Screening method using chromosomal conformations for detecting colorectal cancer

Abstract

A process for analysing chromosome interactions relating to colorectal cancer. The invention allows determination of the colorectal cancer status using a systemic blood-based profile of a chromosome conformation signature (3D genomic) state. This avoids the need to use invasive procedures. In particular, the invention allows blood-based detection of colorectal cancer status and distinguishing between groups of individuals who differ in their colorectal cancer status. This also results in a test of high sensitivity and specificity.

Description

[0001] SCREENING METHOD USING CHROMOSOMAL CONFORMATIONS FOR DETECTING COLORECTAL CANCER

[0002] Field of the Invention

[0003] The invention relates to colorectal cancer.

[0004] Background of the Invention

[0005] Colorectal cancer (also known as bowel cancer, colon cancer, or rectal cancer) is the cancer of parts of the large intestine. Most colorectal cancers are due to old age and lifestyle factors, with only a small number of cases due to underlying genetic disorders. Risk factors include diet, obesity, inflammatory bowel disease (including Crohn's disease and ulcerative colitis). Most of the current commercial tests for colorectal cancer are highly invasive or unpleasant in the least.

[0006] Summary of the Invention

[0007] The invention allows determination of the colorectal cancer status using a systemic blood-based profile of a chromosome conformation signature (3D genomic) state. This avoids the need to use invasive procedures. In particular, the invention allows blood-based detection of colorectal cancer status and distinguishing between groups of individuals who differ in their colorectal cancer status. This also results in a test of high sensitivity and specificity.

[0008] The inventors have identified chromosome interaction signatures that define the colorectal cancer status of an individual. This elucidates the role of the modality of chromosome interactions in colorectal cancer and allows a ‘readout’ in respect of this cancer in an individual. This analysis at the level of the 3D architecture of the genome defined by chromosome interactions offers very early readouts of colorectal cancer status allowing decisions to be made at early disease stages as to therapy. Detection of the relevant chromosome interactions according to the invention has been found to show high specificity and sensitivity.

[0009] Accordingly, the invention provides a method of method of determining colorectal cancer status in an individual comprising detecting the presence or absence in the individual of:

[0010] - all of the chromosome interactions shown in Table 2 to thereby determine whether the individual has colorectal cancer; and / or

[0011] - all of the chromosome interactions shown in Table 3 to thereby determine whether the individual has precancerous lesions (polyps). Brief Description of the Drawings

[0012] Figure 1 shows the 3C / EpiSwitch preferred method for detecting the presence or absence of chromosome interactions.

[0013] Description of the Tables

[0014] Table 1 shows chromosome interaction markers which can be used in the invention. They stratify individuals in respect of having colorectal cancer and polyps.

[0015] Table 2 shows preferred chromosome interaction markers for use in the invention. These markers show high accuracy in stratifying individuals with and without colorectal cancer.

[0016] Table 3 shows preferred chromosome interaction markers for use in the invention. These markers show high accuracy in stratifying individuals in respect of having or not having precancerous lesions (polyps).

[0017] Table 4 shows the shared markers for tables 2 and 3.

[0018] Tables 5 to 10 show the performance of the chromosome interaction markers of tables 2 and 3.

[0019] Tables 11 to 13 show comparator testing for the chromosome interactions markers of tables 2 and 3 which are in the left hand column in each table (EpiSwitch).

[0020] Table 14 shows the cohorts for this work and a description of each sample is provided.

[0021] Chromosome interactions for use in the invention can be selected from any table,

[0022] Detailed Description of the Invention

[0023] Terms Used Herein

[0024] The method of the invention may be referred to as the ‘process’ of the invention herein.

[0025] The chromosome interactions which are typed may be referred to as ‘markers’, ‘CCS’, ‘chromosome conformation signature’, ‘epigenetic interaction’ or ‘EpiSwitch markers’ herein.

[0026] The word ‘type’ will be interpreted as per the context, but will usually refer to detection of whether a specific chromosome interaction is present or absent. The typing will generally be by physical determination of whether the chromosome interaction is present. The typing will preferably be carried out in vitro.

[0027] The word ‘individual’ will be clear from the context, and is usually the individual that is tested in the method of the invention and may also be treated according to the invention. The individual is preferably human. The ‘colorectal cancer status’ of the individual which is determined in the invention relates to any characteristic mentioned herein, and preferably relates to the presence of colorectal cancer (diagnosis or prognosis) and may also relates to any physical manifestation in the individual relating to colorectal cancer, such as precancerous lesions (polyps).

[0028] The chromosome interactions which are typed in the method of the invention are defined in any of the tables herein, and also in tables 2 and 3. Chromosome interactions are defined by means of the probe sequences which detect the ligated product made by an EpiSwitch method (see Figure 1). They are also defined by the position numbers of the interaction which are within the probe name. They may also be defined by other means of defining the ligated sequence, such as primer sequences which allow detection of the ligated sequence. The probe name shows the chromosome number and the also the locations of the regions which define the chromosome interaction by use of the chromosome locations numbers using the HG38 system. Each chromosome interaction can therefore be defined by means of the probe sequence provided in any table which represents the chromosome interaction or by means of language along the lines of ‘a chromosome interaction on chromosome [number] which is formed by region [first chromosome region] and [second chromosome region]’.

[0029] Typing of Chromosome Interactions to Determine Colorectal Cancer Status

[0030] Any aspect of the invention may be carried out on a liquid biopsy sample, preferably a blood sample.

[0031] One or more chromosome interactions are typed in the method which are associated with any aspect of colorectal cancer, including presence or absence of colorectal cancer. The chromosome interactions may be associated with any marker for colorectal cancer, such as precancerous lesions (polyps). The chromosome interactions may be selected from any chromosome interaction shown in any table herein, such as from tables 1 , 2 or 3. Preferably at least 4 chromosome interactions are typed from table 1. Preferably at least 4 chromosome interactions are typed from table 2. Preferably at least 4 chromosome interactions are typed from table 3.

[0032] The Chromosome Interactions Relevant to the Invention

[0033] The chromosome interactions which are typed in the invention are typically interactions between distal regions of a chromosome, said interactions being dynamic and altering, forming or breaking depending upon the state of the region of the chromosome. That state will reflect how the colorectal cancer status. The chromosome interaction may, for example, reflect if it is being transcribed or repressed. Chromosome interactions which are specific to a colorectal cancer status as defined herein have been found to be stable, thus providing a reliable means of measuring the status.

[0034] Chromosome interactions specific to a colorectal cancer status will normally be present before or in the early stages of the disease process, for example compared to other epigenetic markers such as methylation or changes to binding of histone proteins. This allows early intervention (for example treatment) which as a consequence will be more effective and may also allow early choices to be made of the type of treatment which is appropriate for the individual. Chromosome interactions also reflect the current state of the individual and therefore can be used to assess changes to colorectal cancer status. Furthermore there is little variation in the relevant chromosome interactions between individuals within the same colorectal cancer status group.

[0035] 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.

[0036] The changes which lead to the chromosome interactions may be impacted by changes to the underlying nucleic acid sequence which themselves do not directly affect a gene product or the mode of gene expression. Such changes may be for example, 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.

[0037] The Specific Detection Method of the Invention

[0038] The method of the invention comprises a typing system for detecting chromosome interactions relating to colorectal cancer status. Any suitable typing method can be used, for example a method in which the proximity of the chromosomes in the interaction is detected and / or in which a marker that reflects chromosome interaction status is detected. The typing method may be performed using the EpiSwitch system mentioned 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):

[0039] (i) cross-linking regions of chromosome which have come together in a chromosome interaction;

[0040] (ii) optionally isolating the cross-linked DNA from said chromosomal locus;

[0041] (iii) subjecting the cross-linked DNA to cleavage; and

[0042] (iv) ligating the nucleic acids present in the cross-linked entity to derive ligated nucleic acids with sequence from both the regions which formed a chromosomal interaction.

[0043] 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 the chromosome interaction. Preferably the ligated nucleic acid is detected by PCR or a probe based method, including a qPCR method.

