[go: up one dir, main page]

WO2012116019A1 - Compositions et procédés de détection du cancer de la vessie - Google Patents

Compositions et procédés de détection du cancer de la vessie Download PDF

Info

Publication number
WO2012116019A1
WO2012116019A1 PCT/US2012/026038 US2012026038W WO2012116019A1 WO 2012116019 A1 WO2012116019 A1 WO 2012116019A1 US 2012026038 W US2012026038 W US 2012026038W WO 2012116019 A1 WO2012116019 A1 WO 2012116019A1
Authority
WO
WIPO (PCT)
Prior art keywords
methylation
bladder cancer
level
cancer
cpg dinucleotide
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2012/026038
Other languages
English (en)
Inventor
Carmen MARSIT
Karl KELSEY
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Brown University
Original Assignee
Brown University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Brown University filed Critical Brown University
Publication of WO2012116019A1 publication Critical patent/WO2012116019A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • 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
    • C12Q2600/154—Methylation markers

Definitions

  • the present invention relates to compositions and methods related to the detection of bladder cancer.
  • bladder cancer The highly successful treatment of bladder cancer comes at great economic burden to the healthcare system, with lifetime monitoring and treatment making bladder cancer one of the most expensive of all cancers, with diagnosis to death per patient costs ranging from $96,000 to $187,000, accounting for almost 3.7 billion U.S. dollars (2001 dollars) in direct costs to the U.S. medical system each year (Botteman et al., Pharmacoeconomics 21:1315-1330, 2003).
  • Tobacco carcinogen exposure through active smoking is the main established risk factor for bladder cancer; but the attributable risk is far less than for lung cancer, and much of the etiology of bladder cancer remains unclear (Kogevinas et al., Textbook of Cancer Epidemiology, London, Oxford University Press, 2002, pgs. 446-66).
  • Other major risk factors for bladder cancer include occupational exposures, particularly aromatic amine and Via EFS Attorney Docket No.: 35947 -005001 WO
  • the profiles of epigenetic change that are found to be associated with disease may reflect genetic or environmental factors (or their interaction) that establish these gene regulatory marks in a fashion that results in disease susceptibility.
  • genomic CpG dinucleotide sequences associated with bladder cancer As disclosed herein, the methods of the invention have numerous diagnostic and prognostic applications.
  • methods for diagnosing bladder cancer or a susceptibility of developing the cancer, comprising providing a DNA from a non- bladder tissue and detecting methylation of a CpG locus, wherein the locus is located withinl kilobase of a transcription-factor binding site related to immune modulation, and oncogenic transcription factor binding site, or a forkhead family member.
  • the non-bladder tissue comprises peripheral blood.
  • the locus is located within a gene selected from the group consisting of NALP4 (e.g., GenBank Accession number AF442488 (GI: 17064171), incorporated herein by reference), BDKRB1 (e.g.,
  • GenBank Accession Number AY275464 (GI: 18105039), incorporated herein by reference
  • C14ORF103 see, e.g., GenBank Accession Number CM000265 (GL74273668)
  • COX7C e.g., GenBank Accession Number NM_001867 (GL18105039), incorporated herein by reference
  • ZNF322B e.g., GenBank Accession
  • XM_003403446 GL341915480
  • HIGD2A e.g., GenBank Accession Number NM_138820 (GL52851396), incorporated herein by reference
  • TBCA e.g., GenBank Accession Number NM_004607 (GL94421476), incorporated herein by reference
  • BRD7 e.g., GenBank Accession Number AF152604 (GL8452873)
  • PSME2 e.g., GenBank Accession Number
  • NM_002818 (GL30410791), incorporated herein by reference).
  • peripheral blood other bodily fluids such as urine, saliva, and sputum can be tested.
  • the diagnostic methods are non-invasive, a significant advantage over other methods that require biopsied tissues.
  • methods are provided for identification of differentially methylated genomic CpG dinucleotide sequences associated with bladder Via EFS Attorney Docket No.: 35947 -005001 WO
  • the method comprising: (a) obtaining a biological sample comprising genomic DNA from the individual; (b) measuring in the sample the level of one or more methylated genomic CpG dinucleotide sequences in one or more loci selected from the group consisting of NALP4, BDKRBl, C14ORF103, COX7C, ZNF322B, HIGD2A, TBCA, BRD7, and PSME2, and (c) comparing the level of methylation at the one or more genomic CpG dinucleotide sequences in the sample to a reference level of methylated genomic CpG dinucleotide sequences, wherein a difference in the level of methylation of the one or more genomic CpG dinucleotide sequences in the sample compared to the reference level identifies differentially methylated genomic CpG dinucleotide sequences associated with bladder cancer.
  • methods for diagnosing bladder cancer in an individual, the method comprising: (a) obtaining a biological sample comprising genomic DNA from the individual; (b) measuring in the sample the level of one or more methylated genomic CpG dinucleotide sequences in one or more loci selected from the group consisting of NALP4, BDKRBl, C14ORF103, COX7C, ZNF322B, HIGD2A, TBCA, BRD7, and PSME2, and (c) comparing the level of methylation at the one or more genomic CpG dinucleotide sequences in the sample to a reference level of methylated genomic CpG
  • methods for predicting the course of bladder cancer in an individual, the method comprising: (a) obtaining a biological sample comprising genomic DNA from the individual; (b) measuring in the sample the level of one or more methylated genomic CpG dinucleotide sequences in one or more loci selected from the group consisting of NALP4, BDKRBl, C14ORF103, COX7C, ZNF322B, HIGD2A, TBCA, BRD7, and PSME2, and (c) comparing the level of methylation at the one or more genomic CpG dinucleotide sequences in the sample to a reference level of methylated genomic CpG dinucleotide sequences, wherein a difference in the level of methylation of the one or more genomic CpG dinucleotide sequences in the sample compared to the reference Via EFS Attorney Docket No.: 35947 -005001 WO
  • level identifies differentially methylated genomic CpG dinucleotide sequences associated with bladder cancer, and wherein the level of methylation of the differentially methylated genomic CpG dinucleotide sequences is used to predict the course of the cancer in the individual.
