A method and system for brain glioma aid determination of grade

Abstract

The application discloses a method and system for assisting in determining the grade of brain glioma, and belongs to the technical field of biological genes and bioinformatics. The method comprises the following steps: obtaining sample information of a patient and pre-processing the sample; performing FISH detection, NGS detection assisted bioinformatics analysis and pathological detection on the sample to obtain N1 FISH detection indexes, N2 NGS detection indexes and N3 pathological detection indexes, and gene variation phenomenon characteristics corresponding to each detection index; and determining the type and grade of brain glioma based on the detection indexes and the gene variation phenomenon characteristics corresponding to one or more of the detection indexes. The application also discloses corresponding systems, electronic devices and computer readable storage media. The NGS sequencing technology is used to replace part of the conventional FISH detection mode, the NGS data is retained, secondary analysis is performed by using the bioinformatics method to obtain new molecular indexes in the guidelines, the detection types and times are reduced, and the detection mode is optimized.

Description

Technical Field

[0001] This invention relates to the fields of biological genes and bioinformatics, and in particular to a method and system for assisting in the determination of grading of gliomas. Background Technology

[0002] Central nervous system tumors are tumors that occur in the central nervous system tissues such as the brain, spinal cord, and meninges. These tumors can be malignant (cancer) or benign, and their development can severely affect the function of the nervous system. Other types of central nervous system tumors account for only 19%, while gliomas account for 81% of all central nervous system tumors. Based on their malignancy, gliomas are classified into four grades, with grades 1-2 being low-grade gliomas and grades 3-4 being high-grade gliomas.

[0003] Molecular markers play a crucial role in the grading of gliomas. Currently, there are 20 glioma biomarkers. Different methods can be used to detect the same biomarker, and each type of glioma exhibits multiple variations of these molecular markers. This necessitates multiple methods for patients to be tested for molecular markers, increasing the overall cost of testing.

[0004] In summary, existing detection methods have the following drawbacks: (1) At present, there is no glioma grading system. Practitioners can only judge and grade patients according to the guidelines.

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

[0006] (3) Similar detection methods require multiple different indicators.

[0007] (4) After the guidelines are updated, new markers are added, and secondary testing is required as a new test.

[0008] (5) The test results are not standardized, and different operators or companies have different descriptions.

[0009] (6) Judging up to 23 combinations of indicators is time-consuming and prone to errors when done manually.

[0010] (7) There are many detection methods and indicators, and practitioners have a long evaluation cycle. 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 methods for determining the grading of gliomas. Summary of the Invention

[0012] To address the problems existing in the prior art, this invention provides the following technical solution: a method and system for assisting in the grading of gliomas. This method incorporates guideline evaluation criteria and logic, enabling automatic grading determination after data import. In the bioinformatics analysis workflow, WES (Whole-Exome Sequencing) is used to replace some detection methods such as immunohistochemistry, Sanger sequencing, pyrosequencing, PCR, microarrays, and qPCR. RNA (Ribonucleic Acid) sequencing technology replaces some FISH detection methods. Simultaneously, WES and RNA data are retained and secondary analysis is performed using bioinformatics methods to obtain newly added molecular indicators from the guidelines. Since WES and RNA data can cover most gene-based molecular indicators, the number of tests required for patients is reduced, optimizing the testing methods. While ensuring accurate test results, this method improves testing efficiency, reduces the workload and time required for practitioners' evaluation, and enhances the efficiency and accuracy of practitioners' decision-making, allowing patients to receive medication earlier. Furthermore, pathological test conclusions are standardized, and standardized terminology corresponding to all pathological test conclusions is statistically analyzed to reduce ambiguity.

[0013] This invention provides a method for assisting in the grading of gliomas, comprising: S1, Obtain patient sample information and perform clinical description standardization preprocessing on the sample data; The preprocessing described herein refers to preprocessing the samples according to the detection indicators. The preprocessing includes: HE (hematoxylin-eosinstaining), IHC (immunohistochemistry), and FISH (fluorescence in situ hybridization) detection using experimental reagents for staining, and WES and RNA preprocessing using NGS (Next Generation Sequencing) and bioinformatics analysis software; wherein NGS sequencing is a high-throughput sequencing method.