[0044] 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 chromosome interactions are 2 to 10 Angstroms apart.

[0045] The cross-linking is preferably in vitro. The cleaving is preferably by restriction digestion with an enzyme, such as Taql. The ligating may form DNA loops.

[0046] 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. In preferred aspects capable of amplifying ligated nucleic acids as defined in any table are used (corresponding to the chromosome interaction which is being detected). Homologues of such primers or primer pairs may also be used, which can have at least 70% identity to the original sequence.

[0047] Where a probe is used to detect or identify the ligated nucleic acid, this is generally by Watson- Crick based base-pairing between the probe and ligated nucleic acid. Probe sequences as shown in any table herein may be used, for example the probe sequences shown in Table 2 or 3 (corresponding to the chromosome interaction which is being detected). Probe sequences as shown in table 1 may be used. Homologues of such 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.

[0048] The Individual to Tested and / or Treated

[0049] The individual who is tested in the method of the invention is preferably a eukaryote, animal, bird or mammal. Most preferably the individual is a human. In the case of a human individual they are typically aged 50 or above.

[0050] The invention includes detecting and treating particular groups in a population, typically differing in their colorectal cancer status. The inventors have discovered that chromosome interactions differ between these groups, and identifying these differences will allow physicians to categorize their individuals as a part of a particular group of the population. The invention therefore provides physicians with a process of personalizing medicine for an individual based on their epigenetic chromosome interactions. Such testing may be used to select how to subsequently treat the individual, for example the type of drug that will be administered. The process of the invention may be carried out to select treatment for an individual, for example whether or not to give any specific treatment mentioned herein is administered to the individual.

[0051] The individual that is tested in the process of the invention may have been selected in some way, for example based on a risk factor, symptom or physical characteristic. The individual may have been selected based on having a symptom of colorectal cancer.

[0052] The individual may be susceptible to colorectal cancer mentioned and / or may be in need of therapy for colorectal cancer.

[0053] The Data in the Tables Provided Herein

[0054] Tables 1 , 2 and 3 show specific markers which can be used to detect colorectal cancer status. Their presence or absence can be used in such a detection (i.e. they are ‘disseminating’ markers). Table 2 shows markers which can be used to detect presence or absence of colorectal cancer. Table 3 shows markers which can be used to detect presence or absence of precancer lesions (polyps).

[0055] The following information is relevant to chromosome interaction markers and their performance, and some of this is shown in the tables:

[0056] RP - Rsum the Rank Product statistics evaluated per each chromosome interaction. FC - Interaction frequency (positive or negative).

[0057] Pfp - estimated percentage of false positive predictions (pfp), both considering positive and negative chromosome interactions.

[0058] Pval - estimated pvalues per each CCSs being positive and negative.

[0059] Adj. P. value (FDR) - False discovery rate adjusted p. value.

[0060] GeneDist - shows gene distances from the nearest anchor sites in base pairs.

[0061] Simple permutation-based estimation is used to determine how likely a given RP value or better is observed in a random experiment. This has the following steps:

[0062] 1 . Generate p permutations of k rank lists of length n.

[0063] 2. Calculate the rank products of the n CCS in the p permutations.

[0064] 3. Count (c) how many times the rank products of the CCS in the permutations are smaller or equal to the observed rank product. Set c to this value.

[0065] 4. Calculate the average expected value for the rank product by: Erp(g)=c / p.

[0066] 5. Calculate the percentage of false positives as: pfp (g)=Erp(g) / rank (g) where rank(g) is the rank of CCS g in a list of all n CCSs sorted by increasing RP.

[0067] The rank product statistic ranks chromosome interactions according to intensities within each microarray and calculates the product of these ranks across multiple microarrays. This technique can identify chromosome interactions that are consistently detected among the most differential chromosome interactions in a number of replicated microarrays. Where the p-value is 0 this indicates that there is very little variation in the Rank Product of the CCS across the samples, this is a good example of the signal to noise and effect size of CCS. Where p value is 0 and pfp is 0 this means that permutated Rank Product doesn’t differ from the actual observed Rank Product. These methods are described Breitling R and Herzyk P (2005) Rank-based methods as a nonparametric alternative of the t-test for the analysis of biological microarray data. J Bioinf Comp Biol 3, 1171-1189.

[0068] The FC indicates prevalence of marker in each comparison, 2 means twice over average test, 1.5 means 1.5 over the average test, etc., and so FC indicates the weight of a marker to phenotype / group. The FC value 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 typically designed to detect ligated product but their locations from the Taq1 site vary. Probe locations:

[0069] Start 1 - 30 bases upstream of Taql site on fragment 1

[0070] End 1 - Taql restriction site on fragment 1

[0071] Start 2 - Taql restriction site on fragment 2

[0072] End 2 - 30 bases downstream of Taql site on fragment 2

[0073] 4kb Sequence Location:

[0074] Start 1 - 4000 bases upstream of Taql site on fragment 1

[0075] End 1 - Taql restriction site on fragment 1

[0076] Start 2 - Taql restriction site on fragment 2

[0077] End 2 - 4000 bases downstream of Taql site on fragment 2

[0078] Types of Detection

[0079] When detection is performed using a probe, 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. In some aspects probes are used which comprise sequence which is homologous to any of the probe sequences shown in the tables.

[0080] The Approach Taken to Identify Markers and Panels of Markers

[0081] 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 invention relates to genome-wide regulation by chromosome interactions. 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 the genome, across coding and non-coding parts and over large sways of non-coding 5’ and 3’ of known genes 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.

[0082] In selection of the best markers we are driven by statistical data and p values for the marker leads. Selected and 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.

[0083] We are taking the question of clinical phenotype differences and their stratification from the basis of fundamental biology and epigenetic controls over phenotype - including for example from the framework 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 optimal number of markers to stratify the testing cohort with minimal noise. This may end with 3-20 markers.

[0084] 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).

[0085] 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 be perceived as a tailored capture of a chromosome conformation on the gene of know functional value for the question of stratification. The total number of sites for chromosome interaction are 1 .2 million, and so the potential number of combinations is 1 .2 million to the power 1.2 million. The approach that we have followed nevertheless allows the identifying of the relevant chromosome interactions.

[0086] The specific markers that are provided by this application have passed selection, being statistically (significantly) associated with the condition (characteristic) or subgroup. This is what the data in the relevant table demonstrates. 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 individuals when compared to controls. On average, as an example, an individual marker may typically be present in 80% of the relevant colorectal cancer group and in 10% of controls, and therefore the results of the testing by the method of the invention is straightforward to interpret and essentially amounts to a ‘binary readout’.

[0087] 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 (R package) can be brought in. GLMNET package helps to identify interdependence between some of the markers, that reflect their joint role in achieving deregulations leading to disease phenotype. Modelling and then testing markers with highest GLMNET scores offers not only identify the minimal number of markers that accurately identifies the individual cohort, but also the minimal number that offers the least false positive results in the control group of individuals, 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.

[0088] Samples and Sample Treatment

[0089] The process of the invention will normally be carried out on a sample, such as any type of sample mentioned herein. 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 EpiSwitch platforms. 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.

[0090] The sample may be a liquid biopsy sample, preferably a blood sample. The liquid biopsy sample is preferably not from the site of the colorectal cancer or suspected colorectal cancer. The sample is preferably not from the intestine, colon or fecal matter.

[0091] Preferred Aspects for Sample Preparation and Chromosome Interaction Detection

[0092] Methods 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.

[0093] 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.

[0094] 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.

[0095] Digesting DNA with a restriction enzyme is described herein. Typically, DNA restriction is performed at about 55°C to about 70°C, such as for about 65°C, for a period of about 10 to 30 minutes, such as about 20 minutes. Preferably a frequent cutter restriction enzyme is used which results in fragments of ligated DNA with an average fragment size up to 4000 base pair. Optionally the restriction enzyme results in fragments of ligated DNA have an average fragment size of about 200 to 300 base pairs, such as about 256 base pairs. In one 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.

[0096] In one aspect of the EpiSwitch process a DNA precipitation step is not performed between the DNA restriction digest step and the DNA ligation step.

