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WO2018158589A1 - Procédés de diagnostic et de pronostic - Google Patents

Procédés de diagnostic et de pronostic Download PDF

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WO2018158589A1
WO2018158589A1 PCT/GB2018/050544 GB2018050544W WO2018158589A1 WO 2018158589 A1 WO2018158589 A1 WO 2018158589A1 GB 2018050544 W GB2018050544 W GB 2018050544W WO 2018158589 A1 WO2018158589 A1 WO 2018158589A1
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mvps
seq
nos
identified
group
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Andrew FEBER
Timothy Fenton
Kerry Chester
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UCL Business Ltd
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UCL Business Ltd
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/112—Disease subtyping, staging or classification
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/154—Methylation markers

Definitions

  • the present invention relates to methods of diagnosing cervical cancer in an individual, involving determining the methylation status of Methyl ation Variable
  • the invention also relates to methods of treating cervical cancer in an individual comprising providing a diagnosis of cervical cancer by the diagnostic methods defined herein, followed by administering one or more anti- cancer agents to the individual.
  • the present invention also relates to methods of classifying a cervical cancer tumor from a patient having cervical cancer as being either a group 1 tumor or a group 2 tumor, wherein patients having a tumor assigned to group 2 are at risk of a poorer outcome following primary treatment regimes compared to patients having a tumor assigned to group 1.
  • Such methods allow more efficient patient risk stratification for the purposes of providing further treatment and/or to help plan and manage patient care.
  • the invention also relates to methylation-discriminatory arrays comprising probes directed to the MVPs defined herein, as well as kits comprising the arrays.
  • Cervical cancer has been estimated as one of the leading causes of death from cancer among women worldwide [1], with an estimated 527,000 new cases and 265,000 deaths in 2012 [2]. In Europe, there were 58,400 cases diagnosed and 24,400 deaths in 2012 [2]. In the United States, 12200 new diagnoses and 4200 cancer deaths were reported in 2012 [1].
  • Cervical cancer develops from pre-cancerous non-invasive lesions termed cervical intraepithelial neoplasias (CINs) which are scaled according to the severity of the lesion.
  • CINl is classified as a mild dysplasia
  • CIN2 is classified as a moderate dysplasia
  • CIN3 is classified as a severe dysplasia.
  • cervical cancers are staged according to various criteria including the size of the primary tumour, whether it has invaded surrounding organs or tissue and the presence of local or distant metastases.
  • stage cervical cancers are treated with curative intent, 10-20% of patients with stage IB-IIA tumours with no apparent lymph node involvement will suffer recurrence following primary surgery or radiotherapy [8].
  • the gold standard for estimating prognosis in cervical cancer is staging by physical examination and imaging (by CT or, less commonly, by MRI).
  • DNA methylation biomarkers in body fluids, including urine [9-17], plasma/serum [18-20], and sputum [21, 22], for the non-invasive detection of cancer.
  • Changes in DNA methylation play a key role in malignant transformation, leading to the silencing of tumor-suppressor genes and overexpression of oncogenes [23].
  • the ontogenic plasticity and relative stability of DNA methylation makes epigenetic changes ideal biomarkers for diagnosis.
  • methylation status of genes has been examined as a possible approach to the study of the progression of the disease.
  • Virmani et al [24] examined the methylation status of six genes (pi 6, RARb, FHIT, GSTPl, MGMT, and hMLHl). The authors determined that methylation of RARb and GSTPl were early events in disease proion, methylation of pl6 and MGMT were intermediate events, and methylation of FHIT was a late, tumor-associated event.
  • Keratin 17 Keratin 17
  • Diagnostic methods are provided for the detection of cervical cancer from a biological sample from an individual including, but not limited to, a tissue sample, with high sensitivity and specificity, and which have the potential to reduce the need for smear test in patients undergoing screening for disease.
  • the invention provides a method of diagnosing cervical cancer in an individual comprising:
  • the group of MVPs may comprise at least 20 of the MVPs identified in SEQ ID NO:
  • the group of MVPs may comprise at least 20 of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG], and wherein cervical cancer is diagnosed when at least 15 of those MVPs are methylated.
  • the group of MVPs may comprise at least 20 of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG], and wherein cervical cancer is diagnosed when all 20 of those MVPs are methylated.
  • the MVPs determined to be methylated may include the MVPs identified in SEQ ID NOS 1 to 20 and denoted by [CG] and wherein cervical cancer is diagnosed when each one of the MVPs identified in SEQ ID NOS 1 to 20 and denoted by [CG] is methylated.
  • the group of MVPs may comprise all 30 of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG], and wherein cervical cancer is diagnosed when at least 15 of those MVPs are methylated.
  • the MVPs determined to be methylated may include the MVPs identified in SEQ ID NOS 1 to 15 and denoted by [CG] and wherein cervical cancer is diagnosed when each one of the MVPs identified in SEQ ID NOS 1 to 15 and denoted by [CG] is methylated.
  • the group of MVPs may comprise all 30 of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG], and wherein cervical cancer is diagnosed when at least 20 of those MVPs are methylated.
  • the group of MVPs may comprise all 30 of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG], and wherein cervical cancer is diagnosed when all 30 of those MVPs are methylated.
  • the MVPs determined to be methylated may include the MVPs identified in SEQ ID NOS 1 to 20 and denoted by [CG]. Cervical cancer may be diagnosed when each one of the MVPs identified in SEQ ID NOS 1 to 20 and denoted by [CG] is methylated.
  • the present invention also relates to methods of diagnosing cervical cancer in an individual comprising:
  • CG selected from a panel comprising the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG] is methylated, wherein the group comprises at least 20 of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG];
  • the present invention also relates to methods of diagnosing cervical cancer in an individual wherein the step of diagnosing cervical cancer in the individual further comprises:
  • V providing a cervical cancer treatment to the patient.
  • the present invention also relates to methods of classifying a cervical cancer tumor from a patient having cervical cancer as being either a group 1 tumor or a group 2 tumor, wherein patients having a tumor assigned to group 2 are at risk of a poorer outcome following primary treatment regimes compared to patients having a tumor assigned to group 1.
  • Such methods are based on the detection of differential DNA methylation patterns in cervical cancers from patients that experience either better, or worse than expected survival outcomes despite all being diagnosed with early stage tumours and despite all initially receiving uniform treatment.
  • Such methods allow more efficient patient risk stratification for the purposes of providing further treatment and/or to help plan and manage patient care. For example, such methods provide increased accuracy for determining the prognosis of patients following surgery to remove their primary tumour.
  • prognostic methods and related aspects described herein yield more information and outperform clinical staging. These methods outperform both HPV typing and histological subtyping (i.e squamous versus adenocarcinoma).
  • the invention provides a method of classifying a cervical tumor from a patient having cervical cancer as being either a group 1 tumor or a group 2 tumor, the method comprising:
  • the group of MVPs may comprise at least 20 of the MVPs identified in SEQ ID NOS 31 to 175 and denoted by [CG].
  • the group of MVPs may comprise at least 40 of the MVPs, or at least 60 of the MVPs, or at least 80 of the MVPs, or at least 100 of the MVPs, or at least 120 of the MVPs, or at least 140 of the MVPs, or all 145 of the MVPs identified in SEQ ID NOS 31 to 175 and denoted by [CG].
  • the classification algorithm may be based on a support vector machine model (SVM), a k-nearest neighbours model (KNN), a GLMnet model (GLM), or a Random Forest model (RF).
  • SVM support vector machine model
  • KNN k-nearest neighbours model
  • GLMnet model GLMnet model
  • RF Random Forest model
  • the output is the probability (from 0 to 1) of a given tumour belonging to group 2, where 0 is a 100% probability that the sample belongs to group 1, and 1 is a 100% probability that the sample belongs to group 2, and wherein any sample with a probability of equal to or greater than 0.5 is designated as belonging to group 2 and any sample with a probability of than 0.5 is designated as belonging to group 1.
  • the prognostic methods and related aspects described herein may also be applied to samples from individuals suspected to have cervical cancer wherein the samples are analysed for diagnostic purposes. Such methods will allow simultaneous diagnosis and patient risk stratification for the purposes of providing further treatment and/or to help plan and manage patient care.
  • the invention additionally provides methods of managing the treatment or healthcare regime of an individual, comprising:
  • the invention additionally provides methods of treating a cervical cancer patient, the methods comprising:
  • the patient has either a group 1 tumor or a group 2 tumor by performing any of the classification methods of the invention; and (b) treating the patient with one or more cervical cancer treatments.
  • the invention additionally provides methods of treating cervical cancer in a patient comprising:
  • the step of determining whether each one the MVPs is methylated may comprise bisulphite converting the DNA.
  • the step of determining whether each one the MVPs is methylated may comprise:
  • an amplification step may be performed, wherein loci comprising each MVP are amplified.
  • Amplification may be performed by PCR.
  • a capturing step may be performed before the sequencing or hybridization steps.
