WO2021144445A1 - Surveillance de l'évolution d'une tumeur - Google Patents

Surveillance de l'évolution d'une tumeur Download PDF

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WO2021144445A1
WO2021144445A1 PCT/EP2021/050851 EP2021050851W WO2021144445A1 WO 2021144445 A1 WO2021144445 A1 WO 2021144445A1 EP 2021050851 W EP2021050851 W EP 2021050851W WO 2021144445 A1 WO2021144445 A1 WO 2021144445A1
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methylation
seq
tumour
ctdna
sample
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PCT/EP2021/050851
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Andrea SOTTORIVA
Inmaculada SPITERI
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The Institute Of Cancer Research
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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 invention is based on the discovery that the methylation signature of circulating tumour DNA (ctDNA) can be used to determine and monitor tumour status and evolution and that the methylation status of a specific group of polynucleotide sequences is indicative of tumour evolution. Accordingly, the invention relates to methods of determining and tracking tumour evolution using the methylation signature of ctDNA. The invention also relates to methods and kits for determining a phylogenetic relationship between different tumours in a patient and evaluating the effectiveness of treatment.
  • ctDNA circulating tumour DNA
  • Circulating tumour DNA allows tracking the evolution of human cancers at high resolution, overcoming many limitations of tissue biopsies.
  • ctDNA Circulating tumour DNA
  • Profiling of ctDNA almost invariably requires prior knowledge of what genomic alterations to track.
  • the inventors have surprisingly discovered that the methylation profile of ctDNA can be used to track evolutionary changes in the cancer cell population from multiple metastatic sites at single molecule resolution without prior knowledge.
  • the relative frequency of single CpG methylation changes due to methyltransferase errors at cell division compared with DNA point mutations has been leveraged by the inventors.
  • the high methylation error rate means that multiple somatic methylation errors can occur close together in a so-called CpG island, to the extent that more than one CpG change can be found in the same piece of fragmented ctDNA, an event that almost never happens for point mutations and that allows clone haplotyping.
  • This information can be used to track evolutionary changes in the cancer cell population by looking at polynucleotide sequences in ctDNA and montioring shifts in the composition of methylation haplotypes in particular.
  • Profiling somatic (methylation) mutations that are in the same DNA fragment can be done with standard lllumina sequencing (e.g. 150bp pair-ended), thus permitting haplotype reconstruction using CpG methylation changes from standard bisulfite sequencing.
  • Each haplotype from a specific CpG island in the genome is a ‘barcode’ sequence of zeros (non-methylated) and ones (methylated) that corresponds to an identifiable cancer cell lineage that is present in the plasma.
  • the inventors have also found a panel of sensitive polynucleotide sequences which operate as functional methylation clocks in the circulating tumour DNA of cancer patients, in which changes in methylation status are indicative of tumour evolution.
  • Profiling the methylation patterns of these particular loci in ctDNA samples therefore allows tumour evolution to be tracked using a liquid biopsy sample.
  • the present invention provides a method for determining the status and/or composition of at least one tumour comprising; a. providing a liquid biological sample from a subject which comprises ctDNA which originates from the at least one tumour; b. analysing the ctDNA to determine the methylation signature of one or more polynucleotide sequences therein; and c. comparing the methylation signature of the one or more polynucleotide sequences with one or more reference values; and wherein the methylation signature of the one or more polynucleotide sequences and the one or more reference values is indicative of a particular tumour status and/or composition.
  • the one or more polynucleotide sequences comprises SEQ ID NO:
  • SEQ ID NO: 12 [FBX039], SEQ ID NO: 13 [HTR7], SEQ ID NO: 14 [SLCA7] and/or SEQ ID NO: 15 [GPR78] or any combination thereof.
  • the methylation signature of at least one, at least two, at least three, at least four, at least five, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12 or 13 of the above loci are determined.
  • the methylation signature of each of the above loci may be determined.
  • SEQ ID NO: 2 [ZNF454], SEQ ID NO: 3 [ZNF454-short], SEQ ID NO: 4 [NET01], SEQ ID NO: 5 [NET01 -short], SEQ ID NO: 6 [CHAT], SEQ ID NO: 7 [NEUROG2], SEQ ID NO: 8 [TCTEX1D1], SEQ ID NO: 9 [SOX21], SEQ ID NO: 10 [TFAP2B], SEQ ID NO: 11 [GLRA3], SEQ ID NO: 12 [FBX039], SEQ ID NO: 13 [HTR7], SEQ ID NO: 14 [SLCA7] and SEQ ID NO: 15 [GPR78] may be determined.
  • the methylation signature of at least SEQ ID NO: 1 [IRX2] and SEQ ID NO: 2 [ZNF454] are determined, either alone or in combination with one or more of the above loci. It will be appreciated that given the surprising power of the combination of SEQ ID NO: 1 [IRX2] and SEQ ID NO: 2 [ZNF454] in indicating tumour evolution, the methods of the invention may involve determining the methylation signature of SEQ ID NO: 1 [IRX2] and SEQ ID NO: 2 [ZNF454] in the biological sample and comparing the methylation signature of SEQ ID NO: 1 [IRX2] and SEQ ID NO: 2 [ZNF454] in the biological sample with one or more reference values.
  • the methylation signature of SEQ ID NO: 1 [IRX2] and/or SEQ ID NO: 2 [ZNF454] may be investigated in combination with that of any or all of the above polynucleotide sequences in a biological sample.
  • methods of the invention may comprise providing a liquid biological sample from a subject which comprises ctDNA which originates from the at least one tumour; analysing the ctDNA to determine the methylation signature of each of the polynucleotide sequences SEQ ID NO: 1 [IRX2], SEQ ID NO: 2 [ZNF454], SEQ ID NO: 3 [ZNF454-short], SEQ ID NO: 4 [NET01], SEQ ID NO: 5 [NET01 -short], SEQ ID NO: 6 [CHAT], SEQ ID NO: 7 [NEUROG2], SEQ ID NO:
  • ctDNA The methylation signature of the polynucleotide sequences IRX2, ZNF454, NET01, CHAT, NEUROG2, TCTEX1D1, SOX21, TFAP2B, GLRA3, FBX039, HTR7, SLCA7, GPR78 have been found to be informative with samples comprising ctDNA.
  • ctDNA One drawback of ctDNA is that it can fragment into very short polynucleotides (e.g. approximately 80-120 bp in length), and therefore larger quantities of ctDNA may be needed to reconstruct the methylation profile at loci which are longer than the ctDNA fragment size.
  • a longer clock may be selected in large or high-quality samples to increase sequence coverage.
  • a shorter clock may be selected to improve the quality of data from samples where the ctDNA fragments are smaller or less sample and/or ctDNA is available.
  • SEQ ID NO: 3 [ZNF454-short] can be used in place of SEQ ID NO: 2 [ZNF454]
  • SEQ ID NO: 5 [NETOI-short] can be used in place of SEQ ID NO: 4 [NET01]
  • the methods of the invention exploit the methylation signature of polynucleotide sequences of interest in ctDNA in order to reveal information about the status and/or evolution of the tumour from which the ctDNA molecule originates.
  • changes in the methylation signature of the abovementioned polynucleotide sequences are indicative of methylation errors during cell division and therefore reflect changes in tumour status.
  • the degree or level of methylation (whether number or proportion of methylated residues or depth of methylation at a single residue) in a polynucleotide and the pattern of methylation across a polynucleotide may provide useful information.
  • methylation signature may refer to the degree of methylation at the polynucleotide sequence of interest.
  • the methylation signature may refer to the distribution of methylated cytosines across the polynucleotide sequence (i.e. those that have become methylated to form 5-methylcytosine).
  • one or both of the methylation level and pattern of methylated residues may be indicative of a change in tumour status.
  • the degree or level of methylation may refer to the number or proportion of cytosine residues which are methylated across a polynucleotide sequence.
  • the degree or level of methylation may refer to the depth of methylation at an individual cytosine residue across a population of ctDNA sequences.
  • cytosine methylation will accumulate at the ctDNA polynucleotide sequences that have been identified by the inventors as the tumour from which the ctDNA originates evolves. Often it will be simplest to analyse how many cytosine residues are methylated across a given polynucleotide sequence and compare it to the number of methylated cytosine residues in a corresponding control polynucleotide sequence (for example, an unmethylated sequence, a in silico unmethylated haplotype, or that of a tumour biopsy sample or an equivalent liquid biopsy sample taken at an earlier time point).
  • a control polynucleotide sequence for example, an unmethylated sequence, a in silico unmethylated haplotype, or that of a tumour biopsy sample or an equivalent liquid biopsy sample taken at an earlier time point.
  • methylation signature refers to the number of methylated cytosine positions in a polynucleotide sequence of interest.
