Leukemia gene mutation detection method based on next-generation sequencing transcriptome data

Abstract

The invention provides a leukemia gene mutation detection method based on next-generation sequencing transcriptome data, which adopts a next-generation sequencing technology to carry out mutation gene detection on leukemia transcriptome data, and provides an SNV and INDEL detection process based on the transcriptome data so as to improve the positive predicted value of detection. And technical support is provided for precise diagnosis and treatment of leukemia.

Description

Technical Field

[0001] The present invention relates to the technical field of gene detection, and in particular to a method for detecting leukemia gene mutations based on second-generation sequencing transcriptome data. Background Art

[0002] Leukemia is a malignant clonal disease originating from hematopoietic stem cells, characterized by the abnormal proliferation of leukemia cells in the bone marrow and other hematopoietic tissues, resulting in damage to normal hematopoietic function. According to the degree of cell differentiation and the speed of disease progression, leukemia can be divided into two categories: acute leukemia and chronic leukemia. Acute leukemia includes acute myeloid leukemia (AML) and acute lymphocytic leukemia (ALL); chronic leukemia includes chronic myeloid leukemia (CML) and chronic lymphocytic leukemia (CLL).

[0003] In recent years, with the rapid development of genomics technology, the pathogenesis of leukemia has been gradually revealed. The types of leukemia-related mutations mainly include single nucleotide variants (SNVs), insertions and deletions (INDELs), fusion genes, etc. These types of mutations not only affect gene expression and protein function, but are also closely related to the occurrence, development and prognosis of leukemia.

[0004] Common gene mutations in AML include FLT3, NPM1, and CEBPA, while mutations in genes such as ETV6, PAX5, and IKZF1 are common in ALL. The above gene mutations help to classify leukemia and provide important molecular basis for individualized treatment. For AML patients with FLT3 mutations, FLT3 inhibitors have become an important treatment method, and the prognosis of patients has been significantly improved; for ALL, chemotherapy combined with targeted therapy (such as tyrosine kinase inhibitors) and immunotherapy (such as chimeric antigen receptor T cell therapy) has become a new treatment option, significantly improving the efficacy of patients with relapsed or refractory ALL.

[0005] For leukemia patients, DNA and RNA samples from bone marrow or peripheral blood can be used for gene mutation detection. Traditional methods for detecting leukemia gene mutations mainly include Sanger sequencing, fluorescence in situ hybridization (FISH), and polymerase chain reaction (PCR). Sanger sequencing is a classic gene sequencing technology that can accurately detect SNVs and INDELs, but its throughput is low and it is difficult to meet the detection needs of large-scale samples. FISH technology is mainly used to detect chromosomal structural abnormalities, such as translocations and deletions, but its resolution is limited and point mutations cannot be detected. PCR technology is widely used in the detection of specific gene variations, such as the detection of BCR-ABL fusion genes, but its sensitivity and specificity are affected by primer design.

[0006] High-throughput sequencing technology (NGS), mainly including genome sequencing and transcriptome sequencing, has promoted the rapid development of leukemia gene mutation detection. NGS can detect mutations in thousands of genes at the same time, and has the characteristics of high sensitivity, high specificity and high throughput. Genome sequencing, with DNA as the detection level, is a conventional method for detecting leukemia gene mutations (SNV and INDEL). Transcriptome sequencing, with RNA as the detection level, is a conventional method for detecting leukemia gene expression information and fusion gene information. It can also provide richer information than genome sequencing, especially in detecting non-coding region mutations, splicing site mutations and abnormal gene expression regulation areas. Although transcriptome data analysis has shown great potential in gene mutation detection, its application still faces many challenges. The complexity and high-dimensional characteristics of transcriptome data make data analysis difficult, resulting in a high error rate in detecting gene mutations by transcriptome sequencing. Summary of the invention

[0007] The purpose of the present invention is to provide a method for detecting leukemia gene mutations based on second-generation sequencing transcriptome data. The method uses second-generation sequencing technology to perform mutant gene detection on leukemia transcriptome data, and provides SNV and INDEL detection processes based on transcriptome data to improve the positive predictive value of the detection, thereby providing technical support for the accurate diagnosis and treatment of leukemia.

[0008] Based on the second-generation sequencing transcriptome data analysis, in addition to the conventional leukemia gene expression information and fusion gene information analysis, gene mutation analysis is integrated into it to obtain multiple variation information based on one sample and one detection method, which helps to reduce the cost of genetic testing, better apply it to clinical practice, and provide richer information for the accurate diagnosis and treatment of the disease.

[0009] To achieve the above-mentioned purpose, the present invention provides a method for detecting leukemia gene mutations based on second-generation sequencing transcriptome data. The method includes data analysis of gene transcriptomes. In some embodiments, the transcriptome data analysis steps include: I. data collection and gene sequence alignment; II. variation detection and annotation; III. gene mutation site filtering.

[0010] The present invention provides a method for detecting leukemia gene mutations based on second-generation sequencing transcriptome data. In some embodiments, the method includes data analysis of gene transcriptomes, and the transcriptome data analysis steps include: Ⅰ. Data collection and gene sequence alignment; Ⅱ. Variant detection and annotation; Ⅲ. Gene mutation site filtering. The step Ⅰ. Data collection and gene sequence alignment includes: 1) The input data is the original data from the second-generation sequencing machine; 2) Preprocessing the original data from the machine, including removing data aligned to rRNA sequences, removing adapter sequences, and removing low-quality data; 3) Using alignment tools such as STAR, aligning and sorting the processed original data from the machine with the reference genome, and obtaining the alignment results, the data format is bam; 4) Identifying duplication reads on the bam file and removing duplication reads; 5) Removing intron region sequences.

[0011] The present invention provides a method for detecting leukemia gene mutations based on second-generation sequencing transcriptome data. In some embodiments, the method includes data analysis of gene transcriptomes, and the transcriptome data analysis steps include: I. Data collection and gene sequence alignment; II. Variant detection and annotation; III. Gene mutation site filtering. The step II. Variant detection and annotation includes: using GATK Mutect2 software to detect variant sites, and using ANNOVAR software to perform variant annotations, including non-synonymous mutations, synonymous mutations, and splice site mutations.

[0012] The present invention provides a method for detecting leukemia gene mutations based on second-generation sequencing transcriptome data. In some embodiments, the method includes data analysis of gene transcriptomes, and the transcriptome data analysis steps include: I. Data collection and gene sequence alignment; II. Variant detection and annotation; III. Gene mutation site filtering. The step III. Gene mutation site filtering includes: 1) Background library filtering: population databases include 1000 Genomes_EAS database and ExAC_EAS database; 2) Annotation filtering: including filtering out introns, untranslated regions (UTR), intergenic regions, upstream and downstream regions of genes, and filtering out synonymous mutations (synonymous mutations). mutation); 3) Single-base repeat region filtering: filter out regions with single-base repeats greater than 3 to 10 times and mutations on both sides of the region, including single-base indels (insertions and deletions are repeated bases) on both sides of the region and the region, multi-base INDELs (insertions and deletions are repeated bases) on both sides of the region and the region, and SNVs (alt is a repeated base) on both sides of the region and the region; 4) Reads number filtering: filter SNVs with support reads no more than 3 to 10, and filter INDELs with support reads no more than 3 to 15; 5) Mutation frequency filtering: mutation frequency less than 1% to 10%; 6) Filter mutations with a distance of <4 bases from the exon boundary; 7) Filter mutations with support reads of <4 within 10bp of both ends of non-reads, and support reads ratio > 90% within 5bp of both ends of reads. 8) For the supporting mutation reads with base quality lower than 20, if its proportion is higher than 80%, the mutation is filtered; if its proportion is between 70-80%, and the average number of low-quality mismatched bases other than the mutation on each read is ≥2, the mutation is filtered.