  • methods for predict susceptibility to bladder cancer in an individual, the method comprising: (a) obtaining a biological sample comprising genomic DNA from the individual; (b) measuring in the sample the level of one or more methylated genomic CpG dinucleotide sequences in one or more loci selected from the group consisting of NALP4, BDKRB1, C14ORF103, COX7C, ZNF322B, HIGD2A, TBCA, BRD7, and PSME2, and (c) comparing the level of methylation at the one or more genomic CpG dinucleotide sequences in the sample to a reference level of methylated genomic CpG dinucleotide sequences, wherein a difference in the level of methylation of the one or more genomic CpG dinucleotide sequences in the sample compared to the reference level identifies differentially methylated genomic CpG dinucleotide sequences associated with bladder cancer, and wherein the level of
  • methods for staging the progression of bladder cancer in an individual, the method comprising: (a) obtaining a biological sample comprising genomic DNA from the individual; (b) measuring in the sample the level of one or more methylated genomic CpG dinucleotide sequences in one or more loci selected from the group consisting of NALP4, BDKRB1, C14ORF103, COX7C, ZNF322B, HIGD2A, TBCA, BRD7, and PSME2, and (c) comparing the level of methylation at the one or more genomic CpG dinucleotide sequences in the sample to a reference level of methylated genomic CpG dinucleotide sequences, wherein a difference in the level of methylation of the one or more genomic CpG dinucleotide sequences in the sample compared to the reference level identifies differentially methylated genomic CpG dinucleotide sequences associated with bladder cancer, and wherein the level of methyl
  • the method comprising: (a) obtaining a biological sample comprising genomic DNA from the individual; (b)
  • sequences in the sample compared to the reference level identifies differentially methylated genomic CpG dinucleotide sequences associated with bladder cancer, and wherein the level of methylation of the differentially methylated genomic CpG dinucleotide sequences is used to predict the likelihood of overall survival for the individual.
  • the method comprising: (a) obtaining a biological sample comprising genomic DNA from the individual; (b) measuring in the sample the level of one or more methylated genomic CpG dinucleotide sequences in one or more loci selected from the group consisting of NALP4, BDKRB1, C14ORF103, COX7C, ZNF322B, HIGD2A, TBCA, BRD7, and PSME2, and (c) comparing the level of methylation at the one or more genomic CpG dinucleotide sequences in the sample to a reference level of methylated genomic CpG dinucleotide sequences, wherein a difference in the level of methylation of the one or more genomic CpG dinucleotide sequences in the sample compared to the reference level identifies differentially methylated genomic CpG dinucleotide sequences associated with bladder cancer, and wherein the level of methylation of the differentially methylated genomic C
  • methods for determining the effectiveness of a treatment course undergone by an individual with bladder cancer, the method comprising: (a) obtaining a biological sample comprising genomic DNA from the individual; (b) measuring in the sample the level of one or more methylated genomic CpG dinucleotide sequences in one or more loci selected from the group consisting of NALP4, Via EFS Attorney Docket No.: 35947 -005001 WO
  • dinucleotide sequences in the sample compared to the reference level identifies differentially methylated genomic CpG dinucleotide sequences associated with bladder cancer, and wherein the level of methylation of the differentially methylated genomic CpG dinucleotide sequences in the sample is used to determine the effectiveness of a treatment course
  • the reference level corresponds to the level of methylated genomic CpG dinucleotide sequences present in a corresponding sample obtained from the individual prior to treatment.
  • the level of methylation in the biological sample is decreased in comparison to the reference level. In some embodiments, the level of methylation in the biological sample is increased in comparison to the reference level.
  • the level of methylation is elevated compared to normal tissue samples for one of more (e.g. 1 or more, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more) loci selected from the groups consisting of NALP4, BDKRB1, C14ORF103, COX7C, ZNF322B, HIGD2A, TBCA, BRD7, and PSME2.
  • methods comprising: a) providing a biological sample from a subject, the biological sample comprising genomic DNA; b) detecting the presence or absence of DNA methylation in one or more gene loci to generate a methylation profile for the subject, wherein the one or more loci is selected from the group consisting of NALP4, BDKRB1, C14ORF103, COX7C, ZNF322B, HIGD2A, TBCA, BRD7, and PSME2; and c) comparing the methylation profile to one or more standard methylation profiles, wherein the standard methylation profiles are selected from the group consisting of methylation profiles of non cancerous samples and methylation profiles of cancerous samples.
  • the DNA methylation comprises CpG methylation.
  • methods for characterizing bladder cancer comprising: a) providing a biological sample from a subject diagnosed with bladder cancer, the biological sample comprising genomic DNA; and b) detecting increased DNA methylation in one or more loci selected from the groups consisting of NALP4, BDKRB1, Via EFS Attorney Docket No.: 35947 -005001 WO
  • the DNA methylation comprises CpG methylation.
  • the biological sample is a biopsy sample. In some embodiments, the biological sample is a blood sample. In some embodiments, the biological sample is a peripheral blood sample.
  • non-transient computer readable storage medium comprising executable instructions for detecting bladder cancer or a susceptibility of developing said cancer, the executable instructions configured to: receive data characterizing the intensity of DNA methylation in one or more gene loci; compare the intensity of DNA methylation to a model; and calculate a likelihood of bladder cancer.
  • a system for predicting bladder cancer comprises the following elements: an input module for analyzing genomic DNA and detecting the intensity of DNA methylation in the genomic DNA, wherein one or more gene loci of the genomic DNA are inspected; and a processor configured to determine a likelihood of bladder cancer using said intensity of DNA methylation and a model computed from one or more subjects known to have bladder cancer and one or more subjects known to not have bladder cancer.
  • Figure 1 Diagram of the analysis strategy employed in defining the methylation profiles in the training set and applying those profiles for classification in the testing set. Via EFS Attorney Docket No.: 35947 -005001 WO