  • the capturing step may involve binding polynucleotides comprising the MVP loci to binding molecules specific to the MVP loci and collecting complexes comprising MVP loci and binding molecules; and wherein:
  • the capturing step occurs before the step of bisulphite converting the DNA
  • the capturing step occurs after the step of bisulphite converting the DNA but before the amplification or hybridization steps;
  • the binding molecules may be oligonucleotides specific for each MVP, preferably DNA or RNA molecules each comprising a sequence which is
  • the binding molecule may be coupled to a purification moiety.
  • the purification moiety may comprise a first purification moiety and the step of collecting complexes comprising MVP loci and binding molecules may comprise binding the first purification moiety to substrates comprising a second purification moiety, wherein first and second purification moieties form an interaction complex.
  • the first purification moiety may be biotin and the second purification moiety may be streptavidin; or the first purification moiety may be streptavidin and the second purification moiety may be biotin.
  • the step of amplifying loci comprising MVPs may comprise the use of primers which are independent of the methylation status of the MVP.
  • the step of amplifying loci comprising MVPs may be performed by
  • the invention also relates to methylation-discriminatory arrays comprising probes directed to the MVPs defined herein and kits comprising the arrays.
  • the invention provides an array capable of discriminating between methylated and non-methylated forms of MVPs; the array comprising oligonucleotide probes specific for a methylated form of each MVP in a MVP panel and oligonucleotide probes specific for a non-methylated form of each MVP in the panel; wherein the panel consists of at least 10 MVPs selected from the MVPs identified in SEQ ID NOS 1 to 30, or wherein the panel consists of at least 20 MVPs selected from the MVPs identified in SEQ ID NOS 31 to 175.
  • kits comprising any of the arrays described herein.
  • kits may further comprise a DNA modifying regent that is capable of modifying a non-methylated cytosine in a MVP dinucleotide but is not capable of modifying a methylated cytosine in a MVP dinucleotide, optionally wherein the dinucleotide is CpG.
  • a DNA modifying regent that is capable of modifying a non-methylated cytosine in a MVP dinucleotide but is not capable of modifying a methylated cytosine in a MVP dinucleotide, optionally wherein the dinucleotide is CpG.
  • Any of the kits described herein may further comprise forward and reverse primers for amplifying any and all of the MVPs defined herein from a sample of DNA from a patient.
  • Figure 1 shows standard ROC curves for the 30 MVP signature (all 30 of SEQ ID NOs 1 to 30) in four different training models: machine model (SVM), a k- nearest neighbours model (KNN), a GLMnet model (GLM), and a Random Forest model (RF). Performance was assessed in terms of sensitivity, specificity, and Kappa values in the validation dataset.
  • SVM machine model
  • KNN k- nearest neighbours model
  • RF Random Forest model
  • Figure 2 shows separate statistical metrics of sensitivity, specificity, and Kappa values for the 30 MVP signature (all 30 of SEQ ID NOs 1 to 30) in the validation dataset for each each of the four different training models: machine model (SVM), a k-nearest neighbours model (KNN), a GLMnet model (GLM), and a Random Forest model (RF).
  • SVM machine model
  • KNN k-nearest neighbours model
  • RF Random Forest model
  • Figure 3 shows standard ROC curves for a subset of the 30 MVP signature (all 20 of SEQ ID NOs 1 to 20) in the four different training models: machine model (SVM), a k-nearest neighbours model (KNN), a GLMnet model (GLM), and a Random Forest model (RF). Performance was assessed in terms of sensitivity, specificity, and Kappa values in the validation dataset.
  • SVM machine model
  • KNN k-nearest neighbours model
  • GLMnet model GLMnet model
  • RF Random Forest model
  • Figure 4 shows separate statistical metrics of sensitivity, specificity, and Kappa values for the 20 MVP subset signature (all 20 of SEQ ID NOs 1 to 20) in the validation dataset for each each of the four different training models: machine model (SVM), a k-nearest neighbours model (KNN), a GLMnet model (GLM), and a Random Forest model (RF).
  • SVM machine model
  • KNN k-nearest neighbours model
  • RF Random Forest model
  • Figure 5 shows a heatmap showing the 30 probes (all 30 of SEQ ID NOs 1 to 30) that display hypomethylation in normal cervical tissue and
  • Figure 6 shows a heatmap showing stratification of TCGA cervical cancer samples into two groups 'HPV16-like' and 'HPV45-like', on the basis of the pattern of DNA methylation at the 145 informative MVP loci (SEQ ID NOs 31 to 175).
  • Figure 7 shows Kaplan-Meier curves showing overall survival of patients with tumours classified as HPV16-like or HPV45-like in TCGA (discovery) or Norwegian (validation) cohorts.
  • Figure 8 shows shows a Kaplan-Meier curve showing overall survival of patients with tumours classified as FIPV16-like or FIPV45-like on the basis of all 145 MVP loci (SEQ ID NOs 31 to 175).
  • Figure 9 shows shows a Kaplan-Meier curve showing overall survival of patients with tumours classified as FIPV16-like or FIPV45-like on the basis of a subset of the top 20 MVPs out of the total group of 145 MVP loci (i.e. based on MVPs defined by SEQ ID NOs 31 to 50).
  • Histograms are shown for each of two independent validation cohorts.
  • cervical cancer represents one of the most prevalent groups of cancers in women. Infection by human papillomavirus (HPV) is responsible for around 90% of cervical cancer cases. Consistent with this, HPV vaccines have recently been introduced into public healthcare programmes, and HPV screening tests are becoming routine in the diagnosis of the disease. Other risk factors include smoking, prolonged use of oral contraceptives and a weakened immune system [26].
  • HPV human papillomavirus
  • squamous cell carcinomas 90% of cervical cancer cases are referred to as squamous cell carcinomas, 10% are adenocarcinomas, and a smaller proportion are other types [26].
  • cervical cancer is staged according to the International Federation of Gynecology and Obstetrics (FIGO) staging convention.
  • the disease is staged as stage 0 (carcinoma in situ) followed by stages IA, IB, IIA, IIB, IDA, IIIB, IVA and IVB according to severity and degree of metastasis [26].
  • the diagnostic, prognostic and treatment methods described herein are capable of positively identifying malignant cells of all classifications and stages of cervical cancer.
  • any of the methods described herein may be used to diagnose cervical squamous cell carcinomas and cervical adenocarcinomas.
  • the most preferred patient type to which the diagnostic assays described herein are applicable are humans.
  • the diagnostic assays described herein may also be used to identify cervical cancer in a non-human animal.
  • non-human animals may contain tissue derived from humans, e.g. xenografts.
  • diagnostic assays may be used to diagnose human cervical cancer in an animal model of human cervical cancer.
  • Typical non-human animals to which the diagnostic assays described herein are applicable are rodents such as rats or mice.
  • MVPs Methylation Variable Positions
  • Methylation of DNA is a recognised form of epigenetic modification which has the capability of altering the expression of genes and other elements such as
  • methylation may have the effect of e.g. silencing tumor suppressor genes and/or increasing the expression of oncogenes. Other forms of dysregulation may occur as a result of methylation.
  • Methylation of DNA occurs at discrete loci which are predominately dinucleotide consisting of a CpG motif, but may also occur at CHH motifs (where H is A, C, or T).
  • a methyl group is added to the fifth carbon of cytosine bases to create methylcytosine.
  • Methylation can occur throughout the genome and is not limited to regions with respect to an expressed sequence such as a gene. Methylation typically, but not always, occurs in a promoter or other regulatory region of an expressed sequence.
  • a Methylation Variable Position as defined herein is any dinucleotide locus which may show a variation in its methylation status between phenotypes, i.e. between tumour and normal tissue.
  • An MVP is preferably a CpG or a CHH
  • An MVP as defined herein is not limited to the position of the locus with respect to a corresponding expressed sequence.
  • an assessment of DNA methylation status involves analysing the presence or absence of methyl groups in DNA, for example methyl groups on the 5 th position of one or more cytosine nucleotides.
  • the methylation status of one or more cytosine nucleotides present as a CpG dinucleotide is assessed.
  • assessing the methylation status of an MVP or determining whether an MVP is methylated or unmethylated it is meant that a determination is made as to whether an MVP was methylated or unmethylated in the starting sample of DNA obtained from the individual prior to subsequent processing.
  • a panel of 30 MVPs whose methylation status varies as between normal tissue and cervical cancer tissue is provided herein. These 30 MVPs are set out in Table 1 below. For each of the 30 MVPs the CpG dinucleotide, of which the cytosine is subject to differential methylation, is indicated within square brackets.
  • assessing the methylation status of an MVP or “determining whether an MVP is methylated” it is meant that a determination is made as to whether the cytosine of the CpG of the MVP is methylated or unmethylated in the starting sample of DNA obtained from the individual.
  • an MVP is herein defined as methylated if one or more alleles of that MVP in a sample of genomic DNA from the patient is determined to possess one or more methylated CpG dinucleotide loci.
