Method and system for assisting in determining and grading brain glioma

Abstract

The invention discloses a method and a system for auxiliary determination and grading of brain glioma, and belongs to the technical field of biological genes and bioinformatics. The method comprises the following steps: acquiring sample information of a patient and preprocessing a sample; carrying out FISH detection, NGS detection auxiliary biological information analysis and pathological detection on the sample to respectively obtain N1 FISH detection indexes, N2 NGS detection indexes, N3 pathological detection indexes and gene variation phenomenon characteristics corresponding to the detection indexes; and judging the type and classification of the brain glioma based on the detection indexes and the gene variation phenomenon characteristics corresponding to one or more of the detection indexes. The invention further discloses a corresponding system, electronic equipment and a computer readable storage medium, the NGS sequencing technology is used for replacing a part of conventional FISH detection modes, meanwhile, NGS data are reserved, secondary analysis is carried out through a signal generation method to obtain guide newly-added molecular indexes, the detection types and times are reduced, and the detection modes are optimized.

Description

Technical Field

[0001] The present invention relates to the technical fields of biological genes and bioinformatics, and in particular to a method and system for assisting in determining the grade of brain glioma. Background Art

[0002] Central nervous system tumors refer to tumors that occur in central nervous system tissues such as the brain, spinal cord, and meninges. These tumors can be malignant (cancer) or benign, and their development can have a serious impact on the function of the nervous system. Among central nervous system tumors, other tumor types only account for 19%, while gliomas account for 81% of all central nervous system tumors. According to the malignancy of gliomas, gliomas are divided into four grades, of which 1~2 are low-grade gliomas and 3~4 are high-grade gliomas.

[0003] Detection of molecular indicators plays a key role in the grading of gliomas. There are currently 20 glioma markers. Different methods can be used to detect the same marker, and each type of glioma has multiple different molecular marker variations. This means that patients need to use multiple methods when detecting molecular markers, which increases the cost of testing.

[0004] In summary, the existing detection methods have the following defects: (1) There is currently no grading system for gliomas, and practitioners can only assess and grade patients according to guidelines.

[0005] (2) There are many types of patient testing methods.

[0006] (3) The same type of detection method requires multiple different indicators.

[0007] (4) When new markers are added after the guidelines are updated, secondary testing is required as an additional test.

[0008] (5) The test results are not standardized and are not expressed consistently by different operators or companies.

[0009] (6) Manual processing of up to 23 combinations of indicators is time-consuming and prone to errors.

[0010] (7) There are many testing methods and test indicators, and practitioners take a long time to make judgments. Patients have to wait a long time for medication, which can easily delay treatment.

[0011] Therefore, it is necessary to further explore more accurate and reliable auxiliary grading methods to assist in determining the grading of brain gliomas. Summary of the invention

[0012] In order to solve the problems existing in the prior art, the present invention provides the following technical solutions: a method and system for assisting in determining the grade of brain glioma, wherein the guideline judgment criteria and judgment logic are added, so that the grade result can be automatically judged after the data is imported; in the bioinformatics analysis process, WES (Whole-Exome Sequencing, whole exome) sequencing is used to replace some immunohistochemistry, Sanger sequencing, immunohistochemistry, pyrophosphate sequencing, PCR, microarray chips, qPCR and other detection methods, RNA (Ribonucleic Acid, transcriptome) sequencing technology is used to replace some FISH detection methods, while retaining WES and RNA data, and performing secondary analysis through bioinformatics methods to obtain the molecular indicators newly added to the guidelines. Since WES and RNA data can cover most of the gene-based molecular indicators, the types and times of patient tests are reduced, and the detection methods are optimized. While ensuring the accuracy of the test results, the detection efficiency is improved, the workload and cycle of practitioners' judgment are reduced, the decision-making efficiency and accuracy of practitioners are improved, and patients use drugs earlier; in addition, the pathological test conclusions are standardized, and the standard words corresponding to all the conclusions of the pathological test are statistically analyzed to reduce ambiguity.

[0013] In one aspect, the present invention provides a method for assisting in determining the grade of brain glioma, comprising: S1, obtaining the patient's sample information and performing clinical expression standardization preprocessing on the sample data; The pretreatment is to pre-treat the samples according to the detection indicators, and the pre-treatment includes: HE (hematoxylin-eosin staining), IHC (Immunohistochemistry) and FISH (Fluorescence in situ hybridization) detection using experimental reagents for staining, and WES and RNA using NGS (Next Generation Sequencing) sequencing and bioinformatics analysis software to pre-process sample data; NGS sequencing is a high-throughput sequencing method.

[0014] S2, after the pre-processed sample data is subjected to FISH detection, NGS detection-assisted bioinformatics analysis and pathological detection, N1 FISH detection indicators, N2 NGS detection indicators and N3 pathological detection indicators as well as the gene variation phenomenon characteristics corresponding to each detection indicator are obtained respectively; S3, judging the type and grade of the brain glioma based on the N1 FISH detection indicators, N2 NGS detection indicators and N3 pathological detection indicators and one or more corresponding gene variation phenomenon characteristics in each detection indicator.