[0013] The present invention provides a method for detecting leukemia gene mutations based on second-generation sequencing transcriptome data. In some embodiments, the method comprises: analyzing gene transcriptome data, wherein the transcriptome data analysis step comprises step I. data collection and gene sequence alignment: 1) input data is the original data from the second-generation sequencing machine; 2) preprocessing the original data from the machine, including removing data aligned to rRNA sequences, removing adapter sequences, and removing low-quality data; 3) using alignment tools such as STAR to align and sort the processed original data from the machine with the reference genome to obtain alignment results, and the data format is bam; 4) identifying duplication reads for the bam file and removing duplication reads; 5) removing intron region sequences; step II. mutation detection and annotation: using GATK Mutect2 software to detect mutation sites, and using ANNOVAR software to perform mutation annotation, including non-synonymous mutations, synonymous mutations, and splice site mutations; step III. gene mutation site filtering: 1) background library filtering: the population database includes 1000 Genomes_EAS database, ExAC_EAS database; 2) Annotation filtering: including filtering out introns, untranslated regions (UTR), intergenic regions, upstream and downstream regions of genes, and filtering out synonymous mutations. mutation); 3) Filtering of single-base repeat regions: filter out regions with single-base repeats > 5 times and mutations on both sides of the region, including single-base indels (insertions and deletions are repeated bases) on both sides of the region and the region, multi-base INDELs (insertions and deletions are repeated bases) on both sides of the region and the region, and SNVs (alt is a repeated base) on both sides of the region and the region; 4) Filtering of the number of reads: filter SNVs with a support number of reads ≤ 5, filter INDELs with a support number of reads ≤ 10; 5) Mutation frequency filtering: mutation frequency < 4%; 6) Filter mutations with a distance of < 4 bases from the exon boundary; 7) Filter mutations with a support number of reads < 4 within a range of 10bp on both ends of non-reads, and a support ratio of reads within a range of 5bp on both ends of reads> 90% of mutations; 8) For supporting mutation reads with base quality lower than 20, if its proportion is higher than 80%, the mutation is filtered; if its proportion is between 70-80%, and the average number of low-quality mismatched bases other than the mutation on each read is ≥2, the mutation is filtered.

[0014] In the present invention, the term "sample" generally refers to a bone marrow or peripheral blood sample to be tested; "rRNA" generally refers to ribosomal RNA; "adapter" generally refers to a linker sequence; "gene mutation" generally refers to single nucleotide variation (SNV), insertion and deletion (INDEL); "CIGAR" generally refers to a string used to describe the details of a read when it is aligned with a reference sequence; "positive predictive value" generally refers to the proportion of all predicted positive sites that are actually positive, also known as accuracy; "HGVS" generally uses the naming rules established by the Human Genome Variation Society to describe the location, type and effect of the mutation on the protein; "IGV (Integrative Genomics Viewer)" is generally an integrated genome browser, which can visualize various genomic data such as genome variation, gene expression, mutation, fusion, methylation, etc., so that researchers can intuitively view and analyze; "Ref" is generally an allele recorded in the reference genome at a certain gene locus; "Alt" is generally other alleles at the same gene locus that are different from the reference allele. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 , the experimental results of changing the number of single base repeats; Figure 2 , SNV supports the experimental results of changes in the number of reads; Figure 3 , INDEL supports the experimental result graph of read number change; Figure 4 , mutation frequency change experimental results; Figure 5 , IGV map of SNV (based on DNA detection results); Figure 6 , IGV map of SNV (based on RNA detection results); Figure 7 , IGV map of INDEL (based on DNA detection results); Figure 8 , IGV maps of INDEL (based on RNA detection results). DETAILED DESCRIPTION

[0016] In order to more clearly illustrate the technical solution of the present invention, the technical solution in the embodiments of the present invention will be clearly and completely described below. The following embodiments are only used to explain the present invention and should not be regarded as limiting the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention. Example 1: Condition screening of gene mutation site filtering step:

[0017] Based on transcriptome sequencing of bone marrow or peripheral blood samples of leukemia patients, the obtained transcriptome data are analyzed, and the analysis method includes data analysis of gene transcriptomes, and the transcriptome data analysis steps include: I. data collection and gene sequence alignment; II. variation detection and annotation; III. gene mutation site filtering.

[0018] Step I. Data collection and gene sequence alignment, including: 1) The input data is the original data from the second-generation sequencing machine; 2) Preprocessing the original data, including removing the data aligned to the rRNA sequence, removing the adapter sequence, and removing low-quality data; 3) Using alignment tools such as STAR, align and sort the processed original data with the reference genome to obtain the alignment results in bam format; 4) Identify duplication reads in the bam file and remove duplication reads; 5) Remove the intron region sequence.

[0019] Step II. Mutation detection and annotation, including: using GATK Mutect2 software to detect variant sites, and using ANNOVAR software to annotate variants, including non-synonymous mutations, synonymous mutations, and splice site mutations.

[0020] Step III. Filtering of gene mutation sites includes: 1) Background library filtering; 2) Annotation filtering: including filtering out introns, untranslated regions (UTR), intergenic regions, upstream and downstream regions of genes, and filtering out synonymous mutations; 3) Single-base repeat region filtering; 4) Reads number filtering; 5) Mutation frequency filtering; 7) Filtering mutations with less than 4 supporting reads within 10bp of both ends of the reads, and mutations with a supporting read ratio of > 90% within 5bp of both ends of the reads; 8) For supporting mutation reads with base quality lower than 20, if their proportion is higher than 80%, filter the mutation; if their proportion is between 70-80%, and the average number of low-quality mismatched bases other than the mutation on each read is ≥2, filter the mutation.

[0021] In view of the changes in the number of single-base repeats, the number of SNV-supported reads, the number of INDEL-supported reads, and the mutation frequency involved in the gene mutation site filtering step in step III of the above method, a large-scale R&D experiment with different filtering conditions was conducted, and the results are as follows: (1) Changes in the number of single base repeats For the single-base repeat region filtering step in the gene mutation site filtering process, different filtering conditions from >3 to >10 were tried, and the results of positive predictive value and sensitivity were combined, and finally >5 was selected as the optimal condition (positive predictive value: 96.9%, sensitivity: 51.0%). Under the conditions of >6 to >10, although the sensitivity was improved, the positive predictive value was greatly reduced; compared with the conditions of >3 and >4, the positive predictive value under the condition of >5 was not the highest, but the sensitivity was better. The results are as follows Figure 1 shown.

[0022] (2) Changes in the number of reads supported by SNV According to the number of SNV support reads in the gene mutation site filtering process, the filtering steps were changed, and different filtering conditions from ≤3 to ≤10 were tried. After considering the results of positive predictive value and sensitivity, ≤5 was finally selected as the optimal condition (positive predictive value: 96.9%, sensitivity: 51.0%). Compared with the conditions of ≤3 and ≤4, the positive predictive value under the condition of ≤5 increased significantly. Although the sensitivity decreased, it was still within the acceptable range. Under the conditions of ≤5 to ≤10, the positive predictive value remained basically unchanged, but the sensitivity decreased significantly. The results are as follows Figure 2 shown.

