WO2022101646A1 - Procédés d'évaluation du type de communauté du microbiote vaginal - Google Patents

Procédés d'évaluation du type de communauté du microbiote vaginal Download PDF

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Publication number
WO2022101646A1
WO2022101646A1 PCT/GB2021/052958 GB2021052958W WO2022101646A1 WO 2022101646 A1 WO2022101646 A1 WO 2022101646A1 GB 2021052958 W GB2021052958 W GB 2021052958W WO 2022101646 A1 WO2022101646 A1 WO 2022101646A1
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cpgs
individual
seq
panel
nos
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PCT/GB2021/052958
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English (en)
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Martin Widschwendter
Nuno NENE
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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 assays for assessing the vaginal microbiota community type of an individual by determining the methylation status of certain CpGs in a population of DNA molecules in a sample which has been taken from the individual, deriving a vaginal microbiota index value based on the methylation status of the certain CpGs, and assessing the vaginal microbiota community type of the individual based on the vaginal microbiota index value.
  • the invention further relates to a method for screening for and/or preventing ovarian cancer in an individual, the method comprising assessing the vaginal microbiota community type of an individual by performing the assays of the invention, followed by performing one or more ovarian cancer screens and/or administering preventative treatments to the individual based on the assessment.
  • the invention further provides a method of monitoring the ovarian cancer risk of an individual according to changes in the individual's vagina microbiota index value over the course of time.
  • the invention further relates to arrays which are suitable for performing the assays of the invention.
  • composition of the microbiome plays an important role in human health and disease. Whether there is a direct association between the cervicovaginal microbiome and the host's epigenome is largely unexplored.
  • the microbiome plays an essential role in human health and disease, with its composition being one of the most important factors.
  • An "imbalanced" microbiome such as that in bacterial vaginosis or Clostridium difficile infection, can with variable success be treated by directly interfering with its composition (i.e. by applying antibiotics and transplanting the microbiome from healthy individuals).
  • the physiological cervicovaginal microbiome is dominated by four types of Lactobacilli: L crispatus, L gasseri, L iners, and L jensenii. These Lactobacilli are associated with a substantially lower vaginal pH, potentially decreasing the risk of ascending infections.
  • the inventors classified samples according to their proportion of Lactobacilli; samples where at least 50% of the cervicovaginal microbiota belonged to the group of Lactobacilli highlighted above were labelled as having L community-type and samples with less than 50% as O community-type (Other).
  • the current inventors set out to understand whether DNAme (DNA methylation) profiles may be used to the assess vaginal microbiota community type.
  • vaginal microbiota index values are derived from and associated with DNAme profiles established from a sample comprising epithelial cells from a given individual.
  • the sample may particularly be derived from the cervix, the vagina and the buccal area.
  • the sample is preferably a cervical liquid-based cytology sample, and more preferably a cervical smear sample.
  • Subsequent DNAme profiles can be established from a given individual, and which values can be used to stratify the individual in connection with vaginal microbiota community type.
  • a preferred sample for use in any of the assays described and defined herein is a cervical liquid-based cytology sample.
  • a particularly preferred sample for use in any of the assays described and defined herein is a cervical smear sample.
  • the inventors have surprisingly determined that it is possible to derive "vaginal microbiota index values" derived from and associated with DNAme profiles established from samples.
  • tissue(s) from which DNAme profiles of the present assays are established may act to provide surrogate markers for the vaginal microbiota community type.
  • the vaginal microbiota index value is determined from data relating to the methylation status of one or more CpGs in a panel of CpGs as further defined and described herein.
  • CpGs of the panel are methylation sites in DNA from cells derived from/obtained from samples comprising epithelial cells.
  • the sample may particularly be derived from the cervix, the vagina and the buccal area.
  • the sample is preferably a cervical liquid-based cytology sample, and more preferably a cervical smear sample.
  • WID women's risk identification
  • any reference to a vaginal microbiota index value in the context of the present invention may be equally used for the assessment of vaginal microbiota community type in an individual.
  • vaginal microbiota community- type L lactobacilli accounting for at least 50% of the vaginal microbiota species
  • the inventors have established vaginal microbiota index values, using specific panels of CpGs, which have been determined to be associated with/characteristic of vaginal microbiota community type L, i.e. an individual with a community-type L vaginal microbiota.
  • vaginal microbiota community-type O lactobacilli accounting for less than 50% of the vaginal microbiota species
  • the inventors have established vaginal microbiota index values which have been determined to be associated with/characteristic of vaginal microbiota community type O, i.e. an individual with a community-type O vaginal microbiota.
  • the inventors have been able to establish vaginal microbiota index values, using specific panels of CpGs, which can characterize an individual as having community-type L or community-type O vaginal microbiota.
  • the individual By determining the methylation profile-based vaginal microbiota index value from a sample derived from the individual, the individual may be seen to possess a vaginal microbiota index value which correlates with those possessed by individuals which are known, via the inventor's studies described herein, to be of vaginal microbiota community-type L or community-type O.
  • Such correlations have been determined with a high degree of statistical accuracy, particularly with respect to parameters relevant to biological assays such as receiver operating characteristics (ROC) sensitivity and specificity, as well as area under the curve (AUC).
  • ROC receiver operating characteristics
  • AUC area under the curve
  • the individual may be determined to have a vaginal microbiota community- type L or community-type O.
  • the inventors have further determined that statistical accuracy of the vaginal microbiota index value, particularly with respect to parameters relevant to biological assays such as receiver operating characteristics (ROC) sensitivity and specificity, as well as area under the curve (AUC), is enhanced further when the immune cell proportion in the sample is below 50%.
  • ROC receiver operating characteristics
  • AUC area under the curve
  • the vaginal microbiota community type of an individual can be assessed based on the basis of the vaginal microbiota index vale with increased statistical accuracy when the immune cell proportion in a sample obtained from an individual is below 50% relative to a sample with an immune cell proportion of 50% or greater.
  • the inventors have further determined that statistical accuracy of the vaginal microbiota index value, particularly with respect to parameters relevant to biological assays such as receiver operating characteristics (ROC) sensitivity and specificity, as well as area under the curve (AUC), is enhanced yet further when the individual from which a sample is obtained is younger than 50 years old. Accordingly, the vaginal microbiota community type of an individual can be assessed based on the basis of the vaginal microbiota index value with increased statistical accuracy when the individual from which the sample is obtained is younger than 50 years old relative to a sample obtained from an individual that is 50 years old or older.
  • ROC receiver operating characteristics
  • AUC area under the curve
  • the assay methods of the invention are based on a vaginal microbiota index value derived from a methylation profile from DNA originating from samples comprising epithelial cells.
  • the sample may particularly be derived from the cervix, the vagina and the buccal area.
  • the sample is preferably a cervical liquid-based cytology sample, and more preferably a cervical smear sample.
  • the assays provide means for correlating samples derived from the cervix, the vagina and the buccal area-derived DNA methylation profile with a status connected with vaginal microbiota community-type ranging from the individual being community-type L, to the individual being community-type O, with high statistical accuracy.
  • the assays of the invention provide a correlation between the methylation profile and the community type, the skilled person will appreciate that as part of the stratification process and outcome, community type is assigned on the basis of a likelihood.
  • the methods of the invention provide assays which are predictive of an individual's vaginal microbiota community-type.
  • the assays of the invention accordingly provide means for predicting the vaginal microbiota community type of an individual.
  • vaginal microbiota community-type Whilst vaginal microbiota community-type may be assigned on the basis of a likelihood, the inventors have demonstrated herein that correlations between DNA methylation profile and vaginal microbiota community type using vaginal microbiota index values can be achieved with a very high degree of statistical accuracy using parameters relevant to biological assays, as described further herein.
  • the assays of the invention provide means for predicting the vaginal microbiota community type of an individual. This in turn means that the prediction can be made with a high level of confidence.
  • the assays of the invention can be defined to be statistically accurate by means known in the art, as further described and defined herein.
  • the assays of the invention can be defined according to parameters relating to their statistical specificity and sensitivity.
  • the inventors have established CpG panels, as described and defined further herein, wherein the methylation status of CpGs in the panel can be used to establish vaginal microbiota index values such that the assays produce statistically accurate predictions of vaginal microbiota community-type status. Accordingly, the inventors have determined that the assays described herein may be defined according to statistical parameters such as percentage specificity and sensitivity and also by receiver operating characteristics (ROC) area under the curve (AUC). All such means are known in the art and are known to be defined measures of statistical accuracy for biological assays such as those described and defined herein.
  • ROC receiver operating characteristics
  • the methods of the invention provide assays which can be used, with a high degree of statistical accuracy, to stratify an individual with respect to vaginal microbiota community-type status. Accordingly, the methods of the invention provide useful information to individuals and their physicians concerning patient vaginal microbiota community type status. Previous studies have linked vagina microbiota community type with ovarian cancer risk. Particularly, studies have shown that individuals with community type-0 vaginal microbiota are at greater risk of having or developing ovarian cancer than individuals having community-type L vaginal microbiota.
  • the vaginal microbiota community type-information derived from utilisation of the method described herein may inform actual therapeutic treatment measures in an individual, particularly if vaginal microbiota community type-0 is identified in the individual.
  • the vaginal microbiota community type-information derived from utilisation of the method described herein may provoke ovarian cancer screening measures to be subjected to an individual, particularly if vaginal microbiota community type-0 is identified in the individual.
  • the information may help to monitor the vaginal microbiota community type over time, particularly if the individual is being subjected to therapeutic treatment measures in the individual as by monitoring changes in the vaginal microbiota index value over the course of a period of time may provide an indication of the efficacy of the therapeutic treatment measures.
  • the methods of the invention offer significant advantages in the personalised prevention and early detection of ovarian cancer, as well as treatment and management of vaginal microbiota community type in individuals.
  • the invention provides an assay for assessing the vaginal microbiota community type of an individual, the assay comprising: a. providing a sample which has been taken from the individual, the sample comprising a population of DNA molecules; b. determining in the population of DNA molecules in the sample the methylation status of a panel of one or more CpGs selected from a panel of CpGs identified in SEQ ID NOs 1 to 819, wherein the CpGs are denoted by [CG]; c. deriving a vaginal microbiota index value based on the methylation status of the panel of one or more CpGs; and d. assessing the vaginal microbiota community type of the individual based on the vaginal microbiota index value; wherein the assay is characterised as having an area under the curve (AUC) of 0.60 or more as determined by receiver operating characteristics (ROC).
  • AUC area under the curve
  • ROC receiver operating characteristics
  • the assay of the invention may be performed above and additionally wherein the panel of one or more CpGs comprises at least 19 CpGs selected from the CpGs identified in SEQ ID NOs 1 to 819 and denoted by [CG], preferably wherein the assay is characterised as having an AUC of at least 0.71.
  • the assay of the invention may be performed above and additionally wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 801 to 819 and denoted by [CG], preferably wherein the assay is characterised as having an AUC of at least 0.71.
  • the assay of the invention may be performed above and additionally wherein the panel of one or more CpGs comprises at least 50 CpGs selected from the CpGs identified in SEQ ID NOs 1 to 819 and denoted by [CG], preferably wherein the assay is characterised as having an AUC of at least 0.68.
  • the assay of the invention may be performed above and additionally wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 1 to 50 and denoted by [CG], preferably wherein the assay is characterised as having an AUC of at least 0.75.
  • the assay of the invention may be performed above and additionally wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 1 to 100 and denoted by [CG], preferably wherein the assay is characterised as having an AUC of at least 0.74.
  • the assay of the invention may be performed above and additionally wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 1 to 150 and denoted by [CG], preferably wherein the assay is characterised as having an AUC of at least 0.78.
  • the assay of the invention may be performed above and additionally wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 1 to 200 and denoted by [CG], preferably wherein the assay is characterised as having an AUC of at least 0.80.
  • the assay of the invention may be performed above and additionally wherein the panel of one or more CpGs comprises at least the 819 CpGs identified in SEQ ID NOs 1 to 819 and denoted by [CG], and further wherein the assay is characterised as having an AUC of at least 0.82.
  • the assay of the invention may be performed above and additionally wherein the step of determining in the population of DNA molecules in the sample the methylation status of the panel of one or more CpGs further comprises determining a ⁇ value of each CpG.
  • the assay of the invention may be performed above and additionally wherein
  • the step of deriving the vaginal microbiota index value based on the methylation status of the panel of one or more CpGs comprises: a. providing a methylation ⁇ -value data set comprising the methylation ⁇ - values for each CpG in the panel; b. providing a mathematical model capable of generating the vaginal
  • the assay of the invention may be performed above and additionally wherein the vaginal microbiota index value is WID-LO-Index, and wherein the mathematical
  • w 1 ,..., w n are real valued coefficients c.
  • p is a real valued parameter; and d. n refers to the number of CpGs in the panel of one or more CpGs.
  • the assay of the invention may be performed above and additionally wherein when the vaginal microbiota index value for the individual is equal to or more than a threshold, the individual is assessed as having a vaginal flora consisting of less than 50% lactobacilli species (community type O), or wherein when the vaginal microbiota index value for the individual is less than the threshold, the individual is assessed as having a vaginal flora consisting of at least 50% lactobacilli species (community type L), wherein the specificity is 90%, preferably wherein: a.