[0015] Preferably, the N1 FISH detection indicators and their corresponding gene variation phenomenon characteristics are: The first FISH detection index is chromosome 1p / 19q, and the corresponding gene variation phenomenon is characterized by joint deletion; The second FISH detection indicator is the CDKN2A / B gene, and the corresponding gene variation phenomenon is characterized by homozygous deletion; The third FISH detection indicator is the TERT gene, and the corresponding gene variation phenomenon is characterized by promoter mutation (base position is C228T / C250T); The fourth FISH test indicator is the variation of chromosome 7 / 10, and the corresponding gene variation phenomenon is characterized by +7 / -10 (the overall increase of chromosome 7 and the overall loss of chromosome 10); The fifth FISH detection indicator is the EGFR gene, and the corresponding gene mutation phenomenon is characterized by the amplification of the copy number; The sixth FISH test indicator is the EGFR gene, and the corresponding gene variation phenomenon is characterized by the presence of EGFRvIII antigen (the deletion of EGFR exons 2-7 produces a gene corresponding to a truncated extracellular domain capable of constitutive EGFR activation, resulting in a new peptide sequence, thus producing a unique, GBM cell-specific, antibody-reactive EGFRvIII antigen); The seventh FISH detection indicator is the MYCN gene, and the corresponding gene variation phenomenon is characterized by copy number amplification.

[0016] Preferably, the N2 NGS detection indicators and their corresponding gene variation phenomenon characteristics are: The first WES detection index is IDH1, and the corresponding gene variation phenomenon is characterized by amino acid variation R132H / C / L / S / G; The second WES detection indicator is IDH2, and the corresponding gene variation phenomenon is characterized by amino acid variation R172K / M / G / W; The third WES detection index is H3 K27, and the corresponding gene variation phenomenon is characterized by mutation K27M; The fourth WES detection index is H3 G34, and the corresponding gene variation phenomenon is characterized by mutation G34R / V; The fifth WES detection indicator is TP53, and the corresponding gene variation phenomenon is characterized by mutation; The sixth pyrosequencing detection index is MGMT, and the corresponding gene variation phenomenon is characterized by promoter region methylation; The seventh RNA detection indicator is FGFR, and the corresponding gene mutation phenomenon is characterized by FGFR-TACC gene fusion; The eighth RNA detection indicator is MET, and the corresponding gene mutation phenomenon is characterized by mutation METex14; The ninth WES detection index is TSC1 / 2, and the corresponding gene variation phenomenon is characterized by mutation; The tenth RNA detection index is ZFTA, and the corresponding gene mutation phenomenon is characterized by gene Cllorf95-RELA gene fusion; The eleventh RNA detection indicator is YAP1, and the corresponding gene variation phenomenon is characterized by YAP1-MAMLD1 gene fusion; The twelfth WES detection indicator is MYCN, and the corresponding gene variation phenomenon characteristic is mutation; The thirteenth RNA sequencing detection indicator is the MET gene, and the corresponding gene variation phenomenon is characterized by PRPRZ1-MET gene fusion; The fourteenth RNA sequencing detection indicator is miR-181d, and the corresponding variation phenomenon characteristic is the high expression of the transcription regulatory factor miR-181d.

[0017] Preferably, the N3 pathological detection indicators and their corresponding gene variation phenomenon characteristics are: The first IHC detection index is ATRX, and the corresponding gene variation phenomenon characteristic is mutation; The second IHC detection indicator is BRAF, and the corresponding gene mutation phenomenon is characterized by mutation BRAF V600E.

[0018] Preferably, the bioinformatics analysis is used to perform secondary analysis on molecular indicators to obtain newly added molecular indicators, including: (1) Gene fusion to obtain candidate fusion gene sequences; (2) The candidate fusion gene sequence is subjected to the left step and / or the right step for detection, wherein the left step is used to identify inconsistent alignment information of the genome, including aligning and positioning the sequencing reads with the genome, obtaining a read covering the assumed fusion junction site, being able to split and match the reads of the genes on both sides of the fusion junction site, and a pair of reads fragments containing the fusion junction site, wherein the genes corresponding to the two ends of the pair of reads fragments R1 and R2 are different; the right step is used to assemble transcripts, including directly assembling into a longer transcript sequence and then identifying the fusion transcripts consistent with the chromosome rearrangement.

[0019] Preferably, the bioinformatics analysis also includes analyzing and calculating whether there are new gene fusion indicators based on RNA data and STAR-Fusion software, including: (1) Aligning the sequencing reads to the reference genome through STAR, screening one of the sequencing reads containing two gene fusion breakpoints as sequencing Junction reads; screening the sequencing reads that align R1 and R2 to different genes as sequencing Spanning reads, and using the sequencing Spanning reads as candidate fusion gene sequences; (2) After comparing the candidate fusion gene sequence with the reference genome annotation file, predicting the fusion gene based on the overlap method; (3) Based on the minimum sequencing reads support criterion, the prediction result of the fusion gene is further corrected using the verification tool FusionInspector; (4) Filtering out the false positives in the prediction results of the fusion gene after correction to obtain a fusion output result, and the fusion output result is used as a new gene fusion indicator to detect fusion transcripts.