[0097] DNA ligation is described herein. Typically the DNA ligation is performed for 5 to 30 minutes, such as about 10 minutes.

[0098] 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.

[0099] 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.

[0100] Figure 1 shows a preferred process of detecting chromosome interactions.

[0101] Processes and Uses of the Invention

[0102] The process of the invention can be described in different ways. It can be described as a process of making one or more ligated nucleic acids comprising (i) in vitro cross-linking of chromosome regions which have come together in a chromosome interaction; (ii) subjecting said cross-linked DNA to cutting or restriction digestion cleavage; and (iii) ligating said cross-linked cleaved DNA ends to form one or more ligated nucleic acids, wherein optionally detection of the ligated nucleic acid may be used to determine the chromosome state at a locus. Preferably the chromosomal interactions may be 1 , 3, 5, 8 or all the chromosome interactions of Table 2. Preferably the chromosomal interactions may be 1 , 3, 5, 8 or all the chromosome interactions of Table 3.

[0103] Homologues

[0104] Homologues of polynucleotide I nucleic acid (e.g. DNA) sequences are referred to herein. Such homologues typically have at least 70% homology, preferably at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98% or at least 99% homology, for example over a region of at least 10, 15, 20, 30, 100 or more contiguous nucleotides, or across the portion of the nucleic acid which is from the region of the chromosome involved in the chromosome interaction. The homology may be calculated on the basis of nucleotide identity (sometimes referred to as "hard homology").

[0105] Therefore, in a particular aspect, homologues of polynucleotide I 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 least 98% or at least 99% sequence identity across the entire probe, primer or primer pair.

[0106] For example the LIWGCG Package provides the BESTFIT program which can be used to calculate homology and / or % sequence identity (for example used on its default settings) (Devereux et al (1984) Nucleic Acids Research 12, p387-395). The PILEUP and BLAST algorithms can be used to calculate homology and / or % sequence identity and / or line up sequences (such as identifying equivalent or corresponding sequences (typically on their default settings)), for example as described in Altschul S. F. (1993) J Mol Evol 36:290-300; Altschul, S, F et al (1990) J Mol Biol 215:403-10.

[0107] Software for performing BLAST analyses is publicly available through the National Center for Biotechnology Information. This algorithm involves first identifying high scoring sequence pair (HSPs) by identifying short words of length W in the query sequence that either match or satisfy some positive-valued threshold score T when aligned with a word of the same length in a database sequence. T is referred to as the neighbourhood word score threshold (Altschul et al, supra). These initial neighbourhood word hits act as seeds for initiating searches to find HSPs containing them. The word hits are extended in both directions along each sequence for as far as the cumulative alignment score can be increased. Extensions for the word hits in each direction are halted when: the cumulative alignment score falls off by the quantity X from its maximum achieved value; the cumulative score goes to zero or below, due to the accumulation of one or more negative-scoring residue alignments; or the end of either sequence is reached. The BLAST algorithm parameters W5 T and X determine the sensitivity and speed of the alignment. The BLAST program uses as defaults a word length (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.

[0108] 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.

[0109] 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.

[0110] The Threshold of Detection

[0111] The markers which are disclosed herein have been found to be ‘disseminating markers’ capable of determining colorectal cancer status and tables 2 and 3 show which status group each marker is present in. In these tables ‘Control’ means that the relevant marker is associated with absence of the relevant characteristic (i.e. absence of colorectal cancer or absence of polyps).

[0112] In practical terms it means that these markers of table 2 and 3 are prevalent across the relevant colorectal cancer group when compared to controls (as is shown by the FC value, for example). On average, as an example, an individual marker may typically be present in 80% of the relevant status group group and in 10% of controls. When testing an individual the result will be a combination of ‘present’ and ‘absent’ chromosome interactions for each of the markers shown in Table 2 or 3 allowing determination of the colorectal cancer status for the individual. Typically presence / absence of at least 5 markers out of 8 compared to the ‘ideal’ result shown in the table can be used to assign the individual to a colorectal cancer status group.

[0113] Therapeutic Agents and Treatments

[0114] The invention provides therapeutic agents for use in preventing or treating colorectal cancer, such as any therapeutic agent 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 or treat the condition, for example in individuals tested by the method of the invention.

[0115] 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.

[0116] 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.

[0117] The invention also provides therapeutic methods for colorectal cancer, for example any such method mentioned herein.

[0118] Screening for Therapeutic Agents

[0119] The invention provides a screening method to identify therapeutic agents for colorectal cancer comprising determining whether a candidate agent is able to cause a change to all of the chromosome interactions shown in Table 2 and / or Table 3.

[0120] Nucleic Acids of the Inventions

[0121] The invention provides certain nucleic acids, including probes and primers. Preferably the nucleic acids are DNA. It is understood that where a specific sequence is provided the invention may use the complementary sequence as required in the particular aspect.

[0122] The probes shown in Table 2 or 3 may be used in the invention. In one aspect probes are used which comprise any of: the sequences shown in Table 2 or 3; or fragments and / or homologues of any sequence shown in Table 2 or 3. The probes shown in Table 1 may be used in the invention. In one aspect probes are used which comprise any of: the sequences shown in Table 1 ; or fragments and / or homologues of any sequence shown in T able 1. Labelled Nucleic Acids and Pattern of Hybridisation

[0123] 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.

[0124] Forms of the Substance Mentioned Herein

[0125] 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.

[0126] The invention provides a kit for carrying out any process of the invention, including detection of a chromosomal interaction relating to colorectal cancer status. Such a kit can include a specific binding agent capable of detecting the relevant chromosomal interaction, such as agents capable of detecting a ligated nucleic acid generated by processes of the invention. Preferred agents present in the kit include probes capable of hybridising to the ligated nucleic acid or primer pairs, for example as described herein, capable of amplifying the ligated nucleic acid in a PCR reaction. Preferred agents include any of the specific primers and probes disclosed herein and / or homologues of such primers and probes.

[0127] 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.

[0128] Detection Process

[0129] 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 together in an epigenetic chromosome interaction, wherein said process comprises contacting the ligated sequence with the probe during a PCR reaction, and detecting the extent of activation of the probe, and wherein said probe binds the ligation site. The process typically allows particular interactions to be detected in a MIQE compliant manner using a dual labelled fluorescent hydrolysis probe.

[0130] 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.

[0131] 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.

[0132] Use of the Probe in a qPCR Assay

[0133] 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 Target

[0134] The invention includes selecting primers and a probe for use in a PCR process as defined herein comprising 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.

[0135] 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 is calculated for at least 20, 50, 100, 200 or 400 bases, such as for 20 to 400 bases upstream and / or downstream 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.

[0136] Disclosure in Publications and Priority Applications

[0137] The contents of all publications mentioned herein are incorporated by reference into the present specification and may be used to further define the features relevant to the invention. The contents of all priority applications are incorporated by reference into the present specification and may be used to define the features relevant to the invention.

[0138] Paragraph of the Invention Showing How the Invention May Be Carried Out

[0139] A method of determining colorectal cancer status in an individual comprising detecting the presence or absence in the individual of:

[0140] - at least 4 of the chromosome interactions shown in Table 2 to thereby determine whether the individual has colorectal cancer; and / or

[0141] - at least 4 of the chromosome interactions shown in Table 3 to thereby determine whether the individual has precancerous lesions (polyps); and / or

[0142] - at least 4, 10 or 20 of the chromosome interactions shown in Table 1 to thereby determine the colorectal cancer status of the individual. Techniques Used to Identify the Specific Relevant Chromosome Interactions

[0143] The EpiSwitch™ platform technology detects epigenetic regulatory signatures of regulatory changes between normal and abnormal conditions or a phenotype of interest at loci. The EpiSwitch™ platform identifies and monitors the fundamental epigenetic level of gene regulation associated with regulatory high order structures of human chromosomes also known as chromosome conformation signatures. Chromosome signatures are a distinct primary step in a cascade of gene deregulation. They are high order biomarkers with a unique set of advantages against biomarker platforms that utilize late epigenetic and gene expression biomarkers, such as DNA methylation and RNA profiling.

