CN113571130B - Succinct and comprehensive copy number variation pattern recognition method and application thereof - Google Patents
Succinct and comprehensive copy number variation pattern recognition method and application thereof Download PDFInfo
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Abstract
The invention relates to a simple and comprehensive copy number variation pattern recognition method and application thereof, and belongs to the technical field of biomedicine. The invention classifies the absolute copy number information of the obtained tumor sample; forming a matrix of 176 column data information according to the actual distribution of each copy number fragment in the flood data; calculating the copy number variation characteristic mode of each tumor sample; calculating a specific copy number variation characteristic mode of each tumor by using a nonnegative matrix factorization algorithm; quantifying the activity of the pattern of copy number variation characteristics in each tumor; parting the tumor according to the copy number variation characteristic mode; prognosis prediction is carried out on tumor patients according to the activity of the copy number variation characteristic mode. Thereby realizing accurate prediction of prognosis of tumor patients.
Description
Technical Field
The invention relates to a simple and comprehensive copy number variation pattern recognition method and application thereof, and belongs to the technical field of biomedicine.
Background
The precise prediction of the typing and prognosis of malignant tumors is still an unresolved medical problem at present. Cancers are mainly caused by variations in somatic genomic DNA. These cancer-associated DNA variations can be classified into the following four types according to the size and characteristics of genomic variations: single base substitution (single base substitution, SBS), small fragment insertion and deletion (INDEL), structural changes (including translocation/inversion) and copy number variation (copy number alteration). Copy number variation of somatic DNA is extremely common in cancer and has been reported to be an important driver of progression for many types of cancer (1, 2). The characteristic pattern of genomic DNA variation is a repeated genomic pattern, which is a signature of the mutagenesis process accumulated during cancer cell development (3, 4). The pattern of copy number variation characteristics reflects the pattern of repeated copy number variation caused by specific endogenous or exogenous mutation events during cancer progression. The characteristic pattern of single base variation has been extensively studied in different types of cancer (3, 4). Copy number variation is an important driving force for the development and progression of a variety of cancers. However, the utility of systematic studies on copy number variation signature patterns, and in particular, the comprehensive analysis tools for ubiquity and cancer remain lacking.
Reference to the literature
1.Beroukhim,R.et al.The landscape of somatic copy-number alteration across human cancers.Nature 463,899-905,doi:10.1038/nature08822(2010).
2.Zack,T.I.et al.Pan-cancer patterns of somatic copy number alteration.Nat Genet 45,1134-1140,doi:10.1038/ng.2760(2013).
3.Alexandrov,L.B.et al.Signatures of mutational processes in human cancer.Nature 500,415-421,doi:10.1038/nature12477(2013).
4.Alexandrov,L.B.et al.The repertoire of mutational signatures in human cancer.Nature 578,94-101,doi:10.1038/s41586-020-1943-3(2020)。
Disclosure of Invention
The invention aims to solve the technical problem of how to systematically study copy number variation characteristic patterns and obtain a comprehensive analysis tool suitable for cancer.
In order to solve the above problems, the present invention provides a copy number variation pattern recognition method, which includes the following steps:
step 1: extracting genome DNA of tumor samples and normal control tissues, and performing high-throughput whole-exome sequencing (Whole Exome Sequencing, WES), whole-genome sequencing (Whole genome sequencing, WGS) or SNP (single nucleotide polymorphism single nucleotide polymorphism, SNP) chip analysis;
step 2: obtaining absolute copy number information of the tumor sample from the raw WES, WGS or SNP chip data using conventional analysis methods;
step 3: classifying the copy number fragments (copy number segment); the three types are respectively high-low-high, low-high-low and gradient, and the three types refer to the front-back copy number change direction of each specific copy number fragment during classification; the size of copy number change before and after reference in classification is set as two types, namely, any one end is more than 2 and both sides are less than or equal to 2; the absolute copy number values are referred to in classification, and are set to be seven types, namely 0, 1,2, 3,4, 5-8 and more than or equal to 9 respectively; the heterozygosity loss (Loss of heterozygosity, LOH) state is referred to in classification, and is set into two types, namely LOH and no LOH; the reference fragment length information is set into four types when classifying, namely S is less than 50kb in length, M is less than or equal to 50kb in length and less than or equal to 500kb in length, L is less than or equal to 500kb in length and less than or equal to 5Mb in length, and E is less than or equal to 5Mb in length; then according to the actual distribution situation of each copy number fragment in the Pan-cancer data, 176 copy number fragment types are finally formed in total; forming a matrix of 176 columns of data information, each column representing the number of copy number fragments in a particular tumor sample featuring a certain copy number variation fragment;
step 4: calculating the copy number variation characteristic mode of each tumor sample; calculating specific copy number variation characteristic patterns of each tumor by using a non-negative matrix factorization (non-negative matrix factorization, NMF) algorithm;
step 5: quantifying the activity of the pattern of copy number variation characteristics in each tumor; the relative activity of the characteristic pattern of copy number variation and absolute activity values; the relative activity indicates the proportion of contribution of a specific copy number variation characteristic pattern to other variation patterns in a tumor, and can be directly obtained after NMF; absolute activity represents the number of copy number variation records associated with each copy number variation signature pattern;
step 6: parting the tumor according to the copy number variation characteristic mode; classifying each tumor according to the activity of the copy number variation characteristic mode by utilizing cluster analysis;
step 7: prognosis prediction is carried out on tumor patients according to the activity of the copy number variation characteristic mode.
