA A A A A N N N N N N N N N N C C C C C A C A C A C A C A C A C A C A C A C A C A C A C A C

[0005] SF?FN I M MSFSF? M?FN MSFIFN MSFSFN MSFN N M MSFIFN N M MSFF FSF7.37.11.22.0.2.0.9 3318.9.820.2.4.5.2.2.27.9.4.3.9.7 111.3a / 9.9.1 1 1 1 1 1 1 42 0 3 5 1 7 5 86 1 1 n 35 a a a a a a a a a a a a a a a a a a a a a a a a a a mo mo mo mo mo mo mo mo mo mo mo mo mo mo m mmmm mmmmmmmc c c c c c co o o o o o o o o o o oc c c c c c c cn n n n n n n n n n nrarararararararararararar r ralalalalalalalalalala s s s s s s s s s s s sasasase e e e e e e e e eleoeoeoeoeoeoeoeoeoeoeoeo o o oM M M M M M M M M M Mtstststststststst t tetetetetO O O O O O O OsOsOsOsOsOsOsO 63149445 1695 3405 8397482 5 39801 5 1 2 67 5 1 1 1 2 71 3342 2 42 435 091 0 SINSISI1SI1SI1SI1SI1SI1SI1SI1SI2SI2SI2SI2SI2SI2SI2SI2SI2SI2SI2SI2SI1SI2SI2SIA NA NA NA NA NA NA NA NA NA NANA NA N N N N N N N N N N N N N C C C C C C C C C C C C C A C A C A C A C A C A C A C A C A C A C A C A C A C

[0006] SFSFSFIFSFN MSF? MSFSFN MSFN MSFN N M MSFI ? ? ? M M?FM Ma / na / na / n 5.16.9 275.834.83.91.80. .919.85.59.7.4.0.0.7. .15.6.4.01.a / a / a / 1 1 9 6 5 9 8 8 1 7 91 9 n n n a a a a a mo mmmm c l l l r ocrocrocrocrL L L L L L L L L L L o o o asaosasasasZTZTZTZTZTZTZTZTZTZTZL TZL L L L L TZTZTZTZTZTrtnrtnrtn eoeoeoeoeo C o o tst t t tC C OsOsOsOsO 1 38962 8048 2 2 9 5 35 37 7 642 46848 1 4092 86465 7 33 5 2 I 2 2 8 5 42 2 342 2 000 S NSINSI1 NSI2 NSI1 NSI1 NSI1 NSI1 1 1 1 1 1 1 1 1 1 1 1 1 1 000 NSINSINSINSINSINSINSINSINSISISISISISIsisisiA C A C A C A C A C A C A C A C A C A C A C A C A C A C A C A N C A N N N N NnC A C A C A C A C A CanCanCaC