[0144] Epi Switch™ Array Assay

[0145] The custom EpiSwitch™ array-screening platforms come in 4 densities of, 15K, 45K, 100K, and 250K unique chromosome conformations, each chimeric fragment is repeated on the arrays 4 times, making the effective densities 60K, 180K, 400K and 1 million respectively.

[0146] Custom Designed Epi Switch™ Arrays

[0147] The 15K EpiSwitch™ array can screen the whole genome including around 300 loci interrogated with the EpiSwitch™ Biomarker discovery technology. The EpiSwitch™ array is built on the Agilent SurePrint G3 Custom CGH microarray platform; this technology offers 4 densities, 60K, 180K, 400K and 1 Million probes. The density per array is reduced to 15K, 45K, 100K and 250K as each EpiSwitch™ probe is presented as a quadruplicate, thus allowing for statistical evaluation of the reproducibility. The average number of potential EpiSwitch™ markers interrogated per genetic loci is 50, as such the numbers of loci that can be investigated are 300, 900, 2000, and 5000.

[0148] Epi Switch™ Custom Array Pipeline

[0149] The EpiSwitch™ array is a dual colour system with one set of samples, after EpiSwitch™ library generation, labelled in Cy5 and the other of sample (controls) to be compared / analyzed labelled in Cy3. The arrays are scanned using the Agilent SureScan Scanner and the resultant features extracted using the Agilent Feature Extraction software. The data is then processed using the EpiSwitch™ array processing scripts in R. The arrays are processed using standard dual colour packages in Bioconductor in R: Limma*. The normalisation of the arrays is done using the normalisedWithinArrays function in Limma* and this is done to the on chip Agilent positive controls and EpiSwitch™ positive controls. The data is filtered based on the Agilent Flag calls, the Agilent control probes are removed and the technical replicate probes are averaged, in order for them to be analysed using Limma*. The probes are modelled based on their difference between the 2 scenarios being compared and then corrected by using False Discovery Rate. Probes with Coefficient of Variation (CV) <=30% that are <=-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.

[0150] * 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.

[0151] 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).

[0152] Statistical Pipeline

[0153] EpiSwitch™ screening arrays are processed using the EpiSwitch™ Analytical Package in R in orderto select high value EpiSwitch™ markers fortranslation on to the EpiSwitch™ PCR platform.

[0154] Step 1

[0155] 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 fold change (FC), probes FC 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.

[0156] Step 2

[0157] The top 250 markers from the statistical lists are selected based on their FC for selection as markers for PCR translation.

[0158] Step 3

[0159] The resultant markers from step 1 , the statistically significant probes form the bases of enrichment analysis using hypergeometric enrichment (HE). This analysis enables marker reduction from the significant probe list, and along with the markers from step 2 forms the list of probes translated on to the EpiSwitch™ PCR platform.

[0160] 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.

[0161] The most significant enriched loci based on a corrected p-value are selected for probe list generation. Genetic locations below p-value of 0.3 or 0.2 are selected. The statistical probes mapping to these genetic locations, with the markers from step 2, form the high value markers for EpiSwitch™ PCR translation.

[0162] Array design and processing

[0163] Array Design

[0164] Genetic loci are processed using the SI I software (currently v3.2) to:

[0165] - Pull out the sequence of the genome at these specific genetic loci (gene sequence with 50kb upstream and 20kb downstream)

[0166] - Define the probability that a sequence within this region is involved in CCs

[0167] - Cut the sequence using a specific RE

[0168] - Determine which restriction fragments are likely to interact in a certain orientation

[0169] - Rank the likelihood of different CCs interacting together.

[0170] - Determine array size and therefore number of probe positions available (x)

[0171] - Pull out x / 4 interactions.

[0172] - For each interaction define sequence of 30bp to restriction site from part 1 and 30bp to restriction site of 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.

[0173] - Create list of x / 4 probes plus defined control probes and replicate 4 times to create list to be created on array

[0174] - Upload list of probes onto Agilent Sure design website for custom CGH array.

[0175] - Use probe group to design Agilent custom CGH array.

[0176] Array Processing

[0177] - Process samples using EpiSwitch™ Standard Operating Procedure (SOP) for template production.

[0178] - Clean up with ethanol precipitation by array processing laboratory.

[0179] - 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

[0180] - Scan using Agilent C Scanner using Agilent feature extraction software.

[0181] EpiSwitch™ biomarker 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 conformation signatures as a highly informative class of epigenetic biomarkers. Current research methods deployed in academic 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 not have the benefit of the targeted insight provided by the EpiSwitch™ Analytical Package at the design stage.

[0182] Epi Switch™ Array in silico marker identification

[0183] CCS sites across the genome are directly evaluated by the EpiSwitch™ Array on clinical samples from testing cohorts for identification of all relevant stratifying lead biomarkers. The EpiSwitch™ Array platform 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 markers identified through the in silico software to be interrogated.

[0184] Epi Switch™ PCR

[0185] Potential markers identified by EpiSwitch™ Array are then validated either by EpiSwitch™ PCR or DNA sequencers (i.e. Roche 454, Nanopore MinlON, etc.). The top PCR markers which are statistically significant and display the best reproducibility are selected for further reduction into the final EpiSwitch™ Signature Set (top 12 markers) and validated on an independent cohort of samples. EpiSwitch™ PCR can be performed 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 to ensure 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 are sensitive enough to detect abnormalities in very low copy numbers using small volumes of blood.

[0186] Use of a Classifier

[0187] 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 and such training may assist the classifier to detect specific colorectal cancer status groups the classifier was trained on.

[0188] Examples

[0189] Relevant Characteristics of Colorectal Cancer (CRC)

[0190] Some of the inherited genetic disorders can cause colorectal cancer and are responsible for circa 5% of all CRC cases. Those include familial adenomatous polyposis and hereditary non-polyposis colon cancer. However, 75-95% of colorectal cancer cases occur in people with little or no genetic risk.

[0191] CRC often starts as a benign polyp, becoming cancerous over time. The polyp to cancer progression sequence is the classical scenario of pathogenesis, with normal epithelial cells progressing to dysplastic cells such as adenomas, and then to carcinoma. Central to this progression are gene mutations, epigenetic alterations, and local inflammatory changes.

[0192] Treatments for CRC include surgery, radiation therapy, chemotherapy, and immuno-oncology therapy with checkpoint inhibitors. Early CRC stages (1 and 2) are confined within the wall of the colon and could be treated effectively. Late stages (3 and 4) often spread widely and are not curable. The individual likelihood of survival depends on how advanced the cancer is. The five- year survival rate, according to US statistics, was around 65%. Globally, colorectal cancer is the third most common cancer type, accounting for 10% of all cases. There were 1.09 million new cases and 551 ,000 deaths from the disease in 2018. In this context, screening from the age of 45 for an early detection of CRC is considered an effective measure for preventing and decreasing deaths from colorectal cancer.

[0193] Colorectal cancer diagnosis is performed by sampling of areas of the colon suspicious for possible tumor development, typically during colonoscopy or sigmoidoscopy, depending on the location of the lesion.

[0194] Notably, a colorectal cancer is sometimes initially and belatedly discovered on CT scan. Presence of metastases is determined by a CT scan of the chest, abdomen and pelvis. Other potential imaging tests such as PET and MRI also used in certain cases. MRI is particularly useful to determine local stage of the tumor and to plan the optimal surgical approach. MRI is also performed after completion of neoadjuvant chemoradiotherapy of CRC. Patients selected for non- surgical treatment of rectal cancer have periodic MRI scans, physical examinations, and undergo endoscopy procedures to detect any tumor re-growth. In addition, MRI tumor regression grades are monitored after chemoradiotherapy in correlation with patients' survival outcomes.

[0195] The histopathologic characteristics of the tumor are reported from the analysis of tissue taken from a biopsy or surgery. The most common form of colon cancer is adenocarcinoma, constituting between 95% and 98% of all cases of colorectal cancer.

