WO2023075710A2 - Panel de microarn circulant pour la détection d'un carcinome nasopharyngé et procédés associés - Google Patents

Panel de microarn circulant pour la détection d'un carcinome nasopharyngé et procédés associés Download PDF

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WO2023075710A2
WO2023075710A2 PCT/SG2022/050792 SG2022050792W WO2023075710A2 WO 2023075710 A2 WO2023075710 A2 WO 2023075710A2 SG 2022050792 W SG2022050792 W SG 2022050792W WO 2023075710 A2 WO2023075710 A2 WO 2023075710A2
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ebv
mir
bartl
twenty
mirna
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WO2023075710A3 (fr
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Ruiyang ZOU
He Cheng
Qing En LIM
Pan Zhang
Min Xia GU
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MiRXES Lab Pte. Ltd.
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/70—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving virus or bacteriophage
    • C12Q1/701—Specific hybridization probes
    • C12Q1/705—Specific hybridization probes for herpetoviridae, e.g. herpes simplex, varicella zoster
    • 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
    • C12Q2600/158—Expression markers
    • 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
    • C12Q2600/178—Oligonucleotides characterized by their use miRNA, siRNA or ncRNA

Definitions

  • the present invention relates generally to the field of molecular biology.
  • the present invention relates to biomarkers associated with nasopharyngeal carcinoma and methods of using the biomarkers to determine whether a subject suffers from, or is at risk of developing, nasopharyngeal carcinoma.
  • NPC nasopharyngeal carcinoma
  • the present disclosure relates to a method of determining whether a subject suffers from, or is at risk of developing nasopharyngeal carcinoma, wherein the method comprises detecting/determining the expression level of at least one miRNA from a biological sample obtained from the subject, wherein the at least one miRNA is selected from ebv-miR-BART21- 3p, ebv-miR-BART14-5p, ebv-miR-BART19-5p, ebv-miR-BARTl-5p, ebv-miR-BARTl 9- 3p, ebv-miR-BART7-3p, ebv-miR-BART8-3p, ebv-miR-BART13-3p, ebv-miR-BART10-3p, ebv-miR-BART3-3p, ebv-miR-BARTl l-5p, ebv-v-m
  • the method for determining whether a subject suffers from, or is at risk of developing nasopharyngeal carcinoma comprising detecting/determining the expression level of at least one miRNA from a biological sample obtained from the subject, wherein the at least one miRNA is selected from ebv-miR-BART21-3p, ebv-miR-BART14-5p, ebv-miR- BART19-5p, ebv-miR-BARTl-5p, ebv-miR-BART19-3p, ebv-miR-BART7-3p, ebv-miR- BART8-3p, ebv-miR-BART13-3p, ebv-miR-BART10-3p, ebv-miR-BART3-3p, ebv-miR- BARTl l-5p, ebv-miR-BART16, ebv-miR-
  • the at least one miRNA is selected from ebv-miR-BART21-3p, ebv-miR- BART14-5p, ebv-miR-BARTl 9-5p, ebv-miR-BARTl -5p, ebv-miR-BARTl 9-3p, ebv-miR- BART7-3p, ebv-miR-BART8-3p, ebv-miR-BARTl 3 -3p, ebv-miR-BARTl 0-3p, ebv-miR- BART3-3p, ebv-miR-BARTl l-5p, ebv-miR-BARTl 6, ebv-miR-BARTl 7-5p, ebv-miR- BARTl 1 -3p and ebv-miR-BART9-5p.
  • the method for determining whether a subject suffers from, or is at risk of developing nasopharyngeal carcinoma comprising detecting/determining the expression level of at least one miRNA from a biological sample obtained from the subject, wherein the at least one miRNA is selected from ebv-miR-BART21-3p, ebv-miR-BARTl 4-5p, ebv-miR- BARTl 9-5p, ebv-miR-BART9-3p, ebv-miR-BARTl 5, ebv-miR-BARTl 3 -3p, ebv-miR- BART3-3p, ebv-miR-BARTl l-3p, ebv-miR-BART2-5p, ebv-miR-BART8-3p, ebv-miR- BART19-3p, ebv-miR-BART3-5p,
  • the at least one miRNA is selected from ebv-miR-BART21- 3p, ebv-miR-BARTl 4-5p, ebv-miR-BARTl 9-5p, ebv-miR-BART9-3p, ebv-miR-BARTl 5, ebv-miR-BARTl 3 -3p, ebv-miR-BART3-3p, ebv-miR-BARTl l-3p, ebv-miR-BART2-5p, ebv-miR-BART8-3p, ebv-miR-BARTl 9-3p, ebv-miR-BART3-5p, ebv-miR-BART9-5p, ebv- miR-BARTl 0-3p and ebv-miR-BART2-3p.
  • the method for determining whether a subject suffers from, or is at risk of developing NPC comprising detecting/determining the expression level of at least one miRNA from a biological sample obtained from the subject, wherein the at least one miRNA is selected from ebv-miR-BART21-3p, ebv-miR-BART14-5p, ebv-miR-BART19-5p, ebv-miR- BARTl-5p, ebv-miR-BART19-3p, ebv-miR-BART7-3p, ebv-miR-BART8-3p, ebv-miR- BART13-3p, ebv-miR-BART10-3p, ebv-miR-BART3-3p, ebv-miR-BARTl l-5p, ebv-miR- BART16, ebv-miR-BARTl
  • the method may comprise detecting/determining at least one additional miRNA selected from ebv-miR-BARTl 5, ebv-miR-BART3-5p, ebv-miR- BART17-5p, ebv-miR-BARTl 7-3p, ebv-miR-BART2-3p, ebv-miR-BART5-5p, ebv-miR- BART4-5p, ebv-miR-BART21-5p, ebv-miR-BARTl 8-5p, ebv-miR-BART8-5p, ebv-miR- BART4-3p and ebv-miR-BHRFl-2-3p.
  • the level of expression of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve additional miRNAs selected from ebv-miR-BART15, ebv-miR-BART3-5p, ebv-miR-BART17-5p, ebv- miR-BART17-3p, ebv-miR-BART2-3p, ebv-miR-BART5-5p, ebv-miR-BART4-5p, ebv-miR- BART21-5p, ebv-miR-BART18-5p, ebv-miR-BART8-5p, ebv-miR-BART4-3p and ebv-miR- BHRFl-2-3p is determined/detected/measured.
  • the method comprises detecting expression levels of at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven or at least twelve miRNAs.