  • a further panel of 145 MVPs is provided herein. These 145 MVPs are set out in Table 2 below (SEQ ID NOS 31 to 175). For each of these 145 MVPs the CpG dinucleotide, of which the cytosine is subject to differential methylation, is indicated within square brackets. These 145 MVPs and subsets thereof may be used for classifying a cervical tumor from a patient having cervical cancer as being either a group 1 tumor or a group 2 tumor as discussed in more detail herein.
  • an MVP is herein defined as methylated if one or more alleles of that MVP in a sample of genomic DNA from the patient is determined to possess one or more methylated CpG dinucleotide loci.
  • a methylation dataset is generated based on the methylation status of all MVPs analysed.
  • a binary classification algorithm is then applied to the dataset to determine whether a cervical tumor from a patient having cervical cancer is either a group 1 tumor or a group 2 tumor.
  • the MVPs determined to be methylated are methylated relative to normal epithelium control and/or whole blood control.
  • Binary classification is a method of classifying samples or patients into two groups on the basis of a classification rule. Instancing a decision whether an item has or has not some quantitative property or some specified characteristic.
  • binary classification algorthyms include, but are not limted to Logistic regression, Support vector machine (SMV), Relevance vector machine (RVM), Perceptron, Naive Bayes classifier, k-nearest neighbors algorithm (KNN), Artificial neural network, Random Forests (RF).
  • the output of most binary classification algorithms is a continuous probability score.
  • the score indicates the algorithm's certainty that the given observation belongs to the positive class.
  • the decision about whether the observation should be classified as a binary positive or negative is determined by a classification threshold (cut-off). Any observations with scores higher than the threshold are then predicted as the positive class and scores lower than the threshold are predicted as the negative class.
  • Predictions fall into four groups based on the actual known answer and the predicted answer: true positives, true negatives, false positives and false negatives. In a perfect binary classifier the number of true positives will be 100% (1.0) and the number of false positives will be 0% (0.0).
  • MVP Methyl ation Variable Position
  • MVPs Methylation Variable Positions
  • Methyl groups are lost from a starting DNA molecule during conventional in vitro handling steps such as PCR.
  • techniques for the detection of methyl groups commonly involve the preliminary treatment of DNA prior to subsequent processing, in a way that preserves the methylation status information of the original DNA molecule.
  • Such preliminary techniques involve three main categories of processing, i.e. bisulphite modification, restriction enzyme digestion and affinity-based analysis. Products of these techniques can then be coupled with sequencing or array- based platforms for subsequent identification or qualitative assessment of MVP methylation status.
  • methylation-sensitive enzymes can be employed which digest or cut only in the presence of methylated DNA. Analysis of resulting fragments is commonly carried out using microarrays.
  • binding molecules such as anti-5- methylcytosine antibodies are commonly employed prior to subsequent processing steps such as PCR and sequencing.
  • any suitable method can be employed.
  • Preferred methods involve bisulphite treatment of DNA, including amplification of the identified MVP loci for methylation specific PCR and/or sequencing and/or assessment of the methylation status of target loci using methylation-discriminatory microarrays.
  • Amplification of MVP loci can be achieved by a variety of approaches.
  • MVP loci are amplified using PCR.
  • MVP may also be amplified by other techniques such as multiplex ligation-dependent probe amplification (MLPA).
  • MLPA multiplex ligation-dependent probe amplification
  • PCR-based approaches may be used.
  • methylation-specific primers may be hybridized to DNA containing the MVP sequence of interest. Such primers may be designed to anneal to a sequence derived from either a methylated or non-methylated MVP locus.
  • a PCR reaction is performed and the presence of a subsequent PCR product indicates the presence of an annealed MVP of identifiable sequence.
  • DNA is bisulphite converted prior to amplification.
  • MSP methylation specific PCR
  • PCR primers may anneal to the MVP sequence of interest independently of the methylation status, and further processing steps may be used to determine the status of the MVP.
  • Assays are designed so that the MVP site(s) are located between primer annealing sites. This method scheme is used in techniques such as bisulphite genomic sequencing [29], COBRA [30], Ms-SNuPE [31]. In such methods, DNA can be bisulphite converted before or after amplification.
  • small-scale PCR approaches are used. Such approaches commonly involve mass partitioning of samples (e.g. digital PCR). These techniques offer robust accuracy and sensitivity in the context of a highly miniaturised system (pico-liter sized droplets), ideal for the subsequent handling of small quantities of DNA obtainable from the potentially small volume of cellular material present in biological samples, particularly urine samples.
  • a variety of such small-scale PCR techniques are widely available. For example, microdroplet-based PCR instruments are available from a variety of suppliers, including RainDance Technologies, Inc. (Billerica, MA;
  • Microarray platforms may also be used to carry out small-scale PCR. Such platforms may include microfluidic network-based arrays e.g. available from Fluidigm Corp.
  • amplified PCR products may be coupled to subsequent analytical platforms in order to determine the methylation status of the MVPs of interest.
  • the PCR products may be directly sequenced to determine the presence or absence of a methylcytosine at the target MVP or analysed by array-based techniques.
  • any suitable sequencing techniques may be employed to determine the sequence of target DNA.
  • the use of high-throughput, so-called “second generation”, “third generation” and “next generation” techniques to sequence bisulphite-treated DNA are preferred.
  • Third generation techniques are typically defined by the absence of a requirement to halt the sequencing process between detection steps and can therefore be viewed as real-time systems.
  • the base-specific release of hydrogen ions which occurs during the incorporation process, can be detected in the context of microwell systems (e.g. see the Ion Torrent system available from Life Technologies; http://www.lifetechnologies.com/).
  • PPi pyrophosphate
  • nanopore technologies DNA molecules are passed through or positioned next to nanopores, and the identities of individual bases are determined following movement of the DNA molecule relative to the nanopore. Systems of this type are available commercially e.g.
  • a DNA polymerase enzyme is confined in a "zero-mode waveguide" and the identity of incorporated bases are determined with florescence detection of gamma-labeled phosphonucleotides (see e.g. Pacific Biosciences; http://www.pacificbiosciences.com/).
  • sequencing steps may be omitted.
  • amplified PCR products may be applied directly to hybridization arrays based on the principle of the annealing of two complementary nucleic acid strands to form a double-stranded molecule.
  • Hybridization arrays may be designed to include probes which are able to hybridize to amplification products of an MVP and allow discrimination between methylated and non-methylated loci.
  • probes may be designed which are able to selectively hybridize to an MVP locus containing thymine, indicating the generation of uracil following bisulphite conversion of an unmethylated cytosine in the starting template DNA.
  • probes may be designed which are able to selectively hybridize to an MVP locus containing cytosine, indicating the absence of uracil conversion following bisulphite treatment. This corresponds with a methylated MVP locus in the starting template DNA.
  • Detection systems may include, e.g. the addition of fluorescent molecules following a methylation status-specific probe extension reaction. Such techniques allow MVP status determination without the specific need for the sequencing of MVP amplification products.
  • array-based discriminatory probes may be termed methylation-specific probes.
  • Any suitable methylation-discriminatory microarrays may be employed to assess the methylation status of the MVPs described herein.
  • a preferred methylation- discriminatory microarray system is provided by Illumina, Inc. (San Diego, CA;
  • BeadChip array system may be used to assess the methylation status of diagnostic MVPs for cervical cancer as described herein.
  • the array comprises beads to which are coupled oligonucleotide probes specific for DNA sequences corresponding to the unmethylated form of an MVP, as well as separate beads to which are coupled oligonucleotide probes specific for DNA sequences corresponding to the methylated form of an MVP.
  • Candidate DNA molecules are applied to the array and selectively hybridize, under appropriate conditions, to the oligonucleotide probe corresponding to the relevant epigenetic form.
  • corresponding genomic DNA will selectively attach to the bead comprising the methylation-specific oligonucleotide probe, but will fail to attach to the bead
  • RNA comprising the non-methylation-specific oligonucleotide probe Single-base extension of only the hybridized probes incorporates a labeled ddNTP, which is subsequently stained with a fluorescence reagent and imaged.
  • the methylation status of the MVP may be determined by calculating the ratio of the fluorescent signal derived from the methylated and unmethylated sites.
  • the cervical cancer-specific diagnostic MVP biomarkers defined herein were initially identified using the Illumina Infinium HumanMethylation450 BeadChip array system, the same chip system can be used to interrogate those same MVPs in the diagnostic assays described herein.
  • Alternative or customised arrays could, however, be employed to interrogate the cervical cancer-specific diagnostic MVP biomarkers defined herein, provided that they comprise means for interrogating all MVPs for a given method, as defined herein.
  • DNA containing MVP sequences of interest may be hybridized to microarrays and then subjected to DNA sequencing to determine the status of the MVP as described above.
  • sequences corresponding to MVP loci may also be subjected to an enrichment process.
  • DNA containing MVP sequences of interest may be captured by binding molecules such as oligonucleotide probes complementary to the MVP target sequence of interest.
