阅读说明：本技术 用于诊断神经胶质瘤或预测预后的组合物以及提供其相关信息的方法 (Composition for diagnosing glioma or predicting prognosis and method for providing information related thereto ) 是由 南都铉 杰森·炅夏·史 于 2020-04-08 设计创作，主要内容包括：本发明涉及一种包括用于检测靶基因变异的制剂的用于诊断神经胶质瘤或预测预后的组合物、生物标志物组合和试剂盒、提供与神经胶质瘤的诊断或预测预后有关信息的方法、用于神经胶质瘤的个性化医疗的生物标志物组合和提供用于个性化治疗的信息的方法。根据本发明一方面,当检测选自由SAMD11、KLHL21、FAM167B、HPCAL4、GPBP1L1、LPHN2、GPR88、ZNF599、C19ORF33、B9D2、BCAM、CABP5、SIGLEC11、ERVV-2、ZNF865、MZF1、MRTO4、LRIG2、BSND和SLC30A2组成的组中的至少一种靶基因中的基因或蛋白质变异时,由于对神经胶质瘤的诊断具有高准确度和高灵敏度,因此可以有效诊断神经胶质瘤。因此,当使用从所述靶基因中检测基因变异的制剂时,其可以有效地应用于用于诊断神经胶质瘤或预测预后的组合物、试剂盒、提供信息的方法以及提供用于个性化治疗的信息的方法。(The present invention relates to a composition for diagnosing glioma or predicting prognosis, a biomarker combination and a kit comprising an agent for detecting a variation of a target gene, a method for providing information related to the diagnosis or prediction prognosis of glioma, a biomarker combination for personalized medical treatment of glioma, and a method for providing information for personalized treatment. According to an aspect of the present invention, when a gene or protein variation in at least one target gene selected from the group consisting of SAMD11, KLHL21, FAM167B, HPCAL4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, BCAM, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND, and SLC30a2 is detected, glioma can be efficiently diagnosed due to high accuracy and high sensitivity in diagnosis of glioma. Therefore, when an agent for detecting a genetic variation from the target gene is used, it can be effectively applied to a composition for diagnosing glioma or predicting prognosis, a kit, a method of providing information, and a method of providing information for personalized treatment.)

1. A composition for diagnosing glioma or predicting prognosis comprising an agent for detecting variation in at least one gene selected from SAMD11, KLHL21, FAM167B, HPCAL4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, BCAM, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND, and SLC30a 2.

2. The composition for diagnosing glioma or prognosing according to claim 1, wherein the glioma is at least one selected from the group consisting of astrocytoma, oligodendrocyte tumor, mixed glioma and ependymal tumor.

3. The composition for diagnosing glioma or prognosticating a tumor according to claim 1, wherein said agent detects a chromosomal co-deletion of genes 1p and 19 q.

4. The composition for diagnosing glioma or prognosing prognosis as claimed in claim 1, wherein the variation of the gene is 1) a single base sequence variation in base sequence;

2) a deletion or insertion of a base sequence region of 1 to 50 nucleotides;

3) variation in copy number; or

4) A combination of at least two selected from the group consisting of 1) to 3).

5. The composition for diagnosing glioma or prognosticating a prognosis as in claim 1, wherein said agent comprises a primer, a probe or an antisense nucleotide.

6. A biomarker combination for diagnosing glioma or predicting prognosis comprising an agent for measuring variation in at least one gene selected from SAMD11, KLHL21, FAM167B, HPCAL4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, BCAM, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND, and SLC30a 2.

7. A kit comprising a preparation for detecting a variation in at least one gene selected from SAMD11, KLHL21, FAM167B, HPCAL4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, BCAM, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND, and SLC30a 2.

8. A method of providing information relating to the diagnosis or prognosis of glioma comprising: 1) obtaining a nucleic acid sample from a biological sample of an individual;

2) detecting a genetic variation in at least one target gene selected from SAMD11, KLHL21, FAM167B, HPCAL4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, BCAM, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND, and SLC30a2 from the obtained sample; and

3) the level of the detected gene variation is compared with that of a normal sample and analyzed.

9. The method of claim 8, wherein the biological sample is at least one selected from the group consisting of blood, plasma, serum, urine, and saliva of the patient.

10. The method for providing information on the diagnosis or prognosis of glioma according to claim 8, wherein the genetic variation of step 2) is 1) a single base sequence variation;

2) a deletion or insertion of a base sequence region of 1 to 50 nucleotides;

3) variation in copy number; or

4) A combination of at least two selected from the group consisting of 1) to 3).

11. The method of providing information related to the diagnosis or prognosis of glioma according to claim 8 wherein said detecting of step 2) is performed by a next generation sequencer platform.

12. The method of providing information relating to the diagnosis or prognosis of glioma according to claim 11 wherein said next generation sequencer platform is whole genome sequencing, whole exome sequencing or targeted genome sequencing.

13. The method of providing information on the diagnosis or prognosis of glioma according to claim 8, wherein said analysis of step 3) is an analysis confirming whether the chromosomes 1p and 19q of the gene are co-deleted.

14. The method of providing information related to the diagnosis or prognosis of glioma according to claim 13, wherein whether said genes 1p and 19q are co-deleted in chromosome is determined by detecting copy number variation of at least one gene selected from SAMD11, KLHL21, FAM167B, hpal 4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, BCAM, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND and SLC30a 2.

15. A biomarker combination for personalized medicine for gliomas comprising an agent for detecting variation in at least one gene selected from SAMD11, KLHL21, FAM167B, HPCAL4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, BCAM, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND and SLC30a 2.

16. A method of providing information for personalized therapy, comprising: 1) detecting a genetic variation in at least one target gene selected from SAMD11, KLHL21, FAM167B, HPCAL4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, BCAM, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND, and SLC30a2 in a biological sample isolated from an individual; and

2) setting the individual whose genetic variation is detected from the detected result as a treatment target.

Technical Field

The present invention relates to a composition for diagnosing glioma (glioma) or predicting prognosis, a biomarker combination and a kit comprising an agent for detecting a variation of a target gene, a method for providing information related to the diagnosis or predicting prognosis of glioma as well as a biomarker combination for personalized medical treatment of glioma and a method for providing information for personalized treatment.