  • the proportion of different methylation haplotypes in a sample and/or the proportion of unique haplotypes in a sample may also be measured.
  • loci or polynucleotide sequences in the biological sample(s) from the subject are said to be differentially methylated where the degree of methylation at one or more polynucleotide sequences is significantly different from one or more reference values.
  • polynucleotide sequences in the biological sample(s) from the subject are said to be differentially methylated where the degree of methylation at one or more polynucleotide sequences is significantly different from a ctDNA baseline level of methylation, that is, shows more or less cytosine methylation compared to baseline, for example greater or fewer methylated cytosines in a polynucleotide sequence of interest in a ctDNA sample compared with a control ctDNA sample.
  • loci or polynucleotide sequences in the biological sample(s) from the subject are said to be not differentially methylated where the degree of methylation at one or more polynucleotide sequences remains unchanged, or is not significantly increased or decreased compared to one or more reference values but the pattern of methylated nucleotides across the locus is different.
  • the degree of differential methylation at these polynucleotide sequences may be indicative of tumour grade.
  • the degree of differential methylation compared to one or more controls or reference values may also be indicative of the degree of tumour progression. In particular an increase in methylation at one or more polynucleotide sequences when compared to one or more controls or reference values is indicative of tumour progression.
  • a significant increase in methylation is deemed to be an increase of at least 2 methylated residues (i.e. methylated CpG residues) in a polynucleotide sequence of interest compared to a control sample.
  • a significant increase in methylation is deemed to be an increase of at least 3 methylated residues compared to a control sample.
  • a significant increase in methylation is deemed to be an increase of at least 4 methylated residues compared to a control sample.
  • tumour progression may be determined in a biological sample by an increase in methylation level at a polynucleotide sequence, scaled in relation to sample mean and sample variance, relative to a control or reference value.
  • tumour regression may be determined in a biological sample by decrease in methylation level at a polynucleotide sequence, scaled in relation to sample mean and sample variance, relative to a control or reference value.
  • variation in the sensitivity of individual polynucleotide sequences, subject and samples mean that different levels of confidence are attached to each polynucleotide sequence.
  • Methylation of polynucleotide sequences of the invention is said to be significantly increased or decreased when after scaling of methylation levels in relation to sample mean and sample variance, they exhibit a 2-fold change compared with controls or one or more reference values.
  • Preferably polynucleotide sequences of the invention will exhibit a 3-fold change in methylation levels or more compared with the reference value. More preferably polynucleotide sequences of the invention will exhibit a 4-fold change in methylation levels or more compared with the reference value.
  • the level of methylation in the polynucleotide sequence of interest will be more than double that of the control and/or reference value.
  • the methylation level will be more than 3 times the level of the reference value. More preferably, the methylation level will be more than 4 times the level of the reference value.
  • the methylation level will be less than half that of the reference value.
  • the methylation level will be less than one third of the level of the reference value. More preferably, the methylation level will be less than one quarter of the level of the reference value.
  • the reference values are often baseline values, particularly where it is desired to measure the change in methylation signature over time.
  • the baseline reference values can be unmethylated values.
  • fold values for methylation may not be appropriate and absolute values may be employed instead in order to compare the degree or level of methylation (whether this is manifested in the number of methylated residues across a given polynucleotide sequence or the depth of methylation at an individual cytosine residue across a population of ctDNA sequences comprising the same polynucleotide sequence).
  • methylation of polynucleotide sequences of the invention is said to be significantly increased when after scaling of methylation levels in relation to sample mean and sample variance, they exhibit an increase of at least 1 methylated residue compared to a control sample (whether an unmethylated polynucleotide control, in silico unmethylated haplotype or biological sample taken at an earlier time point).
  • a significant increase in methylation is deemed to be an increase of at least 2 methylated residues compared to a control sample.
  • a significant increase in methylation is deemed to be an increase of at least 3 methylated residues compared to a control sample.
  • a significant increase in methylation is deemed to be an increase of at least 4 methylated residues compared to a control sample.
  • the one or more reference values may suitably comprise a methylation signature of a corresponding polynucleotide sequence from one or more control samples.
  • the one or more control samples comprises a panel of in silico unmethylated haplotypes. In this way it is possible to track cancer/tumour evolution without prior knowledge or true biological controls.
  • the one or more control samples includes tumour biopsy samples.
  • tumour biopsy samples may be obtained from primary tumours or secondary tumours.
  • the one or more control samples are breast cancer tumour biopsies.
  • the one or more control samples include a secondary tumour biopsy.
  • the one or more control samples include biopsies from primary and secondary tumours.
  • the one or more control samples include tumour biopsy samples taken before or at the time of administration of an anti-cancer agent.
  • the one or more control samples includes a tumour biopsy sample taken before or at the start of a course of treatment with an anti-cancer agent.
  • the one or more control samples includes a liquid biological sample.
  • the one or more control samples includes a second liquid biological sample taken from a subject at a different time point.
  • the one or more controls includes a liquid biological sample comprising ctDNA taken at a preceding time point to the sample, the methylation profile of the ctDNA in such a control may suitably operate as a ‘baseline’ value to which the methylation profile of the ctDNA in one or more subsequent samples is compared.
  • the one or more control samples may include a liquid biological sample taken before or at the time of administration of an anti-cancer agent.
  • the one or more control samples includes a liquid biological sample taken before or at the start of a course of treatment with an anti-cancer agent.
  • reference value may refer to a pre-determined reference value, for instance specifying a confidence interval or threshold value for the diagnosis of tumour evolution (e.g. progression).
  • the reference value may be derived from the methylation level of a corresponding polynucleotide sequence or sequences in a 'control' biological sample, for example a positive (active) or negative (non-cancerous, inactive or baseline) control, or that of a tumour biopsy sample or an equivalent liquid biopsy sample taken at an earlier time point.
  • the reference value is derived from the methylation level of a corresponding polynucleotide sequence or sequences of ctDNA in a 'control' liquid biological sample comprising ctDNA, taken at an earlier time point than the test sample, the methylation profile of the ctDNA in such a control typically operates as a ‘baseline’ value to which the methylation profile of the ctDNA in one or more subsequent test samples is compared.
  • baseline to which the methylation profile of the ctDNA in one or more subsequent test samples is compared.
  • shifts in the composition of the methylation haplotypes at particular loci can be determined. For example, the number of methylated CpG sites at the IRX2 locus in ctDNA samples isolated from liquid biological samples taken at different time points may be compared.
  • the reference value may be an expected value, for example an expected methylation value which is associated with or indicative of a particular tumour stage or grade.
  • the reference value may be an 'internal' standard or range of internal standards, for example a known methylation level of a polynucleotide sequence. For example, the polynucleotide sequences that have been identified by the inventors will under normal circumstances in healthy tissue remain unmethylated, and cytosine methylation will accumulate at a ctDNA polynucleotide sequence as the tumour from which the ctDNA originates evolves.
  • the reference value may be that of an unmethylated sequence or a in silico unmethylated haplotype.
  • the reference value may be an internal technical control for the calibration of methylation values or to validate the quality of the sample or measurement techniques. This may involve a measurement of the methylation levels of one or several polynucleotide sequences within the sample which are known to be constitutively methylated, un-methylated or methylated at a known level (e.g. an invariant level).
  • the one or more reference values may comprise a methylation signature of a corresponding unmethylated polynucleotide sequence.
  • the one or more reference values may comprise a methylation signature of a corresponding hypermethylated polynucleotide sequence.
  • the one or more reference values may comprise a non zero methylation reference value.
  • the one or more reference values include the methylation signature of the one or more polynucleotide sequences from the ctDNA of a second liquid biological sample from the subject taken at a different time point; and wherein a difference in the methylation signature of the one or more polynucleotide sequences compared to the one or more reference values is indicative of a change in the status and/or composition of a tumour.
  • the method comprises comparing the methylation signature of the one or more polynucleotide sequences of liquid biological samples taken at two time points; wherein the first time point is before administration of a treatment regimen and the second time point is after administration of a treatment regimen.
  • the treatment regime comprises administration of an anti-cancer agent.
  • the anti-cancer agent is an anti-tumour agent.
  • determining the methylation signature comprises determining the degree of methylation of the one or more polynucleotide sequences in the ctDNA.
  • a difference in the degree of methylation of the one or more polynucleotide sequences compared to the one or more reference values is indicative of a change in the status and/or composition of a tumour.
  • a change in the degree of methylation at the polynucleotide sequence of interest is indicative of a change in the tumour.
  • a difference in the degree of methylation of the one or more polynucleotide sequences compared to the one or more reference values is indicative of tumour evolution or progression.