  • FIGS 2A and 2B DNA Methylation Profiles defined by a panel of 9 loci are significantly associated with bladder cancer.
  • A The RPMM based classification of methylation of 9 loci (columns) in the peripheral blood-derived DNA of the 230 subjects (rows) in the testing dataset is depicted in the heatmap, with the 7 classes separated by red lines. The overall mean methylation and confidence intervals (error bars) by class are depicted in the bar graph on the right.
  • B The prevalence of cases and controls (y-axis) in each of the predicted classes (x-axis). A permutation based Chi-square test suggests that case control prevalence is significantly different by methylation class (P ⁇ 0.0001). Top: Cases; Bottom: Control.
  • FIGS. 3 A and 3B Receiver operator curve (ROC) analysis of methylation profiles.
  • A ROC curve based on methylation class only results in a significant AUC of 0.70 (95% CI 0.63, 0.77).
  • B ROC curve including methylation classes, patient gender, age, smoking status (never, former, current), and family history of bladder cancer results in a significant AUC of 0.76 (95% CI 0.70, 0.82).
  • FIG. 4 Diagram of the Gene-Set Enrichment Analysis on DNA Methylation Data.
  • the upper panel depicts the transcription factor binding sites (TFBS) within lkB of differentially methylated loci associated with aging, bladder cancer, and their overlap grouped by functional role or family.
  • the lower panel depicts the KEGG pathways that are over-represented amongst the loci with differential methylation associated with aging, bladder cancer, and their overlap, grouped by higher level pathways.
  • Figure 5 is a flow chart showing a method for detecting cancer or a risk of developing cancer.
  • Epigenetics relates to gene expression or cellular phenotype caused by mechanisms other than changes in the underlying DNA sequence. It refers to functionally relevant modifications to the genome that do not involve a change in the actual nucleotide sequence. Examples of such changes are DNA methylation and histone deacetylation. Such changes serve to regulate gene expression without altering the nucleotide sequence of the regulated gene. Via EFS Attorney Docket No.: 35947 -005001 WO
  • methods are provided for identification of differentially methylated genomic CpG dinucleotide sequences associated with bladder cancer in an individual by obtaining a biological sample comprising genomic DNA from the individual measuring the level or pattern of one or more methylated genomic CpG dinucleotide sequences in two or more of the genomic targets in the sample, and comparing the level of the one or more methylated genomic CpG dinucleotide sequences in the sample to a reference level of methylated genomic CpG dinucleotide sequences, wherein a difference in the level or pattern of methylation of the genomic CpG dinucleotide sequences in the sample compared to the reference level identifies differentially methylated genomic CpG dinucleotide sequences associated with bladder cancer.
  • the methods of the invention are directed to methods for diagnosing an individual with a condition that is characterized by a level and/or pattern of methylated genomic CpG dinucleotide sequences distinct from the level and/or pattern of methylated genomic CpG dinucleotide sequences exhibited in the absence of the particular condition.
  • This invention also is directed to methods for predicting the susceptibility of an individual to a condition that is characterized by a level and/or pattern of methylated genomic CpG dinucleotide sequences that is distinct from the level and/or pattern of methylated genomic CpG dinucleotide sequences exhibited in the absence of the condition.
  • this invention provides diagnostic markers for cancer.
  • the markers of the invention are genomic sequences having methylation states that are diagnostic or prognostic of the presence or severity of cancer.
  • a list of exemplary genes for which methylation state can be used to determine the presence or severity of bladder cancer include CDPCRl/3 (see, e.g., GenBank Accession Number NG_029476 (GL340745288), incorporated herein by reference), CDPCR3HD (see, e.g., GenBank Accession Number NG_029476 (GL340745288), incorporated herein by reference), CHX10 (e.g., GenBank Via EFS Attorney Docket No.: 35947 -005001 WO
  • GenBank Accession Number AY336059 (GL33285957), incorporated herein by reference), GFI1 (e.g., GenBank Accession Number NG_007874 (GL188595651), incorporated herein by reference), HFH1 (e.g., GenBank Accession Number AF225950 (GI: 12655883), incorporated herein by reference), LM02 (e.g., GenBank Accession Number NM_001142316 (GL214832218), incorporated herein by reference), MEF2 (e.g., GenBank Accession Number L16794 (GL401768), incorporated herein by reference), MEIS (see, e.g., GenBank Accession Number NG_029108 (GL335892881), incorporated herein by reference; GenBank Accession Number NG_011467 (GL224809261), incorporated herein by reference), HOXA9 (e.g., GenBank Accession Number NG_029923 (GL345525397), incorporated herein by reference
  • STAT5B e.g., GenBank Accession Number NM_012448 (GL42519913), incorporated herein by reference
  • CREBP1 e.g., GenBank Accession Number U16028 (GI: 1039380), incorporated herein by reference
  • FREAC3/4 see, e.g., GenBank Accession Number BD206053 (GL33015823), incorporated herein by reference
  • PBX1 e.g., GenBank Accession Number NM_002585 (GL326320046), incorporated herein by reference
  • FOXOl e.g., GenBank Accession Number NM_002015 Via EFS Attorney Docket No.: 35947 -005001 WO
  • FOX02 GI: 133930787
  • FOX03 e.g., GenBank
  • C-REL e.g., GenBank Accession Number L41414 (GL8248631), incorporated herein by reference
  • E4BP4 e.g., GenBank Accession Number X64318 (GL30955), incorporated herein by reference
  • SREBP1 e.g., GenBank Accession Number U00968 (U00968.1), incorporated herein by reference
  • ARNT e.g., GenBank Accession Number NM_001178 (NM_001178.4), incorporated herein by reference
  • CDC5 e.g., GenBank Accession
  • NM_001253 GL356640202 incorporated herein by reference
  • SRF GenBank Accession Number NM_003131 (NM_003131.2), incorporated herein by reference
  • GATAl e.g.,GenBank Accession Number NM_002049.3 (GI: 183227689), incorporated herein by reference
  • IRF1 e.g., GenBank Accession Number NM_002198 (GI: 196049386), incorporated herein by reference
  • IRF7 e.g., GenBank Accession Number NM_001572 (GL98985820), incorporated herein by reference
  • NFAT see, e.g., GenBank Accession Number
  • NM_138714 (GI: 342672020), incorporated herein by reference), STAT1 (see, e.g., GenBank Accession Number NM_007315 GL189458859), incorporated herein by reference), STAT3 (e.g., GenBank Accession Number NM_213662 (GL47458819), incorporated herein by reference), HNF1 (e.g., GenBank Accession Number NM_000545 (GL256542296),
