Patent Grant
11434522
This application is a US National stage entry of International Application No. PCT/GB2016/051900, which designated the United States and was filed on Jun. 24, 2016, published in English.
This application claims priority under 35 U.S.C. § 119 or 365 to GB Application No. 1511079.4, filed Jun. 24, 2015, GB Application No. 1511080.2, filed Jun. 24, 2015 and GB Application No. 1519555.5, filed Nov. 5, 2015. The entire teachings of the above applications are incorporated herein by reference.
The invention relates to detecting chromosome interactions.
Health care costs are spiralling and so there is a need to treat people more effectively using existing drugs.
The inventors have investigated the use of epigenetic chromosome interactions as the basis of or for use in conjunction with companion diagnostics, and in particular in the detection of epigenetic states to determine responsiveness to therapy (e.g. pharmaceutical therapy), predisposition to disease/conditions, and/or monitoring residual disease. The inventors' work shows the role played by epigenetic interactions in a diverse set of conditions and provides methods for identifying the relevant chromosomal interactions. The invention includes a method of identifying relevant chromosomal interactions based on looking at the chromosome interactions present in subgroups of individuals. The invention also includes using the identified chromosome interactions as the basis for companion diagnostic tests.
Accordingly a first aspect of the invention provides a method of determining the epigenetic chromosome interactions which are relevant to a companion diagnostic that distinguishes between subgroups, comprising contacting a first set of nucleic acids from the subgroups with a second set of nucleic acids representing an index population of chromosome interactions, and allowing complementary sequences to hybridise, wherein the nucleic acids in the first and second sets of nucleic acids represent a ligated product comprising sequences from both of the chromosome regions that have come together in the epigenetic chromosome interaction, and wherein the pattern of hybridisation between the first and second set of nucleic acids allows a determination of which epigenetic chromosome interactions are specific to subgroups in the population, wherein the subgroups differ in a characteristic relevant to a companion diagnostic.
Preferably, in the first aspect (and/or any other aspect) of the invention:
Preferably, in the first and/or other aspects of the invention, the feature “ . . . the nucleic acids in the first and second sets of nucleic acids represent a ligated product comprising sequences from both of the chromosome regions that have come together in the epigenetic chromosome interaction . . . ” can comprise or be: “ . . . the nucleic acids in the first and second sets of nucleic acids are in the form of a ligated product(s) (preferably a ligated nucleic acid(s), more preferably ligated DNA) comprising sequences from both of the chromosome regions that have come together in the epigenetic chromosome interaction”.
A second aspect of the invention provides a companion diagnostic assay method which selects a subgroup having a characteristic relevant to treatment and/or prophylaxis (in particular pharmaceutical treatment and/or prophylaxis), which method comprises:
Preferably, in the second aspect (and/or any other aspect) of the invention:
Other preferred or particular features of or in the second aspect (and/or any other aspect) of the invention include the following:
The companion diagnostic assay method of the second aspect of the invention can particularly be used to detect the presence of any of the specific conditions or characteristics mentioned herein.
Preferably, the companion diagnostic method of the second aspect of the invention is used to detect:
Preferably, in the second aspect and in all other aspects of the invention, the disease or condition (in particular in a human) comprises:
In one embodiment responsiveness to therapy, preferably anti-PD1 therapy, is detected in any of the following cancers, preferably of the stage or class which is indicated and/or preferably with other indicated characteristics such as viral infection:
The condition or characteristic may be:
A third aspect of the present invention provides a therapeutic agent (in particular a pharmaceutical therapeutic agent) for use in the treatment and/or prophylaxis of a condition in an individual (in particular in a human individual), wherein said individual has been identified as being in need of said therapeutic agent by the method of the second aspect of the invention. The third aspect of the invention also provides the use of a therapeutic agent (e.g. pharmaceutical therapeutic agent) in the manufacture of a medicament (in particular a pharmaceutical composition comprising the therapeutic agent) for use in the treatment and/or prophylaxis of a condition in an individual (in particular in a human individual), wherein said individual has been identified as being in need of said therapeutic agent by the method of the second aspect of the invention. The third aspect of the present invention also provides a method of treatment and/or prophylaxis of a condition in an individual (in particular in a human individual and/or an individual in need thereof), comprising administering a therapeutic agent (e.g. pharmaceutical therapeutic agent and/or an effective amount of a therapeutic agent) to the individual, wherein said individual has been identified as being in need of said therapeutic agent by the method of the second aspect of the invention.
Preferably, in the third aspect (and/or other aspects) of the invention, the therapeutic agent (in particular pharmaceutical therapeutic agent) comprises:
A fourth aspect of the invention provides a method of identifying an agent which is capable of changing the disease state of an individual from a first state to a second state comprising determining whether a candidate agent is capable of changing the chromosomal interactions from those corresponding with the first state to chromosomal interactions which correspond to the second state, wherein preferably the first and second state correspond to presence or absence of:
A fifth aspect of the invention provides a method of determining the effect of a drug comprising detecting the change in epigenetic chromosome interactions caused by the drug, wherein said effect is preferably the mechanism of action of the drug or are the pharmacodynamics properties of the drug, and wherein said the chromosome interactions are preferably specific to:
Additionally or alternatively, according to a preferred embodiment of all aspects of the present invention, the present invention does not relate to a method of determining responsiveness to a specific therapy (in particular a specific pharmaceutical therapy) for rheumatoid arthritis in a subject (e.g. a mammalian such as human subject), comprising detecting the presence or absence of 5 or more (in particular 7 or more, or 10 or more, or 15 or more, or 20 or more) chromosomal interactions; wherein said chromosomal interactions are in particular at 5 or more (for example 5) different loci; and/or wherein said detecting in particular comprises determining for each interaction whether or not the regions of a chromosome which are part of the interaction have been brought together.
The invention has several different aspects:
As used herein, the term ‘epigenetic’ interactions typically refers to interactions between distal regions of a locus on a chromosome, said interactions being dynamic and altering, forming or breaking depending upon the status of the region of the chromosome.
In particular methods of the invention, chromosome interactions are detected by first generating a ligated nucleic acid that comprises sequence(s) from both regions of the chromosomes that are part of the chromosome interactions. In such methods the regions can be cross-linked by any suitable means. In a preferred embodiment, the interactions are cross-linked using formaldehyde, but may also be cross-linked by any aldehyde, or D-Biotinoyl-e-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.
The chromosome interaction may reflect the status of the region of the chromosome, for example, if it is being transcribed or repressed in response to change of the physiological conditions. Chromosome interactions which are specific to subgroups as defined herein have been found to be stable, thus providing a reliable means of measuring the differences between the two subgroups.
In addition, chromosome interactions specific to a disease condition will normally occur early in the disease process, for example compared to other epigenetic markers such as methylation or changes to binding of histone proteins. Thus the companion diagnostic method of the invention is able to detect early stages of a disease state. This allows early treatment which may as a consequence be more effective. Another advantage of the invention is that no prior knowledge is needed about which loci are relevant for identification of relevant chromosome interactions. Furthermore there is little variation in the relevant chromosome interactions between individuals within the same subgroup. Detecting chromosome interactions is highly informative with up to 50 different possible interactions per gene, and so methods of the invention can interrogate 500,000 different interactions.
Location and Causes of Epigenetic Interactions
Epigenetic chromosomal interactions may overlap and include the regions of chromosomes shown to encode relevant or undescribed genes, but equally may be in intergenic regions. It should further be noted that the inventors have discovered that epigenetic interactions in all regions are equally important in determining the status of the chromosomal locus. These interactions are not necessarily in the coding region of a particular gene located at the locus and may be in intergenic regions.
The chromosome interactions which are detected in the invention could be caused by changes to the underlying DNA sequence, by environmental factors, DNA methylation, non-coding antisense RNA transcripts, non-mutagenic carcinogens, histone modifications, chromatin remodelling and specific local DNA interactions. The changes which lead to the chromosome interactions may be caused by changes to the underlying nucleic acid sequence, which themselves do not directly affect a gene product or the mode of gene expression. Such changes may be for example, SNP's 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% SNPs are in non-coding regions, and therefore the method as described is also informative in non-coding situation. In one embodiment the regions of the chromosome which come together to form the interaction are less than 5 kb, 3 kb, 1 kb, 500 base pairs or 200 base pairs apart.
The chromosome interaction which is detected in the companion diagnostic method is preferably one which is within any of the genes mentioned in the Tables herein. However it may also be upstream or downstream of the genes, for example up to 50,000, 30,000, 20,000, 10,000 or 5000 bases upstream or downstream from the gene or from the coding sequence.
The chromosome interaction which is detected may or may not be one which occurs between a gene (including coding sequence) and its regulatory region, such as a promoter. The chromosome interaction which is typed may or may not be one which is inherited, for example an inherited imprinted characteristic of a gene region.
Types of Clinical Situation
The specific case of RA (Rheumatoid Arthritis) illustrates the general principles. There are currently no tests that clinicians can use a priori to determine if patients will respond to methotrexate (MTX) when the patients are first given the drug. Since a significant number (about 30%) of patients do not respond to MTX, being able to predict whether a patient is a responder or non-responder will increase the chances of successfully treating RA, as well as saving time and money.
This is one example of how the inventors visualise the present invention to be used. More broadly speaking, the aim of the present invention is to permit detection and monitoring of disease. In more detail, this technology allows stratification based on biomarkers for specific phenotypes relating to medical conditions, i.e. by recognising a particular chromosome confirmation signature and/or a change in that particular signature.
The methods of the invention are preferably used in the context of specific characteristics relating to disease, such as responsiveness to treatments and/or prophylaxes, identification of the most effective therapy/drug, monitoring the course of disease, identifying predisposition to disease, and/or identifying the presence of residual disease and/or the likelihood of relapse. Therefore the methods may or may not be used for diagnosis of the presence of a specific condition. The methods of the invention can be used to type loci where the mechanisms of disease are unknown, unclear or complex.
Detection of chromosome interactions provides an efficient way of following changes at the different levels of regulation, some of which are complex. For example in some cases around 37,000 non-coding RNAs can be activated by a single impulse.
Subgroups and Personalised Treatment
As used herein, a “subgroup” preferably refers to a population subgroup (a subgroup in a population), more preferably a subgroup in a or the population of a particular animal such as a particular mammal (e.g. human, non-human primate, or rodent e.g. mouse or rat) or a particular nematode worm (e.g. C. elegans). Most preferably, a “subgroup” refers to a subgroup in a or the human population.
Particular populations, e.g. human populations, of interest include: the human population overall, a or the human population suffering from a specific condition/disease (in particular inflammatory disease e.g. RA, blood cancer eg AML, solid cancer eg melanoma or prostate cancer (PC), or neurodegenerative disease/condition e.g. Alzheimer's disease (AD)), the human healthy population (healthy controls), the human population which is healthy in the sense of not suffering from the specific condition/disease of interest or of study (eg RA, AML, melanoma, PC or AD), the human population (e.g. either healthy and/or with a specific condition/disease e.g. RA, AML, melanoma, PC or AD) who are responders to a particular drug/therapy, or the human population (e.g. either healthy and/or with a specific condition/disease e.g. RA, AML, melanoma, PC or AD) who are non-responders to a particular drug/therapy.
The invention relates to detecting and treating particular subgroups in a population, preferably in a or the human population. Within such subgroups the characteristics discussed herein (such as responsiveness to treatment and/or prophylaxis; in particular responsiveness to a specific treatment and/or prophylaxis of one or more conditions or diseases, and/or responsiveness to a specific medicine or therapeutically active substance/therapeutic agent, in particular in the treatment and/or prophylaxis of one or more conditions or diseases) will be present or absent. Epigenetic interaction differences on a chromosome are, generally speaking, structural differences which exist at a genomic level. The inventors have discovered that these differ between subsets (for example two, or two or more subsets) in a given population. Identifying these differences will allow physicians to categorize their patients as a part of one subset of the population as described in the method. The invention therefore provides physicians with a method of personalizing medicine for the patient based on their epigenetic chromosome interactions, and provide an alternative more effective treatment and/or prophylaxis regime.
In another embodiment, threshold levels for determining to what extent a subject is defined as belonging to one subgroup and not to a or the other subgroup of the population are applied. In one preferable embodiment wherein the subgroups comprise responders versus non-responders of a therapy for the treatment and/or prophylaxis of a particular disease, said threshold may be measured by change in DAS28 (Disease Activity Score of 28 joints) score, in particular for rheumatoid arthritis.
