Method for detecting high-risk nasopharyngeal carcinoma

Abstract

The present invention relates generally to the field of oncology. Provided herein is a method of predicting the likelihood of cancer recurrence or prognosis in a subject comprising comparing the expression level of one or more biomarkers selected from the group consisting of fibroblast growth factor 2 (FGF2), complement component 3 (C3) and GTPase, IMAP family member 7 (GIMAP7) in a sample obtained from the subject to a reference. In particular, the cancer is nasopharynx cancer. Also provided are compositions or solid supports comprising a plurality of DNA / RNA complexes, where each DNA / mRNA complex of the plurality of DNA / RNA complexes comprises a biomarker and first and second DNA probes that hybridize to the biomarker, where the plurality of DNA / mRNA complexes comprise one or more biomarkers selected from the group consisting of FGF2, C3, and GIMAP7.

Description

[Technical field]

[0001] The present invention relates generally to the field of oncology. Provided herein is a method for detecting and classifying nasopharyngeal carcinoma. [Background technology]

[0002] Nasopharyngeal carcinoma (NPC) is a type of cancer that arises in the nasopharynx. While NPC is rare in the United States, it occurs frequently in other parts of the world, such as southern China and Southeast Asia. It is the second most common cancer in middle-aged men in Singapore. Other risk factors for NPC include exposure to the Epstein-Barr virus or excessive alcohol consumption. This cancer is difficult to detect early and has a high rate of disease recurrence, approximately 30% to 40%. In addition, treatment options are limited and are often associated with significant morbidity. Currently, no molecular method can accurately identify patients at high risk of recurrence.

[0003] It would be desirable to overcome or ameliorate at least one of the above problems, or to at least provide a useful alternative. [Summary of the Invention]

[0004] Disclosed herein is a method for predicting the likelihood of cancer recurrence in a subject, the method comprising comparing the expression levels of one or more biomarkers selected from the group consisting of fibroblast growth factor 2 (FGF2), complement component 3 (C3) and GTPase, IMAP family member 7 (GIMAP7) in a sample obtained from the subject with a reference, wherein a change in the expression level of the one or more biomarkers compared to the reference or a value derived therefrom predicts the likelihood of cancer recurrence in the subject.

[0005] Disclosed herein is a method for determining the prognosis of cancer in a subject, the method comprising comparing the expression level of one or more biomarkers selected from the group consisting of FGF2, C3 and GIMAP7 in a sample obtained from the subject with a reference, wherein a change in the expression level of one or more biomarkers compared to the reference or a value derived therefrom indicates that the subject may have a high-risk cancer or a low-risk cancer.

[0006] Disclosed herein is a method for treating cancer in a subject, the method comprising: (a) comparing the expression level of one or more biomarkers selected from the group consisting of FGF2, C3 and GIMAP7 in a sample obtained from the subject with a reference, wherein a change in the expression level of one or more biomarkers compared to the reference or a value derived therefrom indicates that the subject may have a high-risk cancer; and (b) administering an anti-cancer agent and / or radiation therapy to the subject found to be likely to have a high-risk cancer.

[0007] In one embodiment, subjects who are found to be likely to have high-risk cancers will be followed up more closely. Subjects may receive further treatment to reduce the risk of cancer recurrence. For example, subjects may be administered second-line anticancer agents. Subjects may also receive targeted therapies for FGF2 and FGFR. Subjects may also receive immunotherapy or anti-vascular endothelial growth factor therapy.

[0008] The present invention also discloses a method for stratifying a subject into a person having high risk or low risk of cancer, the method comprising comparing the expression level of one or more biomarkers selected from the group consisting of FGF2, C3 and GIMAP7 in a sample obtained from the subject with a reference, wherein the change in the expression level of one or more biomarkers compared to the reference or the value derived therefrom classifies the subject into a subject having high risk or low risk of cancer.

[0009] Disclosed herein is a composition or solid support comprising a plurality of DNA / mRNA complexes, wherein each of the plurality of DNA / mRNA complexes comprises a biomarker and a first and a second DNA probe hybridized to the biomarker, wherein: the first probe is a capture probe; the second probe is a reporter probe; the biomarker is an mRNA transcript; and the plurality of DNA / mRNA complexes comprise one or more biomarkers selected from the group consisting of FGF2, C3 and GIMAP7.

Brief Description of the Figures

[0010] Embodiments of the present invention are described below by way of non-limiting examples only, with reference to the accompanying drawings, in which:

[0011] Figure 1 (a) Heatmap of differentially expressed genes in microdissected tumor epithelial compartments in the discovery cohort comparing primary NPC tumors that ultimately relapsed to those that did not (n = 34 gene expression libraries). (b) Unsupervised hierarchical clustering demonstrated that a three-gene signature accurately classified tumors that relapsed from those that did not relapse in the discovery cohort.

[0012] Figure 2 (a) Unsupervised hierarchical clustering showed that the three gene signatures defined clusters of tumors based on their risk profiles for developing recurrent disease in the first validation cohort. (b) The risk scores derived from the three gene signatures showed that tumors that relapsed had higher risk scores compared with tumors that did not relapse. (c) Survival analysis based on Kaplan-Meier plots showed that patients with tumors with higher risk scores had worse disease-free survival compared with patients with tumors with lower risk scores.

[0013] Figure 3(a) Unsupervised hierarchical clustering showed that the three gene signatures defined clusters of tumors based on their risk profiles for developing recurrent disease in the second validation cohort. (b) The risk scores derived from the three gene signatures showed that tumors that relapsed had higher risk scores compared with tumors that did not relapse. (c) Survival analysis based on Kaplan-Meier plots showed that patients with tumors with higher risk scores had worse disease-free survival compared with patients with tumors with lower risk scores.

[0014] Figure 4 (a) Representative images of a cell migration assay (scratch assay) showing increased migration of C666-1 cells in medium containing 5 ng / ml FGF2 compared to control. (b) Quantification of cells migrating to the wounded area from the same cell migration assay. (c) Cell invasion assay showing increased invasion of C666-1 cells when incubated with medium containing FGF2 compared to control. [Detailed description of the invention]

[0016] This specification describes a method for predicting the likelihood of cancer recurrence in a subject.

[0017] Disclosed herein is a method for predicting the likelihood of cancer recurrence in a subject, the method comprising comparing the expression levels of one or more biomarkers selected from the group consisting of FGF2, C3 and GIMAP7 in a sample obtained from the subject with a reference, wherein a change in the expression level of one or more biomarkers compared to the reference or a value derived therefrom predicts the likelihood of cancer recurrence in the subject.

[0018] Without wishing to be bound by theory, the inventors have developed a microdissected gene expression classifier that is robust across different methods of quantifying gene expression (bulk or microdissected). The genes identified here are biologically relevant. FGF2 is a potentially actionable protein as clinical trials have been conducted in other solid organ cancers targeting this pathway. FGFR inhibitors have also been approved for clinical use in other cancers. C3 is a key member of the complement pathway involved in innate immunity, and GIMAP GTPases are also key immune mediators that are potentially targeted.

[0019] The term "biomarker" refers to a measurable characteristic that reflects the presence or nature (e.g., severity) of a physiological and / or pathophysiological state, including an indicator of the risk of developing a particular physiological or pathophysiological state (e.g., cancer). A biomarker may be present in a sample obtained from a subject prior to the onset of the physiological or pathophysiological state (including symptoms thereof). Thus, the presence of a biomarker in a sample obtained from a subject may indicate that the subject is at increased risk for developing the physiological or pathophysiological state or its symptoms. Alternatively, or additionally, a biomarker may be normally expressed in an individual, but its expression may be altered (i.e., increased (upregulated; overexpressed) or decreased (downregulated; underexpressed) prior to the onset of the physiological or pathophysiological state (including symptoms thereof). Thus, a change in the level of biomarker expression may indicate that the subject is at increased risk for developing the physiological or pathophysiological state or its symptoms.