[0007] a / na / na / na / na / na / na / na / na / na / na / na / na / na / na / N N n MSFF MSFM M FIFF a / 6 3 5na / na / na / na / na / na / na / na / na / na / na / na / na / na / na / .n 1a / 4.1 n 02. .1 5 5 43.1 56.6 lo l t o l l l l l l l l l l l l l l l l l l l l l l l r nrto nrto nrto nrto nrto nrto nrto nrto nrtortortortortortortortortortortortortortortorto n n n n n n n n n n n n n n n n C o C o C o C o C o C o C o C o C o C o C o C o C o C o Co C o C o C o C o C o C o C o C o C o C 5667 341475 8615 47 07 5427 45 97 833942 12 33635451 37 46167 000000000000000 6 0000000 333333 3 333 sisisisisisisi0si0si0si0si0si0si0siSINSINSINSINSINSINSINSINSINSISInanananananananananananananaAA A N N C C C C C C C C C C C C C C C C C A C A C A C A C A C A C A C A C eCTG 1 82 88 41uj6 1 7 67 7 7 1 11 1 2 1 1 1 V 3 5.2 20.2 2 55.5.1 5.6 0.1 5.1 5.1 5.d A8.0 13.9013.93.90.821 21 21 01 21 21 21ssalC a 49 e 3 kdoet LM 3M- SFMa / na / n MSFSFa / nIp F F M M M o 7 Ten 3 goai7 rss0 eve1 r 7 p 4 AxE7 6.8 1 6.2.a a 9.9.5.5.1 6 3 540 7 5 / n / n 3 65 5.9.3.4.5 7 C 2 Fg82 ol33 3.0 - tsd d _16 1 n 2 n5 1_9 l l l l l l l l l l_3ya _1 ortortortortortortortortortorytdle _y le _y le rreb 3491_u p dp dpa orm53 n n n n n n n n n n mu ua82 o o o o otStmtm D pF34 C C C C C o C o C o C o C o CeaS SeaS SeaS3na810 saCsasC 148 CaC 2415 81 840 4 0987 0 3 1 . 3 I 33373 53 335 2 8 9 8 o S NSINSINSINSINSI3 NSI3 NSI33 0 1 3 N NSINSINSINSINSIN2A C A C A C A C A C A C A C A C A C A C A C A C AelC b aT1 AG TAGTATTA ATGCAT CATTTATTAGTTT 3 7 4 3 -2.3- - 3- 73 30 42 9 13 0 2 41 7 4 5 0 8 52 5 5 9 8 15 3 2.5 012.6 8 84.2.8 01 81 3 9 1 9 0 41 3 7 1 7 7 42 9 6 2 2 52 8 09 1 5 13 2.0 8 7 03.02.02.- - -0- 1 22__7 6869 44_8304 81_2358 12_78 3425_71_9483_3122_6298 359_13 385_03 34_R m R_a3 F 5621_2 R R m a 89841_1 FFm a 44716_4F 5 R m39 561 7n540_ F641_ F172_aF54 2a8616n3054n3438n86 C2_583901aC4_737300aC1_133896aC2_58 2 3 4 5 AATTTTA TATCCA TAGTTTCATCCA 5 . 5 4 8.-2- 2 86 8 4 6 7 3 78 6 9 2 6 2 38 4 8 1 6 70 6 48 7 2.1 8 1 1.29 81.8 017.9 25 3 5 8 5 5 84 0 98 0 9 8 0 64 5 7 9 8 5 0 7 8 7 17 3 2.20.-02.6 0 -2.0 8282 1123 1276 8106 1_ _103 12_803_ _626 31_222_ _687 42_71_ _70 27 _0 3 m172a1_37113598 2 39780 2 F 8735 R R m a 21_F m36_R m66_R F 989F a514R a535Fn50 a 0942_6n120_ F544_ F929_a2071na8601n9656 C2_504250 C1_225574 C2_585793aC1_992716 6 7 8 9 TATC AACTAT ATATCCTGTGACACT 28 5 9 4 0 1 8 8 0 2 1 4 2 - 2 14 75 6 6 5 4 76 4 06 1 0 07 0 4 4 2 8 97 2 1 2 2.3 0 87.4 89.9.0 9 1 8 5 6 5 1 3 6 46 6 7 9 7 8 3 1 4 8 3 1 0 4 8 8 4 1 6 8 3.2.76 02.0 - 02.0 5_9 8127 22_28 104_ _999 15_92_15 28 _21_8_70466_6_2949 441 31515 364295 3756_2 785_F m132_R m78_1 m109 5744R a12F636F a652a236_R 562_051_ F498_701n17694a5607nC2_056829a09_ Fn 483 C8_5649RF a230F_C96425330 11 21 31 A CCACTTTTTTCTATTTTAATTGCC AGTCTCG 8 4.7 4.6 4 6 -2.-2- 2 79 79 54 5 8 9 4 0 7 4 1 70 6 0 76 2 94 96 6 1 2 3 7.3 88.8 83.8 82.9 80 6 51 3 3 2 8 2 3 18 1 2 0 2 7 8 4 2 5 0 62 1 3 4 8 3.3.6 8 0 -0-2.0 -2.0 _77 __766 95_70 82_63 _116 6577871_22 58 _71_10 23 _61_3_0232 395_7 34892 31170 3183_0 m a 20 F 205_R m a 505_F m172_R m842 F 684R a835R a613_R n 2 a 672F766_n787_ F504_ F277 a 2689na0901na895F_C 5265652 C5_728732 C2_501216 C13388648 41 51 61 71 TATGCTTAAAAATCC GAAGAGG TTTCTA 45 0 8 0 1 4 72 6 9 23 0 4.4 -424.0 2 - 2 1 77 84 4 4 4 0 74 4 8 78 4 93 09 9 4 6 8 9 51 03 7 5.3927 8. .80.9 15 5 6 8 7 4 29 0 4 3 6 4 9 8 6 4 4 64 2 4 10 8 3 39 4 2.6 0 -2.03.6 0 -2.0 66 3_22 _5 _ 97 1133_ _4 082 17 2342_ _0 524 30 _11 01_3_06 232 371726 397_08 358_2 373_0 m62_9 m811_m5813_m3592 aF217a391 FF a944RF a353_R n 82 a 612_ F360_ F802_ F127FRna8484na1087na995_C2_0931 R C1_381670 C3_912005 C13198648 81 91 02 12ATGCAACTACTG TATGGAC TACTGGTC GA 7 3 3.2 2.2 2 0 -2- 2- 49 14 78 81 3 1 3 1 4 5 65 2 3 9 53 4 4 4 1 2 1 7 2 1 3 0 4 . 