Background

Tumors (Tumor), on the other hand, are products of uncontrolled, chaotic proliferation of cells due to an excess of abnormal cells, and are classified as malignant tumors (malignant Tumor) when they are devastating in proliferation, invasiveness, and metastasis. In particular, from the viewpoint of molecular biology, it can be said that it is a genetic disease caused by genetic variation.

Among them, glioma (glioma) originates in neurons and glial cells, mostly permeates into surrounding normal tissues and grows, exhibits rapid growth due to uncontrolled cell growth, and is difficult to completely remove by surgery. In particular, gliomas are divided into several histological and molecular categories, but for accurate diagnosis of this, somatic mutations of IDH1/2, genomic amplification of EGFR, and co-deletions of chromosome 1p and 19q arms have been confirmed so far. However, the IDH1/2 mutation is often found primarily in low-grade gliomas and secondary glioblastomas, and is well known to be closely associated with long-term survival of patients. In addition, localized amplification of EGFR was found in about 50% of glioblastomas. In addition, since it is known that EGFR gene amplification can promote cell proliferation, clinical trials of EGFR inhibitors are often performed in order to treat cancer.

Therefore, in order to effectively diagnose glioma, Immunohistochemistry (IHC) and Fluorescence In Situ Hybridization (FISH) are commonly used for pathological diagnosis of IDH1 targeted mutation and local amplification of EGFR, which are considered to be important genes for the proliferation of the glioma, but there is an increasing demand for effective diagnosis of the glioma in a more convenient manner.

Disclosure of Invention

Technical problem

One aspect of the invention relates to a composition and biomarker combination for diagnosing glioma (glioma) or predicting prognosis comprising a formulation for measuring variation in at least one gene selected from SAMD11, KLHL21, FAM167B, HPCAL4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, BCAM, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND, and SLC30a 2.

One aspect of the invention relates to a kit comprising a preparation for detecting a variation in at least one gene selected from SAMD11, KLHL21, FAM167B, HPCAL4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, BCAM, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND, and SLC30a 2.

Another aspect of the present invention relates to a method of providing information related to the diagnosis or prognosis of glioma, comprising: obtaining a nucleic acid sample from a biological sample of an individual; detecting a genetic variation in at least one target gene selected from SAMD11, KLHL21, FAM167B, HPCAL4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, BCAM, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND, and SLC30a2 from the obtained sample; and comparing and analyzing the level of the detected gene variation with that of a normal sample.

Yet another aspect of the invention relates to a biomarker combination for personalized medicine of gliomas, comprising a preparation for detecting a variation in at least one gene selected from SAMD11, KLHL21, FAM167B, hpmal 4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, BCAM, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND and SLC30a 2.

Another aspect of the invention also relates to a method of providing information for personalized therapy, comprising: detecting a genetic variation in at least one target gene selected from SAMD11, KLHL21, FAM167B, HPCAL4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, BCAM, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND, and SLC30a2 in a biological sample isolated from an individual; and setting the individual whose genetic variation is detected from the detection result as a treatment target.

Technical scheme

One aspect of the invention provides a composition and biomarker combination for diagnosing glioma (glioma) or predicting prognosis comprising a formulation for measuring variation in at least one gene selected from SAMD11, KLHL21, FAM167B, HPCAL4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, BCAM, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND, and SLC30a 2.

Gliomas are tumors originating in glial cells inside the brain and spinal cord, which may be classified into Astrocytic tumors, oligodendrocyte tumors, Ependymal tumors, and the like, according to the main cells constituting the tumors, and a composition that can diagnose or predict prognosis by an aspect of the composition may be, for example, at least one selected from the group consisting of Astrocytic tumors (Astrocytic tumors), oligodendrogliosis tumors (Oligodendroglial tumors), mixed gliomas (mixed gliomas), and Ependymal tumors (Ependymal tumors).

The term "diagnosing" refers to determining the presence or characteristics of a pathological condition. For the purpose, this is to determine whether a glioma has developed. The term "prognosis" refers to determining, for example, the future survival of the individual, recurrence, metastasis, drug reactivity, drug resistance, etc., of the glioma following treatment. This means that, by confirming the level of variation of at least one gene selected from SAMD11, KLHL21, FAM167B, HPCAL4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, BCAM, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND, and SLC30a2 according to one aspect from a sample of an individual, it is possible to predict not only whether the individual has glioma, but also whether the prognosis of future survival of the individual is good.

It is known that individuals having glioma are closely related to the co-deletion of chromosome 1 short arm (1p) and 19 long arm (19q), and that individuals having co-deletion of 1p and 19q among individuals having glioma are known to be related to the encryption of increased sensitivity and survival to anticancer drugs, so that prognosis of individuals can be effectively predicted by co-deletion of 1p and 19 q. It can be confirmed that the preparation of an aspect of the present invention determines 20 target genes for accurate diagnosis of glioma and prediction of prognosis by effectively detecting the co-deletion of 1p and 19q, and can predict individuals having glioma and prognosis thereof with high accuracy and high sensitivity by detecting the variation levels of the 20 genes.

Since the mutations of the 20 target genes confirmed according to the aspect are highly expressed in the blood of glioma patients as compared with normal persons, they are particularly useful for the diagnosis of individuals having gliomas, which are clinically difficult to diagnose.