  • a difference in the degree of methylation of the one or more polynucleotide sequences compared to the one or more reference values is indicative of an actively dividing tumour. In various embodiments a difference in the degree of methylation of the one or more polynucleotide sequences compared to the one or more reference values is indicative of an actively metastasising tumour. In various embodiments the difference is an increase in the methylation levels of the one or more polynucleotide sequences in the sample from the subject compared to the one or more reference values.
  • such a difference may be an increase in the number of methylated cytosine positions in the test sample compared with an equivalent polynucleotide sequence from a control ctDNA sample.
  • the increase in the methylation levels of the one or more polynucleotide sequences in the sample from the subject compared to that of a control sample is at least 2 methylated residues, optionally, at least 3 methylated residues or at least 4 methylated residues compared to a control sample.
  • a difference in the degree of methylation of the one or more polynucleotide sequences compared to the one or more reference values is indicative of tumour regression.
  • the difference is a decrease in the methylation levels of the one or more polynucleotide sequences in the sample from the subject compared to the one or more reference values.
  • the reference value is a baseline level of methylation in a polynucleotide sequence of interest from a control ctDNA sample
  • such a difference may be an decrease in the number of methylated cytosine positions in the test sample compared with an equivalent polynucleotide sequence from a control ctDNA sample.
  • the decrease in the methylation levels of the one or more polynucleotide sequences in the sample from the subject compared to that of a control sample is at least 2 methylated residues, optionally, at least 3 methylated residues or at least 4 methylated residues compared to a control sample.
  • the decrease may result in a complete loss of methylation in the test sample, indicating regression, for example in the case of non-dividing tissues, a decrease in methylation from baseline level to complete unmethylated patterns as a consequence of administration of a therapeutic regimen may indicate effective treatment.
  • determining the status of a tumour comprises identifying the grade of a tumour in a subject, wherein the degree of methylation in the methylation signature of the one or more polynucleotide sequences of the same compared to the one or more reference values is indicative of tumour grade.
  • the relative proportions of ctDNA molecules featuring different methylation signatures in the biological sample may be determined, whether they originate from the same or different tumour locations.
  • the origin of the ctDNA molecules in the liquid biological sample i.e. which tumour location the ctDNA molecule originates from
  • the relative proportions of ctDNA molecules featuring different methylation signatures allows the contribution of different cancer cell subpopulations from distinct lesions to be determined. It is therefore possible to see which of potentially several different lesions is actively dividing and/or which cancer cell subpopulation has seeded other tumour locations. This permits treatment to be tailored accordingly.
  • Methylation in this context generally refers to the addition of methyl groups to the DNA molecule.
  • methylation referred to herein is cytosine methylation to 5-methylcytosine, which is preferably in the CG (or CpG) context.
  • the methylation is a CpG methylation.
  • the one or more polynucleotide sequences of the ctDNA comprises at least 1 methylated CpG site, at least 2 methylated CpG sites, at least 3 methylated CpG sites, at least 4 methylated CpG sites at least 5 methylated CpG sites at least 6 methylated CpG sites at least 7 methylated CpG sites at least 8 methylated CpG sites at least 9 methylated CpG sites at least 10 methylated CpG sites at least 11 methylated CpG sites at least 12 methylated CpG sites at least 13 methylated CpG sites at least 14 methylated CpG sites, at least 15 methylated CpG sites, at least 16 methylated CpG sites, at least 17 methylated CpG sites, at least 18 methylated CpG sites, at least 19 methylated CpG sites or at least 20 methylated CpG sites.
  • the one or more polynucleotide sequences of the ctDNA are not hypermethylated, that is to say not all their CpG sites are methylated.
  • the one or more polynucleotide sequences of interest have at least 2 unmethylated CpG sites.
  • the one or more polynucleotide sequences of the ctDNA comprises a maximum of 60%, 65%, 70%,
  • the one or more polynucleotide sequences of the ctDNA comprises at least 1 methylated CpG site and a maximum of 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 98% or 99% CpG methylation.
  • the one or more polynucleotide sequences of the ctDNA comprises at least 1 methylated CpG site and a maximum of 80% methylated CpG sites.
  • the one or more polynucleotide sequences of the ctDNA comprises at least 1 methylated CpG site and a maximum of 80% CpG methylation.
  • the one or more polynucleotide sequences of the ctDNA comprises at least 1 methylated CpG site and a maximum of 80% CpG methylation.
  • the methods of the invention require the detection of methylated residues in the polynucleotide sequences of interest in ctDNA, and in particular cytosine residues which are methylated.
  • the methylation signature of the one or more polynucleotide sequences in the biological sample is determined using bisulfite sequencing.
  • Bisulfite sequencing is well known in the art and involves treatment of DNA (e.g.
  • a number of other suitable techniques exist for determining the methylation status of a locus of interest. These include, for example, other techniques which are based on the selective conversion of unmethylated cytosines to uracil, such as, for example Methyl Seq (NEB), in which, after DNA is mechanically sheared, end repaired, dA tailed and ligated to adaptors, 5-methylcytosines are oxidised to 5- carboxycytosine (and optionally 5-hydroxymethylcytosines are glucosylated to B-glucosyl-5- hydroxymethylcytosine) in order to protect these bases from subsequent deamination. DNA is converted to single stranded DNA (e.g.
  • cytosines i.e. unmethylated cytosines
  • a cytidine deaminase such as apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like (APOBEC).
  • APOBEC catalytic polypeptide-like
  • Profiling somatic (methylation) mutations that are in the same ctDNA fragment can be done with standard sequencing methods, thus permitting haplotype reconstruction using CpG methylation changes from standard bisulfite sequencing.
  • Each haplotype from a specific CpG island in the genome is a ‘barcode’ sequence of zeros (non-methylated) and ones (methylated) that corresponds to an identifiable cancer cell lineage that is present in the plasma. Accordingly, in the methods of the invention the methylation signature of the one or more polynucleotide sequences in the biological sample is investigated using DNA sequencing.
  • this may be achieved via a variety of (high-throughput) DNA sequencing methods, including, but not limited to, for example, Massively parallel signature sequencing (MPSS), Polony sequencing, 454 pyrosequencing, lllumina (Solexa) sequencing, Combinatorial probe anchor synthesis (cPAS), SOLiD sequencing, Ion Torrent semiconductor sequencing, DNA nanoball sequencing, Heliscope single molecule sequencing, Single molecule real time (SMRT) sequencing, Nanopore DNA sequencing or Microfluidic Systems.
  • MPSS Massively parallel signature sequencing
  • Polony sequencing Polony sequencing
  • 454 pyrosequencing lllumina (Solexa) sequencing
  • cPAS Combinatorial probe anchor synthesis
  • SOLiD sequencing SOLiD sequencing
  • Ion Torrent semiconductor sequencing DNA nanoball sequencing
  • Heliscope single molecule sequencing Single molecule real time sequencing
  • Nanopore DNA sequencing or Microfluidic Systems Nanopore DNA sequencing or Microfluidic Systems.
  • the methylation signature of the one or more polynucleotide sequences in the biological sample is investigated using a combination of bisulfite sequencing and standard lllumina sequencing (e.g. 150bp pair-ended), thus permitting haplotype reconstruction using CpG methylation changes from standard bisulfite sequencing.
  • standard lllumina sequencing e.g. 150bp pair-ended
  • the proportion of different methylation haplotypes in a sample and/or the proportion of unique haplotypes in a sample may also be measured.
  • the methods of the invention are carried out in vitro, but it will be appreciated that the methods and assays of the invention are also capable of being carried out in vivo.
  • in vitro is intended to encompass procedures performed with cells or extracts therefrom in culture whereas the term “in vivo” is intended to encompass procedures with/on intact multi-cellular organisms.
  • Each of the methods of the invention may involve obtaining a sample of biological material from the subject, or may be performed on a pre-obtained sample, e.g. one which has been obtained previously for other clinical purposes.
  • Each of the methods and assays of the invention may include the step of processing the sample before analysis of methylation signature is carried out. This may include for example, filtering and/or enriching the sample, for example in ctDNA.
  • Suitable liquid biological samples are those which comprise circulating tumour DNA (ctDNA).
  • circulating tumour DNA circulating tumour DNA
  • ctDNA circulating tumour DNA
  • the liquid biological sample may conveniently be selected from a blood sample, plasma sample, lymph sample, cerebrospinal fluid, urine, sputum, pleural effusion fluid, saliva, or stool sample.
  • a blood sample plasma sample
  • lymph sample cerebrospinal fluid
  • urine sputum
  • pleural effusion fluid saliva, or stool sample.
  • the liquid biological sample is a blood sample or a plasma sample.
  • the tumour is derived from a sarcoma, carcinoma, lymphoma, myeloma, leukaemia or a mixed type.