4 9 2 0 094.1 85.9 9 84.82.8 16 8 6 8 5 8 7 73 8 3 5 5 7 5 5 9 6 8 0 6 4 32 2 7 5 8 7 6 7 0 82 2.6 7 5 0 -2.02.6 3 0 -2.03.0 - 534 0132_47 660_ 62_14 72__533_328 01_43_2_75571_56 25 _12_7 5 3 2 3771 37 6 36 03_R 14221_162_95 5912_4 022Rm a 365 RFm217 m7207 F m32 19 45_6Fn 309_aF82aF968F_aF7 32262a2513nC1_328785a612_nC2_0997 R Ra5949n5 C2_950589aC2_32 42 52 62 AAC AACACG TGTTAG ACAGCTG TGCT 0 9 4 1.6 2 2- 72 7 8 9 4 0 3 4 0 86 4 0 0 7 28 9 12 8 8 58 5.46 3 3.65 313.9 110.8 7 1 6 7 9 7 2 10 3 9 7 4 89 6 42 3 3 95 1 9 1 5 7 66 4 6 2.02.2 03.02.0 - 10 _461 _109 _1_01 _54 994_50X 7_39171_0R32_1 5_64 475_1103_181_ _25 2_38_4 337_1 3554R_372_4R 7Rm32 9_a4F2524_R m a 09 477F_ F18179_0R m R_a7356 m98 F 54262019aF2286 72na40na137na1492na14 9 1 C44544484 C17021469 C01106196 C5221 72 82 92 03 AGA ATTTCAG AGTCCCG ATCGTGGTATCTAA 4 9 1.2 9 1.0.0.2 - 22- 2- 73 3 6 4 6 0 98 2 0 57 2 7 68 5 6 0 7 6 6 5 7 1 .3.0 0 2 0 1 8 10.5.8 8 65 1 4 6 5 0 67 7 9 3 4 1 9 8 8 2 0 8 1 5 4 7 7 8 8 24 2.6 6 0 -2.02.1 0 -3.0 - 95 _673 517_7_22 5 36922 2__0 5409 590_ 882 82_043_4_85 _ 8222_7_9818 5 3 2 07_6 357 _237064 3827_98 8F m a 89 7FF797_R m a 60_R m85_3 m66 F 061F_aF240aF537_R 3_9 3na184F632_n023 67 a 4284na985_n338 a 895F_88 C 7024819 C2_047817 C4_5689RFC87381031 13 23 33 43 ACACTTAAACGACTATTGCTTCTTTA 44 3 4 3 56 6 69 9 24 2 53 0.4 00.4 00.0 7 4 5 2 3 8 2 4 3 1 1 3 0 3 9 9 3 0 0 5 9 9 7 0 0.8 8 2 -0.20.2 - 10 1 8 3 47 3 8 8 3 1 3 9 5 9 3 02 4 1 0 9 6. .9.8 31 01 72 7 5 4 1 7 1 4 2 6 5 9 8 1 2 3 2 0 4 0 3 3.1 7 0 -3.02.0 - 81__40 _101 _688 158051_71_0F42_9619 381_8_3511_R_ _3629 m31341_m8304468 37_16 aF8 n 4761 FF a6599 m a 112_F a 887 44_87F3 7na16 0338F707R1645na7683_C1_89 8 C 10 164 C 2774794 e G b orecCG TTCC A ATCACCTGTTCGTTC AC ACGTAAGGCC GTC CACCT TGTGGACGCAAPne CCGCCATCCGCCAC AGuGTGG AG CTG GGTA GGTGG AG CTG GGTA GGTG GCGCA CCTCTCAGGTTCAG TTAT srdetIL5 8 6 4 eko1.47.62.5.raM 6 6 Mp n 58 17 12 30 oTegoai4 rss8 91 67 56 eve7 2 9 0 r 9 3 7 7 p 9 4 0 2 AxE0 7.0 98.5 4 82.86.9 73 1 1 9 2 24 64 4 CF9 g 5 8 6 91 6 95 0 2 ol27 3 97 38 6.8 05.2 04.9 03.0 4330 _803 017 _5ya2r_ _81 r e3333_7_68902_8_337 2 32_7 ab 345_11 3 3217_68 33222_5 340 Dorm4640_9R m86847_m485_432 m43 3 paFn 27Fa 1622_aF526RF_aF188_aF9 C3_2170 24n6a5 C83 763 5977n0a1 C81 _7131FFna4 C5_.o 3 N elb aT1 2 3 4 TACAGAAGAC AATCTTC CGTC 4 7 7 2.4 6 5 5 99 5 8 5 0 7 2 9 5 6 2 9 9 5 2 8 0 60 6 7 6 8 0 9 1 8 55 6 7.95.85.8.8 11 94 3 6 9 6 5 2 9 7 37 1 2 7 0 9 3 9 5 09 4 2 7 9 8 9 3.8 5 5 3 03.03. .0 0 9 38 _386 _2 34229 76_5299 _6 221 4987_ _241_66930_39 333_2 37_52X_ _6 392_58 903_R m1 a 3992 43_R m7 a 69 0860_9R m52309 11 9777R_ Fn 710530R_ Fn 52803F3_a7Fn 8056 3917410aC23910651aC6_528966aC6280 6 7 8 CCA GAAGCTA AAATATGAAACGGGTC 1 1 5 7.3 5 9 5.6 4 61 1 6 5 1 03 7 1 2 8 99 9 44 5 7 45 4 4 6 8 9 72 9 1 4.89.9 92.4 80.9 41 97 26 4 04 7 6 12 2 5 6 7 1 53 0 8 6 3 21 4 5 3.5 03.5 6 03.4 03.0 74 _705 214_5_60 9 87162 3__3 4680 031 _ 41 43_363_3_571 _ 53_7_24 6 3 2 75_7 391 335382 3297__1 F m a 29 2F_ F37358_6R m F_a923_F m43_8 m89 F 910209R_aF62513 3_aF9079 7na546na6037na32 Rna04 373 C75750825 C3_266013 C3_0375 R C4710 9 01 11 21 A TAAGGT CTAG CTTCGCATTC AAATTCC 4 8 1 8.7 6 37.5 5 66 9 4 1 36 27 7 3 9 2 7 01 4 8 7 4 98 2 84 5 5 26 2.1.1.9 8 01 117.8 56 9 2 4 6 8 2 9 7 49 8 8 5 5 04 9 25 7 7 46 4 2 2 9 3.4 03.4 3 03.03.0 _868 5 97 _ 988 2_7_07 9 39601_952_3 _623_7_16 _ 01043_4_4969 347_9 398R 31 73 3314_49 F m R_a59 F 7877_R m a 70 F 75_R_m a 68_1 m98 F 310aF014_F 6na95 8973R_n1 5 03 a 965na978_069FFna441_2 C 7895508 C2_2555 C7_4033 R C94148972 31 41 51 61AGCCACG AACACTA AGCCAT GAAAG 71 5 7 1 7 03 6 4 9 8 1 0 0.2 92 62.7 6 1 3 5 5 81 1 7 5 3 3 8 0 8 3 8 1 4 8 5 18 6 3.9 0 6 1 5 3.1 16.49 1.1 8 1 63 49 4 4 4 5 0 4 6 7 00 9 8 2 6 83 6 73 6 8 64 3.3 2 1 03.03.03.0 _558 2_4_82 4 255832__9 5460 _887 41 72_941_7_187 5 931__96 15 _4 384_5 31123507_18 30446 m a 49 F 364_F m a 41_R m60 F 473F aF907_F m a 83310_0F Rn7 a 71 5601F_n824_707 a 4607na011F_ Fn 328_a6455 C 4773153 C2_843093 C87806036 C1_360578 71 81 91 02TTGCG AG TTGTA TGTACCG TTACTCC 25 8 7 2 0 6 1 1 2 06 74 3 2.55.59.4.6 5 97 8 3 2 27 27 9 9 0 7 4 38 08 6 28 0 2 53 1 67 0 2 6.95.2.6 9 119.9 46 1 0 4 3 64 8 6 01 0 4 18 7 6 93 8 7 8 3 3 5 1 1 1 3.1 03.3.1 0 03.0 65__09 7145_51 _022 6525 1 13 95 _01_61_57_3_90421_ _91 532 37881 32899 3333_90 336_1 m a 2 F 7658_9R R m a 59019_5 F R m a 30493_R m a 62 3353_7 F Rn913_ F461_ F028 a 0862na9967na937R_ Fn 019344_5 C5_906333 C1_496300 C03090211aC5_014369 22 32 42 CTTTTCGGG CTAGGT TGCCTA 13 1 2 2 7 09 1 3 7 7 47 1 18.35.6 5 5 80 4 6 6 6 9 2 9 4 3 6 1 1 32 9 0 63 8 7 7 8 2 6 4 0 6 7.5.9.2 8 01 97.9 57 2 2 8 6 9 5 7 7 4 1 5 4 54 5 1 3 8 77 9 1 8 8 6 1 3.0 0 80 03.03.03.0 12 _2 798_ 72_32 _116 706 331_52 F354042 6_3191 _5 33_9R_382_4_3163 7 9468_9 30492_2_1 630 m a 255 m909F m34496_R 831_ Fn 8_9aF283F_aF390 845FF a887nC3_0385a4940n258R_C2_248994aC16325731 62 72 82 AGGGAGCGCTGGTC GTTCTGA 87 3 5 7 7 29 6 8 3 8 17 0 99.56.7 4 5 57 9 8 5 62 19 6 2 6 5 0 1 7 2 9 86 0 3 0 2 49 3 4 . 