[0014] S2, after performing FISH detection, NGS detection-assisted bioinformatics analysis and pathological detection on the preprocessed sample data, N1 FISH detection indicators, N2 NGS detection indicators and N3 pathological detection indicators and the gene variation characteristics corresponding to each detection indicator are obtained respectively. S3. Based on the N1 FISH detection indicators, N2 NGS detection indicators, and N3 pathological detection indicators, as well as one or more corresponding gene variation characteristics of each detection indicator, determine the type and grade of the glioma.

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

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

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

[0018] Preferably, the bioinformatics analysis is used to perform secondary analysis on molecular indicators to obtain new molecular indicators, including: (1) Gene fusion to obtain candidate fusion gene sequences; (2) The candidate fusion gene sequence is tested by performing the left-side step and / or the right-side step. The left-side step is used to identify the alignment information of the genome inconsistency, including aligning the sequencing reads with the genome to locate a read that covers the assumed fusion connection site, and being able to split the reads that match the genes on both sides of the fusion connection site and a pair of reads containing the fusion connection site, wherein the genes corresponding to the two ends of the pair of reads R1 and R2 are different; the right-side step is used to assemble the transcript, including directly assembling it into a longer transcript sequence and then identifying the fusion transcript consistent with the chromosome rearrangement.

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

[0020] Preferably, the step of determining the type and grade of the glioma based on the N1 FISH detection indicators, N2 NGS detection indicators, and N3 pathological detection indicators, as well as one or more corresponding gene variation characteristics of each detection indicator, includes: S31, obtain HE standard language as the first judgment information. The HE standard language is obtained based on HE detection method and morphological characteristics, and describes one or more of the following: cell nucleus, proliferation, visibility of nuclear mitotic figures, degree of pathological nuclear mitotic figures, presence or absence of vascular endothelial cell proliferation and necrosis, visibility of pseudo-"fence"-like small focal necrosis, tumor cell growth status and morphological characteristics. S32, acquire one or more of the following detection indicators from N1 FISH detection indicators, N2 NGS detection indicators and N3 pathological detection indicators, and the gene variation characteristics corresponding to the detection indicators as the second determination information; S33, determine the type and grade of the 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 the grading of gliomas, comprising: The sample information acquisition and preprocessing module is used to acquire the patient's sample information and preprocess the sample; The preprocessing mentioned above refers to preprocessing the samples according to the detection indicators. The preprocessing includes: staining with experimental reagents for HE, IHC and fish detection, and preprocessing sample data for WES and RNA using NGS sequencing and bioinformatics analysis software. The detection feature acquisition module is used to obtain N1 FISH detection indicators, N2 NGS detection indicators, and N3 pathological detection indicators, as well as the gene variation characteristics corresponding to each detection indicator, after performing FISH detection, NGS detection-assisted bioinformatics analysis, and pathological detection on the preprocessed sample data. The grading assistance module is used to determine the type and grade of the glioma based on the N1 FISH detection indicators, N2 NGS detection indicators, and N3 pathological detection indicators, as well as one or more corresponding gene variation characteristics of each detection indicator.

[0022] A third aspect of the present invention provides an electronic device including a processor and a memory, the memory storing a plurality of instructions, the processor being configured to read the instructions and execute the method as described in the first aspect.

[0023] A fourth aspect of the present invention provides a computer-readable storage medium storing a plurality of instructions which can be read by a processor and executed as described in the first aspect.