In one embodiment, a score above 1.2 units indicates a subject falls into the responder subgroup, whilst a score below 1.2 units indicates a subject is defined as a non-responder.
Typically a subgroup will be at least 10%, at least 30%, at least 50% or at least 80% of the general population.
Generating Ligated Nucleic Acids
Certain embodiments of the invention utilise ligated nucleic acids, in particular ligated DNA. These comprise sequences from both of the regions that come together in a chromosome interaction and therefore provide information about the interaction. The EpiSwitch™ method, described herein, uses generation of such ligated nucleic acids to detect chromosome interactions.
One such method, in particular one particular method of detecting chromosome interactions and/or one particular method of determining epigenetic chromosome interactions and/or one particular method of generating ligated nucleic acids (e.g. DNA), comprises the steps of:
(i) in vitro crosslinking of said epigenetic chromosomal interactions present at the chromosomal locus;
(ii) optionally isolating the cross-linked DNA from said chromosomal locus;
(iii) subjecting said cross-linked DNA to restriction digestion with an enzyme that cuts it at least once (in particular an enzyme that cuts at least once within said chromosomal locus);
(iv) ligating said cross-linked cleaved DNA ends (in particular to form DNA loops); and
(v) identifying the presence of said ligated DNA and/or said DNA loops, in particular using techniques such as PCR (polymerase chain reaction), to identify the presence of a specific chromosomal interaction.
One particularly preferable method of detecting, determining and/or monitoring chromosome interactions and/or epigenetic changes, involving inter alia the above-mentioned steps of crosslinking, restriction digestion, ligating, and identifying, is disclosed in WO 2009/147386 A1 (Oxford Biodynamics Ltd), the entire disclosure of which (in particular claims 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or 21 of which) are incorporated herein by reference as though fully set forth. Claim 1 of WO 2009/147386 A1, which can be used in those methods of the present invention which involve a ligated product(s) and/or a ligated nucleic acid(s), discloses a method of monitoring epigenetic changes comprising monitoring changes in conditional long range chromosomal interactions at at least one chromosomal locus where the spectrum of long range interaction is associated with a specific physiological condition, said method comprising the steps of:—
(i) in vitro crosslinking of said long range chromosomal interactions present at the at least one chromosomal locus;
(ii) isolating the cross linked DNA from said chromosomal locus;
(iii) subjecting said cross linked DNA to restriction digestion with an enzyme that cuts at least once within the at least one chromosomal locus;
(iv) ligating said cross linked cleaved DNA ends to form DNA loops; and
(v) identifying the presence of said DNA loops;
wherein the presence of DNA loops indicates the presence of a specific long range chromosomal interaction.
PCR (polymerase chain reaction) may be used to detect or identify the ligated nucleic acid, for example the size of the PCR product produced may be indicative of the specific chromosome interaction which is present, and may therefore be used to identify the status of the locus. The skilled person will be aware of numerous restriction enzymes which can be used to cut the DNA within the chromosomal locus of interest. It will be apparent that the particular enzyme used will depend upon the locus studied and the sequence of the DNA located therein. A non-limiting example of a restriction enzyme which can be used to cut the DNA as described in the present invention is Taq I polymerase.
Embodiments Such as EpiSwitch™ Technology
The EpiSwitch™ Technology relates to the use of microarray EpiSwitch™ marker data in the detection of epigenetic chromosome conformation signatures specific for phenotypes. The present inventors describe herein how the EpiSwitch™ Array Platform has been used for discovery of chromosome signature pool of potential biomarkers specific for particular disadvantageous phenotypes subgroups versus healthy controls. The inventors also provide examples of validated use and translation of chromosome conformation signatures from microarray into PCR platform with examples of several markers specific between subgroups from the cohorts tested on the array.
Embodiments such as EpiSwitch™ which utilise ligated nucleic acids in the manner described herein (for identifying relevant chromosome interactions and in companion diagnostic methods) have several advantages. They have a low level of stochastic noise, for example because the nucleic acid sequences from the first set of nucleic acids of the present invention either hybridise or fail to hybridise with the second set of nucleic acids. This provides a binary result permitting a relatively simple way to measure a complex mechanism at the epigenetic level. EpiSwitch™ technology also has fast processing time and low cost. In one embodiment the processing time is 3 hours to 6 hours.
Samples and Sample Treatment
The sample will contain DNA from the individual. It will normally contain cells. In one embodiment a sample is obtained by minimally invasive means, and may for example be blood. DNA may be extracted and cut up with standard restriction enzymes. This can pre-determine which chromosome conformations are retained and will be detected with the EpiSwitch™ platforms. In one embodiment wherein the sample is a blood sample previously obtained from the patient, the described method is advantageous because the procedure is minimally invasive. Due to the synchronisation of chromosome interactions between tissues and blood, including horizontal transfer, a blood sample can be used to detect the chromosome interactions in tissues, such as tissues relevant to disease. For certain conditions, such as cancer, genetic noise due to mutations can affect the chromosome interaction ‘signal’ in the relevant tissues and therefore using blood is advantageous.
Properties of Nucleic Acids of the Invention
The disclosure herein mentions first and second nucleic acids. In addition the nucleic acids are used in the companion diagnostic method and in other embodiments to detect the presence or absence of chromosome interactions (for example by binding to ligated nucleic acids generated from samples). The nucleic acids of the invention typically comprise two portions each comprising sequence from one of the two regions of the chromosome which come together in the chromosome interaction. Typically each portion is at least 8, 10, 15, 20, 30 or 40 nucleotides in length. Preferred nucleic acids comprise sequence from any of the genes mentioned in the tables, in particular where the nucleic acid is used in an embodiments relevant to the condition relevant for that table. Preferred nucleic acids comprise the specific probe sequences mentioned in the tables for specific conditions or fragments or homologues of such sequences. Preferably the nucleic acids are DNA. It is understood that where a specific sequence is provided the invention may use the complementary as required in the particular embodiment.
The Second Set of Nucleic Acids—the ‘Index’ Sequences
The second set of nucleic acid sequences has the function of being an index, and is essentially a set of nuclei acid sequences which are suitable for identifying subgroup specific sequence. They can represent the ‘background’ chromosomal interactions and might be selected in some way or be unselected. They are in general a subset of all possible chromosomal interactions.
The second set of nucleic acids may be derived by any suitable method. They can be derived computationally or they may be based on chromosome interaction in individuals. They typically represent a larger population group than the first set of nucleic acids. In one particular embodiment, the second set of nucleic acids represents all possible epigenetic chromosomal interactions in a specific set of genes. In another particular embodiment, the second set of nucleic acids represents a large proportion of all possible epigenetic chromosomal interactions present in a population described herein. In one particular embodiment, the second set of nucleic acids represent at least 50% or at least 80% of epigenetic chromosomal interactions in at least 20, 50, 100 or 500 genes.
The second set of nucleic acids typically represents at least 100 possible epigenetic chromosome interactions which modify, regulate or in any way mediate a disease state/phenotype in population. The second set of nucleic acids may represent chromosome interactions that affect a diseases state in a species, for example comprising nucleic acids sequences which encode cytokines, kinases, or regulators associated with any disease state, predisposition to a disease or a disease phenotype. The second set of nucleic acids comprises sequences representing epigenetic interactions relevant and not relevant to the companion diagnostic method.
In one particular embodiment the second set of nucleic acids derive at least partially from a naturally occurring sequences in a population, and are typically obtained by in silico methods. Said nucleic acids may further comprise single or multiple mutations in comparison to a corresponding portion of nucleic acids present in the naturally occurring nucleic acids. Mutations include deletions, substitutions and/or additions of one or more nucleotide base pairs. In one particular embodiment, the second set of nucleic acids may comprise sequence representing a homologue and/or orthologue with at least 70% sequence identity to the corresponding portion of nucleic acids present in the naturally occurring species. In another particular embodiment, at least 80% sequence identity or at least 90% sequence identity to the corresponding portion of nucleic acids present in the naturally occurring species is provided.
Properties of the Second Set of Nucleic Acids
In one particular embodiment, there are at least 100 different nucleic acid sequences in the second set of nucleic acids, preferably at least 1000, 2000 or 5000 different nucleic acids sequences, with up to 100,000, 1,000,000 or 10,000,000 different nucleic acid sequences. A typical number would be 100 to 1,000,000, such as 1,000 to 100,000 different nucleic acids sequences. All or at least 90% or at least 50% or these would correspond to different chromosomal interactions.
In one particular embodiment, the second set of nucleic acids represent chromosome interactions in at least 20 different loci or genes, preferably at least 40 different loci or genes, and more preferably at least 100, at least 500, at least 1000 or at least 5000 different loci or genes, such as 100 to 10,000 different loci or genes.
The lengths of the second set of nucleic acids are suitable for them to specifically hybridise according to Watson Crick base pairing to the first set of nucleic acids to allow identification of chromosome interactions specific to subgroups. Typically the second set of nucleic acids will comprise two portions corresponding in sequence to the two chromosome regions which come together in the chromosome interaction. The second set of nucleic acids typically comprise nucleic acid sequences which are at least 10, preferably 20, and preferably still 30 bases (nucleotides) in length. In another embodiment, the nucleic acid sequences may be at the most 500, preferably at most 100, and preferably still at most 50 base pairs in length. In a preferred embodiment, the second set of nucleic acids comprise nucleic acid sequences of between 17 and 25 base pairs. In one embodiment at least 100, 80% or 50% of the second set of nucleic acid sequences have lengths as described above. Preferably the different nucleic acids do not have any overlapping sequences, for example at least 100%, 90%, 80% or 50% of the nucleic acids do not have the same sequence over at least 5 contiguous nucleotides.
Given that the second set of nucleic acids acts as an ‘index’ then the same set of second nucleic acids may be used with different sets of first nucleic acids which represent subgroups for different characteristics, i.e. the second set of nucleic acids may represent a ‘universal’ collection of nucleic acids which can be used to identify chromosome interactions relevant to different disease characteristics.
The First Set of Nucleic Acids
The first set of nucleic acids are normally from individuals known to be in two or more distinct subgroups defined by presence or absence of a characteristic relevant to a companion diagnostic, such as any such characteristic mentioned herein. The first nucleic acids may have any of the characteristics and properties of the second set of nucleic acids mentioned herein. The first set of nucleic acids is normally derived from a sample from the individual which has undergone treatment and processing as described herein, particularly the EpiSwitch™ cross-linking and cleaving steps. Typically the first set of nucleic acids represent all or at least 80% or 50% of the chromosome interactions present in the samples taken from the individuals.
Typically, the first set of nucleic acids represents a smaller population of chromosome interactions across the loci or genes represented by the second set of nucleic acids in comparison to the chromosome interactions represented by second set of nucleic acids, i.e. the second set of nucleic acids is representing a background or index set of interactions in a defined set of loci or genes.
Library of Nucleic Acids
The nucleic acids described herein may be in the form of a library which comprises at least 200, at least 500, at least 1000, at least 5000 or at least 10000 different nucleic acids from the second set of nucleic acids. The invention provides a particular library of nucleic acids which typically comprises at least 200 different nucleic acids. The library of nucleic acids may have any of the characteristics or properties of the second set of nucleic acids mentioned herein. The library may be in the form of nucleic acids bound to an array.
Hybridisation
The invention requires a means for allowing wholly or partially complementary nucleic acid sequences from the first set of nucleic acids and the second set of nucleic acids to hybridise. In one embodiment all of the first set of nucleic acids is contacted with all of the second set of nucleic acids in a single assay, i.e. in a single hybridisation step. However any suitable assay can be used.
Labelled Nucleic Acids and Pattern of Hybridisation
The nucleic acids mentioned herein may be labelled, preferably using an independent label such as a fluorophore (fluorescent molecule) or radioactive label which assists detection of successful hybridisation. Certain labels can be detected under UV light.
The pattern of hybridisation, for example on an array described herein, represents differences in epigenetic chromosome interactions between the two subgroups, and thus provides a method of comparing epigenetic chromosome interactions and determination of which epigenetic chromosome interactions are specific to a subgroup in the population of the present invention.
The term ‘pattern of hybridisation’ broadly covers the presence and absence of hybridisation between the first and second set of nucleic acids, i.e. which specific nucleic acids from the first set hybridise to which specific nucleic acids from the second set, and so it is not limited to any particular assay or technique, or the need to have a surface or array on which a ‘pattern’ can be detected.