[0020] Biomarkers include, for example, gene expression products, including mRNA transcripts and peptides or proteins, metabolites, etc. expressed from genes. It is understood that referring to a gene as a biomarker (sometimes also referred to herein as a biomarker gene) means that the product of the gene is a biomarker, i.e., mRNA transcripts and / or proteins expressed from biomarker genes are biomarkers. Therefore, for example, using FGF2 as a biomarker refers to mRNA transcripts from FGF2 genes and / or proteins expressed from FGF2 genes. As described herein, citing the expression of a biomarker includes the concentration, level, or activity of a biomarker, such as the concentration or level of an mRNA transcript or the concentration, level, or activity of a protein. For example, when a biomarker is an enzyme, its expression can be determined or measured by the activity level of the enzyme on a known substrate.

[0021] The term "expression" in relation to a gene refers to the transcription of a gene to produce an RNA transcript (e.g., mRNA, antisense RNA, siRNA, shRNA, miRNA, etc.) and, where appropriate, the translation of the resulting mRNA transcript into a protein. Thus, it is clear from the context that the expression of a coding sequence results from the transcription and translation of the coding sequence. In contrast, the expression of a non-coding sequence results from the transcription of the non-coding sequence.

[0022] The term "expression product" or "gene expression product" or "gene product" is used herein to refer to the RNA transcription product (transcript) of a gene, including mRNA, and the polypeptide translation product of such RNA transcript. The expression product can be, for example, unspliced ​​RNA, mRNA, splice variant mRNA, microRNA, fragmented RNA, polypeptide, post-translationally modified polypeptide, splice variant polypeptide, etc.

[0023] The term "gene" as used herein refers to any and all discrete coding regions of a cell's genome, as well as associated non-coding regions and regulatory regions. The term "gene" also refers to an open reading frame, introns, and adjacent 5' and 3' non-coding nucleotide sequences encoding a specific polypeptide that participate in expression regulation. In this regard, the gene may further comprise control signals (e.g., promoters, enhancers, termination and / or polyadenylation signals) naturally associated with a given gene, or heterologous control signals. The DNA sequence may be cDNA or genomic DNA or a fragment thereof. The gene may be introduced into a suitable vector for extrachromosomal maintenance or integration into a host.

[0024] As used herein, the terms "level" and "amount" can be used interchangeably to refer to a quantitative amount (e.g., weight or mole or number), a semi-quantitative amount, a relative amount (e.g., weight % or mole % within a class or ratio), a concentration, etc. Thus, with respect to the amount or level of a biomarker, these terms include the absolute or relative amount or concentration of the biomarker in a sample, including ratios of biomarker levels, and odds ratios of levels or ratios of odds ratios. The level or amount may also reflect a single subject or a cohort of subjects, the latter being expressed as an average or median level.

[0025] As used herein, the term "nucleic acid" or "polynucleotide" refers to mRNA, RNA, cRNA, cDNA or DNA. The term generally refers to a polymeric form of nucleotides of at least 10 bases in length, either ribonucleotides or deoxynucleotides, or a modified form of either type of nucleotide. The term includes single-stranded and double-stranded forms of DNA or RNA. "Protein," "polypeptide," and "peptide" may also be used interchangeably herein to refer to polymers of amino acid residues and their variants and synthetic analogs.

[0026] As used herein, "obtaining" means becoming possessed. For example, reference to acquiring a biomarker signature may include possessing a signature that has already been generated, such as by obtaining the signature from a computer or database, as well as generating the signature by evaluating relevant biomarkers. In another example, acquiring a sample (e.g., a biological sample) may include possessing a sample that has already been collected from a subject, as well as actively collecting a sample from a subject.

[0027] In one embodiment, the method comprises detecting the level of one or more biomarkers. The level of one or more biomarkers can be detected by techniques known in the art (e.g., RNA sequencing). In one embodiment, the method comprises comparing the levels of FGF2, C3, or GIMAP7, or consisting thereof. In one embodiment, the method comprises comparing the levels of FGF2 and C3, or consisting thereof. In one embodiment, the method comprises comparing the levels of FGF2 and GIMAP7, or consisting thereof. In one embodiment, the method comprises comparing the levels of C3 and GIMAP7, or consisting thereof. In one embodiment, the method comprises comparing the levels of FGF2, C3, and GIMAP7, or consisting thereof.

[0028] Disclosed herein is a method for determining the prognosis of cancer in a subject, the method comprising comparing the expression level of one or more biomarkers selected from the group consisting of FGF2, C3 and GIMAP7 in a sample obtained from the subject with a reference, wherein a change in the expression level of one or more biomarkers compared to the reference or a value derived therefrom indicates that the subject may have a high-risk cancer or a low-risk cancer.

[0029] The term "prognosis" as referred to herein refers to a prediction of the possible course and outcome of a clinical condition or disease. The prognosis of a patient is usually made by evaluating disease factors or symptoms that indicate a favorable or unfavorable course or outcome of the disease. The phrase "determining the prognosis" used herein refers to the process by which a skilled technician can predict the course or outcome of a patient's condition. The term "prognosis" does not refer to the ability to predict the course or outcome of a condition with 100% accuracy. On the contrary, a skilled technician will understand that the term "prognosis" refers to an increased likelihood of a certain course or outcome; that is, patients who exhibit a particular condition are more likely to have a course or outcome than those who do not exhibit the condition. The prognosis can be expressed as the expected survival time of the patient. Alternatively, the prognosis may refer to the likelihood that the disease will enter remission or the time the disease can be expected to remain in remission. The prognosis can be expressed in a variety of ways; for example, the prognosis can be expressed as the percentage chance that the patient will survive after one year, five years, ten years, or the like. Alternatively, the prognosis can be expressed as the average number of months that a patient can expect to survive due to a certain condition or disease. The prognosis of a patient may be considered a manifestation of relativism, and many factors can affect the final outcome. For example, for patients with certain diseases, the prognosis may be appropriately expressed as the likelihood that a disease may be treatable or curable, or the likelihood that the disease will go into remission, whereas for patients with more serious conditions, the prognosis may be more appropriately expressed as the likelihood of surviving a specific period of time.

[0030] The term "tumor" as used herein refers to the growth and proliferation of any tumor cell, whether malignant or benign, and all precancerous and cancerous cells and tissues. The terms "cancer" and "cancerous" refer to or describe a physiological condition of a mammal, which is typically characterized in part by unregulated cell growth. The term "cancer" as used herein refers to non-metastatic and metastatic cancers, including early and late stage cancers. The term "precancerous" refers to a condition or growth that usually precedes or develops into a cancer. "Non-metastatic" refers to a benign cancer or a cancer that remains in the primary site and does not penetrate into the lymphatic system or vascular system or tissues outside the primary site. In general, non-metastatic cancer refers to any 0, I or II stage cancer, occasionally III stage cancer. "Early stage cancer" refers to non-invasive or metastatic cancer, or a cancer that is classified as 0, I or II stage cancer. The term "late stage cancer" generally refers to III or IV stage cancer, but may also refer to II stage cancer or a substage of II stage cancer. Those skilled in the art will recognize that the classification of II stage cancer as early stage cancer or late stage cancer depends on the specific type of cancer. Examples of cancer include, but are not limited to, glioma, breast cancer, prostate cancer, ovarian cancer, cervical cancer, pancreatic cancer, colorectal cancer, lung cancer, hepatocellular carcinoma, gastric cancer, liver cancer, bladder cancer, urinary tract cancer, thyroid cancer, kidney cancer, carcinoma, melanoma, brain cancer, non-small cell lung cancer, head and neck squamous cell carcinoma, endometrial cancer, multiple myeloma, rectal cancer, and esophageal cancer. In one embodiment, the cancer is nasopharyngeal carcinoma (NPC). In one embodiment, the cancer is metastatic cancer. In one embodiment, the cancer is metastatic nasopharyngeal carcinoma (NPC).