[0020] Preferably, judging the type and grade of the brain glioma based on the N1 FISH detection indicators, N2 NGS detection indicators, and N3 pathological detection indicators and one or more corresponding gene variation phenomenon characteristics in each detection indicator includes: S31, obtaining HE standard words as the first judgment information, wherein the HE standard words are obtained based on the HE detection method and morphological characteristics, and describe one or more of the following: cell nucleus, proliferation, visibility of nuclear division, degree of pathological nuclear division, presence or absence of vascular endothelial cell proliferation and necrosis, visibility of pseudo-"fence"-like small focal necrosis, tumor cell growth and morphological characteristics; S32, obtaining one or more detection indicators among N1 FISH detection indicators, N2 NGS detection indicators, and N3 pathological detection indicators and gene variation phenomenon characteristics corresponding to the detection indicators as second determination information; S33: Determine the type and grade of the brain glioma based on the first determination information and the second determination information.

[0021] A second aspect of the present invention is to provide a system for assisting in determining the grade of brain glioma, comprising: A sample information acquisition and preprocessing module, used to acquire the sample information of the patient and preprocess the sample; The preprocessing is to preprocess the samples according to the detection indicators, and the preprocessing includes: HE, IHC and FISH detection using experimental reagents for staining and WES and RNA using NGS sequencing and bioinformatics analysis software to preprocess sample data; The detection feature acquisition module is used to perform FISH detection, NGS detection-assisted bioinformatics analysis and pathology detection on the pre-processed sample data to obtain N1 FISH detection indicators, N2 NGS detection indicators and N3 pathology detection indicators and the gene variation phenomenon characteristics corresponding to each detection indicator; The auxiliary determination grading module is used to judge the type and grade of the brain glioma based on the N1 FISH detection indicators, N2 NGS detection indicators and N3 pathological detection indicators and one or more corresponding gene variation phenomenon characteristics in each detection indicator.

[0022] A third aspect of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory stores a plurality of instructions, and the processor is configured to read the instructions and execute the method described in the first aspect.

[0023] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a plurality of instructions, and the plurality of instructions can be read by a processor and execute the method described in the first aspect.

[0024] The method, system and electronic device provided by the present invention have the following beneficial effects: (1) There are few test items, only IHC, WES and RNA are needed; (2) The number of tests is small, and WES and RNA can detect most indicators; (3) After the guidelines are updated, the data can be analyzed again, which can reduce retesting; (4) Reduce the number of tests for patients and help them reduce testing costs; (5) It is convenient for practitioners to collect indicators at one time and the operation is convenient; (6) Reduce the workload of practitioners in grading assessment and eliminate false positives or false negatives caused by lack of experience; (7) The cycle of the entire grading process is shortened, reducing time costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] FIG. 1( a ) is a flow chart of a method for assisting in determining the grade of a glioma according to the present invention; FIG. 1( b ) is a flow chart of a specific method for assisting in determining the grade of a glioma according to an embodiment of the present invention.

[0026] Figure 2 This is a schematic diagram of the bioinformatics analysis principle provided by the present invention.

[0027] Figure 3 A schematic diagram of fusion gene analysis results is provided for the present invention.

[0028] FIG. 4( a ) to FIG. 4( e ) are schematic diagrams of examples of determining the type and grade of the brain glioma based on the first determination information and the second determination information provided by the present invention.

[0029] Figure 5 This is a system architecture diagram for assisting in determining the grade of brain glioma provided by the present invention.

[0030] Figure 6 A schematic structural diagram of an embodiment of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0031] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0032] The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a memory, and a display screen. The memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

[0033] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts of the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory, and calling data stored in the memory.

[0034] The memory may include random access memory (RAM) or read-only memory (ROM). The memory may be used to store instructions, programs, codes, code sets or instructions.

[0035] The display screen is used to display the user interface of each application.

[0036] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

[0037] In addition to biomarkers, specific molecular features, such as gene mutations, can also be incorporated into the prognostic model for patients with early-stage liver cancer after surgery. Gene mutations, especially driver gene mutations, are an important cause of cancer development. Gene mutations are also important targets for cancer treatment and serve as important markers for prognosis.

[0038] Embodiment 1 As shown in FIG. 1( a ) and FIG. 1( b ), this embodiment provides a method for assisting in determining the grade of a brain glioma, comprising: S1, obtaining the patient's sample information and preprocessing the sample information; wherein the preprocessing is preprocessing the sample according to the detection index, and the preprocessing includes: HE, IHC and FISH detection using experimental reagents for staining and WES and RNA using NGS sequencing and bioinformatics analysis software to preprocess sample data; S2, after the pre-processed sample data is subjected to FISH detection, NGS detection-assisted bioinformatics analysis and pathological detection, N1 FISH detection indicators, N2 NGS detection indicators and N3 pathological detection indicators as well as the gene variation phenomenon characteristics corresponding to each detection indicator are obtained respectively; S3, judging the type and grade of the brain glioma based on the N1 FISH detection indicators, N2 NGS detection indicators and N3 pathological detection indicators and one or more corresponding gene variation phenomenon characteristics in each detection indicator.

[0039] See Table 1 for statistical information on 23 test indicators, including markers, characteristics of gene variation phenomena, detection methods, clinical grading, and detection methods used by the system.