Companion Diagnostic Method
The invention provides a companion diagnostic method based on information provided by chromosome interactions. Two distinct companion diagnostic methods are provided which identify whether an individual has a particular characteristic relevant to a companion diagnostic. One method is based on typing a locus in any suitable way and the other is based on detecting the presence or absence of chromosome interactions. The characteristic may be any one of the characteristics mentioned herein relating to a condition. The companion diagnostic method can be carried out at more than one time point, for example where monitoring of an individual is required.
Companion Diagnostic Method Based on Typing a Locus
The method of the invention which identified chromosome interactions that are specific to subgroups can be used to identify a locus, which may be a gene that can be typed as the basis of companion diagnostic test. Many different gene-related effects can lead to the same chromosome interaction occurring. In this embodiment any characteristic of the locus may be typed, such as presence of a polymorphism in the locus or in an expressed nucleic acid or protein, the level of expression from the locus, the physical structure of the locus or the chromosome interactions present in the locus. In one particular embodiment the locus may be any of the genes mentioned herein in the tables, in particular in Tables 1, 3, 5, 6c, 6E, 18a, 18b, 18c, 18d, 18e, 18f, 22, 23, 24 or 25 (in particular Tables 1, 3 and/or 5), or any property of a locus which is in the vicinity of a chromosome interaction found to be linked to the relevant condition.
Companion Diagnostic Method Based on Detecting Chromosome Interactions
The invention provides a companion diagnostic method which comprises detecting the presence or absence of chromosome interactions, typically 5 to 20 or 5 to 500 such interactions, preferably 20 to 300 or 50 to 100 interactions, in order to determine the presence or absence of a characteristic in an individual. Preferably the chromosome interactions are those in any of the genes mentioned herein.
In one particular embodiment the chromosome interactions which are typed are those represented by the nucleic acids disclosed in the tables herein, in particular in Tables 6b, 6D, 18b, 18e, 18f, 22, 23, 24 or 25 herein, for example when the method is for the purpose of determining the presence or absence of characteristics defined in those tables.
Specific Conditions
The companion diagnostic method can be used to detect the presence of any of the specific conditions or characteristics mentioned herein. The companion diagnostic method can be used to detect responsiveness to methotrexate (or another rheumatoid arthritis drug) in rheumatoid arthritis patients, responsiveness to therapy for acute myeloid leukaemia (AML) patients, likelihood of relapse in melanoma, likelihood of developing prostate cancer and/or aggressive prostate cancer, and/or likelihood of developing beta-amyloid aggregate induced Alzheimer's disease.
In one embodiment the method of the invention detects responsiveness to immunotherapy, such as antibody therapy. Preferably the responsiveness to antibody therapy of cancer is detected, for example in immunotherapy using anti-PD-1 or anti-PD-L1 or a combined anti-PD-1/anti-PD-L1 therapy.
Preferably the cancer is melanoma, breast cancer, prostate cancer, acute myeloid leukaemia (AML), diffuse large B-cell lymphoma (DLBCL), pancreatic cancer, thyroid cancer, nasal cancer, liver cancer or lung cancer. In such embodiments detection of chromosome interactions in STAT5B and/or IL15 are preferred, such as described in the Examples. The work in the Examples is consistent with the fact that response to immunotherapy is a feature of the immune system epigenetic set up rather than cancer identity. [‘Anti-PD-1’ is an antibody or antibody derivative or fragment that binds specifically to PD-1 (programmed cell death protein 1). ‘Anti-PD-L1’ is an antibody or antibody derivative or fragment that binds specifically to PD-L1 protein which is a ligand of PD-1.]
The method(s) and/or companion diagnostic method of the invention can be used to:
Preferably the presence or absence of any of the chromosome interactions within any of the relevant genes mentioned in the tables are detected. For example in at least 1, 3, 10, 20, 50 of the genes mentioned in any one of the tables. Preferably the presence or absence of chromosome interactions represented by the probes sequences in the Tables is determined in the method. For example at least 1, 3, 10, 20, 50, or 100 of the relevant chromosome interactions from any one of the tables. These numbers of genes or chromosome interactions can be used in any of the different embodiments mentioned herein.
The Individual Tested Using the Companion Diagnostic Method
The individual to be tested may or may not have any symptoms of any disease condition or characteristic mentioned herein. The individual may be at risk of any such condition or characteristic.
The individual may have recovered or be in the process of recovering from the condition or characteristic. The individual is preferably a mammal, such as a non-human primate, human or rodent.
The individual may be male or female. The individual may be 30 years old or older. The individual may be 29 years old or younger.
Screening Method
The invention provides a method of identifying an agent which is capable of changing the disease state of an individual from a first state to a second state comprising determining whether a candidate agent is capable of changing the chromosomal interactions from those corresponding with the first state to chromosomal interactions which correspond to the second state, wherein preferably the first and second state correspond to presence or absence of:
In one embodiment the method determines whether a candidate agent is capable of changing any chromosomal interaction mentioned herein.
The method may be carried out in vitro (inside or outside a cell) or in vivo (upon a non-human organism). In one embodiment the method is carried out on a cell, cell culture, cell extract, tissue, organ or organism, such as one which comprises the relevant chromosome interaction(s). The cell is The method is typically carried out by contacting (or administering) the candidate agent with the gene, cell, cell culture, cell extract, tissue, organ or organism.
Suitable candidate substances which tested in the above screening methods include antibody agents (for example, monoclonal and polyclonal antibodies, single chain antibodies, chimeric antibodies and CDR-grafted antibodies). Furthermore, combinatorial libraries, defined chemical identities, peptide and peptide mimetics, oligonucleotides and natural agent libraries, such as display libraries (e.g. phage display libraries) may also be tested. The candidate substances may be chemical compounds, which are typically derived from synthesis around small molecules which may have any of the properties of the agent mentioned herein.
Preferred Loci, Genes and Chromosome Interactions
For all aspects of the invention preferred loci, genes and chromosome interactions are mentioned in the tables. For all aspects of the invention preferred loci, genes and chromosome interactions are provided in the tables. Typically the methods chromosome interactions are detected from at least 1, 3, 10, 20, 30 or 50 of the relevant genes listed in the table. Preferably the presence or absence of at least 1, 3, 10, 20, 30 or 50 of the relevant specific chromosome interactions represented by the probe sequences in any one table is detected.
The loci may be upstream or downstream of any of the genes mentioned herein, for example 50 kb upstream or 20 kb downstream.
In one embodiment for each condition the presence or absence of at least 1, 3, 5, 10, 20 of the relevant specific chromosome interactions represented by the top range of p-values or adjusted p-values shown in Table 48 are detected. In another embodiment for each condition the presence or absence of at least 1, 3, 5, 10, 20, 30 or 50 of the relevant specific chromosome interactions represented by the mid range of p-values or adjusted p-values shown in Table 48 are detected. In yet another embodiment for each condition the presence or absence of at least 1, 3, 5, 10, 20, 30 or 50 of the relevant specific chromosome interactions represented by the bottom range of p-values or adjusted p-values shown in Table 48 are detected. In another embodiment for each condition the presence or absence of at least 1, 2, 3, 5 or 10 of the relevant specific chromosome interactions from each of the top, mid and bottom ranges of p-values or adjusted p-values shown in Table 48 are detected, i.e. at least 3, 6, 9, 18 or 30 in total.
Particular combinations of chromosome interactions can be detected (i.e. determining the presence of absence of), which typically represent all of the interactions disclosed in a table herein or a selection from a table. As mentioned herein particular numbers of interactions can be selected from individual tables. In one embodiment at least 10%, 20%, 30%, 50%, 70% or 90% of the interactions disclosed in any table, or disclosed in relation to any condition, are detected.
The interactions which are detected may correspond to presence or absence of a particular characteristic, for example as defined herein, such as in any table herein. If a combination of interactions are detected then they may all correspond with presence of the characteristic or they may all correspond to absence of the characteristic. In one embodiment the combination of interactions which is detected corresponds to at least 2, 5 or 10 interactions which relate to presence of the characteristic and at least 2, 5 or 10 other interactions that relate to absence of the characteristic.
The probe shown in table 49 may be part of or combined with any of the selections mentioned herein, particularly for conditions relating to cancer, and responsiveness to therapy, such as anti-PD1 therapy.
Embodiments Concerning Genetic Modifications
In certain embodiments the methods of the invention can be carried out to detect chromosome interactions relevant to or impacted by a genetic modification, i.e. the subgroups may differ in respect to the genetic modification. Clearly the modification might be of entire (non-human) organisms or parts of organisms, such as cells. In the method of determining which chromosomal interactions are relevant to a biological system state the first set of nucleic acids may be from at least two subgroups, one of which has a defined genetic modification and one which does not have the genetic modification, and the method may determine which chromosomal interactions are relevant to, and/or affected by, the genetic modification. The modification may be achieved by any suitable means, including CRISPR technology.
The invention includes a method of determining whether a genetic modification to the sequence at a first locus of a genome affects other loci of the genome comprising detecting chromosome signatures at one or more other loci after the genetic modification is made, wherein preferably the genetic modification changes system characteristics, wherein said system is preferably the metabolic system, the immune system, the endocrine system, the digestive system, integumentary system, the skeletal system, the muscular system, the lymphatic system, the respiratory system, the nervous system, or the reproductive system. Said detecting chromosome signatures optionally comprises detecting the presence or absence of 5 or more (e.g. 5) different chromosomal interactions, preferably at 5 or more (e.g. 5) different loci, preferably as defined in any of the Tables. Preferably the chromosomal signatures or interactions are identified by any suitable method mentioned herein.
In one embodiment the genetic modification is achieved by a method comprising introducing into a cell (a) two or more RNA-guided endonucleases or nucleic acid encoding two or more RNA-guided endonucleases and (b) two or more guiding RNAs or DNA encoding two or more guiding RNAs, wherein each guiding RNA guides one of the RNA-guided endonucleases to a targeted site in the chromosomal sequence and the RNA-guided endonuclease cleaves at least one strand of the chromosomal sequence at the targeted site.
In another embodiment the modification is achieved by a method of altering expression of at least one gene product comprising introducing into a eukaryotic cell containing and expressing a DNA molecule having a target sequence and encoding the gene product an engineered, non-naturally occurring Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-CRISPR associated (Cas) (CRISPR-Cas) system comprising one or more vectors comprising:
a) a first regulatory element operable in a eukaryotic cell operably linked to at least one nucleotide sequence encoding a CRISPR-Cas system guide RNA that hybridizes with the target sequence, and
b) a second regulatory element operable in a eukaryotic cell operably linked to a nucleotide sequence encoding a Type-II Cas9 protein,
wherein components (a) and (b) are located on same or different vectors of the system, whereby the guide RNA targets the target sequence and the Cas9 protein cleaves the DNA molecule, whereby expression of the at least one gene product is altered; and, wherein the Cas9 protein and the guide RNA do not naturally occur together, wherein preferably each RNA-guided endonuclease is derived from a Cas9 protein and comprises at least two nuclease domains, and optionally wherein one of the nuclease domains of each of the two RNA-guided endonucleases is modified such that each RNA-guided endonuclease cleaves one strand of a double-stranded sequence, and wherein the two RNA-guided endonucleases together introduce a double-stranded break in the chromosomal sequence that is repaired by a DNA repair process such that the chromosomal sequence is modified.
Typically the modification comprised a deletion, insertion or substitution of at least 5, 20, 50, 100 or 1000 bases, preferably up 10,000 or 1000,000 bases.
The modification may be at any of the loci mentioned herein, for example in any of the regions or genes mentioned in any of the tables. The chromosomal interactions which are detected at other (non-modified) loci may also be in any of the loci mentioned herein, for example in any of the regions or genes mentioned in any of the tables.
Embodiments relating to genetic modifications many be performed on any organism, including eukaryotes, chordates, mammals, plants, agricultural animals and plants, and non-human organisms.
Methods and Uses of the Invention
The method of the invention can be described in different ways. It can be described as a method of making a ligated nucleic acid comprising (i) in vitro cross-linking of chromosome regions which have come together in a chromosome interaction; (ii) subjecting said cross-linked DNA to cutting or restriction digestion cleavage; and (iii) ligating said cross-linked cleaved DNA ends to form a ligated nucleic acid, wherein detection of the ligated nucleic acid may be used to determine the chromosome state at a locus, and wherein preferably:
The method of the invention can be described as a method for detecting chromosome states which represent different subgroups in a population comprising determining whether a chromosome interaction is present or absent within a defined region of the genome, wherein preferably:
The invention includes detecting chromosome interactions at any locus, gene or regions mentioned herein. The invention includes use of the nucleic acids and probes (or primers) mentioned herein to detect chromosome interactions, for example use of at least 10, 50, 100 or 500 such nucleic acids or probes to detect chromosome interactions in at least 10, 20, 100 or 500 different loci or genes.