  • the methods above further comprise detecting Epstein-Barr virus DNA (EBV DNA) in a biological sample obtained from the subject.
  • detecting EBV DNA comprises detecting one or more nucleic acid relating to EBNA-1, EBNA-2, EBNA- 3A, EBNA-3B, EBNA-3C, EBNA-LP, LMP1, LMP2A, LMP2B, BMRF1, BALF2, BALF5, EBER1 and EBER 2.
  • detecting EBV DNA comprises detecting one or more nucleic acid relating to EBNA-1.
  • the expression level of the miRNA measured in a biological sample obtained from a subject is higher in a subject with nasopharyngeal carcinoma or is at risk of developing NPC, compared to the control.
  • control is a cancer-free subject, a nasopharyngeal carcinoma-free subject, a subject not suffering from, or not at risk of developing nasopharyngeal carcinoma.
  • the biological sample obtained from the subject is a non -cellular bodily fluid.
  • the non-cellular bodily fluid is plasma and/or serum.
  • the method further comprises treating the subject with one or more therapy selected from the group consisting of an administration of an anti-cancer compound, surgery, immunotherapy, and radiation therapy.
  • the method further comprises performing one or more clinical procedures for determining and/or confirming whether a subject is suffering from or is at risk of developing nasopharyngeal carcinoma.
  • the clinical procedures may be selected from an endoscopy (e.g. nasopharyngoscopy), a biopsy (e.g. nasopharyngeal biopsy examination), a fine needle aspiration or a diagnostic imaging test (e.g. radiological imaging procedures such as computed tomography (CT) scan, magnetic resonance imaging (MRI) scan or positron emission tomography (PET) scan).
  • CT computed tomography
  • MRI magnetic resonance imaging
  • PET positron emission tomography
  • a subject determined to be suffering from, or is at risk of developing nasopharyngeal carcinoma may be further tested using nasopharyngoscopy, nasopharyngeal biopsy examination or radiological imaging procedures such as computed tomography (CT) scan, magnetic resonance imaging (MRI) scan or positron emission tomography (PET) scan.
  • CT computed tomography
  • MRI magnetic resonance imaging
  • PET positron emission tomography
  • kits for determining whether a subject suffers from, or is at risk of developing nasopharyngeal carcinoma can, for example, comprise: (a) an isolated set of probes capable of detecting a panel of miRNAs comprising at least one miRNA selected from ebv- miR-BART21-3p, ebv-miR-BART14-5p, ebv-miR-BART19-5p, ebv-miR-BARTl-5p, ebv- miR-BART19-3p, ebv-miR-BART7-3p, ebv-miR-BART8-3p, ebv-miR-BARTl 3 -3p, ebv- miR-BART10-3p, ebv-miR-BART3-3p, ebv-miR-BARTl l-5p, ebv-miR-BART16, ebv-m-miR-
  • kits comprise an isolated set of probes capable of detecting at least one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty- four, twenty-five, twenty-six, twenty-seven or twenty -eight miRNAs.
  • the kit comprises an isolated set of probes capable of detecting a panel of miRNAs comprises at least one miRNA selected from ebv-miR-BART21-3p, ebv-miR- BART14-5p, ebv-miR-BARTl 9-5p, ebv-miR-BARTl -5p, ebv-miR-BARTl 9-3p, ebv-miR- BART7-3p, ebv-miR-BART21-3p, ebv-miR-BART8-3p, ebv-miR-BARTl 3-3p, ebv-miR- BART10-3p, ebv-miR-BARTl 4-5p, ebv-miR-BART3-3p, ebv-miR-BARTl l-5p, ebv-miR- BART16, ebv-miR- BART
  • the at least one miRNA may be selected from ebv-miR-BART21-3p, ebv-miR- BART14-5p, ebv-miR-BARTl 9-5p, ebv-miR-BARTl -5p, ebv-miR-BARTl 9-3p, ebv-miR- BART7-3p, ebv-miR-BART8-3p, ebv-miR-BART13-3p, ebv-miR-BART10-3p, ebv-miR- BART3-3p, ebv-miR-BARTl l-5p, ebv-miR-BART16, ebv-miR-BART17-5p, ebv-miR- BART11 -3p and ebv-miR-BART9-5p.
  • the kit comprises an isolated set of probes capable of detecting a panel of miRNAs comprises at least one miRNA selected from ebv-miR-BART21-3p, ebv-miR- BARTl 4-5p, ebv-miR-BARTl 9-5p, ebv-miR-BART9-3p, ebv-miR-BARTl 5, ebv-miR- BART13-3p, ebv-miR-BART3-3p, ebv-miR-BARTl l-3p, ebv-miR-BART2-5p, ebv-miR- BART8-3p, ebv-miR-BARTl 9-3p, ebv-miR-BART3-5p, ebv-miR-BART9-5p, ebv-miR- BART10-3p, ebv-miR-B
  • the at least one miRNA may be selected from ebv-miR-BART21-3p, ebv-miR-BARTl 4-5p, ebv-miR-BARTl 9-5p, ebv-miR- BART9-3p, ebv-miR-BARTl 5, ebv-miR-BARTl 3 -3p, ebv-miR-BART3-3p, ebv-miR- BARTl l-3p, ebv-miR-BART2-5p, ebv-miR-BART8-3p, ebv-miR-BARTl 9-3p, ebv-miR- BART3-5p, ebv-miR-BART9-5p, ebv-miR-BARTl 0-3p and ebv-miR-BART2-3p.
  • the kit comprises an isolated set of probes capable of detecting a panel of miRNAs comprises at least one miRNA selected from ebv-miR-BART21-3p, ebv-miR- BART14-5p, ebv-miR-BARTl 9-5p, ebv-miR-BARTl -5p, ebv-miR-BARTl 9-3p, ebv-miR- BART7-3p, ebv-miR-BART8-3p, ebv-miR-BARTl 3 -3p, ebv-miR-BARTl 0-3p, ebv-miR- BART3-3p, ebv-miR-BARTl l-5p, ebv-miR-BARTl 6, ebv-miR-BARTl l-3p, ebv-miR- BART9-5p, eb
  • the kit comprises an isolated set of probes capable of detecting at least one additional miRNAs selected from ebv-miR-BART21-3p, ebv-miR-BARTl 4-5p, ebv-miR-BARTl 9-5p, ebv-miR-BART9- 3p, ebv-miR-BARTl 5, ebv-miR-BARTl 3 -3p, ebv-miR-BART3-3p, ebv-miR-BARTl l-3p, ebv-miR-BART2-5p, ebv-miR-BART8-3p, ebv-miR-BARTl 9-3p, ebv-miR-BART3-5p, ebv- miR-BART9-5p, ebv-miR-BARTl 0-3p and ebv-miR-BART2
  • the kit may further comprise an isolated set of probes capable of detecting Epstein-Barr virus (EBV DNA).