  • the panel of one or more CpGs comprises at least 50 of the CpGs defined by SEQ ID NOs 1 to 819 and denoted by [CG], and wherein the threshold is about 1.78 and the sensitivity is at least 20%; b. the panel of one or more CpGs comprises at least the CpGs defined by SEQ ID NOs 1 to 50 and denoted by [CG], and wherein the threshold is about 1.22 and the sensitivity is at least 39%; c. the panel of one or more CpGs comprises at least the CpGs defined by SEQ ID NOs 1 to 100 and denoted by [CG], and wherein the threshold is about 1.67 and the sensitivity is at least 36%; or d.
  • the panel of one or more CpGs comprises at least the CpGs defined by SEQ ID NOs 1 to 150 and denoted by [CG], and wherein the threshold is about 1.76 and the sensitivity is at least 35%; e. the panel of one or more CpGs comprises at least the CpGs defined by SEQ ID NOs 1 to 819 and denoted by [CG], and wherein the threshold is about 2.11 and the sensitivity is at least 58%; preferably wherein the assay comprises determining methylation ⁇ -values for each CpG in the panel of one or more CpGs.
  • the assay of the invention may be performed above and additionally wherein when the vaginal microbiota index value for the individual is equal to or more than a threshold, the individual is assessed as having a vaginal flora consisting of less than 50% lactobacilli species (community type O), or wherein when the vaginal microbiota index value for the individual is less than the threshold, the individual is assessed as having a vaginal flora consisting of at least 50% lactobacilli species (community type L), wherein the specificity is 90%, preferably wherein: a.
  • the panel of one or more CpGs comprises at least 50 of the CpGs defined by SEQ ID NOs 1 to 819 and denoted by [CG], and wherein the threshold is about 0.50 and the sensitivity is at least 47%; b. the panel of one or more CpGs comprises at least the CpGs defined by SEQ ID NOs 1 to 50 and denoted by [CG], and wherein the threshold is about 0.53 and the sensitivity is at least 52%; c. the panel of one or more CpGs comprises at least the CpGs defined by SEQ ID NOs 1 to 100 and denoted by [CG], and wherein the threshold is about 0.44 and the sensitivity is at least 55%; or d.
  • the panel of one or more CpGs comprises at least the CpGs defined by SEQ ID NOs 1 to 150 and denoted by [CG], and wherein the threshold is about 0.70 and the sensitivity is at least 62%; e. the panel of one or more CpGs comprises at least the CpGs defined by SEQ ID NOs 1 to 819 and denoted by [CG], and wherein the threshold is about - 0.01 and the sensitivity is at least 70%; preferably wherein the assay comprises determining methylation ⁇ -values for each CpG in the panel of one or more CpGs.
  • the assay of the invention may be performed above and additionally wherein the step of determining in the population of DNA molecules in the sample the methylation status of each CpG in the panel of one or more CpGs comprises: a. performing a sequencing step to determine the sequence of each CpG; b. hybridising DNA to an array comprising probes capable of discriminating between methylated and non-methylated forms of the CpGs and applying a detection system to the array so as to determine the methylation status of each CpG; and/or c. performing a PCR step using methylation-specific primers, wherein the methylation status of the CpG is determined by the presence or absence of a PCR product.
  • the assay of the invention may be performed above and additionally wherein the step of determining the methylation status of each CpG in the panel of one or more CpGs comprises: a. bisulphite converting the DNA; or b. performing the steps of oxidising 5-methylcytosine bases (5mC) to 5- carboxylcytosine bases (5caC), preferably by ten-eleven translocation (TET), and/or oxidising 5-hydroxymethylcytosine bases (5hmC) to 5- carboxylcytosine bases (5caC), preferably by ten-eleven translocation (TET); followed by reducing 5-carboxylcytosine bases (5caC) to dihydrouracil bases (DHU), optionally with pyridine borane.
  • 5-methylcytosine bases 5mC
  • 5- carboxylcytosine bases preferably by ten-eleven translocation (TET)
  • TET ten-eleven translocation
  • DHU dihydrouracil bases
  • the invention also provides a method of screening for and/or preventing ovarian cancer in an individual, the method comprising: a. assessing the vaginal microbiota community type of an individual by performing the assay of the invention; b. performing one or more ovarian cancer screens and/or administering preventative treatments to the individual based on the assessment.
  • the method of the invention may be performed above and additionally wherein the individual is assessed as having a vaginal flora consisting of less than 50% lactobacilli species (community type O), the individual is deemed as having a high risk of ovarian cancer, and the individual is subjected to one or more ovarian cancer screens or preventative treatments comprising: a. a test for a BRCA1 and/or BRCA2 germline mutation; b. a test for CA125, preferably wherein the test is repeated annually; c. a test for cell-free tumour DNA methylation in plasma/serum, preferably wherein the test is repeated annually; d. a test for cell-free tumour DNA methylation in vaginal fluid, preferably wherein the test is repeated annually; e. a vaginal lactobacillus transplantation.
  • the invention also provides a method of monitoring the ovarian cancer risk of an individual according to the individual's vaginal microbiota index value, the method comprising: (a) assessing the vaginal microbiota community type of an individual by performing the assay of the invention at a first time point; (b) assessing the vaginal microbiota community type of an individual by performing the assay of the invention at one or more further time points; and (c) monitoring for change in vaginal flora community type L to community type O at one or more further time points as this would indicate that the individual is at an increased risk of ovarian cancer.
  • the method of the invention may be performed as above and additionally wherein the further time points are monthly, three monthly, six monthly, yearly or two yearly basis following an initial assessment.
  • the method of the invention may be performed as above and additionally wherein if the vaginal flora community type changes from L to community type O the individual is subjected to one or more ovarian cancer screens or preventative treatments according to the method of the invention.
  • the invention also provides an array capable of discriminating between methylated and non-methylated forms of CpGs; the array comprising oligonucleotide probes specific for a methylated form of each CpG in a CpG panel and oligonucleotide probes specific for a non-methylated form of each CpG in the panel; wherein the panel consists of at least 50 CpGs selected from the CpGs identified in SEQ ID NOs 1 to 819.
  • the array of the invention may be as above and additionally provided that the array is not an Infmium MethylationEPIC BeadChip array or an Infmium HumanMethylation450, and/or provided that the number of CpG-specific oligonucleotide probes of the array is 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.
  • the array of the invention may be performed as above and additionally wherein the panel comprises any set of CpGs defined in the assays of the invention.
  • the array of the invention may be as above and additionally further comprising one or more oligonucleotides comprising any set of CpGs defined in the assays of any one of the invention, wherein the one or more oligonucleotides are hybridized to corresponding oligonucleotide probes of the array.
  • the invention also provides a hybridized array, wherein the array is obtainable by hybridizing to an array of the invention a group of oligonucleotides comprising any panel of one or more CpGs defined in the assays of the invention.
  • the invention also provides a process for making the hybridized array of the invention, comprising contacting an array of the invention with a group of oligonucleotides comprising any panel of one or more CpGs defined in any one of the assays of the invention.
  • Figure 1 shows enrichment of input CpG feature space.
  • a Input feature pool enrichment for CpG region
  • b Input feature pool enrichment for cell-type (eFORGE). See Methods section for details.
  • Figure 2 shows WID-LO-index performance in cervical samples.
  • d Adjusted odds-ratios for the association of WID-LO-index with community-type determined from sample microbiota proportions, Age ⁇ 50 years subgroup. See also Fig. 11.
  • (*) corresponds to adjustment for Age.
  • (**) corresponds to adjustment for IC.
  • (***) corresponds to adjustment for Age and IC.
  • Odds-ratios, 95% Confidence Intervals and p values were calculated under a logistic regression model with a bias reduction method.
  • IC Immune Cell proportion.
  • the WID-LO-index was generated with 819 selected CpGs. See Methods section for details.
  • FIG. 3 shows association of the WID-LO-index with each of the additional covariates collected.
  • a Age ⁇ 50 years, b Age >50 years. See Methods section for details.
  • BMI Body Mass Index (kg/m 2 ).
  • OCP Oral Contraceptive Pill.
  • HRT Hormone Replacement Therapy.
  • CH Combined Hormone. See also Tables 10 and 11.
  • Figure 4 shows validation in buccal and blood samples.
  • a b Receiver operating characteristic curves for the linear WID-LO-index classifier in buccal (a) and blood samples (b).
  • c Receiver operating characteristic curves for the non-linear (NL) WID-LO-index, in the validation set (c) and in buccal samples (d). See also Figs. 14, 15, 16 and 17.
  • the WID-LO-index was generated with 819 CpGs.
  • the NL WID-LO-index was generated with 1,162 features (c, d).
  • IC Immune Cell proportion. See Methods section for details.
  • Figure 5 shows distribution of subjects in the training and validation sets according to microbiota community-type L/O.
  • a, d, g Contour plots for the distribution of subjects with age and immune cell proportion for all subjects (a), the training set (d) and the validation set (g).
  • b, e, h Density with age and community-type for all subjects (b), the training set (e) and the validation set (h).
  • c, f, i Density with immune cell proportion and type for all subjects (c), the training set (f) and the validation set (i).
  • Figure 6 shows overall species abundance per subject for the training set.
  • Heatmap colours are proportional to abundance in each subject.
  • the legend represents membership of community-types L or O. ⁇ 50 and > 50 indicates the age of the individuals, younger or older than 50 years. Here the inventors plot only the top abundant species.
  • For the purposes of developing a classifier samples were divided into people whose cervicovaginal microbiota consisted of at least 50% community type L (L. crispatus. L. iners. L. gasseri and L.jensenii) and those whose microbiota consisted of less than 50% community type L, which the inventors referred to as community type O.
  • community type O For full list of species making community-type O see data availability statement.
  • Figure 7 shows cell type proportion differences between subjects with community-type.
  • a Training set.
  • b Validation set.
  • Cell proportions were determined with EpiDISH (see Methods). Significant differences between the distributions for each community-type (L or O) are identified by * (p ⁇ 0.05) and ** (p ⁇ 0.01). P values were calculated with a Wilcoxon-test.
  • Figure 8 shows association of top CpGs with gene region and gene set enrichment analysis, linear classifier (WID-LO-index).
  • a, b Association of top 50,000 CpGs (absolute numbers) ranked according to a logistic regression model adjusted for age and immune cell proportion with gene region
  • Figure 9 shows association of the linear WID-LO-index 819 CpGs with CpG and gene region.
  • a GpG region, b, gene region (b).
  • c Odds ratios for (a), where the p-values were calculated via the exact Fisher method (two-sided) against the proportions in the entirety of the Illumina InfiniumMethylation EPIC BeadChip array,
  • d Odds ratios for
  • Figure 10 shows performance profile of the linear classifier (WID-LO-index) in the training set.
  • a Average performance across training folds.
  • ElNet Elastic Net
  • b Number of selected CpGs at each input pool size. Best performer displayed in (a) has 819 selected CpGs (indicated). Due to the discrepancy between the number of selected CpGs in ElNet, Lasso and Ridge, the inventors only display the first two algorithms in (b). See also Methods section in the main text and Table 12.
  • Figure 11 shows WID-LO-index in the training set.
  • a Linear index scatter plot with IC.
  • b Linear index scatter plot with Age. See also Fig. 2 in the main text.
  • This linear classifier involves 819 CpGs. See also Fig. 10 and Table 12.
  • IC Immune Cell proportion.
  • Figure 12 shows performance of the WID-LO-index in the validation set.
  • Figure 13 shows odds-ratios and p-values for the association between community-type LO and the WID-LO-index.
  • a All ages, b, Age > 50 (years). See also Tables 10 and 11 for the epidemiological covariates.
  • (*) corresponds to adjustment for Age.
  • (**) corresponds to adjustment for IC.
  • (***) corresponds to adjustment for Age and IC.
  • Odds-ratios, 95% Confidence Intervals and p values were calculated under a logistic regression model with a bias reduction method. See also Fig. 2d for results in the sub-group Age ⁇ 50 years. This linear classifier involves 819 CpGs. See also Fig. 10 and Table 12.
  • Figure 14 shows WID-LO-index for Buccal and Blood samples.
  • a Performance in buccal samples
  • c Age
  • d Performance in blood samples versus granulocyte proportion
  • e f
  • e Index in blood samples versus IC
  • f Age
  • Fig 4 This linear classifier involves 819 CpGs. See also Fig. 10 and Table 12.
  • IC Immune Cell proportion.
  • Figure 15 shows performance of the non-linear classifier (NL WID-LO-index) in the training and validation sets.
  • a, d ROC curves and AUC in the training set (a) and validation set (d).
  • b, e Non-linear index scatter plot with IC, in the training (b) and validation set (e).
  • c, f Non- linear index scatter plot with Age, in the training (c) and validation set (f).
  • IC Immune Cell proportion.
  • Figure 16 shows association of top CpGs with gene region, CpG region, eFORGE analysis and gene set enrichment analysis, non-linear classifier (NL WID-LO- index).
  • a, b Association of the top 60,000 CpGs (absolute numbers) ranked according to the geometric mean across 5 ranking algorithms, with CpG region (a) and gene region (b).
  • Figure 17 shows performance of the non-linear classifier (NL WID-LO-index) in buccal and blood samples.
  • a, d ROC curves and AUC in buccal (a) and blood (d).
  • b, e Non-linear index scatter plot with IC, in the buccal (b) and blood samples (e).
  • c, f Non-linear index scatter plot with Age, in buccal (c) and blood samples (f).
  • IC Immune Cell proportion.
  • Figures 18 and 19 show cutpoints applied to the patient data, and consequent specificity and sensitivity for cancer status discrimination achieved when these cutpoints are applied.