  • LHX3 e.g., GenBank Accession Number NM_178138 (GL315013528), incorporated herein by reference
  • MIF1 e.g., GenBank Accession Number BD248115 (GL33057885)
  • NFY e.g., GenBank Accession Number NM_001142588 (GL217272830), incorporated herein by reference
  • NALP4 E.G., genbank accession number AF442488 (GI: 17064171), incorporated herein by reference
  • BDKRB1 e.g.,
  • GenBank Accession Number AY275464 (GI: 18105039), incorporated herein by reference
  • C14ORF103 see, e.g., GenBank Accession Number CM000265 (GL74273668)
  • COX7C e.g., GenBank Accession Number NM_001867 (GL18105039), incorporated herein by reference
  • ZNF322B e.g., GenBank Accession
  • TBCA GenBank Accession Number NM_004607 (GL94421476), incorporated herein by reference
  • BRD7 e.g., GenBank Accession Number AF152604 (GL8452873)
  • PSME2 e.g., GenBank Accession Number
  • the prognostic methods of the invention are useful for determining if a patient is at risk for recurrence.
  • Cancer recurrence is a concern relating to a variety of types of cancer.
  • the prognostic methods of the invention can be used to identify surgically treated patients likely to experience cancer recurrence so that they can be offered additional therapeutic options, including preoperative or postoperative adjuncts such as chemotherapy, radiation, biological modifiers and other suitable therapies.
  • the methods are especially effective for determining the risk of metastasis in patients who demonstrate no measurable metastasis at the time of examination or surgery.
  • the prognostic methods of the invention also are useful for determining a proper course of treatment for a patient having cancer.
  • a course of treatment refers to the therapeutic measures taken for a patient after diagnosis or after treatment for cancer. For example, a determination of the likelihood for cancer recurrence, spread, or patient survival, can assist in determining whether a more conservative or more radical approach to therapy should be taken, or whether treatment modalities should be combined. For example, when cancer recurrence is likely, it can be advantageous to precede or follow surgical treatment with chemotherapy, radiation, immunotherapy, biological modifier therapy, gene therapy, vaccines, and the like, or adjust the span of time during which the patient is treated.
  • the diagnosis or prognosis of cancer state is typically correlated with the degree to which one or more of the genes selected from NALP4, BDKRB1, C14ORF103, COX7C, ZNF322B, HIGD2A, TBCA, BRD7, and PSME2 is methylated.
  • the invention can include a determination made based on the methylation state for the entire set of genes or a subset of the genes.
  • This invention provides methods for determining a prognosis for survival for a cancer patient.
  • One method involves (a) measuring a level of methylation for one or more of the genes selected from the groups consisting of NALP4, BDKRB1, C14ORF103, COX7C, ZNF322B, HIGD2A, TBCA, BRD7, and PSME2 in a biological sample from an individual, and (b) comparing the level of methylation in the sample to a reference level of methylation Via EFS Attorney Docket No.: 35947 -005001 WO
  • a low level of methylation for the gene in the sample correlates with increased survival of the patient.
  • the invention also provides a method for monitoring the effectiveness of a course of treatment for a patient with cancer.
  • the method involves (a) determining a level of one or more of the genes listed in selected from the groups consisting of NALP4, BDKRB1, C14ORF103, COX7C, ZNF322B, HIGD2A, TBCA, BRD7, and PSME2 in a biological sample from an individual prior to treatment, and (b) determining the level of methylation for the gene in a biological sample from the patient after treatment, whereby comparison of the level of methylation for the gene prior to treatment with the level of methylation for the gene after treatment indicates the effectiveness of the treatment.
  • the level of methylation of the differentially methylated genomic CpG dinucleotide sequences can provide a variety of information about the cancer and can be used, for example, to diagnose bladder cancer in the individual; to predict the course of the cancer in the individual; to predict the susceptibility to cancer in the individual, to stage the progression of the cancer in the individual; to predict the likelihood of overall survival for the individual; to predict the likelihood of recurrence of cancer for the individual; to determine the effectiveness of a treatment course undergone by the individual.
  • the level of methylation that is detected in a biological sample can be decreased or increased in comparison to the reference level and alterations that increase or decrease methylation can be detected and provide useful prognostic or diagnostic information.
  • the present invention also allows for the detection of patterns of methylation. Analysis of methylation patterns across these chromosome in biological samples from afflicted individuals can reveal epigenetic changes in the form of altered levels of methylation of subsets of genomic CpG dinucleotide sequences that make up a pattern of affected genomic targets that can be correlated with a condition.
  • Methylation of CpG dinucleotide sequences can be measured using any method known in the art. Methylation of CpG dinucleotide sequences can be measured using any of a variety of techniques used in the art for the analysis of specific CpG dinucleotide methylation status. For example, methylation can be measured by employing a restriction Via EFS Attorney Docket No.: 35947 -005001 WO
  • Restriction enzyme based technology which utilizes methylation sensitive restriction endonucleases for the differentiation between methylated and unmethylated cytosines.
  • Restriction enzyme based technologies include, for example, restriction digest with methylation- sensitive restriction enzymes followed by Southern blot analysis, use of methylation- specific enzymes and PCR, restriction landmark genomic scanning (RLGS) and differential methylation hybridization (DMH).
  • composition includes a plurality of such compositions, as well as a single composition, and a reference to "a therapeutic agent” is a reference to one or more therapeutic and/or
  • methylation profile refers to a presentation of
  • the methylation profile is compared to a standard methylation profile
  • methylation profiles are generated using the methods of the present invention.