[0040] Table 1 Serial number Markers Genetic variation Detection Methods Clinical classification Detection method used by the system 1 IDH1 Mutation (R132H / C / L / S / G) IHC, Sanger sequencing, pyrosequencing, NGS IHC (Pathology) WES 2 IDH2 Mutation (R172K / M / G / W) Sanger sequencing, pyrosequencing, NGS NGS (Molecular) WES 3 Chromosome 1p / 19q Joint deletion FISH, PCR, methylation array / expression array / NGS FISH (Pathology) FISH 4 H3 K27 Mutation (K27M) IHC, Sanger sequencing, NGS IHC (Pathology) WES 5 H3 G34 Mutation (G34R / V) Sequencing, NGS NGS (Molecular) WES 6 ATRX mutation IHC, Sanger sequencing, NGS IHC (Pathology) IHC 7 TP53 mutation IHC, Sanger sequencing, NGS IHC (Pathology) WES 8 CDKN2A / B Homozygous deletion FISH, qPCR, MLPA, methylation array / expression array / NGS related methods FISH (Pathology) FISH 9 TERT Promoter mutation (C228T / C250T) Sanger sequencing, pyrosequencing, NGS, FISH FISH (Pathology) FISH 10 Chromosome 7 / 10 +7 / -10 FISH, NGS, microarray FISH (Pathology) FISH 11 EGFR Amplification FISH, digital PCR, NGS, microarray FISH (Pathology) FISH 12 EGFR EGFRvIII rearrangement RT-PCR, digital PCR, IHC, MLPA, NGS NGS (Molecular) FISH 13 BRAF Mutation (BRAFV600E) IHC, Sanger sequencing, pyrosequencing, NGS IHC (Pathology) IHC 14 MGMT Promoter region methylation Methylation-specific PCR, pyrosequencing, methylation microarray Pyrosequencing (molecular) Pyrosequencing 15 FGFR Fusion gene (FGFR-TACC) Sanger sequencing, qPCR, NGS NGS (Molecular) RNA 16 MET Fusion gene (PTPRZ1-MET) Sanger sequencing, qPCR, NGS NGS (Molecular) FISH 17 MET Mutation (METex14) Sanger sequencing, qPCR, NGS NGS (Molecular) RNA 18 miR-181d High expression microRNA expression profile array, qPCR, in situ hybridization staining In situ hybridization staining (pathology) FISH 19 TSC1 / 2 mutation Sanger sequencing, NGS NGS (Molecular) WES 20 ZFTA Gene fusion (C11orf95-RELA) FISH, NGS NGS (Molecular) RNA 21 YAP1 Gene fusion (YAP1-MAMLD1) FISH, NGS NGS (Molecular) RNA 22 MYCN Amplification FISH, NGS FISH (Pathology) FISH 23 MYCN mutation Sanger sequencing, NGS NGS (Molecular) WES As a preferred embodiment, the N1 FISH detection indicators and their corresponding gene variation phenomenon characteristics are: The first FISH detection index is chromosome 1p / 19q, and the corresponding gene variation phenomenon is characterized by joint deletion; The second FISH detection indicator is the CDKN2A / B gene, and the corresponding gene variation phenomenon is characterized by homozygous deletion; The third FISH detection indicator is the TERT gene, and the corresponding gene variation phenomenon is characterized by promoter mutation (base position is C228T / C250T); The fourth FISH test indicator is the variation of chromosome 7 / 10, and the corresponding gene variation phenomenon is characterized by +7 / -10 (the overall increase of chromosome 7 and the overall loss of chromosome 10); The fifth FISH detection indicator is the EGFR gene, and the corresponding gene mutation phenomenon is characterized by the amplification of the copy number; The sixth FISH test indicator is the EGFR gene, and the corresponding gene variation phenomenon is characterized by the presence of EGFRvIII antigen (the deletion of EGFR exons 2-7 produces a gene corresponding to a truncated extracellular domain capable of constitutive EGFR activation, resulting in a new peptide sequence, thus producing a unique, GBM cell-specific, antibody-reactive EGFRvIII antigen); The seventh FISH detection indicator is the MYCN gene, and the corresponding gene variation phenomenon is characterized by copy number amplification.

[0041] As a preferred embodiment, the N2 NGS detection indicators and their corresponding gene variation phenomenon characteristics are: The first WES detection index is IDH1, and the corresponding gene variation phenomenon is characterized by amino acid variation R132H / C / L / S / G; The second WES detection indicator is IDH2, and the corresponding gene variation phenomenon is characterized by amino acid variation R172K / M / G / W; The third WES detection index is H3 K27, and the corresponding gene variation phenomenon is characterized by mutation K27M; The fourth WES detection index is H3 G34, and the corresponding gene variation phenomenon is characterized by mutation G34R / V; The fifth WES detection indicator is TP53, and the corresponding gene variation phenomenon is characterized by mutation; The sixth pyrosequencing detection index is MGMT, and the corresponding gene variation phenomenon is characterized by promoter region methylation; The seventh RNA detection indicator is FGFR, and the corresponding gene mutation phenomenon is characterized by FGFR-TACC gene fusion; The eighth RNA detection indicator is MET, and the corresponding gene mutation phenomenon is characterized by mutation METex14; The ninth WES detection index is TSC1 / 2, and the corresponding gene variation phenomenon is characterized by mutation; The tenth RNA detection index is ZFTA, and the corresponding gene mutation phenomenon is characterized by gene Cllorf95-RELA gene fusion; The eleventh RNA detection indicator is YAP1, and the corresponding gene variation phenomenon is characterized by YAP1-MAMLD1 gene fusion; The twelfth WES detection indicator is MYCN, and the corresponding gene variation phenomenon characteristic is mutation; The thirteenth RNA sequencing detection indicator is the MET gene, and the corresponding gene variation phenomenon is characterized by PRPRZ1-MET gene fusion; The fourteenth RNA sequencing detection indicator is miR-181d, and the corresponding variation phenomenon characteristic is the high expression of the transcription regulatory factor miR-181d.