Preferably, the conventional analysis method in step 2 includes using FACET, sequenza software.
The invention provides an application of a copy number variation pattern recognition method in prognosis prediction evaluation of tumor patients.
The copy number variation pattern recognition method provided by the invention is applied to the accurate typing of tumor patients.
The invention provides an application of a copy number variation pattern recognition method in preparing a detection kit for prognosis prediction of tumor patients.
The invention provides an application of a copy number variation pattern recognition method in preparing a detection kit for accurately typing tumor patients.
Compared with the prior art, the invention has the following beneficial effects:
the invention starts from the characteristic mode of identifying copy number variation (copy number alteration) of genome of tumor patient, and performs accurate typing on the tumor patient, thereby realizing accurate prediction of prognosis of the tumor patient. The invention provides a genomics marker for accurate diagnosis and typing of tumors, which is realized by identifying a characteristic mode of copy number variation (copy number alteration) of the genome of a tumor patient. The tumor genome copy number variation pattern recognition method developed by the invention has the characteristics of simple design and comprehensive copy number variation types. The biomarker developed by the invention can be applied to accurate prognosis prediction of the cancer species.
Drawings
FIG. 1 shows a classification method of copy number variant fragments according to the present invention.
Fig. 2 shows 14 copy number variation signature patterns extracted from the pcag database.
FIG. 3 is a graph showing the effect of absolute activity of different copy number variation patterns on tumor patient prognosis (Hazard ratio) in PCAWG database by multifactor Cox survival analysis.
FIG. 4 shows 20 copy number variation signature patterns extracted from TCGA database.
Fig. 5 is a similarity comparison between TCGA copy number variation pattern and PCAWG copy number variation pattern, with cosine similarity shown numerically.
FIG. 6 is a graph showing the effect of absolute activity of different copy number variation patterns on tumor patient prognosis (Hazard ratio) in TCGA database by multifactor Cox survival analysis.