  • Sequences corresponding to MVP loci may be captured before or after bisulphite conversion or before or after amplification. Probes may be designed to be complementary to bisulphite converted DNA. Captured DNA may then be subjected to further processing steps to determine the status of the MVP, such as DNA sequencing steps.
  • Capture/separation steps may be custom designed.
  • a variety of such techniques are available commercially, e.g. the SureSelect target enrichment system available from Agilent Technologies ( hu;v ⁇ .3 ⁇ 4 ; k: ; u -n/h -no).
  • biotinylated "bait” or “probe” sequences e.g. RNA
  • RNA complementary to the DNA containing MVP sequences of interest
  • Streptavidin-coated magnetic beads are then used to capture sequences of interest hybridized to bait sequences. Unbound fractions are discarded. Bait sequences are then removed (e.g. by digestion of RNA) thus providing an enriched pool of MVP target sequences separated from non-MVP sequences.
  • template DNA is subjected to bisulphite conversion and target loci are then amplified by small-scale PCR such as microdroplet PCR using primers which are independent of the methylation status of the MVP.
  • small-scale PCR such as microdroplet PCR using primers which are independent of the methylation status of the MVP.
  • samples are subjected to a capture step to enrich for PCR products containing the target MVP, e.g. captured and purified using magnetic beads, as described above.
  • PCR reaction is carried out to incorporate DNA sequencing barcodes into MVP-containing amplicons. PCR products are again purified and then subjected to DNA sequencing and analysis to determine the presence or absence of a methylcytosine at the target genomic MVP [32].
  • the MVP biomarker loci defined herein are identified e.g. by Illumina® identifiers (IlmnID). These MVP loci identifiers refer to individual MVP sites used in the commercially available Illumina® Infinium Human Methyl ation450 BeadChip kit. The identity of each MVP site represented by each MVP loci identifier is publicly available from the Illumina, Inc. website under reference to the MVP sites used in the Infinium Human Methyl ation450 BeadChip kit.
  • Illumina® identifiers refer to individual MVP sites used in the commercially available Illumina® Infinium Human Methyl ation450 BeadChip kit.
  • the identity of each MVP site represented by each MVP loci identifier is publicly available from the Illumina, Inc. website under reference to the MVP sites used in the Infinium Human Methyl ation450 BeadChip kit.
  • Illumina® has developed a method to consistently designate MVP/CpG loci based on the actual or contextual sequence of each individual MVP/CpG locus. To unambiguously refer to MVP/CpG loci in any species, Illumina® has developed a consistent and deterministic MVP loci database to ensure uniformity in the reporting of methylation data. The Illumina® method takes advantage of sequences flanking a MVP locus to generate a unique MVP locus cluster ID. This number is based on sequence information only and is unaffected by genome version. Illumina' s standardized nomenclature also parallels the TOP/BOT strand nomenclature (which indicates the strand orientation) commonly used for single nucleotide polymorphism (S P) designation.
  • S P single nucleotide polymorphism
  • Illumina® Identifiers for the Infinium Human Methyl ation450 BeadChip system are also available from public repositories such as Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/).
  • GEO Gene Expression Omnibus
  • An MVP as defined herein thus refers to the CG dinucleotide motif identified in relation to each SEQ ID NO. and Illumina Identifier (limn ID) as listed in Table 1, wherein the cytosine base of the dinucleotide (noted in bold and square brackets in the sequences listed at Table 1) may (or may not) be modified.
  • determining the methylation status of a CpG defined by or identified in a given SEQ ID NO., or determining whether such a CpG is methylated it is meant that a determination is made as to whether the cytosine of the CG dinucleotide motif identified in bold and in square brackets in a sequence shown in Table 1 is methylated or not at one or more loci in the sample of DNA from the individual, accepting that variation in the sequence upstream and downstream of any given CpG may exist due to sequencing errors or variation between individuals.
  • the Inventors have surprisingly discovered a panel of 30 diagnostic MVPs whose methylation status varies as between normal tissue and cervical cancer tissue. These 30 MVPs are set out in Table 1 below. For each of the 30 diagnostic MVPs the CpG dinucleotide, of which the cytosine is subject to differential methylation, is indicated within square brackets (see Table 1). Polynucleotide sequence both upstream and downstream of the relevant CpG dinucleotide is shown for each diagnostic MVP.
  • the Inventors have discovered that within the panel of 30 diagnostic MVPs, MVPs are methylated in DNA samples from tissue from individuals with cervical cancer, whereas MVPs are unmethylated in DNA samples from tissue from individuals without cervical cancer - the determination of whether a given diagnostic MVP is methylated or unmethylated being made in accordance with the methods defined herein.
  • the disclosed panel provides a pool of MVPs which have high discriminatory power in the diagnosis of cervical cancer.
  • the invention provides a method of diagnosing cervical cancer in an individual comprising:
  • the group of MVPs may comprise 11 or more of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG]; or the group may comprise 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more, 21 or more, 22 or more, 23 or more, 24 or more, 25 or more, 26 or more, 27 or more, 28 or more, 29 or more, or all 30 of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG].
  • cervical cancer may be diagnosed when each MVP in the group of MVPs analysed are methylated.
  • the group of MVPs i.e. those MVPs, the methylation status of which are to be determined
  • the group of MVPs may comprise 10 or more of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG]
  • cervical cancer may be diagnosed when all 10 MVPs are methylated.
  • the group of MVPs may comprise 11 or more of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG], and cervical cancer may be diagnosed when all
  • the group of MVPs may comprise 12 or more of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG], and cervical cancer may be diagnosed when all
  • the group of MVPs may comprise 13 or more of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG], and cervical cancer may be diagnosed when all
  • the group of MVPs may comprise 14 or more of the MVPs identified in SEQ
  • ID NOS 1 to 30 and denoted by [CG] and cervical cancer may be diagnosed when all
  • the group of MVPs may comprise 15 or more of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG], and cervical cancer may be diagnosed when all 15 MVPs are methylated.
  • the group of MVPs may comprise 16 or more of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG], and cervical cancer may be diagnosed when all
  • the group of MVPs may comprise 17 or more of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG], and cervical cancer may be diagnosed when all
  • the group of MVPs may comprise 18 or more of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG], and cervical cancer may be diagnosed when all
  • the group of MVPs may comprise 19 or more of the MVPs identified in SEQ
  • ID NOS 1 to 30 and denoted by [CG] and cervical cancer may be diagnosed when all
  • the group of MVPs may comprise 20 or more of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG], and cervical cancer may be diagnosed when all 20 MVPs are methylated.
  • the group of MVPs may comprise 21 or more of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG], and cervical cancer may be diagnosed when all
  • the group of MVPs may comprise 22 or more of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG], and cervical cancer may be diagnosed when all
  • the group of MVPs may comprise 23 or more of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG], and cervical cancer may be diagnosed when all
  • the group of MVPs may comprise 24 or more of the MVPs identified in SEQ
  • ID NOS 1 to 30 and denoted by [CG] and cervical cancer may be diagnosed when all
  • the group of MVPs may comprise 25 or more of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG], and cervical cancer may be diagnosed when all 25 MVPs are methylated.
  • the group of MVPs may comprise 26 or more of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG], and cervical cancer may be diagnosed when all
  • the group of MVPs may comprise 27 or more of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG], and cervical cancer may be diagnosed when all
  • the group of MVPs may comprise 28 or more of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG], and cervical cancer may be diagnosed when all
  • the group of MVPs may comprise 29 or more of the MVPs identified in SEQ
  • ID NOS 1 to 30 and denoted by [CG] and cervical cancer may be diagnosed when all
  • the group of MVPs may comprise all 30 of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG], and cervical cancer may be diagnosed when all 30 MVPs are methylated.
  • cervical cancer may be diagnosed when all 30 of the MVPs identified in SEQ ID NO S 1 to 30 and denoted by [CG] are methylated.
  • the MVPs determined to be methylated may include the MVPs identi fied in SEQ ID NOS 1 to 10 and denoted by [CG], or may include the MVPs identified n SEQ ID NOS 1 to 11 and denoted by [CG or may include the MVPs identified n SEQ ID NOS 1 to 12 and denoted by [CG or may include the MVPs identified n SEQ ID NOS 1 to 13 and denoted by [CG or may include the MVPs identified n SEQ ID NOS 1 to 14 and denoted by [CG or may include the MVPs identified n SEQ ID NOS 1 to 15 and denoted by [CG or may include the MVPs identified n SEQ ID NOS 1 to 16 and denoted by [CG or may include the MVPs identified n SEQ ID NOS 1 to 17 and denoted by [CG or may include the MVPs identified n SEQ ID NOS 1 to 18 and denoted by [CG or may include the MVPs identified n SEQ ID NOS 1 to 19 and denote
  • Any of the diagnoistic methods described herein may be used to diagnose squamous cell carcinoma of the cervix.
  • Any of the diagnoistic methods described herein may be used to diagnose squamous cell carcinoma of the cervix at any of the FIGO stages of the disease.