The same 11 is known to be present in chr 1: 860235 and 879558, the Ensemble ID number is ENSG 00000187634. KLHL21 is known to be present in chromosome in chr 1: 6653400 and 6674692 with Ensemble ID number ENSG 00000162413. Chr1, which is known to be present in chromosomes, shows FAM 167B: 32712793 and 32714227, Ensemble ID number is ENSG 00000183615. HPCAL4 is known to be present in chromosome chr 1: 40148183 and 40157407, Ensemble ID number is ENSG 00000116983. Gprp 1L1 is known to be present in chromosome chr 1: 46093903-. LPHN2 is known to be present in chromosomal chr 1: 81771820 and 82458145, and the Ensemble ID number is ENSG 00000117114. GPR88 is known to be present in chromosome chr 1: 101003668-. ZNF599 is known to be present in chromosome chr 19: 35249914 and 35264159, wherein the Ensemble ID number is ENSG 00000153896. The C19ORF33 is known to be present in chromosome chr 19: 38794776-38795671 and Ensemble ID number ENSG 00000167644. Chr19, known to be present in chromosomes, of B9D 2: 41860580 and 41870103, Ensemble ID number ENSG 00000123810. Chr19, known to be present in chromosomes, of BCAM: 45312291 and 45324698, Ensemble ID number ENSG 00000187244. Chr19, which is known to be present in chromosomes of CABP 5: 48533789 and 48547336, Ensemble ID number is ENSG 00000105507. SIGLEC11 is known to be present in chromosome chr 19: 50453202-. ERVV-2 is known to be present in chromosomes chr 19: 53547966 and 53554405, Ensemble ID number is ENSG 00000268964. Chr19 known to be present within chromosomes for ZNF 865: 56116746-. MZF1 is known to be present in chromosomes chr 19: 59073414-. MRTO4 is known to be present in chromosome chr 1: 19578008 and 19585349, and the Ensemble ID number is ENSG 00000053372. Known to be present in the chromosome of LRIG2 chr 1: 113615767 and 113669847, with Ensemble ID number ENSG 00000198799. Chr1, known to be present in chromosomes: 55464581 and 55474326, wherein the Ensemble ID number is ENSG 00000162399. SLC30a2 is known to be present in chromosome chr 1: 26365626-.

According to an aspect of the invention, the agent included in the composition may be 1) a single base sequence variation; 2) a deletion or insertion of a base sequence region of 1 to 50 nucleotides; 3) copy Number variation (Copy Number Variant); or 4) at least two combinations selected from the group consisting of 1) to 3).

The "mutation" refers to a change (alteration) in a base, nucleotide, polynucleotide or nucleic acid in a genome. Variations may include substitutions (substitutions), insertions (insertions), deletions (deletions) (also referred to as "indels"), etc., of bases, nucleotides, polynucleotides, or nucleic acids. A substitution refers to an alteration in which one base, nucleotide, polynucleotide or nucleic acid is substituted for another base, nucleotide, polynucleotide or nucleic acid. Insertion refers to the addition of another base, nucleotide, polynucleotide or nucleic acid change. Deletion refers to the removal of a base, nucleotide, polynucleotide or nucleic acid change.

Single base sequence variation or Single Nucleotide Variation (SNV) refers to a sequence variation or variation in a genome that exhibits a single base or nucleotide difference. Single base variation may be used interchangeably with single nucleotide polymorphism (SNP, hereinafter referred to as "SNP"). An SNP refers to the diversity of a base or nucleotide sequence that occurs when a single base or nucleotide (A, T, C or G, nucleotide A representing adenine, nucleotide T representing thymine, nucleotide C representing cytosine, nucleotide G representing guanine) differs on the genome between members of a species or between paired chromosomes of an individual (individual). The SNP may be at least 2 allelic bases or nucleotide sequences present in a population (population) at a frequency of a few, 1% or more, or 5% or more. SNPs are the most common genetic polymorphisms in the human genome, and may cause great differences among individuals depending on the genetic location of the SNP. For example, when a SNP is present at a position encoding a protein, it can affect the structure of the protein, alter the function of the protein, and cause disease. When a SNP is present in a non-coding region that does not encode a protein, i.e., in a promoter (promoter) or an intron (intron), there is a difference in the expression level of each protein, so that the overall activity of its protein may be increased or decreased, and an abnormal protein may also be expressed by alternative splicing (alternative splicing).

The copy number variation refers to a variation segment with a length of 1kb or more, which has a different copy number compared to a reference sequence, and methods for measuring the copy number variation may be, for example, Fluorescence In Situ Hybridization (FISH), chromatin co-immunoprecipitation (ChIP), and Next Generation Sequencer (NGS), but are not limited thereto. In a specific embodiment, when copy number variation of SAMD11, KLHL21, FAM167B, HPCAL4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, BCAM, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND, and SLC30a2 genes is detected, co-deletion of 1p and 19q of chromosomes 1 and 19 of an individual occurs, so that glioma can be effectively detected.

The term "biomarker combination" is constructed using any combination of biomarkers for diagnosing glioma, which may refer to the entire collection, or any subset or subcombination thereof. That is, a biomarker combination may refer to a set of biomarkers, and may refer to any type of biomarker that is measured. Thus, when RPL23 is part of a biomarker combination, for example, RPL23mRNA or RPL23 protein may be considered part of the group. Although individual biomarkers can be used as diagnostic agents, sometimes biomarker combinations can provide greater values in determining a particular condition than using individual biomarkers alone. In particular, detecting multiple biomarkers in a sample can improve the sensitivity and/or specificity of the test. Thus, in a particular embodiment, a biomarker panel may comprise more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 biomarker types. In another specific embodiment, the biomarker combinations are combined from a minimum number of biomarkers to generate a maximum amount of information. Thus, in various specific embodiments, a biomarker combination may consist of more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 biomarker types. When a biomarker combination consists of a "set of biomarkers", there are no biomarkers present other than to make up the collection. In a specific embodiment, the biomarker combination consists of 1 biomarker disclosed herein. In another specific embodiment, the biomarker combination consists of 2 biomarkers disclosed herein. In another specific embodiment, the biomarker combination consists of 3 biomarkers disclosed herein. In another specific embodiment, the biomarker combination consists of 4 biomarkers disclosed herein. In another specific embodiment, the biomarker combination consists of 5 biomarkers disclosed herein. In another specific embodiment, the biomarker combination consists of 6 biomarkers disclosed herein. In another specific embodiment, the biomarker combination consists of 7 biomarkers disclosed herein. In another specific embodiment, the biomarker combination consists of 8 biomarkers disclosed herein. In another specific embodiment, the biomarker combination consists of 9 biomarkers disclosed herein. In another specific embodiment, the biomarker combination consists of 10 biomarkers disclosed herein. In another specific embodiment, the biomarker combination consists of 11 biomarkers disclosed herein. In another specific embodiment, the biomarker combination consists of 12 biomarkers disclosed herein. In another specific embodiment, the biomarker combination consists of 13 biomarkers disclosed herein. In another specific embodiment, the biomarker combination consists of 14 biomarkers disclosed herein. In another specific embodiment, the biomarker combination consists of 15 biomarkers disclosed herein. In another specific embodiment, the biomarker combination consists of 16 biomarkers disclosed herein. In another specific embodiment, the biomarker combination consists of 17 biomarkers disclosed herein. In another specific embodiment, the biomarker combination consists of 18 biomarkers disclosed herein. In another specific embodiment, the biomarker combination consists of 19 biomarkers disclosed herein. In another specific embodiment, the biomarker combination consists of 20 biomarkers disclosed herein. The biomarkers of the invention show statistically significant differences in the diagnosis and prognosis of gliomas. In a particular embodiment, diagnostic tests using these biomarkers, alone or in combination, exhibit sensitivity and specificity of about 85% or greater, about 90% or greater, about 95% or greater, about 98% or greater, and about 100%.