  • the profile may be presented as a graphical representation (e.g., on paper or on a computer screen), a physical representation (e.g., a gel or array) or a digital representation stored in computer memory.
  • one or more includes 1 or more, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, or 9 or more.
  • DNA methylation Methylation of bases contained in the DNA double helix, resulting in a loss of gene function. Generally occurring on cytosine residues in the DNA, methylation is important in regulating cell growth and differentiation and has resulted in the testing of DNA methyltransferase inhibitors as anti-cancer agents and differentiation agents. Via EFS Attorney Docket No.: 35947 -005001 WO
  • Epigenetic The transfer of information from one cell to its descendants without the information's being encoded in the nucleotide sequence of the DNA. The methylation of the promoter to inactivate a gene is an example of an epigenetic change. Epigenetic inheritance is typically transmitted in dividing cells. Al-though rare, it is occasionally seen in traits being transmitted from one generation to another. Epigenetic variants can arise spontaneously and just as spontaneously revert.
  • Epigenome The overall epigenetic state of a cell.
  • GSEA Gene Set Enrichment Analysis
  • K EGG Kyoto Encyclopedia of Genes and Genomes is an integrate bioinformatic database resource that is used as a reference knowledge base for biologic interpretation of large-scale data sets generated by sequencing and other high-throughput experimental technologies (online at The KEGG website at www.kegg.jp has become the primary site of the KEGG database developed by Kanehisa Laboratories. The GenomeNet website at www.genome.jp operated by Kyoto University Bioinformatics Center will continue to mirror the KEGG database and provide additional KEGG-based analysis services.
  • Population-based Study in which the subjects are drawn from a defined population in a manner that is representative of the source population studied. Such a design can avoid bias arising from the selective factors that guide affected individuals to a particular medical facility, allowing for greater generalizability of the findings.
  • RPMM Recursively partitioned mixture model
  • Regulatory T cells are a specialized subpopulation of T cells that act to suppress activation of the immune system and thereby maintain immune system homeostasis and tolerance to self-antigens. This is an important "self-check” built into the immune system so that responses do not go haywire. Regulatory T cells come in many forms, including those that express the CD8 trans-membrane glycoprotein (CD8 T cells), Via EFS Attorney Docket No.: 35947 -005001 WO
  • CD4CD25 regulatory T cells those that express CD4, CD25 and Foxp3 (CD4CD25 regulatory T cells or "Tregs") and other T cell types that have suppressive function. These cells are involved in closing down immune responses after they have successfully tackled invading organisms and also in keeping in check immune responses that may potentially attack one's own tissues (autoimmunity).
  • Example 1 DNA Methylation Array Analysis Identifies Profiles of Blood-derived DN A Methylation Associated with Bladder Cancer
  • methylation was significantly associated with bladder cancer in a masked, independent testing series of 111 cases and 119 controls (P ⁇ 0.0001). Membership in 3 of the most methylated classes was associated with a 5.2 fold increased risk of bladder cancer (95% CI 2.8, 9.7), and a model including the methylation classification, subject age, gender, smoking status, and family history of bladder cancer was a significant predictor of bladder cancer (AUC 0.76, 95% CI 0.70, 0.82). CpG loci associated with bladder cancer and aging had neighboring sequences enriched for transcription-factor binding sites related to immune modulation and forkhead family members.
  • DNA Methylation and Statistical Analysis DNA was extracted from peripheral blood buffy coats using the QIAmp DNA mini kit according to the manufacturer's protocol (Qiagen, Valencia, CA), and was subjected to sodium bisulfite modification using the EZ DNA Methylation Kit (Zymo Research, Orange, CA) following the manufacturer's protocol. Methylation profiling was performed using the Illumina Infinium Methylation27 Bead Array at the UCSF Institute for Human Genetics Genomic Core Facility.
  • RPMM partitioned mixture model
  • J3 0j is the intercept
  • a ik is a normally distributed random intercept assumed to have the same value for all arrays on the same plate/beadChip
  • £ ijk is a normally distributed error term (now assumed to represent technical variation)
  • ⁇ 1] is a fixed effect term for case control status
  • the resulting model provides a latent class structure on the pre-selected loci, which was then used in conjunction with Empirical Bayes to predict class for the observations in the testing data.
  • the ROC curves and corresponding AUC were generated (1) based only on the predicted methylation classes in the testing data and (2) based on the predicted methylation classes in the testing data
  • Kegg protein interaction pathway data sets were constructed by parsing Kegg XML files for homo sapiens and matching gene nodes to CpG sites by Entrez ID. Transcription factor binding site data sets were obtained by querying the Genomes Browser table
  • tfbsConsSites and excluding sites with Z-score less than or equal to 2.
  • a separate gene set for each of 252 types of transcription factor binding sites was obtained by determining which CpG sites were within lkbp of the midpoint of the nearest instance of the transcription factor binding sites.
  • GSEA Gene Set Enrichment Analysis
  • the profile of DNA methylation was obtained for 460 peripheral blood samples using the Human Methylation Beadarray (Houseman et al., BMC Bioinformatics 9:365, 2008; hereby incorporated by reference).
  • the first step of our semi-supervised strategy was to identify those CpG loci whose methylation state was most significantly associated with being bladder cancer case than control. To do this we fit a series of linear mixed effects model using the training data only for each of the 26,486 CpGs in the data set. This allowed us to model each methylation value as the dependent variable, with a random effect for plate (to allow for inter-plate
  • CpG loci were ranked based on the absolute value of the t-statistic derived from the model, and the top 9 loci were chosen based on a nested cross-validation procedure (Materials and Methods, Figure 1) for inclusion in the RPMM, which clustered the samples based on the methylation profile of these 9 loci in the training data.
  • a nested cross-validation procedure (Materials and Methods, Figure 1) for inclusion in the RPMM, which clustered the samples based on the methylation profile of these 9 loci in the training data.