  • the present inventors sought to identify CpG methylation-based assays capable of assessing the vaginal microbiota community type of an individual. Any of the assays described herein for assessing the vaginal microbiota community type of an individual in an individual are capable of being utilised for assessing whether an individual has a community type-L or community type O vaginal microbiota. Based on studies with patients known to have vaginal microbiota community-type L (lactobacilli accounting for at least 50% of the vaginal microbiota species), the inventors have established vaginal microbiota index values, using specific panels of CpGs, which have been determined to be associated with/characteristic of vaginal microbiota community type L or type O.
  • a CpG as defined herein refers to the CG dinucleotide motif identified in relation to each SEQ ID NO., wherein the CG dinucleotide of interest is denoted by [CG],
  • CG CG dinucleotide of interest
  • determining the methylation status of any panel of one or more CpGs defined by a panel of one more of SEQ ID NOs 1 to 819 it is meant that a determination is made as to the methylation status of the cytosine of the CG dinucleotide motif identified in square brackets in the panel of one or more CpGs in each sequence shown in Table 1, accepting that variations in the sequence upstream and downstream of any given CpG, as denoted by [CG], may exist due to sequencing errors or variation between individuals.
  • the methylation status of sub- selections of the 819 CpGs, as identified in SEQ ID NOs 1 to 819, may be determined in order to assess an individual for the vaginal microbiota community type with high sensitivity and specificity.
  • a panel of one or more of the CpGs identified in SEQ ID NOs 1 to 819 may be utilised to derive a vaginal microbiota index for an individual in accordance with the invention described herein.
  • the methylation status of a panel of one or more CpGs of the 819 CpGs defined according to SEQ ID NOs: 1 to 819 may be assessed by any suitable technique.
  • one particular exemplary technique which the inventors have used is an array-based analysis technique coupled with beta value analysis.
  • SEQ ID NOs 1 to 819 correspond to the sequences of commercial probes utilised in said array.
  • the Illumina identifiers for the commercial probes the correspond to SEQ ID NOs 1 to 819 are set out in Table 1 below.
  • the sample in a sample which has been taken from an individual, the sample comprises a population of DNA molecules.
  • the assay of the invention further comprises determining in the population of DNA molecules in the sample the methylation status of a panel of one or more CpGs selected from a panel of CpGs identified in SEQ ID NOs 1 to 819, wherein CpGs are denoted by [CG], A vaginal microbiota index value is then derived based on the methylation status of the panel of one or more CpGs, which is used to assess the vaginal microbiota community type of the individual based on the vaginal microbiota index value.
  • the panel of one or more CpGs may comprise at least 19 CpGs selected from the CpGs identified in SEQ ID NOs 1 to 819 and denoted by [CG], preferably wherein the assay is characterised as having a receiver operating characteristics (ROC) area under the curve (AUC) of at least 0.71.
  • the panel of one or more CpGs may comprise at least the CpGs identified in SEQ ID NOs 801 to 819 and denoted by [CG], preferably wherein the assay is characterised as having an ROC AUC of at least 0.71.
  • the panel of one or more CpGs may comprise at least 50 CpGs selected from the CpGs identified in SEQ ID NOs 1 to 819 and denoted by [CG], preferably wherein the assay is characterised as having a ROC AUC of at least 0.68.
  • the panel of one or more CpGs may comprise at least the CpGs identified in SEQ ID NOs 1 to 50 and denoted by [CG], preferably wherein the assay is characterised as having a ROC AUC of at least 0.75.
  • the panel of one or more CpGs may comprise at least the 819 CpGs identified in SEQ ID NOs 1 to 819 and denoted by [CG], and further wherein the assay is characterised as having a ROC AUC of at least 0.82.
  • the assay may be characterised as having a ROC AUC of 0.68 or more, 0.69 or more, 0.70 or more, 0.71 or more, 0.72 or more,
  • the methylation status of the panel of one or more CpGs is preferably determined by a ⁇ -value analysis.
  • the panel of one or more CpGs may comprise at least 19 CpGs selected from the CpGs identified in SEQ ID NOs 1 to 819 and denoted by [CG], optionally wherein:
  • the assay is characterised as having an ROC AUC (AUC) of at least 0.75, and more preferably wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 1 to 50 and denoted by [CG];
  • the assay is characterised as having an ROC AUC (AUC) of at least 0.68, and more preferably wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 51 to 100 and denoted by [CG];
  • the assay is characterised as having an ROC AUC (AUC) of at least 0.74, and more preferably wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 101 to 150 and denoted by [CG];
  • the assay is characterised as having an ROC AUC (AUC) of at least 0.73, and more preferably wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 151 to 200 and denoted by [CG];
  • the assay is characterised as having an ROC AUC (AUC) of at least 0.73, and more preferably wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 201 to 250 and denoted by [CG];
  • the assay is characterised as having an ROC AUC (AUC) of at least 0.72, and more preferably wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 251 to 300 and denoted by [CG]; 7. the assay is characterised as having an ROC AUC (AUC) of at least 0.80, and more preferably wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 301 to 350 and denoted by [CG];
  • the assay is characterised as having an ROC AUC (AUC) of at least 0.73, and more preferably wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 351 to 400 and denoted by [CG];
  • the assay is characterised as having an ROC AUC (AUC) of at least 0.74, and more preferably wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 401 to 450 and denoted by [CG];
  • the assay is characterised as having an ROC AUC (AUC) of at least 0.70, and more preferably wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 451 to 500 and denoted by [CG];
  • the assay is characterised as having an ROC AUC (AUC) of at least 0.76, and more preferably wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 501 to 550 and denoted by [CG];
  • the assay is characterised as having an ROC AUC (AUC) of at least 0.75, and more preferably wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 551 to 600 and denoted by [CG];
  • the assay is characterised as having an ROC AUC (AUC) of at least 0.73, and more preferably wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 601 to 650 and denoted by [CG];
  • the assay is characterised as having an ROC AUC (AUC) of at least 0.75, and more preferably wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 651 to 700 and denoted by [CG];
  • the assay is characterised as having an ROC AUC (AUC) of at least 0.71, and more preferably wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 701 to 750 and denoted by [CG]; or
  • the assay is characterised as having an ROC AUC (AUC) of at least 0.71, and more preferably wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 751 to 819 and denoted by [CG],
  • the methylation status of the panel of one or more CpGs is preferably determined by a ⁇ -value analysis.
  • the assay may be characterised as having a ROC AUC of 0.60 or more, 0.61 or more, 0.62 or more, 0.63 or more, 0.64 or more, 0.65 or more, 0.66 or more, 0.67 or more, 0.68 or more, 0.69 or more, 0.70 or more,
  • the panel of one or more CpGs may comprise:
  • CpGs selected from the CpGs identified in SEQ ID NOs 1 to 819 and denoted by [CG], preferably wherein the assay is characterised as having an AUC of at least 0.74, and more preferably wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 1 to 100 and denoted by [CG];
  • the methylation status of the panel of one or more CpGs is preferably determined by a ⁇ -value analysis.
  • the assay may be characterised as having a ROC AUC of 0.74 or more, 0.75 or more, 0.76 or more, 0.77 or more, 0.78 or more, 0.79 or more, 0.80 or more, 0.81 or more, 0.82 or more, 0.83 or more, 0.84 or more, 0.85 or more, 0.86 or more, 0.87 or more, 0.88 or more, 0.89 or more or 0.90 or more.
  • the panel of one or more CpGs may comprise:
  • CpGs selected from the CpGs identified in SEQ ID NOs 1 to 819 and denoted by [CG]
  • the assay is characterised as having an AUC of at least 0.82, and more preferably wherein the panel of one or more CpGs comprises at least the CpGs identified in SEQ ID NOs 1 to 819 and denoted by [CG]
  • the methylation status of the panel of one or more CpGs is preferably determined by a ⁇ -value analysis.
  • the assay may be characterised as having a ROC AUC of 0.71 or more, 0.72 or more, 0.73 or more, 0.74 or more, 0.75 or more, 0.76 or more, 0.77 or more, 0.78 or more, 0.79 or more, 0.80 or more, 0.81 or more, 0.82 or more, 0.83 or more, 0.84 or more, 0.85 or more, 0.86 or more, 0.87 or more, 0.88 or more, 0.89 or more or 0.90 or more.
  • the invention also provides a variety of assays, each comprising any 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more (or any range derivable therein) of a variety of steps and in no particular order, including methods of the following: measuring in a sample; analyzing a sample; assessing a sample; evaluating a sample; measuring nucleic acids in a sample; assessing nucleic acids in a sample; detecting nucleic acids in a sample; measuring methylation in nucleic acids in a sample; analyzing nucleic acids in a sample; assessing nucleic acids in a sample; measuring methylation at one or more CpG dinucleotides in a sample; detecting methylation at one or more CpG dinucleotides in a sample; assaying methylation at one or more CpG dinucleotides in a sample; assessing methylation at one or more CpG dinucleotides in a sample; measuring a methylation status in a sample; assay
  • an individual who is administered a therapy or treatment has been subjected to any of the methods and steps described herein.
  • a vaginal microbiota index value may be derived thus enabling stratification of individuals according to whether they are community type-L or community type-0 with statistically robust sensitivity and specificity.
  • the methylation status of each CpG within a panel of one or more CpGs can be determined by any suitable means in order to thereby derive the vaginal microbiota index value. Any one method, or a combination of methods, may be used to determine the methylation status of each CpG within a panel of one or more CpGs.
  • a percent methylated reference (PMR) value of a CpG may be determined.
  • the methylation ⁇ -values of a CpG may be determined.
  • Different mechanisms may be employed to determine specific values depending on the circumstances, such as PCR-based mechanisms or array-based mechanisms.
  • Vaginal microbiota index values as vaginal microbiota community type assessment tools are assessed for vaginal microbiota community type assessment tools.
  • the assessment of the microbiota community type of an individual is based on the vaginal microbiota index value of the individual at the time of testing.
  • vaginal microbiota index values can be established which correspond with community type-0 samples, because they are based on values derived from individuals known to have less than 50% lactobacilli in their vaginal microbiota and obtained from samples from the cervix, the vagina and the buccal area, particularly from a liquid-based cytology sample, and more preferably a cervical smear sample.
  • vaginal microbiota index values can be established which correspond with community type-L samples, because they are based on values derived from samples from the cervix, the vagina and the buccal area, particularly from a liquid- based cytology sample, and more preferably a cervical smear sample, as noted above, from tissue samples from individuals known to have 50% or more lactobacilli in their vaginal microbiota.
  • a user can then apply these vaginal microbiota index values to assess the vaginal microbiota community type in any test individual whose vaginal microbiota community type is required to be tested.
  • the assays of the invention are capable of being performed with a high degree of statistical accuracy.
  • the described assays particularly relate to the assessment of the vaginal microbiota community type.
  • vaginal microbiota index value provides a value that indicates a "likelihood” or “risk” or “prediction” of any of the assays of the invention correctly assessing the vaginal microbiota community type in an individual. This is because the assessment is based upon a correlation between DNA methylation profiles of tissue samples and individual microbiota community type status.
  • the assays of the invention provide such correlations with high statistical accuracy, thus providing the skilled person with a high degree of confidence that the vaginal microbiota index value which is determined for any test individual whose microbiota community-type status is required to be tested will provide an accurate correlation with actual microbiota community type for the individual.
  • the assay of the invention represents a ' prediction' because any vaginal microbiota index value (WID-LO-Index) derived in accordance with the invention is unlikely to be capable of identifying every individual as, for example, having or not having a vaginal microbiota L-type community with 100% specificity and 100% sensitivity. Rather, depending on the vaginal microbiota index cutpoint threshold applied by the user for positively predicting the vaginal microbiota community type in an individual, the false positive and false negative rate will vary.
  • WID-LO-Index vaginal microbiota index value
  • the inventors have discovered that the assays of the invention can achieve variable levels of sensitivity and specificity for predicting the vaginal microbiota community type, as defined by receiver operating characteristics, depending on the vaginal microbiota index cutpoint threshold chosen and applied by the user.
  • Such sensitivity and specificity can be seen from the data disclosed herein to be achievable at high proportions, demonstrating accurate and statistically-significant discriminatory capability.
  • the step of assessing the vaginal microbiota community type in an individual based on a vaginal microbiota index value may involve the application of a threshold value.
  • Threshold values can provide a risk- based indication of an individual's vaginal microbiota community type, whether that is community type-L, or community type-O.
  • any assay of the invention is an assay for assessing the vaginal microbiota community type of an individual.
  • the types of vaginal microbiota community type are set out further herein.
  • the assays of the invention provide means for assessing whether an individual has a particular vaginal microbiota community type based on specific cutpoint thresholds. Such assessments can be provided with a high degree of confidence based on the statistical parameters which characterise the assay.
  • the vaginal microbiota index threshold may be used for assessment purposes.
  • the cutpoint threshold value may be used to specify whether or not an individual has a particular vaginal flora community type as a pure diagnostic test. Again, such diagnostic tests can be provided with a high degree of confidence based on the statistical parameters which characterise the assay.
  • any assay described herein which specifies that a vaginal microbiota index value for the individual is a specific value or more, or is "about" a specific value or more the individual may be assessed as having vaginal microbiota community type-L.
  • a vaginal microbiota index value for the individual is less than a specific value, or is less than "about” a specific value
  • the individual may be assessed as having vaginal microbiota community type-O.