[0042] As a preferred embodiment, the N3 pathological detection indicators and their corresponding gene variation phenomenon characteristics are: The first IHC detection index is ATRX, and the corresponding gene variation phenomenon characteristic is mutation; The second IHC detection indicator is BRAF, and the corresponding gene mutation phenomenon is characterized by mutation BRAF V600E.

[0043] like Figure 2 As shown, as a preferred embodiment, the bioinformatics analysis is used to perform secondary analysis on molecular indicators to obtain newly added molecular indicators, including: (1) Gene fusion to obtain candidate fusion gene sequences; (2) The candidate fusion gene sequence is subjected to the left step and / or the right step for detection, wherein the left step is used to identify inconsistent alignment information of the genome, including aligning and positioning the sequencing reads with the genome, obtaining a read covering the assumed fusion junction site, being able to split and match the reads of the genes on both sides of the fusion junction site, and a pair of reads fragments containing the fusion junction site, wherein the genes corresponding to the two ends of the pair of reads fragments R1 and R2 are different; the right step is used to assemble transcripts, including directly assembling into a longer transcript sequence and then identifying the fusion transcripts consistent with the chromosome rearrangement.

[0044] In this embodiment, the newly added gene fusion is the fusion of gene1 and gene2.

[0045] A fusion gene is two genes "fused" together. Under normal circumstances, the two genes are far apart and do not interfere with each other, but due to pathological chromosomal rearrangements, genes that are far apart or even not on the same chromosome are combined together. Fusion genes are produced by chromosomal rearrangements, including chromosomal translocation, insertion, inversion, and deletion. Gene fusions produce gene fusion transcripts and chimeric protein products, which have been used as therapeutic targets. Well-known examples are Gleevec (Imantinib), which targets the BCR-ABL1 gene fusion, and Crizotinib, which targets the EML4-ALK gene fusion.

[0046] 1. Step 1 on the left: Identify inconsistent genome alignment information Align the sequencing reads with the genome and find Junction / Spanning_reads; JunctionReadsCount refers to the number of reads that cover the assumed fusion junction site and can be split and matched to the genes on both sides of the fusion junction site; SpanningFragsCount refers to the number of reads that include the fusion junction site, and the genes corresponding to the two ends of a pair of read fragments R1 and R2 are different.

[0047] 2. Right step: assembling transcripts Directly assemble into longer transcript sequences and then identify fusion transcripts consistent with chromosomal rearrangements; it is possible that most reads align to both sides of the fusion junction site and do not directly cover the fusion junction site itself.

[0048] There are many softwares for detecting fusion genes, most of which are based on the above two schemes for predictive analysis. STAR-Fusion recommended by NCIP (National Cancer Institute Cancer Research Center) is a software that uses the fusion output results of STAR alignment (sequencing sequence and reference sequence alignment) to detect fusion transcripts. In the analysis process developed by NCIP, the software is used to obtain predicted fusion transcripts in the first step. Therefore, as a preferred embodiment, the bioinformatics analysis also includes analyzing and calculating whether there are new gene fusion indicators based on RNA data and STAR-Fusion software, including: (1) Align the sequencing reads to the reference genome through STAR, and select one of the sequencing reads containing two gene fusion breakpoints as Junction reads; select the sequencing reads that align R1 and R2 to different genes as Spanning reads, and use the Spanning reads as candidate fusion gene sequences; (2) After comparing the candidate fusion gene sequence with the reference genome annotation file, predicting the fusion gene based on the overlap method; (3) Based on the minimum sequencing reads support criterion, the prediction result of the fusion gene is further corrected using the verification tool FusionInspector; (4) Filtering out the false positives in the prediction results of the fusion gene after correction to obtain a fusion output result, and the fusion output result is used as a new gene fusion indicator to detect fusion transcripts.

[0049] In this embodiment, in the analysis process developed by NCIP, the software is used to obtain the predicted fusion transcripts in the first step. STAR-Fusion starts from the FASTQ file. After the genome database and the second-generation sequencing file are prepared, STAR-Fusion can be used to predict the fusion gene.

[0050] During the analysis, STAR is used to align sequencing reads to the genome, and the most appropriate analysis results are obtained through various hard index screening, such as removing MT information, limiting the minimum FFPM value, and dividing splicing isoforms into multiple entries. The output result is a table file, such as Figure 3 As shown, Figure 3 The table headers are as follows.

[0051] STAR-Fusion Results Explanation: FusionName: fusion gene name; JunctionReadsCount: Contains the number of reads that can be split and matched to the fusion genes on both sides at the assumed fusion junction site; SpecificType: specific type; SpanningFragsCount: The number of sequence reads containing fusion connections, where the genes corresponding to the R1 and R2 ends of the sequence read fragments are different; SpliceType: whether the fusion gene breakpoint position appears at the reference exon junction provided by the reference transcript structure annotation (e.g., gencode); Left / RightGene: The gene on the left / right side of the fusion gene; Left / RightBreakpoint: left / right chromosome location information of the fusion gene breakpoint; LargeAnchorSupport: whether there are longer base sequences (>=25bp) matching reads on both sides of the putative breakpoint. Fusion genes lacking LargeAnchorSupport are usually false positives; Left / RightBreakDinuc: left / right chromosome position information of the breakpoint dinucleotide; FFPM: supports the normalization of fused reads, i.e., the number of fused reads per million total reads; Left / RightBreakEntropy: The "Shannon entropy" of the 15 exonic bases on both sides of the breakpoint. The maximum entropy is 2, the minimum entropy is 0, and the results of low entropy should generally be considered low confidence.