[0023] (3) INDEL supports changes in the number of reads According to the number of INDEL support reads in the gene mutation site filtering process, the filtering step was changed, and different filtering conditions from ≤3 to ≤15 were tried. After considering the results of positive predictive value and sensitivity, ≤10 was finally selected as the optimal condition (positive predictive value: 96.9%, sensitivity: 51.0%). Compared with the conditions from ≤11 to ≤15, the sensitivity under the condition of ≤10 increased significantly. Although the positive predictive value decreased slightly, it was within an acceptable range. Under the conditions from ≤3 to ≤10, the positive predictive value increased significantly, and the sensitivity decreased. The results are as follows Figure 3 shown.

[0024] (4) Changes in mutation frequency According to the mutation frequency in the gene mutation site filtering process, the filtering steps were changed, and different filtering conditions from <1% to <10% were tried. After combining the results of positive predictive value and sensitivity, <4% was finally selected as the optimal condition (positive predictive value: 96.9%, sensitivity: 51.0%). Under the <1% to <4% condition, the positive predictive value increased significantly, and although the sensitivity decreased slightly, it was within an acceptable range; compared with the <5% to <10% condition, although the positive predictive value increased, the sensitivity decreased significantly. The results are as follows: Figure 4 shown. Example 2: Detection of leukemia gene mutation sites:

[0025] Bone marrow or peripheral blood samples from 97 leukemia patients were simultaneously subjected to DNA panel genomic sequencing and transcriptome sequencing targeting 521 leukemia-related genes. The transcriptome analysis region was consistent with the DNA coverage region of 521 leukemia-related genes, and the DNA panel genomic sequencing results were used as the gold standard.

[0026] After the condition screening of the transcriptome data analysis in Example 1, a better leukemia gene mutation transcriptome data analysis method was selected, transcriptome sequencing was performed based on bone marrow or peripheral blood samples of leukemia patients, and the acquired transcriptome data was analyzed. The steps of the analysis method are as follows.

[0027] Step I. Data collection and gene sequence alignment: 1) The input data is the original data from the second-generation sequencing machine, and the data format is fastq.

[0028] 2) Preprocess the original data, including removing data aligned to rRNA sequences, removing adapter sequences, and removing low-quality data.

[0029] 3) Use STAR and other alignment tools to align and sort the processed raw data with the reference genome to obtain the alignment results in bam format.

[0030] 4) Identify duplication reads in bam files and remove duplication reads.

[0031] 5) Identify all N elements in the CIGAR string and split each read containing N into multiple new reads.

[0032] Step II. Variant detection and annotation: GATK Mutect2 software was used to detect variant sites, and ANNOVAR software was used to annotate variants, including nonsynonymous mutations, synonymous mutations, and splice site mutations.

[0033] Step III. Filtering of gene mutation sites: 1) Population database filtering: The databases used for filtering include the 1000 Genomes_EAS database and the ExAC_EAS database. If the population frequency of the above variants in any population database is higher than 1%, they will be filtered out.

[0034] 2) Annotation filtering: Based on the annotation information of ANNOVAR software, the variants in introns, untranslated regions (UTR), intergenic regions, and upstream and downstream regions of genes were filtered out, and synonymous mutations were also filtered out.

[0035] 3) Single-base repeat region filtering: Filter out regions with single-base repeats > 5 times on the reference genome and mutations on both sides of the region, including the region and the single-base INDELs on both sides of the region (insertions and deletions are repeated bases), the region and the poly-base INDELs on both sides of the region (insertions and deletions are repeated bases), and the region and the SNVs on both sides of the region (alt is a repeated base).

[0036] 4) Reads number filtering: filter SNVs with support reads ≤ 5, and filter INDELs with support reads ≤ 10.

[0037] 5) Mutation frequency filtering: mutation frequency <4%.

[0038] 6) Filter mutations that are <4 bases away from exon boundaries.

[0039] 7) Filter out mutations with less than 4 supporting reads within 10 bp of the non-read ends and mutations with > 90% supporting reads within 5 bp of the read ends.

[0040] 8) For the supporting mutation reads with base quality lower than 20, if its proportion is higher than 80%, the mutation is filtered; if its proportion is between 70-80%, and the average number of low-quality mismatched bases other than the mutation on each read is ≥2, the mutation is filtered.

[0041] Based on the analysis method of the preferred conditions of Example 1, transcriptome data collection and gene sequence alignment, variation detection and annotation, and gene mutation site filtering are performed.

[0042] The gene loci detected by DNA genome sequencing and RNA transcriptome sequencing in bone marrow or peripheral blood samples of 97 leukemia patients are summarized in Table 1.