  • the term "about” is to be understood as providing a range of +/- 5% of the value.
  • any assay of the invention is an assay for assessing the vaginal microbiota community type of an individual, the assay comprising: a. providing a sample which has been taken from the individual, the sample comprising a population of DNA molecules; b. determining in the population of DNA molecules in the sample the methylation status of a panel of one or more CpGs selected from a panel of CpGs identified in SEQ ID NOs 1 to 819, wherein the CpGs are denoted by [CG]; c. deriving a vaginal microbiota index value based on the methylation status of the panel of one or more CpGs; and d. assessing the vaginal microbiota community type of the individual based on the vaginal microbiota index value; wherein the assay is characterised as having an area under the curve (AUC) of 0.60 or more as determined by receiver operating characteristics (ROC).
  • AUC area under the curve
  • ROC receiver operating characteristics
  • Such an assay may be performed in accordance with any of the methods disclosed and defined herein.
  • any assay of the invention for assessing the vaginal microbiota community type in an individual may alternatively be referred to as an assay for stratifying an individual in accordance with their vaginal microbiota community type.
  • any assay of the invention is an assay for stratifying an individual for vaginal microbiota community type in an individual, the assay comprising: a. providing a sample which has been taken from the individual, the sample comprising a population of DNA molecules; b. determining in the population of DNA molecules in the sample the methylation status of a panel of one or more CpGs selected from a panel of CpGs identified in SEQ ID NOs 1 to 819, wherein the CpGs are denoted by [CG]; c. deriving a vaginal microbiota index value based on the methylation status of the panel of one or more CpGs; and d. stratifying the individual for vaginal microbiota community type based on the vaginal floa index value; wherein the assay is characterised as having an area under the curve (AUC) of 0.60 or more as determined by receiver operating characteristics (ROC).
  • AUC area under the curve
  • ROC receiver operating characteristics
  • Such an assay may be performed in accordance with any of the methods disclosed and defined herein.
  • any assay of the invention is an assay for stratifying an individual on the basis of vaginal microbiota community type, the assay comprising: a. providing a sample which has been taken from the individual, the sample comprising a population of DNA molecules; b. determining in the population of DNA molecules in the sample the methylation status of a panel of one or more CpGs selected from a panel of CpGs identified in SEQ ID NOs 1 to 819, wherein CpGs are denoted by [CG]; c. deriving a vaginal microbiota index value based on the methylation status of the panel of one or more CpGs; and d.
  • the assay is characterised as having an area under the curve (AUC) of 0.60 or more as determined by receiver operating characteristics (ROC).
  • Such an assay may be performed in accordance with any of the methods disclosed and defined herein.
  • the vaginal microbiota index value may be derived by any suitable means.
  • the vaginal microbiota index value may be derived by assessing the methylation status of the panel of one or more CpGs selected from a panel of CpGs identified in SEQ ID NOs 1 to 819, wherein CpGs are denoted by [CG], in a sample provided from an individual.
  • the methylation status of the CpGs may be determined by any suitable means.
  • the step of determining the methylation status of each CpG in the panel of one or more CpGs may comprise: a. performing a sequencing step to determine the sequence of each CpG; b.
  • the hybridising DNA to an array comprising probes capable of discriminating between methylated and non-methylated forms of the CpGs and applying a detection system to the array so as to determine the methylation status of each CpG; and/or c. performing a PCR step using methylation-specific primers, wherein the methylation status of the CpG is determined by the presence or absence of a PCR product.
  • the step of determining in the population of DNA molecules in the sample the methylation status of a panel of one or more CpGs further comprises determining a P value of each CpG.
  • Deriving the vaginal microbiota index value may involve providing a methylation ⁇ -value data set comprising the methylation ⁇ -values for each CpG in the panel of one or more CpGs.
  • deriving the vaginal microbiota index value may also involve estimating the fraction of contaminating DNA within the DNA provided from a sample, particularly wherein the contaminating fraction is immune cell DNA.
  • DNA may be DNA originating from a particular source organism, tissue or cell type.
  • the contaminating DNA originates from one or more different cell types to one or more cell types of interest.
  • a cell type of interest may particularly be an epithelial cell.
  • the assays described herein may optionally involve estimating a contaminating DNA fraction within DNA in the sample by any suitable means.
  • the contaminating DNA fraction for the sample is estimated via any suitable bioinformatics analysis tool.
  • a bioinformatics analysis tool that may be used to estimate a contaminating DNA fraction may be EpiDISH.
  • vaginal microbiota index value used for vaginal microbiota community type in an individual may, in some instances, only be reliably derived from determining the methylation status of a set of CpGs from DNA of a particular cell type of interest.
  • methylation status beta-values may differ in the one or more cell types of interest within a sample relative to methylation status beta-values in contaminating DNA from different cell types within the same sample.
  • the derived vaginal microbiota index value may in some instances have a decreased predictive power without estimating and controlling for the contaminating DNA fraction within the DNA provided from the sample.
  • assays of the invention that involve estimating the fraction of contaminating DNA and accordingly controlling for said contaminating DNA, it is preferable to estimate an immune cell DNA fraction within the DNA provided from the sample.
  • the assay may preferably involve controlling for the immune cell contamination by deriving the vaginal microbiota index, in accordance with the invention, solely from the DNA molecules derived from epithelial cells.
  • any of the assays described herein comprising a step of deriving a vaginal microbiota index value based on the methylation status of the panel of one or more CpGs may further comprise applying an algorithm to the methylation beta-value dataset to obtain the vaginal microbiota index value.
  • the step of deriving the vaginal microbiota index value based on the methylation status of the panel of CpGs comprises providing a methylation beta-value data set comprising the methylation beta-values for each CpG in the panel and applying an algorithm to the methylation beta-value data set to obtain the vaginal microbiota index value.
  • the step of deriving the vaginal microbiota index value based on the methylation status of the panel of one or more CpGs comprises: a. providing a methylation ⁇ -value data set comprising the methylation P- values for each CpG in the panel; b. providing a mathematical model capable of generating the vaginal microbiota index from the methylation ⁇ -value data set; and c. applying the mathematical model to the methylation ⁇ -value data set, thereby generating the vaginal microbiota index.
  • the vaginal microbiota index value may be calculated by any suitable mathematical model such as an algorithm or formula.
  • the vaginal microbiota index value is termed Women's risk Identification for Lactobacillus Index (WID-LO-index) and wherein the mathematical model which is applied to the methylation ⁇ -value data set to generate the vaginal microbiota index is calculated by an algorithm according to the following formula: wherein: a. ⁇ 1 , ... , ⁇ n are methylation beta-values (between 0 and 1) b. w 1 , ... , w n are real valued coefficients c. p is a real valued parameter used to scale the index; and d. n refers to the number of CpGs in the panel of one or more CpGs.
  • Ten- fold cross-validation was used internally by the cv. glmnet function in order to determine the optimal value of the regularisation parameter lambda.
  • the beta values from n CpGs for individual are used as inputs to the ridge classifier.
  • the coefficients w 1 , ..., w n and parameter ⁇ are obtained from the fitted model
  • Any suitable real valued coefficients may be applied to the WID-LO-Index in any of the assays described herein.
  • any suitable p real valued parameter may be applied to the WID-LO-index in any of the assays described herein.
  • Any suitable training data set may be applied to the assays described herein in order to infer real value parameters and coefficients that can subsequently be applied to the WID-LO-index formula according to the present invention. Exemplary ways of utilising a training dataset in accordance with the present invention are further described in the ' Statistical Analyses' section of the Materials and Methods section of the Examples.
  • Exemplary ⁇ real valued parameters are provided in Table 2 for CpG subsets identified in SEQ ID NOs 1 to 819. These real valued parameters may be applied to any of the assays described herein wherein the real parameters correspond to any one of the sets of CpGs identified in SEQ ID NOs 1 to 819 and set out in the left hand column of Table 2.
  • Table 2 Exemplary ⁇ real valued parameters are provided in Table 2 for CpG subsets identified in SEQ ID NOs 1 to 819 Exemplary w 1 , ... , w n real value coefficients are provided in the fifth column of
  • the predicting the vaginal microbiota community type in an individual may particularly involve a threshold vaginal microbiota index value being applied in order to assess or stratify an individual has having vaginal microbiota community type-L or type-O.
  • the individual when the vaginal microbiota index value for the individual is equal to or more than a threshold, the individual is assessed as having a vaginal flora consisting of less than 50% lactobacilli species (community type O), or wherein when the vaginal microbiota index value for the individual is less than the threshold, the individual is assessed as having a vaginal flora consisting of at least 50% lactobacilli species (community type L), wherein the specificity is 90%, preferably wherein the assay comprises determining methylation ⁇ - values for each CpG in the panel of one or more CpGs, and more preferably wherein the assessing vaginal microbiota community type in an individual is based on the WID-LO- Index.
  • the panel of one or more CpGs used to derive the vaginal microbiota index value may particularly comprise: a. at least 50 of the CpGs defined by SEQ ID NOs 1 to 819 and denoted by [CG], and wherein the threshold is about 1.78 and the sensitivity is at least 20%; b. at least the CpGs defined by SEQ ID NOs 1 to 50 and denoted by [CG], and wherein the threshold is about 1.22 and the sensitivity is at least 39%; c. at least the CpGs defined by SEQ ID NOs 1 to 100 and denoted by [CG], and wherein the threshold is about 1.67 and the sensitivity is at least 36%; d.
  • the threshold is about 1.76 and the sensitivity is at least 35%; or e. at least the CpGs defined by SEQ ID NOs 1 to 819 and denoted by [CG], and wherein the threshold is about 2.11 and the sensitivity is at least 58%.
  • the individual when the vaginal microbiota index value for the individual is equal to or more than a threshold, the individual is assessed as having a vaginal flora consisting of less than 50% lactobacilli species (community type O), or wherein when the vaginal microbiota index value for the individual is less than the threshold, the individual is assessed as having a vaginal flora consisting of at least 50% lactobacilli species (community type L), wherein the specificity is 90%, preferably wherein the assay comprises determining methylation ⁇ - values for each CpG in the panel of one or more CpGs, and more preferably wherein the assessing vaginal microbiota community type in an individual is based on the WID-LO- Index.
  • the panel of one or more CpGs used to derive the vaginal microbiota index value may particularly comprise:
  • the threshold is about 1.69 and the sensitivity is at least 42%; 4. at least the CpGs defined by SEQ ID NOs 1 to 400 and denoted by [CG], and wherein the threshold is about 1.77 and the sensitivity is at least 44%; 5. at least the CpGs defined by SEQ ID NOs 1 to 450 and denoted by [CG], and wherein the threshold is about 1.61 and the sensitivity is at least 42%; 6. at least the CpGs defined by SEQ ID NOs 1 to 500 and denoted by [CG], and wherein the threshold is about 1.15 and the sensitivity is at least 53%; 27. at least the CpGs defined by SEQ ID NOs 1 to 550 and denoted by [CG], and wherein the threshold is about 1.14 and the sensitivity is at least 55%;
  • the methylation status of the panel of one or more CpGs is preferably determined by a ⁇ -value analysis.
  • the individual when the vaginal microbiota index value for the individual is equal to or more than a threshold, the individual is assessed as having a vaginal flora consisting of less than 50% lactobacilli species (community type O), or wherein when the vaginal microbiota index value for the individual is less than the threshold, the individual is assessed as having a vaginal flora consisting of at least 50% lactobacilli species (community type L), wherein the specificity is 75%, preferably wherein the assay comprises determining methylation ⁇ - values for each CpG in the panel of one or more CpGs, and more preferably wherein the assessing the vaginal microbiota community type in an individual is based on the WID- LO-Index.
  • the panel of one or more CpGs used to derive the vaginal microbiota index value may particularly comprise: a. at least 50 of the CpGs defined by SEQ ID NOs 1 to 819 and denoted by [CG], and wherein the threshold is about 0.50 and the sensitivity is at least 47%; b. at least the CpGs defined by SEQ ID NOs 1 to 50 and denoted by [CG], and wherein the threshold is about 0.53 and the sensitivity is at least 52%; c. at least the CpGs defined by SEQ ID NOs 1 to 100 and denoted by [CG], and wherein the threshold is about 0.44 and the sensitivity is at least 55%; d.
  • the threshold is about 0.70 and the sensitivity is at least 62%; or e. at least the CpGs defined by SEQ ID NOs 1 to 819 and denoted by [CG], and wherein the threshold is about -0.01 and the sensitivity is at least 70%.
  • the individual when the vaginal microbiota index value for the individual is equal to or more than a threshold, the individual is assessed as having a vaginal flora consisting of less than 50% lactobacilli species (community type O), or wherein when the vaginal microbiota index value for the individual is less than the threshold, the individual is assessed as having a vaginal flora consisting of at least 50% lactobacilli species (community type L), wherein the specificity is 75%, preferably wherein the assay comprises determining methylation ⁇ - values for each CpG in the panel of one or more CpGs, and more preferably wherein the assessing the vaginal microbiota community type in an individual is based on the WID- LO-Index.
  • the panel of one or more CpGs used to derive the vaginal microbiota index value may particularly comprise:
  • the threshold is about 0.19 and the sensitivity is at least 68%; 4. at least the CpGs defined by SEQ ID NOs 1 to 400 and denoted by [CG], and wherein the threshold is about 0.14 and the sensitivity is at least 68%; 5. at least the CpGs defined by SEQ ID NOs 1 to 450 and denoted by [CG], and wherein the threshold is about 0.13 and the sensitivity is at least 65%; 6. at least the CpGs defined by SEQ ID NOs 1 to 500 and denoted by [CG], and wherein the threshold is about 0.08 and the sensitivity is at least 68%; 7.