[0052] As a preferred embodiment, the method of judging the type and grade of the brain glioma based on the N1 FISH detection indicators, N2 NGS detection indicators and N3 pathological detection indicators and one or more corresponding gene variation phenomenon characteristics in each detection indicator includes: S31, obtaining HE standard words as the first judgment information, wherein the HE standard words are obtained based on the HE detection method and morphological characteristics, and describe one or more of the following: cell nucleus, proliferation, visibility of nuclear division, degree of pathological nuclear division, presence or absence of vascular endothelial cell proliferation and necrosis, visibility of pseudo-"fence"-like small focal necrosis, tumor cell growth and morphological characteristics; S32, obtaining one or more detection indicators among N1 FISH detection indicators, N2 NGS detection indicators, and N3 pathological detection indicators and gene variation phenomenon characteristics corresponding to the detection indicators as second determination information; S33: Determine the type and grade of the brain glioma based on the first determination information and the second determination information.

[0053] As shown in Table 2, as a preferred implementation, the HE standard speech includes: HE standard statement 1: No nuclear atypia, inactive proliferation, no mitotic figures, endothelial cell proliferation and necrosis; HE standard statement 2: obvious nuclear atypia, active proliferation, occasional nuclear division, no endothelial cell proliferation and necrosis; HE standard phrase 3: increased tumor cell density, obvious nuclear atypia, active proliferation, easy to see nuclear division, no endothelial cell proliferation and necrosis; HE standard phrase 4: Tumor cell density and nuclear atypia significantly increase, hyperplasia is extremely active, mitotic figures and pathological mitotic figures are common, and glomerular endothelial cell hyperplasia and / or pseudo-"fence"-like small focal necrosis can be seen; HE standard wording 5: Cubic or columnar tumor cells grow in a pseudopapillary or perivascular manner, anucleated areas can be seen around the blood vessels, and the blood vessels and tumor cells are transparent.

[0054] Table 2 Morphological characteristics Detection method The cells have no nuclear atypia, are not actively proliferating, have no mitotic figures, and have no endothelial cell proliferation or necrosis. HE The cell nucleus is more dysplastic, the proliferation is more active, nuclear division is occasionally seen, and there is no endothelial cell proliferation and necrosis. HE The tumor cell density increases, the nuclear atypia is obvious, the proliferation is active, the nuclear division is easy to see, and there is no endothelial cell proliferation and necrosis. HE The density of tumor cells and nuclear atypia increased significantly, the proliferation was extremely active, nuclear division and pathological nuclear division were common, and glomerular endothelial cell proliferation and / or pseudo-"fence"-like small focal necrosis were seen. HE Cuboidal or columnar tumor cells grow in pseudopapillary or perivascular patterns, with anucleated areas around blood vessels and clearing around blood vessels and tumor cells HE As a preferred implementation, the S33, judging the type and grade of the brain glioma based on the first determination information and the second determination information, comprises: (1) As shown in FIG. 4 (a), based on the HE standard statement 2: the nuclear atypia is obvious, the proliferation ratio is active, nuclear division is occasionally seen, and there is no endothelial cell proliferation and necrosis; the IHC detection index has IDH1 mutation, ki67 <5%; the WES detection index has IDH2 mutation, ATRX deletion and p53 mutation, the type and grade of the brain glioma are judged to be astrocytoma, IDH mutant, grade 2; (2) As shown in FIG4(b), based on the HE standard statement 2: the nuclear atypia is obvious, the proliferation ratio is active, nuclear division is occasionally seen, and there is no endothelial cell proliferation and necrosis; the IHC detection index has IDH1 mutation, ki67 <5%; the WES detection index has IDH2 mutation, and the FISH detection index has 1p / 19q co-deletion. The type and grade of the brain glioma are judged to be oligodendroglioma, IDH mutation with 1p / 19q co-deletion, grade 2; (3) As shown in FIG. 4 (c), based on the HE standard statement 3: the tumor cell density is increased, the nuclear atypia is obvious, the proliferation is active, the nuclear division is easy to see, and there is no endothelial cell proliferation and necrosis; the IHC detection index has IDH1 mutation, and ki67 is between 5% and 10%; the WES detection index has IDH2 mutation, and the FISH detection index has 1p / 19q co-deletion. The type and grade of the brain glioma are judged to be oligodendroglioma, IDH mutation with 1p / 19q co-deletion, grade 3; (4) As shown in FIG. 4( d ), based on any of the HE standard terms, 1: no nuclear atypia, inactive proliferation, no mitotic figures, endothelial cell proliferation and necrosis; HE standard term 2: obvious nuclear atypia, more active proliferation, occasional mitotic figures, no endothelial cell proliferation and necrosis; HE standard term 3: increased tumor cell density, obvious nuclear atypia, active proliferation, easy to see mitotic figures, no endothelial cell proliferation and necrosis; HE standard term 4: tumor cell density and nuclear atypia Significant increase, extremely active proliferation, frequent nuclear division and pathological nuclear division, glomerular endothelial cell proliferation and / or pseudo "fence"-like small focal necrosis can be seen; the IHC detection index is IDH1 wild type, ki67>10%; the WES detection index is IDH2 wild type, the FISH detection index is TERT promoter mutation, EGFR amplification and any one of +7 / -10, the type and grade of the brain glioma are judged to be glioblastoma, IDH wild type, grade 4; (5) As shown in FIG4(e), based on the HE standard statement 1: no nuclear atypia, inactive proliferation, no nuclear division, endothelial cell proliferation and necrosis; the IHC detection index is IDH1 wild type; the WES detection index is IDH2 mutation; the RNA-SEQ detection index is any one of MYB-QK1, ESR1, MMP16, MAML2, PCDHGA1 and MYBL1-RAD51B, MAML2, ZFHX4, TOX, the type and grade of the brain glioma is judged to be diffuse astrocytoma with MYB / MYBL1 changes, grade 1.