  • Any of the diagnoistic methods described herein may be used to diagnose adenocarcinoma of the cervix. Any of the diagnoistic methods described herein may be used to diagnose adenocarcinoma of the cervix at any of the FIGO stages of the disease.
  • any of the diagnoistic methods described herein may be used to diagnose FIGO stage 0 cervical cancer.
  • Any of the diagnoistic methods described herein may be used to diagnose FIGO stage IDA cervical cancer.
  • the Inventors have surprisingly discovered a panel of 145 prognostic MVPs whose methylation status may be used to classify tumors into either a group 1 tumor (also referred to as 'HPV-16 like') or a group 2 tumor (also referred to as 'HPV-45 like'). These 145 MVPs are set out in Table 2 below. For each of the 145 prognostic MVPs the CpG dinucleotide, of which the cytosine is subject to differential
  • methylation is indicated within square brackets (see Table 2). Polynucleotide sequence both upstream and downstream of the relevant CpG dinucleotide is shown for each diagnostic MVP.
  • the Inventors have discovered that methylation datasets may be generated using the 145 MVPs set out in Table 2 below, or subsets thereof, by identifying in a given patient sample whether each MVP is methylated or unmethylated.
  • Application of a binary classification algorithm to the generated datasets may be used to determine the probability of a patient's tumor being either or a group 1 tumor or a group 2 tumor. Any sample with a probability value of greater than or equal to 0.5 is considered to derive from a group 2 tumour ('HPV45-like'). Any sample with a probability value of less than 0.5 is considered to derive from a group 1 tumour ('HPV16-like').
  • cervical cancer patients may be stratified into two groups.
  • the disclosed panel provides a pool of MVPs which have high
  • the invention provides a method of classifying a cervical tumor from a patient having cervical cancer as being either a group 1 tumor or a group 2 tumor, the method comprising:
  • the group of MVPs may comprise at least 20 of the MVPs identified in SEQ ID NOS 31 to 175 and denoted by [CG].
  • the group of MVPs may comprise at least 21 of the MVPs identified in SEQ ID NOS 31 to 175 and denoted by [CG], or at least 22 of the MVPs, or at least 23 of the MVPs, or at least 24 of the MVPs, or at least 25 of the MVPs, or at least 26 of the MVPs, or at least 27 of the MVPs, or at least 28 of the MVPs, or at least 29 of the MVPs, or at least 30 of the MVPs, or at least 31 of the MVPs, or at least 32 of the MVPs, or at least 33 of the MVPs, or at least 34 of the MVPs, or at least 35 of the MVPs, or at least 36 of the MVPs, or at least 37 of the MVPs, or at least 38 of the MVPs, or at least 39 of the MVPs, or at least 40 of the MVPs, or at least 41 of the MVPs, or at least 42 of the
  • MVPs or at least 44 of the MVPs, or at least 45 of the MVPs, or at least 46 of the
  • MVPs or at least 47 of the MVPs, or at least 48 of the MVPs, or at least 49 of the
  • MVPs or at least 50 of the MVPs, or at least 51 of the MVPs, or at least 52 of the
  • MVPs or at least 53 of the MVPs, or at least 54 of the MVPs, or at least 55 of the
  • MVPs or at least 56 of the MVPs, or at least 57 of the MVPs, or at least 58 of the
  • MVPs or at least 59 of the MVPs, or at least 60 of the MVPs, or at least 61 of the
  • MVPs or at least 62 of the MVPs, or at least 63 of the MVPs, or at least 64 of the
  • MVPs or at least 65 of the MVPs, or at least 66 of the MVPs, or at least 67 of the
  • MVPs or at least 68 of the MVPs, or at least 69 of the MVPs, or at least 70 of the
  • MVPs or at least 71 of the MVPs, or at least 72 of the MVPs, or at least 73 of the
  • MVPs or at least 74 of the MVPs, or at least 75 of the MVPs, or at least 76 of the
  • MVPs or at least 77 of the MVPs, or at least 78 of the MVPs, or at least 79 of the
  • MVPs or at least 80 of the MVPs, or at least 81 of the MVPs, or at least 82 of the
  • MVPs or at least 83 of the MVPs, or at least 84 of the MVPs, or at least 85 of the
  • MVPs or at least 86 of the MVPs, or at least 87 of the MVPs, or at least 88 of the
  • MVPs or at least 89 of the MVPs, or at least 90 of the MVPs, or at least 91 of the
  • MVPs or at least 92 of the MVPs, or at least 93 of the MVPs, or at least 94 of the
  • MVPs or at least 95 of the MVPs, or at least 96 of the MVPs, or at least 97 of the
  • MVPs or at least 98 of the MVPs, or at least 99 of the MVPs, or at least 100 of the
  • MVPs or at least 101 of the MVPs, or at least 102 of the MVPs, or at least 103 of the
  • MVPs or at least 104 of the MVPs, or at least 105 of the MVPs, or at least 106 of the
  • MVPs or at least 107 of the MVPs, or at least 108 of the MVPs, or at least 109 of the
  • MVPs or at least 110 of the MVPs, or at least 111 of the MVPs, or at least 112 of the
  • MVPs or at least 113 of the MVPs, or at least 114 of the MVPs, or at least 115 of the
  • MVPs or at least 116 of the MVPs, or at least 117 of the MVPs, or at least 118 of the
  • MVPs or at least 119 of the MVPs, or at least 120 of the MVPs, or at least 121 of the
  • MVPs or at least 122 of the MVPs, or at least 123 of the MVPs, or at least 124 of the
  • MVPs or at least 125 of the MVPs, or at least 126 of the MVPs, or at least 127 of the
  • MVPs or at least 128 of the MVPs, or at least 129 of the MVPs, or at least 130 of the
  • the group of MVPs may comprise all 145 of the MVPs identified in SEQ ID NOS 31 to 175 and denoted by [CG].
  • the group of MVPs may also comprise subsets of the MVPs identified in SEQ ID NOS 31 to 175 and denoted by [CG], as defined further herein.
  • Any of the classification and prognostic methods described herein may be applied to a squamous cell carcinoma of the cervix.
  • Any of the classification and prognostic methods described herein may be applied to a squamous cell carcinoma of the cervix at any of the FIGO stages of the disease.
  • any of the classification and prognostic methods described herein may applied to an adenocarcinoma of the cervix at any of the FIGO stages of the disease.
  • any of the classification and prognostic methods described herein may be applied to FIGO stage 0 cervical cancer.
  • any of the classification and prognostic methods described herein may be applied to FIGO stage IIIB cervical cancer. Any of the classification and prognostic methods described herein may be applied to FIGO stage IVA cervical cancer.
  • Sensitivity and specificity metrics for cervical cancer diagnosis based on the
  • MVP methylation status assays described herein may be defined using standard receiver operating characteristic (ROC) statistical analysis [36] .
  • ROC analysis 100% sensitivity corresponds to a finding of no false negatives, and 100%> specificity corresponds to a finding of no false positives.
  • a cervical cancer diagnostic assay in accordance with the invention described herein can achieve a ROC sensitivity of 90% or greater, 91% or greater, 92%> or greater, 93% or greater, 94%> or greater, 95% or greater, 96% or greater, 97% or greater, 98% or greater or 99%.
  • the ROC sensitivity may be 100%.