  • the threshold is about 0.12 and the sensitivity is at least 68%; 8. at least the CpGs defined by SEQ ID NOs 1 to 600 and denoted by [CG], and wherein the threshold is about 0.04 and the sensitivity is at least 71%; 9. at least the CpGs defined by SEQ ID NOs 1 to 650 and denoted by [CG], and wherein the threshold is about 0.05 and the sensitivity is at least 67%;
  • the methylation status of the panel of one or more CpGs is preferably determined by a ⁇ -value analysis.
  • the sensitivity and specificity of the vaginal microbiota index threshold values vary depending on the number of CpGs comprised within the set, and specifically what CpGs are comprised within the set. Tables 3, 4 and 5 exemplify this assertion.
  • the inventors derived a vaginal microbiota index based on an analysis of methylation status (DNAme; as described above) for use in assays for assessing the presence or development of cancer in an individual.
  • the described assays particularly relate to the assessment of assessing the microbiota community type of an individual.
  • any of the assays described herein involve deriving a vaginal microbiota index value based on the methylation of status of a panel of one or more CpGs assayed in a sample provided from an individual, as described and defined herein.
  • the vaginal microbiota index value may be derived by any suitable means.
  • the inventors have identified specific CpGs, as described and defined herein, which may be used to form a panel of CpGs whose methylation status is determined in order to establish vaginal microbiota index values in accordance with the assays described and defined herein. Using these panels the inventors have demonstrated that it is possible to derive a vaginal microbiota index value which correlates with and is indicative of L- type microbiota community, i.e. the vaginal flora of the individual is formed of 50% or more lactobacilli. Using these panels the inventors have demonstrated that it is possible to derive a vaginal microbiota index value which correlates with and is indicative of O-type microbiota community, i.e. the vaginal flora of the individual is formed of less than 50% lactobacilli.
  • the inventors have used certain methods for determining the methylation status of specific CpGs in the population of DNA molecules in the sample. For example, in one method a percent methylated reference (PMR) value of a CpG may be determined. In another method the methylation ⁇ -values of a CpG may be determined. Different mechanisms may be employed to determine specific values depending on the circumstances, such as PCR-based mechanisms or array-based mechanisms. As will be apparent to a skilled person, in the assays of the invention the steps of determining the methylation status of specific CpGs in the population of DNA molecules in the sample are not limited to any one specific methodology.
  • PMR percent methylated reference
  • vaginal microbiota index value is based on the methylation status of CpGs
  • the methylation status of CpGs can be represented by values which may be specific to a specific methodology, e.g. percent methylated reference (PMR) value or methylation ⁇ -value
  • PMR percent methylated reference
  • methylation ⁇ -value the range of vaginal microbiota index values which define samples as L-type or O-type community may be dependent upon the methodology used to determine the methylation status of CpGs.
  • PMR percent methylated reference
  • vaginal microbiota index values which define microbiota community type by determining the methylation status of CpGs in panels constituting the specific CpGs disclosed herein from known L-type or O-type vaginal microbiota community samples.
  • vaginal microbiota index values are established using the CpGs identified herein, a user may use these values as a basis for assessing the presence, absence or development of vaginal microbiota community type in any test individual whose vaginal microbiota community type is to be determined.
  • vaginal microbiota index values according to the present invention are not limited to specific methods of determination of methylation status of CpGs.
  • vaginal microbiota index values can be established which reflect the intrinsic capabilities of the CpGs identified herein to correlate methylation status with vaginal microbiota community type.
  • the vaginal microbiota index value may be derived by assessing the methylation status of the panel of one or more CpGs in a sample provided from an individual by any suitable means.
  • the step of determining the methylation status of each CpG in the panel of one or more CpGs may be achieved by determining a percent methylated reference (PMR) value of each one of the one or more CpGs.
  • the step of determining the methylation status of each CpG in the panel of one or more CpGs may be achieved by determining the methylation ⁇ -value of each one of the one or more CpGs.
  • the methylation status of the CpGs may be determined by any suitable means.
  • the step of determining the methylation status of each CpG in the panel of one or more CpGs may comprise: a. performing a sequencing step to determine the sequence of each CpG; b. hybridising DNA to an array comprising probes capable of discriminating between methylated and non-methylated forms of the CpGs and applying a detection system to the array so as to determine the methylation status of each CpG; and/or c. performing a PCR step using methylation-specific primers, wherein the methylation status of the CpG is determined by the presence or absence of a PCR product.
  • the step of determining the methylation status of each CpG in the panel of one or more CpGs may comprise a conversion step in order to distinguish methylated CpG dinucleotides relative to non-methylated CpG dinucleotides.
  • the conversion step may comprise e.g. bisulfite conversion or TAPS (TET-assisted pyridine borane sequencing) conversion of the DNA in a sample that is to be applied to any one or more of a. to c. above.
  • TAPS may particularly involve the steps of oxidising 5-methylcytosine bases (5mC) to 5-carboxylcytosine bases (5caC), preferably by ten-eleven translocation (TET), and/or oxidising 5-hydroxymethylcytosine bases (5hmC) to 5-carboxylcytosine bases (5caC), preferably by ten-eleven translocation (TET); followed by reducing 5- carboxylcytosine bases (5caC) to dihydrouracil bases (DHU), optionally with pyridine borane.
  • TET ten-eleven translocation
  • DHU dihydrouracil bases
  • the step of determining the methylation status of each CpG in the panel of one or more CpGs may additionally, or alternatively, comprise the use of TempO-seq (templated Olig-sequencing).
  • TempO-seq template Olig-sequencing
  • the oligoniclueotides in the context of TempO-seq may or may not be designed such that they hybridise with methylated CpG dinucleotides following a prior conversion as described herein.
  • the step of determining the methylation status of each CpG in the panel of one or more CpGs may comprise the contacting the DNA in the sample with one or more methylation sensitive restriction endonucleases that cleave methylated and/or unmethylated forms of their restriction sites, and preferably the contacting of the DNA is prior to performing any one of a. to c. above.
  • one or more control reactions are performed.
  • the one or more control reactions involve interrogation of known loci that contain (i) no restriction endonuclease sites; (ii) a restriction site that is methylated; (iii) a restriction site that is unmethylated.
  • the proportion of methylated and unmethylated CpGs at any given locus may be determined, thereby enabling generation of a vaginal microbiota index value.
  • the step of determining in the population of DNA molecules in the sample the methylation status of a panel of one or more CpGs further comprises determining a ⁇ value of each CpG.
  • Deriving the vaginal microbiota index value may involve providing a methylation ⁇ -value data set comprising the methylation ⁇ -values for each CpG in the panel of one or more CpGs.
  • 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 microRNAs.
  • 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 dinucleotides consisting of a CpG motif, but may also occur at CHH motifs (where H is A, C, or T). During methylation, 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 such as enhancer elements. Most typically, the methylation status of CpGs is clustered in CpG islands, for example CpG islands present in the regulatory regions of genes, especially in their promoter regions.
  • an assessment of DNA methylation status involves analysing the presence or absence of methyl groups in DNA, for example methyl groups on the 5 position of one or more cytosine nucleotides.
  • the methylation status of one or more cytosine nucleotides present as a CpG dinucleotide is assessed.
  • 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 CpG 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 mircroarrays.
  • binding molecules such as anti-5- methylcytosine antibodies are commonly employed prior to subsequent processing steps such as PCR and sequencing.
  • Olkhov-Mitsel and Bapat (2012) provide a comprehensive review of techniques available for the identification and assessment of biomarkers involving methyl cytosine.
  • any suitable assay can be employed.
  • Assays described herein may comprise determining methylation status of CpGs by bisulphite converting the DNA.
  • Preferred assays involve bisulphite treatment of DNA, including amplification of the identified CpG loci for methylation specific PCR and/or sequencing and/or assessment of the methylation status of target loci using methylation- discriminatory microarrays.
  • CpG loci are amplified using PCR.
  • a variety of PCR-based approaches may be used.
  • methylation-specific primers may be hybridized to DNA containing the CpG sequence of interest.
  • Such primers may be designed to anneal to a sequence derived from either a methylated or non-methylated CpG locus.
  • a PCR reaction is performed and the presence of a subsequent PCR product indicates the presence of an annealed CpG of identifiable sequence.
  • DNA is bisulphite converted prior to amplification.
  • MSP methylation specific PCR
  • PCR primers may anneal to the CpG sequence of interest independently of the methylation status, and further processing steps may be used to determine the status of the CpG.
  • Assays are designed so that the CpG site(s) are located between primer annealing sites. This assay scheme is used in techniques such as bisulphite genomic sequencing, COBRA, Ms-SNuPE. In such assay, DNA can be bisulphite converted before or after amplification.
  • Small-scale PCR approaches may be 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.
  • microdroplet-based PCR instruments are available from a variety of suppliers, including RainDance Technologies, Inc. (Billerica, MA; http://raindancetech.com/) and Bio-Rad, Inc. (http://www.bio-rad.com/).
  • 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, (www.fluidigm.com).
  • amplified PCR products may be coupled to subsequent analytical platforms in order to determine the methylation status of the CpGs of interest.
  • the PCR products may be directly sequenced to determine the presence or absence of a methylcytosine at the target CpG 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 can be used.
  • 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/).
  • hybridization arrays may be designed to include probes which are able to hybridize to amplification products of a CpG and allow discrimination between methylated and non- methylated loci.
  • probes may be designed which are able to selectively hybridize to an CpG 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 a CpG locus containing cytosine, indicating the absence of uracil conversion following bisulphite treatment. This corresponds with a methylated CpG 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 CpG status determination without the specific need for the sequencing of CpG 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 CpGs described herein.
  • One particular methylation- discriminatory microarray system is provided by Illumina, Inc. (San Diego, CA; http://www.illumina.com/).
  • Illumina, Inc. (San Diego, CA; http://www.illumina.com/).
  • the Infmium Methyl ationEPIC BeadChip array and the Infmium HumanMethylation450 BeadChip array systems may be used to assess the methylation status of CpGs for predicting cancer development as described herein.
  • Such a system exploits the chemical modifications made to DNA following bisulphite treatment of the starting DNA molecule.
  • the array comprises beads to which are coupled oligonucleotide probes specific for DNA sequences corresponding to the unmethylated form of a CpG, as well as separate beads to which are coupled oligonucleotide probes specific for DNA sequences corresponding to the methylated form of an CpG.
  • Candidate DNA molecules are applied to the array and selectively hybridize, under appropriate conditions, to the oligonucleotide probe corresponding to the relevant epigenetic form.
  • a DNA molecule derived from a CpG which was methylated in the corresponding genomic DNA will selectively attach to the bead comprising the methylation-specific oligonucleotide probe, but will fail to attach to the bead 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 CpG is determined by calculating the ratio of the fluorescent signal derived from the methylated and unmethylated sites.
  • Infmium HumanMethylation450 BeadChip array systems can be used to interrogate CpGs in the assays described herein.
  • Alternative or customised arrays could, however, be employed to interrogate the cancer-specific CpG biomarkers defined herein, provided that they comprise means for interrogating all CpG for a given assay, as defined herein.
  • Techniques involving combinations of the above-described assays may also be used. For example, DNA containing CpG sequences of interest may be hybridized to microarrays and then subjected to DNA sequencing to determine the status of the CpG as described above.
  • sequences corresponding to CpG loci may also be subjected to an enrichment process if desired.
  • DNA containing CpG sequences of interest may be captured by binding molecules such as oligonucleotide probes complementary to the CpG target sequence of interest.
  • Sequences corresponding to CpG 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 CpG, such as DNA sequencing steps.
  • Capture/separation steps may be custom designed. Alternatively a variety of such techniques are available commercially, e.g. the SureSelect target enrichment system available from Agilent Technologies (http://www.agilent.com/home).
  • biotinylated "bait” or “probe” sequences e.g. RNA
  • 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 CpG target sequences separated from non-CpG sequences.
  • Template DNA may be subjected to bisulphite conversion and target loci amplified by small-scale PCR such as microdroplet PCR using primers which are independent of the methylation status of the CpG.
  • samples may be subjected to a capture step to enrich for PCR products containing the target CpG, e.g. captured and purified using magnetic beads, as described above.
  • a standard PCR reaction is carried out to incorporate DNA sequencing barcodes into CpG- 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 CpG.
  • CpG biomarker loci defined herein by SEQ ID NOs 1 to 14,000 are identified e.g. by Illumina® identifiers (IlmnlD) (see Table 1). These CpG loci identifiers refer to individual CpG sites used in the commercially available Illumina® Infinium Methylation EPIC BeadChip kit and Illumina® Infinium Human Methylation450 BeadChip kit. The identity of each CpG site represented by each CpG loci identifier is publicly available from the Illumina, Inc. website under reference to the CpG sites used in the Infinium Methylation EPIC BeadChip kit and the Infinium Human Methylation450 BeadChip kit.
  • Illumina® has developed a method to consistently designate CpG loci based on the actual or contextual sequence of each individual CpG locus. To unambiguously refer to CpG loci in any species, Illumina® has developed a consistent and deterministic CpG loci database to ensure uniformity in the reporting of methylation data. The Illumina® method takes advantage of sequences flanking a CpG locus to generate a unique CpG 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 (SNP) designation.