[0031] The terms "subject", "individual" and "patient" - used interchangeably herein, are intended to refer to any subject, preferably a mammalian subject, and more preferably still a human subject. Mammalian subjects include humans, livestock, farm animals, sports animals, and zoo animals, for example, including humans, non-human primates, dogs, cats, mice, rats, guinea pigs, etc. In some embodiments, the subject has or is suspected of having nasopharyngeal carcinoma (NPC).

[0032] As used herein, the term "sample" is used in its broadest sense. In a sense, it is meant to include specimens or cultures obtained from any source, as well as biological and environmental samples. Biological samples can be obtained from animals (including humans) and include liquids, solids, tissues, and gases. Biological samples include blood products such as plasma, serum, etc. However, such examples should not be construed as limiting the types of samples applicable to the present disclosure.

[0033] The sample can be a biological sample, which refers to the fact that it is derived from or obtained from a living organism. The organism can be in vivo (e.g., a whole organism) or in vitro (e.g., cells or organs grown in culture). "Biological sample" also refers to cells or cell groups or a certain amount of tissue or fluid from a subject. In most cases, the sample has been taken from the subject, but the term "biological sample" can also refer to cells or tissues analyzed in vivo, i.e., without being taken from the subject. Typically, "biological samples" contain cells from the subject, but the term can also refer to non-cellular biological materials, such as non-cellular components of blood, saliva or urine. Biological samples may come from resection of primary, secondary or metastatic tumors, bronchoscopic biopsies or core needle biopsies, or cell blocks from pleural effusions. In addition, fine needle aspiration biological samples are also useful. In one embodiment, the biological sample is ascites. Biological samples also include explants from patient tissues and primary and / or transformed cell cultures. A biological sample can be provided by removing a sample of cells from a subject, but can also be accomplished using previously isolated cells or cell extracts (e.g., isolated by another person, at another time, and / or for another purpose). Archived tissues, such as tissues with a history of treatment or outcome, can also be used. Biological samples include, but are not limited to, tissue biopsies, scrapings (e.g., oral scrapings), whole blood, plasma, serum, urine, saliva, cell culture, or cerebrospinal fluid. In one embodiment, the sample is a tissue sample. The tissue sample can be fresh tissue, frozen tissue, or paraffin-embedded formalin-fixed (FFPE) tissue sample. In one embodiment, the tissue sample is obtained by microdissection. In one embodiment, the sample is obtained by laser capture microdissection.

[0034] The biological sample can be processed and analyzed for assessment of biomarkers almost immediately after collection (i.e., as a fresh sample), or it can be stored for later analysis. If it is desired or necessary to store the biological sample, one skilled in the art will appreciate that ideally, it should be stored under conditions that maintain the integrity of the biomarkers of interest in the sample (e.g., at -80°C).

[0035] The assessment of biomarkers can include the assessment of the level of mRNA expressed by the referenced gene and / or the level of protein expressed by the referenced gene. Methods for measuring expression products (such as transcripts and proteins) are well known to those skilled in the art, and some illustrative examples are described below.

[0036] Nucleic acid-based assays are well known in the art and include low-throughput and high-throughput assays. In an illustrative nucleic acid-based assay, nucleic acids are isolated from cells contained in a biological sample according to standard methods (Sambrook, et al., 1989, supra; and Ausubel et al., 1994, supra). Nucleic acids are typically fractionated (e.g., poly A+RNA) or whole-cell RNA. When RNA is used as a test subject, it may be necessary to convert the RNA into complementary DNA. In some embodiments, nucleic acids are amplified by template-dependent nucleic acid amplification techniques. Many template-dependent processes can be used to amplify biomarker sequences present in a given template sample. Polymerase chain reaction (PCR for short) is a typical nucleic acid amplification technique, which is described in detail in U.S. Patents Nos. 4,683,195, 4,683,202, and 4,800,159, Ausubel et al. (above) and Innis et al. ("PCR Protocols", Academic Press, Inc., San Diego Calif., 1990). Briefly, in PCR, two primer sequences are prepared that are complementary to regions on opposite complementary strands of the biomarker sequence. Excess deoxynucleotide triphosphates are added to the reaction mixture along with a DNA polymerase (e.g., Taq polymerase). If a homologous biomarker sequence is present in the sample, the primers will bind to the biomarker, and the polymerase will cause the primers to extend along the biomarker sequence by adding nucleotides. By raising and lowering the temperature of the reaction mixture, the extended primers will dissociate from the biomarker to form a reaction product, the excess primers will bind to the biomarker and the reaction product, and the process will be repeated. A reverse transcriptase PCR amplification procedure can be performed to quantify the amount of amplified mRNA. Methods for reverse transcription of RNA into cDNA are well known and described in Sambrook et al. 1989, above. Alternative methods of reverse transcription utilize thermostable RNA-dependent DNA polymerases. These methods are described in WO 90 / 07641. Polymerase chain reaction methods are well known in the art.

[0037] In certain embodiments, template-dependent amplification involves real-time quantification of transcripts. For example, RNA or DNA can be quantified using real-time PCR technology (Higuchi et al., Biotechnology 10: 413-417, 1992). By measuring the concentration of target DNA amplification products in PCR reactions that have completed the same number of cycles and are in the linear range, the relative concentration of a specific target sequence in the original DNA mixture can be determined. If the DNA mixture is cDNA synthesized from RNA isolated from different tissues or cells, the relative abundance of specific mRNA derived from the target sequence of the corresponding tissue or cell can be determined. This direct ratio between PCR product concentration and relative mRNA abundance is only correct within the linear range of the PCR reaction. The final concentration of the target DNA in the platform portion of the curve is determined by the availability of reagents in the reaction mixture and is independent of the original concentration of the target DNA. In a specific embodiment, multiple tandem PCR (MT-PCR) is used, which uses a two-step process to characterize gene expression of a small amount of RNA or DNA, such as described in U.S. Patent Application No. 20070190540. In the first step, RNA is converted into cDNA and amplified using multiple gene-specific primers. In the second step, each individual gene was quantified by real-time PCR.

[0038] In certain embodiments, the target nucleic acid is quantified using blotting techniques, which are well known to those skilled in the art. Southern blotting involves the use of DNA as a target, while Northern blotting involves the use of RNA as a target. Each method provides different types of information, although cDNA blotting is similar to blotting or RNA species in many ways. In short, probes are used to target DNA or RNA species that have been immobilized on a suitable matrix, usually a nitrocellulose filter. The different species should be separated spatially to facilitate analysis. This is usually done by gel electrophoresis of the nucleic acid material and then "blotting" it onto the filter. Subsequently, the blotted target is incubated with the probe (usually labeled) under conditions that promote denaturation and re-hybridization. Since the probe is designed to base pair with the target, the probe will bind to a portion of the target sequence under renaturing conditions. The unbound probe is then removed and detection is completed as described above. After detection / quantification, the results seen in a given subject can be compared with a control reaction defined herein or a statistically significant reference group or control subject population.

[0039] Biochip-based techniques, such as those described by Hacia et al. (1996, Nature Genetics 14: 441-447) and Shoemaker et al. (1996, Nature Genetics 14: 450-456), are also contemplated. In short, these techniques involve quantitative methods for analyzing large numbers of genes quickly and accurately. By labeling genes with oligonucleotides or using fixed probe arrays, biochip technology can be used to separate target molecules into high-density arrays and screen these molecules based on hybridization. See also Pease et al. (1994, Proc. Natl. Acad. Sci. USA 91: 5022-5026); Fodor et al. (1991, Science 251: 767-773). In short, nucleic acid probes for biomarker polynucleotides are prepared and attached to biochips for screening and diagnostic methods, as described herein. The nucleic acid probes attached to the biochip are designed to be substantially complementary to a specifically expressed biomarker nucleic acid, i.e., a target sequence (either of a sample or with other probe sequences, such as in a sandwich assay), such that hybridization occurs between the target sequence and the probe of the invention. This complementarity is not necessarily perfect; there may be any number of base pair mismatches, which will interfere with hybridization between the target sequence and the nucleic acid probe of the invention. However, if the number of mismatches is so great that hybridization does not occur even under the least stringent hybridization conditions, the sequence is not a complementary target sequence. In certain embodiments, more than one probe is used per sequence, using overlapping probes or probes directed to different parts of the target. That is, two, three, four or more probes (three of which are ideal) are used to build redundancy for a particular target. The probes can be overlapping (i.e., have some common sequence) or independent.