  • Diagnostic assays in accordance with the invention can achieve a ROC specificity of 50% or greater, 51% or greater, 52% or greater, 53% or greater, 54% or greater, 55% or greater, 56% or greater, 57% or greater, 58% or greater, 59% or greater, 60%) or greater, 61% or greater, 62% or greater, 63% or greater, 64% or greater, 65% or greater, 66% or greater, 67% or greater, 68% or greater, 69% or greater, 70% or greater, 71%) or greater, 72% or greater, 73% or greater, 74% or greater, 75% or greater, 76% or greater, 77% or greater, 78% or greater, 79% or greater, 80% or greater, 81% or greater, 82%o or greater, 83% or greater, 84% or greater, 85% or greater, 86% or greater, 87% or greater, 88% or greater, 89% or greater, 90% or greater, 91% or greater, 92% or greater, 93%o or greater, 94% or greater, 95% or greater, 96% or greater,
  • the ROC sensitivity may be 100% and the ROC specificity may be 50% or greater, 51%> or greater, 52% or greater, 53% or greater, 54% or greater, 55% or greater, 56%o or greater, 57% or greater, 58% or greater, 59% or greater, 60% or greater, 61% or greater, 62% or greater, 63% or greater, 64% or greater, 65% or greater, 66% or greater, 67%o or greater, 68% or greater, 69% or greater, 70% or greater, 71% or greater, 72% or greater, 73% or greater, 74% or greater, 75% or greater, 76% or greater, 77% or greater, 78%o or greater, 79% or greater, 80% or greater, 81% or greater, 82% or greater, 83% or greater, 84% or greater, 85% or greater, 86% or greater, 87% or greater, 88% or greater, 89%o or greater, 90% or greater, 91% or greater, 92% or greater, 93% or greater, 94% or greater, 95% or greater, 96% or greater, 9
  • the ROC sensitivity may be 95% or greater and the ROC specificity may be
  • the ROC sensitivity may be 90% or greater and the ROC specificity may be
  • the ROC sensitivity may be 85%> or greater and the ROC specificity may be 50%) or greater, 51%> or greater, 52% or greater, 53% or greater, 54% or greater, 55% or greater, 56% or greater, 57% or greater, 58% or greater, 59% or greater, 60% or greater, 61%o or greater, 62% or greater, 63% or greater, 64% or greater, 65% or greater, 66% or greater, 67% or greater, 68% or greater, 69% or greater, 70% or greater, 71% or greater, 72%o or greater, 73% or greater, 74% or greater, 75% or greater, 76% or greater, 77% or greater, 78% or greater, 79% or greater, 80% or greater, 81% or greater, 82% or greater, 83%o or greater, 84% or greater, 85% or greater, 86% or greater, 87% or greater, 88% or greater, 89% or greater, 90% or greater, 91% or greater, 92% or greater, 93% or greater, 94% or greater, 95% or greater, 96% or
  • the ROC sensitivity may be 80% or greater and the ROC specificity may be 50%o or greater, 51% or greater, 52% or greater, 53% or greater, 54% or greater, 55% or greater, 56% or greater, 57% or greater, 58% or greater, 59% or greater, 60% or greater, 61%o or greater, 62% or greater, 63% or greater, 64% or greater, 65% or greater, 66% or greater, 67% or greater, 68% or greater, 69% or greater, 70% or greater, 71% or greater, 72%o or greater, 73% or greater, 74% or greater, 75% or greater, 76% or greater, 77% or greater, 78% or greater, 79% or greater, 80% or greater, 81% or greater, 82% or greater, 83%o or greater, 84% or greater, 85% or greater, 86% or greater, 87% or greater, 88% or greater, 89% or greater, 90% or greater, 91% or greater, 92% or greater, 93% or greater, 94% or greater, 95% or greater, 96% or greater,
  • the ROC sensitivity may be 75% or greater and the ROC specificity may be 50%o or greater, 51% or greater, 52% or greater, 53% or greater, 54% or greater, 55% or greater, 56% or greater, 57% or greater, 58% or greater, 59% or greater, 60% or greater, 61%o or greater, 62% or greater, 63% or greater, 64% or greater, 65% or greater, 66% or greater, 67% or greater, 68% or greater, 69% or greater, 70% or greater, 71% or greater, 72%o or greater, 73% or greater, 74% or greater, 75% or greater, 76% or greater, 77% or greater, 78% or greater, 79% or greater, 80% or greater, 81% or greater, 82% or greater, 83%o or greater, 84% or greater, 85% or greater, 86% or greater, 87% or greater, 88% or greater, 89% or greater, 90% or greater, 91% or greater, 92%> or greater, 93% or greater, 94% or greater, 95% or greater, 96% or greater
  • the ROC sensitivity may be 70% or greater and the ROC specificity may be 50%) or greater, 51% or greater, 52% or greater, 53% or greater, 54% or greater, 55% or greater, 56% or greater, 57% or greater, 58% or greater, 59% or greater, 60% or greater, 61%o or greater, 62% or greater, 63% or greater, 64% or greater, 65% or greater, 66% or greater, 67% or greater, 68% or greater, 69% or greater, 70% or greater, 71% or greater, 72%o or greater, 73% or greater, 74% or greater, 75% or greater, 76% or greater, 77% or greater, 78% or greater, 79% or greater, 80% or greater, 81% or greater, 82% or greater, 83%o or greater, 84% or greater, 85% or greater, 86% or greater, 87% or greater, 88% or greater, 89% or greater, 90% or greater, 91% or greater, 92% or greater, 93% or greater, 94% or greater, 95% or greater, 96% or greater, 9
  • the ROC sensitivity may be 65% or greater and the ROC specificity may be 50%o or greater, 51% or greater, 52% or greater, 53% or greater, 54% or greater, 55% or greater, 56% or greater, 57% or greater, 58% or greater, 59% or greater, 60% or greater, 61%o or greater, 62% or greater, 63% or greater, 64% or greater, 65% or greater, 66% or greater, 67% or greater, 68% or greater, 69% or greater, 70% or greater, 71% or greater, 72%o or greater, 73% or greater, 74% or greater, 75% or greater, 76% or greater, 77% or greater, 78% or greater, 79% or greater, 80% or greater, 81% or greater, 82% or greater, 83%o or greater, 84% or greater, 85% or greater, 86% or greater, 87% or greater, 88% or greater, 89% or greater, 90% or greater, 91% or greater, 92% or greater, 93% or greater, 94% or greater, 95% or greater, 96% or greater,
  • the ROC sensitivity may be 60% or greater and the ROC specificity may be 50%o or greater, 51% or greater, 52% or greater, 53% or greater, 54% or greater, 55% or greater, 56% or greater, 57% or greater, 58% or greater, 59% or greater, 60% or greater, 61%o or greater, 62% or greater, 63% or greater, 64% or greater, 65% or greater, 66% or greater, 67% or greater, 68% or greater, 69% or greater, 70% or greater, 71% or greater, 72%o or greater, 73% or greater, 74% or greater, 75% or greater, 76% or greater, 77% or greater, 78% or greater, 79% or greater, 80% or greater, 81% or greater, 82% or greater, 83%o or greater, 84% or greater, 85% or greater, 86% or greater, 87% or greater, 88% or greater, 89% or greater, 90% or greater, 91% or greater, 92%> or greater, 93% or greater, 94% or greater, 95% or greater, 96% or greater,
  • the ROC sensitivity may be 55% or greater and the ROC specificity may be 50%) or greater, 51% or greater, 52% or greater, 53% or greater, 54% or greater, 55% or greater, 56% or greater, 57% or greater, 58% or greater, 59% or greater, 60% or greater, 61%) or greater, 62% or greater, 63% or greater, 64% or greater, 65% or greater, 66% or greater, 67% or greater, 68% or greater, 69% or greater, 70% or greater, 71% or greater, 72%) or greater, 73% or greater, 74% or greater, 75% or greater, 76% or greater, 77% or greater, 78% or greater, 79% or greater, 80% or greater, 81% or greater, 82% or greater, 83%) or greater, 84% or greater, 85% or greater, 86% or greater, 87% or greater, 88% or greater, 89% or greater, 90% or greater, 91% or greater, 92% or greater, 93% or greater, 94% or greater, 95% or greater, 96% or greater, 9
  • a further metric which can be employed to classify the accuracy of the MVP- based assays is ROC AUC.
  • AUC area under the curve of a ROC plot
  • the AUC score for the ROC plot will be 0.5.
  • the number of true positives will be 100% and the number of false positives will be 0%.
  • the AUC score for the ROC plot will be 1.
  • a cervical cancer diagnostic assay in accordance with the invention can achieve a ROC AUC of 0.90 or greater, 0.91 or greater, 0.92 or greater, 0.93 or greater, 0.94 or greater, 0.95 or greater, 0.96 or greater, 0.97 or greater, 0.98 or greater, 0.99 or 1.00.
  • the diagnostic assay can achieve a ROC AUC of 0.98 or greater.
  • Cervical cancer diagnostic tests based on the 30 diagnostic MVP biomarkers described herein may also be characterised using a Negative Predictive Value (NPV) metric.
  • NPV Negative Predictive Value
  • a cervical cancer diagnostic assay in accordance with the invention described herein can achieve an NPV of 90% or greater, 91% or greater, 92%> or greater, 93% or greater, 94%) or greater, 95% or greater, 96%> or greater, 97% or greater, 98% or greater or 99% or 100%.
  • Biological samples 91% or greater, 92%> or greater, 93% or greater, 94%) or greater, 95% or greater, 96%> or greater, 97% or greater, 98% or greater or 99% or 100%.
  • the cervical cancer diagnostic assays described herein may be performed on any suitable biological material obtained from the patient.
  • Preferred biological material is a Liquid Based Cytology (LBC) sample.
  • LBC Liquid Based Cytology
  • cryopreserved material tissue sections etc.
  • Samples of biological material may also include solid tissue samples, aspirates, samples of biological fluids, blood, serum, plasma, ascitic fluid, lymph, peripheral blood, cerebrospinal fluid, fine needle aspirate, saliva, sputum, bone marrow, skin, epithelial samples (including buccal, cervical or vaginal epithelia) or other tissue derived from the ectoderm, vaginal fluid, semen etc.
  • Tissue scrapes may include biological material from e.g. buccal,
  • the cells of the sample may comprise inflammatory cells, such as lymphocytes.
  • any of the assays and methods described herein may involve providing a biological sample from the patient as the source of patient DNA for methylation analysis.
  • any of the assays and methods described herein may involve obtaining patient DNA from a biological sample which has previously been obtained from the patient.
  • any of the assays and methods described herein may involve obtaining a biological sample from the patient as the source of patient DNA for methylation analysis.