2 8 0 484.98.2 93.9 51 3 1 1 2 33 3 3 1 4 29 3 43 2 0 58 8 7 5 4 0 5 5 4 3.0 03.0 03.0 03.0 05 _633 _27R8X 3_26145005_2151 82_25 2_4_3432_32_71_491_52 8F 3 R m4_016 6 23_F m717 3 12_7 0 7 m381427_7 F 10_a83Fn 797aF041aF38F_443a105R_n242 n24555 C63811045aC0291_0 RaC1_328440 03 13 23 TCTTTGCC GGGAGT AGGCCT 1 3.3 45.9 5 9.5 67 6 6 4 79 9 28 9 13 9 4 1 1 86 9 4 0.8 6 13.9.8 01 24 1 2 4 42 9 4 6 9 06 8 63 4 02 0 3 2 3.0 03.0 03.0 5 32__5 4409 787 _ 44 02_51_2_848 2 92 5__89 52 _4 3554 2 356091 33369 m a 95_m33_5 m025_R F 787 RF a060a992Rn45 a 489_ F833434na88_ Fn 242_a9950 C2_49856 C2_1824RFC5_291200 43 53 63 AGTCATC GCTGTCC ATCGTAGCTATCCA 14 6 9 4 0 5 02 7 15 5 8 15.5 61.1 6 5 67 9 3 4 2 58 2 5 65 9 2 3 0 1 1 4 1 4 3 7 . 0 6 8 4 57.74 019.1 9 16.9 45 2 4 5 5 62 9 9 3 1 33 1 2 4 2 2 6 1 9 17 3 9 9 2.9 69 59 02.02.02.0 0 84 77 344 46 14__911 5 14_82 1__31 14 _ _73_2_739 7342_ _81 34 _4 3361 4 30621 3162_73 39461 m a 49_R m801_F m94 F 805R aF789R a732_F m a 99435_3RFn66 a 181 64_8n693_ F116 a 1771na793F_ Fn 298_a1057 C4_18034 C1_615475 C32175311 C3_318233 73 83 93 04 ACTCCG AGCATCTT TTCTTCCG GAAACG 91 5 5 7 4 4 39 4 0 2 4 5.6.9 6 59.6 6 9.5 77 66 3 2 2 2 61 7 0 4 7 0 7 0 7 00 1 6 46 1 56 08 9.7 8.2.5.9 9 11 01 69 9 4 2 0 64 4 8 34 4 4 24 6 5 24 4 4 54 1 4 9 2.9 9 9 02.2.8 0 02.0 4 21__4 867 13 66 42_35 _1 _ 66 _06 57_3_53 08 _93 692_30 905 365_56 3560 383_5_392_3 m021_m683_46 m4350 8979 aF714RF a396a983_R m702_R n 242_ F40 a 6817na603_ Fn 736 a 356F_aFn 012557F_C1_264346 C3_6437RFC03838426aC92119153 24 34 44 AGCTGCCATATTTATC CGTG ATGTAC GA 9 7.3 7 4 8 7 50.6 2.5 6 78 7 9 6 4 4 81 5 8 8 9 9 98 3 0 3 7 1 90 1 91 1 9 4 16 9 0 6 8 1 6.3 2.2.5 9.8.9 9 01 8 8 14 5 3 8 1 4 5 2 6 6 1 65 6 1 0 4 2 58 2 6 4 6 2 20 8 98 8 8 8 36 2.8 8 8 8 02.02.02.02.0 82__7 686 _418 598_ 022_84 _7 1 9 _71_2_15313_1_01501_10 22 _87_1_38698 3552_15 309164 387 0 33 m a 682_F m26 m32_1 m8202_m15 F 727F a492_Ra311a872 FF a7 n 69 a 8755_0Fn 602 a 979R_ Fn 87 a 912_ F631_ F3 Rna0955na3 C2_680961 C42695178 C1_6898 R C2_602284 C71 54 64 74 84 94 TAGGC AGGACTA ACCTGACCCATTTG GGTA 8 9 1 65- 6- 81 62 96 6 31 9 2 56 4 1 5 4 9 76 24 2 64 6 9.5 0 4 5.0.8 5.8 9 21 11 3 60 8 8 1 0 46 8 14 6 6 93 4 32 8 3 5 4 53 2 58 5 4 23 2.05.05.5.-0-0- 346 73 211 _ 141 1_68 _639 77_01 089_ 11_98 1 12_88 _4_37 337 2129_1 3764 47_0_64_226 385052 387_0 615_R m a 81_8 m10 347F_ F20121 1_aF70171_5 R m R_a52_7 m06 F 21060 2_aF2836 451na127na004na84na83 34 7 C1_077RFC67109761 C1_5133RFC2_88 05 15 25 35ATCTAAGGTTACGATCTATATGTACGA 6 9 5 9.0 4 8 46.4 .- - -4- 16 3 7 5 5 69 6 3 86 8 71 42 0 8 1 4 7 96 4 9 6.06.4 2.08 1 41 018.8 32 5 4 1 8 78 9 1 76 6 9 94 2 4 5 4 2 5 3 5 0 9 0 8 6 4.9 04.4.4 -0-0-4.0 - 4 85 15 727 51 12__663 4 22_82 2__26 25 _ _13_1_779 2532_ _08 56 _5 3176 34556 3401_77 37398 m a 822_F m452_R m05 m635_R F 680F a591R a741_Fa600Fn67 a 6726_1Fn 060_ F512 a 6594na507F_ Fn 380725_0 C2_663272 C2_069469 C71656634aC5_389528 55 65 75 T AATAGTGTATCGGG AACTTAA CCCTCCT 6 0 4 4 -6.1 6 -4.7 6 - .5 - 89 56 30 1 8 4 1 6 7 8 0 96 0 3 2 6 11 3 20 1 0.7.5 9 07.6 22.9 1 1 01 34 2 1 1 2 0 4 6 7 61 0 6 00 6 2 0 5 3 4 36 2 9 3 4.1 0 6 4 9 04. .0 -3.-0-0- 30_ _539 62_3_08 _091 273 _0F7_7_06142_15 2906_6_0149 3376F_3717_0 33_5 326_0 m104 m606 m40902_R m9861 aF758a327_Ra891F a356_F n 8_6F905 a 635na954F_ Fn 952_ F908 a 4836na956F_C3_1015 C57999761 C2_946984 C06997241 85 95 06 16 AACCGGCTCCTGGACACACC 52 1 7 9 40 64 8 6 8 6 4 5 1 0 5 5.-6-9.4 - .4 - 1 76 60 3 94 8 7 95 8 1 7 3 2 75 6 0 1 4 41 9 4.5 3 07.7.1 010.8 21 41 84 23 5 51 8 1 78 6 7 5 4 5 12 7 1 6 3 03 1 9 3.8 8 28 03.03.3.- -0-0- 00 _791 721_67 _944 _F 5R X_9_47592941662_27 3694_312_4_39 242 5_5 25_07 314 3 82_8_9 632 m 0aF55992_9F m aF84338R m6 1F_aF80 3921_F 436n440F_n4811n0092F_043aC79641116aC2_445176aC92500087 36 46 56ACCAATA ATCTAGTCCACGCTCTGTAAA 49 6 5 3 1 1 0 3 6 35 92 8 98.66 54.4 .