Tables Provided Herein
Tables herein either show probe (Episwitch™ marker) data or gene data representing chromosome interactions present in a condition (the first mentioned group) and absent in a control group, typically but not necessarily healthy individuals (the second mentioned group). The probe sequences show sequence which can be used to detect a ligated product generated from both sites of gene regions that have come together in chromosome interactions, i.e. the probe will comprise sequence which is complementary to sequence in the ligated product. The first two sets of Start-End positions show probe positions, and the second two sets of Start-End positions show the relevant 4 kb region. The following information is provided in the probe data table:
The gene table data shows genes where a relevant chromosome interaction has been found to occur. The p-value in the loci table is the same as the HyperG_Stats (p-value for the probability of finding that number of significant EpiSwitch™ markers in the locus based on the parameters of hypergeometric enrichment).
The probes are designed to be 30 bp away from the Taq1 site. In case of PCR, PCR primers are also designed to detect ligated product but their locations from the Taq1 site vary.
Probe Locations:
Start 1—30 bases upstream of TaqI site on fragment 1
End 1—TaqI restriction site on fragment 1
Start 2—TaqI restriction site on fragment 2
End 2—30 bases downstream of TaqI site on fragment 2
4 kb Sequence Location:
Start 1—4000 bases upstream of TaqI site on fragment 1
End 1—TaqI restriction site on fragment 1
Start 2—TaqI restriction site on fragment 2
End 2—4000 bases downstream of TaqI site on fragment 2
The following information is also provided in the tables:
Methods of preparing samples and detecting chromosome conformations are described herein. Optimised (non-conventional) versions of these methods can be used, for example as described in this section.
Typically the sample will contain at least 2×105 cells. The sample may contain up to 5×105 cells. In one embodiment, the sample will contain 2×105 to 5.5×105 cells
Crosslinking of epigenetic chromosomal interactions present at the chromosomal locus is described herein. This may be performed before cell lysis takes place. Cell lysis may be performed for 3 to 7 minutes, such as 4 to 6 or about 5 minutes. In some embodiments, cell lysis is performed for at least 5 minutes and for less than 10 minutes.
Digesting DNA with a restriction enzyme is described herein. Typically, 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 embodiment, the typical fragment size is from 200 base pairs to 4,000 base pairs, such as 400 to 2,000 or 500 to 1,000 base pairs.
In one embodiment of the EpiSwitch method a DNA precipitation step is not performed between the DNA restriction digest step and the DNA ligation step.
DNA ligation is described herein. Typically the DNA ligation is performed for 5 to 30 minutes, such as about 10 minutes.
The protein in the sample may be digested enzymatically, for example using a proteinase, optionally Proteinase K. The protein may be enzymatically digested for a period of about 30 minutes to 1 hour, for example for about 45 minutes. In one embodiment after digestion of the protein, for example Proteinase K digestion, there is no cross-link reversal or phenol DNA extraction step.
In one embodiment PCR detection is capable of detecting a single copy of the ligated nucleic acid, preferably with a binary read-out for presence/absence of the ligated nucleic acid.
Homologues
Homologues of polynucleotide/nucleic acid (e.g. DNA) sequences are referred to herein. Such homologues typically have at least 70% homology, preferably at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98% or at least 99% homology, for example over a region of at least 10, 15, 20, 30, 100 or more contiguous nucleotides, or across the portion of the nucleic acid which is from the region of the chromosome involved in the chromosome interaction. The homology may be calculated on the basis of nucleotide identity (sometimes referred to as “hard homology”).
Therefore, in a particular embodiment, homologues of polynucleotide/nucleic acid (e.g. DNA) sequences are referred to herein by reference to % sequence identity. Typically such homologues have at least 70% sequence identity, preferably at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98% or at least 99% sequence identity, for example over a region of at least 10, 15, 20, 30, 100 or more contiguous nucleotides, or across the portion of the nucleic acid which is from the region of the chromosome involved in the chromosome interaction.
For example the UWGCG Package provides the BESTFIT program which can be used to calculate homology and/or % sequence identity (for example used on its default settings) (Devereux et al (1984) Nucleic Acids Research 12, p 387-395). 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.
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) oi 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 % sequence identity.
Arrays
The second set of nucleic acids may be bound to an array, and in one embodiment there are at least 15,000, 45,000, 100,000 or 250,000 different second nucleic acids bound to the array, which preferably represent at least 300, 900, 2000 or 5000 loci. In one embodiment one, or more, or all of the different populations of second nucleic acids are bound to more than one distinct region of the array, in effect repeated on the array allowing for error detection. The array be based on an Agilent SurePrint G3 Custom CGH microarray platform. Detection of binding of first nucleic acids to the array may be performed by a dual colour system.
Therapeutic Agents
Therapeutic agents are mentioned herein. The invention provides such agents for use in preventing or treating the relevant condition. This may comprise administering to an individual in need a therapeutically effective amount of the agent. The invention provides use of the agent in the manufacture of a medicament to prevent or treat the disease. The methods of the invention may be used to select an individual for treatment. The methods of the invention, and in particular the companion diagnostic assay method, may include a treatment step where a person identified by the method may then be administered with an agent that prevents or treats the relevant condition.
The formulation of the agent will depend upon the nature of the agent. The agent will be provided in the form of a pharmaceutical composition containing the agent and a pharmaceutically acceptable carrier or diluent. Suitable carriers and diluents include isotonic saline solutions, for example phosphate-buffered saline. Typical oral dosage compositions include tablets, capsules, liquid solutions and liquid suspensions. The agent may be formulated for parenteral, intravenous, intramuscular, subcutaneous, transdermal or oral administration.
The dose of agent may be determined according to various parameters, especially according to the substance used; the age, weight and condition of the individual to be treated; the route of administration; and the required regimen. A physician will be able to determine the required route of administration and dosage for any particular agent. A suitable dose may however be from 0.1 to 100 mg/kg body weight such as 1 to 40 mg/kg body weight, for example, to be taken from 1 to 3 times daily.
Forms of the Substance Mentioned Herein
Any of the substances, such as nucleic acids or therapeutic agents, mentioned herein may be in purified or isolated form. The may be in a form which is different from that found in nature, for example they may be present in combination with other substance with which they do not occur in nature. The nucleic acids (including portions of sequences defined herein) may have sequences which are different to those found in nature, for example having at least 1, 2, 3, 4 or more nucleotide changes in the sequence as described in the section on homology. The nucleic acids may have heterologous sequence at the 5′ or 3′ end. The nucleic acids may be chemically different from those found in nature, for example they may be modified in some way, but preferably are still capable of Watson-Crick base pairing. Where appropriate the nucleic acids will be provided in double stranded or single stranded form. The invention provides all the of specific nucleic acid sequences mentioned herein in single or double stranded form, and thus includes the complementary strand to any sequence which is disclosed.
The invention also provides a kit for carrying out any method of the invention, including detection of a chromosomal interaction associated with a particular subgroup. Such a kit can include a specific binding agent capable of detecting the relevant chromosomal interaction, such as agents capable of detecting a ligated nucleic acid generated by processes of the invention. Preferred agents present in the kit include probes capable of hybridising to the ligated nucleic acid or primer pairs, for example as described herein, capable of amplifying the ligated nucleic acid in a PCR reaction.
The invention also provides a device that is capable of detecting the relevant chromosome interactions. The device preferably comprises any specific binding agents, probe or primer pair capable of detecting the chromosome interaction, such as any such agent, probe or primer pair described herein.
Preferred Therapeutic Agents for Use in the Invention for Specific Stated Condition
A. Predisposition to Relapsing-Remitting Multiple Sclerosis (RRMS)
Treatments for MPNST include surgery, radiotherapy and chemotherapy.
J. Likelihood of developing prostate cancer and/or aggressive prostate cancer
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 EpiSwitch™ platform technology detects epigenetic regulatory signatures of regulatory changes between normal and abnormal conditions at loci. The EpiSwitch™ platform identifies and monitors the fundamental epigenetic level of gene regulation associated with regulatory high order structures of human chromosomes also known as chromosome conformation signatures. Chromosome signatures are a distinct primary step in a cascade of gene deregulation. They are high order biomarkers with a unique set of advantages against biomarker platforms that utilize late epigenetic and gene expression biomarkers, such as DNA methylation and RNA profiling.
EpiSwitch™ Array Assay
The custom EpiSwitch™ array-screening platforms come in 4 densities of, 15K, 45K, 100K, and 250K unique chromosome conformations, each chimeric fragment is repeated on the arrays 4 times, making the effective densities 60K, 180K, 400K and 1 Million respectively.
Custom Designed EpiSwitch™ Arrays
The 15K EpiSwitch™ array can screen the whole genome including around 300 loci interrogated with the EpiSwitch™ Biomarker discovery technology. The EpiSwitch™ array is built on the Agilent SurePrint G3 Custom CGH microarray platform; this technology offers 4 densities, 60K, 180K, 400K and 1 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.
EpiSwitch™ Custom Array Pipeline
The EpiSwitch™ array is a dual colour system with one set of samples, after EpiSwitch™ library generation, labelled in Cy5 and the other of sample (controls) to be compared/analyzed labelled in Cy3. The arrays are scanned using the Agilent SureScan Scanner and the resultant features extracted using the Agilent Feature Extraction software. The data is then processed using the EpiSwitch™ array processing scripts in R. The arrays are processed using standard dual colour packages in Bioconductor in R: Limma*. The normalisation of the arrays is done using the normalised within Arrays function in Limma* and this is done to the on chip Agilent positive controls and EpiSwitch™ positive controls. The data is filtered based on the Agilent Flag calls, the Agilent control probes are removed and the technical replicate probes are averaged, in order for them to be analysed using Limma*. The probes are modelled based on their difference between the 2 scenarios being compared and then corrected by using False Discovery Rate. Probes with Coefficient of Variation (CV)<=30% that are <=−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 Methods for Assessing Differential Expression in Microarray Experiments. Limma is a R package for the analysis of gene expression data arising from microarray or RNA-Seq.
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).
The invention is illustrated by the following non-limiting Examples.
Statistical Pipeline
EpiSwitch™ screening arrays are processed using the EpiSwitch™ Analytical Package in R in order to select high value EpiSwitch™ markers for translation on to the EpiSwitch™ PCR platform.
Step 1
Probes are selected based on their corrected p-value (False Discovery Rate, FDR), which is the product of a modified linear regression model. Probes below p-value
<=0.1 are selected and then further reduced by their Epigenetic ratio (ER), probes ER have to be <=−1.1 or =>1.1 in order to be selected for further analysis. The last filter is a coefficient of variation (CV), probes have to be below <=0.3.
Step 2
The top 40 markers from the statistical lists are selected based on their ER for selection as markers for PCR translation. The top 20 markers with the highest negative ER load and the top 20 markers with the highest positive ER load form the list.
Step 3
The resultant markers from step 1, the statistically significant probes form the bases of enrichment analysis using hypergeometric enrichment (HE). This analysis enables marker reduction from the significant probe list, and along with the markers from step 2 forms the list of probes translated on to the EpiSwitch™ PCR platform.
The statistical probes are processed by HE to determine which genetic locations have an enrichment of statistically significant probes, indicating which genetic locations are hubs of epigenetic difference.
The most significant enriched loci based on a corrected p-value are selected for probe list generation. Genetic locations below p-value of 0.3 or 0.2 are selected. The statistical probes mapping to these genetic locations, with the markers from step 2, form the high value markers for EpiSwitch™ PCR translation.
Array Design and Processing
Array Design
Source: Glasgow Scottish Educational Research Association (SERA) cohort.
Introduction to and Brief Summary of Example 1
Stable epigenetic profiles of individual patients modulate sensitivity of signalling pathways, regulate gene expression, influence the paths of disease development, and can render ineffective the regulatory controls responsible for effective action of the drug and response to treatment. Here we analysed epigenetic profiles of rheumatoid arthritis (RA) patients in order to evaluate its role in defining the non-responders to Methotrexate (MTX) treatment.
Reliable clinical prediction of response to first-line disease modifying anti-rheumatic drugs (DMARDs, usually methotrexate (MTX)) in rheumatoid arthritis is not currently possible. Currently the ability to determine response to first line DMARDs (in particular, methotrexate (MTX)) is dependent on empiric clinical measures after the therapy.