  • the at least one tumour is selected from a colorectal cancer tumour, a prostate cancer tumour and a breast cancer tumour.
  • the at least one tumour is a breast cancer tumour or glioblastoma multiforme tumour. More preferably, the at least one tumour is a breast cancer tumour.
  • the subject is human or a non-human animal.
  • the subject is a human.
  • the subject is a human having at least one tumour.
  • the subject is a human with a primary tumour.
  • the subject is a human with a metastatic tumour. Even more preferably, the subject is a human having a primary tumour and a secondary tumour.
  • the work presented here amongst other things enables a difference in methylation signature as between two samples to be used as a proxy for a change in status or composition of a tumour and allows tumour evolution to be monitored over time in a non-invasive manner.
  • This offers a comparatively cheap and fast alternative to more invasive and/or expensive screening or monitoring methods or as an initial screen which identifies a need for further investigation, using for example, more invasive techniques.
  • the methods and kits of the present invention may additionally make use of a range of biological samples and/or measurements taken from a subject to further determine or confirm the precise pathological status of the patient or on which to base clinical decisions.
  • the methods of the invention may further involve detecting and/or assaying immunohistochemistry markers from biopsies (e.g. Ki67, ER, PR, HER2) and/or other blood biomarkers (e.g. CA125, CEA, PSA).
  • the methods of the invention may further involve conducting scans (e.g. MRI, CT, DOT, DCS), physiological measurements and/or observations (e.g. oxy-, deoxy-haemoglobin concentration, tissue blood oxygenation, metabolic rate of oxygen, nuclear morphology, CD34 stained mean- vessel-area).
  • the methods of the invention may further comprise imaging the at least one tumour, for example by performing positron-emission tomography (PET), computed tomography (CT) or magnetic-resonance imagining (MRI) of the at least one tumour.
  • PET positron-emission tomography
  • CT computed tomography
  • MRI magnetic-resonance imagining
  • the method may further comprise analysing a biological sample obtained from the subject to determine the levels of one or more biomarkers.
  • the biological sample may be, for example, a tumour biopsy.
  • the biological sample may be, for example, a tissue biopsy.
  • the biological sample may be, for example, a liquid biological sample.
  • the additional measurements may be performed on the same biological sample that the ctDNA was isolated from, further reducing the need for repeated invasive testing.
  • the liquid biological sample is a blood sample, plasma sample, lymph sample, cerebrospinal fluid, urine, sputum, pleural effusion fluid, saliva, or stool sample.
  • the one or more biomarkers is a cell-surface protein.
  • the one or more biomarkers is selected from Ki67, CA125, CEA, PSA.
  • the method further comprises measuring one or more physiological parameters selected from oxy-, deoxy-haemoglobin concentration, tissue blood oxygenation, metabolic rate of oxygen, CD34 stained mean-vessel-area.
  • the method may method further comprise administration of an anti-cancer agent.
  • the anti-cancer agent is an anti-tumour agent.
  • the present invention In addition to monitoring the evolution of a tumour over time by comparing samples which are taken at two or more different time points, advantageously, the present invention also allows the evolutionary history of the tumours in a patient to date to be reconstructed from a single sample by comparing the methylation signature(s) of ctDNA molecules (which originate from different tumour locations) in the same liquid biological sample. This method therefore allows changes that have occurred over time to be reconstructed but from a sample extracted from the patient at a single time point, rather than requiring a comparison between two samples taken at different time points which further reduces the invasiveness of sampling required. Accordingly the present invention provides a method of determining a phylogenetic relationship between tumours from different locations in a subject comprising; a.
  • a liquid biological sample from a subject which comprises a first ctDNA molecule which originates from a first tumour location and a second ctDNA molecule which originates from a second tumour location, wherein the first and second ctDNA molecules originate from tumours in different locations; b. analysing the first ctDNA molecule to determine the methylation signature of at least one polynucleotide sequence therein; c. analysing the second ctDNA molecule to determine the methylation signature of the corresponding at least one polynucleotide sequence therein; and d. comparing the methylation signatures of the at least one polynucleotide sequence of the first and second ctDNA molecules; wherein the degree of difference between the methylation signatures is indicative of the phylogenetic distance between the first and second tumour locations.
  • tumours in different locations in the body can share a common origin.
  • Reconstructing the evolutionary history of the tumours in a patient to date, by comparing the methylation signature(s) of ctDNA molecules (which originate from tumours from different locations) in the same liquid biological sample can provide very useful information about disease evolution and can inform treatment decisions.
  • the origin of the ctDNA molecules in the liquid biological sample (i.e. which tumour the ctDNA molecule originates from) can be established for example, by a regression analysis from which the contribution of different tumour sites to ctDNA can be calculated.
  • the contribution of ctDNA by different tumour sites can be determined using linear regression.
  • Model selection for the linear regression can be performed by carrying out a non-negative least squares estimation, for example, using the nnls R package. For example, firstly, a forward stepwise regression may be performed to introduce a parameter in the model that maximises adjusted R-squared. Then the least significant variable may be removed to recursively remove non-significant (p > 0.05) variables.
  • the method may further comprise comparing the methylation signatures of the at least one polynucleotide sequence of the first and second ctDNA molecules with a first reference value and a second reference value, wherein the first reference value comprises a methylation signature of the corresponding at least one polynucleotide sequence from a sample from a first tumour location and wherein the second reference value comprises a methylation signature of the corresponding at least one polynucleotide sequence from a sample from a second tumour location; wherein the difference between the methylation signature of the respective ctDNA molecules and the reference values is indicative of the origin of the ctDNA. For example a greater similarity in methylation signature indicates a similar origin.
  • determining the status of a tumour may comprise identifying the grade of a tumour in a subject; wherein the degree of methylation in the methylation signature of the one or more polynucleotide sequences of the sample compared to the one or more reference values is indicative of tumour grade.
  • malignant cells are those cells that exhibit multiple characteristics selected from the group consisting of uncontrolled proliferation, evading growth suppressors, avoiding cell death, limitless proliferative capacity (immortality), metastatic capacity and genetic instability. Details of cancer cell properties are described in Hanahan etai, 2011, Cell 144: 646-674.
  • Tumour progression i.e. from a low-grade (benign) tumour to a high-grade (malignant) tumour may be determined if the methylation signature of the one or more polynucleotides of the ctDNA sample exhibits a greater degree of methylation than that of a control.
  • the control value is a baseline level of methylation in a polynucleotide sequence of interest from a control ctDNA sample
  • such a difference may be an increase in the number of methylated cytosine positions in the test sample compared with an equivalent polynucleotide sequence from a control ctDNA sample.
  • the increase in the methylation levels of the one or more polynucleotide sequences in the sample from the subject compared to that of a control sample is at least 2 methylated residues, optionally, at least 3 methylated residues or at least 4 methylated residues compared to a control sample is determinative of tumour progression.
  • tumour regression may be determined by a decrease in the methylation levels of the one or more polynucleotide sequences in the sample from the subject compared to that of a baseline control ctDNA sample, for example a reduction of at least 2 methylated residues, optionally, at least 3 methylated residues or at least 4 methylated residues compared to a control sample. In some cases, the decrease may be manifested in a complete loss of methylation in the test sample.
  • tumour in a subject is used herein to refer to a tumour that does not progress to result in death of the subject, e.g. a benign tumour.
  • a benign tumour may be a premalignant tumour.
  • a "high grade tumour” in a subject is used herein to refer to a tumour that progresses to result in death of the subject, e.g. a malignant tumour.
  • the relative frequency of single CpG methylation changes due to methyltransferase errors at cell division compared with DNA point mutations can be harnessed to track evolutionary changes in the cancer cell population over time by comparing the methylation profiles of polynucleotide sequences in ctDNA molecules from biological samples taken at different time points.
  • the invention also provides a method of detecting changes in the cancer cell population of a subject comprising; a. providing a first liquid biological sample taken at a first time point from a subject which comprises ctDNA; b. analysing the ctDNA of the first sample to determine the methylation signature of one or more polynucleotide sequences therein; c. providing a second liquid biological sample taken at a second time point from the subject which comprises ctDNA; d. analysing the ctDNA of the second sample to determine the methylation signature of the one or more polynucleotide sequences therein; and e.
  • the difference is an increase in the methylation levels of the one or more polynucleotide sequences in the sample from the subject compared to the one or more reference values.