- - -4- 93 13 9 8 3 1 44 4 6 0 1 8 2 7 0 47 4 6 0 8.7 8 3 9 2 4 22 0.1 3.1 217.9 81 10 7 1 3 5 20 8 8 0 0 6 9 6 2 06 8 7 07 9 26 28 3.6 0 -3.6 0 -3.5 03.-0- _72555 _58216 _94247 81_912 X_9_724_4_5862_9432149 3629_5 394_2_332_2_33_1 m a 5507 99_R m8 a 1275 14_R m8 a 6809 92_R m9 a 15 1816_5 R F n 525 a 440F_ Fn 694 a 047F_ Fn 319 a 002F_ Fn 91414F0_3 C79640238 C54700576 C92500087aC1_916348 66 76 86 96 AATGA CTGGCTGGA CATATGAATGAT 58 55 4 4 3 4 36 2 1 2 9 8 5 8 5 -6.5 -2.5 - .3 - 76 3 3 8 97 22 4 1 1 1 9 48 90 3 79 2 5.1 3 29 3 8 42 018.0.9 318.9 16 9 1 4 61 22 6 3 1 7 6 99 87 7 11 5 3 42 5 94 9 8 3.03.4 3.4 3.-0-0-0- 23_3064 03_6455 65_2023 _1857 1_1332_0361_3528_6_148 378_00 379_74 301_0 336_5 m113_F m593_F 7905_R 3361 aF202R a660Rm a 195Rm196_F n 351_ F571_ F025_aF536 a 0251na4766na0245n523F_C3_300431 C3_547435 C5_002213aC16551111 07 17 27 37 GTAATAAACCGCC CACAGA 82 6 6 4 88 2 3 2.3 5 16-2. .5 - 5- 53 5 8 3 58 1 73 4 19 3 3 7 7 6 7.5 21 34.11.1 1 11 98 3 2 3 04 2 20 4 41 5 5 2 6 8 4 4 89 3.4 0 -3.3 03.-0- _76 3_3_23 75 _79 6X 9_41 75 _56 082_20 527 323_2_369_5_362_2 m3127 F3m2507 m5055 a 63_Fa599_Fa722_R n 875 a 172F_ Fn 521 a 446F_ Fn 288 a 159F_C73813287 C79641568 C52317229 57 67 77 TAGA CTGATTAATACCCAA GTTCTAAC 33 6 2 2 7 79 6 0 89 2 5 5 -6.5- - 4- 55 1 7 2 8 1 0 8 6 54 2 9 15 0 3 21 0 4 6 2.8 8 2 77.94 1.0 1 01 10.9 44 7 1 7 9 15 36 7 86 9 4 53 4 8 5 8 9 19 1 8 3 3.3 83 73 03.03.3.- -0-0- 38 2_49 _7 _ 93 6242_ _5 499 22 _75 5X_9_42 74 7462_ _3 810 453 372906 307_19 339_4 33_1 m a 040_400 m a 92722_7 RFm5 a 6077 4 39_F m a 93 3147_1 R F n 26 a 845_ Fn 449_ F676 a 9314na232R_ Fn 21345F0_5 C4_9249F R C2_498226 C79625509aC4_217352 97 08 18ACCATT ATTAG CTGACCAAG AATTGAG 49 5 3 2 1 1 9 5 3 68 41 8 75.55 55.5 .- - -4- 19 89 2 5 02 8 6 1 0 9 8 4 0 5 1 03 8 9 6 8.2 3 9 99.08 0.1 1 0 1 11.8 8 67 1 3 4 4 1 0 5 6 2 2 0 8 2 50 0 7 5 6 7 3 5 4 3 3.3 3 3 0 -3.0 -3.3.0 -0- _7 X_97 _47 73 _9 8X_99 _91 90 _76 0X_9_53 79 9261_ _2 088 262 3629_47 3919_0 369_5 35_8 m a 5 F 5099_R m a 0970 81_F R m2 a 5507 99_F m7 a 58 2825_7 FFn524 a 448F_ Fn 839_ F522 a 7333na443F_ Fn 279_a8278 C79640968 C99978566 C79640335 C2_282678 28 38 48 58 CTCTCATTAAAGATC ATTATTATGG AG TTT 0 1 7 3 8 7 57.61.5 9. .- - -4-5- 53 7 9 1 1 1 8 5 5 9 5 0 39 4 7 2 1 4 24 4 4 8 7 1 3 1 41 4 6.2.3 02.4 5.21 9 1 01 017.9 44 3 5 5 7 8 5 75 6 2 2 6 5 68 2 0 2 4 5 5 8 90 0 8 7 8 7 3 2 52 3 2 3.3.0 -0-3.2 0 -3.2 0 -3.0 - _85386 21_2908 1337 _8 44_958 _1 3_4_851_7122 7_872249966_3474_38 38_9_3542_36_5_30 m a 7 F 07 8 44_F m a 10 7416_4R 0 R m a 778_8 09 2 4 m24 4245_8R 7 R m10 n 377 a 274R_ Fn 057_ F83 a 9787na387_aFn 854_aF6 a 5455na0 C84329326 C1_093762 C7_4913RFC4_853942 C16 68 78 88 98 09 CCTGTATTTC CTATGA CTAGTTTTCTACCC 6 2 3 5 6 7 4.2. .-5-6-5- 80 8 7 9 8 6 86 9 31 2 0 1 9 01 4 3 0 7 8 7 3 6.1 14.48 1 1 119.8.9 01 53 75 70 9 9 8 4 6 4 1 5 70 1 7 71 3 92 61 2 1 9 8 3.2 03.1 3.1 -0-0-3.0 - 14 939_36 _879 33_82 _79 01_69 39 _31_7_38871_29 36 _8X_9_47 1 33 6 259 3717_38 38679 369__8F m a 823_R m63 6F_ F714097F_aF08677_1 F m F_a7 F 41375_96R 4Rm2 _a5 F 50 592 7na9892na176na9468na44 241 C3_190884 C87916590 C3_790632 C7964 19 29 39 49TTGC GATTGAT ATCTATATTTTTGG 0 0 8 2 96 3 9 8 38 6 9 7 0 8 4.-3-0.4 - .5 - 42 9 3 6 70 12 5 0 9 6 0 35 0 1 2 0 62 3 5 3 6 8 9 7 8.9.9 05.8 1 310.8 83 46 7 2 7 7 00 8 99 9 6 94 7 7 3 8 7 57 86 5 1 3.1 3 1 1 33.0. .-0-0-0- 0 35 _922 62_4586 35_012 _867 80692_352_451_2553 _016_5_4886 63_0 3 3 355_4 91 m78332_R m74395_9178 6763_Ra355R a042 R m835_F 76 0857R_ Fn 216_ F988F_aF897 3771956a5416nC2_254627a0093n224F_C5_904271aC85034233 69 79 89 AAACCTGA 70 7 9 3 0 4 8 2 4 2 0 0 0.0 00.0 84 6 9 0 6 - 2E6 0 3 0 7 0.2.0 1 2 3 9 8 96 3 6 3 7 3 7 6 9 3 0 8 9.7 3 -4.5 - 83 2 1 4 6 7 1 8 8 5 7 2 2.8 07.1 01 11 7 1 0 7 4 4 5 6 8 4 4 1 3 3.1 0 -3.0 - 2307 _7653 73X 9_9 4_3_7 3676 29_5 24m507 46_36FF599_81_aF522 RF_1752n9a4 C74 963 40335 001 5 e GG b orecATACAACCTTCCTCCTG CACTATCATTCTCG AGTTCTACGTC CCTACTGACAGG PneuGTGCGTTG TGGG TACAA AC CGGA GTCAGTG CTACTATAG TA TACATCTTA GG GGT TCTTGTCCGGTAAGGT p doetIL8 47.4 8 o.M1 8.0.T2- -3-3- n 51 69 4 1 ego0 8 90 42 airss94 9 4 4 5 8 5 0 everp 2 4 9 2 8 31 36 3 AxE3.82.83.1 79.9 1 5 7 1 6 5 2 1 4 4 1 C 8 3 1 F 1 g 7 1 8 0 ol4 3 0 8 7 2 1 0 4.0 9 2 0 -4.0 -4.4.0 -0- 72_0625 81_6824 _1_010 62_2 y20 018 6X61_R206 arre_ab 3626_6_381_8_3431_R_2_6482_211_R 587_3168 Dorm a 708F m02654Rm74909665 m6795 3 pFn 84 a 587F6_a2F53280_a0F8563aF18 C2_85944n2a8 C1_58931n7a1 C08 117 082 19n8a6 C27 _16 .o 4 N elb aT1 2 3 4 AATTAGTTGCCGTCTTCTGCTAGA GGCGT 6 5 1 3 1 6 8 1.3. .