  • EBV DNA may comprise one or more nucleic acid relating to EBNA-1, EBNA-2, EBNA-3A, EBNA-3B, EBNA-3C, EBNA-LP, LMP1, LMP2A, LMP2B, BMRF1, BALF2, BALF5, EBER1 and EBER 2.
  • EBV DNA may comprise one or more nucleic acid relating to EBNA-1.
  • the probe is selected from the group consisting of an aptamer, an antibody, an affibody, a peptide and a nucleic acid.
  • the isolated set of probes is capable of determining the level of expression of the at least one miRNA.
  • the level of expression of the at least one miRNA can, for example, be determined by methods selected from sequencing, nucleic acid hybridization, microarray and nucleic acid amplification (such as a quantitative reverse transcription- polymerase chain reaction (qRT-PCR), reverse transcription-polymerase chain reaction (RT- PCR), quantitative polymerase chain reaction (qPCR), a locked nucleic acid PCR, a clustered regularly interspaced short palindromic repeat (CRISPR) -based assay, or isothermal amplification assay).
  • qRT-PCR quantitative reverse transcription- polymerase chain reaction
  • RT-PCR reverse transcription-polymerase chain reaction
  • qPCR quantitative polymerase chain reaction
  • CRISPR clustered regularly interspaced short palindromic repeat
  • miRNA refers to microRNA, small non-coding RNA molecules, which in some examples contain about 19 to 25 nucleotides, and are found in plants, animals and some viruses. miRNAs are known to have functions in RNA silencing and post- transcriptional regulation of gene expression. These highly conserved RNAs regulate the expression of genes by binding to the 3'-untranslated regions (3'-UTR) of specific mRNAs. For example, each miRNA is thought to regulate multiple genes, and since hundreds of miRNA genes are predicted to be present in higher eukaryotes. miRNAs tend to be transcribed from several different loci in the genome.
  • RNAs with a hairpin structure that when processed by a series of RNase III enzymes (including Drosha and Dicer) form a miRNA duplex of usually about 19 to 25 nucleotides long with 2nt overhangs on the 3 ’end.
  • RNase III enzymes including Drosha and Dicer
  • nasopharyngeal carcinoma As used herein, “nasopharyngeal carcinoma”, “nasopharyngeal cancer” or “NPC” refers to a disease in which malignant (cancer) cells form in the tissues of the nasopharynx, which is located behind the nose and above the back of the throat.
  • biomarker can refer to a gene, protein, or miRNA whose level of expression or concentration in a sample is altered compared to that of a control.
  • a control refers to a level of expression or concentration of a biomarker that is indicative or correlated with a different outcome compared to the outcome of interest.
  • a biomarker can be amiRNA whose level of expression or concentration is altered (e.g., increased or decreased) compared to that of a control in a sample of a subject with a condition (i.e., NPC).
  • comparing the level of expression in the control does not necessarily entail obtaining a sample from a subject without NPC and testing said sample at the same time as the test subject.
  • said control could be a control sample incorporated in the kit or a threshold set to represent the range of expression of the biomarker where expression levels falling into this range would identify a subject as suffering from, or is at risk of suffering from, NPC.
  • biological sample is intended to include any sampling of cells, tissues, or bodily fluids in which expression of a biomarker can be detected.
  • biological samples include, but are not limited to, biopsies, smears, blood, lymph, urine, saliva, or any other bodily secretion or derivative thereof.
  • Blood can, for example, include whole blood, plasma, serum, or any derivative of blood.
  • the biological sample is a liquid biological sample.
  • the biological sample is a non-cellular bodily fluid.
  • the non-cellular bodily fluid may comprise serum and/or plasma. Samples can be obtained from a subject by a variety of techniques, which are known to those skilled in the art.
  • the term “differential expression” refers to the measurement of a cellular component in comparison to a control or another sample, and thereby determining the difference in, for example concentration, presence or intensity of said cellular component.
  • the result of such a comparison can be given in the absolute, that is a component is present in the samples and not in the control, or in the relative, that is the expression or concentration of component is increased or decreased compared to the control.
  • the terms “increased” and “decreased” in this case can be interchanged with the terms “upregulated” and “downregulated” which are also used in the present disclosure.
  • probe refers to any molecule or agent that is capable of selectively detecting an intended target biomolecule, for example, by binding directly or indirectly to the target biomolecule.
  • the target molecule can be a biomarker, for example, a nucleotide transcript or a protein encoded by or corresponding to a biomarker.
  • Probes can be synthesized by one of skill in the art, or derived from appropriate biological preparations, in view of the present disclosure. Probes can be specifically designed to be labelled. Examples of molecules that can be utilized as probes include, but are not limited to, oligonucleotides, RNA, DNA (e.g., primers), proteins, peptides, antibodies, aptamers, affibodies, and organic molecules.
  • a probe designed for the detection of a nucleic acid biomarker such a probe may be directed to the target region, the complementary nucleic acid sequence on the reverse strand, or copies of the same generated via an amplification process.
  • (statistical) classification refers to the problem of identifying to which of a set of categories (sub -populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. An example is assigning a diagnosis to a given patient as described by observed characteristics of the patient (gender, blood pressure, presence or absence of certain symptoms, etc.).
  • classification is considered an instance of supervised learning, i.e., learning where a training set of correctly identified observations is available.
  • the corresponding unsupervised procedure is known as clustering and involves grouping data into categories based on some measure of inherent similarity or distance.
  • the individual observations are analysed into a set of quantifiable properties, known variously as explanatory variables or features. These properties may variously be categorical (e.g., "A”, “B”, “AB” or “O”, for blood type), ordinal (e.g., "large”, “medium” or “small”), integer-valued (e.g., the number of occurrences of a part word in an email) or real-valued (e.g. a measurement of blood pressure).
  • classifiers work by comparing observations to previous observations by means of a similarity or distance function.
  • An algorithm that implements classification, especially in a concrete implementation, is known as a classifier.