[0043] Table 1. Summary of DNA genome sequencing and RNA transcriptome detection sites in 97 samples Sample ID Chr Start End Ref Alt mut_belongs HGVS Sample 1 chr1 27106333 27106333 G A both NM_006015(ARID1A):c.5944G>A(p.V1982I) Sample 1 chr11 32417913 32417913 - GTACAAGA DNA_only NM_024426(WT1):c.1138_1139insTCTTGTAC(p.R380fs) Sample 1 chr11 119156112 119156112 C T DNA_only NM_005188(CBL):c.1777C>T(p.R593W) Sample 1 chr11 32417947 32417947 - C DNA_only NM_024426(WT1):c.1104dupG(p.R369fs) Sample 2 chr10 104852955 104852955 C T both NM_012229(NT5C2):c.1100G>A(p.R367Q) Sample 2 chr12 25378562 25378562 C G both NM_004985(KRAS):c.436G>C(p.A146P) Sample 2 chr12 122252181 122252181 - C DNA_only NM_015048(SETD1B):c.2066dupC(p.L690fs) Sample 3 chr12 25378562 25378562 C T both NM_004985(KRAS):c.436G>A(p.A146T) Sample 3 chr12 112926888 112926888 G C both NM_002834(PTPN11):c.1508G>C(p.G503A) Sample 3 chr1 149858181 149858181 G A both NM_003528(HIST2H2BE):c.10C>T(p.P4S) Sample 4 chr12 25378562 25378562 C T both NM_004985(KRAS):c.436G>A(p.A146T) Sample 4 chr1 115258747 115258747 C T both NM_002524(NRAS):c.35G>A(p.G12D) Sample 4 chr13 28608320 28608320 A G both NM_004119(FLT3):c.1736T>C(p.V579A) Sample 4 chr17 7577120 7577120 C T both NM_000546(TP53):c.818G>A(p.R273H) Sample 4 chr12 25380283 25380283 C T both NM_004985(KRAS):c.175G>A(p.A59T) Sample 4 xR 44941883 44941886 TAAG - DNA_only NM_021140(KDM6A):c.3207_3209+1del Sample 5 chr12 25378562 25378562 C T DNA_only NM_004985(KRAS):c.436G>A(p.A146T) Sample 5 chr12 25398281 25398281 C T DNA_only NM_004985(KRAS):c.38G>A(p.G13D) Sample 5 chr12 25380281 25380281 - GGG DNA_only NM_004985(KRAS):c.176_177insCCC(p.A59_G60insP) Sample 5 chr12 112888195 112888195 T C DNA_only NM_002834(PTPN11):c.211T>C(p.F71L) Sample 6 chr1 115258747 115258747 C T both NM_002524(NRAS):c.35G>A(p.G12D) Sample 6 chr13 28592612 28592612 T C DNA_only NM_004119(FLT3):c.2533A>G(p.R845G) Sample 6 chr12 112888189 112888189 G A DNA_only NM_002834(PTPN11):c.205G>A(p.E69K) Sample 7 chr1 115258747 115258747 C T both NM_002524(NRAS):c.35G>A(p.G12D) Sample 7 chr12 12043913 12043913 - A both NM_001987(ETV6):c.1293dupA(p.D432fs) Sample 7 chr17 30264406 30264406 - G DNA_only NM_015355(SUZ12):c.146dupG(p.G50fs) Sample 7 chr7 148513840 148513840 - CCCCG DNA_only NM_001203247(EZH2):c.1425_1426insCGGGG(p.I476fs) Sample 8 chr1 115258747 115258747 C T both NM_002524(NRAS):c.35G>A(p.G12D) Sample 8 chr19 33792408 33792408 - CAA both NM_004364(CEBPA):c.912_913insTTG(p.K304_Q305insL) Sample 8 chr19 33793082 33793082 - C both NM_004364(CEBPA):c.238dupG(p.D80fs) Sample 8 chr4 55599321 55599321 A T RNA_only NM_000222(KIT):c.2447A>T(p.D816V) Sample 8 chr1 43818310 43818311 GA - DNA_only NM_005373(MPL):c.1775_1776del(p.R592fs) Sample 9 chr1 115258747 115258747 C G both NM_002524(NRAS):c.35G>C(p.G12A) Sample 9 chr12 12006411 12006411 - G both NM_001987(ETV6):c.381dupG(p.I128fs) Sample 9 chr2 25972585 25972585 G A DNA_only NM_018263(ASXL2):c.1840C>T(p.R614X) Sample 10 chr3 47058668 47058668 - TC both NM_014159(SETD2):c.7609_7610insGA(p.K2537fs) Sample 10 chr13 28592642 28592642 C A both NM_004119(FLT3):c.2503G>T(p.D835Y) Sample 10 chr8 117866672 117866672 - TAGC DNA_only NM_006265(RAD21):c.972_973insGCTA(p.I325fs) Sample 10 chrX 123195075 123195075 T A DNA_only NM_001042749(STAG2):c.1418T>A(p.L473X) Sample 11 chr1 115258748 115258748 C T both NM_002524(NRAS):c.34G>A(p.G12S) Sample 12 chr1 115258748 115258748 C G both NM_002524(NRAS):c.34G>C(p.G12R) Sample 12 chr1 1737942 1737942 A T both NM_001282539(GNB1):c.239T>A(p.I80N) Sample 12 chr11 108186742 108186742 C T both NM_000051(ATM):c.6100C>T(p.R2034X) Sample 12 chr1 1735942 1735942 C T DNA_only NM_001282539(GNB1):c.346G>A(p.G116S) Sample 12 chr7 140453155 140453155 C T DNA_only NM_004333(BRAF):c.1780G>A(p.D594N) Sample 13 chr1 115258748 115258748 C T both NM_002524(NRAS):c.34G>A(p.G12S) Sample 13 chr9 37015165 37015165 G C both NM_016734(PAX5):c.239C>G(p.P80R) Sample 14 chr1 115258748 115258748 C T both NM_002524(NRAS):c.34G>A(p.G12S) Sample 14 chr12 12022822 12022822 G T both NM_001987(ETV6):c.928G>T(p.E310X) Sample 14 chr11 108206581 108206581 G A DNA_only NM_000051(ATM):c.8161G>A(p.D2721N) Sample 14 chr19 11488917 11488917 - CGGG DNA_only NM_000121(EPOR):c.1269_1270insCCCG(p.F424fs) Sample 15 chr1 115258748 115258748 C T both NM_002524(NRAS):c.34G>A(p.G12S) Sample 16 chr1 115258748 115258748 C T DNA_only NM_002524(NRAS):c.34G>A(p.G12S) Sample 16 chr12 12022535 12022535 C T both NM_001987(ETV6):c.641C>T(p.P214L) Sample 17 chr1 115258748 115258748 C A DNA_only NM_002524(NRAS):c.34G>T(p.G12C) Sample 17 chr12 25398284 25398284 C T DNA_only NM_004985(KRAS):c.35G>A(p.G12D) Sample 17 chr4 55599340 55599340 T G both NM_000222(KIT):c.2466T>G(p.N822K) Sample 17 chr16 11349292 11349292 G A both NM_003745(SOCS1):c.44C>T(p.T15I) Sample 17 chr9 5055680 5055680 - TTTACC DNA_only NM_004972(JAK2):c.949_950insTTACCT(p.L316_Y317insFT) Sample 17 chr3 47108559 47108559 C G DNA_only NM_014159(SETD2):c.6109+1G>C Sample 17 chr20 31022245 31022245 C G DNA_only NM_015338(ASXL1):c.1730C>G(p.S577X) Sample 18 chr1 115258748 115258748 C A DNA_only NM_002524(NRAS):c.34G>T(p.G12C) Sample 18 chr13 28592635 28592637 ATG - both NM_004119(FLT3):c.2508_2510del(p.I836del) Sample 20 chr2 209108317 209108317 C T both NM_001282386(IDH1):c.532G>A(p.V178I) Sample 20 chr1 115258744 115258744 C T DNA_only NM_002524(NRAS):c.38G>A(p.G13D) Sample 