- -3-2- 83 6 3 5 7 75 5 3 07 2 99 08 9 6 6 8 6 71 83 8 0 2.2 82.3 2 80.1 87.8 24 7 7 7 7 8 4 3 8 87 72 4 75 8 6 84 5 63 3 2 6 6 76 5 3.04.04.3 4.- -0-0- 44 8 261 1__5 1832 _5 71_58X_477__549 84 7 R3_2_155 7 52 2__85 28 _8 2 347136_01 3272_15 39415 _3F 4Fm a 87_F m57R_m98 9_ F988R a384a282_F m a 75227_0F R 70n408_ F3 335a8021nC1_580265a17_83Fn 216487F_ Fn 756666_4 C761088aC52213838aC2_756780 5 6 7 8 A CATTTAA CTCATA TCACTTTAAACTCTT 9 . 2 1.4 5 -32-8.4 - 4- 60 1 9 0 6 8 3 5 0 1 4 2 8 33 4 2 5 0 5 2 4 8 3 8 7 .40 03. .8.8 7 7 01 13 6 3 9 6 5 6 6 1 27 05 7 92 0 6 48 1 3 5 5 2 4 . 8 8 2 40 3.3 4.5 4.-0-0-0- 3 02 023 50 85 16__32 65 _ _07_5_700 7131_ _12 11 _08F__55_391 33402 3945_70 31202 3_93 m a 2 F 6362_6F R m a 94995_R m a 77614_0F R m0 a 009 03_50 n 95 a 1618_1Fn 636 a 605F_ Fn 405_ F531 a 2981na883C6_917426 C05767819 C1_423010 C0306_3 R 9 01 11 21ATTGCTTATA TGTGAG GGTCAATG 01 81 57 4 78 0 3 39 . 5 -0.9 3 -43 3 92 39 6 6 9 1 4 5 0 5 08 0 97 0 8 09 9 5 4 3 7 5 0.3 4 0.9 4.80.8 01 11 89 6 9 0 6 7 6 8 4 9 4 0 1 4 9 5 9 7 9 6 0 1 7 2 0 0 4 4.0 0 -4.4 2 0 -6.6.0 0 17 _534 _26F 448 132_35538083_2 1_60 2_6_32723_8_574_764_46 3R m9_285 31 82_R m415 3 28_8 1445 m7705_4R 3F6_a59Fn 265aF861aF29R_691a142F_n188 n67189 C52214080aC0875_2 FaC4_261051 41 51 61CTTTTCG 398 4.1 80235906 6.6 85967687 0.0 70-E999 8.1 78 21776699 5.6 34752749 9.7 26494647 5.0 _208 3_4_1210 334_1 m2 a 282 F 14n173_7 F a 0R_C22 412 03042 5 01 e T becCAC AAAGTT CA CG TC ACAAT TTTAACTCT TAGCAAATG AGGTTCTA A CTCGATorPne GACA C u AGA CAGTTA AACTTTAAGTGTTACAGA GTCTATCATTA CTTGAG AAGGC GAGTATACA TCAATTTAT krdoetIL9 3.4 8 0 7 M M4- 2 3 p oTn 72 6 3 4 ego3 65 60 45 airss12 15 99 15 ever8 5 3 7 p 51 4 8 6 AxE6.0 74.1 88.2 78.6 14 1 2 1 2 2 C 0 5 01 10 Fg7 8 7 4 2 8 0 73 3 ol28 1 8 01 4.1 4 7 4 7 0- .0.04.0 3028_8 _95549 _6998 92_1 yarre3_3_ab 33547_6_951_2_6592_50 2 314 386_93_95 5_59 3782_96 3486 Dorm a 881 m65146_F m54822_R m3650 3 pFn 65aa 013_ F14071a4R_ F941F_aF06 C3_1776RFnaC9614089n9a8 C68 296 8035n2a0 C26 _00 .o 5 N elb aT1 2 3 4CTAAT CGTAGTCGTCGGG 9 8 9 8 77 14 5 5 0 6 5 3 1 91 6 35 3 72 9 0 8 5.56.4 -0.2 - 22 98 2 7 4 1 6 7 3 70 3 64 7 83 9 9 1 5.2 64.7 74.7 1 1 9 0 0 4 3 2 7 1 06 7 6 8 7 62 7 94 6 6 4.04.9 0 -3.0 - 5216455 46_ 1_ _428 5 92_121_ _812 5 04_513_3_91 299 3 m2 a 912F 02 14_394409R m0 0R_aF05 46_334 33 8F R m6_10 _aF255 na2180 160670 84383_C2_12282n4aC4_41714n9aC3_4820R R 6 7 e A b orecCACTC GTTACAATATCACTCGATT T TCTTCCAGAA AG CGAT GAAG CG AGAGGTTPneTu AA GC TTTGGTAC A CAACACT CGT TGGC TTAAATACATGATA G GGCACTA TGTCCGTC A CGTG AGTATT srd ekoetIL9 3.7 02.raM1-2.3 -1.2 -4- M 3 9 5 p n 1 7 35 2 oTegoairs9 s 7 8 6 1 4 5 4 8 1 0 4 ever8 p 7 7 9 7 E 0 9 .6.0 Ax 57 79.2 9 6 12.8 25 45 92 1 5 4 6 5 7 9 8 CFg39 7 73 34 4 6 4 4 ol7 35 62 2 3.04.09.6 -4.- -0 0- 5174 4342 _2963ya1r_ _98 r e1672_ _59 3059_3_225 3 92_ _6 ab 370_28 1_310_49 3 3903_22 355 Dorm44253R m1141_8F m01703_F m59 3 paFn 76Fa 8402_a0F323F_aF106R_aF4 C1_78365n9a9 C31 _39 997 93n1a1 C27 316 1917n4a5 C3_.o 6 N elb aT1 2 3 4 TAAC TATTGGA TCTCATGTTACTAGC TATT 4 7 -3.4 - 4- 20 81 1 6 4 3 58 5 84 8 8 13 0 58 6 4 1 1 26 6 5 . 8 7 9 1 0 83.76.71.8 39 2 4 6 8 19 3 4 05 2 9 6 8 3 9 3 3 0 5 76 9 9 7 4.2 3 0 1 4 04.03.04.- -0- 651 35 5486 _025 9185 546 _62_786865_1151_982_8 35_833844_78_4 37 2 95_1_711 340_8_7469 1 389_2 7_74FFm a 47_R m13 97F357F aF305_R m a 8613_2 FFm a 1498 42_4n652_893 55391a8331nC4_689526a942R_ Fn 616_ F54 C05892370a9797n84 C1_693855aC4_58 5 6 7 8 ATGTGGCTCATC 68870 9.2 - 29327179 0.0 9940121 0.0 2 6 9 9154116 6.2 - 70060673 9.7 16842197 3.0 - 53 43 6906_9 81_ _6459 4_3 F 695 _2 78Rm a 36455 4_47F269_37n5a35 C9_9309F R 9 5 e T b orecATT TGTCAAG GGAGGTCTAAT TCTTTAATG TTA AA AAAAGA GC G GCAT TG A GA PneTu AA GCAA GGAAAGATT ATTGTACGA GCTCTC GGTAA ATATATGATTTAT TCTCATCG ACCTTAGCTC GTT kradoetIL2 3.4 7 1 24.27.28.M M- -2 p oTn 66 15 4 2 egoai6 rss0 8 9 9 3 7 1 5 1 6 8 02 ever6 7 6 0 p 9 E 3 9 7 7 Ax 8.5 74.5 77.8 75.6 58 7 5 4 7 66 7 C 4 0 04 Fg2 1 7 4 9 2 0 57 8 ol55 0 9 03 4.6 -4.1 1 00-4.04.0 _32R4218 4522ya1rre_7 830_83_ _34 24 _ 12 8_040 7 62_2 ab 3_o 03865 327_84_3008_0_4 360 r m388_43 m26223_8F m2368_F m9 D paFanFR_aF3aF9 3 7 a 2248 855 053R_2 C78382_n6 Fa3 C26 _84 371 46n5a0 C44 807 0745n1a9 C2_.o 7 N elb aT1 2 3 4ATGTATACTATA GTTAATAC AGTGTTATCT 6 1 7 32.