[0024] The method, system, and electronic device provided by this invention have the following beneficial effects: (1) Fewer tests are required, only IHC, WES and RNA need to be tested; (2) Fewer tests are required; WES and RNA can detect most indicators. (3) After the guidelines are updated, the data can be analyzed a second time, which can reduce the need for repeated testing; (4) Reduce the number of tests for patients and help them reduce testing costs; (5) It facilitates practitioners to collect indicators at once, and the operation is convenient; (6) Reduce the workload of grading and assessment practitioners and eliminate false positives or false negatives due to lack of experience; (7) The time cost is reduced by shortening the entire grading process. Attached Figure Description

[0025] Figure 1(a) is a flowchart of the method for assisting in the determination of grading of glioma according to the present invention; Figure 1(b) is a flowchart of the specific method for assisting in the determination of grading of glioma according to an embodiment of the present invention.

[0026] Figure 2 A schematic diagram illustrating the principle of bioinformatics analysis provided by this invention.

[0027] Figure 3 This diagram illustrates the results of fusion gene analysis for the present invention.

[0028] Figures 4(a)-4(e) are schematic diagrams illustrating examples of determining the type and grade of 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 the grading of gliomas, provided by the present invention.

[0030] Figure 6 This is a schematic diagram of the structure of an embodiment of the electronic device provided by the present invention. Detailed Implementation

[0031] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

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

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

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

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

[0036] In addition, those skilled in the art will understand that the structure of the terminal described above does not constitute a limitation on the terminal. The terminal may include more or fewer components, or combine certain components, or have different component arrangements. For example, the terminal may also include radio frequency circuits, input units, sensors, audio circuits, power supplies, and other components, which will not be described in detail here.

[0037] Besides biomarkers, specific molecular characteristics, such as gene mutations, can also be incorporated into prognostic models for early-stage liver cancer patients after surgery. Gene mutations, especially driver gene mutations, are a significant cause of cancer development and progression. Gene mutations are also important targets for cancer treatment and serve as crucial prognostic markers.

[0038] Example 1 As shown in Figures 1(a) and 1(b), this embodiment provides a method for assisting in the grading of gliomas, including: S1, Obtain patient sample information and preprocess the sample information; wherein the preprocessing is to preprocess 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 performing FISH detection, NGS detection-assisted bioinformatics analysis and pathological detection on the preprocessed sample data, N1 FISH detection indicators, N2 NGS detection indicators and N3 pathological detection indicators and the gene variation characteristics corresponding to each detection indicator are obtained respectively. S3. Based on the N1 FISH detection indicators, N2 NGS detection indicators, and N3 pathological detection indicators, as well as one or more corresponding gene variation characteristics of each detection indicator, determine the type and grade of the glioma.

[0039] See Table 1 for statistical information on 23 detection indicators, including biomarkers, characteristics of gene variation, detection methods, clinical classification, and the detection methods used in the system.