  • classifier sometimes also refers to the mathematical function, implemented by a classification algorithm, which maps input data to a category.
  • the term “pre-trained” or “supervised (machine) learning” refers to a machine learning task of inferring a function from labelled training data.
  • the training data can consist of a set of training examples.
  • each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal).
  • a supervised learning algorithm that is the algorithm to be trained, analyses the training data and produces an inferred function, which can be used for mapping new examples.
  • An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way.
  • the term “score” refers to an integer or number, that can be determined mathematically, for example by using computational models a known in the art, which can include but are not limited to, SMV, as an example, and that is calculated using any one of a multitude of mathematical equations and/or algorithms known in the art for the purpose of statistical classification. Such a score is used to enumerate one outcome on a spectrum of possible outcomes. The relevance and statistical significance of such a score depends on the size and the quality of the underlying data set used to establish the results spectrum. For example, a blind sample may be input into an algorithm, which in turn calculates a score based on the information provided by the analysis of the blind sample. This results in the generation of a score for said blind sample.
  • a decision can be made, for example, how likely the patient, from which the blind sample was obtained, has cancer or not.
  • the ends of the spectrum may be defined logically based on the data provided, or arbitrarily according to the requirement of the experimenter. In both cases the spectrum needs to be defined before a blind sample is tested.
  • the score generated by such a blind sample for example the number “45” may indicate that the corresponding patient has cancer, based on a spectrum defined as a scale from 1 to 50, with “1” being defined as being cancer-free and “50” being defined as having cancer.
  • MiRNAs are evolutionary conserved, single -stranded non-coding RNAs of 19 to 25 nucleotides which primarily function in mediating the degradation or translational repression of mRNA targets. Under normal physiological conditions, miRNAs are key components of feedback mechanisms for a wide range of biological pathways such as cell proliferation, differentiation, and apoptosis. Conversely, dysregulated miRNAs have been implicated in the hallmarks of cancer including supporting tumour growth by inhibiting growth suppression, sustaining proliferative signalling and resisting cell death, activating invasion and metastasis, and promoting angiogenesis. It is now known that miRNAs regulate oncogenesis through their tumour suppressor or oncogenic activities, with increasing evidence of aberrant miRNA expression in a variety of malignancies.
  • NPC The diagnosis of NPC is commonly via nasopharyngoscopy, nasopharyngeal biopsy examination or radiological imaging procedures such as computed tomography (CT) scan, magnetic resonance imaging (MRI) scan or positron emission tomography (PET) scan.
  • CT computed tomography
  • MRI magnetic resonance imaging
  • PET positron emission tomography
  • Cancers of the nasopharynx are notorious for being submucosal, located deep in the pharyngeal recess, or masked by adenoidal hyperplasia in the nasopharyngeal roof. Accordingly, endoscopic tumour detection is challenging at this site, not only for asymptomatic patients in the screening setting but also for symptomatic patients in the clinical setting.
  • NPC the 10-year overall survival rate for stage I patients is as high as 98% and hence, highlighting the importance of early detection.
  • an effective liquid biopsy test able to detect NPC with high specificity and sensitivity would greatly assist early detection of the disease without the discomfort, costs or radiation exposure that limits the utility of present diagnostics methods for screening and early detection of NPC.
  • MiRNAs are deemed suitable as biomarkers because of altered miRNA expression profdes in cancer that reflect disease development, as well as the stability and the accessibility of circulating miRNAs in a myriad of body fluids including blood, urine and saliva.
  • Minimally invasive methods, such as miRNA-based liquid biopsies, can potentially overcome these disadvantages and improve overall detection accuracy.
  • Epstein-Barr virus is a herpes virus that is closely associated with the initiation and development of nasopharyngeal carcinoma (NPC), lymphoma and other malignant tumours.
  • EBV encodes 44 mature microRNAs (miRNAs) that regulate viral and host cell gene expression and plays a variety of roles in biological functions and the development of cancer.
  • the present invention relates to methods of determining whether a subject suffers from, or is at risk of developing NPC, comprising the detection and/or measurement and/or determination of the expression level of one or more biomarkers present in a biological sample obtained from the subject, specifically EBV-specific miRNAs (listed in Table 1).
  • EBV-specific miRNAs listed in Table 1.
  • miRNAs that had been identified to be useful for this purpose include, but not limited to, ebv-miR-
  • Table 1 Sequences of the miRNA biomarkers described herein (from miRbase).
  • the methods of determining whether a subject suffers from, or is at risk of developing NPC comprises detecting and/or measuring and/or determining the expression level of one or more miRNAs selected from ebv-miR-BART21-3p, ebv-miR- BART14-5p, ebv-miR-BART19-5p, ebv-miR-BARTl-5p, ebv-miR-BART19-3p, ebv-miR- BART7-3p, ebv-miR-BART8-3p, ebv-miR-BART13-3p, ebv-miR-BART10-3p, ebv-miR- BART3-3p, ebv-miR-BARTl l-5p, ebv-miR-BART16, ebv-miR-BART17-5p, ebv-m
  • the methods of determining whether a subject suffers from, or is at risk of developing NPC comprises detecting and/or measuring and/or determining the expression level of one or more miRNAs selected from ebv-miR-BART21-3p, ebv-miR-BARTl 4-5p, ebv- miR-BARTl 9-5p, ebv-miR-BART9-3p, ebv-miR-BARTl 5, ebv-miR-BARTl 3 -3p, ebv-miR- BART3-3p, ebv-miR-BARTl l-3p, ebv-miR-BART2-5p, ebv-miR-BART8-3p, ebv-miR- BART19-3p, ebv-miR-BART3-5p, ebv-miR-BART9-5p, ebv-miRNAs
  • the methods of determining whether a subject suffers from, or is at risk of developing NPC comprises detecting and/or measuring and/or determining the expression level of one or more miRNAs selected from miRNAs selected from ebv-miR-BART21-3p, ebv- miR-BARTl 4-5p, ebv-miR-BARTl 9-5p, ebv-miR-BARTl -5p, ebv-miR-BARTl 9-3p, ebv- miR-BART7-3p, ebv-miR-BART8-3p, ebv-miR-BARTl 3-3p, ebv-miR-BARTl 0-3p, ebv- miR-BART3-3p, ebv-miR-BARTl l-5p, ebv-miR-BARTl 6, ebv-miR-BARTl l-3
  • the present disclosure also discloses the utility of combining the measurement of EBV-encoded miRNA with the measurement of EBV DNA in a biological sample obtained from the test subject in improving the accuracy of the method to determine whether said subject is suffering from or is at risk of developing NPC.