21 chr2 209108317 209108317 C T DNA_only NM_001282386(IDH1):c.532G>A(p.V178I) Sample 21 chr12 112926910 112926910 G T both NM_002834(PTPN11):c.1530G>T(p.Q510H) Sample 21 chr13 28592621 28592621 A T DNA_only NM_004119(FLT3):c.2524T>A(p.Y842N) Sample 21 chr3 78666907 78666907 C T DNA_only NM_002941(ROBO1):c.4160G>A(p.R1387H) Sample 22 chr12 112888211 112888211 A C both NM_002834(PTPN11):c.227A>C(p.E76A) Sample 22 chr6 26235140 26235140 C T DNA_only NM_005320(HIST1H1D):c.22G>A(p.A8T) Sample 23 chr1 115256529 115256529 T C both NM_002524(NRAS):c.182A>G(p.Q61R) Sample 23 chr3 47144882 47144882 G C both NM_014159(SETD2):c.4871C>G(p.S1624C) Sample 23 chr3 47079250 47079250 G - both NM_014159(SETD2):c.7256delC(p.P2419fs) Sample 23 chr12 25378561 25378561 G A DNA_only NM_004985(KRAS):c.437C>T(p.A146V) Sample 24 chr1 115256529 115256529 T C both NM_002524(NRAS):c.182A>G(p.Q61R) Sample 24 chr19 4054224 4054224 - C both NM_015898(ZBTB7A):c.1006dupG(p.D336fs) Sample 24 chr15 42003439 42003439 - A DNA_only NM_001164273(MGA):c.2976_2977insA(p.V993fs) Sample 24 chr2 25967115 25967115 - C DNA_only NM_018263(ASXL2):c.2090dupG(p.G698fs) Sample 24 chr2 25973090 25973094 TTCTT - DNA_only NM_018263(ASXL2):c.1331_1335del(p.K444fs) Sample 25 chr1 115256529 115256529 T A both NM_002524(NRAS):c.182A>T(p.Q61L) Sample 25 chr1 115258744 115258744 C T both NM_002524(NRAS):c.38G>A(p.G13D) Sample 25 chr12 25398284 25398284 C T DNA_only NM_004985(KRAS):c.35G>A(p.G12D) Sample 25 chr2 48036361 48036361 T C both NM_001190274(FBXO11):c.2491A>G(p.S831G) Sample 25 chr1 115256530 115256530 G T DNA_only NM_002524(NRAS):c.181C>A(p.Q61K) Sample 26 chr1 115256529 115256529 T C DNA_only NM_002524(NRAS):c.182A>G(p.Q61R) Sample 26 chr12 112926888 112926888 G C both NM_002834(PTPN11):c.1508G>C(p.G503A) Sample 26 chr18 42531906 42531906 C A DNA_only NM_015559(SETBP1):c.2601C>A(p.S867R) Sample 26 chr12 112888210 112888210 G C DNA_only NM_002834(PTPN11):c.226G>C(p.E76Q) Sample 27 chr12 112926888 112926888 G T both NM_002834(PTPN11):c.1508G>T(p.G503V) Sample 28 chr12 111884833 111884833 C T both NM_005475(SH2B3):c.922C>T(p.R308X) Sample 28 chrX 39930312 39930312 C T DNA_only NM_001123383(BCOR):c.3152G>A(p.W1051X) Sample 28 chrX 123182858 123182858 C T DNA_only NM_001042749(STAG2):c.823C>T(p.Q275X) Sample 28 chrX 48649679 48649679 - CTGCAGCTGCGGCACTGGCCTA DNA_only NM_002049(GATA1):c.187_188insGCAGCTGCGGCACTGGCCTACT(p.Y63fs) Sample 29 chr9 139399344 139399344 A T both NM_017617(NOTCH1):c.4799T>A(p.L1600Q) Sample 29 chr5 35874569 35874569 - ATGCCC both NM_002185(IL7R):c.726_727insTGCCCA(p.L242_L243insCP) Sample 29 chrX 70339232 70339232 - CGC DNA_only NM_005120(MED12):c.111_112insCCG(p.T37_A38insP) Sample 29 chr17 30320334 30320334 - TAAATGAACC DNA_only NM_015355(SUZ12):c.1276_1277insAAATGAACCT(p.L426X) Sample 30 chr9 139399344 139399344 A T both NM_017617(NOTCH1):c.4799T>A(p.L1600Q) Sample 30 chr6 135509033 135509033 A T both NM_001130173(MYB):c.203A>T(p.N68I) Sample 30 chr9 139399422 139399422 A G both NM_017617(NOTCH1):c.4721T>C(p.L1574P) Sample 30 chr19 10940974 10940974 - CCAATAGGGA both NM_001005361(DNM2):c.2463_2464insCCAATAGGGA(p.S822fs) Sample 30 chrX 133551254 133551254 G T both NM_001015877(PHF6):c.890G>T(p.C297F) Sample 30 chr9 139399293 139399293 A G DNA_only NM_017617(NOTCH1):c.4850T>C(p.F1617S) Sample 30 chr9 139399288 139399288 A T DNA_only NM_017617(NOTCH1):c.4855T>A(p.Y1619N) Sample 30 chr9 139399296 139399296 A G DNA_only NM_017617(NOTCH1):c.4847T>C(p.I1616T) Sample 31 chr9 139399422 139399422 A G both NM_017617(NOTCH1):c.4721T>C(p.L1574P) Sample 33 chr1 226924291 226924291 G A both NM_002221(ITPKB):c.869C>T(p.A290V) Sample 33 chr9 37006469 37006469 - GGGTCGTGTCA DNA_only NM_016734(PAX5):c.475_475+1insTGACACGACCC Sample 34 chr1 115258744 115258744 C T both NM_002524(NRAS):c.38G>A(p.G13D) Sample 34 chr1 190067554 190067554 C T DNA_only NM_199051(BRINP3):c.1895G>A(p.R632H) Sample 35 chr1 115258744 115258744 C A both NM_002524(NRAS):c.38G>T(p.G13V) Sample 37 chr1 115258744 115258744 C T both NM_002524(NRAS):c.38G>A(p.G13D) Sample 38 chr15 89858576 89858576 - T both NM_001113378(FANCI):c.3881dupT(p.D1295fs) Sample 39 chr16 11348788 11348788 A G both NM_003745(SOCS1):c.548T>C(p.V183A) Sample 40 chr9 5080672 5080672 T G both NM_004972(JAK2):c.2423T>G(p.L808W) Sample 41 chr1 27059250 27059250 C A both NM_006015(ARID1A):c.1887C>A(p.N629K) Sample 41 chr9 139399320 139399320 C T both NM_017617(NOTCH1):c.4823G>A(p.R1608H) Sample 42 chr9 139399325 139399325 - GGCTCT both NM_017617(NOTCH1):c.4817_4818insAGAGCC(p.F1606delinsLEP) Sample 42 chr9 139390777 139390777 - CT both NM_017617(NOTCH1):c.7413_7414insAG(p.L2472fs) Sample 42 chr16 67645908 67645908 C A DNA_only NM_006565(CTCF):c.836C>A(p.S279X) Sample 43 chr5 137627720 137627720 T C both NM_001287582(CDC25C):c.701A>G(p.D234G) Sample 43 chr19 33792654 33792654 C T both NM_004364(CEBPA):c.667G>A(p.G223S) Sample 43 chr6 41903745 41903745 - G DNA_only NM_001760(CCND3):c.811dupC(p.R271fs) Sample 43 chr11 108129749 108129749 C T DNA_only NM_000051(ATM):c.2413C>T(p.R805X) Sample 44 chr12 92538069 92538069 T A both NM_001731(BTG1):c.303A>T(p.E101D) Sample 44 chr4 106157539 106157539 C T DNA_only NM_001127208(TET2):c.2440C>T(p.R814C) Sample 44 chr3 176769446 176769446 T - DNA_only NM_024665(TBL1XR1):c.273delA(p.V92fs) Sample 44 chr3 176769430 176769430 - T DNA_only NM_024665(TBL1XR1):c.288dupA(p.Q97fs) Sample 45 chr22 