  • Procedures for obtaining a biological sample from the patient may be noninvasive, such as collecting cells from urine. Alternatively, invasive procedures such as biopsies may be used.
  • the level of detection is such that 2 tumor cells may be detected in a sample comprising 150,000 cells or more.
  • the sample may comprise 160,000 cells or more, 170,000 cells or more, 180,000 cells or more, 190,000 cells or more, 200,000 cells or more, 210,000 cells or more, 220,000 cells or more, 230,000 cells or more, 240,000 cells or more, 250,000 cells or more, 260,000 cells or more, 270,000 cells or more, 280,000 cells or more, 280,000 cells or more, or 300,000 cells or more.
  • the number of tumor cells that can be detected is 10 or more, 20 or more, 30 or more, 40 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, 100 or more, 200 or more, 300 or more, 400 or more, 500 or more, 600 or more, 700 or more, 800 or more, 900 or more, 1000 or more, 2000 or more, 3000 or more, 4000 or more, 5000 or more, 6000 or more, 7000 or more, 8000 or more, 9000 or more, 10000 or more, 20000 or more, 30000 or more, 40000 or more, 50000 or more, 60000 or more, 70000 or more, 80000 or more, 90000 or more or 100000 or more.
  • the invention encompasses methods wherein following a positive diagnosis of cervical cancer, and/or assignment of a patient's tumor as a group 1 tumor, the patient may be administered of one or more urogical procedures, one or more surgical procedures, one or more chemotherapeutic agents, one or more immunotherapeutic agents, one or more radiotherapeutic agents, one or more small molecule agents, one or more kinase inhibitor agents or any combination of the above.
  • Urogical procedures include radical trachelectomy or radical hysterectomy resection of cervical tumor
  • Chemotherapeutic agents include the following. Alkylating agents, which include the nitrogen mustards, nitrosoureas, tetrazines, aziridines, cisplatin and platinum based derivatives, as well as the non-classical alkylating agents. Antimetabolites, which include the anti-folates, fluoropyrimidines, deoxynucleoside analogues and thiopurines. Microtubule disrupting agents, which include the vinca alkaloids and taxanes, as well as dolastatin 10 and derivatives thereof. Topoisomerase inhibitors, which include camptothecin, irinotecan and topotecan.
  • Topoisomerase II poisons which include etoposide, doxorubicin, mitoxantrone and teniposide.
  • Topoisomerase II catalytic inhibitors which include novobiocin, merbarone, and aclarubicin.
  • Cytotoxic antibiotics which include anthracyclines, actinomycin, bleomycin, plicamycin, and mitomycin. Combinations of agents include but are not limited to MVAC (Methotrexate, Vinblastine, Vinblastine and Vinblastine), Gem-Cis (GC) (Gemcitabine and Cisplatin), Lapatinib and gemcitabine.
  • MVAC Metalhotrexate, Vinblastine, Vinblastine and Vinblastine
  • GC Gem-Cis
  • Cisplatin Lapatinib and gemcitabine.
  • Ilmmune checkpoint modulators include Ipilimumab, Nivolumab,
  • immunotherapeutics include chimeric antigen receptor (CAR) T-cell therapy, HPV therapeutic vaccines and antibody-drug conjugates (ADC).
  • CAR chimeric antigen receptor
  • ADC antibody-drug conjugates
  • Antibody- drug conjugates include antibodies conjugated to microtubule disrupting agents and DNA modifying agents as described above.) or currently in clinical development.
  • Targeted kinase inhibitor therapies include Lapatinib, Dasatinib, Gefinitinib,
  • Cancer therapeutic agents are administered to a subject already suffering from a disorder or condition, in an amount sufficient to cure, alleviate or partially arrest the condition or one or more of its symptoms. Such therapeutic treatment may result in a decrease in severity of disease symptoms, or an increase in frequency or duration of symptom-free periods. An amount adequate to accomplish this is defined as
  • terapéuticaally effective amount Effective amounts for a given purpose will depend on the severity of the disease as well as the weight and general state of the subject. As used herein, the term “subject” includes any human.
  • the invention also encompasses arrays capable of discriminating between methylated and non-methylated forms of MVPs as defined herein; the arrays may comprise oligonucleotide probes specific for methylated forms of MVPs as defined herein and oligonucleotide probes specific for non-methylated forms of MVPs as defined herein.
  • the probes comprise sequences which are complementary to those of the oligonucleotides comprising the MVP such they may hybridize, particularly under stringent conditions.
  • the array is not an Illumina Infinium
  • HumanMethylation450 BeadChip array Infinium HumanMethylation450 BeadChip array.
  • the number of MVP-specific oligonucleotide probes of the array is less than 482,421, preferably 482,000 or less, 480,000 or less, 450,000 or less, 440,000 or less, 430,000 or less, 420,000 or less, 410,000 or less, or 400,000 or less, 375,000 or less, 350,000 or less, 325,000 or less, 300,000 or less, 275,000 or less, 250,000 or less, 225,000 or less, 200,000 or less, 175,000 or less, 150,000 or less, 125,000 or less, 100,000 or less, 75,000 or less, 50,000 or less, 45,000 or less, 40,000 or less, 35,000 or less, 30,000 or less, 25,000 or less,
  • the invention further encompasses the use of any of the arrays as defined herein in any of the methods which require determining the methylation status of MVPs for the purposes of diagnosing cervical cancer cells in an individual.
  • any of the arrays as defined herein may be comprised in a kit.
  • the kit may comprise any array as defined herein.
  • the kit may comprise any array as defined herein together with instructions for use.
  • the kit may additionally comprise a DNA modifying regent, such as a bisulphite reagent.
  • a DNA modifying regent such as a bisulphite reagent.
  • the kit may additionally comprise reagents for amplifying DNA, such as primers directed to any of the MVPs as defined herein as identified in SEQ ID NOS 1 to 175 (see Tables 1 and 2).
  • the invention further encompasses a method of determining a methylation profile of a sample from an individual, the method comprising:
  • the group of MVPs which are selected from a panel comprising the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG] may comprise any number of MVPs as described and defined herein, provided that the group comprises at least 10 of the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG].
  • the group of MVPs which are selected from a panel comprising the MVPs identified in SEQ ID NOS 31 to 175 and denoted by [CG] i.e. the group of MVPs whose methylation status is to be determined
  • the methylation status of MVPs may be determined using any of the arrays described herein.
  • the step of diagnosing cervical cancer in the individual may further comprise:
  • the invention also encompasses a method of determining the risk of the development of cervical cancer in an individual, the method comprising:
  • the group of MVPs which are selected from a panel comprising the MVPs identified in SEQ ID NOS 1 to 30 and denoted by [CG] may comprise any number of MVPs as described and defined herein.
  • the methylation status of MVPs may be determined using any of the arrays described herein.
  • the invention also encompasses the use of a group of MVPs in the diagnosis of cervical cancer in an individual, in determining the risk of the development of cervical cancer in an individual or predicting the prognosis of an individual with cervical cancer.
  • the group of MVPs are selected from a panel comprising the
  • next-generation DNA sequencing platforms hold particular promise for the development of highly sensitive epigenetic biomarker panels.
  • the microdroplet-based PCR amplification of bisulphite converted DNA, followed by next-generation sequencing of the amplified target loci developed by RainDance Technologies [37] enables the sensitive, specific and simultaneous amplification of up to 20,000 bisulfite-converted target loci.
  • Highly parallel microdroplet-based PCR amplification of bisulphite-converted DNA has shown utility in the validation of epigenetic alterations in a range of tissues [32, 38, 39].
  • Genome-wide DNA methylation profiling was performed on DNA from 97 cervical cancer samples and 29 matched normal cervix tissues collected from
  • Example 1 Materials and methods for the development of a panel of methylation biomarkers for use in the diagnosis of cervical cancer
  • Genome-wide methylation profiles were obtained from 77 cervical cancer and 29 normal cervix samples using bead arrays (Illumina Infinium 450k). 500 ng of DNA was bisulphite converted and hybridised to the Infinium 450K Human Methylation array and processed in accordance with the manufacturer's recommendations. DNA bisulphite conversion was carried out using the EZ DNA Methylation kit (Zymo
  • Raw IDAT files were between-array- normalised using the FunNorm function in the minfi Bioconductor package (Aryee, Jaffe et al. 2014), with initial noob correction of intensities using dye-swap (Triche, Weisenberger et al. 2013). Beta values were then retrieved and corrected for probe-type differences using BMIQ normalisation
  • methylated ( ⁇ >50%) as discussed herein in relation to the development of this initial DNA methylation biomarker panel, it is meant that for any given locus, >50% of cells in a patient sample are determined to be methylated with respect to that MVP.
  • Example 2 Development of a panel of methylation biomarkers for use in the diagnosis of cervical cancer
  • the four different training models used were a support vector machine model (SVM), a k-nearest neighbours model (KNN), a GLMnet model (GLM), and a Random Forest model (RF). Performance was assessed in terms of sensitivity, specificity, and Kappa values in the validation dataset.