  • SNP single nucleotide polymorphism
  • Illumina® Identifiers for the Infinium Methyl ationEPIC BeadChip and Infinium Human Methylation450 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
  • methylation status of a CpG By assessing the methylation status of a CpG it is meant that a determination is made as to whether a given CpG is methylated or unmethylated. In addition, it is meant that a determination is made as to the degree to which a given CpG site is methylated across a population of CpG loci in a sample.
  • CpG methylation status may be measured indirectly using a detection system such as fluorescence.
  • a methylation-discriminatory microarray may be used.
  • the Illumina® definition of beta-values may be used.
  • methylation status of any one or more CpGs of the 819 CpGs defined according to SEQ ID NOS: 1 to 819 may be assessed by any suitable technique.
  • a methylation discriminatory array such as an Illumina InfiniumMethylation EPIC BeadChip. These assays utilise probes directed to methylated and unmethylated CpGs at a given locus.
  • MethyLight Another exemplary technique which the inventors have used to determine the methylation status of any one or more CpGs is a fluorescence-based PCR technique referred to as MethyLight.
  • These assays utilise forward and reverse PCR primers specific for sequences encompassing any one or more of the 819 CpGs defined according to SEQ ID NOS: 1 to 819.
  • the methylation status of one or more of the 819 CpGs defined according to SEQ ID NOS: 1 to 819 may therefore be determined by MethyLight analysis.
  • the detectable probes are typically designed such that they hybridise only to methylated forms of the one or more CpGs to be assayed.
  • ROC receiver-operating- characteristic
  • AUC area under the curve
  • Each point on the ROC curve shows the effect of a rule for turning a risk/likelihood estimate into a prediction of the microbiota community type in an individual.
  • the AUC measures how well the model discriminates between case subjects and control subjects.
  • An ROC curve that corresponds to a random classification of case subjects and control subjects is a straight line with an AUC of 50%.
  • An ROC curve that corresponds to perfect classification has an AUC of 100%.
  • the 95% confidence interval for the ROC AUC may be between 0.60 and 1.
  • the interval may be defined as a range having as an upper limit any number between 0.60 and 1.
  • the upper limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99 or 1.00.
  • the interval may be defined as a range having as a lower limit any number between 0.60 and 1.
  • the lower limit number may be
  • the interval range may comprise any of the above lower limit numbers combined with any of the above upper limit numbers as appropriate.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 1 and as a lower limit any number between 0.60 and 1.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99 or 1.00.
  • the upper limit number may be 0.99.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.99 and as a lower limit any number between 0.60 and 0.99.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98 or 0.99.
  • the upper limit number may be 0.98.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.98 and as a lower limit any number between 0.60 and 0.98.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97 or 0.98.
  • the upper limit number may be 0.97.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.97 and as a lower limit any number between 0.60 and 0.97.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96 or 0.97.
  • the upper limit number may be 0.96.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.96 and as a lower limit any number between 0.60 and 0.96.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95 or 0.96.
  • the upper limit number may be 0.95.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.95 and as a lower limit any number between 0.60 and 0.95.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94 or 0.95.
  • the upper limit number may be 0.94.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.94 and as a lower limit any number between 0.60 and 0.94.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93 or 0.94.
  • the upper limit number may be 0.93.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.93 and as a lower limit any number between 0.60 and 0.93.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92 or 0.93.
  • the upper limit number may be 0.92.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.92 and as a lower limit any number between 0.60 and 0.92.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91 or 0.92.
  • the upper limit number may be 0.91.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.91 and as a lower limit any number between 0.60 and 0.91.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90 or 0.91.
  • the upper limit number may be 0.90.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.90 and as a lower limit any number between 0.60 and 0.90.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89 or 0.90.
  • the upper limit number may be 0.89.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.89 and as a lower limit any number between 0.60 and 0.89.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88 or 0.89.
  • the upper limit number may be 0.88.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.88 and as a lower limit any number between 0.60 and 0.88.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87 or 0.88.
  • the upper limit number may be 0.87.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.87 and as a lower limit any number between 0.60 and 0.87.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86 or 0.87.
  • the upper limit number may be 0.86.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.86 and as a lower limit any number between 0.60 and 0.86.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85 or 0.86.
  • the upper limit number may be 0.85.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.85 and as a lower limit any number between 0.60 and 0.85.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84 or 0.85.
  • the upper limit number may be 0.84.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.84 and as a lower limit any number between 0.60 and 0.84.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83 or 0.84.
  • the upper limit number may be 0.83.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.83 and as a lower limit any number between 0.60 and 0.83.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82 or 0.83.
  • the upper limit number may be 0.82.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.82 and as a lower limit any number between 0.60 and 0.82.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81 or 0.82.
  • the upper limit number may be 0.81.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.81 and as a lower limit any number between 0.60 and 0.81.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80 or 0.81.
  • the upper limit number may be 0.80.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.80 and as a lower limit any number between 0.60 and 0.80.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79 or 0.80.
  • the upper limit number may be 0.79.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.79 and as a lower limit any number between 0.60 and 0.79.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78 or 0.79.
  • the upper limit number may be 0.78.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.78 and as a lower limit any number between 0.60 and 0.78.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77 or 0.78.
  • the upper limit number may be 0.77.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.77 and as a lower limit any number between 0.60 and 0.77.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76 or 0.77.
  • the upper limit number may be 0.76.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.76 and as a lower limit any number between 0.60 and 0.76.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75 or 0.76.
  • the upper limit number may be 0.75.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.75 and as a lower limit any number between 0.60 and 0.75.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74 or 0.75.
  • the upper limit number may be 0.74.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.74 and as a lower limit any number between 0.60 and 0.74.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73 or 0.74.
  • the upper limit number may be 0.73.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.73 and as a lower limit any number between 0.60 and 0.73.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72 or 0.73.
  • the upper limit number may be 0.72.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.72 and as a lower limit any number between 0.60 and 0.72.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71 or 0.72.
  • the upper limit number may be 0.71.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.71 and as a lower limit any number between 0.60 and 0.71.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70 or 0.71.
  • the upper limit number may be 0.70.
  • the 95% confidence ROC AUC interval may be defined as a range having an upper limit of 0.70 and as a lower limit any number between 0.60 and 0.70.
  • the lower limit number may be 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69 or 0.70.
  • screening refers to any intervention or procedure performed on an individual for the assessment of a disease state.
  • treatment is intended to refer to any preventative or prophylactic intervention or procedure performed on an individual, including a surgical intervention or a pharmacological intervention such as the administration of a compound or drug.
  • vaginal microbiota community type-information derived from utilisation of the method described herein may inform actual therapeutic treatment measures in an individual, particularly if vaginal microbiota community type-0 is identified in the individual.
  • the vaginal microbiota community type-information derived from utilisation of the method described herein may provoke ovarian cancer screening measures to be subjected to an individual, particularly if vaginal microbiota community type-0 is identified in the individual.
  • the invention also encompasses the performance of one or more screening procedures following assessment the vaginal microbiota community type of an individual, particularly when the individual is assessed as having a type-0 vaginal microbiota community, particularly wherein the screening procedure is a screen for ovarian cancer.
  • the invention also encompasses the performance of one or more treatment steps following assessment of an individual as having a type-0 vaginal microbiota community, particularly wherein the treatment involves the administration of an intervention to the individual to increase the proportion of lactobacilli in the vaginal microbiota community of the individual.
  • an individual having a type-0 vaginal microbiota community subject to intervention in accordance with the method of the invention leads to an increase in lactobacilli proportion within the vaginal microbiota following the intervention, and more preferably wherein the vaginal microbiota community of the individual transforms to type-L.
  • Said treatments/interventions may be considered "risk prevention", “preventative” or “prophylactic” treatments with respect to ovarian cancer or prospective ovarian cancer development.
  • the individual may be subjected to any one or more of the following interventions in accordance with the method of the invention in order to promote an increase in lactobacilli proportion within the vaginal microbiota following the intervention, and more preferably wherein the vaginal microbiota community of the individual transforms to type-L:
  • Lactobacillus-based products including yoghurt, acidophilus milk, and available Lactobacillus powder and tablets, in order to replenish the vagina with lactobacilli;
  • VMT vaginal microbiomial transplantation
  • the individual may be subject to vaginal microbiomial transplantation (VMT), wherein before transplantation, patients are treated with an intravaginal antibiotic regimen consisting of 5 g clindamycin cream (2%) for 7 days or 5 g metronidazole gel (0.75%) for 5 d; VMT will be performed 1 week after completion of antibiotic treatment; during the procedure, vaginal fluid for transplantation will be collected from the donors starting from the seventh day of the menstrual cycle and taken from the upper half of the vagina and cervical fomices, while avoiding the cervix; and the collected discharge will be evaluated by pH and microscopy, diluted with 1 ml of sterile saline and transferred to the recipient's posterior fornix, without the use of a speculum.
  • VMT vaginal microbiomial transplantation
  • the one or more treatment steps may additionally, or alternatively, following assessment of an individual as having a type-0 vaginal microbiota community, comprise administration of one or more of Aspirin, oral contraceptive pill, selective estrogen receptor modulators (SERMS), and selective progesterone receptor modulators (SPRMs).
  • the SERMs may comprise Anordin, Bazedoxifene, Broparestrol, Broparestrol, Clomifene, Cyclofenil, Lasofoxifene, Ormeloxifene, Ospemifene, Raloxifene, Tamoxifen, preferably wherein the SERMs comprise Tamoxifen, Bazedoxifene and Raloxifene.
  • Thee SPRMs may comprise Mifepristone, Ulipristal, Asoprisnil, Proellex, Onapristone, Asoprisnil and Lonaprisan. Said treatments/interventions may be considered “risk prevention”, “preventative” or “prophylactic” treatments with respect to ovarian cancer or prospective ovarian cancer development.
  • the invention thus encompasses a method of screening for and/or preventing ovarian cancer in an individual, the method comprising: a. assessing the vaginal microbiota community type of an individual by performing any one of the assays of the invention described herein; b. performing one or more ovarian cancer screens and/or administering preventative treatments to the individual based on the assessment.
  • the invention further encompasses a method of treating ovarian cancer in an individual, the method comprising: a. assessing the vaginal microbiota community type of an individual by performing any one of the assays of the invention described herein; and b. when the individual is assessed as having an O-type vaginal microbiota community, administering an ovarian cancer therapeutic to the individual based on the assessment.
  • any preventative ovarian cancer treatment should evoke an increase in lactobacilli proportion in the vaginal microbiota in the individual, most preferably wherein the vaginal microbiota community of the individual transforms to type-L.
  • the screens or preventative treatments preferably comprise: a. a test for a BRCA1 and/or BRCA2 germline mutation; b. a test for CA125, preferably wherein the test is repeated annually; c. a test for cell-free tumour DNA methylation in plasma/serum, preferably wherein the test is repeated annually; d. a test for cell-free tumour DNA methylation in vaginal fluid, preferably wherein the test is repeated annually; e. a pelvic MRI scan, preferably wherein the test is repeated annually; f. administration of one or more of Aspirin, oral contraceptive pill, selective estrogen receptor modulators (SERMS), and selective progesterone receptor modulators (SPRMs); g.
  • SERMS selective estrogen receptor modulators
  • SPRMs selective progesterone receptor modulators
  • a total hysterectomy and bilateral salpingo-oophorectomy h. a vaginal lactobacillus transplantation; and/or i. a local (ie vaginal) or systemic (transdermal or orally) treatment with oestrogens, preferably wherein the treatment supports the growth of protective lactobacilli.
  • screening tests may be repeated at any suitable interval.
  • a test for CA125 may be performed three-monthly, six- monthly, annually or about once every two, three or four years.
  • a test for cell-free tumour DNA methylation in plasma/ serum may be performed three-monthly, six-monthly, annually or about once every two, three or four years.
  • a test for cell-free tumour DNS methylation in vaginal fluid may be performed three-monthly, six-monthly, annually or about once every two, three or four years.
  • a pelvic MRI scan may be performed three- monthly, six-monthly, annually or about once every two, three or four years.
  • the one or more screens or preventative treatments that the individual is subjected to may be repeated on one or more occasions.
  • the one or more screens or preventative treatments may be repeated at regular intervals.
  • the repetitive nature of the treatment administration may depend on the particular treatment being administered. Some screens or preventative treatments may require repetitive administration at greater frequency than others. The skilled person would be aware of the frequency of administration required for therapies known in the art.
  • the one or more screens or preventative treatments may be repeated weekly, two weekly, three weekly, four weekly, monthly, three monthly, six monthly, yearly, two yearly, three yearly, four yearly, or five yearly.
  • the following therapeutic agents may be administered to an individual based on their screening assessment following assessment of O-type microbiota community in an individual, optionally in combination with any other treatment described herein.
  • the therapeutic agent may be directly attached, for example by chemical conjugation, to an antibody.
  • Methods of conjugating agents or labels to an antibody are known in the art.
  • carbodiimide conjugation (Bauminger & Wilchek (1980) Methods Enzymol. 70, 151-159) may be used to conjugate a variety of agents, including doxorubicin, to antibodies or peptides.
  • the water-soluble carbodiimide, l-ethyl-3-(3- dimethylaminopropyl) carbodiimide (EDC) is particularly useful for conjugating a functional moiety to a binding moiety.