[0040] In an illustrative biochip analysis, oligonucleotide probes on a biochip are exposed to or contacted with a nucleic acid sample suspected of containing one or more biomarker polynucleotides under conditions that favor specific hybridization. Single-stranded or double-stranded sample extracts of DNA or RNA can be prepared from a liquid suspension of biological material, or by grinding biological material, or following a cell lysis procedure, including but not limited to lysis by SDS (or other detergents), osmotic shock, guanidine isothiocyanate, and lysozyme treatment. Suitable DNA that can be used in the methods of the present invention includes cDNA. Such DNA can be prepared by any of a number of commonly used protocols, such as those described in Ausubel, et al., 1994, and Sambrook, et al., et al., 1989, above.

[0041] Methods for assessing mRNA levels that do not require conversion of mRNA to cDNA are also known in the art and are suitable for use in the practice of the present invention. In a specific example, digital molecular barcoding technology is used to measure mRNA levels. Among such technologies are NanostringnCounter, for example. TM , color-coded molecular barcodes are used in multiplexed assays, for example. In this approach, each color-coded barcode is attached to a target-specific reporter probe, such as any number between about 50 bases to about 100 bases or 50 to 100 bases in length that hybridizes to the gene of interest. Two probes are used to hybridize to the mRNA transcript of interest: a reporter probe that carries a color signal and a capture probe that allows the probe-target complex to be immobilized on a reliable support for data collection. The probe-target complex can be immobilized on a substrate for data collection, such as an nCounter TM The cartridge is then analyzed, for example, in a digital analyzer, so that the color code for each target molecule is counted and tabulated.

[0042] Suitable RNA (including messenger RNA, complementary RNA (cRNA) transcribed from DNA, or genomic or subgenomic RNA) can be used in the methods of the present invention. Such RNA can be prepared using standard protocols, such as described in the relevant parts of Ausubel, et al. 1994, and Sambrook, et al. 1989 above.

[0043] The cDNA can be fragmented, for example by sonication or by treatment with restriction endonucleases. The cDNA is suitably fragmented so that the length of the resulting DNA fragments is greater than the length of the fixed oligonucleotide probe, but small enough to allow rapid access under suitable hybridization conditions. Alternatively, cDNA fragments can be selected and amplified using suitable nucleotide amplification techniques, for example, as described above, involving appropriate random or specific primers.

[0044] The target biomarker polynucleotide (e.g., mRNA or cDNA) or a probe that hybridizes to the target polynucleotide is typically detectably labeled so that hybridization can be detected. Detectable labels include, for example, chromogens, catalysts, enzymes, fluorescent dyes, chemiluminescent molecules, bioluminescent molecules, lanthanide ions (e.g., Eu34), radioactive isotopes, and direct visual labels. In the case of direct visual labels, colloidal metal or non-metal particles, dye particles, enzymes or substrates, organic polymers, latex particles, liposomes, or other vesicles containing signals that produce substances, etc., can be used. Illustrative labels of this type include macrocolloids, such as metal colloids, such as colloids from gold, selenium, silver, tin, and titanium oxide. In some embodiments, where an enzyme is used as a direct visual label, a biotinylated base is incorporated into the target polynucleotide.

[0045] Hybridization formation step can be carried out under suitable conditions, for hybridization oligonucleotide probe to test nucleic acid, including DNA or RNA. In this respect, can refer to, for example, NUCLEIC ACID HYBRIDIZATION, A PRACTICAL APPROACH (Homes and Higgins, eds.) (IRL press, Washington DC, 1985). Usually, whether hybridization occurs is subject to the length of oligonucleotide probe and polynucleotide sequence to be measured, pH value, temperature, monovalent and divalent cation concentration, hybridization formation region G and C nucleotide ratio, the viscosity of medium and the influence of possible denaturing agent. These variables also can affect the required time of hybridization. Therefore, optimal conditions will depend on specific application. However, this empirical condition can be routinely determined, without the need to carry out unnecessary experiments.

[0046] After the hybrid formation step, the probes are typically washed with a hybridization buffer to remove any unbound nucleic acids. This washing step leaves only the bound target polynucleotides. The probes are then examined to determine which probes hybridize to the target polynucleotide.

[0047] The hybridization reaction is then evaluated to detect the target polynucleotide / probe complex. Depending on the nature of the reporter molecule associated with the target polynucleotide or probe, the signal may be instrumentally detected by: irradiating a fluorescent label with light and detecting fluorescence in a fluorimeter; by providing an enzyme system to produce a dye that can be detected using a spectrophotometer; or detecting dye particles or colored colloidal metal or non-metal particles using a reflectometer; or in the case of radioactive labels or chemiluminescent molecules, using a radiation counter or autoradiography. Thus, the detection device may be adapted to detect or scan light associated with the label, which may include fluorescent light, luminescent light, a focused beam of light, or a laser. In this case, a charge-coupled device (CCD) or photocell may be used to scan the emitted light of the probe:target polynucleotide hybrid from each position in the microarray and the data recorded directly in a digital computer. In some cases, electronic detection of the signal may not be required. For example, for enzymatically generated color spots associated with a nucleic acid array format, visual inspection of the array will allow the pattern on the array to be interpreted. In the case of a nucleic acid array, the detection means is appropriately interfaced with pattern recognition software to convert the signal pattern from the array into a plain language genetic map. In certain embodiments, oligonucleotide probes specific for different biomarker polynucleotides are in the form of a nucleic acid array, and a "chip reader" is used to detect the signal generated from the reporter molecules on the array. For example, Pirrung et al. describe a detection system that can be used with a "chip reader" (U.S. Patent No. 5,143,854). The chip reader will also typically include some signal processing to determine whether the signal at a particular array location or feature is a true positive signal or a spurious signal. For example, Fodor et al. describe a typical chip reader (U.S. Patent No. 5,925,525). Alternatively, when the array is prepared using a mixture of individually addressable labeled microbeads, flow cytometry can be used to detect the reaction.

[0048] In other embodiments, the level of a protein expressed from a gene is assessed, for example using a protein-based assay known in the art. Antibody-based techniques can also be used to determine the level of a biomarker in a sample, non-limiting examples of which include immunoassays such as enzyme-linked immunosorbent assays (ELISA) and radioimmunoassays (RIA).

[0049] In a specific embodiment, a protein capture array is employed that allows for the simultaneous detection and / or quantification of a large number of proteins. For example, low-density protein arrays on filter membranes, such as the Universal Protein Array System (Ge, 2000 Nucleic Acids Res. 28(2): e3), allow for imaging of the array antigens using standard ELISA techniques and a scanning charge-coupled device (CCD) detector. In addition, immunosensor arrays have been developed that are capable of simultaneous detection of clinical analytes. Protein arrays can now be used to characterize protein expression in body fluids, such as serum from healthy or diseased subjects, and in subjects before and after drug treatment.