  • measuring EBV DNA for this purpose comprises detecting one or more nucleic acid relating to EBV genes, antigens, antibodies and/or proteins, which include, but not limited to, EBV Nuclear Antigen (EBNA) 1 (EBNA-1), EBV Nuclear Antigen 2 (EBNA-2), EBV Nuclear Antigen 3A (EBNA-3A), EBV Nuclear Antigen 3B (EBNA-3B), EBV Nuclear Antigen 3C (EBNA-3C), EBV Nuclear Antigen Leader Protein (EBNA-LP), Latent Membrane Protein 1 (LMP1), Latent Membrane Protein 2A (LMP2A), Latent Membrane Protein 2B (LMP2B), BMRF1, BALF2, BALF5, EBV-encoded RNA 1 (EBER 1) and EBV-encoded RNA 2 (EBER 2).
  • EBNA EBV Nuclear Antigen
  • LMP1 Latent Membrane Protein 1
  • LMP2A Latent Membrane Protein 2A
  • LMP2B Latent Membrane Protein 2B
  • detecting and/or measuring and/or determining the level of EBNA-1 comprises detecting one or more nucleic acid relating to EBNA-1.
  • EBV DNA Epstein-Barr virus DNA
  • Ct threshold cycle
  • the EBV DNA may be determined by methods such as sequencing, nucleic acid hybridization, microarray and nucleic acid amplification (e.g. quantitative polymerase chain reaction).
  • EBV DNA may be carried out before, after or concurrently with the detection of EBV miRNAs and such analysis may be carried out in the same sample, different aliquots of the same sample or samples obtained separately from the same subject.
  • the level of expression of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve additional miRNAs selected from ebv-miR-BARTl 5, ebv-miR-BART3-5p, ebv-miR- BART17-5p, ebv-miR-BARTl 7-3p, ebv-miR-BART2-3p, ebv-miR-BART5-5p, ebv-miR- BART4-5p, ebv-miR-BART21-5p, ebv-miR-BARTl 8-5p, ebv-miR-BART8-5p, ebv-miR- BART4-3p and ebv-miR-BHRFl-2-3p is determined and/or detected and/or measured.
  • detection/measurement/determination of the one or more miRNAs above may be combined with detection of EBV DNA in the same subject.
  • the method as described herein comprises determining and/or detecting and/or measuring the level of expression of at least one or at least two or at least three miRNA selected from ebv-miR-BART21-3p, ebv-miR-BART14-5p, and ebv-miR-BART19-5p, and at least one or more miRNA selected from ebv-miR-BART21-3p, ebv-miR-BART14-5p, ebv- miR-BART19-5p, ebv-miR-BARTl-5p, ebv-miR-BART19-3p, ebv-miR-BART7-3p, ebv- miR-BART8-3p, ebv-miR-BART13-3p, eb
  • the level of expression of miRNA panels as described in Table 8, Table 9, Table 11, Table 12, Table 14, or Table 15, or as described herein, is determined and/or detected and/or measured.
  • detection/measurement/determination of the one or more miRNAs above may be combined with measurement of EBV DNA in the same subject.
  • the level of expression of miRNA panels as described in Table 16a, Table 16b, or Table 16c, or as described herein, is determined and/or detected and/or measured.
  • the present disclosure refers to a method of determining whether a subject suffers from, or is at risk of developing NPC, the method comprising: (i) detecting the presence of miRNA in a biological sample obtained from the subject; (ii) detection and/or measuring and/or determining the expression level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty, at least twenty-one, at least twenty-two, at least twenty-three, at least twenty -four, at least twenty -five, at least twenty-six , at least twenty-seven or at least twenty-eight miRNAs selected from ebv-miR-BART21-3p, ebv-miR-BARTl 4-5p, ebv-
  • the level of expression of miRNA panels as described in Table 8, Table 9, Table 11, Table 12, Table 14, or Table 15, or as described herein, is determined and/or detected and/or measured.
  • the methods provided herein further comprises detecting the EBV DNA in a biological sample obtained from the subject.
  • EBV DNA is detected and the miRNAs disclosed above are also determined to be differentially expressed in the sample from the subject compared to a control, said subject is determined to suffer from NPC or to be at risk of developing NPC.
  • the level of expression of miRNA panels as described in Table 16a, Table 16b, or Table 16c, or as described herein, is determined and/or detected and/or measured.
  • a subject determined to be suffering from, or is at risk of developing NPC may be further tested using endoscopy, biopsy, fine needle aspiration or diagnostic imaging tests including but not limited to nasopharyngoscopy, nasopharyngeal biopsy examination or radiological imaging procedures such as computed tomography (CT) scan, magnetic resonance imaging (MRI) scan or positron emission tomography (PET) scan.
  • CT computed tomography
  • MRI magnetic resonance imaging
  • PET positron emission tomography
  • the present disclosure provides for a method of treating a subject determined to be suffering from NPC using the methods disclosed herein.
  • a subject would be referred to a medical practitioner and, where determined to be appropriate, treated with a suitable anti-cancer compound, surgery, immunotherapy or radiation therapy or combinations of these.
  • treatment options may include surgery, radiation therapy (including but not limited to external radiation therapy, intensity-modulated radiation therapy, proton therapy, stereotactic radiosurgery or brachytherapy), chemotherapy (including but not limited to combinations of gemcitabine and cisplatin; docetaxel with cisplatin and 5 -fluorouracil (5-FU); cisplatin and 5 -fluorouracil; cisplatin and capecitabine; or docetaxel and cisplatin; nedaplatin; carboplatin, or oxaliplatin, adjuvant chemotherapy including cisplatin, 5 -fluorouracil, and/or carboplatin, targeted therapy (such as monoclonal antibodies targeting the epidermal growth factor receptor (EGFR) such as cetuximab) or immunotherapy (e.g. PD-1 and PDL-1 inhibitors such as pembrolizumab or nivolumab).