  • the latent class structure from the RPMM solution fit to the training data was used in conjunction with an empirical Bayes procedure.
  • FIG. 2a The methylation profile of these 9 loci in the testing data is depicted in Figure 2a, which also shows the mean methylation across loci within a given class and the relationships among the classes through the dendrogram.
  • the right branch classes (those beginning with the letter R) had overall mean methylation that was significantly greater than the left branch classes (P ⁇ 0.0001).
  • GSEA gene set enrichment analysis
  • differential methylation profiles may represent a response of the hematopoietic system to a developing tumor, i.e. the methylation profiles capture the downstream effects of this response, which may be through differential binding of transcription factors near sites of altered methylation.
  • the top half of Figure 4 depicts the results of this GSEA-based analysis, depicting binding sites of
  • transcription factors over-represented within lkB of loci whose DNA methylation related to age, bladder cancer status, or both, grouped by similar structure or functional response.
  • Binding sites for a forkhead containing transcription factor and a transcription factor involved in immune modulation overlapped between loci associated with age and disease status. Loci with differential methylation strongly associated with age were nearby binding sites of a large number of transcription factors related to developmental processes including homeobox containing transcription factors, as well as factors involved in immune modulation and stress response. Oncogenic transcription factor binding sites as well as immune
  • results of LASSO Analysis To identify if the association between methylation profiles and bladder cancer is sensitive to the statistical methodology employed in the examination, we also performed our analysis utilizing a LASSO approach, utilizing the same training and testing datasets. In the same training series used for the SS-RPMM, we fit two models, first investigating the association between the top 1000 most variable loci on the array and bladder cancer risk, and the second with the 1000 loci most associated with bladder cancer risk. The first mode resulted in 48 loci being identified with non-zero estimates of the coefficient representing correlation with bladder cancer status, and the second with 66 loci.
  • ROC curves were constructed and the AUC calculated.
  • the AUCs and corresponding 95% bootstrap CIs using only the predicted probabilities of case/control status in the testing data were 0.76 (0.69, 0.82) and 0.78 (0.72, 0.84) for the LASSO models fit to the top 1000 most variable CpG loci and to the top 1000 CpG loci most associated with bladder case/control status, respectively.
  • the profiles represent a directed alteration and by looking at the genomic context of those loci whose differential methylation was associated with aging or bladder cancer, the manner in which these pathologic processes are influencing methylation status is defined.
  • BRD7 is an activator of the WNT signaling pathway, which plays a critical role in stem cell maintenance and a pathways whose alteration has been linked to bladder cancer (Marsit et al., Cancer Res. 65:7081-5, 2005).
  • TBCA encodes a member of the multiprotein complex responsible for appropriate folding of the tubulin protein, and may be involved in responding to cellular stress events leading to an unfolded protein response (Tian et al., Cell 86:287-96, 1996).
  • COX7C is one member of the cytochrome c oxidase complex responsible for mitochondrial respiration and changes in its expression have been observed in skin squamous cell
  • Figure 5 illustrates the method for detecting bladder cancer or a susceptibility of developing bladder cancer.
  • the intensity, level or pattern of DNA methylation is
  • the measurement/characterization of intensity, level, or pattern is compared to a model 504.
  • the model may be predetermined from data derived from a set of subjects known to have bladder cancer and a set of subjects known to not have bladder cancer.
  • a likelihood of cancer is computed 506.
  • the subject matter described herein can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structural means disclosed in this specification and structural equivalents thereof, or in combinations of them.
  • the subject matter described herein can be implemented as one or more computer program products, such as one or more computer programs tangibly embodied in an information carrier (e.g., in a machine-readable storage device), or embodied in a propagated signal, for Via EFS Attorney Docket No.: 35947 -005001 WO
  • a computer program (also known as a program, software, software application, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.
  • a computer program does not necessarily correspond to a file.
  • a program can be stored in a portion of a file that holds other programs or data, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code).
  • a computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network.
  • the subject matter described herein can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, (e.g., a mouse or a trackball), by which the user can provide input to the computer.
  • a display device e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor
  • a keyboard and a pointing device e.g., a mouse or a trackball
  • Other kinds of devices can be used to provide for interaction with a user as well.
  • feedback provided to the user can be any form of sensory feedback, (e.g., visual feedback, auditory feedback, or tactile feedback), and input from the user can be received in any form, including acoustic, speech, or tactile input.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Immunology (AREA)
  • Engineering & Computer Science (AREA)
  • Molecular Biology (AREA)
  • Organic Chemistry (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Pathology (AREA)
  • Analytical Chemistry (AREA)
  • Biotechnology (AREA)
  • Biomedical Technology (AREA)
  • Urology & Nephrology (AREA)
  • Hematology (AREA)
  • Zoology (AREA)
  • Wood Science & Technology (AREA)
  • Physics & Mathematics (AREA)
  • Hospice & Palliative Care (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Oncology (AREA)
  • Microbiology (AREA)
  • Genetics & Genomics (AREA)
  • General Physics & Mathematics (AREA)
  • Biophysics (AREA)
  • Cell Biology (AREA)
  • Medicinal Chemistry (AREA)
  • Food Science & Technology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • General Engineering & Computer Science (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