[0055] Embodiment 2 like Figure 5 As shown, this embodiment provides a system for assisting in determining the grade of brain glioma, including: The sample information acquisition and preprocessing module 101 is used to acquire the sample information of the patient and preprocess the sample; The detection feature acquisition module 102 is used to perform FISH detection, NGS detection-assisted bioinformatics analysis and pathology detection on the pre-processed sample data to obtain N1 FISH detection indicators, N2 NGS detection indicators and N3 pathology detection indicators and the gene variation phenomenon characteristics corresponding to each detection indicator; The auxiliary determination grading module 103 is used to determine the type and grade of the brain glioma based on the N1 FISH detection indicators, N2 NGS detection indicators and N3 pathological detection indicators and one or more corresponding gene variation phenomenon characteristics in each detection indicator.

[0056] The system can implement the method provided above. The specific method can be found in the above description and will not be repeated here.

[0057] The present invention also provides a memory storing a plurality of instructions, wherein the instructions are used to implement the method as in the first embodiment.

[0058] like Figure 6 As shown, the present invention also provides an electronic device, including a processor 301 and a memory 302 connected to the processor 301, the memory 302 stores a plurality of instructions, and the instructions can be loaded and executed by the processor so that the processor can execute the method as in the first embodiment.

[0059] Although preferred embodiments of the present invention have been described, additional changes and modifications may be made to these embodiments by those skilled in the art once the basic inventive concepts are known. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for assisting in determining the grade of brain glioma, characterized in that: include: S1, obtaining sample information of the patient and performing clinical expression standardization preprocessing on the sample information; The preprocessing is to preprocess the samples according to the detection indicators, and the preprocessing includes: HE, IHC and FISH detection using experimental reagents for staining and WES and RNA using NGS sequencing and bioinformatics analysis software to preprocess sample data; S2, performing FISH detection, NGS detection-assisted bioinformatics analysis and pathological detection on the pre-processed sample data to obtain N1 FISH detection indicators, N2 NGS detection indicators and N3 pathological detection indicators as well as the gene variation phenomenon characteristics corresponding to each detection indicator; S3, judging the type and grade of the brain glioma based on the N1 FISH detection indicators, N2 NGS detection indicators and N3 pathological detection indicators and one or more corresponding gene variation phenomenon characteristics in each detection indicator.

2. A method for assisting in determining the grade of brain glioma according to claim 1, characterized in that: The N1 FISH detection indicators and their corresponding gene variation characteristics are: The first FISH detection index is chromosome 1p / 19q, and the corresponding gene variation phenomenon is characterized by joint deletion; The second FISH detection indicator is the CDKN2A / B gene, and the corresponding gene variation phenomenon is characterized by homozygous deletion; The third FISH detection indicator is the TERT gene, the corresponding gene variation phenomenon is characterized by promoter mutation, and the corresponding base position is C228T / C250T; The fourth FISH detection index is the variation of chromosome 7 / 10, and the corresponding gene variation phenomenon characteristic is +7 / -10. The characteristic +7 / -10 indicates the overall increase of chromosome 7 and the overall loss of chromosome 10. The fifth FISH detection indicator is the EGFR gene, and the corresponding gene mutation phenomenon is characterized by the amplification of the copy number; The sixth FISH detection indicator is the EGFR gene, and the corresponding gene mutation phenomenon is characterized by the presence of EGFRvIII antigen; The seventh FISH detection indicator is the MYCN gene, and the corresponding gene variation phenomenon is characterized by copy number amplification.

3. A method for assisting in determining the grade of brain glioma according to claim 2, characterized in that: The N2 NGS detection indicators and their corresponding gene variation characteristics are: The first WES detection index is IDH1, and the corresponding gene variation phenomenon is characterized by amino acid variation R132H / C / L / S / G; The second WES detection indicator is IDH2, and the corresponding gene variation phenomenon is characterized by amino acid variation R172K / M / G / W; The third WES detection index is H3 K27, and the corresponding gene variation phenomenon is characterized by mutation K27M; The fourth WES detection index is H3 G34, and the corresponding gene variation phenomenon is characterized by mutation G34R / V; The fifth WES detection indicator is TP53, and the corresponding gene variation phenomenon is characterized by mutation; The sixth pyrosequencing detection index is MGMT, and the corresponding gene variation phenomenon is characterized by promoter region methylation; The seventh RNA detection indicator is FGFR, and the corresponding gene mutation phenomenon is characterized by FGFR-TACC gene fusion; The eighth RNA detection indicator is MET, and the corresponding gene mutation phenomenon is characterized by mutation METex14; The ninth WES detection index is TSC1 / 2, and the corresponding gene variation phenomenon is characterized by mutation; The tenth RNA detection index is ZFTA, and the corresponding gene mutation phenomenon is characterized by gene Cllorf95-RELA gene fusion; The eleventh RNA detection indicator is YAP1, and the corresponding gene variation phenomenon is characterized by YAP1-MAMLD1 gene fusion; The twelfth WES detection indicator is MYCN, and the corresponding gene variation phenomenon characteristic is mutation; The thirteenth RNA sequencing detection indicator is the MET gene, and the corresponding gene variation phenomenon is characterized by PRPRZ1-MET gene fusion; The fourteenth RNA sequencing detection indicator is miR-181d, and the corresponding variation phenomenon characteristic is the high expression of the transcription regulatory factor miR-181d.