[0040] Table 1 1 IDH1 Mutations (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 (molecule) WES 3 Chromosome 1p / 19q Joint missing FISH, PCR, methylation microarray / expression profiling microarray / 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 (molecule) WES 6 ATRX mutation IHC, Sanger sequencing, NGS IHC (pathology) IHC 7 TP53 mutation IHC, Sanger sequencing, NGS IHC (pathology) WES 8 CDKN2A / B Loss of homozygosity FISH, qPCR, MLPA, methylation microarrays / expression profiling microarrays / 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 chips FISH (pathology) FISH 11 EGFR Amplification FISH, digital PCR, NGS, microarray chips FISH (pathology) FISH 12 EGFR EGFRvIII rearrangement RT-PCR, digital PCR, IHC MLPA, NGS NGS (molecule) 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 (molecule) RNA 16 MET Fusion gene (PTPRZ1-MET) Sanger sequencing, qPCR, NGS NGS (molecule) FISH 17 MET Mutation (METex14) Sanger sequencing, qPCR, NGS NGS (molecule) RNA 18 miR-181d High expression microRNA expression profiling microarray, qPCR, in situ hybridization staining In situ hybridization staining (pathology) FISH 19 TSC1 / 2 mutation Sanger sequencing, NGS NGS (molecule) WES 20 ZFTA Gene fusion (C11orf95-RELA) FISH, NGS NGS (molecule) RNA 21 YAP1 Gene fusion (YAP1-MAMLD1) FISH, NGS NGS (molecule) RNA 22 MYCN Amplification FISH, NGS FISH (pathology) FISH 23 MYCN mutation Sanger sequencing, NGS NGS (molecule) WES In a preferred embodiment, the N1 FISH detection indicators and their corresponding gene variation characteristics are as follows: The first FISH test indicator is chromosome 1p / 19q, and the corresponding gene variation is characterized by combined deletion. The second FISH test indicator is the CDKN2A / B gene, and the corresponding gene variation is characterized by homozygous loss. The third FISH test indicator is the TERT gene, and the corresponding gene variation is characterized by promoter mutation (base position C228T / C250T). The fourth FISH test indicator is the variation of chromosome 7 / 10, and the corresponding gene variation characteristics are +7 / -10 (overall increase of chromosome 7 and overall deletion of chromosome 10). The fifth FISH detection indicator is the EGFR gene, and the corresponding gene variation phenomenon is copy number amplification; The sixth FISH detection indicator is the EGFR gene. The corresponding gene mutation is characterized by the presence of EGFRvIII antigen (the deletion of EGFR exons 2-7, resulting in a gene corresponding to a truncated extracellular domain that can constitutively activate EGFR, producing a new peptide sequence, thereby producing a unique, GBM cell-specific, antibody-reactive EGFRvIII antigen). The seventh FISH detection indicator is the MYCN gene, and the corresponding gene mutation characteristic is copy number amplification.

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

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

[0043] like Figure 2 As shown, in a preferred embodiment, the bioinformatics analysis is used to perform secondary analysis on molecular indicators to obtain new molecular indicators, including: (1) Gene fusion to obtain candidate fusion gene sequences; (2) The candidate fusion gene sequence is tested by performing the left-side step and / or the right-side step. The left-side step is used to identify the alignment information of the genome inconsistency, including aligning the sequencing reads with the genome to locate a read that covers the assumed fusion connection site, and being able to split the reads that match the genes on both sides of the fusion connection site and a pair of reads containing the fusion connection site, wherein the genes corresponding to the two ends of the pair of reads R1 and R2 are different; the right-side step is used to assemble the transcript, including directly assembling it into a longer transcript sequence and then identifying the fusion transcript 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 that have "fused" together. Normally, two genes are far apart and do not interfere with each other. However, due to pathological chromosomal rearrangements, genes that are far apart, or even on different chromosomes, can combine together. Fusion genes are produced by chromosomal rearrangements, including translocations, insertions, inversions, and deletions. Gene fusion produces gene fusion transcripts and chimeric protein products, which have been used as therapeutic targets. Well-known examples are Imantinib, which targets the BCR-ABL1 gene fusion, and crizotinib, which targets the EML4-ALK gene fusion.

[0046] 1. The left-hand step identifies alignment information that indicates genomic inconsistencies. Sequencing reads are aligned with the genome to locate Junction / Spanning reads. JunctionReadsCount refers to the number of reads that can be split and match the genes on both sides of a fusion junction site when a read covers the assumed fusion junction site. SpanningFragsCount refers to the number of reads that contain fusion junction sites, where the genes on both sides of a pair of read fragments R1 and R2 are different.

[0047] 2. Right-hand step: Transcript assembly The transcripts were directly assembled into longer sequences, and then fusion transcripts consistent with chromosomal rearrangements were identified; most reads may have aligned to the sides of the fusion junction site rather than directly to the fusion junction site itself.