-2- 3- 85 9 5 2 17 25 9 0 4 4 0 63 31 9 23 9 3 14 1 4 9 8 5.2 61.0 75.6.7 31 53 44 2 8 8 9 28 2 5 2 3 2 0 7 6 22 3 3 2 6 51 1 2 4.4 03.2 0 04.5 04.- - -0- 54 0 391 1__8 846 13 2 25_8672 2 23_5310 4 3 21_1 2_362_8_4514 1 318_8_2366 342_6_6133 349_5 3R 9Rm a 2334_7 F R m a 89751_0FFm a 47831_7 FFm a 5294 2_16Fn 738_ F611_ F560_ F22 382a4303nC1_743347a6009n3720n13 C5_764212aC3_536911aC1_21 5 6 7 8 ACGTAG C ATCCTTTTCTTTTAATTTTA T 2 3 9.3 5 -1.4 - 2 58 13 4 3 8 2 00 7 94 1 9 95 8 4 4 2 3 52 9 1 7 6.5 7 9 75.6.9 6 013.6 2 5 8 2 67 8 0 2 7 8 4 1 1 8 8 4 3 7 0 9 7 7 9 19 1 7 2 0 4 3.04.0 -4.5 0 -3.0 026 21 81_0159 34_4291 _83679 _03_1102_6445_8_83 42 316_51 333_12 3468_6 62_R m111_m494_F m568 399R a142 RF a749R a008_F 09 82_9Fn 936_ F302_ F990 13167a0162nC1_909786a2850nC4_325110a522F_C88951803 01 11 21 GCTTGTCA TTCTGAG 58 5 5 0 2 - 4E5 0 2 0 5 0.2.0 6 6 3 9 9 4 7 1 1 2 6 8 1 0 0 1 3 4 9.0 3 -6.4 - 90 7 4 2 7 1 4 8 0 5 8 9 9 1 4.0.8 11 94 3 0 0 3 5 1 6 0 1 1 9 0 9 4.7 0 -3.0 - 15__40 _857 200 39594X 6_2895 4_90_376_2 m a 2 F 585_F m4088 6_n 2854F_aF21 F a 05918 718695F_C5_20982n7aC86815888 41 51 5 e C b orecCATAAACGTCTA CGCTTTATTCCTTATT TT CAT TAGCCTGAG T CATGATGA GTTA Pne AGCCGTAAC GAGTCCTCCGAAGGTCAG u A AGC GGACT TATTCGTTTGAATAA AAAAAAACAG GCAGA GGTG G radoetIL4 2.2 2 4 M M2-3.15.1 -6.2 po Ten 29 7 2 2 go1 95 2 3 airss9 9 5 1 42 89 6 ever10 92 5 7 7 50 ApxE5 9.8 77.5.5 7 315.7 93 9 52 3 3 3 0 6 C 6 4 F 4 2 59 g 51 0 27 7 ol2 1 7 5 4 51 4.3 1 4 3 0 -3.0.0 -4.0 7356_5 11_407 _749 _80 yae3_2_942_015 _4X_8_27545_5_68 rrab 3914 20 _8 39780 1_3788_27 3275_Dorm43994 m23791 R R m89748_F m0987 3 paFn 63aa 352_ F36394_a7F790F_aF67 C2_1716RnRa0 C1_30453n0a1 C75 884 1448n7a4 C82 574 1.8 o elN b aT1 2 3 4 ACTTC TTGGAT ACTTTTGA GATCG 1 7 1.5 3 7 23-3- 3- 96 32 6 8 0 2 8 5 1 5 89 8 7 7 5 9 77 0 8 39 2 9 8 4 8.7 74.6 8 96.67.7 6 7 2 2 0 59 6 7 5 8 4 6 43 0 7 97 7 9 48 9 1 2 1 8 2 0 3.04.4.2 0 -0-4.0 - 5 71 _4_7 23_384 25_47 _00 62 35543_7 23286245 5_20 343_8_375_6_31547_72 15543_235_8_105 m118F m65855 R m558 07aF108F_aF32F_aF2 5 737__3n8286n66032 7148 059R RaC3_188022aC5_36622n3aC0571 6 7 8 AG AGTTGG TACTAAATTACTGACGTG CTAG 0 . 9 0 20.-33- 3 50 7 3 5 8 65 79 8 77 3 1 97 6 16 2 7 0 2 48 3 8 . 9 4 2 6 8 77.74.98.7 69 1 5 4 5 6 9 3 0 5 76 5 4 03 6 9 16 6 7 25 4 1 1 . 7 7 4 4 9 0 -3.03.0 -3.0 82 6211_71 5480871_25 2340033_34 6305 48231_992 2_0 30259_10_3240_20_3332_68_343_09 _3R m a 22_4_3_363_2F_ F50 7447 RFm4 510_a7F83 2264RFm66 F m55 R 89_aF217588F_aF899628F_54na0na0 68na8386na8833 89 C2_707775 C4_208949 C3_188022 C3_988105 9 01 11 21 A TACTTAC 3.1 - 68048282 9.0 76247627 0.0 2894100 0.0 00 6 1 7289310 3.4 - 77331819 8.6 7305211 4.0 - _04 1_5_64 07 33 4 m25_60 a 7 F 92 n 745_R a 52 0154R_C 5850295 31 5 01 e TTGGTA ATTGTT CCAATA GG p etsM 4 oTdoetM -tI5 8 L 7 5 - 4 - 5 2.6 1 2 . 8 3 6 7 M2-3- .54.5 2 2 en 6 7 9 1 goairs9 7 14 83 s 63 80 5 6 v 4 2 9 eer7 7 7 p 2 0 5 9 AxE0 2.2 8 9 87.69.81.9 67 9 4 4 6 47 2 9 CF9 g - 9 0 3 ol2 - 3 8 2 - 0 9 - 7 7 4 8 9 7 6 . 9 5 8 404.4 05.4 05.0 6 _ _ _ yarre1__62_4 b 36802_R R5_3_71 23_7 3F5_4_33 34_3 4F4_1_5 8847_346_R_373_F_33 aorm918974 m0171456 m314 m4 Da3 pFn 8 a 409681a2Fn 3004796a0F3 n 82 047 987a820F3 n 13 C2_429499aC73070161aC34031382aC91 .o 9 N elb aT1 2 3 4 - 73 9 7 1 5 3 9 4. 90 6.59.3 62 75 5 4 4 2 7 2 0 3 4 8 97 0 30 9.53 41 010.72.8 74 33 7 5 1 8 3 6 27 - 95 2 6 6 64 1 1 4 5.4 9 06.05.0 2 3_4_099 4 3_4_1 0 F1_6_37 0 24_1_6 9 378545_ _7 4R_3001 m a 20 F 364_R m a 45988844 m a 43 245 n 501 a 776R_ Fn 5564F72 a 1511n57 C4_578795 C4_147127aC6_56 6 7 8 GAG GCC G G CAC G G 301 5 6_R 5R9_32 26 81 85 588 Table 10. Dog Breed Prevalence US. Table 11. Dog breed prevalence UK, based on percentage most common 40 dog breeds. Table 12