[0196] As more than 80% of colorectal cancers arise from adenomatous polyps, screening for this cancer is effective for both early detection and for prevention. Diagnosis of cases of colorectal cancer through screening tends to occur 2-3 years before diagnosis of cases with symptoms, mainly at the asymptomatic stage. Any polyps that are detected can be removed, usually by colonoscopy or sigmoidoscopy, and thus prevent them from turning into cancer. It is evaluated that effective screening has the potential to reduce colorectal cancer deaths by 60%.

[0197] The main screening tests are based colonoscopy (and flexible sigmoidoscopy), fecal occult blood testing, and monitoring of cell-free DNA from CRC tumor in blood. Fecal occult blood testing (FOBT) of the stool is either guaiac-based or immunochemical-based in its detection of blood in stool, as a symptom of CRC. If abnormal FOBT results are found, participants are typically referred for a follow-up colonoscopy examination. Latest studies confirm that these tests may miss more than half of bowel cancer cases.

[0198] Other options include CT scan and stool DNA screening testing (FIT-DNA). Colonoscopy via a CT scan is expensive, associated with radiation exposure, and cannot remove any detected abnormal growths as standard colonoscopy can. Faecal Immunochemical Test (FIT) measures blood in faeces, and people with levels above a certain threshold may have bowel tissue examined for signs of cancer. Stool FIT-DNA screening test looks also for altered DNA associated with colorectal cancer and precancerous lesions. A positive result should be followed by colonoscopy. FIT-DNA has a high level of false positives results. The UK Bowel Cancer Screening Programme recommends a faecal immunochemical test (FIT) every two years.

[0199] The treatment of colorectal cancer can be aimed at cure or palliation. At an early stage, colorectal cancer may be removed during a colonoscopy. For people with localized cancer, the preferred treatment is complete surgical removal, with the attempt of achieving a cure. The procedure of choice is a partial colectomy. If there are a few metastases in the liver or lungs, these may also be removed.

[0200] Chemotherapy may be used in addition to surgery in certain cases. The decision to add chemotherapy in management of colon and rectal cancer depends on the stage of the disease. In Stage I colon cancer, no chemotherapy is offered, and surgery is the definitive treatment. The role of chemotherapy in Stage II colon cancer is debatable and is usually not offered unless risk factors such as undifferentiated tumor, vascular invasion or inadequate lymph node sampling is identified. For Stage III and Stage IV colon cancer, chemotherapy is an integral part of treatment.

[0201] For Stages III and IV, when cancer has spread to the lymph nodes or distant organs, chemotherapy treatment with fluorouracil, capecitabine or oxaliplatin increases life expectancy. If the lymph nodes do not contain cancer, the benefits of chemotherapy are controversial. Chemotherapy drugs may also irinotecan, oxaliplatin. The drugs capecitabine and fluorouracil are interchangeable, with capecitabine being an oral medication and fluorouracil being an intravenous medicine. Some specific regimens used for CRC can also include CAPOX, FOLFOX, FOLFOXIRI, and FOLFIRI. Antiangiogenic drugs such as bevacizumab are often added in first line therapy. Another class of drugs used in the second line setting are epidermal growth factor receptor inhibitors, of which the three FDA approved ones are aflibercept, cetuximab and panitumumab.

[0202] If the cancer is widely metastatic or unresectable, treatment is then palliative. Notably, low stage rectal cancer is often treated with radio therapy in conjunction with chemotherapy in a neoadjuvant fashion to enable surgical resection.

[0203] The average five-year recurrence rate in people with colon cancer where surgery is successful is 5% for stage I cancers, 12% in stage II and 33% in stage III. However, depending on the number of risk factors it ranges from 9-22% in stage II and 17-44% in stage III. The average five-year recurrence rate in people with rectal cancer where surgery is successful is 9% for stage 0 (after pre-treatment) cancers, 8% for stage I cancers, 18% in stage II and 34% in stage III. Depending on the number of risk factors (0-2) the risk for distant metastasis in rectal cancer ranges from 4- 11% in stage 0, 6-12% in stage I, 11-28% in stage II and 15-43% in stage III.

[0204] Five-year overall survival (OS) in rectal cancer after preoperative treatment and surgery was 90% for stage 0, 86% for stage I, 78% for stage II, and 67% for stage III according to a nationwide, population-based study.

[0205] Immunotherapy with immune checkpoint inhibitors has been found to be beneficial for patients with mismatch repair deficiency and microsatellite instability, as potential responders. Pembrolizumab (Keytruda) is approved for advanced CRC tumours that are MMR deficient and have failed usual treatments.

[0206] Regarding CRC prevalence, in the UK about 41 ,000 people a year get colon cancer making it the fourth most common type. In US, in 2022, the incidence of colorectal cancer was anticipated to be about 151 ,000 adults, including over 106,000 new cases of colon cancer (some 54,000 men and 52,000 women) and about 45,000 new cases of rectal cancer. Moreover, incidence of colorectal cancer has almost doubled over the last 25 years in individuals aged 25 to 50. Colorectal cancer also disproportionately affects the Black community, where the rates are the highest of any racial / ethnic group in the US. African Americans are about 20% more likely to get colorectal cancer and about 40% more likely to die from it than most other groups.

[0207] Pre-cancerous lesions: colorectal polyps

[0208] A colorectal polyp is a fleshy growth occurring on the lining of the colon or rectum]. They are not usually associated with symptoms. Untreated colorectal polyps can develop into colorectal cancer. Of the main types, the following polyps could be mentioned:

[0209] 1) hyperplastic, found in the distal colon and rectum, with no malignant potential.

[0210] 2) neoplastic, with lost differentiation and with either being or malignant potential.

[0211] 3) adenomas, i.e. neoplastic polyps of the bowl with not yet gained properties of cancer. Normally, as a rule, an adenoma that is greater than 0.5 cm is removed.

[0212] 4) inflammatory, associated with conditions such as ulcerative colitis and Crohn's disease.

[0213] By United States guidelines, the colonoscopy follow up recommendation is dependent on the initial result of the check up, withing the period of 10 years after normal results, down to 1 year following the discovery of >10 adenomas on a single examination.

[0214] Biomarkers for Colorectal Polyps and Colorectal Cancer

[0215] This work includes 12 preferred EpiSwitch qPCR blood-based biomarkers (see tables 2 and 3) that have been discovered in patients representing the pathophysiological onset of precancerous lesions (polyps), with most of them further advancing into colorectal cancer. Every marker in these tables is effective in a test (for example based on the p-values which are shown). However the markers will be more effective when used together. Analysis in these Examples with groups of markers provides additional evidence of performance, but in fact the p-value alone is sufficient evidence of effectiveness.

[0216] Today the main challenge in colorectal cancer is a reliable early detection, with active treatments and prophylactics of pre-cancerous lesions offering effective cure and reduced mortality. Unfortunately, most of the current early detection tests in CRC and polyps performs poorly in detection of early stages of CRC, produces a lot of false positives and misses most of the polyps. The biomarkers presented offer two step classifier identifying patients with CRC for direct intervention, and then presence of polyps for prophylactic intervention in the remaining cases.

[0217] Table 1 - Top EpiSwitch array markers were identified as statistically significant and consistently present, based on standard statistical analysis based on P Value and adjusted P value, when screened on bloods from patients with cases of healthy, colorectal cancer and polyps.

[0218] Table 2 - Colorectal cancer Classifier (CRC). Top array markers were selected based on statistical analysis conducted using LIMMA statistical package, then translated into qPCR format and further reduced to the top 12 qPCR markers using feature extraction and marker reduction. Based on the data from the training cohort (Cohorts 1 and 2), the top 8 markers from 12 were built into a classifier model using machine learning package XGBoost which in validation across 325 samples in validation cohorts Train and Test demonstrated high efficacy of 88.31% accuracy, 91.23% sensitivity, 85.06% specificity, and 87.15% positive predictive value stratifying patients, with and without colorectal cancer.