  • EGFR epidermal growth factor receptor
  • the method as described herein comprises determining and/or detecting and/or measuring the level of expression of at least one or at least two or at least three miRNA selected from ebv-miR-BART21-3p, ebv-miR-BARTl 4-5p, and ebv-miR-BARTl 9-5p, and at least one or more miRNA selected from ebv-miR-BART21-3p, ebv-miR-BARTl 4-5p, ebv- miR-BARTl 9-5p, ebv-miR-BARTl -5p, ebv-miR-BARTl 9-3p, ebv-miR-BART7-3p, ebv- miR-BART8-3p, ebv-miR-BARTl 3 -3p, ebv-miR-BARTl 0-3p, ebv-miR-BART3-3
  • the level of expression of miRNA panels as described in Table 8, Table 9, Table 11, Table 12, Table 14, or Table 15, or as described herein, is determined and/or detected and/or measured. In some embodiments, the level of expression of miRNA panels as described in Table 16a, Table 16b, or Table 16c, or as described herein, is determined and/or detected and/or measured.
  • control may include a healthy subject, a non-diseased subject, a cancer-free subject, aNPC-free subject, a subject not suffering from, or not at risk of developing nasopharyngeal carcinoma.
  • the present disclosure refers to a kit for use in determining whether a subject suffers from, or is at risk of developing NPC in a biological sample obtained from the subject
  • the kit comprises: (a) an isolated set of probes capable of detecting at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty, at least twenty-one, at least twenty-two, at least twenty-three, at least twenty-four, at least twenty-five, at least twenty-six , at least twenty-seven or at least twenty -eight miRNAs selected from ebv- miR-BART21-3p, ebv-miR-BARTl 4-5p, ebv-miR-BARTl 9-5p, ebv-m
  • the kit as described herein comprises probes capable of detecting at least one or at least two or at least three miRNA selected from ebv-miR-BART21-3p, ebv-miR- BART14-5p, and ebv-miR-BART19-5p, and at least one or more miRNA selected from ebv- miR-BART21-3p, ebv-miR-BART14-5p, ebv-miR-BART19-5p, ebv-miR-BARTl-5p, ebv- miR-BART19-3p, ebv-miR-BART7-3p, ebv-miR-BART8-3p, ebv-miR-BART13-3p, ebv- miR-BART10-3p, ebv-miR-BART3-3p, ebv-miR-BARTl l-5p, e
  • the kit comprises: the isolated set of probes capable of detecting the level of expression of miRNA panels as described in Table 8, Table 9, Table 11, Table 12, Table 14, or Table 15, or as described herein.
  • said kit may further comprise an isolated set of probes capable of detecting EBV DNA.
  • EBV DNA may further comprise one or more nucleic acid relating to EBNA-1, EBNA-2, EBNA-3A, EBNA-3B, EBNA-3C, EBNA-LP, LMP1, LMP2A, LMP2B, BMRF1, BALF2, BALF5, EBER1 and EBER 2.
  • EBV DNA may further comprise one or more nucleic acid relating to EBNA-1.
  • the kit comprises: the isolated set of probes capable of detecting the level of expression of miRNA panels as described in Table 16a, Table 16b, or Table 16c, or as described herein.
  • compositions/agents/reagents for use in the methods disclosed herein.
  • such compositions/agents/reagents may include, but are not limited to, probes, antibodies, affibodies, nucleic acids, and/or aptamers.
  • the compositions can detect (or detects) the level of expression (e.g., miRNA) of a panel of biomarkers from a biological sample.
  • kits can include all components necessary or sufficient for assays, which can include, but is not limited to, target enrichment reagents, detection reagents (e.g., probes and/or fluorescent dyes), buffers, control reagents (e.g., positive and negative controls), amplification reagents, solid supports, labels, instruction manuals, calibrators, and reference materials, etc.
  • the kit comprises a set of probes for the panel of biomarkers and a solid support to immobilize the set of probes.
  • the kit comprises a set of probes for the panel of biomarkers, a solid support, and reagents for processing the sample to be tested (e.g., reagents to isolate the protein or nucleic acids from the sample).
  • DNA-, RNA-, and protein-based detection methods that either directly or indirectly detect the biomarkers described herein.
  • the present invention also provides compositions, reagents, and kits for such diagnostic purposes.
  • the diagnostic methods described herein may be qualitative or quantitative. Quantitative diagnostic methods may be used, for example, to compare a detected biomarker level to a cut-off or threshold level. Where applicable, qualitative or quantitative diagnostic methods can also include amplification of target, signal, or intermediary.
  • biomarkers are detected at the nucleic acid (e.g., DNA or RNA) level.
  • the amount of biomarker RNA (e.g., miRNA) present in a sample is determined (e.g., to determine the level of biomarker expression).
  • Biomarker nucleic acid e.g., miRNA, amplified cDNA, etc.
  • Biomarker nucleic acid can be detected/quantified using a variety of nucleic acid techniques known to those of ordinary skill in the art, including but not limited to, sequencing, nucleic acid hybridization (e.g., northern blot), microarray and nucleic acid amplification (e.g., quantitative reverse transcription polymerase chain reaction (qRT-PCR), reverse transcription polymerase chain reaction (RT-PCR), quantitative polymerase chain reaction (qPCR), a locked nucleic acid (LNA) real-time PCR, a CRISPR-based assay, or isothermal amplification assay.
  • qRT-PCR quantitative reverse transcription polymerase chain reaction
  • RT-PCR reverse transcription polymerase chain reaction
  • qPCR quantitative polymerase chain reaction
  • LNA locked nucleic acid
  • An isothermal amplification assay can, for example, include, but is not limited to, a nicking endonuclease amplification reaction (NEAR) assay, a transcription mediated amplification (TMA) assay, a loop-mediated isothermal amplification (LAMP) assay, a helicase -dependent amplification (HD A) assay, a clustered regularly interspaced short palindromic repeat (CRISPR) assay, or a strand displacement amplification (SDA) assay.
  • NEAR nicking endonuclease amplification reaction
  • TMA transcription mediated amplification
  • LAMP loop-mediated isothermal amplification
  • HD A helicase -dependent amplification
  • CRISPR clustered regularly interspaced short palindromic repeat
  • SDA strand displacement amplification
  • the method used to detect miRNA biomarkers may comprise the use of the assay methodologies disclosed in WO2011159256A1 and a kit for the detection of miRNAs may comprise the stem-loop oligonucleotides designed based on the teachings of WO2011159256A1, the disclosure of which is incorporated herein by reference.
  • range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the disclosed ranges. Accordingly, the description of a range should be considered to have specifically disclosed all the possible sub-ranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed sub-ranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.
  • biomarkers e.g., miRNAs
  • a biomarker panel can be combined to calculate the disease risk score, for example using a linear model.
  • An example would be to calculate such a risk score using logistic regression, a form of linear model.