[0050] Exemplary protein capture arrays include arrays containing spatially manipulated antigen-binding molecules, often referred to as antibody arrays, which can facilitate extensive parallel analysis of numerous proteins defining a proteome or subproteome. Antibody arrays have been demonstrated to have the desired specificity and acceptable background characteristics, and some are commercially available (e.g., BD Biosciences, Clontech, BioRad, and Sigma). Various methods for preparing antibody arrays have been reported (e.g., see Lopez et al., 2003 J. Chromatogr. B 787:19-27; Cahill, 2000 Trends in Biotechnology 7:47-51; U.S. Patent Application Publication 2002 / 0055186; U.S. Patent Application Publication 2003 / 0003599; PCT Publication WO 03 / 062444; PCT Publication WO 03 / 077851; PCT Publication WO 02 / 59601; PCT Publication WO 02 / 39120; PCT Publication WO01 / 79849; PCT Publication WO 99 / 39210). The antigen-binding molecules of such an array can recognize a subset of proteins expressed by at least one cell or cell population, illustrative examples of which include growth factor receptors, hormone receptors, neurotransmitter receptors, catecholamine receptors, amino acid derivative receptors, cytokine receptors, extracellular matrix receptors, antibodies, lectins, cytokines, serine protease inhibitors, proteases, kinases, phosphatases, ras-like GTPases, hydrolases, steroid hormone receptors, transcription factors, heat shock transcription factors, DNA binding proteins, zinc finger proteins, leucine zipper proteins, homeodomain proteins, intracellular signal transduction regulators and effectors, apoptosis-related factors, DNA synthesis factors, DNA repair factors, DNA recombination factors, and cell surface antigens.

[0051] Individual spatially distinct protein capture agents are typically attached to a support surface, which is usually planar or contoured. Common physical supports include glass slides, silicon, microwells, nitrocellulose or PVDF membranes, and magnetic and other microbeads.

[0052] Particles in suspension can also be used as the basis for arrays, provided they are coded for identification; systems include microbeads (e.g., available from Luminex, Bio-Rad, and Nanomics Biosystems) and semiconductor nanocrystals (e.g., QDots TM , available from Quantum Dots), and magnetic beads (UltraPlex TM , available from Smartbeads) and multi-metal microrods (Nanobarcodes TM Beads can also be assembled into planar arrays on semiconductor chips (e.g., available from LEAPS Technologies and BioArray Solutions). When using particles, a single protein capture agent is typically attached to a single particle to provide spatial definition or separation of the array. The particles can then be analyzed individually but in parallel in a compartmentalized manner, such as in the wells of a microtiter plate or in separate test tubes.

[0053] In one illustrative example, a protein sample, optionally fragmented to form peptide fragments (see, e.g., U.S. Patent Application Pub. 2002 / 0055186), is delivered to a protein capture array under conditions suitable for protein or peptide binding, and the array is washed to remove unbound or non-specifically bound components of the sample from the array. Next, a suitable detection system is used to detect the presence or quantity of proteins or peptides bound to each feature of the array. The amount of protein bound to an array feature can be determined relative to the amount of a second protein bound to a second feature of the array. In some embodiments, the amount of the second protein in the sample is known or known to be constant.

[0054] In another illustrative example of a protein capture array, a Luminex-based multiplex assay is a bead-based multiplex assay in which the beads are internally dyed with fluorescent dyes to produce a specific spectral address. Biomolecules (such as oligomers or antibodies) can be coupled to the surface of magnetic beads to capture target analytes. Flow cytometry or other suitable imaging techniques known to those skilled in the art can then be used for characterization of the beads, as well as detection of the analytes. Luminex technology enables the detection of a large number of proteins, genes, or other gene expression products (e.g., 100 or more, 200 or more, 300 or more, 400 or more) using very small sample amounts (e.g., in 96 or 384 well plates). In some embodiments, as previously described, the protein capture array is a Bio-Plex Luminex-100 Station (Bio-Rad).

[0055] To analyze the differential expression of proteins between two cells or cell populations, a protein sample of a first cell or cell population is delivered to an array under conditions suitable for protein binding. In a similar manner, a protein sample of a second cell or cell population is delivered to a second array that is identical to the first array. Both arrays are then washed to remove unbound or non-specifically bound components of the sample from the array. In the final step, the amount of protein that remains bound to the features of the first array is compared to the amount of protein that remains bound to the corresponding features of the second array. To determine the differential protein expression pattern of two cells or cell populations, the amount of protein that is respectively bound to the features of the first array is subtracted from the amount of protein that is bound to the corresponding features of the second array.

[0056] In other examples, when the biomarker protein is an enzyme, the protein can be quantified based on its catalytic activity or based on the number of molecules of the protein contained in the sample.

[0057] In some embodiments, the level of a biomarker is normalized to a housekeeping biomarker (i.e., relative to a housekeeping biomarker), or expressed as a ratio between the level of a biomarker and the level of a housekeeping biomarker. A housekeeping biomarker is a biomarker or a group of biomarkers (e.g., polynucleotides and / or polypeptides) that are typically present at a constant level in the cell type being analyzed and under the conditions being evaluated. In some embodiments, a housekeeping biomarker is a housekeeping gene, i.e., a gene or group of genes that encodes a protein whose activity is essential for maintaining cellular function and that is typically present at a constant level in the cell type being analyzed and under the conditions being evaluated.

[0058] In one embodiment, the method comprises comparing the expression levels of mRNA or protein of one or more biomarkers.

[0059] The term "reference" can refer to samples from healthy individuals (e.g., individuals without gliomas) or to non-cancerous samples. Alternatively, "reference" can refer to samples from individuals with cancer or to cancerous (e.g., NPC) samples. It can also refer to a predetermined value.

[0060] A change in the expression level of one or more biomarkers or a value derived therefrom, compared to a reference, may refer to a change in the expression level of one or more biomarkers, or a change in a value derived from the expression level of one or more biomarkers. A change may be an increase or decrease in the expression level of one or more biomarkers. It may also be an increase or decrease in a value derived from the expression level of one or more biomarkers.

[0061] As used herein, the term "increase" or "increased" in the level of one or more biomarkers, or a value derived therefrom, refers to a statistically significant and measurable increase compared to a reference. An increase may refer to at least an increase of about 10%, or at least an increase of about 20%, or at least an increase of about 30%, or at least an increase of about 40%, or at least an increase of about 50% or more.

[0062] In one embodiment, an increase in the level of one or more biomarkers, or a value derived therefrom, compared to a reference, predicts the likelihood of cancer recurrence in a subject. The increase may be 1.1 times, 1.2 times, 1.3 times, 1.4 times, 1.5 times, 1.6 times, 1.7 times, 1.8 times, 1.9 times, 2 times, 3 times, 4 times, 5 times, 6 times, 7 times, 8 times, 9 times, 10 times, 11 times, 12 times, 13 times, 14 times, 15 times, 16 times, 17 times, 18 times, 19 times, 20 times, 21 times, 22 times, 23 times, 24 times, 25 times, 26 times, 27 times, 28 times, 29 times, 30 times, 31 times, 32 times, 33 times, 34 times, 35 times, 36 times, 37 times, 38 times, 39 times, 40 times, 41 times, 42 times, 43 times, 44 times, 45 times, 46 times, 47 times, times, 48 ​​times, 49 times, 50 times, 51 times, 52 times, 53 times, 54 times, 55 times, 56 times, 57 times, 58 times, 59 times, 60 times, 61 times, 62 times, 63 times, 64 times, 65 times, 66 times, 67 times, 68 times, 69 times, 70 times, 71 times, 72 times, 73 times, 74 times, 75 times, 76 times, 77 times, 78 times, 79 times, 80 times, 81 times, 82 times, 83 times, 84 times, 85 times, 86 times, 87 times, 88 times, 89 times, 90 times, 91 times, 92 times, 93 times, 94 times, 95 times, 96 times, 97 times, 98 times, 99 times, or 100 times or any multiple in between.

[0063] As used herein, the term "decrease" or "decreased" in the level of one or more biomarkers, or a value derived therefrom, refers to a statistically significant and measurable decrease compared to a reference. A decrease may refer to a decrease of at least about 10%, or at least about 20%, or at least about 30%, or at least about 40%, or at least about 50% or more.