The biomarkers can be obtained from transcript data of the cell.

The agent for detecting a genetic variation of an aspect of the present invention may include a primer pair, a probe, or an antisense nucleotide. Specifically, it may be an agent for measuring a variation level of the biomarker gene, and may also be a primer pair, a probe, or an antisense nucleotide that specifically binds to the gene. In a particular embodiment, the biomarker combination may comprise at least two primer pairs, probes or antisense nucleotides, each of which may specifically bind to SAMD11, KLHL21, FAM167B, HPCAL4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, bcam, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND and SLC30a 2.

In particular, the primers are characterized by amplification of sequences such that the amplified polymerase chain reaction products can be optimally sized to compare sequences for effective glioma diagnosis and prognosis using a Next Generation Sequencer (NGS).

The primer, probe or antisense nucleotide may be a polynucleotide labeled with a detectable label. The detectable label is a labeling substance capable of generating a detectable signal, and may be a labeling substance capable of generating a detectable signal including a fluorescent material, such as Cy3 and Cy 5. The result of hybridization of the nucleic acid can be confirmed by the detectable label.

The term "primer" refers to a single-stranded oligonucleotide that can serve as a starting point in a nucleotide polymerization reaction by a polymerase. For example, the primer may be a single-stranded oligonucleotide that can be used as a template-directed DNA synthesis initiation point in the presence of four different nucleoside triphosphates and a polymerase under suitable conditions, i.e., in a suitable buffer at a suitable temperature. The appropriate length of the primer may vary depending on various factors, such as temperature and the use of the primer. The length of the primer may be 5nt to 100nt, 5nt to 70nt, 10nt to 50nt, or 15nt to 30 nt. For example, the shorter the primer length, the more stable the hybridization complex formed with the template at low annealing temperatures.

The primer may further comprise, for example, phosphorothioate (phosphothioate), a nucleotide analogue of alkyl phosphorothioate (analogue), a peptide nucleic acid (peptide nucleic acid) or an intercalating agent (intercalating agent). In addition, a labeling substance that emits fluorescence, phosphorescence, or radioactivity may be further included. The fluorescent labeling substance may be VIC, NED, FAM, PET, or a combination thereof. The labeling substance may be labeled at the 5' end of the polynucleotide. In addition, the radioactive labeling substance may be incorporated into the amplification product by a PCR reaction using a solution to which a radioisotope Polymerase Chain Reaction (PCR) such as 32P or 35S is added.

The term probe refers to a polynucleotide that can sequence-specifically bind to a complementary polynucleotide strand. The length of the probe may be 5nt to 100nt, 10nt to 90nt, 15nt to 80nt, 20nt to 70nt, or 30nt to 50 nt. The probe can be used for hybridization methods such as methods using microarray (microarray), Southern blotting, dynamic allele-specific hybridization (dynamic allele-hybridization), and DNA chip, and the like. Microarrays are used in the sense known in the art and may, for example, refer to probes or sets of probes immobilized on a plurality of discrete areas on a substrate. The substrate is a suitable rigid or semi-rigid support and may include, for example, membranes, filters, chips, slides, wafers, fibers, magnetic or non-magnetic beads, gels, tubes, plates, polymers, microparticles, and capillaries. The probe or a probe complementary thereto may be used in a method which can hybridize to a nucleic acid obtained from an individual and measure the degree of hybridization thus obtained.

One aspect of the invention provides a kit comprising a preparation for detecting a variation in at least one gene selected from SAMD11, KLHL21, FAM167B, HPCAL4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, BCAM, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND, and SLC30a 2.

The kit (kit) may be a kit for predicting genetic variation of a target gene. The kit is used in the sense known in the art. The kit may, for example, comprise a polynucleotide as described above and items required for its particular use. It may include reagents required for its method of use together with the polynucleotide as described above.

The kit of one aspect may include reagents for performing an amplification reaction, and may include thermostable DNA polymerases, dNTPs, buffers, and the like. In addition, the kit of the present invention may further comprise a user guide describing the optimal reaction conditions. The guide is to explain how to use the printed matter of the kit, such as the preparation method of the PCR buffer, suggested reaction conditions, and the like. The instructions include instructions in the form of pamphlets or leaflets, labels affixed to the kits, and instructions on the surface of the packaging containing the kits. In addition, the guide includes information disclosed or provided through electronic media such as the internet.

Another aspect of the present invention provides a method for providing information related to the diagnosis or prognosis of glioma, comprising: obtaining a nucleic acid sample from a biological sample of an individual; detecting a genetic variation in at least one target gene selected from SAMD11, KLHL21, FAM167B, HPCAL4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, BCAM, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND, and SLC30a2 from the obtained sample; and comparing and analyzing the level of the detected gene variation with that of a normal sample.

The method includes a step of obtaining a nucleic acid sample from a biological sample of an individual.

The subject may be a mammal, including a human.