4. A method for assisting in determining the grade of brain glioma according to claim 3, characterized in that: The N3 pathological detection indicators and their corresponding gene variation characteristics are: The first IHC detection index is ATRX, and the corresponding gene variation phenomenon characteristic is mutation; The second IHC detection indicator is BRAF, and the corresponding gene mutation phenomenon is characterized by mutation BRAF V600E.

5. A method for assisting in determining the grade of brain glioma according to claim 4, characterized in that: The bioinformatics analysis is used to perform secondary analysis on the molecular indicators to obtain new molecular indicators, including: (1) Gene fusion to obtain candidate fusion gene sequences; (2) The candidate fusion gene sequence is subjected to the left step and / or the right step for detection, wherein the left step is used to identify inconsistent alignment information of the genome, including aligning and positioning the sequencing reads with the genome, obtaining a read covering the assumed fusion junction site, being able to split and match the reads of the genes on both sides of the fusion junction site, and a pair of reads fragments containing the fusion junction site, wherein the genes corresponding to the two ends of the pair of reads fragments R1 and R2 are different; the right step is used to assemble transcripts, including directly assembling into a longer transcript sequence and then identifying the fusion transcripts consistent with the chromosome rearrangement.

6. A method for assisting in determining the grade of brain glioma according to claim 5, characterized in that: The bioinformatics analysis also includes analyzing and calculating whether there are new gene fusion indicators based on RNA data and STAR-Fusion software, including: (1) Aligning the sequencing reads to the reference genome through STAR, screening one of the sequencing reads containing two gene fusion breakpoints as sequencing Junction reads; screening the sequencing reads that align R1 and R2 to different genes as sequencing Spanning reads, and using the sequencing Spanning reads as candidate fusion gene sequences; (2) After comparing the candidate fusion gene sequence with the reference genome annotation file, the fusion gene is predicted based on the overlap method; (3) Based on the minimum sequencing reads support criterion, the prediction result of the fusion gene is further corrected using the verification tool FusionInspector; (4) Filtering out the false positives in the prediction results of the fusion gene after correction to obtain a fusion output result, and the fusion output result is used as a new gene fusion indicator to detect fusion transcripts.

7. A method for assisting in determining the grade of brain glioma according to claim 6, characterized in that: The method of judging the type and grade of the brain glioma based on the N1 FISH detection indicators, the N2 NGS detection indicators, the N3 pathological detection indicators, and one or more corresponding gene variation phenomenon characteristics in each detection indicator includes: S31, obtaining HE standard words as the first judgment information, wherein the HE standard words are obtained based on the HE detection method and morphological characteristics, and describe one or more of the following: cell nucleus, proliferation, visibility of nuclear division, degree of pathological nuclear division, presence or absence of vascular endothelial cell proliferation and necrosis, visibility of pseudo-"fence"-like small focal necrosis, tumor cell growth and morphological characteristics; S32, obtaining one or more detection indicators among N1 FISH detection indicators, N2 NGS detection indicators, and N3 pathological detection indicators and gene variation phenomenon characteristics corresponding to the detection indicators as second determination information; S33: Determine the type and grade of the brain glioma based on the first determination information and the second determination information.

8. A system for assisting in determining the grade of a glioma, for implementing the method for assisting in determining the grade of a glioma as claimed in any one of claims 1 to 7, characterized in that: include: A sample information acquisition and preprocessing module (101), used to acquire the sample information of the patient and preprocess the sample; The preprocessing is to preprocess the samples according to the detection indicators, and the preprocessing includes: HE, IHC and FISH detection using experimental reagents for staining and WES and RNA using NGS sequencing and bioinformatics analysis software to preprocess sample data; A detection feature acquisition module (102) is used to perform FISH detection, NGS detection-assisted bioinformatics analysis and pathology detection on the pre-processed sample data to obtain N1 FISH detection indicators, N2 NGS detection indicators and N3 pathology detection indicators and the gene variation phenomenon characteristics corresponding to each detection indicator; The auxiliary determination grading module (103) is used to determine the type and grade of the brain glioma based on the N1 FISH detection indicators, the N2 NGS detection indicators and the N3 pathological detection indicators and one or more corresponding gene variation phenomenon characteristics in each detection indicator.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores a plurality of instructions, and the processor is used to read the instructions and execute the method for assisting in determining the grade of brain glioma as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the plurality of instructions can be read by a processor and executed by the method for assisting in determining the grade of a brain glioma as described in any one of claims 1-7.

Images