  • the prediction score may also be calculated using a classification algorithm selected from the group comprising support vector machine algorithm, logistic regression algorithm, multinomial logistic regression algorithm, Fisher’s linear discriminant algorithm, quadratic classifier algorithm, perceptron algorithm, k-nearest neighbours algorithm, artificial neural network algorithm, random forests algorithm, decision tree algorithm, naive Bayes algorithm, adaptive Bayes network algorithm, and ensemble learning method combining multiple learning algorithms.
  • the challenge in the field pertains to identifying relevant biomarkers, such as circulatory miRNAs, that could be applied to identify an individual at risk of a disease such as NPC.
  • relevant miRNAs could be identified via exhaustive and well-designed studies, it would be within the skill of someone aware of the state of the art to apply the measured level of the relevant miRNAs in such statistical models to generate a score for the prediction of the risk of a subject having NPC.
  • Examples of such mathematical methods used to perform the calculations disclosed herein, for example, the calculation of a prediction score can be, but are not limited to, support vector machine algorithm, logistic regression algorithm, multinomial logistic regression algorithm, Fisher’s linear discriminant algorithm, quadratic classifier algorithm, perceptron algorithm, k- nearest neighbours algorithm, artificial neural network algorithm, random forests algorithm, decision tree algorithm, naive Bayes algorithm, adaptive Bayes network algorithm, and ensemble learning method combining multiple learning algorithms.
  • the calculation of the prediction score is calculated using linear models and support vector machine algorithms.
  • the control and subjects with NPC have different disease risk score values calculated. Fitted probability distributions of the disease risk scores for the control and subjects with NPC show a separation between the two groups can be found. Based on this prior probability and the fitted probability distributions previously determined, the probability (risk) of an unknown subject having NPC can be calculated based on their disease risk score values. With higher score, the subject has higher risk of having NPC. Furthermore, the disease risk score can, for example, tell the fold change of the probability (risk) of an unknown subject having NPC compared to, for example, the NPC rate in high-risk population.
  • Formula 1 below exemplifies the use of a linear model for NPC risk prediction, where the disease risk score (unique for each subject) indicates the likelihood of a subject having NPC. This is calculated by the summing the weighted measurements for, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, or 19 miRNAs.
  • Ki - the coefficients used to weight multiple miRNA targets and C - constant, can be derived through the application of a linear model. Subjects with disease risk score lower than 0 will be considered as 0 and subjects with disease risk score higher than 100 will be considered as 100. It would be within the understanding of someone skilled in the art, knowing the identity of the relevant miRNA biomarkers, to derive the relevant disease risk scores and cut-offs to identify a subject at risk of having NPC.
  • a further example of such an algorithm for risk score calculation includes the use of logistic regression models:
  • a further example of such an algorithm further incorporates the use of a reference sample with known levels of assayed miRNAs to normalize the score for each assay run to account for run- to-run variations (which may be referred to as the Quantitative Reference (QR)), and in some embodiments, the mean value for the QR scores may be used to calculate the expected QR scores.
  • QR Quantitative Reference
  • the mean value for the QR scores may be used to calculate the expected QR scores.
  • Maxwell® RSC miRNA Plasma and Serum Kit (AS1680, Promega, USA) was used to purify total nucleic acid from plasma samples.
  • a set of 3 proprietary spike -in controls (MiRXES, Singapore) representing high, medium, and low expression levels of miRNA were added into a mixture of 80 pL of Proteinase K and 230 pL of Lysis Buffer C. This mixture was transferred into 400 pL plasma sample, followed by vortexing and incubating the lysate at 37 °C for 15 minutes. Lysate was pipetted into Maxwell® RSC Cartridge and then automatically purified by the Maxwell® RSC Instrument using miRNA Plasma and Serum method and eluted in 60 pL of Nuclease-Free Water. RNA was stored at -80°C until further processing.
  • Cq Quantification cycle
  • Multivariate analyses using logistic regression were undertaken to classify the biomarkers suitable to be combined into a panel for determining whether a subject is suffering from or is at risk of developing NPC.
  • a five-fold cross-validation procedure that incorporated the sequential forward floating search (SFFS) algorithm and the logistic regression model was used for building and optimizing miRNA biomarker panels to discriminate between NPC cases and non-cancer controls.
  • the SFFS was used to select miRNA biomarkers for inclusion in each biomarker panel built.
  • the 40 (or 48 for batch 2) samples were randomly partitioned into two groups: Group A and Group B.
  • Group A was first used as the training set for building an NPC prediction model while Group B was used as the test set.
  • a logistic regression prediction model was built, and the diagnostic ability of each panel was evaluated using the area under the curve of the receiver operating characteristics (AUC) analysis.
  • the cross-validation procedure was carried out five times.
  • the diagnostic power (AUC) of each optimized multi-miRNA panel for classifying NPC and non-cancer patient samples was then calculated and compared with other panels optimized in each iteration.
  • AUC receiver operating characteristics
  • Example 1 Discovery and validation of EBV miRNAs for the diagnosis of nasopharyngeal carcinoma
  • the expression levels of EBV miRNAs are quantitated in study subjects from a cohort in two different batches (i.e., batch 1 and 2).
  • the study subjects from batch 1 comprises 20 NPC plasma samples and 20 healthy controls, while those from batch 2 comprises 24 NPC plasma samples and 24 healthy controls. All samples in this cohort were obtained from a single source and the clinical characteristics of these samples are as shown in Table 2.
  • multi-miRNA panels were assessed.
  • the bestperforming multi-miRNA panel comprising between one to fifteen miRNAs were formed from all 44 EBV miRNAs using a stratified five-fold cross-validation procedure that incorporated the Sequential Forward Floating Selection (SFFS) algorithm.
  • AUC of the multi-miRNA panels in the training and test group was calculated for five iterations.
  • the mean AUC for NPC prediction from five iterations of training and testing was calculated for each miRNA from the results of cross-validation experiments comprising two to eight miRNAs (Tables 3 and 4).
  • the median AUC increased significantly (p ⁇ 0.001) with a mean AUC of >0.9 achievable with a panel comprising at least 2 miRNAs; hence indicating that a panel of at least two miRNAs is useful for determining whether a subject is suffering from or is at risk of developing nasopharyngeal carcinoma.
  • the AUC of the miRNAs individually may be below that of EBV DNA, but the combinations of several of these miRNAs produces multivariate panels able to identify subjects with NPC, or at risk of developing NPC with AUC higher than measuring EBV DNA.