[0219] Table 3 - Polyps / No Polyps Classifier. For patients classified as No CRC, a second assessment has been evaluated. Based on the data from the Polyps training cohort (Cohorts 1 and 2), a separate set of top 8 markers from 12 were built into a classifier model using machine learning approach XGBoost, which in validation across 154 samples in validation cohorts Train and Test demonstrated high efficacy of 83.77% accuracy, 84.09% sensitivity, 83.64% specificity, and 67.27% positive predictive value stratifying patients without colorectal cancer, with and without polyps.

[0220] The two developed sets of 8 marker classifiers from the common pool of top 12 markers reflect pathophysiological relationship between precancerous lesions, also known as adenomas or polyps, and colorectal cancer. In fact, the two sets of 8 markers for two classifier models share 4 markers (shown in table 4), reflecting those shared pathological dysregulations, that could be detected in blood for both types of pathology: precancerous and cancerous.

[0221] Validation of the markers was performed not only on the retrospective independent cohorts, but also on prospective cohorts. Cohorts 3 & 4 were prospective collections from Malaysia. Cohort 4 was based on 'all comers' reporting to the doctor with a potential for getting a colonoscopy, i.e. early detection of CRC. They were all getting a blood sample for EpiSwitch CRC / Polyp testing first and then underwent the colonoscopy test assessment.

[0222] Table 5 shows Cancer vs No Cancer (Independent testing cohort only). The testing cohort consisted of 251 samples, 125 samples were correctly classified as Cancer (CRC) and 89 samples were correctly classified as Non-Cancer, the remaining 37 samples were either false positives or false negatives.

[0223] Table 6 shows Cancer vs No Cancer (Train and Test cohorts combined). The combined cohorts consisted of 325 samples, 156 samples were correctly classified as cancer (CRC) and 131 samples were correctly classified as Non-Cancer, the remaining 38 samples were either false positives or negatives.

[0224] Table 7 shows Cancer vs No Cancer (No late stage samples i.e. stage 3 and 4). The early stage cohort consists of 149 samples, 31 samples were correctly classified as Cancer (CRC) and 89 samples were correctly classified as Non-Cancer, the remaining 29 samples were either false positives or negatives. Table 8 shows Polyp vs No Polyp (Train and Test cohorts combined). The combined Train and Test cohort contained 154 samples, 37 samples were correctly classified as Polyp and 92 samples were correctly classified as No Polyp. The remaining 25 samples were either false positive or negatives. Table 9 shows Polyp vs No Polyp (All samples classified as “No Cancer” by clinical annotation, Testing cohorts only). The clinical non cancer control cohort consists of 142 samples, 27 samples were correctly classified as Polyp and 90 samples were correctly classified as No Polyp. The remaining 25 samples are either false positivise or negatives.

[0225] Table 10 shows Polyp vs No Polyp (All samples classified as “No Cancer” by the CRC classifier, Testing cohorts only). A total of 122 samples were classified as “No Cancer” by the CRC classifier.

[0226] Out of 122 samples, 22 samples were correctly classified as Polyp and 81 samples were correctly classified as No Polyp, 19 samples were either False positive or negative samples.

[0227] Tables 11 to 13 show comparisons of the performance of the markers of tables 2 and 3 compared to performance of other tests for colorectal cancer based on the available data for those other tests.

[0228] - 1 - Table 1

[0229]

[0230]

[0231]

[0232] Table 2

[0233] Table 3

[0234]

[0235] Table 4

[0236] Table 5

[0237] Table 6 Model 47 - Cancer or No Cancer (All Data: Train and Test Cohorts)

[0238] Table 7 Model 47 - Cancer or No Cancer (No Late Stage Samples S3+S4)

[0239] Table 8 Model 50 - Polyp or No Polyp (All Samples: Train and Test Cohorts)

[0240] Table 9

[0241] Model 50 - Polyp or No Polyp (All Clinical no-CRC Controls, No Train Cohort)

[0242] Table 10 Model 50 - Polyp or No Polyp (All Model 47 no-CRC Calls, No Train Cohort)

[0243] Table 11

[0244] Table 12 Table 13

[0245] Detection of colorectal cancer (stages I - IV)

[0246] Freenome

[0247] EpiSwitch® Guardant CoIoguard FIT PREEMPT Colonoscopy NST Shield® CRC®

[0248] Sensitivity

[0249] Specificity

[0250] PPV

[0251] NPV

[0252] able 14

[0253] 0118 2 Polyp Train cohort Colorectal Cancer Retrospective0138 2 Colorectal Cancer Retrospective0140 2 Colorectal Cancer Retrospective0150 2 Control Retrospective0151 2 Control Retrospective0153 2 Control Retrospective0156 2 Control Retrospective0159 2 Control Retrospective0162 2 Control Retrospective0165 2 Control Retrospective0166 2 Control Retrospective0169 2 Control Retrospective0172 2 Control Retrospective0174 2 Control Retrospective0177 2 Control Retrospective0180 2 Control Retrospective0185 2 Control Retrospective0187 2 Control Retrospective0188 2 Control Retrospective0189 2 Control Retrospective0190 2 Control Retrospective0195 2 Control Retrospective0209 2 Polyp Train cohort Colorectal Cancer Retrospective0216 2 Polyp Train cohort Colorectal Cancer Retrospective 021 2 Polyp Train cohort Colorectal Cancer Retrospective 022 2 Polyp Train cohort Colorectal Cancer Retrospective

[0254] 028 2 Colorectal Cancer Retrospective031 2 Colorectal Cancer Retrospective001 l(Train) Colorectal Cancer Train cohort Colorectal Cancer Retrospective002 l(Train) Colorectal Cancer Train cohort Colorectal Cancer Retrospective004 l(Train) Colorectal Cancer Train cohort Colorectal Cancer Retrospective009 l(Train) Colorectal Cancer Train cohort Colorectal Cancer Retrospective003 l(Train) Colorectal Cancer Train cohort Colorectal Cancer Retrospective007 l(Train) Colorectal Cancer Train cohort Control Retrospective010 l(Train) Colorectal Cancer Train cohort Control Retrospective011 l(Train) Colorectal Cancer Train cohort Control Retrospective013 l(Train) Colorectal Cancer Train cohort Control Retrospective016 l(Train) Colorectal Cancer Train cohort Control Retrospective019 l(Train) Colorectal Cancer Train cohort Control Retrospective027 l(Train) Colorectal Cancer Train cohort Control Retrospective034 l(Train) Colorectal Cancer Train cohort Control Retrospective035 l(Train) Colorectal Cancer Train cohort Control Retrospective036 l(Train) Colorectal Cancer Train cohort Control Retrospective037 l(Train) Colorectal Cancer Train cohort Control Retrospective039 l(Train) Colorectal Cancer Train cohort Control Retrospective042 l(Train) Colorectal Cancer Train cohort Control Retrospective0015 2 (Train) Colorectal Cancer Train cohort Polyp Train cohort Colorectal Cancer Retrospective0021 2 (Train) Colorectal Cancer Train cohort Colorectal Cancer Retrospective0022 2 (Train) Colorectal Cancer Train cohort Colorectal Cancer Retrospective0046 2 (Train) Colorectal Cancer Train cohort Colorectal Cancer Retrospective0049 2 (Train) Colorectal Cancer Train cohort Polyp Train cohort Colorectal Cancer Retrospective0050 2 (Train) Colorectal Cancer Train cohort Colorectal Cancer Retrospective