[0064] In one embodiment, the value derived from the level of one or more biomarkers is a probability. The probability may come from a logistic regression model, wherein the level of one or more biomarkers is a predictor of the logistic regression model. The value can be compared with a reference, for example, it may be a cutoff value specified as 0.5.

[0065] In one embodiment, the value derived therefrom is a cancer recurrence risk score obtained by processing the expression levels of one or more biomarkers selected from the group consisting of FGF2, C3, and GIMAP7 using a risk scoring model.

[0066] In one embodiment, the risk score model is generated by performing regression on a dataset comprising gene expression data and clinical outcomes of a cohort of cancer patients.

[0067] The term "recurrence," as used herein, may refer to a cancer that has come back (come back), usually after a period of time when the cancer can't be detected. This type of cancer may be called a recurrent cancer. A recurrent cancer may come back in the same place as the original (primary) tumor or in another place in the body. When the cancer is in the same place as the original cancer or very close to the original cancer, the recurrence may be considered a "local recurrence." When the tumor has grown into lymph nodes or tissues near the original cancer, the recurrence may be a "regional recurrence." When the cancer has spread to organs or tissues far away from the original cancer, this recurrence may be called a distant recurrence. When the cancer spreads to distant places in the body, a recurrent cancer may be called a metastasis or metastatic cancer.

[0068] The term "likelihood of recurrence" may refer to how likely it is that cancer will recur in a subject. An increase in the level of one or more biomarkers, or a value derived therefrom, compared to a reference may indicate a 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 99% or greater likelihood that the subject will relapse.

[0069] It is apparent to one skilled in the art that the predicted likelihood of cancer recurrence in a subject can change, for example, from a low (including negligible) or reduced likelihood to a high or increased likelihood of recurrence. A subject with a low or reduced likelihood of recurrence means that the cancer is less likely to recur in the subject than a subject with a high or increased likelihood of recurrence. Conversely, a subject with a high or increased likelihood of recurrence means that the cancer is more likely to recur in the subject than a subject with a low or reduced likelihood of recurrence.

[0070] Likelihood is suitably based on mathematical modeling. For example, increased likelihood may be relative or absolute and may be expressed qualitatively or quantitatively. For example, increased risk may be expressed as simply determining a given biomarker level in a subject at one or more time points and placing the test subject in an "increased risk" category, based on an established reference (e.g., a reference biomarker), for example, from a previous population study at the same time point. Alternatively, a numerical representation of the test subject's increased risk may be determined based on an analysis of the biomarker level.

[0071] In some embodiments, the likelihood is assessed by comparing the level or abundance of at least one biomarker to one or more preselected levels (also referred to herein as thresholds or reference levels). Thresholds can be selected that provide acceptable ability to predict risk, treatment success, etc. In an illustrative example, a receiver operating characteristic (ROC) curve is calculated by plotting the value of a variable against its relative frequency in two populations, where the first population is considered to be at risk for cancer recurrence and the second population is considered to be at no risk or low risk for cancer recurrence (arbitrarily designated, e.g., "healthy controls").

[0072] In some embodiments, as described herein, a subject is considered to be at risk for cancer recurrence if at least one biomarker in the biomarker profile of the subject's sample is upregulated or downregulated compared to the corresponding biomarker in a healthy subject.

[0073] For any particular biomarker, the distributions of biomarker levels for subjects at risk of cancer recurrence or those not at risk of cancer recurrence may overlap. In this case, the test may not be able to absolutely (i.e., 100%) accurately distinguish subjects at risk of cancer recurrence from subjects not at risk of cancer recurrence, and the overlapping area indicates that the test cannot distinguish between the two subjects. A threshold can be selected above which (or below which, depending on how the biomarker varies with risk) the test is considered "positive" and below which it is considered "negative." The area under the ROC curve (AUC) provides a C statistic, which is a measure of the probability that the perceptual measure allows the correct identification of the condition (e.g., see Hanley et al., Radiology 143:29-36 (1982)).

[0074] In some embodiments, the positive likelihood ratio, negative likelihood ratio, odds ratio and / or AUC or receiver operating characteristic (ROC) value are used as a measure of the ability of the method for predicting the risk of cancer recurrence. As used herein, the term "likelihood ratio" refers to the probability of observing a given test result in a subject with such risk possibility divided by the probability of observing the same result in a subject without such risk possibility. Therefore, the positive likelihood ratio is the probability of observing a positive result in a subject with a specified risk divided by the probability of a positive result occurring in a subject without a specified risk. The negative likelihood ratio is the probability of a negative result occurring in a subject without a specified risk divided by the probability of a negative result occurring in a subject with a specified risk. The term "odds ratio" used herein refers to the ratio of the probability of an event occurring in one group (e.g., a healthy control group) to the probability of another group (e.g., a cancer recurrence risk group), or to the ratio of an estimate based on the data of the ratio. The term "area under the curve" or "AUC" refers to the area under the curve of a receiver operating characteristic (ROC) curve, both of which are well known in the art. The AUC metric can be used to compare the accuracy of the classifier across the entire data range. A classifier with a larger AUC has a greater ability to correctly classify unknowns between two groups of interest (e.g., healthy controls and cancer recurrence risk groups). ROC curves can be used to plot the performance of a particular feature (e.g., any biomarker described herein and / or any additional biomedical information) in distinguishing or discriminating between two groups (e.g., cases with problems and controls without problems). Typically, the feature data for the entire population (e.g., cases and controls) is sorted in ascending order based on the value of a single feature. Then, for each value of that feature, the true positive rate and false positive rate of the data are calculated. Sensitivity is determined by counting the number of samples above the feature value and then dividing by the total number. Specificity is determined by counting the number of controls below the feature value and then dividing by the total number of controls. Although this definition refers to the case where the feature is elevated in the case compared to the control, this definition also applies to the case where the feature is low in the case compared to the control (in which case, the samples below the feature value are calculated). ROC curves can be generated for a single feature as well as other single outputs, for example, a combination of two or more features can be mathematically combined (e.g., added, subtracted, multiplied, etc.) to generate a single value, and this single value can be plotted in the ROC curve. In addition, any combination of multiple features can be plotted in an ROC curve, where the combination derives a single output value. These combinations of features may constitute a test. An ROC curve is a plot of the sensitivity of a test against the specificity of the test, where sensitivity is traditionally shown on the vertical axis and specificity is traditionally shown on the horizontal axis. Thus, an "AUC ROC value" is equal to the probability that a classifier will rank a randomly selected positive instance higher than a randomly selected negative instance.The AUC ROC value can be considered equivalent to the Mann-Whitney U test, which tests the median difference between the scores obtained when the two groups are continuous data, or to the Wilcoxon test for ranks.

[0075] In one embodiment, the method further comprises treating the subject. As used herein, the term "treating" may refer to (1) preventing or delaying the onset of one or more symptoms of a disease; (2) inhibiting the development of a disease or one or more symptoms of a disease; (3) ameliorating a disease, i.e., causing regression of the disease or at least one or more symptoms of a disease; and / or (4) causing a reduction in the severity of one or more symptoms of a disease.

[0076] As used herein, the term "effective amount" or "therapeutically effective amount" refers to an amount of an active agent as described herein that is sufficient to achieve or contribute to one or more desired clinical outcomes, such as those described in the "treatment" and "prevention" descriptions above. In any individual case, an appropriate "effective" amount can be determined using standard techniques known in the art (e.g., dose escalation studies) and can be determined taking into account factors such as the desired route of administration (e.g., systemic vs. intracranial), the desired frequency of administration, etc. In addition, the "effective amount" can be determined based on the co-administration method to be used.

[0077] Disclosed herein is a method for preventing or treating cancer recurrence in a subject, the method comprising: (a) comparing the expression level of one or more biomarkers selected from the group consisting of FGF2, C3 and GIMAP7 in a sample obtained from the subject with a reference, wherein the change in the expression level of one or more biomarkers compared to the reference or the value derived therefrom predicts the possibility of cancer recurrence in the subject; and (b) when the subject is found to have the possibility of cancer recurrence, administering an anti-cancer agent and / or radiotherapy to the subject.