[0048] Currently, many software programs exist for detecting gene fusions, most of which are based on the two methods mentioned above for predictive analysis. STAR-Fusion, recommended by NCIP (National Cancer Institute Research Center), is software that uses the fusion output results of STAR alignment (sequencing sequence alignment with reference sequence) to detect fusion transcripts. In the analysis workflow developed by NCIP, this software is used to obtain predicted fusion transcripts in the first step. Therefore, as a preferred embodiment, the bioinformatics analysis also includes calculating whether there are new gene fusion indicators based on RNA data and STAR-Fusion software analysis, including: (1) Align the sequencing reads to the reference genome using 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 select the Spanning reads as candidate fusion gene sequences; (2) The candidate fusion gene sequence is compared with the reference genome annotation file and the fusion gene is predicted based on the overlap method; (3) Based on the minimum sequencing reads support criterion, the prediction results of the fusion gene were further corrected using the verification tool FusionInspector; (4) Filter out false positives in the prediction results of the fusion gene after correction to obtain the fusion output result, which is used as a new gene fusion indicator to detect fusion transcripts.

[0049] In this embodiment, the software is used to obtain predicted fusion transcripts in the first step of the analysis workflow developed by NCIP. Starting with the FASTQ file, STAR-Fusion, after preparing the genome database and next-generation sequencing files, can then be used to predict fusion genes.

[0050] The analysis uses STAR to align sequencing reads to the genome, and employs various stringent criteria to select the most suitable analytical results, such as removing MT information, limiting the minimum FFPM, and separating splice isoforms into multiple entries. The output is a tabular file, such as... Figure 3 As shown, Figure 3 The table header is as follows.

[0051] STAR-Fusion Results Explanation: FusionName: Name of the fusion gene; JunctionReadsCount: The number of reads that can be split into two fusion genes at the assumed fusion junction site; SpecificType: Specific type; SpanningFragsCount: Includes the number of fusion-linked sequence reads, where the genes at the R1 and R2 ends of the sequence read fragments are different; SpliceType: Whether the breakpoint location of the fusion gene appears at the reference exon connection provided by the reference transcript structural annotation (e.g., gencode); Left / RightGene: Left / Right gene of the fusion gene; Left / RightBreakpoint: Information on the left / right chromosome location of the fusion gene breakpoint; LargeAnchorSupport: Whether there is a longer base sequence (>=25bp) matching reads on both sides of the assumed breakpoint. Fusion genes lacking LargeAnchorSupport are usually false positives. Left / RightBreakDinuc: Breakpoint dinucleotide information indicating the left / right chromosome position; FFPM: The standardized result of fused reads, i.e., the amount of fused reads per million total reads; Left / RightBreakEntropy: The Shannon entropy of the 15 exon bases on either side of the breakpoint. The maximum entropy is 2, and the minimum entropy is 0. Results with low entropy should generally be considered to have low confidence.

[0052] In a preferred embodiment, determining the type and grade of the glioma based on the N1 FISH detection indicators, N2 NGS detection indicators, and N3 pathological detection indicators, as well as one or more corresponding gene variation characteristics of each detection indicator, includes: S31, obtain HE standard language as the first judgment information. The HE standard language is obtained based on HE detection method and morphological characteristics, and describes one or more of the following: cell nucleus, proliferation, visibility of nuclear mitotic figures, degree of pathological nuclear mitotic figures, presence or absence of vascular endothelial cell proliferation and necrosis, visibility of pseudo-"fence"-like small focal necrosis, tumor cell growth status and morphological characteristics. S32, acquire one or more of the following detection indicators from N1 FISH detection indicators, N2 NGS detection indicators and N3 pathological detection indicators, and the gene variation characteristics corresponding to the detection indicators as the second determination information; S33, determine the type and grade of the glioma based on the first determination information and the second determination information.