The biological sample refers to a sample obtained from a living organism. The biological sample may be at least one selected from the group consisting of blood, plasma, serum, urine and saliva of a patient.

Can further comprise a conventional DNA isolation method for detecting a gene mutation in the target gene from the biological sample. For example, the isolation method may be obtained by amplifying and purifying a target nucleic acid by Polymerase Chain Reaction (PCR), Ligase Chain Reaction (LCR), transcription amplification (transcription amplification), or real-time Nucleic Acid Sequence Based Amplification (NASBA).

In the step of analyzing the genetic variation, it may be determined whether an individual or a subject is at risk of having glioma or a patient having glioma, based on the presence or absence of a specific variation in the target gene.

The step of detecting a genetic variation in the target gene may be performed, for example, by a next generation sequencer platform, which may be, but is not limited to, Whole genome sequencing (Whole-genome sequencing), Whole exome sequencing (Whole-exome sequencing), or targeted genome sequencing (target gene panel sequencing).

In addition, in the comparative analysis step of the detected gene variation level and the normal sample level, whether or not the gene 1p and 19q are chromosomal co-deleted (co-deletion) can be confirmed by the gene variation level analysis.

Providing information about the diagnosis or prognosis of the individual glioma may be aimed at predicting or detecting the relative risk against glioma by predicting the relative risk against chromosomal co-deletions of genes 1p and 19q highly correlated with glioma expression and measuring the level of variation of at least one gene selected from SAMD11, KLHL21, FAM167B, hpal 4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, BCAM, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND and SLC30a 2. For example, the risk can be used to predict or diagnose whether an increased likelihood of developing glioma occurs when the level of variation in the target gene is higher than the group having the reference genomic sequence. When the target gene has a variation of 1 or more, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9or more, or 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19or more, or 20 or more, the gene 1p and 19q chromosomes are co-deleted with high probability, so that it can be determined that the risk of developing glioma highly correlated therewith is high, and thus the accuracy of diagnosis or prognosis prediction of glioma can be improved. In one embodiment, when copy number variation was measured in all of the 20 target genes, the occurrence of genome 1p and 19q chromosome co-deletions was confirmed, thereby confirming effective diagnosis of glioma.

According to an aspect of the present invention, the step of comparing and analyzing the level of the detected variation of the gene with the level of a normal sample may comprise: when copy number variation was measured in all of the 20 target genes, an individual having a co-deletion of 1p and 19q chromosomes in the genome was determined as an individual having a glioma in which the co-deletion of 1p and 19q chromosomes occurred. In addition, since it can be determined whether the survival rate of a patient with glioma is high when the individual has co-deletions of 1p and 19q chromosomes, the step of comparing and analyzing the level of the detected variation of the gene with the level of the normal sample may further comprise: when copy number variation is measured in all of the 20 target genes and the individual is determined to be an individual having glioma with 1p and 19q chromosomal co-deletions, the survival rate of glioma patients is high, thereby predicting good prognosis of glioma patients.

In the method, the step of detecting a genetic variation from the gene is a method of determining a nucleotide or base sequence, may be sequencing, such as Whole-genome sequencing (Whole-exome sequencing), Whole-exome sequencing (Whole-exome sequencing) or targeted-genome sequencing (target-genome sequencing), and may be performed by at least one technique selected from microarray hybridization, allele-specific PCR (allele specific PCR), dynamic allele-specific hybridization (dynamic allele-specific hybridization), PCR extension analysis, PCR-single strand conformation polymorphism (PCR-SSCP), and taq method. In the step of detecting a genetic variation from the gene, the composition or the kit may be used.

In another aspect, the present invention also provides a biomarker combination for personalized medicine for glioma, comprising a preparation for detecting variation in at least one gene selected from SAMD11, KLHL21, FAM167B, hpmal 4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, BCAM, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND, and SLC30a 2.

In another aspect, the present invention provides a method of providing information for personalized treatment, comprising: detecting a genetic variation in at least one target gene selected from SAMD11, KLHL21, FAM167B, HPCAL4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, BCAM, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND, and SLC30a2 in a biological sample isolated from an individual; and setting the individual whose genetic variation is detected from the detection result as a treatment target.

As described above, since the biomarker gene combination according to a specific embodiment is based on the tumor gene of each patient, the therapeutic effect of glioma can be enhanced by customized gene therapy. In addition, copy number variation in the gene combination is a target, so that effective targeted therapy can be performed on glioma which is a plurality of diseases caused by the gene variation.

In one embodiment, when copy number variation was measured in all of the 20 target genes, the occurrence of genome 1p and 19q chromosome co-deletions was confirmed, thereby confirming effective diagnosis of glioma. Since the survival rate of patients with glioma is high when the 1p and 19q chromosome co-deletions occur, when copy number variation occurs in all of the 20 target genes, good prognosis of patients with glioma can be predicted.

It is known in the art that when a 1p and 19q chromosomal co-deletion occurs, the susceptibility to anticancer drugs (chemosensitivity) and survival to specific anticancer drug therapies may vary. For example, in oligodendrocyte tumors, it is well known that patients with 1p and 19q chromosomal co-deletions respond differently to chemotherapy, and that radiotherapy combined with procarbazine/lomustine/vincristine (PCV) chemotherapy can improve survival compared to radiotherapy alone in patients with 1p and 19q chromosomal co-deletions, as oligodendroglioma patients with 1p and 19q chromosomal co-deletions are sensitive to anticancer drugs and therefore have a good prognosis, allowing personalized treatment, and possibly also targeted treatment, of individuals with or without 1p and 19q chromosomal co-deletions with individuals with genetic variations in 20 target genes according to an aspect.