[0255] -67-

[0256] 0055 2 (Train) Colorectal Cancer Train cohort Polyp Train cohort Colorectal Cancer Retrospective0056 2 (Train) Colorectal Cancer Train cohort Colorectal Cancer Retrospective0057 2 (Train) Colorectal Cancer Train cohort Polyp Train cohort Colorectal Cancer Retrospective0073 2 (Train) Colorectal Cancer Train cohort Polyp Train cohort Colorectal Cancer Retrospective0076 2 (Train) Colorectal CancerTrain cohort Polyp Train cohort Colorectal Cancer Retrospective0079 2 (Train) Colorectal CancerTrain cohort Polyp Train cohort Colorectal Cancer Retrospective0080 2 (Train) Colorectal CancerTrain cohort Colorectal Cancer Retrospective0084 2 (Train) Colorectal CancerTrain cohort Polyp Train cohort Colorectal Cancer Retrospective0087 2 (Train) Colorectal CancerTrain cohort Polyp Train cohort Colorectal Cancer Retrospective0095 2 (Train) Colorectal CancerTrain cohort Polyp Train cohort Colorectal Cancer Retrospective0102 2 (Train) Colorectal CancerTrain cohort Polyp Train cohort Colorectal Cancer Retrospective0114 2 (Train) Colorectal CancerTrain cohort Polyp Train cohort Colorectal Cancer Retrospective0141 2 (Train) Colorectal CancerTrain cohort Colorectal Cancer Retrospective0143 2 (Train) Colorectal CancerTrain cohort Polyp Train cohort Colorectal Cancer Retrospective0146 2 (Train) Colorectal CancerTrain cohort Polyp Train cohort Colorectal Cancer Retrospective0147 2 (Train) Colorectal CancerTrain cohort Colorectal Cancer Retrospective0149 2 (Train) Colorectal CancerTrain cohort Control Retrospective0152 2 (Train) Colorectal CancerTrain cohort Control Retrospective0154 2 (Train) Colorectal CancerTrain cohort Control Retrospective0157 2 (Train) Colorectal CancerTrain cohort Control Retrospective0158 2 (Train) Colorectal CancerTrain cohort Control Retrospective0160 2 (Train) Colorectal CancerTrain cohort Control Retrospective0161 2 (Train) Colorectal CancerTrain cohort Control Retrospective0163 2 (Train) Colorectal CancerTrain cohort Control Retrospective0167 2 (Train) Colorectal CancerTrain cohort Control Retrospective0168 2 (Train) Colorectal CancerTrain cohort Control Retrospective

[0257] 0170 2 (Train) Colorectal Cancer Train cohort Control Retrospective0171 2 (Train) Colorectal Cancer Train cohort Control Retrospective0173 2 (Train) Colorectal Cancer Train cohort Control Retrospective0175 2 (Train) Colorectal Cancer Train cohort Control Retrospective0176 2 (Train) Colorectal Cancer Train cohort Control Retrospective0179 2 (Train) Colorectal Cancer Train cohort Control Retrospective0181 2 (Train) Colorectal Cancer Train cohort Control Retrospective0182 2 (Train) Colorectal Cancer Train cohort Control Retrospective0183 2 (Train) Colorectal Cancer Train cohort Control Retrospective0184 2 (Train) Colorectal Cancer Train cohort Control Retrospective0186 2 (Train) Colorectal Cancer Train cohort Control Retrospective0191 2 (Train) Colorectal Cancer Train cohort Control Retrospective0192 2 (Train) Colorectal Cancer Train cohort Control Retrospective0193 2 (Train) Colorectal Cancer Train cohort Control Retrospective0194 2 (Train) Colorectal Cancer Train cohort Control Retrospective0196 2 (Train) Colorectal Cancer Train cohort Control Retrospective0197 2 (Train) Colorectal Cancer Train cohort Control Retrospective0198 2 (Train) Colorectal Cancer Train cohort Control Retrospective0199 2 (Train) Colorectal Cancer Train cohort Control Retrospective0202 2 (Train) Colorectal Cancer Train cohort Colorectal Cancer Retrospective0212 2 (Train) Colorectal Cancer Train cohort Colorectal Cancer Retrospective0218 2 (Train) Colorectal Cancer Train cohort Polyp Train cohort Colorectal Cancer Retrospective010 2 (Train) Colorectal Cancer Train cohort Colorectal Cancer Retrospective030 2 (Train) Colorectal Cancer Train cohort Polyp Train cohort Colorectal Cancer Retrospective008 1 (St3_St4) Colorectal Cancer Retrospective012 1 (St3_St4) Colorectal Cancer Retrospective

[0258] 016 1 (St3_St4) Colorectal Cancer Retrospective019 1 (St3_St4) Colorectal Cancer Retrospective024 1 (St3_St4) Colorectal Cancer Retrospective025 1 (St3_St4) Colorectal Cancer Retrospective027 1 (St3_St4) Colorectal Cancer Retrospective026 1 (St3_St4) Colorectal Cancer Retrospective0001 2 (St3_St4) Colorectal Cancer Retrospective0002 2 (St3_St4) Colorectal Cancer Retrospective0004 2 (St3_St4) Colorectal Cancer Retrospective0005 2 (St3_St4) Colorectal Cancer Retrospective0006 2 (St3_St4) Colorectal Cancer Retrospective0007 2 (St3_St4) Colorectal Cancer Retrospective0008 2 (St3_St4) Colorectal Cancer Retrospective0009 2 (St3_St4) Colorectal Cancer Retrospective0010 2 (St3_St4) Colorectal Cancer Retrospective0012 2 (St3_St4) Colorectal Cancer Retrospective0013 2 (St3_St4) Colorectal Cancer Retrospective0014 2 (St3_St4) Colorectal Cancer Retrospective0016 2 (St3_St4) Colorectal Cancer Retrospective0017 2 (St3_St4) Colorectal Cancer Retrospective0018 2 (St3_St4) Colorectal Cancer Retrospective0019 2 (St3_St4) Colorectal Cancer Retrospective0020 2 (St3_St4) Colorectal Cancer Retrospective0023 2 (St3_St4) Colorectal Cancer Retrospective0024 2 (St3_St4) Colorectal Cancer Retrospective0025 2 (St3_St4) Colorectal Cancer Retrospective

[0259] 0026 2 (St3_St4) Colorectal Cancer Retrospective0027 2 (St3_St4) Colorectal Cancer Retrospective0029 2 (St3_St4) Colorectal Cancer Retrospective0030 2 (St3_St4) Colorectal Cancer Retrospective0031 2 (St3_St4) Colorectal Cancer Retrospective0032 2 (St3_St4) Colorectal Cancer Retrospective0033 2 (St3_St4) Colorectal Cancer Retrospective0034 2 (St3_St4) Colorectal Cancer Retrospective0035 2 (St3_St4) Colorectal Cancer Retrospective0036 2 (St3_St4) Colorectal Cancer Retrospective0039 2 (St3_St4) Colorectal Cancer Retrospective0040 2 (St3_St4) Colorectal Cancer Retrospective0042 2 (St3_St4) Colorectal Cancer Retrospective0044 2 (St3_St4) Colorectal Cancer Retrospective0047 2 (St3_St4) Colorectal Cancer Retrospective0052 2 (St3_St4) Colorectal Cancer Retrospective0053 2 (St3_St4) Colorectal Cancer Retrospective0058 2 (St3_St4) Colorectal Cancer Retrospective0060 2 (St3_St4) Colorectal Cancer Retrospective0061 2 (St3_St4) Colorectal Cancer Retrospective0062 2 (St3_St4) Colorectal Cancer Retrospective0063 2 (St3_St4) Colorectal Cancer Retrospective0064 2 (St3_St4) Colorectal Cancer Retrospective0065 2 (St3_St4) Colorectal Cancer Retrospective0066 2 (St3_St4) Colorectal Cancer Retrospective0069 2 (St3_St4) Colorectal Cancer Retrospective