  • the methylation signature of one or more of SEQ ID NO: 1 [IRX2], SEQ ID NO: 2 [ZNF454], SEQ ID NO: 3 [ZNF454-short], SEQ ID NO: 4 [NET01], SEQ ID NO: 5 [NET01 -short], SEQ ID NO: 6 [CHAT], SEQ ID NO: 7 [NEUROG2], SEQ ID NO: 8 [TCTEX1D1], SEQ ID NO: 9 [SOX21], SEQ ID NO: 10 [TFAP2B], SEQ ID NO: 11 [GLRA3], SEQ ID NO: 12 [FBX039], SEQ ID NO: 13 [HTR7], SEQ ID NO: 14 [SLCA7] and/or SEQ ID NO: 15 [GPR78] in ctDNA from a biological sample may conveniently be used as rapid, sensitive and reliable proxies for making clinical decisions, whether alone or in combination with other measures.
  • the invention provides a method of treating a patient with an anti-cancer agent, wherein the patient has at least one tumour, the method comprising the steps of; a. determining the status and/or composition of the least one tumour comprising; b. providing a liquid biological sample from a patient which comprises ctDNA which originates from the at least one tumour; c. analysing the ctDNA to determine the methylation signature of one or more polynucleotide sequences therein; and d. comparing the methylation signature of the one or more polynucleotide sequences with one or more reference values; and e.
  • the one or more reference values include the methylation signature of the one or more polynucleotide sequences from the ctDNA of a second liquid biological sample from the subject taken at a second time point; and wherein a difference in the methylation signature of the one or more polynucleotide sequences compared to the one or more reference values is indicative of a change in the status and/or composition of a tumour.
  • the invention provides a method of treating a patient having at least one tumour, comprising the steps of; a. providing a liquid biological sample obtained from a patient which comprises ctDNA which originates from the at least one tumour; b. analysing the ctDNA to determine the methylation signature of one or more polynucleotide sequences therein; c. comparing the methylation signature of the one or more polynucleotide sequences with the methylation signature of the one or more polynucleotide sequences in a control sample, wherein the control sample is a liquid biological sample which comprises ctDNA and which was obtained from the patient at an earlier time point; and d.
  • the invention provides a method of treating a patient having at least one tumour, wherein the patient is already receiving anti-tumour treatment, comprising the steps of; a. providing a liquid biological sample obtained from a patient which comprises ctDNA which originates from the at least one tumour; b. analysing the ctDNA to determine the methylation signature of one or more polynucleotide sequences therein; c.
  • control sample is a liquid biological sample which comprises ctDNA and which was obtained from the patient at an earlier time point; and d. administering a different therapeutic agent where the level of methylation of the one or more polynucleotide sequences in the biological sample is elevated compared to that the one or more polynucleotide sequences in the control sample; and e. optionally withdrawing the original treatment regime.
  • the invention provides a kit of parts for selectively determining, in a sample, the methylation signature of a polynucleotide sequence
  • the kit comprises: a. at least one binding partner that selectively binds to a polynucleotide selected from SEQ ID NO: 16 [IRX2_BC], SEQ ID NO: 17 [ZNF454_BC], SEQ ID NO: 18 [ZNF454_BC-short], SEQ ID NO: 19 [NET01_BC], SEQ ID NO: 20 [NET01_BC- short], SEQ ID NO: 21 [CHAT_BC], SEQ ID NO: 22 [NEUROG2_BC], SEQ ID NO: 23 [TCTEX1 D1_BC], SEQ ID NO: 24 [SOX21_BC], SEQ ID NO: 25 [TFAP2B_BC], SEQ ID NO: 26 [GLRA3_BC], SEQ ID NO: 27 [FBX039_BC], SEQ ID NO: 28 [HTR7_BC], SEQ ID NO: 16 [IR
  • an agent which is capable of selectively deaminating cytosine to uracil but not 5- methylcytosine c. a DNA polymerase capable of amplifying uracil-containing DNA; d. at least one binding partner that selectively binds to a nucleic acid which operates as an internal control; and e. optionally an internal standard.
  • the binding partners comprise; [IRX2_Fwd] SEQ ID NO: 31 and [IRX2_Rev] SEQ ID NO: 32; or [ZNF454_Fwd (long)] SEQ ID NO: 33 and [ZNF454_Rev (long)] SEQ ID NO: 34 or [ZNF454_Fwd (short)] SEQ ID NO: 35 and [ZNF454_Rev (short)] SEQ ID NO: 36.
  • the agent capable of selectively deaminating cytosine to uracil is sodium bisulfite.
  • the agent capable of selectively deaminating cytosine to uracil but not 5- methylcytosine is sodium bisulfite.
  • Suitable DNA polymerases capable of amplifying uracil-containing DNA are Kapa 2G Robust (manufactured by Kapa Biosystems), AmpliTaq (manufactured by ThermoFisher scientific) and Platinum Taq (manufactured by ThermoFisher scientific).
  • the invention includes the combination of the aspects and preferred features described except where such a combination is clearly impermissible or expressly avoided.
  • in vitro is intended to encompass procedures performed with cells in culture whereas the term “in vivo” is intended to encompass procedures with/on intact multi-cellular organisms.
  • Figure 1 shows genomic profiling analysis of LEGACY patient 1.
  • A Multiple samples from distinct metastatic deposit in different organs were collected from this patient, together with blood germline reference (buffy) and plasma.
  • B Copy number alterations analysis highlights genome-wide copy neutral LOH and overall homogeneous copy number profiles. Median total copy number in 1Mb bins with a minimum mappability score of 0.8.
  • C Single-nucleotide variants analysis identified a clonal PIK3CA driver mutation and convergent evolution for drug resistant ESR1 mutants.
  • D Diagram of inferred genome-wide copy neutral LOH event that can be explained by haploidisation followed by convergent re-diploidisation after few cell divisions.
  • E Mutational signature analysis identifies dominant signature 1A (aging) in all samples.
  • Figure 2 shows clonal evolution analysis of LEGACY patient 1.
  • A Subclonal decomposition allows constructing the tumour clone tree from the sample tree, thus revealing the evolutionary history of the metastatic cascade.
  • B Only 2/12 samples were found to be polyclonal (Liver Met R3 and Ovary Met R2) and indeed exchanged clones between each other. The same clones went to seed distinct metastatic deposit in a monoclonal fashion.
  • C Reconstructed metastatic cascade in breast cancer LEGACY patient 1 shows polyclonal seeding between one liver and one ovary sample, but otherwise early monoclonal seeding was the dominant pattern of metastatic spread.
  • Figure 3 shows genomic profiling and clonal evolution analysis of LEGACY patient 2.
  • A Multiple samples from distinct metastatic deposit in different organs were collected from this patient, together with blood germline reference (buffy) and plasma.
  • B Copy number alterations analysis highlights homogeneous copy number profiles.
  • C Single-nucleotide variants analysis identified clonal PIK3CA and ERBB2 putative driver mutations multiple subclonal truncating mutations in mismatch repair genes that seem to be responsible for the higher number of private mutations in Liver Met R2 and R3.
  • D Mutational signature analysis highlights aging (1A) but also APOBEC (2) as dominant mutational processes.
  • E Clone tree shows early divergence for liver and lung metastases.
  • Figure 4 shows tracking the metastatic cascade in the plasma of LEGACY patients.
  • Figure 5 shows clinical history of LEGACY patients. Chronological clinical history of patient 1 (A) and patient 2 (B).
  • Figure 6 shows genome-Wide LOH in Patient 1.
  • A B-Allele frequency across the genome of LEGACY patient 1 indicates genome-wide loss of heterozygosity.
  • B Allele-specific copy number analysis confirms a largely diploid genome, but with the major allele contributing both copies.
  • C Minor allele heatmap shows that there are zero copies of the minor allele throughput the genome of this patient.
  • Figure 7 shows separate whole genome duplication (WGD) clusters in Patient 1. There were two separate clusters of SNVs that occurred before whole-genome reduplication but were not truncal, rather they were present in all primary and lymph node samples and in all metastases separately. The VAF-VAF distributions show the two clusters are entirely distinct.
  • Figure 8 shows mutational signature analysis for Patient 1.
  • the dominant mutational process in this patient is cytosine deamination, which is the result of aging.
  • Figure 9 shows re-analysis of Hoadley etal. 2016 using a stricter and more conservative bioinformatics analysis and confirmation that in both patients all lesions were of monoclonal origin. Previously inferred polyclonality was supported by few mutant reads of unclear significance and confounded by missed copy number events.
  • Figure 10 shows sample tree for patient 1. This was reconstructed the phylogenetic tree using parsimony prior to subclonal deconvolution (sample tree).
  • Figure 11 shows 2D Cancer Cell Fraction plots of Patient 1. All mutational clusters plotted for all samples from LEGACY patient 1 following sciClone analysis.
  • Figure 12 shows 2D Cancer Cell Fraction plots of Patient 2. All mutational clusters plotted for all samples from LEGACY patient 2 following sciClone analysis.