One embodiment provides a method for providing information comprising the step of detecting a variation in a defined combination of genes in a biological sample isolated from an individual. Specifically, the individual refers to a subject for predicting the risk of developing glioma due to genetic variation. The subject may comprise a vertebrate, a mammal or a human (Homo sapiens). For example, the person may be a korean person. The biological sample may be tissue, cells, whole blood, serum, plasma, saliva, sputum, cerebrospinal fluid or urine. Mutation detection of the gene combination as defined above can be performed by measuring copy number variation after isolating nucleic acid from the biological sample, and the method of isolating nucleic acid and the method of measuring copy number variation are known in the art. The method for isolating nucleic acids can be performed, for example, by isolating DNA directly from the biological sample or amplifying a specific region by a nucleic acid amplification method such as PCR. The isolated nucleic acid sample includes not only a pure isolated nucleic acid but also a roughly isolated nucleic acid, for example, a cell lysate containing a nucleic acid. The nucleic acid amplification methods include PCR, Ligase Chain Reaction (LCR), transcription amplification (transcription amplification), self-sustained sequence replication, and nucleic acid sequence dependent amplification (NASBA). The isolated nucleic acid may be DNA or RNA. The DNA may be genomic DNA, cDNA or recombinant DNA. The RNA may be mRNA. In addition, the method of determining the mutation site, for example, the nucleotide of the mutation site can be directly determined by a nucleotide sequencing method (sequencing method) of a known nucleic acid. Methods for determining nucleotide sequencing may include the Sanger (or dideoxy) sequencing method or the Maxam-Gilbert (chemical modification) method. In addition, the nucleotide of the mutation site can be determined by hybridizing a probe including the sequence of the mutation site with a target polynucleotide and analyzing the hybridization result. The degree of hybridization can be confirmed, for example, by labeling a target nucleic acid with a detectable label and detecting the hybridized target nucleic acid, or by an electrical method or the like. In addition, a single base primer extension (SBE) method may be used.

Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. In addition, although the present specification describes preferred methods or samples, methods or samples similar or equivalent thereto are also included in the scope of the present invention. The contents of all publications cited in this specification as references are incorporated herein by reference in their entirety.

Advantageous effects

According to an aspect of the present invention, when a gene or protein variation in at least one target gene selected from SAMD11, KLHL21, FAM167B, HPCAL4, GPBP1L1, LPHN2, GPR88, ZNF599, C19ORF33, B9D2, BCAM, CABP5, SIGLEC11, ERVV-2, ZNF865, MZF1, MRTO4, LRIG2, BSND, and SLC30a2 is detected, glioma can be efficiently diagnosed due to high accuracy and high sensitivity in diagnosis of glioma. Therefore, when an agent for detecting a genetic variation from the target gene is used, it can be effectively used for a composition for diagnosing glioma or predicting prognosis, a kit, a method for providing information, and a method for providing information for personalized treatment.

Drawings

FIG. 1 is a graph showing the results of comparative evaluation of the sensitivity of GliomasCAN with Whole Exome Sequencing (WES) data.

FIG. 2 is a graph showing the results of confirming the mutation spectrum of the target therapeutic gene from 46 samples.

Fig. 3 is a schematic diagram of information identifying common variations in glioma affecting an individual glioma.

FIG. 4a is a schematic representation of the confirmation of the accuracy of the detection of 1p and 19q copy number deletions on 20 selected target genes.

FIG. 4b is a graph of the results of verifying the validity of the selected 20 target genes by comparing GliomasCAN and FISH data.

FIG. 4c is a graph of the results of confirming the co-deletion of 1p and 19q compared to CNA at the chromosomal level for wild type samples of 1p and 19 q.

Detailed Description

The present invention will be described in detail below with reference to experimental examples and examples. The following examples are only for more specifically describing the present invention, and it will be apparent to those of ordinary skill in the art that the scope of the present invention is not limited by these examples according to the gist of the present invention.

Experimental example: selection of mutations for biomarkers for diagnosis of glioma

Experimental example 1: collection and analysis of tumor samples from subjects for diagnosis of gliomas

Cancer patient samples for accurate diagnosis of glioma were collected from a subset of patients at the seoul hospital visit from 1 month 2014 to 2017 and from 8 months to samsung, with patient consent and with approval by the life ethics committee to collect tumor samples. The IRB of the Saxiseoul Hospital (IRC #: 2010-04-004) approved this study. The tumor sample is diagnosed. The main criteria for selecting cancer patient samples for diagnosis are as follows: (i) the possibility of patient enrollment into clinical trial when a viable mutation is found; and (2) the likelihood that a patient's specimen will be stored as a sufficient number of tumor scores in the pathology department. The tumor samples were processed without binding to normal tissue.

Tumor samples collected by the method were sequenced using the targeted sequencing combination GliomaSCAN designed by the samsung medical center. 232 target genes associated with a therapy targeting cancer glioma, which have been approved by the combination of korean MFDS and the U.S. FDA, or reported in the public database cosmic (category of viral Mutations in cancer) and literature as useful as cancer therapeutics, were screened and panel analyzed for the genes in the sample.

Cancer samples were analyzed using combinations based on specificity, detection limit, PPV and NPV from a gene repertoire consisting of HD753 (horizons, Inc., usa), NA12878 and 10 cell lines (NA07014, NA10840, NA18488, NA18511, NA18595, NA18867, NA18924, NA18957, NA19108 and NA 19114). The sensitivity of the HD753 cell line was calculated by comparing the known variation and the expected value of the Variant Allele Frequency (VAF) with the single nucleotide sequence variation (SNV), insertion and deletion (InDel) and gene Copy Number Variation (CNV) measured in combination. For the NA12878 cell line, the genotype of single base sequence variations in combination was compared to NA12878 in the coding region for the calculation of sensitivity, specificity, PPV and NPV. In addition, to predict the limits of detection and sensitivity, the measured variant allele frequencies of single base sequence variations in the combinations are compared to the expected variant allele frequencies in the entire genome.

Experimental example 2: comparison with Whole exome sequencing to cross-validate the collected glioma samples

In a study approved by the institutional review boards of the samsung medical center, korea university of astronomy, and seoul national university hospital, tumor samples and clinical information were obtained from brain tumor patients who underwent tumor resection surgery at the institution, and histological diagnosis of tumors was performed after obtaining informed consent of all patients. For genetic analysis, a portion of the tumor sample was snap frozen and stored in liquid nitrogen until use. Genomic DNA was extracted using DNeasy kit (Qiagen). Target Sequencing was performed on 46 samples, with 28 samples being simultaneously analyzed by Whole-Exome Sequencing (WES). To confirm chromosomes 1p and 19q, whole exome sequencing and targeted sequence analysis were performed on 52 samples and 45 samples.