  • Other methods for conjugating a moiety to antibodies can also be used. For example, sodium periodate oxidation followed by reductive alkylation of appropriate reactants can be used, as can glutaraldehyde cross- linking.
  • glutaraldehyde cross- linking can be used, as can glutaraldehyde cross- linking.
  • a cytotoxic moiety may be directly and/or indirectly cytotoxic.
  • directly cytotoxic it is meant that the moiety is one which on its own is cytotoxic.
  • indirectly cytotoxic it is meant that the moiety is one which, although is not itself cytotoxic, can induce cytotoxicity, for example by its action on a further molecule or by further action on it.
  • the cytotoxic moiety may be cytotoxic only when intracellular and is preferably not cytotoxic when extracellular.
  • Cytotoxic chemotherapeutic agents are well known in the art. Cytotoxic chemotherapeutic agents, such as anticancer agents, include: alkylating agents including nitrogen mustards such as mechlorethamine (HN2), cyclophosphamide, ifosfamide, melphalan (L-sarcolysin) and chlorambucil; ethylenimines and methylmelamines such as hexamethylmelamine, thiotepa; alkyl sulphonates such as busulfan; nitrosoureas such as carmustine (BCNU), lomustine (CCNU), semustine (methyl -CCNU) and streptozocin (streptozotocin); and triazenes such as decarbazine (DTIC; dimethyltriazenoimidazole- carboxamide); Antimetabolites including folic acid analogues such as methotrexate (amethopterin); pyrimidine analogues such
  • Natural Products including vinca alkaloids such as vinblastine (VLB) and vincristine; epipodophyllotoxins such as etoposide and teniposide; antibiotics such as dactinomycin (actinomycin D), daunorubicin (daunomycin; rubidomycin), doxorubicin, bleomycin, plicamycin (mithramycin) and mitomycin (mitomycin C); enzymes such as L-asparaginase; and biological response modifiers such as interferon alphenomes.
  • VLB vinblastine
  • epipodophyllotoxins such as etoposide and teniposide
  • antibiotics such as dactinomycin (actinomycin D), daunorubicin (daunomycin; rubidomycin), doxorubicin, bleomycin, plicamycin (mithramycin) and mitomycin (mitomycin C)
  • enzymes such as L-asparaginas
  • Miscellaneous agents including platinum coordination complexes such as cisplatin (cis-DDP) and carboplatin; anthracenedione such as mitoxantrone and anthracycline; substituted urea such as hydroxyurea; methyl hydrazine derivative such as procarbazine (N-methylhydrazine, MIH); and adrenocortical suppressant such as mitotane (o,p'-DDD) and aminoglutethimide; taxol and analogues/derivatives; and hormone agonists/antagonists such as flutamide and tamoxifen.
  • platinum coordination complexes such as cisplatin (cis-DDP) and carboplatin
  • anthracenedione such as mitoxantrone and anthracycline
  • substituted urea such as hydroxyurea
  • methyl hydrazine derivative such as procarbazine (N-methylhydrazine, MIH)
  • a cytotoxic chemotherapeutic agent may be a cytotoxic peptide or polypeptide moiety which leads to cell death.
  • Cytotoxic peptide and polypeptide moieties are well known in the art and include, for example, ricin, abrin, Pseudomonas exotoxin, tissue factor and the like. Methods for linking them to targeting moieties such as antibodies are also known in the art.
  • Other ribosome inactivating proteins are described as cytotoxic agents in WO 96/06641. Pseudomonas exotoxin may also be used as the cytotoxic polypeptide.
  • Certain cytokines, such as TNFa and IL-2, may also be useful as cytotoxic agents.
  • Radioactive atoms may also be cytotoxic if delivered in sufficient doses.
  • Radiotherapeutic agents may comprise a radioactive atom which, in use, delivers a sufficient quantity of radioactivity to the target site so as to be cytotoxic.
  • Suitable radioactive atoms include phosphorus-32, iodine-125, iodine-131, indium-i l l, rhenium- 186, rhenium-188 or yttrium-90, or any other isotope which emits enough energy to destroy neighbouring cells, organelles or nucleic acid.
  • the isotopes and density of radioactive atoms in the agents of the invention are such that a dose of more than 4000 cGy (preferably at least 6000, 8000 or 10000 cGy) is delivered to the target site and, preferably, to the cells at the target site and their organelles, particularly the nucleus.
  • the radioactive atom may be attached to an antibody, antigen-binding fragment, variant, fusion or derivative thereof in known ways.
  • EDTA or another chelating agent may be attached to the binding moiety and used to attach 11 Un or 90Y.
  • Tyrosine residues may be directly labelled with 1251 or 1311.
  • a cytotoxic chemotherapeutic agent may be a suitable indirectly-cytotoxic polypeptide.
  • the indirectly cytotoxic polypeptide is a polypeptide which has enzymatic activity and can convert a non-toxic and/or relatively non-toxic prodrug into a cytotoxic drug.
  • ADEPT Antibody-Directed Enzyme Prodrug Therapy
  • the system requires that the antibody locates the enzymatic portion to the desired site in the body of the patient and after allowing time for the enzyme to localise at the site, administering a prodrug which is a substrate for the enzyme, the end product of the catalysis being a cytotoxic compound.
  • the object of the approach is to maximise the concentration of drug at the desired site and to minimise the concentration of drug in normal tissues.
  • the cytotoxic moiety is capable of converting a non-cytotoxic prodrug into a cytotoxic drug.
  • the invention also provides methods of monitoring the vaginal microbiota community type, and consequent risk of cancer development, in an individual.
  • Monitoring in the context of the present invention may refer to longitudinal assessment of an individual's vaginal microbiota community type, and consequent risk of cancer development.
  • This longitudinal assessment may be carried out according to the assays of the invention described herein.
  • This longitudinal assessment may involve performance of the assays of the invention described herein to assess vaginal microbiota community type in an individual at more than one time point over the course of an undetermined time window.
  • the time window may be any period of time whilst the individual is still living.
  • the time window may persist for the lifetime of the individual.
  • the time window may, for example, persist until the individual's microbiota community type transforms from type-0 to type-L.
  • the invention thus encompasses a method of monitoring monitoring the ovarian cancer risk of an individual according to the individual's vaginal microbiota index value, the method comprising: (a) assessing the vaginal microbiota community type of an individual by performing the assay according to the invention at a first time point; (b) assessing the vaginal microbiota community type of an individual by performing the assay according to the invention at one or more further time points; and (c) monitoring for change in vaginal flora community type L to community type O at one or more further time points as this would indicate that the individual is at an increased risk of ovarian cancer
  • the steps of assessing the vaginal microbiota community type in an individual based on a vaginal microbiota index value may involve the application of threshold values.
  • Threshold values can provide an indication of an individual's vaginal microbiota community type.
  • vaginal microbiota index values may indicate a type-0 or type-L vaginal microbiota community type.
  • the step of assessing microbiota community type in an individual involves deriving a vaginal microbiota index value.
  • the vaginal microbiota index value may change between any two or more time points. For this reason, longitudinal monitoring of an individual's vaginal microbiota index value could be of particular benefit to the assessment of, for example, ovarian cancer risk and/or ovarian cancer prevention treatment efficacy.
  • the one or more further time points may be any suitable time point.
  • the one or more further time points may of suitable distance apart for sufficiently frequent screening in order to predict any particularly early onset cases of ovarian cancer in an individual.
  • the one or more further time points may be of suitable distance apart for assessing the efficacy of one or more treatments.
  • the one or more further time points may be of suitable distance apart for predicting whether the vaginal microbiota community type has transformed to type-L after a successful course of treatment.
  • the one or more further time points may be about monthly, about two monthly, about three monthly, about four monthly, about five monthly, about six monthly, about seven monthly, about eight monthly, about nine monthly, about ten monthly, about eleven monthly, about yearly, about two yearly, or more than two yearly.
  • Treatments may be changed in accordance with the methods of treatments described herein. Treatments may particularly be changed if the individual's vaginal microbiota community type, based on their vaginal microbiota index value, fails to transform from O-type to L-type.
  • the step of assessing the vaginal microbiota community type in an individual may involve the use of any one of the arrays described herein.
  • the assays described herein are preferably performed on samples comprising epithelial cells, particularly obtained from an anatomical site other than the ovary or endometrium.
  • the sample may particularly be derived from the cervix, the vagina and the buccal area.
  • the sample is preferably a cervical liquid-based cytology sample, and more preferably a cervical smear sample.
  • any one of the assays described herein for assessing vaginal microbiota community type in an individual comprises providing a sample which has been taken from the individual.
  • the assay may or may not encompass the step of obtaining the sample from the individual.
  • assays which do not encompass the step of obtaining the sample from the individual a sample which has previously been obtained from the individual is provided.
  • the sample may be provided directly from the individual for analysis or may be derived from stored material, e.g. frozen, preserved, fixed or cryopreserved material.
  • the sample may be self-collected or collected by any suitable medical professional.
  • the sample may comprise cells.
  • the sample may comprise genetic material such as DNA and/or RNA.
  • any of the assays described herein may involve providing a biological sample from the patient as the source of patient DNA for methylation analysis.
  • any of the assays described herein may involve obtaining patient DNA from a biological sample which has previously been obtained from the patient. Any of the assays described herein may involve obtaining a biological sample from the patient as the source of patient DNA for methylation analysis. The sample may be self-collected or collected by any suitable medical professional. Procedures for obtaining a biological sample include biopsy.
  • the methods described herein may be applied to any vaginal microbiota community type. Preferably, the methods described herein may be applied to assess whether an individual has vaginal microbiota community type-0 or type-L.
  • a type-0 community is formed of less than 50% lactobacilli and a type-L community is formed of at least 50% lactobacilli.
  • Lactobacilli in the vaginal microbiota may be any species of lactobacilli.
  • the lactobacilli may be lactobacilli species that maintain a low vaginal pH, particularly by producting lactic acid.
  • the lactobacilli may particularly be Lactobacillus crispatus. Lactobacillus iners. Lactobacillus gasseri, and/ or Lactobacillus jensenii.
  • vaginal flora other than lactobacilli may be any flora.
  • the at least 50% lactobacilli community may be formed of any one or more lactobacilli species described above.
  • arrays and kits The invention also encompasses arrays capable of discriminating between methylated and non-methylated forms of CpGs as defined herein; the arrays may comprise oligonucleotide probes specific for methylated forms of CpGs as defined herein and oligonucleotide probes specific for non-methylated forms of CpGs as defined herein.
  • the array may comprise oligonucleotide probes specific for a methylated form of each CpG in a CpG panel and oligonucleotide probes specific for a non-methylated form of each CpG in the panel; wherein the panel consists of at least 50 CpGs selected from the CpGs identified in SEQ ID NOs 1 to 819.
  • the panel may consist of at least 100 CpGs selected from the CpGs identified in SEQ ID NOs 1 to 819, preferably wherein the CpGs are the CpGs identified in SEQ ID NOs 1 to 100.
  • the panel may consist of at least 200 CpGs selected from the CpGs identified in SEQ ID NOs 1 to 819, preferably wherein the CpGs are the CpGs identified in SEQ ID NOs 1 to 200.
  • the panel may consist of at least 300 CpGs selected from the CpGs identified in SEQ ID NOs 1 to 819, preferably wherein the CpGs are the CpGs identified in SEQ ID NOs 1 to 300.
  • the panel may consist of at least 400 CpGs selected from the CpGs identified in SEQ ID NOs 1 to 819, preferably wherein the CpGs are the CpGs identified in SEQ ID NOs 1 to 400.
  • the panel may consist of at least 500 CpGs selected from the CpGs identified in SEQ ID NOs 1 to 819, preferably wherein the CpGs are the CpGs identified in SEQ ID NOs 1 to 500.
  • the panel may consist of at least 600 CpGs selected from the CpGs identified in SEQ ID NOs 1 to 819, preferably wherein the CpGs are the CpGs identified in SEQ ID NOs 1 to 600.
  • the panel may consist of at least 700 CpGs selected from the CpGs identified in SEQ ID NOs 1 to 819, preferably wherein the CpGs are the CpGs identified in SEQ ID NOs 1 to 700.
  • the panel may consist of at least 800 CpGs selected from the CpGs identified in SEQ ID NOs 1 to 819, preferably wherein the CpGs are the CpGs identified in SEQ ID NOs 1 to 800
  • the panel may consist of all CpGs identified in SEQ ID NOs 1 to 819.
  • the array is not an Infmium Methyl ationEPIC BeadChip array or an Illumina Infmium HumanMethylation450 BeadChip array.
  • the number of CpG-specific oligonucleotide probes of the array is 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, 20,000 or less, 15,000 or less, 10,000 or less, 5,000 or less, 4,000 or less, 3,000 or less or 2,000 or less.
  • the CpG panel may comprise any set of CpGs defined in the assays of the invention described herein.
  • the arrays of the invention may comprise one or more oligonucleotides comprising any set of CpGs defined in the assays of the invention, wherein the one or more oligonucleotides are hybridized to corresponding oligonucleotide probes of the array.
  • the invention also encompasses a process for making a hybridized array described herein, comprising contacting an array according to the present invention with a group of oligonucleotides comprising any set of CpGs defined in the assays of the invention.
  • any of the arrays as defined herein may be comprised in a kit.
  • the kit may comprise any array as defined herein together with instructions for use.
  • the invention further encompasses the use of any of the arrays as defined herein in any of the assays for determining the methylation status of CpGs for the purposes of assessing the vaginal microbiota community type of an individual.