[0078] Anticancer drugs used to treat NPC may include gemcitabine (Gemzar), cisplatin (Platinol), docetaxel (Taxotere), 5-fluorouracil (5-FU), or capecitabine (Xeloda). Combinations of chemotherapy drugs may include, for example, gemcitabine (Gemzar) and cisplatin (Platinol); docetaxel (Taxotere) with cisplatin and 5-fluorouracil (5-FU); cisplatin and 5-fluorouracil; cisplatin and capecitabine (Xeloda); or docetaxel and cisplatin. Treatment of NPC may include using an anticancer drug with radiation therapy. The anticancer agent may also be an immunotherapy. The immunotherapy may be pembrolizumab, nivolumab, or an anti-vascular endothelial growth factor therapy, such as bevacizumab.

[0079] The present invention also discloses a method for grading a subject into an individual with high risk or low risk of cancer, the method comprising comparing the expression level of one or more biomarkers selected from the group consisting of FGF2, C3 and GIMAP7 in a sample obtained from the subject with a reference, wherein the change in the expression level of one or more biomarkers compared to the reference or the value derived therefrom classifies the subject into an individual with high risk or low risk of cancer.

[0080] The kits provided herein comprise one or more nucleic acid molecules that specifically bind to an expression product of a biomarker in a sample, wherein the biomarker is selected from the group consisting of FGF2, C3, and GIMAP7. The one or more nucleic acid molecules may be detectably labeled.

[0081] The compositions provided herein include a sample and one or more nucleic acid molecules that specifically bind to the expression products of biomarkers in the sample, wherein the biomarkers are selected from the group consisting of FGF2, C3 and GIMAP7.

[0082] In one embodiment, a composition or solid support is provided, comprising a plurality of DNA / mRNA complexes, wherein each of the plurality of DNA / mRNA complexes comprises a biomarker and a first and a second DNA probe hybridized to the biomarker, wherein: the first probe is a capture probe; the second probe is a reporter probe; the biomarker is an mRNA transcript; and the plurality of DNA / mRNA complexes comprise one or more biomarkers selected from the group consisting of fibroblast growth factor 2 (FGF2), complement component 3 (C3), and GTPase, IMAP family member 7 (GIMAP7).

[0083] As used herein, "and / or" refers to and encompasses any and all possible combinations of one or more of the associated listed items, as well as combinations that are absent when construed in the alternative (or).

[0084] As used in this application, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. For example, the term "an agent" includes a plurality of agents, including mixtures thereof.

[0085] In this specification and subsequent statements, unless the context requires otherwise, the word "comprise" and variations such as "comprises" and "comprising", will be understood to mean the inclusion of a stated integer or step or groups of integers or steps but not the exclusion of any other integer or step or groups of integers or steps.

[0086] Reference in this specification to any previous publication (or information derived therefrom) or any known matter is not and should not be taken as an acknowledgement or permission or any form of implication that the previous publication (or information derived therefrom) or known matter forms part of the common general knowledge in the field of endeavour relevant to this specification.

[0087] Those skilled in the art will recognize that the invention described herein is susceptible to variations and modifications other than those specifically described. It is to be understood that the present invention includes all such variations and modifications, which fall within the spirit and scope of the present invention as described. The present invention also includes all steps, features, compositions and compounds mentioned or indicated in this specification, either individually or collectively, and any and all combinations of any two or more of said steps or features.

[0088] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0089] Certain embodiments of the invention will now be described with reference to the following examples, which are provided for illustrative purposes only and are not intended to limit the general scope of the foregoing description. [Example]

[0090] Due to the strong inflammatory infiltrate in NPC tumors, gene expression signals from a large number of gene expression characterizations of the tumor are mixed, including signals from tumor epithelial cells and cells of the inflammatory microenvironment. Defining gene signatures that are unique to different tumor compartments (tumor epithelium or microenvironment) remains a challenge.

[0091] Microdissected gene expression characterization was performed in a discovery cohort of NPC patients recruited from Singapore following the methods of Foley et al. Primary NPC tumors from patients who subsequently developed recurrent or metastatic disease (group A) and those who did not (group B) were characterized. Primary pre-treatment tumors from archived paraffin blocks of these patients were characterized. Tumor epithelial and microenvironmental compartments were microdissected separately using laser capture microdissection, and library preparation was performed using Smart-3SEQ. Samples were prepared in biological replicates. Quality control was performed using tapestation and libraries quantified by qPCR. Libraries were pooled, sequenced, and disassembled. Raw fastq files were mapped to concatenated human hg19 and Epstein-Barr virus genomes using STAR sequence alignment software, and gene expression was then quantified using featureCounts in the Subread package.

[0092] After filtering high-quality libraries, differential gene expression analysis was performed using the DESeq2 package in R studio to compare gene expression between tumors that eventually relapsed and those that did not relapse (group A vs. group B). The 17 gene expression libraries of 11 tumors in group A were compared with the 17 gene expression libraries of 12 tumors in group B.

[0093] From the analysis of this cohort, 27 differentially expressed genes were identified in the tumor epithelial compartment at an adjusted p-value < 0.01 ( Figure 1 a). Most differentially expressed genes were enriched in non-recurrent tumors, including several immune-related genes (C3, GIMAP genes, CCL21, IL8, etc.). Two genes were identified to be enriched in recurrent tumors (FGF2, EFHC1).

[0094] To refine the classification power of this set of genes and to rationalize the practical significance of using gene expression signatures to classify patients, the lasso method was used to reduce the number of genes enriched in non-recurrent tumors in an unbiased manner, selecting genes with the largest effect (|coefficient|>0.001) at λ(minimum). C3 and GIMAP7 were observed to be the best classifiers among the genes enriched in group B. Based on the biological relevance of the FGF signaling pathway in cancer, FGF2 was also included in the three-gene signature containing FGF2, C3, and GIMAP7. When applied to the discovery cohort, the three-gene signature accurately divided tumors into recurrent and non-recurrent groups on unsupervised clustering ( Figure 1 b). A generalized linear model was developed to generate a risk score incorporating the expression of the three genes (“three-gene classifier”).

[0095] The ability of the three-gene classifier to identify high-risk NPC patients was validated in two independent cohorts. In the first cohort, Zhang et al. 2 , relied on bulk RNA-Seq (without microdissection) to characterize NPC tumors (n = 88 tumors). The gene expression matrix and clinical outcomes of this cohort are available from the NCBI Gene Expression Omnibus (accession number GSE102349). Despite the different gene expression characterization methods used by Lin et al., the three-gene classifier was observed to perform robustly ( Figure 2 ). Unsupervised hierarchical clustering showed that tumors were divided into high-risk and low-risk groups based on the expression of FGF2, C3, and GIMAP7. When the three-gene classifier was applied, tumors from patients who developed progressive disease had higher risk scores compared with those who remained healthy. Survival analysis based on Kaplan-Meier curves showed that patients with tumors with higher risk scores had worse disease-free survival compared with patients with tumors with lower risk scores (p=0.0029).

[0096] The second validation cohort was a microdissected NPC tumor cohort from Stanford University (n=85 tumor epithelial libraries). This cohort was used only for validation and was not required for discovery of the three-gene signature. We observed that the three-gene classifier performed well ( Figure 3 ). Unsupervised hierarchical clustering showed that tumors spontaneously clustered into high-risk and low-risk groups. Patients who developed progressive disease also had elevated tumor risk scores compared with those who remained healthy. Finally, survival analysis based on Kaplan-Meier curves again showed that patients with tumors with higher risk scores had worse disease-free survival (p=0.0015).