[0053] As shown in Table 2, in a preferred embodiment, the HE standard script includes: HE Standard Statement 1: The cell nucleus shows no atypia, proliferation is not active, there are no mitotic figures, vascular endothelial cell proliferation and necrosis; HE Standard Statement 2: The cell nuclear atypia is relatively obvious, the proliferation ratio is active, mitotic figures are occasionally seen, and there is no proliferation and necrosis of vascular endothelial cells; HE Standard Statement 3: Increased tumor cell density, obvious nuclear atypia, active proliferation, easily visible mitotic figures, no vascular endothelial cell proliferation and necrosis; HE Standard Statement 4: Tumor cell density and nuclear atypia are significantly increased, proliferation is extremely active, mitotic figures and pathological mitotic figures are common, and glomerular-like vascular endothelial cell proliferation and / or pseudo-"fence"-like focal necrosis can be seen; HE Standard Statement 5: Cubic or columnar tumor cells grow in a pseudopapillary or perivascular manner, with anucleate areas visible around the vessels, and the area around the vessels and tumor cells becoming transparent.

[0054] Table 2 The cell nucleus showed no atypia, proliferation was inactive, and there were no mitotic figures, vascular endothelial cell proliferation, or necrosis. HE The cells showed significant nuclear atypia, active proliferation, and occasional mitotic figures, but no vascular endothelial cell proliferation or necrosis. HE Tumor cells show increased density, marked nuclear atypia, active proliferation, and frequent mitotic figures, but no vascular endothelial cell proliferation or necrosis. HE Tumor cell density and nuclear atypia were significantly increased, with extremely active proliferation. Mitotic figures and pathological mitotic figures were frequently observed. Glomerular-like vascular endothelial cell proliferation and / or pseudo-"palisading" focal necrosis were also visible. HE Cubic or columnar tumor cells grow in a pseudopapillary or perivascular pattern, with anucleate areas visible around the vessels, and the area around the vessels and tumor cells becoming transparent. HE In a preferred embodiment, step S33, determining the type and grade of the glioma based on the first determination information and the second determination information, includes: (1) As shown in Figure 4(a), based on the HE standard statement 2: the cell nuclear atypia is obvious, the proliferation ratio is active, mitotic figures are occasionally seen, and there is no vascular endothelial cell proliferation and necrosis; the IHC detection index shows IDH1 mutation, ki67<5%; the WES detection index shows IDH2 mutation, ATRX deletion and p53 mutation, the type and grade of the glioma are determined to be astrocytoma, IDH mutation type, grade 2; (2) As shown in Figure 4(b), based on the HE standard statement 2: the cell nuclear atypia is obvious, the proliferation ratio is active, mitotic figures are occasionally seen, and there is no vascular endothelial cell proliferation and necrosis; the IHC detection index shows IDH1 mutation, ki67<5%; the WES detection index shows IDH2 mutation, and the FISH detection index shows 1p / 19q co-deletion, the type and grade of the glioma are determined to be oligodendroglioma, IDH mutation with 1p / 19q co-deletion, grade 2; (3) As shown in Figure 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 mitotic figures are easily seen, and there is no vascular endothelial cell proliferation and necrosis; the IHC detection index shows IDH1 mutation, and Ki67 is between 5% and 10%; the WES detection index shows IDH2 mutation, and the FISH detection index shows 1p / 19q co-deletion. The type and grade of the glioma are determined to be oligodendroglioma, IDH mutation with 1p / 19q co-deletion, grade 3; (4) As shown in Figure 4(d), based on any of the above HE standard statements, 1: No nuclear atypia, inactive proliferation, no mitotic figures, vascular endothelial cell proliferation and necrosis; HE standard statement 2: Relatively obvious nuclear atypia, more active proliferation, occasional mitotic figures, no vascular endothelial cell proliferation and necrosis; HE standard statement 3: Increased tumor cell density, obvious nuclear atypia, active proliferation, easily seen mitotic figures, no vascular endothelial cell proliferation and necrosis; HE standard statement 4: Tumor cell density and nuclear atypia Significantly increased, extremely active proliferation, frequent mitotic figures and pathological mitotic figures, glomerular-like vascular endothelial cell proliferation and / or pseudo-"fence"-like focal necrosis are visible; the IHC detection index shows IDH1 wild-type, ki67>10%; the WES detection index shows IDH2 wild-type; the FISH detection index shows TERT promoter mutation, EGFR amplification, and any of +7 / -10; the type and grade of the glioma are determined to be glioblastoma, IDH wild-type, grade 4; (5) As shown in Figure 4(e), based on the HE standard statement 1: the cell nucleus is not atypic, proliferation is not active, there are no mitotic figures, vascular endothelial cell proliferation and necrosis; the IHC detection index is wild-type IDH1; the WES detection index is IDH2 mutated; and 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 glioma are determined to be diffuse astrocytoma with MYB / MYBL1 changes, grade 1.