Claims

CLAIMS1. A method of detecting a systemic blood-based profile of a chromosome conformation signatureassociated with cancer in a canine comprising:(a) detecting the presence or absence of at least 5 chromosome interactions as shown in any of tables 2,3 or 9 to determine the class of cancer to be a lymphoma or sarcoma, or to determine the specificcancer to be malignant melanoma, and(b) in the case where the at least 5 chromosome interactions detected in (a) are from table 2 thenadditionally detecting the presence or absence of at least 5 chromosome interactions as shown in any of table 4 or 5 to determine the specific cancer to be a diffuse large B cell lymphoma or T-zone lymphoma, and(c) in the case where the at least 5 chromosome interactions detected in (a) are from table 3 thenadditionally detecting the presence or absence of at least 5 chromosome interactions as shown in any oftables 6, 7 or 8 to determine the specific cancer to be hemangiosarcoma, histiocytic sarcoma orosteosarcoma, wherein said detecting in (a), (b) or (c) is in a blood sample from the canine.

2. A method according to claim 1 wherein the presence or absence of the chromosome interactions isdetermined:- by detecting the presence or absence of a DNA loop at the site of the chromosome interactions, and / or- detecting the presence or absence of distal regions of a chromosome being brought together in achromosome conformation, and / or- by detecting the presence of a ligated nucleic acid which is generated during said typing and whosesequence comprises two regions each corresponding to the regions of the chromosome which come together in the chromosome interaction, and / or- by a process which detects the proximity of the chromosome regions which have come together in thechromosome interaction.

3. A method according to claim 1 or 2 wherein said detecting of the presence or absence of thechromosome interactions is by a process comprising:(i) in vitro crosslinking of chromosome interactions of the canine;(ii) subjecting said cross-linked DNA to cleaving;(iii) ligating said cross-linked cleaved DNA ends to form ligated DNA; and(iv) identifying the presence or absence of the said ligated DNA;to thereby determine the presence or absence of each chromosome interaction.

4. A method according to claim 2 or 3 wherein said ligated DNA is detected by PCR or by use of a probe.

5. A method according to claim 4 wherein detection is by use of a probe, wherein said probe preferablyhas at least 70% identity to any of the probes shown in any of tables 2 to 9.

6. A method according to any one of the preceding claims wherein:(i) the method is carried out prior to the canine receiving therapy for cancer and / or is carried out toselect which therapy the individual should receive for cancer, and / or(ii) the method is carried out on a canine that has cancer or is suspected of having cancer, and / or(iii) the method is carried out on a canine that has been preselected based on a physical characteristic,risk factor or the presence of a symptom for cancer, and / or(iv) the method is carried out to diagnose cancer.

7. A method according to any one of the preceding claims which further comprises selecting a cancertherapy for administering to a canine identified as having a chromosome state associated with cancer.

8. A method according to any one of claims 3 to 5, wherein the typing of the chromosome interactionscomprises specific detection of the ligated DNA by quantitative PCR (qPCR) which uses primers capableof amplifying the ligated DNA and a probe which binds the ligation site during the PCR reaction, whereinsaid probe comprises sequence which is complementary to sequence from each of the chromosome regions that have come together in the chromosome interaction9. A method according to any one of claims 3 to 5 or 8 in which said probe comprises:- a fluorophore covalently attached to the 5’ end of the probe, and / or- a quencher covalently attached to the 3’ end of the probe, and10. A method according to claim 9 wherein said fluorophore is selected from HEX, Texas Red and FAM.

11. A method according to any one of the preceding claims in which:(i) the presence or absence of chromosome interactions relating to 2 or 3 classes of cancer isdetermined, and / or(ii) the presence or absence of at least 5 chromosome interactions from each of tables 2 to 9 isdetermined, and / or(iii) the presence or absence of all of the chromosome interactions from any of tables 5, 6 or 9 aredetermined, and / or(iv) the presence or absence of at least 10 chromosome interactions from any of tables 2, 3, 4, 7 or 8 isdetermined.

12. An anti-cancer therapy for use in a method of treating a cancer in a canine, wherein said method oftreating comprises:- identifying whether the canine has a chromosome state associated with cancer individual by themethod of any one of the preceding claims, and- administering the anti-cancer therapy to a canine that has been identified as having a chromosomestate associated with cancer.

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