[0260] -71-

[0261] 0070 2 (St3_St4) Colorectal Cancer Retrospective0071 2 (St3_St4) Colorectal Cancer Retrospective0072 2 (St3_St4) Colorectal Cancer Retrospective0074 2 (St3_St4) Colorectal Cancer Retrospective0077 2 (St3_St4) Colorectal Cancer Retrospective0078 2 (St3_St4) Colorectal Cancer Retrospective0081 2 (St3_St4) Colorectal Cancer Retrospective0083 2 (St3_St4) Colorectal Cancer Retrospective0086 2 (St3_St4) Colorectal Cancer Retrospective0088 2 (St3_St4) Colorectal Cancer Retrospective0089 2 (St3_St4) Colorectal Cancer Retrospective0094 2 (St3_St4) Colorectal Cancer Retrospective0096 2 (St3_St4) Colorectal Cancer Retrospective0098 2 (St3_St4) Colorectal Cancer Retrospective0099 2 (St3_St4) Colorectal Cancer Retrospective0100 2 (St3_St4) Colorectal Cancer Retrospective0101 2 (St3_St4) Colorectal Cancer Retrospective0103 2 (St3_St4) Colorectal Cancer Retrospective0104 2 (St3_St4) Colorectal Cancer Retrospective0105 2 (St3_St4) Colorectal Cancer Retrospective0106 2 (St3_St4) Colorectal Cancer Retrospective0109 2 (St3_St4) Colorectal Cancer Retrospective0110 2 (St3_St4) Colorectal Cancer Retrospective0112 2 (St3_St4) Colorectal Cancer Retrospective0113 2 (St3_St4) Colorectal Cancer Retrospective0115 2 (St3_St4) Colorectal Cancer Retrospective

[0262] -72-

[0263]

[0264] Screen 3 0227 (CR26) Polyp Train cohort Control No Polyp Prospective

[0265] Screen 3 0228 (CR26) Polyp Train cohort Control No Polyp Prospective

[0266] Screen 3 0229 (CR26) Polyp Train cohort Control Polyp Prospective

[0267] Screen 3 0230 (CR26) Polyp Train cohort Control No Polyp Prospective

[0268] Screen 3 0231 (CR26) Control Polyp Prospective

[0269] Screen 3 0232 (CR26) Polyp Train cohort Control No Polyp Prospective

[0270] Screen 3 0233 (CR26) Polyp Train cohort Control Polyp Prospective

[0271] Screen 3 0234 (CR26) Control Polyp Prospective

[0272] Screen 3 0235 (CR26) Control Polyp Prospective

[0273] Screen 3 0236 (CR26) Control Polyp Prospective

[0274] Screen 3 0237 (CR26) Polyp Train cohort Control Polyp Prospective

[0275] Screen 3 0238 (CR26) Control Polyp Prospective

[0276] Screen 3 0239 (CR26) Polyp Train cohort Control Polyp Prospective

[0277] Screen 3 0240 (CR26) Control Polyp Prospective

[0278] Screen 3 0241 (CR26) Control Polyp Prospective

[0279]

[0280] 0262 Screen 4 Colorectal Cancer Prospective0263 Screen 4 Colorectal Cancer Prospective0264 Screen 4 Colorectal Cancer Prospective0265 Screen 4 Control No Polyp Prospective0266 Screen 4 Control No Polyp Prospective0267 Screen 4 Control No Polyp Prospective0268 Screen 4 Control No Polyp Prospective0269 Screen 4 Control No Polyp Prospective0270 Screen 4 Control No Polyp Prospective0271 Screen 4 Control No Polyp Prospective0272 Screen 4 Control No Polyp Prospective0273 Screen 4 Control No Polyp Prospective0274 Screen 4 Control No Polyp Prospective0275 Screen 4 Control No Polyp Prospective0276 Screen 4 Control No Polyp Prospective0277 Screen 4 Control Polyp Prospective0278 Screen 4 Control Polyp Prospective0279 Screen 4 Control Polyp Prospective0280 Screen 4 Control Polyp Prospective0281 Screen 4 Control No Polyp Prospective0282 Screen 4 Control Polyp Prospective0283 Screen 4 Control Polyp Prospective0284 Screen 4 Control Polyp Prospective0285 Screen 4 Control No Polyp Prospective0286 Screen 4 Control No Polyp Prospective0287 Screen 4 Control Polyp Prospective

[0281] -76-

[0282] 0288 Screen 4 Control No Polyp Prospective0289 Screen 4 Control Polyp Prospective0290 Screen 4 Control No Polyp Prospective0291 Screen 4 Control No Polyp Prospective0292 Screen 4 Control No Polyp Prospective0293 Screen 4 Control Polyp Prospective0294 Screen 4 Control No Polyp Prospective0295 Screen 4 Control Polyp Prospective0296 Screen 4 Control No Polyp Prospective0297 Screen 4 Control No Polyp Prospective0298 Screen 4 Control No Polyp Prospective0299 Screen 4 Control Polyp Prospective0300 Screen 4 Control Polyp Prospective0301 Screen 4 Control No Polyp Prospective0302 Screen 4 Control No Polyp Prospective0303 Screen 4 Control No Polyp Prospective0304 Screen 4 Control No Polyp Prospective0305 Screen 4 Control Polyp Prospective0306 Screen 4 Control No Polyp Prospective0307 Screen 4 Control No Polyp Prospective0308 Screen 4 Control No Polyp Prospective0309 Screen 4 Control Polyp Prospective0310 Screen 4 Control No Polyp Prospective0311 Screen 4 Control Polyp Prospective0312 Screen 4 Control Polyp Prospective0313 Screen 4 Control No Polyp Prospective

[0283] 0314 0315 0316 0317 0318 0319 0320 0321 0322 0323 0324

Claims

1. CLAIMS1. A method of determining colorectal cancer status in an individual comprising detecting the presence or absence in the individual of:- all of the chromosome interactions shown in Table 2 to thereby determine whether the individual has colorectal cancer; and / or- all of the chromosome interactions shown in Table 3 to thereby determine whether the individual has precancerous lesions (polyps).

2. A method according to claim 1 wherein the presence or absence of the chromosome interactions is determined in a sample which is obtained by non-invasive means, preferably a blood sample.

3. A method according to claim 1 or 2 wherein the presence or absence of the chromosome interactions is determined:- 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 a chromosome conformation, and / or- by detecting the presence of a ligated DNA which is generated during said typing and whose sequence 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 the chromosome interaction.

4. A method according to any one of the preceding claims wherein said detecting of the presence or absence of the chromosome interactions is by a process comprising:(i) in vitro crosslinking of chromosome interactions which are present in the DNA of the individual;(ii) subjecting the cross-linked DNA to cleaving;(iii) ligating the cross-linked cleaved DNA ends to form ligated DNA; and(iv) identifying the presence or absence in said ligated DNA of a DNA sequence that corresponds to each chromosome interaction; to thereby determine the presence or absence of each chromosome interaction.-79-5. A method according to claim 3 or 4 wherein said ligated DNA is detected by PCR or by use of a probe.

6. A method according to claim 5 wherein detection is by use of a probe, wherein said probe preferably has at least 70% identity to any of the probes shown in Table 2 or 3.

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

8. A method according to any one of the preceding claims wherein the individual:- has no genetic risk of colorectal cancer; and / or- has previously had treatment for colorectal cancer; and / or- has been tested for colorectal cancer by colonoscopy, fecal occult blood testing or monitoring of cell-free DNA in blood.

9. A method according to any one of the preceding claims, wherein the typing of chromosome interactions comprises specific detection of a ligated DNA by quantitative PCR (qPCR) which uses primers capable of amplifying the ligated DNA and a probe which binds the ligation site during the PCR reaction, wherein said probe comprises sequence which is complementary to sequence from each of the chromosome regions that have come together in the chromosome interaction.

10. A method according to any one of the preceding claims which comprises using a probe which comprises sequence which is complementary to sequence from each of the chromosome regions that have come together in the chromosome interaction, and 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, and optionally- said fluorophore is selected from HEX, Texas Red and FAM.11 . A method according to any one of the preceding claims which comprises using a probe which comprises sequence which is complementary to sequence from each of the chromosome regionsthat have come together in the chromosome interaction, and said probe comprises a nucleic acid sequence of length 10 to 40 nucleotide bases, preferably a length of 20 to 30 nucleotide bases.

12. A method according to any one of the preceding claims further comprising detecting the presence or absence in the individual of one or more of the chromosome interactions shown in Table 1.

13. A therapy for colorectal cancer for use in a method of treating a colorectal cancer in an individual, wherein said method of treating comprises:(a) identifying whether the individual has colorectal cancer or precancerous lesions by the method of any one of the preceding claims, and (b) administering said therapy to an individual that has been identified as having colorectal cancer or precancerous lesions in step (a).-81-

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