  • Figure 13 shows CCF heatmaps including ctDNA. Heatmap of CCF clusters that includes ctDNA as a sample for Patient 1 (A) and Patient 2 (B). Patterns reflect the analysis in Figure 4.
  • Figure 14 shows linear methylation over time in clocks.
  • Autopsy samples from healthy tissues from donors of different age shows linear accumulation of methylation errors in fast dividing epithelial tissues such as colon, but not in non-dividing or slowly dividing tissues like heart and brain.
  • Figure 15 shows quality control of the methylation haplotypes. Hypomethylated (in our case completely unmethylated) haplotypes are probably deriving from non-cancer cells and were excluded from our analysis. Saturated (>80% methylated) haplotypes were also excluded.
  • Figure 16 shows percent methylation for each CpG per sample. For each CpG in each clock we plot the average methylation in each sample. Whereas for Patient 1 the data showed a good signal of haplotype variability in both IRX2 (A) and ZNF454 (B), for Patient 2 the patterns were characterised by hypermethylation in almost all CpGs in both IRX2 (C) and ZNF454 (D). Patient 1 was filtered for hypo- and hypermethylation. To represent hypermethylation, Patient 2 is only filtered for hypo-methylation.
  • Figure 17 shows correlation between haplotype frequencies from an orthogonal cohort.
  • ctDNA circulating tumour DNA
  • Example 1 Sample collection, preparation and whole-genome sequencing
  • Samples were retrieved from LEGACY patients within 4 hours of death. Large (6-8 mm) punch biopsies of solid organ metastases or whole lymph nodes were rapidly sampled, split and snap frozen in liquid nitrogen and stored at -80 degrees Celsius prior to nucleic acid extraction and sequencing. Adjacent samples from the same sites were preserved in 10% phosphate buffered formaldehyde and processed for paraffin embedding and routine haematoxylin and eosin staining and immunohistochemistry. In preparation for nucleic acid extraction frozen section analysis of slices flanking the samples were also stained to assess the proportion of tumour for the entire extract.
  • Example 2 Standard bioinformatics analysis of whole-genome sequencing and phylogenetic analysis
  • FASTQ files are trimmed for adaptor content using skewer (Jiang etal., BMC Bioinformatics 15: 182 (2014)) with a minimum length allowed after trimming of 35bp, only on reads with a minimum mean quality of 10 and with the filter to remove highly degenerative reads (-I 35 -Q 10 -n). Trimmed reads are then aligned to hg38 (GRCh38) using bwa mem (Li etal., Bioinformatics 25: 2078-2079 (2009)). Sam files are sorted, compressed to bam files and duplicates marked using Picard tools (http://broadinstitute.github.io/picard/). Bam files can be indexed using samtools (Li etal., Bioinformatics 25: 1754-1760 (2009)).
  • Mutations in each sample are called first using Mutect2 (Cibulskis etal., Nature Biotechnology 31: 213-219 (2013)). Variants are only kept if the coverage in both the tumour and normal tissue is greater than 10 reads and the variant was present in three or more reads in the tumour. The variant must have the genotype “0/0” in the normal tissue but not in the tumour. Mutations with the flag ‘artifact_in_normal’ are kept but variants called in each tumour sample are removed if their variant allele frequency (VAF) is less than ten times greater than in the normal blood sample.
  • VAF variant allele frequency
  • Per sample results are then merged and used as input for Platypus (Rimmer et a!., Nature Genetics 46: 912-918 (2013)) that is run in genotyping mode.
  • the following filtering criteria cane be used to filter variants after platypus genotyping: i) only variants with Platypus filter PASS, alleleBias, Q20, QD, SC and HapScore arre kept, ii) minimum coverage and genotype quality of 10 is required for all samples iii) minimum of 3 reads covering the variant in at least one of the tumour samples per patient is required, iv) the highest VAF in the tumour samples must be 10 times greater than the VAF in the normal tissue, and v) the germline sample must have a genotype of “0/0” and at least one tumour sample must not have a genotype of “0/0”. Variants are annotated using VEP (McLaren etal., Bioinformatics 26: 64-70 (2014)).
  • Sequenza is used to identify heterozygous germline single nucleotide polymorphisms (SNPs) in the bam files (0.4-0.6 allele frequency in the matched normal sample) and normalise depth ratios for GC content (Favero et a!., Annals of Oncology 26: 64-70 (2014). Loci are filtered for a minimum of 25 reads in the matched normal sample. Log2 ratios (LRR) are derived from the depth ratios by calculating the log (base 2) of the depth ratio and subtracting by the global median. LRR outliers were smoothed using CGHcall (van de Wiel etal., Bioinformatics 23: 892- 894 (2007)).
  • CGHcall van de Wiel etal., Bioinformatics 23: 892- 894 (2007).
  • PCF piece-wise constant fitting
  • the fixed standard deviation used for the Kolmogorov- Smirnov test is estimated from an initial pass of two-component Gaussian mixture modelling on all segments and also is used to restrict a grid search of parameters for modelling allelic imbalance (code available as an R package atwww.github.com/georgecresswell/MiMMAI).
  • CCF cancer cell fraction
  • Mutations with a CCF of 0.2 or greater were used to calculate mutational signatures using deconstructSigs (Rosenthal etai, Genome Biology 17: 31 (2016)) using the signatures from Alexandrov etai, 2013 as a reference and a minimum signature contribution of 0.05 (Alexandrov etai, Nature 500: 415-421 (2013)).
  • Mutations are filtered for clonal diploid regions and mappability to ensure the mutations are reliable prior to inferring potential subclones.
  • the average mappability of the region is calculated ⁇ 25bp of each mutation from a bigWig generated for hg38 using the gem- mappability tool in the GEM Iibrary50 and bigWigAverageOverBed (Kent etal., Bioinformatics 26: 2204-2207 (2010)). In the examples below the average mappability value in the bins was required to be 1 (uniquely mappable). Mutations that overlapped with simple repeats and low complexity regions in accordance with annotations from RepeatMasker (http://www.repeatmasker.org) were removed.
  • SciClone was run with a maximum number of clusters determined as twice the number of tumour samples, minus one. This is the number of edges in a tree of double the number of samples taken without private edges (i.e. SciClone should be able to detect a maximum of two subclones in all samples).
  • Ovary Met R2 and Liver Met R3 were polyclonal. Mutations unique to the major clone in Liver Met R3 were defined as belonging to kmeans cluster 8, mutations unique to the minor clone in the sample were defined as kmeans cluster 7.
  • IRX2 a 205 bp locus containing 9 CpGs
  • ZNF454 a 133 bp locus bearing 16 CpGs
  • IRX2 a 205 bp locus containing 9 CpGs
  • ZNF454 a 133 bp locus bearing 16 CpGs
  • ZNF454 a 133 bp locus bearing 16 CpGs
  • ZNF454 a shorter version of ZNF454 containing 14 CpGs but which is amenable to ctDNA analysis where DNA fragments are particularly small was also profiled.
  • 50ng of genomic DNA was bisulfite- converted using the EZ Methylation Direct kit (Zymo) following the manufacturer’s recommendations.
  • the resulting ssDNA was quantified with the Qubit ssDNA assay (Invitrogen).
  • the molecular clocks were PCR amplified using the following primers: IRX2-fwd: GTAT ATTTT GTT AGG
  • TTCTATTACCTTCCAAACCTTTT (SEQ ID NO: 36).
  • ssDNA was used to amplify each molecular clock in 25mI PCR reactions made up from 12.5mI of Kapa2G Robust ready mix (Kapa), 2mI of the forward and reverse primer mix (10mM), 1 mI of MgCI2 (50mM),
  • PCR reaction 1 5mI of DNA and 8mI of DNase/RNase free water.
  • the thermal profile of the PCR reaction was as follows: 95°C/2min, [95°C/10sec, 58°C/10sec, 72°C/35sec]x30, 72°C/2min.
  • PCR products were purified with the QIAquick kit (Qiagen) and quantified with the Qubit dsDNA HS assay (Invitrogen).
  • NEBnext Ultra II kit 25mI (approximately 10-30ng) from each pool was mixed with 1.5mI of the NEBnext Ultra II End Prep Enzyme Mix and 3.5mI of the NEBnext Ultra II End Prep Reaction buffer. After 30-minute incubation at 20°C followed by 30-minute incubation at 65°C, 1 25mI of the NEBnext Adaptor (diluted 5-fold) was added to the reactions.
  • NEBnext Ultra II Ligation Master Mix 15mI
  • NEBnext Ligation Enhancer (1mI) were added to the previous reaction mixtures and incubated at 20°C for 15-minutes. Further, 1.5mI of USER enzyme was added to the ligation mixtures and then incubated at 37°C for 15 minutes. The reactions were then purified with 1.4X SPRI-beads prior to PCR amplification. PCR reactions were prepared with: 10.5mI of ligated DNA fragments, 12.5mI of NEBnext Ultra II Q5 Master Mix, 1mI of i7-NEBnext Dual-Index Primer, 1mI of i5- NEBnext Dual-Index Primer.