23237792 23237792 A G both NM_001178126(IGLL5):c.563A>G(p.K188R) Sample 45 chr12 49442903 49442909 GGAAGAA - DNA_only NM_003482(KMT2D):c.3999_4005del(p.S1334fs) Sample 45 chr12 49442913 49442913 G - DNA_only NM_003482(KMT2D):c.3995delC(p.T1332fs) Sample 45 chr12 49442915 49442915 - CCGG DNA_only NM_003482(KMT2D):c.3992_3993insCCGG(p.T1332fs) Sample 45 chr3 176769295 176769295 - GGGG DNA_only NM_024665(TBL1XR1):c.423_424insCCCC(p.A142fs) Sample 46 chr13 28592640 28592640 A C both NM_004119(FLT3):c.2505T>G(p.D835E) Sample 47 chr5 170832399 170832412 ATAGAAAAAGTGAG - both NM_002520(NPM1):c.763_771+5del Sample 47 chr5 170832395 170832396 AA - both NM_002520(NPM1):c.759_760del(p.S254fs) Sample 47 chr2 43452457 43452457 - CC both NM_006887(ZFP36L2):c.485_486insGG(p.F162fs) Sample 47 chr3 128204839 128204842 CTAC - both NM_032638(GATA2):c.599_602del(p.G200fs) Sample 47 chr19 33792755 33792763 GGCGGCGGC - RNA_only NM_004364(CEBPA):c.558_566del(p.P187_P189del) Sample 47 chr11 32417912 32417912 - CT DNA_only NM_024426(WT1):c.1139_1140insAG(p.S381fs) Sample 47 chr3 128204841 128204841 - C DNA_only NM_032638(GATA2):c.599dupG(p.S201X) Sample 48 chr6 135517044 135517044 - TCGCCAGCAAGGTGCATGA both NM_001130173(MYB):c.1129_1130insCCAGCAAGGTGCATGATCG(p.V377fs) Sample 48 chr12 112888166 112888166 A T DNA_only NM_002834(PTPN11):c.182A>T(p.D61V) Sample 49 chr12 25398284 25398284 C A both NM_004985(KRAS):c.35G>T(p.G12V) Sample 49 chr11 108196143 108196143 C T both NM_000051(ATM):c.6679C>T(p.R2227C) Sample 49 chr9 139391797 139391803 TGCCCCC - both NM_017617(NOTCH1):c.6388_6394del(p.G2130fs) Sample 49 chr9 139397768 139397768 A G both NM_017617(NOTCH1):c.5033T>C(p.L1678P) Sample 49 chr11 108121458 108121458 G - DNA_only NM_000051(ATM):c.1266delG(p.K422fs) Sample 49 chr4 153268147 153268147 G A DNA_only NM_033632(FBXW7):c.661C>T(p.Q221X) Sample 50 chr12 25398284 25398284 C T both NM_004985(KRAS):c.35G>A(p.G12D) Sample 51 chr12 25398284 25398284 C T DNA_only NM_004985(KRAS):c.35G>A(p.G12D) Sample 51 chr12 25398281 25398281 C T both NM_004985(KRAS):c.38G>A(p.G13D) Sample 51 chr1 115256528 115256528 T A both NM_002524(NRAS):c.183A>T(p.Q61H) Sample 52 chr12 25398284 25398284 C A DNA_only NM_004985(KRAS):c.35G>T(p.G12V) Sample 52 chr12 49438733 49438748 CGAAAGTACTGGGGCT - DNA_only NM_003482(KMT2D):c.4742_4757del(p.E1581fs) Sample 52 chr12 11992224 11992224 - GGGGGTTTC DNA_only NM_001987(ETV6):c.314_315insGGGGGTTTC(p.R105_S106insGVS) Sample 52 chr12 111885467 111885467 - T DNA_only NM_005475(SH2B3):c.1244_1245insT(p.L416fs) Sample 52 chr4 106157539 106157539 C T DNA_only NM_001127208(TET2):c.2440C>T(p.R814C) Sample 53 chr9 139397768 139397768 A G DNA_only NM_017617(NOTCH1):c.5033T>C(p.L1678P) Sample 53 chr4 153249384 153249384 C T both NM_033632(FBXW7):c.1394G>A(p.R465H) Sample 53 chr9 139399397 139399399 CGG - both NM_017617(NOTCH1):c.4744_4746del(p.P1582del) Sample 53 chrX 41200809 41200809 - ACCTCTA DNA_only NM_001193416(DDX3X):c.224_225insACCTCTA(p.S76fs) Sample 53 chr9 139391170 139391170 - G DNA_only NM_017617(NOTCH1):c.7020dupC(p.S2341fs) Sample 54 chr21 36171701 36171701 - AGGTATTGGT both NM_001754(RUNX1):c.863_864insACCAATACCT(p.G289fs) Sample 54 chr7 92734909 92734909 C A DNA_only NM_017654(SAMD9):c.502G>T(p.E168X) Sample 55 chr4 1962801 1962801 G A both NM_133330(NSD2):c.3295G>A(p.E1099K) Sample 56 chr4 1962801 1962801 G A both NM_133330(NSD2):c.3295G>A(p.E1099K) Sample 56 chr20 31023963 31023963 G C both NM_015338(ASXL1):c.3448G>C(p.G1150R) Sample 57 chr4 1962801 1962801 G A DNA_only NM_133330(NSD2):c.3295G>A(p.E1099K) Sample 57 chr9 139399284 139399284 T G DNA_only NM_017617(NOTCH1):c.4859A>C(p.Y1620S) Sample 58 chr20 31023717 31023717 C T both NM_015338(ASXL1):c.3202C>T(p.R1068X) Sample 58 chr16 3788617 3788617 C T both NM_004380(CREBBP):c.4337G>A(p.R1446H) Sample 58 chr7 92764976 92764976 - T RNA_only NM_152703(SAMD9L):c.308dupA(p.N103fs) Sample 58 chr20 31022441 31022441 - G DNA_only NM_015338(ASXL1):c.1934dupG(p.G646fs) Sample 59 chr8 128750677 128750681 CCCAC - both NM_002467(MYC):c.217_221del(p.T73fs) Sample 59 chr8 128750684 128750684 - GG both NM_002467(MYC):c.222_223insGG(p.P75fs) Sample 59 chr2 43452622 43452622 - C DNA_only NM_006887(ZFP36L2):c.320dupG(p.G108fs) Sample 59 chr8 128750678 128750678 - CGG DNA_only NM_002467(MYC):c.216_217insGGC(p.P72_T73insG) Sample 60 chr13 32953942 32953942 A - both NM_000059(BRCA2):c.9011delA(p.K3004fs) Sample 60 chr22 23235990 23235990 C G RNA_only NM_001178126(IGLL5):c.317C>G(p.T106S) Sample 61 chr7 50450372 50450372 G T both NM_006060(IKZF1):c.556G>T(p.D186Y) Sample 61 chr13 41240294 41240294 C A DNA_only NM_002015(FOXO1):c.56G>T(p.R19L) Sample 61 chr12 49433758 49433758 - CCCCTGAAGAGAGA DNA_only NM_003482(KMT2D):c.7794_7795insTCTCTCTTCAGGGG(p.G2599fs) Sample 61 chr15 42059447 42059447 - G DNA_only NM_001164273(MGA):c.9172dupG(p.A3058fs) Sample 62 chr7 5568000 5568013 CTTCTCCAGGGAGG - both NM_001101(ACTB):c.701_714del(p.S234X) Sample 62 chr7 5568028 5568028 - TGGCCACCGAGGGA both NM_001101(ACTB):c.685_686insTCCCTCGGTGGCCA(p.T229fs) Sample 62 chr1 1737914 1737914 - TGG DNA_only NM_001282539(GNB1):c.266_267insCCA(p.K89delinsNQ) Sample 64 chr17 8132097 8132097 - G both NM_025099(CTC1):c.3334dupC(p.Q1112fs) Sample 65 chr6 26156791 26156791 C T both NM_005321(HIST1H1E):c.173C>T(p.S58F) Sample 65 chr10 89720857 89720857 - T both NM_000314(PTEN):c.1012dupT(p.S338fs) Sample 65 chr9 139390821 