  • miRNAs selected from ebv-miR-BARTl-5p, ebv-miR-BART19-3p, ebv-miR-BART7-3p, ebv-miR-BART21-3p, ebv-miR-BART8-3p, ebv-miR-BART13-3p, ebv-miR-BART10-3p, ebv-miR-BART14-5p, ebv-miR-BART3-3p, ebv-miR-BARTl l-5p, ebv-miR-BART16, ebv-miR-BART17-5p, ebv-miR-BARTl l-3p, ebv- miR-BART9-5p, ebv-miR-BART2-5p, ebv-miR-BARTl 7-3
  • EBV DNA is also included as a feature in the analysis and it was found that the combination of the presence of EBV DNA with one or more EBV miRNA significantly improved the accuracy for determining whether a subject is suffering from or is at risk of developing NPC. Therefore, adding one or more miRNA to the measurement of circulating EBV DNA could improve the utility of this test to provide AUC values greater than the measurement of EBV DNA alone.
  • Batch 1 mean AUC values for the use of: (a) miRNAs alone or in combinations of up to 8 miRNAs; (b) selected miRNAs with lower single-feature AUCs combined only with each other; and (c) miRNAs in combination with EBV DNA for diagnosing NPC.
  • the AUC of the miRNAs individually may be below that of EBV DNA, but the combinations of several of these miRNAs produces multivariate panels able to identify subjects with NPC, or at risk of developing NPC with AUC higher than measuring EBV DNA.
  • Batch 2 mean AUC values for the use of: (a) miRNAs alone or in combinations of up to 8 miRNAs; (b) selected miRNAs with lower single-feature AUCs combined only with each other; and (c) miRNAs in combination with EBV DNA for diagnosing NPC.
  • Example 3 Exemplary panels comprising EBV miRNAs for the diagnosis of nasopharyngeal carcinoma
  • exemplary panels of both batches comprising miRNAs listed in above Tables 3a and 4a alone and in combination with EBV DNA were analysed.
  • the mean AUC, mean sensitivity, mean specificity, mean Positive Predictive Value (PPV) and mean Negative Predictive Value (NPV) were as shown in Tables 5 and 6, respectively, for the two batches.
  • exemplary panels comprising 1-8 EBV encoded miRNAs or 1-7 EBV encoded miRNA with EBV DNA detectable in human plasma may be used for the use to detect NPC with the mean AUC ranging from 0.84 up to 0.94.
  • a person skilled in the art with knowledge of the identity of the miRNAs listed in Tables 3 and 4, and their utility disclosed herein, would be able to design other panels comprising any number of features wherein the measurement of any number of the miRNAs disclosed herein, whether measured alone or in combination with EBV DNA, shall yield a panel suitable for determining whether a subject is suffering from, or is at risk of developing NPC with similarly high performance.
  • Batch 1 mean AUC, sensitivity, specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) values from the combination of one to eight features where: (a) the features comprise the miRNAs listed in Table 3a); and (b) where the features further comprise EBV DNA.
  • PPV Positive Predictive Value
  • NPV Negative Predictive Value
  • Batch 2 mean AUC, sensitivity, specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) values from the combination of one to eight features where: (a) the features comprise the miRNAs listed in Table 4(a); and (b) where the features further comprise EBV DNA.
  • PPV Positive Predictive Value
  • NPV Negative Predictive Value
  • top performing miRNAs from both batches were selected, followed by addition of other miRNAs to improve the AUC of the panels.
  • these top performing miRNAs include, but not limited to, ebv-miR-BART21-3p, ebv-miR-BART14-5p and ebv-miR-BART19-5p, which demonstrated good performance in both batches.
  • one or more of these top miRNAs may be selected and may be combined with other miRNAs to form a panel. The selection of these miRNAs is not limited to any order.
  • Table 8 Exemplary panels of EBV miRNAs combinations with ebv-miR-BART21-3p, and their respective AUC, sensitivity, specificity, PPV and NPV in identifying subjects with, or at risk of developing nasopharyngeal carcinoma.
  • Table 9 Further exemplary panels of EBV miRNAs combinations with ebv-miR- BART21-3p, and their respective AUC, sensitivity, specificity, PPV and NPV in identifying subjects with, or at risk of developing nasopharyngeal carcinoma.
  • Exemplary panels with ebv-miR-BART14-5p as one of the miRNAs are as shown in Tables 11 and 12. With each successive addition of a miRNA, it was observed that the AUCs increased accordingly.
  • Table 11 Exemplary panels of EBV miRNAs combinations with ebv-miR-BART14-5p, and their respective AUC, sensitivity, specificity, PPV and NPV in identifying subjects with, or at risk of developing nasopharyngeal carcinoma.
  • Exemplary panels with ebv-miR-BART19-5p as one of the miRNAs are as shown in Tables 14 and 15. With each successive addition of a miRNA, it was observed that the AUCs increased accordingly.
  • Table 14 Exemplary panels of EBV miRNAs combinations with ebv-miR-BART19-5p, and their respective AUC, sensitivity, specificity, PPV and NPV in identifying subjects with, or at risk of developing nasopharyngeal carcinoma.
  • Table 15 Further exemplary panels of EBV miRNAs combinations with ebv-miR- BART19-5p, and their respective AUC, sensitivity, specificity, PPV and NPV in identifying subjects with, or at risk of developing nasopharyngeal carcinoma.
  • Exemplary panels with EBV DNA Similarly, exemplary panels having EBV DNA with miRNAs are as shown in Table 16. With each successive addition of a miRNA, it was observed that the AUCs increased accordingly.
  • Tan LP Tan GW
  • Sivanesan VM Goh SL, Ng XJ, Lim CS, Kim WR, Mohidin TBBM, Mohd Dali NS, Ong SH, Wong CY, Sawali H, Yap YY, Hassan F, Pua KC, Koay CE,

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Abstract

L'invention concerne des biomarqueurs associés au carcinome nasopharyngé (CNP) et des procédés de détermination du fait qu'un sujet est ateint ou présente un risque de développer un CNP, le procédé comprenant la détection du niveau d'expression différentielle d'au moins un ou plusieurs miARN à partir d'un échantillon biologique du sujet.
PCT/SG2022/050792 2021-11-01 2022-11-01 Panel de microarn circulant pour la détection d'un carcinome nasopharyngé et procédés associés WO2023075710A2 (fr)

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