Detailed Description
In order to make the invention more comprehensible, preferred embodiments accompanied with the accompanying drawings are described in detail as follows:
the invention adopts the technical scheme that the copy number variation pattern recognition method comprises the following steps:
step 1: extracting genome DNA of tumor samples and normal control tissues, and performing high-throughput whole-exome sequencing (Whole Exome Sequencing, WES), whole-genome sequencing (Whole genome sequencing, WGS) or SNP (single nucleotide polymorphism single nucleotide polymorphism, SNP) chip analysis;
step 2: obtaining absolute copy number information of the tumor sample from the raw WES, WGS or SNP chip data using conventional analysis methods;
step 3: classifying the copy number fragments (copy number segment); the three types are respectively high-low-high, low-high-low and gradient, and the three types refer to the front-back copy number change direction of each specific copy number fragment during classification; the size of copy number change before and after reference in classification is set as two types, namely, any one end is more than 2 and both sides are less than or equal to 2; the absolute copy number values are referred to in classification, and are set to be seven types, namely 0, 1,2, 3,4, 5-8 and more than or equal to 9 respectively; the heterozygosity loss (Loss of heterozygosity, LOH) state is referred to in classification, and is set into two types, namely LOH and no LOH; the reference fragment length information is set into four types when classifying, namely S is less than 50kb in length, M is less than or equal to 50kb in length and less than or equal to 500kb in length, L is less than or equal to 500kb in length and less than or equal to 5Mb in length, and E is less than or equal to 5Mb in length; then according to the actual distribution situation of each copy number fragment in the Pan-cancer data, 176 copy number fragment types are finally formed in total; forming a matrix of 176 columns of data information, each column representing the number of copy number fragments in a particular tumor sample featuring a certain copy number variation fragment;
step 4: calculating the copy number variation characteristic mode of each tumor sample; calculating specific copy number variation characteristic patterns of each tumor by using a non-negative matrix factorization (non-negative matrix factorization, NMF) algorithm;
step 5: quantifying the activity of the pattern of copy number variation characteristics in each tumor; the relative activity of the characteristic pattern of copy number variation and absolute activity values; the relative activity indicates the proportion of contribution of a specific copy number variation characteristic pattern to other variation patterns in a tumor, and can be directly obtained after NMF; absolute activity represents the number of copy number variation records associated with each copy number variation signature pattern;
step 6: parting the tumor according to the copy number variation characteristic mode; classifying each tumor according to the activity of the copy number variation characteristic mode by utilizing cluster analysis;
step 7: prognosis prediction is carried out on tumor patients according to the activity of the copy number variation characteristic mode.
The conventional analysis method used in step 2 above includes using FACET, sequencza software.
The invention provides an application of a copy number variation pattern recognition method in prognosis prediction evaluation of tumor patients.
The copy number variation pattern recognition method provided by the invention is applied to the accurate typing of tumor patients.
The invention provides an application of a copy number variation pattern recognition method in preparing a detection kit for prognosis prediction of tumor patients.
The invention provides an application of a copy number variation pattern recognition method in preparing a detection kit for accurately typing tumor patients.
The copy number variation pattern recognition basis is a brand-new and creative genome analysis method. The implementation details of the method are described below:
1. genomic DNA of tumor samples and normal control tissues was extracted and subjected to high throughput whole exome sequencing (Whole Exome Sequencing, WES), whole genome sequencing (Whole genome sequencing, WGS) or SNP (single nucleotide polymorphism marker technique single nucleotide polymorphism single nucleotide polymorphism, SNP) chip analysis.
2. Absolute copy number information of tumor samples was obtained from raw WES, WGS or SNP chip data using conventional analytical methods, such as FACET, sequenza software.
3. The copy number fragments (copy number segment) are classified. The classification method considers the front-back copy number change direction (three types are high-low-high H-L-H; low-high-low L-H-L; gradient Ladder), the front-back copy number change size (divided into two types: any one end > 2; both sides are less than or equal to 2), the absolute copy number value (divided into seven types: 0;1;2;3;4;5-8; more than or equal to 9), the heterozygosity deletion (Loss of heterozygosity, LOH) state (divided into two types: LOH; no LOH), the fragment length information (divided into four types: S [ length <50kb ]; M [50kb ] less than or equal to length <500kb ]; L [500kb ] less than or equal to 5Mb ]; E [5Mb ] less than or equal to length (see FIG. 1). A total of 176 copy number fragment types were then finally determined based on the actual distribution of each copy number fragment in the Pan-cancer data (see table 1). And classifying all the copy number fragments of each tumor sample according to the type classification mode of the copy number fragments, and finally forming a matrix with 176 column data information, wherein each row represents the copy number fragment number with a certain copy number variation fragment characteristic in a specific tumor sample.
4. Copy number variation signature patterns were calculated for each tumor sample. Specific copy number variation signature patterns for each tumor were calculated using a non-negative matrix factorization (non-negative matrix factorization, NMF) algorithm.
5. The activity of the pattern of copy number variation characteristic in each tumor was quantified. The method provides both the relative activity of the copy number variation signature pattern and the absolute activity value. Relative activity indicates the proportion of contribution of a particular copy number variation profile to other variation profiles in a tumor, and can be obtained directly after NMF. Absolute activity represents the number of copy number variation records associated with each copy number variation signature pattern.
6. And (5) parting the tumor according to the copy number variation characteristic mode. And (3) classifying each tumor according to the activity of the copy number variation characteristic mode by using cluster analysis.
7. Prognosis prediction is carried out on tumor patients according to the activity of the copy number variation characteristic mode.