Les modifications épigénétiques des tissus ciblés pour le cancer jouent un rôle causal dans la carcinogenèse. Des changements dans la méthylation de l'ADN dans les tissus non cibles, en particulier le sang périphérique, peuvent également affecter le risque de maladie maligne. La présente invention concerne des profils spécifiques de méthylation de l'ADN dans le sang périphérique qui sont associés à un risque de cancer de la vessie et servent donc de marqueur épigénétique d'une prédisposition à une maladie.
PCT/US2012/026038 2011-02-22 2012-02-22 Compositions et procédés de détection du cancer de la vessie Ceased WO2012116019A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201161445270P 2011-02-22 2011-02-22
US61/445,270 2011-02-22

Publications (1)

Publication Number Publication Date
WO2012116019A1 true WO2012116019A1 (fr) 2012-08-30

Family

ID=46721207

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2012/026038 Ceased WO2012116019A1 (fr) 2011-02-22 2012-02-22 Compositions et procédés de détection du cancer de la vessie

Country Status (1)

Country Link
WO (1) WO2012116019A1 (fr)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014168346A1 (fr) * 2013-04-10 2014-10-16 (주)지노믹트리 Méthode de dépistage du cancer de la vessie au moyen d'un gène marqueur épigénétique spécifique du cancer de la vessie
EP2915883A1 (fr) 2014-03-07 2015-09-09 Ruprecht-Karls-Universität Heidelberg Analyse non invasive pour la détection précoce du cancer
CN111118160A (zh) * 2020-02-12 2020-05-08 中国医学科学院肿瘤医院 结直肠癌诊断中的甲基化标志物
CN115927610A (zh) * 2022-07-19 2023-04-07 武汉艾米森生命科技有限公司 检测foxo6基因中目标区域的甲基化水平的试剂在制备膀胱癌诊断产品中的应用