In early rheumatoid arthritis (ERA), it has not been possible to predict response to first line DMARDs (in particular, methotrexate (MTX)) and as such treatment decisions rely primarily on clinical algorithms. The capacity to classify drug naïve patients into those that will not respond to first line DMARDs would be an invaluable tool for patient stratification. Here we report that chromosome conformational signatures (highly informative and stable epigenetic modifications that have not previously been described in RA) in blood leukocytes of early RA patients can predict non-responsiveness to MTX treatment.
Methods:
Peripheral blood mononuclear cells (PBMCs) were obtained from DMARD naïve ERA patients recruited in the Scottish early rheumatoid arthritis (SERA) inception cohort. Inclusion in this study was based on diagnosis of RA (fulfilling the 2010 ACR/EULAR Criteria) with moderate to high disease activity (DAS28≥3.2) and subsequent monotherapy with methotrexate (MTX). DAS28=Disease Activity Score of 28 joints. EULAR=The European League Against Rheumatism. ACR=American College of Rheumatology. MTX responsiveness was defined at 6 months using the following criteria: Responders—DAS28 remission (DAS28<2.6) or a good response (DAS28 improvement of >1.2 and DAS28 3.2). Non-responders—no improvement in DAS28 (50.6). Initial analysis of chromosome conformational signatures (CCS) in 4 MTX responders, 4 MTX non-responders and 4 healthy controls was undertaken using an EpiSwitch™ array containing 13,322 unique probes covering 309 RA-related genetic loci.
Differentiating CCS were defined by LIMMA* linear modeling, subsequent binary filtering and cluster analysis. A validation cohort of 30 MTX responders and 30 non-responders were screened for the differentiating CCS using the EpiSwitch™ PCR platform. The differentiating signature was further refined using binary scores and logistical regression modeling, and the accuracy and robustness of the model determined by ROC analysis**.
*Note: LIMMA is Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments. Limma is a R package for the analysis of gene expression data arising from microarray or RNA-Seq.
**Note: ROC means Receiver Operating Characteristic and refers to ROC curves. An ROC curve is a graphical plot that illustrates the performance of a binary classifier system as its discrimination threshold is varied. The curve is created by plotting the true positive rate against the false positive rate at various threshold settings.
CCS EpiSwitch™ array analysis identified a 30-marker stratifying profile differentiating responder and non-responder ERA patients. Subsequent evaluation of this signature in our validation cohort refined this to a 5-marker CCS signature that was able to discriminate responders and non-responders. Prediction modeling provided a probability score for responders and non-responders, ranging from 0.0098 to 0.99 (0=responder, 1=non-responder). There was a true positive rate of 92% (95% confidence interval [95% CI] 75-99%) for responders and a true negative rate of 93% (95% CI 76-99%) for non-responders. Importantly, ROC analysis to validate this stratification model demonstrated that the signature had a predictive power of sensitivity at 92% for NR to MTX.
We have identified a highly informative systemic epigenetic state in the peripheral blood of DMARD naïve ERA patients that has the power to stratify patients at the time of diagnosis. The capacity to differentiate patients a priori into non-responders, using a blood-based clinical test, would be an invaluable clinical tool; paving the way towards stratified medicine and justifying more aggressive treatment regimes in ERA clinics.
Detailed Version of Example 1
The capacity to differentiate patients a priori into responders (R) and non-responders (NR) would be an invaluable tool for patient stratification leading to earlier introduction of effective treatment. We have used the EpiSwitch™ biomarker discovery platform to identify Chromosome Conformation Signatures (CCS) in blood-derived leukocytes, which are indicative of disease state and MTX responsiveness. Thereby we identified an epigenetic signature contained in the CXCL13, IFNAR1, IL-17A, IL-21R and IL-23 loci that provide the first prognostic molecular signature that enables the stratification of treatment naïve early RA (ERA) patients into MTX R and NR. Importantly, this stratification model had a predictive power of sensitivity at 92% for NR to MTX. This epigenetic RA biomarker signature can distinguish between ERA and healthy controls (HC). This combinatorial, predictive peripheral blood signature can support earlier introduction of more aggressive therapeutics in the clinic, paving the way towards personalized medicine in RA.
RA is a chronic autoimmune disease affecting up to 1% of the global population. Pathogenesis is multifactorial and characterized by primarily immune host gene loci interacting with environmental factors, particularly smoking and other pulmonary stimuli. The exposure of a genetically susceptible individual to such environmental factors suggests an epigenetic context for disease onset and progression. Recent studies of chromatin markers (e.g. methylation status of the genome) provide the first evidence of epigenetic differences associated with RA. However, to date neither genetic associations, nor epigenetic changes, have provided a validated predictive marker for response to a given therapy. Moreover, clinical presentation only weakly predicts the efficacy and toxicity of conventional DMARDs. MTX8, the commonest first-choice medication recommended by EULAR (The European League Against Rheumatism) and ACR (American College of Rheumatology) management guidelines, delivers clinically meaningful response rates ranging from 50 to 65% after 6 months of treatment. Such responses, and especially the rather smaller proportion that exhibits high hurdle responses, cannot currently be predicted in an individual patient. This begets a ‘trial and error’ based approach to therapeutic regimen choice (mono or combinatorial therapeutics). The ability to predict drug responsiveness in an individual patient would be an invaluable clinical tool, given that response to first-line treatment is the most significant predictor of long-term outcome.
Herein we focused on epigenetic profiling of DMARD-naïve, ERA patients from the Scottish Early Rheumatoid Arthritis (SERA) inception cohort in order to ascertain if there is a stable blood-based epigenetic profile that indicates NR to MTX treatment and thus enables a priori identification and stratification of such patients to an alternate therapeutic. The source Epigenetic modulation can strongly influence cellular activation and transcriptional profiles. Conceivably, the mode of action for a drug could be affected by epigenetically modified loci. We have focused on CCS, also known as long-range chromatin interactions, because they reflect highly informative and stable high-order epigenetic status which have significant implications for transcriptional regulation. They also offer significant advantages15 and early functional links to phenotypic differences16, and have been reported as informative biomarkers candidates in oncology and other disease areas.
We used early RA (ERA) patients provided by the Scottish early rheumatoid arthritis (SERA) inception cohort. Demographic, clinical and immunological factors were obtained at diagnosis and 6 months. Inclusion in this study was based on a diagnosis of RA (fulfilling the 2010 ACR/EULAR Criteria) with moderate to high disease activity (DAS28≥3.2) and subsequent monotherapy with MTX. Responders were defined as patients who upon receiving MTX achieved DAS28 remission (DAS28<2.6) or a good response (DAS28 improvement of >1.2 and DAS28≤3.2) at 6 months. Non-responders were defined as patients who upon receiving MTX had no improvement in DAS28 (≤0.6) at 6 months. Blood samples for epigenetic analysis were collected at diagnosis. (DAS28=Disease Activity Score of 28 joints.)
We used a binary epigenetic biomarker profiling by analysing over 13,322 chromosome conformation signatures (CCS) (13,322 unique probes) across 309 genetic loci functionally linked to RA. CCS, as a highly informative class of epigenetic biomarkers (1), were read, monitored and evaluated on EpiSwitch™ platform which has been already successfully utilized in blood based stratifications of Mayo Clinic cohort with early melanoma (2) and is currently used for predictive stratification of responses to immunotherapies with PD-1/PD-L1.
Identified epigenetic profiles of naïve RA patients were subject to statistical analysis using GraphPad Prism, WEKA and R Statistical language. By using EpiSwitch™ platform and extended cohort of 90 clinical samples we have identified a pool of over 922 epigenetic lead biomarkers, statistically significant for responders, non-responders, RA patients and healthy controls.
To identify a pre-treatment circulating CCS status in ERA patients, 123 genetic loci (Table 1) associated with RA pathogenesis were selected and annotated with chromosome conformations interactions predicted using the EpiSwitch™ in silico prediction package. The EpiSwitch™ in silico prediction generated 13,322 high-confidence CCS marker candidates (Table 1). These candidates were used to generate a bespoke discovery EpiSwitch™ array (
*Note: LIMMA is Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments. Limma is a R package for the analysis of gene expression data arising from microarray or RNA-Seq.
To refine and validate the CCS signature, the 30 identified markers were screened in a second ERA patient cohort of R and NR (
Importantly, the composition of the stratifying signature identifies the location of chromosomal conformations that potentially control genetic locations of primary importance for determining MTX response. Principal component analysis (PCA) of the binary scores for the classifying 5 EpiSwitch™ CCS markers provided clear separation of ERA patients based on their MTX response (
In order to test the ‘accuracy’ and ‘robustness of performance’ of the logistic classifying model that determined the 5 EpiSwitch™ CSS markers, 150 ROC** curves (with unique start points) were generated by random data re-sampling of the R and NR data (
**Note: ROC means Receiver Operating Characteristic and refers to ROC curves. An ROC curve is a graphical plot that illustrates the performance of a binary classifier system as its discrimination threshold is varied. The curve is created by plotting the true positive rate against the false positive rate at various threshold settings.
As an independent evaluation of the discerning powers of the selected 5 EpiSwitch™ CCS markers, factor analysis of mixed data (FAMD) incorporating 30 HC was performed. This illustrated that the signature not only has the power to differentiate between MTX R and NR but also retains sufficient disease-specific features to differentiate between healthy individuals and RA patients (
Table 6D, and continuation Table 6E, presented hereinafter after Tables 6b+6c from Example 1A, show inter alia a list of about 54 DNA probes (60mers) and their DNA sequences. These probes represent some of the probes used in Example 1. Without being bound, most of the probes illustrated in Table 6D+6E are thought likely to be significant to/useful in stratifying between RA-MTX responders and RA-MTX non-responders. The shown probes were investigated further by PCR. P Value=Probability value; adj.=adjusted.
In conclusion, our study of the epigenetic profile classification of DMARD naïve ERA patients on the basis of prospective clinical assessment for R/NR has identified a consistent epigenetic signature, which discriminates an epigenetic state that is conducive and non-conducive to MTX response. This is to our knowledge, the first example of a stable and selectively differentiating blood based epigenetic biomarker in early RA patients that appears disease related (versus healthy controls) and that can predict non-responsiveness to first-line MTX therapy. This model offers direct and practical benefits with a validated classifier based on 5 conditional CCS and their detection by the industrial ISO-13485 EpiSwitch™ platform, which has the potential to be routinely available in the near future within clinical practice. Importantly, by adopting this predictive signature it should be possible to stratify MTX naïve ERA patients into R and NR cohorts. This offers the potential to accelerate patient progression through the currently approved treatment strategy for ERA seeking earlier use of effective therapeutics, hence leading to a ‘personalised’ treatment regime. Furthermore, it is conceivable that alternative CCS signatures are present in RA patients (and patients with other autoimmune diseases) that could be used to justify fast-tracked biological treatment regimes in the clinic. This would have far reaching socio-economic implications, providing more cost effective and robust therapeutic approaches.
ERA patients in this study are part of the Scottish early rheumatoid arthritis (SERA) inception cohort. Demographic, clinical and immunological factors were obtained at diagnosis and 6 months (Table 1). Inclusion in the inception cohort was based on clinical diagnosis of undifferentiated polyarthritis or RA (≥1 swollen joint) at a secondary care rheumatology unit in Scotland. Exclusion criteria were previous or current DMARD/biological therapy and/or established alternative diagnosis (i.e. psoriatic arthritis, reactive arthritis). Inclusion in this study was based on a diagnosis of RA (fulfilled the 2010 ACR/EULAR criteria for RA) with moderate to high disease activity (DAS28≥3.2) and subsequent monotherapy with MTX. [DAS28=Disease Activity Score of 28 joints. EULAR=European League Against Rheumatism. ACR=American College of Rheumatology.] Responders were defined as patients who upon receiving MTX achieved DAS28 remission (DAS28<2.6) or a good response (DAS28 improvement of >1.2 and DAS28≤3.2) at 6 months. Non-responders were defined as patients who upon receiving MTX had no improvement in DAS28 (50.6) at 6 months. Blood samples were collected at diagnosis (Baseline) in EDTA tubes and centrifuged to generate a buffy layer containing PBMCs, which was harvested and stored at −80° C. Local ethics committees approved the study protocol and all patients gave informed consent before enrolment into the study.
Pattern recognition methodology was used to analyse human genome data in relation to the transcriptional units in the human genome. The proprietary EpiSwitch™ pattern recognition software18, 20 provides a probabilistic score that a region is involved in chromatin interaction. Sequences from 123 gene loci were downloaded and processed to generate a list of the 13,322 most probable chromosomal interactions. 60mer probes were designed to interrogate these potential interactions and uploaded as a custom array to the Agilent SureDesign website. Sequence-specific oligonucleotides were designed using Primer323, at the chosen sites for screening potential markers by nested PCR. Oligonucleotides were tested for specificity using oligonucleotide specific BLAST.