[0097] Since FGF2 was observed to be upregulated in group B tumors that developed recurrent or metastatic disease, we evaluated the ability of FGF2 to increase cell migration in NPC cell lines using a scratch assay (cell migration assay). C666-1 cells were seeded in triplicate in 6-well plates and cultured in R10 medium (RPMI 1640 supplemented with 1% non-essential amino acids (MEM non-essential amino acids solution (100×), Gibco), 10% fetal bovine serum (Fetal Bovine Serum, certified, United States, Gibco), and 1% penicillin-streptomycin (Penicillin-Streptomycin (10,000 U / mL), Gibco). After confluence, the previous medium was replaced with serum-free medium. After 24 hours of serum starvation, scratches were made using a pipette tip and the cells were washed three times with phosphate-buffered saline (PBS) to remove detached cell debris. Serum-free medium containing 5 ng / ml FGF2 (human basic FGF (FGF-2 / bFGF) recombinant protein, Gibco, dissolved in PBS) was then added to the wounded cell layer. For control wells, serum-free medium containing an equal volume of PBS was added. These cells were then incubated at 37°C for 24 and 48 hours after the scratch was made. The migration of cells to the wounded area was monitored and captured at 0h, 24h, and 48h using an Olympus IX51 inverted microscope coupled with a DP26 Olympus digital camera. ImageJ software was used to quantify the migrated cells. The cell migration assay was performed in biological triplicates. Statistical analysis of the quantitative data was performed using a two-tailed Student's t-test to compare the FGF2-treated group with the control group. The error bars on the graphs are the standard deviations from the experimentally obtained values. It was observed that C666-1 cells treated with 0.5 ng / ml FGF2 exhibited an increased migration ability ( Figure 4 a, b, p<0.05).

[0098] We also evaluated the ability of FGF2 to increase the invasiveness of NPC cell lines using a cell invasion assay. CytoSelect TM 96-well cell invasion assay kit (basement membrane, fluorescence measurement format). Briefly, C666-1 cells were serum starved for 20-24 hours and then harvested. 2×10 6 Cells / ml were seeded in triplicate in serum-free medium containing 5 ng / ml and 20 ng / ml FGF in the upper chamber, with serum-free medium alone serving as a control. The cells were then incubated at 37°C for 24 h and 48 h. The feed tray contained 10% FBS as a chemoattractant for the cells. At each time point, cells that invaded the basement membrane and attached to the bottom of the membrane were harvested using a cell dissociation reagent. The cells were then incubated at 37°C for 30 min. Lysis buffer and fluorescent dye were then added to the detached cells, and the mixture was incubated at ambient temperature for 20 min. The number of cells that invaded the membrane was quantified using fluorescence measurements at 485 nm / 528 nm using a Synergy HT microplate reader. The cell invasion assay was performed in biological triplicate. Statistical analysis of the quantitative data was performed using a two-tailed Student's t-test to compare the FGF2-treated group with the control group. One replicate of the 20 ng / ml FGF2 group was excluded due to excessively high fluorescence readings. The error bars on the graphs are standard deviations from the experimentally obtained values. It was observed that C666-1 cells treated with 20 ng / ml FGF2 showed increased invasiveness ( Figure 4 c, p<0.05).

[0099] In conclusion, the gene expression classifier derived from a matched cohort of NPC tumors with and without recurrence accurately classified NPC into high-risk and low-risk groups.

[0100]

References

[0101] Foley JW, Zhu C, Jolivet P, et al. Gene-expression profiling of single cells from archival tissue with laser-capture microdissection andSmart-3SEQ. Genome Res. 2019:doi: 10.1101 / gr.234807.234118.

[0102] Zhang L, MacIsaac KD, Zhou T, et al. Genomic Analysis ofNasopharyngeal Carcinoma Reveals TME-Based Subtypes. Mol Cancer Res. 2017;15(12):1722-1732.

[0103] Liu SL, Sun XS, Chen QY, et al. Development and validation of atranscriptomics-based gene signature to predict distant metastasis and guideinduction chemotherapy in locoregionally advanced nasopharyngeal carcinoma.Eur J Cancer. 2022;163:26-34.

[0104] Zhao S, Dong X, Ni X, et al. Exploration of a Novel Prognostic RiskSignature and Its Effect on the Immune Response in Nasopharyngeal Carcinoma.Front Oncol. 2021;11:709931.

[0105] Tang XR, Li YQ, Liang SB, et al. Development and validation of a geneexpression-based signature to predict distant metastasis in locoregionallyadvanced nasopharyngeal carcinoma: a retrospective, multicentre, cohortstudy. Lancet Oncol. 2018;19(3):382-393.

Claims

1. A method for predicting the likelihood of cancer recurrence in a subject, the method comprising comparing the expression level of one or more biomarkers selected from the group consisting of fibroblast growth factor 2 (FGF2), complement component 3 (C3) and GTPase, IMAP family member 7 (GIMAP7) in a sample obtained from the subject with a reference, wherein: A change in the expression level of one or more biomarkers, or a value derived therefrom, compared to a reference, predicts the likelihood that the cancer will recur in the subject.

2. The method according to claim 1, wherein: The cancer is nasopharyngeal carcinoma (NPC).

3. The method according to claim 1 or 2, wherein: The method comprises comparing the expression levels of mRNA or protein of the one or more biomarkers.

4. The method according to any one of claims 1 to 3, wherein: The method comprises detecting the level of the one or more biomarkers.

5. The method according to any one of claims 1 to 4, wherein: The method comprises, or consists of, comparing the levels of FGF2, C3 and GIMAP7.

6. The method according to any one of claims 1 to 5, wherein: Increased, unchanged and / or decreased expression levels of FGF2, C3 and GIMAP7 compared to a reference predicts the likelihood of recurrence of said cancer in said subject.

7. The method according to any one of claims 1 to 5, wherein: The value derived therefrom is a cancer recurrence risk score obtained by processing the expression levels of one or more biomarkers selected from the group consisting of FGF2, C3 and GIMAP7 using a risk score model.

8. The method according to claim 7, wherein: The risk score model was generated by performing regression on a dataset containing gene expression data and clinical outcomes of a cohort of cancer patients.

9. The method according to any one of claims 1 to 8, wherein: The sample is a tissue sample.

10. A method for determining the prognosis of cancer in a subject, the method comprising comparing the expression level of one or more biomarkers selected from the group consisting of FGF2, C3 and GIMAP7 in a sample obtained from the subject with a reference, wherein: A change in the expression level of one or more biomarkers, or a value derived therefrom, compared to a reference indicates that the subject may have a high risk cancer or a low risk cancer.

11. A method of treating cancer in a subject, the method comprising: (a) comparing the expression level of one or more biomarkers selected from the group consisting of FGF2, C3 and GIMAP7 in a sample obtained from the subject with a reference, wherein a change in the expression level of the one or more biomarkers, or a value derived therefrom, compared to the reference indicates that the subject may have a high-risk cancer; and (b) administering anticancer agents and / or radiation therapy to subjects found to be at high risk of cancer.

12. A method for stratifying a subject as having a high risk or low risk of cancer, the method comprising comparing the expression level of one or more biomarkers selected from the group consisting of FGF2, C3 and GIMAP7 in a sample obtained from the subject with a reference, wherein: A change in the expression level of one or more biomarkers, or a value derived therefrom, compared to a reference, stratifies the subject as an individual with a high risk or a low risk of having cancer.

13. A composition or solid support comprising a plurality of DNA / mRNA complexes, wherein: Each DNA / mRNA complex in the plurality of DNA / mRNA complexes comprises a biomarker and first and second DNA probes hybridized to the biomarker, wherein: The first probe is a capture probe; The second probe is a reporter probe; The biomarker is an mRNA transcript; and The plurality of DNA / mRNA complexes comprises one or more biomarkers selected from the group consisting of fibroblast growth factor 2 (FGF2), complement component 3 (C3), and GTPase, IMAP family member 7 (GIMAP7).

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