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050021240A1 (en) * 2000-11-02 2005-01-27 Epigenomics Ag Systems, methods and computer program products for guiding selection of a therapeutic treatment regimen based on the methylation status of the DNA
WO2007050777A2 (fr) * 2005-10-25 2007-05-03 Illumina, Inc. Procedes et compositions servant a diagnostiquer le cancer des poumons au moyen de profils specifiques de methylation d'adn
CA2759312A1 (fr) * 2009-04-20 2010-10-28 Erasmus University Medical Center Rotterdam Methode de diagnostic du cancer de la vessie
US20100317000A1 (en) * 2007-07-23 2010-12-16 Shanghai Cancer Institute Method for diagnosing bladder cancer by analyzing dna methylation profiles in urine sediments and its kit

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050021240A1 (en) * 2000-11-02 2005-01-27 Epigenomics Ag Systems, methods and computer program products for guiding selection of a therapeutic treatment regimen based on the methylation status of the DNA
WO2007050777A2 (fr) * 2005-10-25 2007-05-03 Illumina, Inc. Procedes et compositions servant a diagnostiquer le cancer des poumons au moyen de profils specifiques de methylation d'adn
US20100317000A1 (en) * 2007-07-23 2010-12-16 Shanghai Cancer Institute Method for diagnosing bladder cancer by analyzing dna methylation profiles in urine sediments and its kit
CA2759312A1 (fr) * 2009-04-20 2010-10-28 Erasmus University Medical Center Rotterdam Methode de diagnostic du cancer de la vessie

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014168346A1 (fr) * 2013-04-10 2014-10-16 (주)지노믹트리 Méthode de dépistage du cancer de la vessie au moyen d'un gène marqueur épigénétique spécifique du cancer de la vessie
KR101573467B1 (ko) * 2013-04-10 2015-12-01 (주)지노믹트리 방광암 특이적 후성유전적 마커 유전자를 이용한 방광암의 검출방법
EP2915883A1 (fr) 2014-03-07 2015-09-09 Ruprecht-Karls-Universität Heidelberg Analyse non invasive pour la détection précoce du cancer
CN111118160A (zh) * 2020-02-12 2020-05-08 中国医学科学院肿瘤医院 结直肠癌诊断中的甲基化标志物
CN111118160B (zh) * 2020-02-12 2023-06-06 中国医学科学院肿瘤医院 结直肠癌诊断中的甲基化标志物
CN115927610A (zh) * 2022-07-19 2023-04-07 武汉艾米森生命科技有限公司 检测foxo6基因中目标区域的甲基化水平的试剂在制备膀胱癌诊断产品中的应用

Similar Documents

Publication Publication Date Title
Hua et al. Genetic and epigenetic intratumor heterogeneity impacts prognosis of lung adenocarcinoma
Teixeira et al. Deciphering the genomic, epigenomic, and transcriptomic landscapes of pre-invasive lung cancer lesions
Croes et al. Large-scale analysis of DFNA5 methylation reveals its potential as biomarker for breast cancer
Xi et al. Multi-omic characterization of genome-wide abnormal DNA methylation reveals diagnostic and prognostic markers for esophageal squamous-cell carcinoma
Marsit et al. DNA methylation array analysis identifies profiles of blood-derived DNA methylation associated with bladder cancer
Onken et al. An accurate, clinically feasible multi-gene expression assay for predicting metastasis in uveal melanoma
Aref-Eshghi et al. Genomic DNA methylation-derived algorithm enables accurate detection of malignant prostate tissues
Hou et al. m6A RNA methylation regulators have prognostic value in papillary thyroid carcinoma
Pedersen et al. Leukocyte DNA methylation signature differentiates pancreatic cancer patients from healthy controls
Fehrmann et al. Profiling studies in ovarian cancer: a review
JP2020503850A (ja) 腫瘍抑制ｆｏｘｏ活性を酸化ストレスから区別する方法
JP2009528825A (ja) デュークスｂ大腸がんの再発を予測するための分子的解析
JP2008521412A (ja) 肺癌予後判定手段
US20090192045A1 (en) Molecular staging of stage ii and iii colon cancer and prognosis
Exner et al. Potential of DNA methylation in rectal cancer as diagnostic and prognostic biomarkers
Gonzalez Bosquet et al. Creation and validation of models to predict response to primary treatment in serous ovarian cancer
Wang et al. Identification of a 5-gene signature for clinical and prognostic prediction in gastric cancer patients upon microarray data
Dai et al. Identification of hub methylated‐CpG sites and associated genes in oral squamous cell carcinoma
Wang et al. A robust blood gene expression-based prognostic model for castration-resistant prostate cancer
Wei et al. Identification of potential predictor of biochemical recurrence in prostate cancer
US20150294062A1 (en) Method for Identifying a Target Molecular Profile Associated with a Target Cell Population
Kim et al. Genomic hypomethylation in cell-free DNA predicts responses to checkpoint blockade in lung and breast cancer
WO2012116019A1 (fr) Compositions et procédés de détection du cancer de la vessie
Wang et al. Genome-wide analysis of DNA methylation identifies two CpG sites for the early screening of colorectal cancer
Houseman et al. Copy number variation has little impact on bead-array-based measures of DNA methylation

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 12749181

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 12749181

Country of ref document: EP

Kind code of ref document: A1