Template preparation: Chromatin from 50 μl of each PBMC sample was extracted using the EpiSwitch™ assay following the manufacturer's instructions (Oxford BioDynamics Ltd). Briefly, the higher order structures are fixed with formaldehyde, the chromatin extracted, digested with TaqI, dilution and ligation in conditions to maximize intramolecular ligation, and subsequent proteinase K treatment. EpiSwitch™ microarray: EpiSwitch™ microarray hybridization was performed using the custom Agilent 8×60 k array using the Agilent system, following the manufacturer's instructions (Agilent). Each array contains 55088 probes spots, representing 13,322 potential chromosomal interactions predicted by the EpiSwitch™ pattern recognition software quadruplicated, plus EpiSwitch™ and Agilent controls. Briefly, 1 μg of EpiSwitch™ template was labelled using the Agilent SureTag labelling kit. Processing of labelled DNA was performed. Array analysis was performed immediately after washing using the Agilent scanner and software. In order to compare all the experiments the data was background corrected and normalized. Since each spot in the array is present in quadruplicate, the median of the four spots of each probe in the array was calculated and its log 2 transformed value was used for further analysis. The coefficient of variation and p-value was calculated for each probe replicate. EpiSwitch™ PCR detection: Oligonucleotides were tested on template to confirm that each primer set was working correctly. To accommodate for technical and replicate variations, each sample was processed four times. All the extracts from these four replicates were pooled and the final nested PCR was performed on each sample. This procedure permitted the detection of limited copy-number templates with higher accuracy. All PCR amplified samples were visualised by electrophoresis in the LabChip* GX from Perkin Elmer, using the LabChip DNA 1K Version2 kit (Perkin Elmer) and internal DNA marker was loaded on the DNA chip according to the manufacturer's protocol using fluorescent dyes. Fluorescence was detected by laser and electropherogram read-outs translated into a simulated band on gel picture using the instrument software. The threshold we set for a band to be deemed positive was 30 fluorescence units and above.
GraphPad Prism and SPSS were used for all statistical analyses of clinical data. The chi-square test and Fisher's exact test (for categorical variables), the t-test for independent samples (for continuous normally distributed variables), and the Mann-Whitney U test (for continuous variables without normal distribution) were used to identify differences. The level of statistical significance was set at 0.05, and all tests were 2-sided. R (and appropriate packages) were used for evaluation of EpiSwitch™ data. This included Stats package for Chi-square test and GLM (logit), ROCR package for ROC curves from WEKA odds probabilities, gplot & stats package in R for Heatmaps. FactorMiner package was used for PCA and Factor plots. Weka was used for Attribute Reduction, data randomisation and re-sampling, Logistic Model Classifier, AUC calculations and model accuracy calculations.
$n = 3
€Physician global assessment (VAS, 0-100 mm)
€CDAI
#DAS28-CRP
§DAS28-ESR
¢RF (IU/ml)
∞CCP (U/ml)
#C-reactive protein (mg/liter)
§Erythrocyte sedimentation rate (mm/hour)
¶Whole Blood cell count
¶Lymphocytes
¶Monocytes
¶Platelets
$One patient “other” (non-white, non-South East Asian, non-Indian Sub-Continent, Non-Afro-Caribbean), one patient did not give an answer.
$$n = 25 in responders for BMI
€Baseline - n = 29 non-R, n = 30 R; 6m - n = 30 non-R, n = 29
#Baseline - n = 26 non-R, n = 29 R; 6m - n = 21 non-R, n = 29
§Baseline - n = 19 non-R, n = 23 R; 6m - n = 19 non-R, n = 22
¢Baseline - n = 13 non-R, n = 23 R
∞Baseline - n = 26 non-R, n = 29 R
¶Baseline - n = 29 non-R, n = 27 R; 6m - n = 28 non-R, n = 25
Following on after Example 1, in Example 1A, a biostatistical hypergeometric analysis was carried out, using the “Statistical Pipeline” method(s) at the beginning of the Examples section in the present specification, to generate further refined DNA probes stratifying between MTX responders vs MTX non-responders, for RA patients on MTX monotherapy.
Example 1A Results: Table 6b (and continuation part Table 6c) hereinafter discloses Probe and Loci data for RA-MTX—DNA probes stratifying between responders (R) and non-responders (NR). B=B-statistic (lods or B), which is the log-odds that that gene is differentially expressed. FC is the non-log Fold Change. FC_1 is the non-log Fold Change centred around zero. It is seen that Table 6b+6c includes the sequences of 25 refined preferable DNA probes (60mers) for identifying MTX responders (MTX-R), and of 24 refined preferable DNA probes (60mers) for identifying MTX responders (MTX-NR), from the hypergeometric analysis.
Source: Institute of Cancer Research UK.
Pharmacodynamic Biomarkers
Pharmacodynamic (PD) biomarkers are molecular indicators of drug effect on the target in an organism. A PD biomarker can be used to examine the link between drug regimen, target effect, and biological tumour response. Coupling new drug development with focused PD biomarker measurements provides critical data to make informed, early go/no-go decisions, to select rational combinations of targeted agents, and to optimise schedules of combination drug regimens. Use of PD endpoints also enhances the rationality and hypothesis-testing power throughout drug development, from selection of lead compounds in preclinical models to first-in-human trials (National Cancer Institute).
The inventors have discovered that chromosome signatures could be used as pharmacodynamic biomarkers to monitor response to a number of drugs at time points consistent with phenotypic changes observed.
EpiSwitch™ Markers—Ideal Pharmacodynamic Biomarkers
Work on BET (bromodomain and extra-terminal) inhibitors on MV4-11 cell lines has shown that BET inhibition causes the transcriptional repression of key oncogenes BCL2, CDK6, and C-MYC BET inhibitors like LSD1 inhibitors are epigenetic therapies, targeting the acetylated and methylation states of histones. As topological changes at loci precede any regulatory changes, the findings at the MYC locus with EpiSwitch™ show evidence of regulatory change with LSD1 inhibition. MV4-11 cell line harbours translocations that express MLL-AF4 and FLT3-ITD whereas THP-1 only expresses MLL-AF9.
EpiSwitch™ LSD1 Inhibition Biomarker Study for AML (Acute Myeloid Leukemia)
Epigenetic biomarkers identified by EpiSwitch™ platform are well suited for delineating epigenetic mechanisms of LSD1 demethylase and for stratification of different specificities of LSD1 inhibitors within and between cell lines. This work demonstrates that chromosome conformation signatures could be used as mechanism-linked predictive biomarkers in LSD1 inhibition. A standard LSD1 inhibitor is investigated in this study, tranylcypromine (TCP).
EpiSwitch™ LSD1 Pharmacodynamic Biomarker Discovery
The cells were treated with 1 uM of tranylcypromine (TCP). Two AML (acute myeloid leukemia) cell lines THP-1 and MV4-11 were tested with the above compound. Chromosome signatures identified in the vicinity of MYD88 gene in THP-1 cells are shown in Table 7. Chromosome signatures identified in the vicinity of MYD88 gene in MV4-11 cells are shown in Table 8. Each number combination, points to individual chromosome interaction. The positions across the gene have been created and selected based on restriction sites and other features of detection and primer efficiency and were then analysed for interactions. The result in tables 7 and 8 represent no signature detection. A signature detection is represented with the number 1. Below are the PCR EpiSwitch™ marker results for the MyD88 locus for cell lines THP-1 and MV4-11. FACS analysis was used to sort for the expression of CD11b±cells, as an indicator of differentiation. MyD88 and MYC loci were selected on the basis of previously published studies, as key genetic drivers of treatment changes at 72 hrs.
LSD1 Inhibitor (TCP) Experiments—Discovery Findings
The conformations that change at the later time point (72 hrs) relative to the untreated cells show the most consistency between the 2 cell types. These are the markers above the bold double line shown in the THP-1 data, and highlighted by the shaded cells in the MV4-11 data.
LSD1 inhibition removes a long range interaction with 5′ upstream to the ORF of MYD88, changing the regulatory landscape for the locus.
LSD1 Inhibition Analysis Versus Gene Expression Data—Temporal and Structural Correlation of MYC Locus Conformations with Gene Expression (GEX)
MYC is the target gene that drives the AML (acute myeloid leukemia) pathology, but at 72 hrs treatment, the fold change is too small to be significant for a marker. The changes seen in Table 9 at the MYC locus at 72 hrs for GEX data correlates to the conformation changes identified at 72 hrs. The negative GEX change at MYC relative to the untreated cells is in keeping with the requirement to perturb MYC proliferation effect. The change is small also in keeping with the tight control elicited on this locus by numerous signal cascades.
Unlike GEX data above, the EpiSwitch™ biomarkers clearly detect changes in chromosome conformation signatures at 72 hr treatments correspondent with cells differentiation and their death by apoptosis (phenotypic change).
LSD1 Inhibition Analysis Versus Gene Expression Data—Temporal and Structural Correlation of MyD88 Locus Conformations with Gene Expression (GEX)
The changes seen at MyD88 at 72 hrs for the GEX data correlate to the conformation changes identified at 72 hrs. The GEX change is positive relative to untreated cells, which is in keeping with the differential seen in these AML (acute myeloid leukemia) cells after treatment with the LSD1 inhibitor.
Only 1.5 fold change observed at 72 hr treatment with TCP at MYD88 locus identified both by GEX and EpiSwitch™. This level of change is too affected by noise in microarray gene expression analysis. However, epigenetic changes observed for chromosome signatures are clean to follow a binary format of 0 or 1. The data shows distinct pattern of changes. Both MYC and MYD88 are epigenetic drivers that, as shown in the GEX data, may not present with the strong response in gene expression, but can be identified as key epigenetic changes are visible through chromosome signatures. These two genetic drivers define phenotypic changes required for successful therapy treatment. At 72 hrs cells differentiate and undergo apoptosis.
Source: Mayo Clinic metastatic melanoma cohort, USA
A prognostic biomarker predicts the course or outcome (e.g. end, stabilisation or progression) of disease. This study discovers and validates chromosome signatures that could act as prognostic biomarkers for relapse to identify clear epigenetic chromosome conformation differences in monitored melanoma patients, who undergone surgery treatment, for signs of relapse or recovery, and to validate such biomarkers for potential to be prognostic biomarkers for monitoring relapse of melanoma. Here we want to present our example of validated prognostic use of chromosome conformation signatures in application to confirmed melanoma patients who have undergone treatment by the resection of the original growth in order to identify the candidates who are likely to relapse within 2 years of treatment.
224 melanoma patients were treated with surgery to remove their cancer. They were then observed for a period of two years with blood being drawn for analysis at >100 days after the surgery.
EpiSwitch™ Prognostic Biomarker Discovery
Chromosome signatures of 44 genes associated with melanoma and the rest of the genome for any disease-specific long range interaction by Next Generation Sequencing NGS were tested. Non-biased assessment of chromosome signatures associated with melanoma through deep sequencing provided initial pool of 2500 candidate markers. Further analysis by EpiSwitch™ platform on expanding sets of blood samples from melanoma patients and patients with non-melanoma skin cancers (NMSQ) as control, reduced the initial pool of candidate markers to 150. With further expansion on sample numbers it has been reduced to 32, as shown in Table 13.
Prognosis of Relapse
Top 15 markers previously identified for stratification of melanoma from non-melanoma skin cancers comprise TBx2 7/15, TYR 1/9, TYR 13/17, TYR 3/11, TYR 3/23, P1611/19, P16 7/23, P16 9/29, MITF 35/51, MITF 43/61, MITF 49/55, BRAF 5/11, BRAF 27/31, BRAF 21/31, BRAF 13/21, which were taken from a total of 8 genes: TBx2; TYR; BRAF; MiTF; p 16; BRN2; p 21; TBx3
3C analysis of melanoma patients' epigenetic profiles revealed 150 chromosome signatures with a potential to be prognostic biomarkers, reduced to three in expanding sets of testing sample cohorts. The three chromosome signatures which show the switches in chromosome conformational signature highly consistent with treatment and 2 year outcome for relapse, and this are the best potential prognostic melanoma markers are: BRAF 5/11, p 16-11/19 and TYR 13/17. Finally, three chromosome signatures were carried out to the validation stage as prognostic biomarkers.