Experimental example 3: performing targeted sequence analysis

Sequence gene reads from the FASTQ file were mapped to human genome assembly using a Burrows-Wheeler Aligner version 0.6.2 or mutation calling (mutation calling) (human genome assembly, hg 19). For a highly reliable prediction of Somatic mutations in tumor and normal tissue pairs, a MuTect and a matic index Detector were used. The Variant Effect Predictor (VEP) version was used to annotate potential functional outcomes and other relevant information to predicted somatic mutations. When the total gene reading is greater than or equal to 20, or the variant allele frequency is greater than 0.05, significant mutations are selected.

In addition, the ngCGH python package 0.4.4 version was used to measure gene copy number. Normal whole exome sequencing data matched to the patient was used as a criterion to predict fold change in tumor copy number. Genomic amplification and deletion were determined using a log2 scale above 0.585 and a log2 scale below-0.5, respectively.

Experimental example 4: performing statistical analysis of targeted sequencing

The frequency dependence of variant alleles between whole exome sequencing and the targeted sequence was calculated by Pearson. To evaluate the 1p/19q status using targeted base sequence analysis, the copy number status of genes located on chromosomes 1p and 19q was investigated using a receiver operating characteristic curve (ROC) and measured as a trapezoid. Oncoprint graphs are rendered using the R-package and composite heatmaps for visual alteration. The somatic mutations of IDH1 were compared to the local amplification of EGFR between GliomaSCAN and fluorescence in situ hybridization or immunohistochemistry using a two-sided Fisher's exact test.

Example 1: confirmation of the effectiveness of the combination from tumor cell lines

1.1 confirmation of the effectiveness of the combination from the HD753 cell line

A total of 15 of 18 variants were identified from the targeted region by analysis using GliomaSCAN. Compared to the variants identified from the combination of mutation measurements, 15 total variations were detected, which included 3 single base sequence variations, 2 high GC content single base sequence variations, 1 low GC content single base sequence variation, 1 long insertion, 1 long deletion and 3 short deletions. There is a high correlation (r) between the expected and measured allele frequencies²0.9356). The results showed that the sensitivity of single base sequence variation, insertion and deletion and gene copy number variation was 100%.

1.2 determination of reference sequences from NA12878 cell line

To estimate the sensitivity and specificity of single base sequence variation to 4330 targeted regions (1269kb) in the combination, a comprehensive analysis of genotype was performed and the NA12878 cell line was used as a reference sequence. In comparison of the single-base sequence variations confirmed in the combinations with the genotype of NA12878 (ftp:// ftptrace. ncbi. nlm. nih. gov/gib/ftp/release/NA 12878-HG 001/latest/GRCh37/), the single-base sequence variations confirmed between the two were identical. In addition, the comparison of 846058 reference genotypes in the targeted region was consistent with the genotypes identified in the combination.

1.3 verifying the effectiveness of the combination by measuring the limits of detection and sensitivity of the genetic variations

The limit of detection (LOD) and sensitivity of detection were measured using a gene corpus (expected allele frequency (VAF) range: 4% to 100%, number of variations in the gene set used to verify the combination: 689). When the combination was used to evaluate the effectiveness of the combination, the sensitivity and the correlation ratio (r2) were 99.2% and 0.9856, respectively, thereby confirming that the variation of the gene set can be measured very accurately. 135 of 136 variants (99.26%) were detected in the range of low expected variant allele frequencies (4.1% to 5%), and in summary, the detection limit of the genetic variants according to the combinations was confirmed to be 5% or more.

Example 2 validation of combinations by comparing variant allele frequencies between targeted sequencing combinations and Whole exome sequencing

To confirm the accuracy of the targeted sequencing combination, experiments were performed comparing the variable allele frequencies of somatic mutations obtained from the targeted combination with whole exome sequencing. 28 tumor tissue samples matched to DNA extracted from normal blood were exposed to a combination of whole exome sequencing and targeted sequencing. The threshold for significant mutations from single base sequence variations, insertions, or deletions is set to a depth range of greater than or equal to 20 and a variant allele frequency of greater than or equal to 5%. A comparison of variant allele frequencies for all somatic mutations measured in whole exome sequencing and targeted sequence analysis is shown in figure 1.

From the results confirmed in fig. 1, a total of 118 genomic variations were detected in the two sequencing combinations, and 24 or 9 variations could be detected in whole exome sequencing or targeted sequence analysis, respectively. Proprietary mutations (private mutations) with relatively low variant allele frequencies were detected because glioblastomas exhibit excessive intratumoral heterogeneity (intratumoral heterogeneity) when DNA samples applied to targeted sequencing combinations were compared to tumor fragments applied to whole exome sequencing, which was thought to result because they were from different sites of the same tumor. We could confirm the presence of subclone (sub-clone) mutations, such as in tumor cell proliferation, from lower variant allele frequencies and confirm that a variety of proprietary mutations were available. In contrast, in the whole exome sequencing platform, proprietary mutations with relatively high frequencies of minor variant alleles can be identified. By analyzing each private mutation, it can be confirmed as a true germ cell mutation because the genetic variation is simultaneously detected in matching normal blood and does not appear in the targeted sequencing combination. Deletion of the whole exome sequencing blood combination was mainly due to a significant difference in sequencing depth (depth) between the whole exome sequencing and the targeted sequencing combination. In particular, the range of measured GliomasCAN was approximately 800X, which is much higher than 200X for standard whole exome sequencing, but it was confirmed that the whole exome sequencing and targeted sequencing combination showed a high correlation in terms of somatic mutation frequency (r 0.814, P-value 4.77 e-37; Fisher's exact test). Thus, the targeted sequencing combinations identified in the experimental examples were demonstrated to be able to accurately identify and measure potential somatic variations with high confidence, compared to previously established full exome sequencing platforms.