139390824 AGAA - both NM_017617(NOTCH1):c.7367_7370del(p.I2456fs) Sample 65 chr5 170832369 170832369 G C both NM_002520(NPM1):c.733G>C(p.E245Q) Sample 66 chr12 25398281 25398281 C T both NM_004985(KRAS):c.38G>A(p.G13D) Sample 66 chr16 9002284 9002284 - T DNA_only NM_003470(USP7):c.1184_1185insA(p.F395fs) Sample 66 chr6 27835074 27835074 C G DNA_only NM_005322(HIST1H1B):c.234G>C(p.K78N) Sample 66 chr7 140477807 140477807 C T DNA_only NM_004333(BRAF):c.1501G>A(p.E501K) Sample 66 chr4 153259049 153259049 - AC DNA_only NM_033632(FBXW7):c.765_766insGT(p.L256fs) Sample 67 chr9 139390981 139390981 G - both NM_017617(NOTCH1):c.7210delC(p.Q2404fs) Sample 68 chr10 8106004 8106004 G A both NM_002051(GATA3):c.824G>A(p.R275Q) Sample 68 chr6 26124462 26124462 T C both NM_003512(HIST1H2AC):c.2T>C(p.M1?) Sample 68 chr5 35874564 35874566 TAT - both NM_002185(IL7R):c.720_722del(p.I241del) Sample 68 chr1 65310517 65310517 C T both NM_002227(JAK1):c.2171G>A(p.R724H) Sample 68 chr9 139397651 139397651 T - both NM_017617(NOTCH1):c.5150delA(p.K1717fs) Sample 68 chr9 139397655 139397655 - C both NM_017617(NOTCH1):c.5145_5146insG(p.Y1716fs) Sample 68 chr4 153249384 153249384 C T both NM_033632(FBXW7):c.1394G>A(p.R465H) Sample 68 chr19 17945969 17945969 C T DNA_only NM_000215(JAK3):c.1970G>A(p.R657Q) Sample 68 chr9 139397654 139397654 T A DNA_only NM_017617(NOTCH1):c.5147A>T(p.Y1716F) Sample 68 chr9 139397660 139397660 A C DNA_only NM_017617(NOTCH1):c.5141T>G(p.I1714S) Sample 68 chr9 139397719 139397719 - CCA DNA_only NM_017617(NOTCH1):c.5081_5082insTGG(p.Q1694delinsHG) Sample 69 chr11 32417913 32417916 CGTA - both NM_024426(WT1):c.1136_1139del(p.V379fs) Sample 69 chr11 32417910 32417910 G T DNA_only NM_024426(WT1):c.1142C>A(p.S381X) Sample 70 chrX 39916463 39916463 G A both NM_001123383(BCOR):c.4438C>T(p.R1480X) Sample 71 chr3 128200142 128200142 A G both NM_032638(GATA2):c.1163T>C(p.M388T) Sample 71 chr3 128200744 128200744 G C both NM_032638(GATA2):c.1061C>G(p.T354R) Sample 72 chr6 26234953 26234953 G A both NM_005320(HIST1H1D):c.209C>T(p.A70V) Sample 72 chr13 28608320 28608320 A G both NM_004119(FLT3):c.1736T>C(p.V579A) Sample 72 chr7 50450367 50450367 G C both NM_006060(IKZF1):c.551G>C(p.R184P) Sample 72 chr1 155874261 155874261 C T both NM_006912(RIT1):c.270G>A(p.M90I) Sample 72 chr4 153259049 153259049 - AC DNA_only NM_033632(FBXW7):c.765_766insGT(p.L256fs) Sample 72 chr15 42042447 42042447 G - DNA_only NM_001164273(MGA):c.6642delG(p.Q2216fs) Sample 73 chr12 112926884 112926884 T C both NM_002834(PTPN11):c.1504T>C(p.S502P) Sample 73 chr13 28602329 28602329 G A DNA_only NM_004119(FLT3):c.2039C>T(p.A680V) Sample 74 chr16 67645871 67645871 C T both NM_006565(CTCF):c.799C>T(p.Q267X) Sample 74 chr17 37947711 37947711 G A DNA_only NM_012481(IKZF3):c.550C>T(p.Q184X) Sample 75 chr20 31022403 31022425 CACCACTGCCATAGAGAGGCGGC - DNA_only NM_015338(ASXL1):c.1900_1922del(p.E635fs) Sample 75 chr1 43803899 43803899 C T DNA_only NM_005373(MPL):c.209C>T(p.P70L) Sample 76 chrX 123191805 123191805 T - DNA_only NM_001042749(STAG2):c.1400delT(p.F467fs) Sample 78 chr16 3817745 3817745 - GG DNA_only NM_004380(CREBBP):c.3225_3226insCC(p.S1076fs) Sample 79 chr2 25972860 25972860 - T DNA_only NM_018263(ASXL2):c.1564_1565insA(p.V522fs) Sample 79 chr22 41572833 41572840 GAAACTAG - DNA_only NM_001429(EP300):c.5119_5126del(p.K1707fs) Sample 80 chr1 43818431 43818431 G T DNA_only NM_005373(MPL):c.1896G>T(p.W632C) Sample 81 chr9 37020768 37020768 A C DNA_only NM_016734(PAX5):c.77T>G(p.V26G) Sample 82 chr16 3779765 3779765 - GG DNA_only NM_004380(CREBBP):c.5282_5283insCC(p.K1762fs) Sample 83 chr11 119169138 119169138 T G DNA_only NM_005188(CBL):c.2322T>G(p.Y774X) Sample 84 chr17 58740642 58740642 C A DNA_only NM_003620(PPM1D):c.1547C>A(p.S516X) Sample 84 chr12 25362828 25362828 G C DNA_only NM_004985(KRAS):c.468C>G(p.F156L) Sample 85 chrX 44910984 44910984 - AA DNA_only NM_021140(KDM6A):c.687_688insAA(p.Q230fs) Sample 85 chr20 57428744 57428744 G T DNA_only NM_080425(GNAS):c.424G>T(p.G142X) Sample 86 chr2 32476322 32476322 C T DNA_only NM_001199139(NLRC4):c.611G>A(p.R204H) Sample 89 chr1 115258748 115258748 C A DNA_only NM_002524(NRAS):c.34G>T(p.G12C) Sample 89 chr13 28602340 28602340 G T both NM_004119(FLT3):c.2028C>A(p.N676K) Sample 89 chr1 115256529 115256529 T C DNA_only NM_002524(NRAS):c.182A>G(p.Q61R) Sample 92 chr1 117086962 117086962 - C both NM_001144822(CD58):c.334dupG(p.D112fs) Sample 92 chr1 117113591 117113605 CCATGGCTCGTCGGG - DNA_only NM_001144822(CD58):c.1_4del(p.M1?) Sample 93 chr2 198267359 198267359 C G both NM_012433(SF3B1):c.1998G>C(p.K666N) Sample 96 chr1 115258744 115258744 C T both NM_002524(NRAS):c.38G>A(p.G13D) Sample 96 chr3 47163512 47163512 G A DNA_only NM_014159(SETD2):c.2614C>T(p.Q872X) Sample 97 chr12 112926852 112926852 C T DNA_only NM_002834(PTPN11):c.1472C>T(p.P491L) Sample 97 chr9 36846834 36846840 ACTCACC - DNA_only NM_016734(PAX5):c.1099_1099+6del Sample 97 chr1 226924414 226924414 T A DNA_only NM_002221(ITPKB):c.746A>T(p.Q249L) Table Notes: Chr is chromosome; Start is the starting position; End is the ending position; Ref is the allele recorded in the reference genome at a certain gene locus; Alt is other alleles that are different from the reference allele, usually at the same gene locus; HGVS uses the naming rules established by the Human Genome Variation Society to describe the location, type of mutation and its effect on protein.