[0055] Example 2 like Figure 5 As shown, this embodiment provides a system for assisting in the grading of gliomas, comprising: The sample information acquisition and preprocessing module 101 is used to acquire the patient's sample information and preprocess the sample. The detection feature acquisition module 102 is used to obtain N1 FISH detection indicators, N2 NGS detection indicators, and N3 pathological detection indicators, as well as the gene variation characteristics corresponding to each detection indicator, after performing FISH detection, NGS detection-assisted bioinformatics analysis, and pathological detection on the preprocessed sample data. The grading assistance module 103 is used to determine the type and grade of the glioma based on the N1 FISH detection indicators, N2 NGS detection indicators, and N3 pathological detection indicators, as well as one or more corresponding gene variation characteristics of each detection indicator.

[0056] This system can implement the methods provided above. For details on the methods, please refer to the above description, which will not be repeated here.

[0057] The present invention also provides a memory that stores multiple instructions for implementing the method as described in Embodiment 1.

[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, which can be loaded and executed by the processor to enable the processor to perform the method as described in Embodiment 1.

[0059] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and modifications.

Claims

1. A system for assisting in the grading of gliomas, comprising a method for assisting in the grading of gliomas, characterized in that, include: The sample information acquisition and preprocessing module (101) is used to acquire the patient's sample information and perform clinical expression standardization preprocessing on the sample information; The standardized preprocessing of clinical expression mentioned above refers to the preprocessing of samples according to the detection indicators. The preprocessing includes: staining with experimental reagents for HE, IHC and FISH detection, and preprocessing sample data for WES and RNA using NGS sequencing and bioinformatics analysis software. The detection feature acquisition module (102) is used to obtain N1 FISH detection indicators, N2 NGS detection indicators, and N3 pathological detection indicators, as well as the gene variation characteristics corresponding to each detection indicator, after performing FISH detection, NGS detection-assisted bioinformatics analysis, and pathological detection on the preprocessed sample data; wherein, 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 tested by performing the left-side step and / or the right-side step. The left-side step is used to identify genomic inconsistencies in alignment information, including aligning the sequencing reads with the genome to locate a read that covers the assumed fusion junction site, and being able to separate reads that match the genes on both sides of the fusion junction site and a pair of read fragments containing the fusion junction site, wherein the genes at both ends of the pair of read fragments R1 and R2 are different; the right-side step is used to assemble transcripts, including directly assembling them into longer transcript sequences and then identifying fusion transcripts consistent with chromosomal rearrangements; wherein most reads align to both sides of the fusion junction site, but do not directly cover the fusion junction site itself; The bioinformatics analysis also includes calculating whether there are any new gene fusion indicators based on RNA data and STAR-Fusion software, including: (1) Align the sequencing reads to the reference genome using STAR, and select one sequencing read containing two gene fusion breakpoints as sequencing Junction reads; select sequencing reads that align R1 and R2 to different genes as sequencing Spanning reads, and select sequencing Spanning reads as candidate fusion gene sequences; (2) The candidate fusion gene sequence is compared with the reference genome annotation file and then the fusion gene is predicted based on the re-alignment method; (3) Based on the minimum sequencing reads support criterion, the prediction results of the fusion gene were further corrected using the verification tool FusionInspector; (4) Filter out false positives in the prediction results of the fusion gene after correction to obtain the fusion output result, which is used as a new gene fusion index to detect fusion transcripts; The grading assistance module (103) is used to determine the type and grade of the glioma based on the N1 FISH detection indicators, N2 NGS detection indicators, and N3 pathological detection indicators, as well as one or more corresponding gene variation characteristics of each detection indicator; including: The HE standard script is obtained as the first judgment information. The HE standard script is obtained based on the HE detection method and morphological characteristics, and describes one or more of the following: cell nucleus, proliferation, visibility of mitotic figures, degree of pathological mitotic figures, presence or absence of vascular endothelial cell proliferation and necrosis, visibility of pseudo-palisading foci of necrosis, tumor cell growth status and morphological characteristics. One or more of the N1 FISH detection indicators, N2 NGS detection indicators, and N3 pathological detection indicators, along with the gene variation characteristics corresponding to the detection indicators, are obtained as the second determination information. The type and grade of the glioma are determined based on the first determination information and the second determination information.