Example 3 confirmation of Gene variation in biomarker Gene that can diagnose glioblastoma

Experiments were conducted to confirm gene mutations as biomarkers for easy diagnosis of glioblastoma, and the glioblastoma could only be confirmed by clinical judgment so far. As confirmed in the experimental examples, among the 94 genes known to have therapeutic effects, clinically useful mutations that can effectively detect glioblastoma were systematically evaluated in all the genes, and the evaluation results are shown in fig. 2.

As confirmed in fig. 2, 32 out of 46 patients (69.6%) demonstrated at least one individual cell mutation in the clinically viable gene. In addition, 11 of the 46 patients (23.9%) were confirmed to have previously identifiable variation in cosinc, with mutations in the NF1 gene being the most common mutation (31.3%), and mutations in the PTEN, PIK3CA, and PTPN11 genes occurring at 28.1%, 15.6%, and 12.5%, respectively, which in turn showed the most mutations after the genes. In addition, as confirmed in fig. 2, mutations of 19 genes were observed in at least two or more patients. Among the mutations that were repeatedly identified, the number of somatic mutations of the gene in 32 samples varied from 2 to 81. In summary, it was confirmed that the gene identified in 43 of 46 patients (93.5% of the sample) that could be used as a predictor of potential therapeutic response could have at least one genetic variation.

Example 4 confirmation of common somatic mutations in gliomas

Since glioma can be effectively diagnosed by mutations that are common in glioma using targeted combination of serialization, experiments to analyze the mutations were performed. Single base variation, short insertions and deletion analyses were performed on samples of 10 low-grade gliomas (LGG) and 36 Glioblastomas (GBM). Since such genetic variations regulate a key oncogenic pathway in glioblastoma that is often uncontrolled, it is important to confirm the presence of such variations in gliomas, and current studies indicate that TP53 mutations occur at an early stage of tumor progression in a variety of tumor types. Thus, in WHO re-classification, mutations in IDH1/IDH2 and TP53 should be considered for glioma diagnosis, and especially, it is important to identify TP53 because the co-deletion of chromosome 1p and 19q arms (arm) is not consistent with the TP53 mutation. In addition, glioblastoma can be divided into four different molecular subtypes based on the genetic variation accompanying transcript expression, which are known to be classified based on CNA and oncogene somatic variations such as EGFR, NF1 and PDGFRA. Since mutations common in the genes are largely different between molecular subtypes having significant biological properties, it is very important to select a target gene candidate group that can be used as a biomarker for accurately diagnosing glioma. The occurrence of CNA was confirmed in 9 genes (TP53, ATRX, EGFR, PTEN, PDGFRA, RB1, NF1, MDM2, and CDKN2A) in the target gene candidate group, as shown in fig. 3.

As confirmed in FIG. 3, mutations in IDH1 and ATRX genes had significant expression rates, and several important genetic variations including local amplification of EGFR and gene deletion of PTEN were confirmed in gliomas, consistent with the fact that the addition of chromosome 7 and deletion of chromosome 10 induced significant tumorigenesis in glioblastomas. In addition, it was confirmed that, in the low-grade glioma, mutations of IDH1 gene and ATRX gene occurred at a frequency of 8 and 4 out of 10 mutations. It was confirmed that in IDH gene mutation, the amino acid was changed to R132H except that a change of the stop codon was obtained as R20 in IDH1 protein.

Example 5 determination of biomarkers for the diagnosis of glioma and prognosis thereof

Experiments were performed to determine biomarkers for measuring co-deletions of chromosome 1p/19q to diagnose glioma and more accurately predict its prognosis. Experiments for determining biomarkers that can be used to measure the co-deletion of the genes 1p/19q with validity were performed using the GliomaSCAN whose validity was confirmed in the experimental examples to perform targeting sequence analysis data, which was again verified to be valid compared to the existing standard method.

It was confirmed that the results of fisher's exact test for EGFR amplification by fluorescence in situ hybridization experiment independently confirmed from targeted sequence analysis of GliomaSCAN confirmed in the experimental examples agreed with the P-value of 2.06e-04, and showed a higher agreement rate (P-value of 6.68e-07) even compared with the immunohistochemical method, which is the standard detection method currently used for detecting IDH1 mutation in glioma.

Accordingly, 20 genes effective for confirmation of chromosomal 1p/19q co-deletion were selected from the 232 target genes of said experimental example 1, said experimental example 1 was used to determine biomarkers of chromosomal 1p/19q co-deletion for effective confirmation of effectiveness as described above, and the genes are shown in table 1 below.

[ TABLE 1 ]

To assess whether the actual chromosomal co-deletion status can be reflected by the genes, the levels of 1p and 19q segments of chromosomes were compared using whole exome sequencing. Deletion of 1p and 19q at the chromosomal level was confirmed in each tumor when 9 genes out of 11 genes and 8 genes out of 9 genes were deleted simultaneously from the chromosomal arms of 1p and 19q, respectively. To assess the accuracy of the selected genes as biomarkers for confirming 1p/19q co-deletion status, the subject working characteristic curves were confirmed using 52 samples, as shown in fig. 4a, 4b and 4 c.

As confirmed in FIG. 4a, when the gene set and the whole exome sequencing results were integrated, the 20 biomarker genes could accurately predict chromosomal co-deletions of 1p and 19q, with AUC values of 0.929 and 95% CI values of 0.8862-1, both higher.

The targeting sequence was evaluated to determine whether the combination could accurately predict the 1p/19q co-deletion state compared to the prior known diagnostic methods such as fluorescence in situ hybridization, and the results are shown in FIG. 4b, with AUC values of 0.917 and 95% CI values of 0.8596-0.9737 confirmed by subject working profile analysis, which consistently showed high agreement.

As confirmed in fig. 4c, by comparing the 1p and 19q chromosomal co-deletions from whole exome sequencing with the chromosomal deletions predicted from the targeted sequencing combination, it was confirmed that both combinations showed very similar chromosomal profiles.

Therefore, in summary, with the 20 gene biomarkers, co-deletions of 1p and 19q, which can be used for accurate diagnosis of glioma or prediction of prognosis, can be confirmed with high accuracy and sensitivity.