Table 14 shows that relapse has been observed within two years after the treatment among the above patients. Through completely non-biased analysis of chromosome signatures these disease-specific three markers remained present and unchanged after treatment in majority of patients who relapsed after treatment.
Table 15 provides evidence that chromosome signatures change as a result of treatment to reflect more healthy profile. Through completely non-biased analysis of chromosome signatures the same disease-specific three markers have changed and were absent in majority of patients after treatment, with no signs of relapse for 2 years.
Table 16 shows that the same three prognostic biomarkers show a strong tendency to be absent in healthy population. From all melanoma specific biomarkers identified in initial discovery stage, only these three markers carried prognostic value due to their change after treatment, in that they were different from diagnostic markers.
These results confirm that the three identified chromosome signatures exemplify the evidence for chromosome signatures acting as valid and robust prognostic biomarkers.
Prognosis for Relapse (Residual Disease Monitoring in Treated Melanoma Patients)
Cross-validation for the 224 melanoma patients, observed for 2 years after the treatment for a relapse, on the basis of stratification with the three prognostic chromosome signatures from post-operational blood test. Table 17 shows the relevant confusion table.
Predictive/Pharmacodynamic Biomarkers for Drug Response: Anti-PD-1 in Metastatic Melanoma Patients (Array Data)
Melanoma
Malignant melanoma is the least common, but most aggressive form of skin cancer. It occurs in melanocytes, cells responsible for synthesis of the dark pigment melanin. The majority of malignant melanomas are caused by heavy UV exposure from the sun. Most of the new melanoma cases are believed to be linked to behavioural changes towards UV exposure from sunlight and sunbeds. Globally, in 2012, melanoma occurred in 232,000 people and resulted in 55,000 deaths. Incidence rates are highest in Australia and New Zealand. The worldwide incidence has been increasing more rapidly amongst men than any other cancer type and has the second fastest incidence increase amongst women over the last decade. The survival rates are very good for individuals with stage 1 and 2 melanomas. However, only 7-19% of melanoma patients whose cancer has spread to distant lymph nodes or other parts of the body will live for more than 5 years. Currently, the only way to accurately diagnose melanoma is to perform an excision biopsy on the suspicious mole. The treatment includes surgical removal of the tumour. There is no melanoma screening programme in the UK, but educational programmes have been created to raise awareness of risks and symptoms of melanoma. There is a high demand for screening programmes in countries where melanoma incidence is very high e.g. in Australia. This work concerns biomarkers for diagnosis, prognosis, residual disease monitoring and companion diagnostics for melanoma immunotherapies.
Study Background
The major issue with all immunomodulators currently tested in the treatment of cancers is their low response rates. In the case of late melanoma, for anti-PD-1 or anti-PD-L1 monoclonal antibodies, the objective response rate is only 30-40%. Such therapy is in strong need of biomarkers predicting responders vs. non-responders. The PD-1 locus is regulated by cytokines epigenetically through resetting of long range chromosome conformation signatures.
OBD Technology
EpiSwitch™ platform technology is ideally suited for stratification of PD-1 epigenetic states prior to and in response to immunotherapy. An EpiSwitch™ array has been designed for analysis of >332 loci implicated in controls and modulation of response to anti-PD-1 treatment in melanoma patients.
Methods
Biomarker identification using EpiSwitch™ array analysis:
All patients have been previously treated with chemotherapy and anti-CTLA-4 therapy. Two time points considered pre-treatment (baseline samples) and post-treatment (12 week samples)
Discovery Cohort
As the last step of the array data analysis, the hypergeometric analysis was carried out in order to identify regulatory hubs i.e. most densely regulated genes as being potential causative targets and preferred loci for stratification. The data is ranked by the Epigenetic Ratio for R vs R 12W (12W_FC_1), 1 in BL Binary indicates the loop is present in Responders vs Non-Responders, but when Responders baseline are compared to Responders at 12 weeks. The epigenetic ratio indicates that the presence of the loop is more abundant in the 12 week Responder patient samples. This indicates that there has been an expansion of this signature.
Summary
This epigenetic screen of anti-PD1 therapy for potential predictive and pharmacodynamic biomarkers provides a wealth of new regulatory knowledge, consistent with prior biological evidence. The work provides a rich pool of predictive and pharmacodynamic/response EpiSwitch™ markers to use in validation analysis. The results show presence of a defined epigenetic profile permissive for anti-PD-1 therapy. The epigenetic profile permissive for anti-PD1 therapy is present in naïve patients at baseline and is strengthened with treatment over 12 weeks period.
Further Information
This work concerns EpiSwitch™ as the basis for a diagnostic test to address the issue of poor melanoma diagnosis by general practitioners. 15 lead EpiSwitch™ biomarkers were screened and identified from an initial set of 86 patient samples representing true clinical setting. The biomarkers were then trained and validated in 2 independent patient cohorts: one from Australia (395 patients) and one from the Mayo Clinic (119 patients):
68 EpiSwitch™ Markers identified by statistical processing as predictive biomarkers at baseline for anti-PD-1 therapy. (PD1-R vs NR BL). R is Responder, and NR Is Non-Responder. 63 EpiSwitch™ Markers identified by statistical processing as response biomarkers for anti-PD-1 therapy. (PD1 R-BL v R-12W). 10 Markers are both good candidates for predictive and response markers.
Fisher-Exact test results: top 8 predictive EpiSwitch™ Array Markers validated with the EpiSwitch™ PCR platform on the independent patient cohort (see Table 37). See Table 38 for the discerning markers from the Fisher-Exact analysis for PCR analysis between Responders at Baseline and Responders at 12 weeks. 1 is Conformation Present. 0 is Conformation Absent/Array: R12_W indicates that the conformation was present in the Responders at 12 weeks.
The STAT5B_17_40403935_40406459_40464294_40468456_FR probe was measured in Responder v Non-Responder at Baseline and the conformation is present in the Responder. In this comparison the marker is in Responders at 12 weeks, this is the case as the concentrating of DNA used to detect the conformation in Responder vs Non Responder is greater than in Responder baseline v Responder at 12 weeks, indicating the Epigenetic Load has increased in the anti-PD-1 responding patients. Markers STAT5B and IL15 are of particular interest and are involved in key personalised medical and regulatory events responsible for the efficacies response to anti-PD1 therapies (see tables 39 to 40, 43 to 47).
The following Tables 36a to 36f, 37a, 37b, 38a, and 38b also pertain to Example 3 and are as follows:
Table 36a. Top Probes—Anti PD1 (Melanoma)—responders
Table 36b. Top Probes—Anti PD1 (Melanoma)—responders—probe sequences
Table 36c. Top Probes—Anti PD1 (Melanoma)—Responders—Loci
Table 36d. Top Probes—Anti PD1 (Melanoma) Non-responders
Table 36e. Top Probes—Anti PD1 (Melanoma) Non-responders
Table 36f. Top Probes—Anti PD1 (Melanoma) Non-responders—probes sequences and loci
Table 37a. Anti-PD1: pharmacodynamic response markers
Table 37b. Anti-PD1: pharmacodynamic response markers
Table 38a. Anti-PD1: pharmacodynamic response markers—No difference in baseline Responders and baseline Non-Responders but show a significant change in 12 week Responder
Table 38b. Probe location—Anti-PD1: pharmacodynamic response markers—No difference in baseline Responders and baseline Non-Responders but shows a significant change in 12 week Responders
The motor neurone disease Amyotrophic lateral sclerosis (ALS or Lou Gehrig's disease) is a fatal neurodegenerative disease characterised by progressive death of the primary motor neurones in the central nervous system. Symptoms include muscle weakness and muscle wasting, difficulty in swallowing and undertaking everyday tasks. As the disease progresses, the muscles responsible for breathing gradually fail, causing difficulty in breathing, and finally death. ALS has an average prevalence of 2 per 100,000, but is higher in the UK and USA with up to 5 per 100,000. There are estimated to be over 50,000 patients in the USA and 5,000 patients in the UK with the condition. The mortality rate for ALS sufferers is high: the median survival from diagnosis with ALS (i.e. the time when 50% of patients have died) varies in different studies, but in the most reliable (unbiased) population studies it is about 22 months with a range of 18-30 months. With no known cure, treatment of ALS focuses on supportive care. There is only one drug currently approved for treatment, riluzole which provides a modest increase in lifespan for ALS patients but minimal improvement in symptoms. Despite intensive research into the biological basis of ALS, diagnosis and methods of treatment, as well as monitoring of disease progression remains a challenge. Such prognostic tests would greatly benefit ALS sufferers by allowing sub-stratification of patients according to the biological mediators of clinical heterogeneity, potentially allowing a more precise prognosis and care planning by identifying fast and slow progressors. OBD has been discovering EpiSwitch™ markers to stratify ALS vs. healthy controls, and fast progressing ALS vs. slow progressing ALS, to develop and validate diagnostic, prognostic and predictive EpiSwitch™ biomarkers for ALS.
Source: Northeast Amyotrophic Lateral Sclerosis Consortium (NEALS)—USA.
See Tables 1, 2, 18, and 42 and hereinafter for ALS Probes—EpiSwitch™ markers to stratify ALS vs. healthy controls. Table 27 shows the gene data for this indication.
Further work was performed to validate the top ALS array markers and identify primers that could study the interactions. Statistical analysis of the array markers informed shortlist selection for PCR based assay development. From the list of the best stratifying ALS array probes, 99 markers were taken to the PCR stage.
Primers were designed using Integrated DNA Technologies (IDT) software (and Primer3web version 4.0.0 software if required) from markers identified from the microarray. Primer testing was carried out on each primer set; each set was tested on a pooled subset of samples to ensure that appropriate primers could study the potential interactions. Presence of an amplified product from PCR was taken to indicate the presence of a ligated product, indicating that a particular chromosome interaction was taking place. If the primer testing was successful then the primer sets were taken through to screening.
The signature set was isolated using a combination of univariate (LIMMA package, R language) and multivariate (GLMNET package, R language) statistics and validated using logistic modelling within WEKA (Machine learning algorithms package). The best 10 stratifying PCR markers were selected for validation on 58 individuals (29×ALS; 29× Healthy controls—HC) using data from the Northeast Amyotrophic Lateral Sclerosis Consortium (NEALS). These were selected based on their Exact Fisher's P-value. A consistently good marker from all 3 tests was the EpiSwitch marker in CD36. The first 9 PCR markers shown in Table 41 stratified between ALS and HC with 90% rank discrimination index.
The ALS marker set was analysed against a small independent cohort of samples provided by Oxford University. Even in a small subset of samples stratification of the samples was shown based on the biomarkers. Four markers stratify the subset of 32 (16 ALS, 16 Healthy Control) samples with p-value<0.3. These core markers are ALS.21.23_2, DNM3.5.7_8, ALS.61.63_4 and NEALS.101.103_32, in genes EGFR, DNM3, CD36 and GLYCAM1 respectively. The Fisher-Exact test, GLMNET and Bayesian Logistic modelling marked CLIC4 as a valuable addition to the four core markers.
The sequences of the primers for the PCR markers given in Table 41 are provided in Table 42.
Type 2 diabetes (also known as T2DM) is the most common form of diabetes. Diabetes may occur through either, the pancreas not producing enough hormone insulin which regulates blood sugar levels, or the body not being able to effectively use the hormone it produces due to reduced insulin sensitivity. Until recently, T2DM has only been diagnosed in adults, but it is now occurring in children and young adults. According to World Health Organisation (WHO), diabetes reached pandemic levels with 346 million sufferers worldwide and its incidence is predicted to double by 2030. In 2004 alone, approximately 3.4 million people died as a consequence of diabetes and its complications with the majority of deaths occurring in low- and middle-income countries. The incidence of T2DM is increasing due to an ageing population, changes in lifestyle such as lack of exercise and smoking, as well as diet and obesity. T2DM is not insulin dependent and can be controlled by changes in lifestyle such as diet, exercise and further aided with medication. Individuals treated with insulin are at a higher risk of developing severe hypoglycaemia (low blood glucose levels) and thus their medication and blood glucose levels require routine monitoring. Generally, older individuals with established T2DM are at a higher risk of cardiovascular disease (CVD) and other complications and thus usually require more treatment than younger adults with a recently-recognised disease. It has been estimated that seven million people in the UK are affected by pre-diabetic conditions, which increase the risk of progressing to T2DM. Such individuals are characterised by raised blood glucose levels, but are usually asymptomatic and thus may be overlooked for many years having a gradual impact on their health. Inventors develop prognostic stratifications for pre-diabetic state and T2DM. Presented herein are EpiSwitch™ markers to stratify pre-diabetic state (Pre-T2DM) vs. healthy controls, as well as the discovery of EpiSwitch™ markers to stratify T2DM vs. healthy control, and prognostic markers to stratify aggressive T2DM vs. slow T2DM.