  • Figure 1 shows standard ROC curves for the 30 MVP signature (all 30 of SEQ ID NOs 1 to 30) in each model, showing a high degree of sensitivity and good specificity.
  • Figure 2 shows separate statistical metrics for each each model.
  • Figure 3 shows standard ROC curves for a subset of the 30 MVP signature in each model, showing a high degree of sensitivity and good specificity. In this case the MVPs analysed were all 20 of SEQ ID NOs 1 to 20.
  • Figure 4 shows separate statistical metrics for each each model.
  • Figure 5 shows a heatmap demonstrating that probes corresponding to MVPs defined by SEQ ID NOs 1 to 30 display hypomethylation in normal cervical tissue and hypermethylation in cervical cancer samples.
  • Example 3 Materials and methods for the development of a panel of methylation biomarkers for use in the prognosis of cervical cancer patient outcome
  • Level 3 RSEM upper-quartile normalised count values were downloaded from the TCGA data portal for 307 CESCs and parsed into a feature-by-sample matrix using an in-house package. Methylation beta values were extracted for this subset of samples from the discovery set established for the identification of Pan-tissue UPV methylome profiles. Clinical data were isolated from the TCGA data portal. The analysis was conducted on tumours histologically classified as Cervical Squamous Cell Carcinomas. Expression modelling
  • Cox regression modelling indicated significant differences in survival between patients having 'HPV45-like' and 'HPV16-like' tumors, especially amongst early stage samples, with HPV18+ tumours comprising an intermediate group. To explore the extent and significance of molecular heterogeneity associated with UPV-type tumors, comparisons were therefore performed between HPV45-like' and 'HPV16-like' tumours.
  • RNA seq RSEM fractional counts were quantile normalised and filtered to remove low-expressed genes (less than 1 count per million in more than a quarter of samples). Limma voom was then used to estimate mean-variance precision weights for robust linear modelling. Differentially expressed genes were identified at a mean fold- change of 2 and a BH-corrected false-discovery rate of 0.01. Methylation modelling
  • Differentially methylated probes were identified using a custom R wrapper to limma with correction for mean-variance trend upon empirical bayes modelling to account for the heteroscedasticity of beta-values.
  • Significant MVPs were defined at a mean delta-beta of 0.1 at a two-step BH adjusted false discovery rate of 0.01. Small beta-value thresholds were used to permit more sensitive detection of methylation differences.
  • DMRs were carried out using fDMR, and DMRs were required to have at least 3 mvp's at a false discovery rate of 5% and an average deltaBeta difference of 0.1 and a DMR FDR of 0.05. Both these signatures were reduced to features associated with differential expression at an FDR of 0.01 and a fold change of 2, following limma-trend analysis of log2-cpm matrices reduced to genes in the signatures.
  • Example 4 Development of a panel of methylation biomarkers for use in the prognosis of cervical cancer patient outcome
  • the inventors sought to define the DNA methylation landscape of cervical cancer and to identify epigenetic markers of prognosis and to understand the biological differences underlying the differential clinical behaviours of disease subtypes.
  • Genome-wide methylation profiles were obtained from 77 cervical cancer and 29 normal cervix samples using bead arrays (Illumina Infinium 450k).
  • Publicly available DNA methylation data for samples profiled by The Cancer Genome Atlas (TCGA) cervical cancer project was analysed and integrated with HPV type information gained from RNA-sequencing data. Data on HPV type and DNA methylation were used to identify a methylation signature of 145 differentially methylated loci that defined 'HPV16-like' and 'HPV45-like' tumours.
  • Clinical data were integrated with these findings to examine survival in either molecular subtype using multivariate analysis. Methylation data were used to estimate differences in infiltrating immune cell fractions between the subgroups using a novel computational approach, which we term
  • Linear-modelling was carried out to identify probes differentially methylated between 'HPV16-like' and 'HPV45-like' tumour clusters at an FDR of 0.01 (Two-step BH corrected), dB > 0.3.
  • Significant MVPs were then used to train an assortment of classifiers with the tuning parameters set out in the Table below using the caret R package using 10 iterations of 10 fold Cross- Validation.
  • Random Forest mtry 1/3 the number of features.
  • Kappa values from the cross-validation tuning/fitting process were made using out-of-fold calls to generate a majority vote.
  • the different models were compared on the basis of Kappa values and how closely they recapitulated the Hazard Ratios of Cox regressions carried out on type-associated clusters to select a model for application to validation datasets. Performance in validation data was then used to select the optimum set of features and the model on the basis of magnitude of separation of KM curves and statistical significance of separation.
  • Figure 7 shows Kaplan-Meier curves showing overall survival of patients with tumours classified as HPV16-like or HPV45-like in TCGA (discovery) or Norwegian (validation) cohorts.
  • Figure 8 shows shows a Kaplan- Meier curve showing overall survival of patients with tumours classified as HPV16-like or HPV45-like on the basis of all 145 MVP loci (SEQ ID NOs 31 to 175).
  • Figure 9 shows shows a Kaplan-Meier curve showing overall survival of patients with tumours classified as HPV16-like or HPV45-like on the basis of a subset of the top 20 MVPs out of the total group of 145 MVP loci (i.e. based on MVPs defined by SEQ ID NOs 31 to 50).
  • Cervical cancers can be stratified according to their DNA methylation profiles, into two groups that display differences in HPV type, the immune microenvironment and clinical outcome.
  • the Table below provides a list of 30 methylation variable positions (MVPs or CpGs) as used in the cervical cancer diagnostic methods described herein.
  • CpG Illumina Identifier
  • CHR chromosome number
  • MAPINFO chromosome position
  • the CpGs are listed in rank order from 1 to 30. The rank order is in respect of the methylation bet value.
  • the MVP corresponding to SEQ ID NO: 1 has the highest beta value
  • the MVP corresponding to SEQ ID NO: 30 has th lowest beta value.
  • the Table below provides a list of 145 methylation variable positions (MVPs or CpGs) as used in the cervical cancer prognostic methods described herein.
  • CpG Illumina Identifier
  • CHR chromosome number
  • MAPINFO chromosome position
  • the CpGs are listed in rank order from 1 to 30. The rank order is in respect of the methylation be value.
  • the MVP corresponding to SEQ ID NO: 1 has the highest beta value
  • the MVP corresponding to SEQ ID NO: 30 has th lowest beta value.
  • Kandimalla R, van Tilborg AA, Zwarthoff EC DNA methylation-based biomarkers in bladder cancer. Nat Rev Urol 2013.
  • Methylation-specific PCR a novel PCR assay for methylation status of CpG islands. Proc. Natl Acad. Sci. USA 1996, 93 : 9821-9826.
  • Butcher LM Beck S: Probe Lasso: A novel method to rope in differentially methylated regions with 450K DNA methylation data. Methods 2015, 72:21-28.

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Abstract

La présente invention concerne des procédés de diagnostic du cancer du col de l'utérus chez une patiente, consistant à déterminer l'état de méthylation des positions variables de méthylation (MVP) dans l'ADN provenant d'un échantillon de la patiente et à fournir un diagnostic fondé sur les données de l'état de méthylation. L'invention concerne également des méthodes de traitement du cancer du col de l'utérus chez une patiente consistant à fournir un diagnostic de cancer du col de l'utérus par les procédés de diagnostic tels que définis dans la description, puis à administrer un ou plusieurs agents anticancéreux à la patiente. La présente invention concerne également des procédés de classification d'une tumeur du cancer du col de l'utérus d'une patiente ayant un cancer du col de l'utérus comme étant soit une tumeur du groupe 1, soit une tumeur du groupe 2, les patientes ayant une tumeur attribuée au groupe 2 risquant d'obtenir de moins bons résultats après des régimes de traitement primaires par comparaison aux patientes ayant une tumeur attribuée au groupe 1. De tels procédés permettent une stratification plus efficace des risques pour les patientes dans le but de fournir un traitement supplémentaire et/ou d'aider à planifier et à gérer des soins aux patientes. L'invention concerne également des réseaux discriminants de méthylation comprenant des sondes dirigées vers les MVP telles que définies dans l'invention, ainsi que des nécessaires comprenant ces réseaux.
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CN116705159A (zh) * 2023-06-15 2023-09-05 杭州布平医学检验实验室有限公司 甲基化标志物的筛选方法、识别甲基化特征的方法和装置
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RU2817569C2 (ru) * 2018-12-04 2024-04-16 ЭйчКейДжи ЭПИТЕРАПЬЮТИКС ЛИМИТЕД Биомаркеры метилирования днк для раннего выявления рака шейки матки
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JP2022551148A (ja) * 2020-03-17 2022-12-07 バイオチェーン（ペキン）サイエンス アンド テクノロジー、インコーポレイテッド １１種類の癌を検出するプローブ組成物
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CN116705159A (zh) * 2023-06-15 2023-09-05 杭州布平医学检验实验室有限公司 甲基化标志物的筛选方法、识别甲基化特征的方法和装置

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