2. The system for assisting in the grading of gliomas according to claim 1, characterized in that, The N1 FISH detection indicators and their corresponding gene variation characteristics are as follows: The first FISH test indicator is chromosome 1p / 19q, and the corresponding gene variation is characterized by combined deletion. The second FISH test indicator is the CDKN2A / B gene, and the corresponding gene variation is characterized by homozygous loss. The third FISH detection indicator is the TERT gene, and the corresponding gene variation is a promoter mutation, with the corresponding base position being C228T / C250T. The fourth FISH test indicator is the variation of chromosome 7 / 10, and the corresponding gene variation characteristics are +7 / -10. The characteristic +7 / -10 indicates an overall increase in chromosome 7 and an overall deletion in chromosome 10. The fifth FISH detection indicator is the EGFR gene, and the corresponding gene variation phenomenon is copy number amplification; The sixth FISH test indicator is the EGFR gene, and the corresponding gene mutation characteristic is the presence of EGFRvIII antigen; The seventh FISH detection indicator is the MYCN gene, and the corresponding gene mutation characteristic is copy number amplification.

3. The system for assisting in the grading of gliomas according to claim 2, characterized in that, The N2 NGS detection indicators and their corresponding gene variation characteristics are as follows: The first WES detection index is IDH1, and the corresponding gene variation characteristics are amino acid variations R132H / C / L / S / G. The second WES detection index 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 mutation characteristic is the K27M mutation. The fourth WES detection index is H3 G34, and the corresponding gene variation characteristic is the mutation G34R / V; The fifth WES detection index is TP53, and the corresponding gene variation phenomenon is mutation. The sixth pyrosequencing assay detects MGMT, which corresponds to promoter methylation as a gene mutation characteristic. The seventh RNA detection indicator is FGFR, and the corresponding gene variation characteristic is FGFR-TACC gene fusion; The eighth RNA detection indicator is MET, and the corresponding gene variation characteristic is the mutation METex14. The ninth WES detection index is TSC1 / 2, and the corresponding gene variation phenomenon is mutation. The tenth RNA detection indicator is ZFTA, and the corresponding gene variation characteristic is the fusion of the Cllorf95-RELA gene; The eleventh RNA detection index is YAP1, and the corresponding gene mutation characteristic is YAP1-MAMLD1 gene fusion. The twelfth WES detection index is MYCN, and the corresponding gene variation phenomenon is mutation. The thirteenth RNA sequencing detection index is the MET gene, and the corresponding gene variation phenomenon is the PTPRZ1-MET gene fusion. The fourteenth RNA sequencing detection index was miR-181d, and the corresponding variation was characterized by high expression of the transcriptional regulatory factor miR-181d.

4. The system for assisting in the grading of gliomas according to claim 3, characterized in that, The N3 pathological detection indicators and their corresponding gene variation characteristics are as follows: The first IHC detection indicator is ATRX, and the corresponding gene variation phenomenon is mutation. The second IHC test indicator is BRAF, and the corresponding gene mutation characteristic is the BRAF V600E mutation.

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