[0044] Based on the preferred leukemia gene mutation transcriptome data analysis method of Example 1, transcriptome data collection and gene sequence alignment, variation detection and annotation, and gene mutation site filtering were performed. The results of the DNA genome and RNA transcriptome detection site alignment in the bone marrow or peripheral blood samples of 97 leukemia patients are shown in Table 2 below.

[0045] Table 2. Comparison results of DNA genome and RNA transcriptome detection sites in samples Number of mutation sites detected Both DNA and RNA were detected 126 DNA detection only 121 RNA detection only 4 Positive predictive value 126 / （126+4）=96.9% Sensitivity 126 / （126+121）=51.0% Using the leukemia gene mutation transcriptome data analysis method provided in Example 1 of the present invention, the IGV (Integrative Genomics Viewer) of the mutation site SNV and INDEL detected in the DNA genome and RNA transcriptome of the sample is as follows Figure 5-Figure 8 shown.

[0046] Taking "Chromosome: chr9, start position: 139399422, end position: 139399422, HGVS named variant site: NM_017617 (NOTCH1): c.4721T>C (p.L1574P)" as an example, the IGV map of SNV based on DNA detection results is as follows: Figure 5 As shown; the IGV map of SNV based on RNA detection results is as follows Figure 6 shown.

[0047] Taking "Chromosome: chr19, start position: 10940974, end position: 10940974, HGVS named variant site: NM_001005361 (DNM2): c.2463_2464insCCAATAGGGA (p.S822fs)" as an example, the IGV map of INDEL based on DNA detection results is as follows: Figure 7 As shown; the IGV map of INDEL based on RNA detection results is as follows Figure 8 shown.

[0048] Based on the SNV and INDEL detection analysis of the sample transcriptome data, the results show that the mutation site detection detected by the leukemia gene mutation transcriptome data analysis method provided by the present invention has a high positive predictive value and can detect positive mutations more accurately.

[0049] The method of the present invention can improve the positive predictive value of leukemia transcriptome gene mutation detection, and provide an important reference for clinical diagnosis and treatment. In addition to the gene expression and fusion information obtained based on conventional transcriptome data, this method can provide accurate gene mutation detection results, and achieve multiple mutation information based on one sample and one detection method, which helps to reduce the cost of gene testing and has important clinical practical value and application prospects.

Claims

1. A method for detecting leukemia gene mutations based on second-generation sequencing transcriptome data, characterized in that: The method includes data analysis of gene transcriptomes, and the steps of transcriptome data analysis include: I. data collection and gene sequence comparison; II. variation detection and annotation; III. gene mutation site filtering; the step III. gene mutation site filtering includes: 1) Background library filtering: Crowd databases include 1000 Genomes_EAS database and ExAC_EAS database; 2) Annotation filtering: including filtering out introns, untranslated regions (UTR), intergenic regions, upstream and downstream regions of genes, and filtering out synonymous mutations; 3) Filtering of single-base repeat regions: Filter out regions with single-base repeats greater than 3 to 10 times and mutations on both sides of the region, including single-base indels (insertions and deletions are repeated bases) on both sides of the region and multi-base INDELs (insertions and deletions are repeated bases) on both sides of the region and the region, and SNVs (alt is a repeated base) on both sides of the region and the region; 4) Reads number filtering: filter SNVs with support reads no more than 3 to 10, and filter INDELs with support reads no more than 3 to 15; 5) Mutation frequency filtering: mutation frequency is less than 1% to 10%; 6) Filter mutations that are less than 4 bases away from exon boundaries; 7) Filter out mutations with less than 4 supporting reads within 10 bp of the non-read ends and mutations with > 90% supporting reads within 5 bp of the read ends; 8) For the supporting mutation reads with base quality lower than 20, if its proportion is higher than 80%, the mutation is filtered; if its proportion is between 70-80%, and the average number of low-quality mismatched bases other than the mutation on each read is ≥2, the mutation is filtered.

2. A method for detecting leukemia gene mutations based on second-generation sequencing transcriptome data according to claim 1, characterized in that: The step I. data collection and gene sequence comparison includes: 1) The input data is the original data from the second-generation sequencing machine; 2) Preprocess the original data, including removing data aligned to rRNA sequences, removing adapter sequences, and removing low-quality data; 3) Use STAR and other alignment tools to align and sort the processed raw data with the reference genome to obtain the alignment results in bam format; 4) Identify duplication reads in bam files and remove duplication reads; 5) Remove intron region sequences.

3. A method for detecting leukemia gene mutations based on next-generation sequencing transcriptome data according to claims 1-2, characterized in that: The step II. mutation detection and annotation includes: using GATK Mutect2 software to detect mutation sites, and using ANNOVAR software to perform mutation annotation, including non-synonymous mutations, synonymous mutations, and splice site mutations.

4. A method for detecting leukemia gene mutations based on next-generation sequencing transcriptome data according to claims 1-3, characterized in that: The method includes data analysis of gene transcriptome, and the transcriptome data analysis step includes: Ⅰ. Data collection and gene sequence alignment: 1) The input data is the original data from the second-generation sequencing machine; 2) Preprocess the original data, including removing data aligned to rRNA sequences, removing adapter sequences, and removing low-quality data; 3) Use STAR and other alignment tools to align and sort the processed raw data with the reference genome to obtain the alignment results in bam format; 4) Identify duplication reads in bam files and remove duplication reads; 5) Removal of intron region sequences; II. Mutation detection and annotation: GATK Mutect2 software was used to detect variant sites, and ANNOVAR software was used to annotate variants, including non-synonymous mutations, synonymous mutations, and splice site mutations; III. Gene mutation site filtering: 1) Background library filtering: Crowd databases include 1000 Genomes_EAS database and ExAC_EAS database; 2) Annotation filtering: including filtering out introns, untranslated regions (UTR), intergenic regions, upstream and downstream regions of genes, and filtering out synonymous mutations; 3) Filtering of single-base repeat regions: Filter out regions with single-base repeats greater than 3 to 10 times and mutations on both sides of the region, including single-base indels (insertions and deletions are repeated bases) on both sides of the region and multi-base INDELs (insertions and deletions are repeated bases) on both sides of the region and the region, and SNVs (alt is a repeated base) on both sides of the region and the region; 4) Reads number filtering: filter SNVs with support reads no more than 3 to 10, and filter INDELs with support reads no more than 3 to 15; 5) Mutation frequency filtering: mutation frequency is less than 1% to 10%; 6) Filter mutations that are less than 4 bases away from exon boundaries; 7) Filter out mutations with less than 4 supporting reads within 10 bp of the non-read ends and mutations with > 90% supporting reads within 5 bp of the read ends; 8) For the supporting mutation reads with base quality lower than 20, if its proportion is higher than 80%, the mutation is filtered; if its proportion is between 70-80%, and the average number of low-quality mismatched bases other than the mutation on each read is ≥2, the mutation is filtered.

5. A method for detecting leukemia gene mutations based on next-generation sequencing transcriptome data according to claims 1-4, characterized in that: The method includes data analysis of gene transcriptome, and the transcriptome data analysis step includes: Ⅰ. Data collection and gene sequence alignment: 1) The input data is the original data from the second-generation sequencing machine; 2) Preprocess the original data, including removing data aligned to rRNA sequences, removing adapter sequences, and removing low-quality data; 3) Use STAR and other alignment tools to align and sort the processed raw data with the reference genome to obtain the alignment results in bam format; 4) Identify duplication reads in bam files and remove duplication reads; 5) Removal of intron region sequences; II. Mutation detection and annotation: GATK Mutect2 software was used to detect variant sites, and ANNOVAR software was used to annotate variants, including non-synonymous mutations, synonymous mutations, and splice site mutations; III. Gene mutation site filtering: 1) Background library filtering: Crowd databases include 1000 Genomes_EAS database and ExAC_EAS database; 2) Annotation filtering: including filtering out introns, untranslated regions (UTR), intergenic regions, upstream and downstream regions of genes, and filtering out synonymous mutations; 3) Filtering of single-base repeat regions: Filter out regions with single-base repeats > 5 times and mutations on both sides of the region, including single-base indels (insertions and deletions are repeated bases) on both sides of the region and multi-base INDELs (insertions and deletions are repeated bases) on both sides of the region and the region, and SNVs (alt is a repeated base) on both sides of the region and the region; 4) Reads number filtering: filter SNVs with ≤5 supporting reads and filter INDELs with ≤10 supporting reads; 5) Mutation frequency filtering: mutation frequency <4%; 6) Filter mutations that are less than 4 bases away from exon boundaries; 7) Filter out mutations with less than 4 supporting reads within 10 bp of the non-read ends and mutations with > 90% supporting reads within 5 bp of the read ends; 8) For the supporting mutation reads with base quality lower than 20, if its proportion is higher than 80%, the mutation is filtered; if its proportion is between 70-80%, and the average number of low-quality mismatched bases other than the mutation on each read is ≥2, the mutation is filtered.

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