  • PCR cycling conditions were as follows: 98°C/30sec, [98°C/10sec, 65°C/75sec]x6, 65°C/5min.
  • Libraries were purified with 1.2X SPRI-beads and quality control was done on DNA-HS Bioanalyser Chips (Agilent). Finally, equimolar amounts of each library were mixed and the resulting pool was sequenced using MiSeq 300-cycle. v2 reagents (lllumina).
  • FASTQ files from the methylation clock assays were trimmed for adaptor content using skewer (Jiang etai, BMC Bioinformatics 15: 182 (2014)) with a minimum length allowed after trimming of 35bp, only on reads with a minimum mean quality of 10 and with the filter to remove highly degenerative reads (-I 35 -Q 10 -n). Paired reads were then aligned to hg19 using Bismark53. Bismark methylation extractor was then used to extract methylation states of possible CpG sites from the original top and bottom strands. For each molecular clock loci called CpGs are identified.
  • CpGs are used for analysis if they have a total number of calls for a position (methylated plus unmethylated) greater than or equal to 1,000. Reads with a call missing for a genomic position that passes this coverage filter are removed to leave only complete reads with a CpG call on all locations.
  • each tumour sample reads are required to have at least 1 methylated CpG site and reads can only have a maximum of 80% methylation, to remove reads that are likely produced by cells that have a low turnover (normal cells) and clocks that have reached saturation and are therefore noninformative, respectively.
  • remaining haplotypes (reads) with an overall abundance of 1% or less are removed due to their rarity.
  • 100 random haplotypes are selected with replacement and an additional set of 100 ‘synthetic’ unmethylated haplotypes are created as a reference for each case.
  • a similarity measurement is then performed pairwise between tumour samples and the unmethylated reference as used previously (Sottoriva etai, Cancer Res. 73: 41-49 (2013)).
  • the Hamming distance of each haplotype combination between the two samples is measured and the shortest distance of all these combinations is recorded and the haplotype pair is removed from consideration. This is performed iteratively until all haplotype pairs have been removed and the Hamming distances of the chosen pairs is summed. This similarity measurement between all tumour samples and the unmethylated reference is used to create a Neighbour Joining tree using phangorn (Schliep etai, PLoS ONE 7: e30377 (2012)).
  • the WGS bam files are processed identically to the corresponding tissue sample WGS bam files to generate bam files and copy number profiles to enable direct comparison. Mutations called in the tissues samples are then searched for in the ctDNA bam file using platypus in genotyping mode. Copy number analysis is used to determine local copy number and purity for calculating CCF as described in Example 2 and used to perform a linear regression to determine organ contributions to the ctDNA. Unfortunately in Patient 2 the callSubclones function in Battenberg failed, therefore the purity was estimated from the ASCAT fit generated by the fit.copy. number function and we subset only clonal diploid regions in the tissue samples to assess organ contributions.
  • methylation clock sequencing was employed (as described in Examples 4 and 5) the same regression analysis was performed as for whole-genome sequencing to determine the contribution of each tumour site to the ctDNA. Frequencies of haplotypes were calculated after filtering for a minimum of 1 methylated site and less than 80% methylation and used for linear regression. The contribution of ctDNA by different tumour sites was determined using linear regression. Model selection for the linear regression was performed by carrying out a non negative least squares estimation using the nnls R package. Firstly, a forward stepwise regression was performed to introduce a parameter in the model that maximises adjusted R- squared. Then the least significant variable was removed to recursively remove non-significant (p > 0.05) variables.
  • the predominant mutational signature in all sites was Signature 1A and the only other detectable signature in the patient was Signature 2 (APOBEC) in the breast, lymph nodes and a single ovary sample (R2) indicating low levels of early APOBEC activity that may be diluted by an increasing mutational burden.
  • Panel E of figure 1 shows the mutational signature analysis that identifies the dominant signature as signature 1A (aging) in all samples.
  • Example 8 Parsimonious reconstruction of the metastatic cascade in Patient 1
  • Subclonal decomposition allows construction of the tumour clone tree from the sample tree, thus revealing the evolutionary history of the metastatic cascade as illustrated in Figure 2A.
  • Parsimonious reconstruction of the metastatic cascade in Patient 1 revealed a relatively simple dissemination pattern where a single lineage from the primary tumour seeded all distant metastases very early during the evolution of the tumour ( Figure 2A and Figure 10). Lymph node dissemination also occurred early but independently. Early dissemination has been documented before both in breast (Barry et ai, Clin. Cancer Res. doi:10.1158/1078-0432. CCR- 17-3374 (2016)) and colon cancer (Hu et ai, Nature Genetics 24: 1 (2019)). Hence, dissemination to the large majority of sites was consistent with monoclonal seeding.
  • Example 9 Identification of neoantigens in metastatic primary tumour and metastatic sites
  • HLA type was classified for each region and then the putative number of neoantigens estimated.
  • Polysolver V1.0 was run on each of the BAM files (generated during whole-genome sequence analysis) coming from the different sites of Patient 1 to predict the HLA type.
  • Pvactools v1.4.4 was run using the previously classified HLA types to identify the putative neoantigens present in each region. Both tools were run using default parameters. It was observed that the HLA locus of Patient 1 was homozygous for most of the regions (11/14), confirming the previous observation of a haploidization event across the genome ( Figure 6 and Table 2).
  • Second-line chemotherapy epirubicin plus cyclophosphamide, 5 cycles
  • Tumour markers fluctuating.
  • Months 16-30 Third-line chemotherapy (paclitaxel) o tumour marker and PET/CT response until Month 30
  • Single-nucleotide variants analysis identified clonal PIK3CA and ERBB2 putative driver mutations and multiple subclonal truncating mutations in mismatch repair genes that seem to be responsible for the higher number of private mutations in Liver Met R2 and R3. Specifically, this single-nucleotide variants analysis revealed a clonal PIK3CA G1049R and a clonal ERBB2 E770_A771insAYVM mutation (Figure 3C). Chromosome 13 loss was identified in all samples ( Figure 3B).
  • the para-aortic lymph node samples appear to have expanded from the lung metastases which may reflect the lymph drainage pattern from the lung.
  • Liver Met R3 diverged early from the other metastatic samples and Liver Met R2 contained the highest burden of private mutations perhaps due to increased APOBEC expression and ERCC3 mutation.
  • each metastatic site appeared monoclonal in origin ( Figure 3E). Purity and ploidy data are available from Table 1. Copy number, SNV calling and clustering assignment information were also determined.
  • Example 11 Circulating tumour DNA reflects dynamics of metastasis and treatment resistance
  • Example 12 Tracking clonal dynamics in ctDNA at single-molecule resolution using methylation clocks
  • haplotype reconstruction using CpG methylation changes from standard bisulfite sequencing.
  • Each haplotype from a specific CpG island in the genome is a ‘barcode’ sequence of zeros (non-methylated) and ones (methylated) that corresponds to an identifiable cancer cell lineage that is present in the plasma.
  • passenger methylation haplotypes can be used to track the ancestry of normal somatic cells by leveraging on epigenetic drift (Yatabe et al., Proc. Natl. Acad Sci. U.S.A. 98: 10839-10844 (2001), Nicolas etal., PLoS Comput.
  • the idea of using somatic variation as a molecular clock has been ‘rediscovered’ more recently with next-generation sequencing (Alexandrov etal., Genetics 47: 1402-1407 (2015)).
  • the resolution of methylation clocks is several orders of magnitude higher than point mutation clocks, such as mutational signature 1 (aging) as the mutation rate is orders of magnitude larger (Shibtata etal, Carcinogenesis 32: 123-128 (2011)).
  • polynucleotide sequences have been developed which operate as functional methylation clocks in circulating tumour DNA in the plasma of cancer patients.
  • IRX2 a 205 bp locus containing 9 CpGs
  • ZNF454 a 133 bp locus bearing 16 CpGs
  • Both IRX2 and ZNF454 clocks were discovered to be amenable to methylation analysis in ctDNA samples.
  • a new PCR assay was designed to allow for a shorter version of these clocks and their use in analysis of ctDNA (see Examples 4 and 5).
  • a shorter version of the ZNF454 methylation clock has been developed which is more amenable to ctDNA analysis where, for example, ctDNA fragments are particularly small.
  • a shorter version of NET01 has also been developed.
  • High-depth targeted bisulfite sequencing was performed using the IRX2 and ZNF454 methylation clocks (median 16,000X for ZNF454 and 22,000X for IRX2) in a total of 13 tissue samples and the ctDNA of LEGACY Patient 1.