Source: Norfolk and Norwich University Hospitals (NNUH), NHS Foundation Trust—Norwich UK
See Tables 19a, 19b, 19c and 19d hereinafter for Pre-type 2 diabetes mellitus probes—EpiSwitch™ markers to stratify pre-type 2 diabetes vs. healthy controls. Table 28 shows the gene data.
See also Tables 20a, 20b, 20c, 20d hereinafter for Type 2 diabetes mellitus probes—EpiSwitch™ markers to stratify type 2 diabetes mellitus vs. healthy controls. Table 29 shows the gene data.
Diabetes mellitus (DM) type 1 (also known as T1DM; formerly insulin-dependent diabetes or juvenile diabetes) is a form of diabetes that results from the autoimmune destruction of the insulin-producing beta cells in the pancreas. The classical symptoms are polyuria (frequent urination), polydipsia (increased thirst), polyphagia (increased hunger) and weight loss. Although, T1DM accounts for 5% of all diabetes cases, it is one of the most common endocrine and metabolic conditions among children. Its cause is unknown, but it is believed that both genetic factors and environmental triggers are involved. Globally, the number of people with T1DM is unknown, although it is estimated that about 80,000 children develop the disease each year. The development of new cases varies by country and region. The United States and northern Europe fall between 8-17 new cases per 100,000 per year. Treatment of diabetes involves lowering blood glucose and the levels of other known risk factors that damage blood vessels. Administration of insulin is essential for survival. Insulin therapy must be continued indefinitely and does not usually impair normal daily activities. Untreated, diabetes can cause many serious long-term complications such as heart disease, stroke, kidney failure, foot ulcers and damage to the eyes. Acute complications include diabetic ketoacidosis and coma. OBD's diabetes programme is focused on a development of EpiSwitch™ biomarkers for diagnostic and prognostic stratifications of T1DM.
Presented herein are EpiSwitch™ markers to stratify T1DM versus healthy controls.
Source: Caucasian samples collected by Procurement Company Tissue Solutions based in Glasgow (Samples collected in Russia); NEALS consortium controls (USA).
See Tables 21a, 21b, 21c and 21d hereinafter for Type 1 diabetes mellitus (T1DM) probes—EpiSwitch™ markers to stratify T1DM vs. healthy controls. Table 30 shows the gene data.
Ulcerative colitis (UC), a chronic inflammatory disease of the gastrointestinal tract, is the most common type of inflammatory disease of the bowel, with an incidence of 10 per 100,000 people annually, and a prevalence of 243 per 100,000. Although, UC can occur in people of any age, it is more likely to develop in people between the ages of 15 and 30 and older than 60. The exact cause of ulcerative colitis is unknown. However, it is believed that an overactive intestinal immune system, family history and environmental factors (e.g. emotional stress) may play a role in causing UC.
It is more prevalent in people of Caucasian and Ashkenazi Jewish origin than in other racial and ethnic subgroups. The most common signs and symptoms of this condition are diarrhoea with blood or pus and abdominal discomfort. It can also cause inflammation in joints, spine, skin, eyes, and the liver and its bile ducts. UC diagnosis is carried out through taking family history, physical exam, lab tests and endoscopy of large intestine. This lifelong disease is associated with a significant morbidity, and the potential for social and psychological sequelae particularly if poorly controlled. An estimated 30-60% of people with ulcerative colitis will have at least one relapse per year. About 80% of these are mild to moderate and about 20% are severe. Approximately 25% of people with UC will have one or more episodes of acute severe colitis in their lifetime. Of these, 20% will need a surgical removal of all or part of the colon (colectomy) on their first admission and 40% on their next admission. Although mortality rates have improved steadily over the past 30 years, acute severe colitis still has a mortality rate of up to 2%. Mortality is directly influenced by the timing of interventions, including medical therapy and colectomy.
Ulcerative colitis has a well-documented association with the development of colorectal cancer, with greatest risk in longstanding and extensive disease. Treatment of relapse may depend on the clinical severity, extent of disease and patient's preference and may include the use of aminosalicylates, corticosteroids or immunomodulators. The resulting wide choice of agents and dosing regimens has produced widespread heterogeneity in management across the UK, and emphasises the importance of comprehensive guidelines to help healthcare professionals provide consistent high quality care.
Presented herein are EpiSwitch™ markers to stratify UC versus healthy controls for a development of disease-specific signatures for UC.
Source: Caucasian samples collected by Procurement Company Tissue Solutions based in Glasgow (Samples collected in Russia); NEALS consortium controls (USA).
See Tables 22a, 22b, 22c and 22d hereinafter for Ulcerative colitis (UC) probes—EpiSwitch™ markers to stratify UC vs. healthy controls. Table 31 shows the gene data.
Systemic lupus erythematosus (SLE), also known as discoid lupus or disseminated lupus erythematosus, is an autoimmune disease which affects the skin, joints, kidneys, brain, and other organs. Although “lupus” includes a number of different diseases, SLE is the most common type of lupus. SLE is a disease with a wide array of clinical manifestations including rash, photosensitivity, oral ulcers, arthritis, inflammation of the lining surrounding the lungs and heart, kidney problems, seizures and psychosis, and blood cell abnormalities. Symptoms can vary and can change over time and are not disease specific which makes diagnosis difficult. It occurs from infancy to old age, with peak occurrence between ages 15 and 40. The reported prevalence of SLE in the population is 20 to 150 cases per 100,000. In women, prevalence rates vary from 164 (white) to 406 (African American) per 100,000. Due to improved detection of mild disease, the incidence nearly tripled in the last 40 years of the 20th century. Estimated incidence rates are 1 to 25 per 100,000 in North America, South America, Europe and Asia. The exact cause of SLE is not known, but several factors have been associated with the disease. People with lupus often have family members with other autoimmune conditions. There may be environmental triggers like ultraviolet rays, certain medications, a virus, physical or emotional stress, and trauma. There is no cure for SLE and the treatment is to ease the symptoms. These will vary depending on expressed symptoms and may include anti-inflammatory medications, steroids, corticosteroids and anti-malarial drugs. Survival has been improving, suggesting that more or milder cases are being recognised. OBD has been developing prognostic signatures for SLE.
See Tables 23a, 23b, 23c and 23d for SLE probes—EpiSwitch™ markers to stratify SLE vs. healthy controls. Table 32 shows the gene data.
Source: Caucasian samples collected by Procurement Company Tissue Solutions based in Glasgow (Samples collected in US); NEALS consortium controls.
Multiple sclerosis (MS) is an acquired chronic immune-mediated inflammatory condition of the central nervous system (CNS), affecting both the brain and spinal cord. The cause of MS is unknown. It is believed that an abnormal immune response to environmental triggers in people who are genetically predisposed results in immune-mediated acute, and then chronic, inflammation. The initial phase of inflammation is followed by a phase of progressive degeneration of the affected cells in the nervous system. MS is more common among people in Europe, the United States, Canada, New Zealand, and sections of Australia and less common in Asia and the tropics. It affects approximately 100,000 people in the UK. In the US, the number of people with MS is estimated to be about 400,000, with approximately 10,000 new cases diagnosed every year. People with MS typically develop symptoms between the ages 20 and 40, experiencing visual and sensory disturbances, limb weakness, gait problems, and bladder and bowel symptoms. They may initially have partial recovery, but over time develop progressive disability. Although, there is no cure, there are many options for treating and managing MS. They include drug treatments, exercise and physiotherapy, diet and alternative therapies. MS is a potentially highly disabling disorder with considerable personal, social and economic consequences. People with MS live for many years after diagnosis with significant impact on their ability to work, as well as an adverse and often highly debilitating effect on their quality of life and that of their families. OBD's MS programme involves looking at prognostic stratifications between primary progressive and relapsing-remitting MS.
The most common (approx. 90%) pattern of disease is relapsing-remitting MS (MSRR). Most people with this type of MS first experience symptoms in their early 20s. After that, there are periodic attacks (relapses), followed by partial or complete recovery (remissions). The pattern of nerves affected, severity of attacks, degree of recovery, and time between relapses all vary widely from person to person. Eventually, around two-thirds of people with relapsing-remitting MS enter a secondary progressive phase of MS. This occurs when there is a gradual accumulation of disability unrelated to relapses, which become less frequent or stop completely.
Presented herein are EpiSwitch™ monitoring markers to stratify MS patients who are responders to IFN-B treatment versus non-responders; EpiSwitch™ markers to stratify MSRR versus healthy controls and EpiSwitch™ markers to stratify MSRR (relapsing remitting type of MS) versus MSPP (primary progressive type of MS).
Source: Caucasian samples collected by procurement company Tissue Solutions, based in Glasgow (Samples collected in MS-RR: Russia: MS IFN-B R vs NR: USA); NEALS consortium controls (USA
See Tables 24a, b, c and d hereinafter for Relapsing-Remitting Multiple Sclerosis (MSRR) probes—EpiSwitch™ markers to stratify MSRR vs. healthy controls. Table 33 shows the gene data.
See also Tables 25a, 25b, 25c and 25d hereinafter for Multiple Sclerosis (MS) probes—EpiSwitch™ monitoring markers to stratify MS patients who are (B) responders to IFN-B (IFN-beta) treatment vs. (A) non-responders. Table 34 shows the gene data.
In patients with NF1 mutation transformation into malignant state is difficult to predict, as it is governed by epigenetic context of the patient. In NF2 mutants, prognosis of the disease is very reliable and strongly defined by the genetics itself. Presented herein are EpiSwitch™ markers to stratify Malignant Peripheral Nerve Sheath Tumours (MPNSTs) vs. Benign plexiform showing 329 top probes in enriched data.
Source: Belgium—University of Leuven
See Tables 26a and 26b hereinafter for Neurofibromatosis (NF) probes—EpiSwitch™ markers to stratify Benign plexiform vs. Malignant Peripheral Nerve Sheath Tumours (MPNSTs). Table 35 shows the gene data.
Table 47 shows the pattern of chromosome interactions present in responders to anti-PD1 (unless otherwise stated with NR (non-responder)) in individuals with particular cancers. The terminology used in the table is explained below.
DLBCL_ABC: Diffuse large B-cell lymphoma subtype activated B-cells
DLBCL_GBC: Diffuse large B-cell lymphoma subtype germinal centre B-cells
HCC: hepatocellular carcinoma
HCC_HEPB: hepatocellular carcinoma with hepatitis B virus
HCC_HEPC: hepatocellular carcinoma with hepatitis C virus
HEPB+R: Hepatitis B in remission
Pca_Class3: Prostate cancer stage 3
Pca_Class2: Prostate cancer stage 2
Pca_Class1: Prostate cancer stage 1
BrCa_Stg4: Breast cancer stage 4
BrCa_Stg3B: Breast cancer stage 3B
BrCa_Stg2A: Breast cancer stage 2A
BrCa_Stg2B: Breast cancer stage 2B
BrCa_Stg1A: Breast cancer stage 1A
BrCa_Stg1: Breast cancer stage 1
PD_1_R_Melanoma: Melanoma responder
PD_1_NR_Melanoma: Melanoma non responder
| Number | Date | Country | Kind |
|---|---|---|---|
| 1511079 | Jun 2015 | GB | national |
| 1511080 | Jun 2015 | GB | national |
| 1519555 | Nov 2015 | GB | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/GB2016/051900 | 6/24/2016 | WO |
| Publishing Document | Publishing Date | Country | Kind |
|---|---|---|---|
| WO2016/207653 | 12/29/2016 | WO | A |
| Number | Name | Date | Kind |
|---|---|---|---|
| 10508303 | Ren et al. | Dec 2019 | B2 |
| 20070238094 | Chaussabel et al. | Oct 2007 | A1 |
| 20100075861 | De et al. | Mar 2010 | A1 |
| 20100130373 | Dekker | May 2010 | A1 |
| 20180274015 | Akoulitchev et al. | Sep 2018 | A1 |
| 20190071715 | Ramadass et al. | Mar 2019 | A1 |
| Number | Date | Country |
|---|---|---|
| 2005118873 | Dec 2005 | WO |
| 2007093819 | Aug 2007 | WO |
| 2008084405 | Jul 2008 | WO |
| 2009147386 | Dec 2009 | WO |
| 2012159025 | Nov 2012 | WO |
| 2015077414 | May 2015 | WO |
| Entry |
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