  • WID-LO-Index is a vaginal microbiota index value wherein the index value has been determined by assaying in a population of DNA molecules derived from a given sample from an individual the methylation status of a panel of CpGs selected from the CpGs defined by SEQ ID NOs: 1 to 819.
  • all CpGs defined by SEQ ID NOs: 1 to 819 have been included in the panel which has been assayed to obtain a vaginal microbiota index value.
  • specific sub-selections of CpGs from among the 819 CpGs defined by SEQ ID NOs: 1 to 819 have been included in the panel which has been assayed to obtain a vaginal microbiota index value.
  • the vaginal microbiota index value's ability to discriminate between type-0 and type-L vaginal microbiota community types in women is described, wherein discriminatory ability of the index is characterised by AUC and received operating characteristics.
  • vaginal sample collection and transportation, sample matching, the consent process and wet laboratory processing of cervicovaginal samples the inventors refer the reader to previously published work.
  • the demultiplexed sequencing reads were quality checked, trimmed and filtered (Sickle vl.33) and adapters and primers removed (Cutadapt vl.10). Overlapping paired- end reads were merged for fulll2 length V1-V3 16S amplicons (FLASh vl .2.11), clustered (CD-HIT v4.6), and chimeric sequences removed (UCHIME v4.2.40). Operational taxonomic units (OTUs) were assigned with BLASTN+ (v2.4.0) via a non-redundant 16S rRNA reference database from the Ribosomal Database Project (RDP, Release 11) and filtered for high quality. Taxonomic classification was based on the NCBI Taxonomy. Please check previously published work for further details.
  • the inventors collapsed four Lactobacillus community groups, previously identified by Ravel and colleagues (groups I, II, III, and V) into one microbial community, seen as the prevalent community in a' healthy' microbiome.
  • This community which the inventors referred to as community type L, is comprised of four types of Lactobacillus, L crispatus, L gasseri, L iners, and L jensenir and they are associated with a substantially lower vaginal pH, which has the potential to reduce the risk of ascending infections.
  • community-type O containing higher proportions of typical obligate and facultative anaerobe genera (such as Gardnerella or Atopobium species), which are associated with aerobic vaginitis and bacterial vaginosis, and is highly diverse.
  • typical obligate and facultative anaerobe genera such as Gardnerella or Atopobium species
  • the inventors divided the 448 samples used in the inventors' analysis into people whose cervicovaginal microbiota consisted of at least 50% community type L and those whose microbiota consisted of less than 50% community type L (community type O).
  • a heat map showing the abundance patterns across all subjects is shown in Fig. 6.
  • DNA was isolated from cervical, buccal and blood cells using AllPrep DNA/RNA Mini Kits (#80204, Qiagen Ltd), following the manufacturer's protocol. DNA concentration and quality absorbance ratios were measured using a Nanodrop-8000 (Thermo Scientific Inc). Extracted DNA was stored at -80°C until further analysis. DNA was normalized to 25 ng/pl and 500 ng total DNA was bisulfite modified using the EZ-96 DNA Methylation-Lightning kit (Zymo Research Corp, cat #D5047) on a Hamilton Star Liquid handling platform. 8 pl of modified DNA was subjected to methylation analysis on the Illumina Infinium MethylationEPIC BeadChip microarray (Illumina, CA, USA) at UCL Genomics, according to the manufacturer's standard protocol.
  • Illumina Infinium MethylationEPIC BeadChip microarray Illumina, CA, USA
  • All methylation microarray data were processed through the same standardized pipeline.
  • Raw data was loaded using the R package minfi. Any samples with median methylated and unmethylated intensities ⁇ 9.5 were removed. Any probes with a detection p-value >0.01 were regarded as failed. Any samples with >10% failed probes, and any probes with >10% failure rate were removed from the dataset.
  • Beta values from failed probes (approximately 0.001% of the dataset) were imputed using the impute.knn function as part of the impute R package.
  • Non-CpG probes (2,932), SNP -related probes as identified by Zhou et.al. (82,108), and chromosome Y probes were removed from the dataset. An additional 6,102 previously identified probes that followed a trimodal methylation pattern characteristic of an underlying SNP were removed.
  • the fraction of immune cell contamination, and the relative proportions of different immune cell subtypes in each sample, were estimated using the EpiDISH algorithm using the epithelial, fibroblast and immune cell reference datasets.
  • the top 1,000 most variable probes (ranked by standard deviation) were used in a principal component analysis. Statistical tests were performed in order to identify any anomalous associations between plate, sentrix position, date of array processing, date of DNA creation, study centre, immune contamination fraction, age, type (case versus control) and the top ten principal components.
  • the inventors divided this joint set into a training (2/3) and validation (1/3) set, by stratifying by age, immune cell proportion (determined by EpiDISH), and community-type (L or O).
  • the resulting distributions can be seen in Fig. 5.
  • the inventors resorted to the logistic regression model implemented in the logistfR. package (version 1.23). This approach fits a logistic regression model using Firth's bias reduction method.
  • the linear WID-LO-index was developed by combining a ranking method, based on a logistic regression model adjusted for age and estimated immune cell fraction, of the CpGs associated with community-type, and an elastic-net regularization path for logistic regression approach for feature extraction (glmnet R package, version 2.0.18).
  • the best classifiers were determined by scanning the ranked list of CpGs (from top to lower rank) and gradually adding a larger input pool of features. For each pool size, the inventors tested 11 values for the glmnet hyperparameter a, ranging from 0 to 1. For the hyperparameter X, the inventors followed the default settings of the package. From the performance profile in the training set, the inventors chose the best with a 10-fold cross- validation resampling algorithm (see for example Fig. 10).
  • NL WID-LO- index which includes a differently ranked pool of input CpGs; in this case the ranks were calculated by the geometric means of the ranks found by five ranking methods, i.e. Welch's test, Bartlett's test, Adjusted logistic regression test (Age+IC), ⁇ method test (described below) and the CellDMC test.
  • Welch's test Welch's test
  • Bartlett's test Adjusted logistic regression test (Age+IC)
  • ⁇ method test described below
  • the geometric mean of all five methods provided a measure of consistency highlighting different distinguishing features, from association with epithelial or immune cells (CellDMC test) to differential variability between community-types (Bartlett's test).
  • this classifier In addition to the methylation values, this classifier also incorporates non-linear terms of second order characterized by the product of the original ⁇ methylation values and the estimated immune cell proportion calculated with EpiDISH, for each subject.
  • a similar parameter scanning strategy used to select the optimal linear classifier described above was also employed for the non-linear case, i.e. the NL WID-LO- index.
  • the WID-LO-index and the NL WID-LO-index share 573 CpGs associated with first order terms.
  • the model with 1,162 selected features (detailed signature not provided), which includes 1,109 unique CpGs, i.e. certain CpGs are used both in linear and non-linear terms, was the one tested in the validation set and in the buccal samples (see for example Fig. 4c and d).
  • the AUC for the ROC curves was used as the performance metric.
  • ROC curves were generated with the pROC R package (version 1.15.3). 95% CI for AUCs were determined by stratified bootstrapping (DeLong's method).
  • the ⁇ ranking method identifies CpGs associated with a signal stemming from epithelial cells by ranking them according to
  • . ⁇ corresponds to the difference in methylation, for a specific CpG, between the y-intercepts at IC 0 for linear regression models generated for the community-type O subgroup and the community-type L subgroup, independently.
  • the abundance patterns plotted in Fig. 6 within each age group and each microbiota community type were clustered by a hierarchical clustering algorithm, hclust in R, by employing the Ward's method.
  • the patterns were scaled column-wise.
  • the species selected for the heatmaps correspond to those belonging to community-type L, i.e. L crispatus. L iners. L gasseri, or L jensenii. in addition to those that ranked highest in terms of average. For all species analysed please see data availability statement.
  • the inventors analyzed the DNA methylation of 448 cervicovaginal smear samples for which the inventors had microbiome data available (see Table 6 for the association between covariates and community-type).
  • the inventors found that methylation differences vary due to immune cell type composition in cases compared to controls and it is therefore important to assess the level of cell type heterogeneity in each cervical smear sample as a first step in the analysis pathway.
  • EpiDISH an algorithm that infers the relative proportion of epithelial cells, fibroblasts, and seven subtypes of immune cells (ICs) in each sample.
  • the estimated cell-type distributions were broadly similar between microbiota community-types (L and O) (Fig. 7). Although the inventors found eosinophils to be higher in O- compared to L-type samples, 95% and 99% of O- and L-type samples in the training set and 91% and 100% of O- and L-type samples in the validation set, contained no eosinophils.
  • the inventors assessed the number of CpGs which were significantly differentially methylated in samples classified as community-type L and community-type O, by applying a logistic regression model adjusted for age and proportion of immune cells (IC, see Methods). After adjustment for multiple comparisons (with False Discovery Rate, qvalue R package, version 2.16.0), 173,245 CpGs showed a significant difference between L- and O-type samples; 109,500 were hyper- and 63,745 were hypo-methylated in O-type samples.
  • the optimum input pool size of features for a linear classifier determined under a penalized logistic regression model (see Methods), was significantly enriched for CpGs that were the furthest away from CpG islands with a considerable over-representation of open sea CpGs (Fig la).
  • the inventors further utilized the eFORGE tool in order to search for enrichment of cell-type specific CpGs in the top 1,000 hyper- and hypom ethylated CpGs. The strongest enrichment was observed in hypo-methylated CpGs for cells that are part of the gastrointestinal tract (Fig. lb).
  • WID-LO-index Wild's risk IDentification Lactobacilli or Other index
  • the inventors used elastic-net, ridge and lasso generalized linear models to classify individuals as community-type L or O (see Methods).
  • the classifiers that included only a linear combination of features, were trained on the training set, which was used for both the optimization of hyper-parameters and optimization of the pool size of input CpG under a cross-validation resampling strategy (see Fig. 8 and 9) and the ROC AUC was used as a measure of performance.
  • the WID-LO-index was developed consisting of 819 CpGs (565 hyper- and 254 hypo-methylated) which were selected by elastic-net logistic regression (see Methods).
  • the association between LO-type and the WID-LO- index was stable after adjustment for age and IC proportion in both age groups of women (Fig. 2d).
  • the 819 group of CpGs comprising the WID-LO index did not show any enrichment for terms under an eFORGE or GSEA analysis.
  • the inventors next sought to assess whether the epigenetic signature, which was derived in cervical smear samples, is also able to correctly classify the cervicovaginal L- or O-type microbiome when analyzed in cells from other anatomical regions.
  • the inventors analyzed the WID-LO-index in buccal (Fig. 4a) and blood (Fig. 4b) samples of 96 women (ages ranging from 18.7 to 69.3 years, median of 38.45 years). As these women were younger, the L-type was more prevalent (76% L-type, 24% O-type) compared to the group of women in the training (52% L-type, 48% O-type) and validation sets (52% L-type, 48% O-type).
  • the inventors included only those CpGs that remained significant after a logistic regression adjusted for age and IC proportion. The inventors did not however consider other methods with which to identify informative CpGs such as those associated with outliers, which the inventors have demonstrated to provide independent information and potentially could capture further systemic epigenetic alterations. Hence, the inventors revisited the training set, applied various ranking algorithms (see Methods) and took the geometric mean across the ranked lists. This final ordered pool was used to once again apply the same three penalized regression strategies, but with high-order terms included (see Methods).
  • the inventors did not explore the molecular mechanisms driving the epigenome-microbiome strong local interaction, the inventors showed, with a non-linear index based on the same principles of the WID-LO-index, that the predictive methylation signal identified in cervicovaginal samples is present in a completely different and unrelated region, which is naturally subjected to different local stressors. This finding enhances the likelihood of a potential underlying systemic causal link, possibly shaped by environmental and hormonal factors, with the epigenome having a clear role in shifting the host cell differentiation towards an environment which facilitates growth of lactobacilli or non-lactobacilli community types.
  • mice demonstrated that the microbiome impacts on the DNA methylome of the host. Future research will need to further assess the interaction between the epigenome and the microbiome in order to find novel strategies for disease prevention.

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Abstract

La présente invention concerne des dosages pour évaluer le type de communauté de microbiote vaginal d'un individu en déterminant le statut de méthylation de certains CpG dans une population de molécules d'ADN dans un échantillon prélevé sur l'individu, en dérivant une valeur d'indice de microbiote vaginal basée sur le statut de méthylation des certains CpG, et en évaluant le type de communauté de microbiote vaginal de l'individu sur la base de la valeur d'indice de microbiote vaginal. L'invention concerne également un procédé de dépistage et/ou de prévention du cancer de l'ovaire chez un individu, le procédé comprenant l'évaluation du type de communauté de microbiote vaginal d'un individu en réalisant les dosages de l'invention, suivie de la réalisation d'un ou plusieurs dépistages du cancer de l'ovaire et/ou de l'administration de traitements préventifs à l'individu sur la base de l'évaluation. La présente invention propose en outre un procédé de surveillance du risque de cancer ovarien chez un individu en fonction des changements de la valeur de l'indice du microbiote du vagin de l'individu au fil du temps. L'invention concerne également des matrices adaptées à la réalisation des dosages de l'invention.
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WO2013033627A2 (fr) * 2011-09-01 2013-03-07 The Regents Of The University Of California Diagnostic et traitement de l'arthrite à l'aide de l'épigénétique
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