  • Methylation clock analysis demonstrates the contribution of metastatic sites to ctDNA in LEGACY patient 1 using two clock-like methylation regions ZNF454 and IRX2 that have been previously validated as subject to methylation drift.
  • a large number of methylation haplotypes were identified that did not show signs of saturation and could therefore be used to trace ancestry.
  • the contamination from non-cancer cells e.g.
  • lymphocytes in cell free DNA in the blood of cancer patients can be filtered out as blood cells have extremely low methylation levels in those clocks and hence unmethylated haplotypes were excluded from the analysis.
  • Methylation levels from the tissue were conserved in the ctDNA, indicating that methylation clocks are a stable marker of cell fate.
  • the same regression method was applied that was used to calculate the contribution of different sites to ctDNA from whole-genome sequencing data. It was found that the methylation clocks largely recapitulated what was reported for whole- genome sequencing, confirming that metastatic disease in the liver dominated plasma DNA (Figure 4C,D). This demonstrates the power of methylation clocks in trace ancestral cell populations applied for the first time to ctDNA.
  • Example 13 Methylation clock analysis of ctDNA in patients with lymph node metastasis
  • Example 14 Summary statistics, sensitivity a) Summary statistics for methylation patterns
  • Proportion of distinct methylation patterns calculate proportion of different methylation haplotypes in a sample.
  • Proportion of singletons patterns calculate proportion of haplotypes that are unique in a sample.
  • metastatic progression is instead often a tractable problem with the right data and models. Unravelling the metastatic cascade can be interesting biologically, but may also be exploited in a clinical setting.

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Abstract

L'invention concerne des procédés de détermination et de suivi de l'évolution d'une tumeur à l'aide de la signature de méthylation de l'ADNtc. L'invention concerne également des procédés et des kits pour déterminer une relation phylogénétique entre différentes tumeurs chez un patient et évaluer l'efficacité de régimes thérapeutiques.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114606316A (zh) * 2022-03-12 2022-06-10 中山大学肿瘤防治中心（中山大学附属肿瘤医院、中山大学肿瘤研究所） 一种nk/t细胞淋巴瘤早期诊断和预后预测模型构建方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018027176A1 (fr) * 2016-08-05 2018-02-08 The Broad Institute, Inc. Procédés pour la caractérisation génomique
WO2018119452A2 (fr) * 2016-12-22 2018-06-28 Guardant Health, Inc. Procédés et systèmes pour analyser des molécules d'acide nucléique
WO2018209361A2 (fr) * 2017-05-12 2018-11-15 President And Fellows Of Harvard College Diagnostic de cancer précoce universel
WO2019006269A1 (fr) * 2017-06-30 2019-01-03 The Regents Of The University Of California Procédés et systèmes d'évaluation de la méthylation de l'adn dans l'adn acellulaire
WO2019084659A1 (fr) * 2017-11-03 2019-05-09 University Health Network Détection, classification, pronostic, prédiction de thérapie et surveillance de thérapie du cancer à l'aide d'une analyse du méthylome

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018027176A1 (fr) * 2016-08-05 2018-02-08 The Broad Institute, Inc. Procédés pour la caractérisation génomique
WO2018119452A2 (fr) * 2016-12-22 2018-06-28 Guardant Health, Inc. Procédés et systèmes pour analyser des molécules d'acide nucléique
WO2018209361A2 (fr) * 2017-05-12 2018-11-15 President And Fellows Of Harvard College Diagnostic de cancer précoce universel
WO2019006269A1 (fr) * 2017-06-30 2019-01-03 The Regents Of The University Of California Procédés et systèmes d'évaluation de la méthylation de l'adn dans l'adn acellulaire
WO2019084659A1 (fr) * 2017-11-03 2019-05-09 University Health Network Détection, classification, pronostic, prédiction de thérapie et surveillance de thérapie du cancer à l'aide d'une analyse du méthylome

Non-Patent Citations (39)

* Cited by examiner, † Cited by third party
Title
"The Cancer Genome Atlas, Nature", vol. 490, 2012, pages: 61 - 70
ALEXANDROV ET AL., GENETICS, vol. 47, 2015, pages 1402 - 1407
ALEXANDROV ET AL., NATURE, vol. 500, 2013, pages 415 - 421
BARRY ET AL., CLIN. CANCER RES., 2018
BENAGLIA ET AL., J. STAT. SOFT., vol. 32, 2009
CHRISTOPHER ABBOSH ET AL: "Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution", NATURE, vol. 545, no. 7655, 26 April 2017 (2017-04-26), London, pages 446 - 451, XP055409582, ISSN: 0028-0836, DOI: 10.1038/nature22364 *
CIBULSKIS ET AL., NATURE BIOTECHNOLOGY, vol. 31, 2013, pages 213 - 219
CRESSWELL: "Mapping the breast cancer metastatic cascade onto ctDNA using genetic and epigenetic clonal tracking", NAT COMMUN, vol. 11, 27 March 2020 (2020-03-27), pages 1446, Retrieved from the Internet <URL:https://doi.org/10.1038/s41467-020-15047-9>
CURTIS ET AL., NATURE, vol. 486, 2012, pages 346 - 352
DENTRO ET AL., COLD SPRING HARBOUR PERSPECT MED, vol. 7, 2017, pages a026625
FAVERO, ANNALS OF ONCOLOGY, vol. 26, 2014, pages 64 - 70
HANAHAN ET AL., CELL, vol. 144, 2011, pages 646 - 674
HU ET AL., NATURE GENETICS, vol. 24, 2019, pages 1
ISAAC GARCIA-MURILLAS ET AL: "Mutation tracking in circulating tumor DNA predicts relapse in early breast cancer", SCIENCE TRANSLATIONAL MEDICINE, vol. 7, no. 302, 26 August 2015 (2015-08-26), US, pages 302ra133 - 302ra133, XP055440860, ISSN: 1946-6234, DOI: 10.1126/scitranslmed.aab0021 *
JIANG ET AL., BMC BIOINFORMATICS, vol. 15, 2014, pages 182
KENT ET AL., BIOINFORMATICS, vol. 26, 2010, pages 2204 - 2207
LI ET AL., BIOINFORMATICS, vol. 25, 2009, pages 1754 - 1760
MCLAREN ET AL., BIOINFORMATICS, vol. 26, 2014, pages 64 - 70
MCPHERSON ET AL., NATURE GENETICS, vol. 48, no. 7, 2016, pages 758 - 767
NICOLAS ET AL., PLOS COMPUT. BIOL., vol. 3, 2007, pages e28
NIK-ZAINAL ET AL., CELL, vol. 149, 2012, pages 994 - 1007
NILSEN ET AL., BMC GENOMICS, vol. 13, no. 1, 2012, pages 591
O' LEARY, CANCER DISCOVERY, vol. 8, 2018, pages 1390 - 1403
RIMMER, NATURE GENETICS, vol. 46, 2013, pages 912 - 918
ROSENTHAL ET AL., GENOME BIOLOGY, vol. 17, 2016, pages 31
ROSENTHAL ET AL., NATURE, vol. 567, 2019, pages 479 - 485
SCHLIEP ET AL., PLOS ONE, vol. 7, 2012, pages e30377
SHIBATA D.: "Mutation and epigenetic molecular clocks in cancer", CARCINOGENESIS, vol. 32, no. 2, 12 November 2010 (2010-11-12), GB, pages 123 - 128, XP055794652, ISSN: 0143-3334, DOI: 10.1093/carcin/bgq239 *
SHIBATA ET AL., J. PATHOL, vol. 217, 2009, pages 199 - 205
SHIBTATA ET AL., CARCINOGENESIS, vol. 32, 2011, pages 123 - 128
SIEGMUND ET AL., CELL CYCLE, vol. 8, 2009, pages 2187 - 2193
SIRAVEGNA ET AL., NAT REV CLIN ONCOL, vol. 14, 2017, pages 531 - 548
SOTTORIVA ET AL., CANCER RES., vol. 73, 2013, pages 41 - 49
SOTTORIVA ET AL., CANCER RESEARCH, vol. 73, 2013, pages 41 - 49
STEELE ET AL., CANCER CELL, vol. 35, 2019, pages 346 - 352
TSAO ET AL., PROC. NATL. ACAD. SCI. USA, vol. 97, 2000, pages 1236 - 1241
VAN DE WIEL ET AL., BIOINFORMATICS, vol. 23, 2007, pages 892 - 894
YATABE ET AL., PROC. NATL. ACAD SCI. U.S.A., vol. 98, 2001, pages 10839 - 10844
ZHANG ET AL., CELL, vol. 173, no. 7, 2018, pages 1755 - 1769

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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