Table 1, classification of genome copy number fragments in the present invention, specific number of each class in the PCAWG database.
Examples
The following shows 2 examples of application of the invention in the precise typing of cancer and prognosis prediction.
Application example one:
absolute genome copy number information for 2778 tumor samples of 32 tumor types in the Pan-Cancer Analysis of Whole Genomes (PCAWG) database was downloaded.
The method of the invention is used for extracting copy number variation characteristic patterns (Copy number alteration signature) of the PCAWG database. A total of 14 copy number variation patterns were obtained, named position CNS1, CNS2 … CNS14 in turn (see fig. 2).
For each sample, the activity of each copy number variation pattern (i.e., the number of copy number fragments each copy number variation pattern contributed to in a particular patient's genome copy number variation) was calculated. The multi-factor Cox analysis (activity values considering tumor type, all copy number variation patterns) results indicate that: the activity of the partial copy number variation pattern was somewhat significantly correlated with prognosis of the ubiquitous cancer (see figure 3). The increased activity of CNS11, CNS12 is a significant predictor of poor patient prognosis.
Application example two:
absolute copy number information of the genome of 10822 tumor samples was downloaded from the The Cancer Genome Atlas (TCGA) database for 33 tumor types.
The copy number variation characteristic pattern of the TCGA database is extracted using the method of the present invention (Copy number alteration signature). A total of 20 copy number variation patterns were obtained, named bits Sig1, sig2 … Sig20 in sequence (see fig. 4).
It is also expected that more copy number variation patterns will be extracted from tumor samples in the TCGA database that are approximately 4 times that of the PCAWG database. It is worth noting that most of the copy number variation patterns extracted from PCAWG can find the corresponding variation patterns in TCGA, which also suggests that the copy number variation pattern recognition method developed by the invention can stably recognize variation rules in tumors. A comparison between the copy number variation patterns extracted from PCAWG and TCGA is shown in FIG. 5
For each sample, the activity of each copy number variation pattern (i.e., the number of copy number fragments each copy number variation pattern contributed to in a particular patient's genome copy number variation) was calculated. The multi-factor Cox analysis (activity values considering tumor type, all copy number variation patterns) results indicate that: the activity of the partial copy number variation pattern was somewhat significantly correlated with prognosis of the ubiquitous cancer (see figure 6). An increase in activity of Sig13,14,17,19 is a significant predictor of poor patient prognosis. Of note, sig13, sig17 is the pattern of corresponding copy number variation of CNS11, CNS12 within PCWAG. This also suggests that copy number variation patterns can serve as stable prognostic markers for ubiquity.
While the invention has been described with respect to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Equivalent embodiments of the present invention will be apparent to those skilled in the art having the benefit of the teachings disclosed herein, when considered in the light of the foregoing disclosure, and without departing from the spirit and scope of the invention; meanwhile, any equivalent changes, modifications and evolution of the above embodiments according to the essential technology of the present invention still fall within the scope of the technical solution of the present invention.
Claims (6)
1. A copy number variation pattern recognition method is characterized in that: the method comprises the following steps:
step 1: extracting genome DNA of tumor samples and normal control tissues, and carrying out high-throughput whole-exon group sequencing, whole genome sequencing or SNP chip analysis;
step 2: obtaining absolute copy number information of the tumor sample from the raw WES, WGS or SNP chip data using conventional analysis methods;
step 3: classifying the copy number fragments; the three types are respectively high-low-high, low-high-low and gradient, and the three types refer to the front-back copy number change direction of each specific copy number fragment during classification; the size of copy number change before and after reference in classification is set as two types, namely, any one end is more than 2 and both sides are less than or equal to 2; the absolute copy number values are referred to in classification, and are set to be seven types, namely 0, 1,2, 3,4, 5-8 and more than or equal to 9 respectively; the heterozygosity loss state is referred to in classification, and is divided into two types, namely LOH and LOH-free; the reference fragment length information is set into four types when classifying, namely S is less than 50kb in length, M is less than or equal to 50kb in length and less than or equal to 500kb in length, L is less than or equal to 500kb in length and less than or equal to 5Mb in length, and E is less than or equal to 5Mb in length; then, according to the actual distribution situation of each copy number fragment in the flood data, 176 copy number fragment types are finally formed in total; forming a matrix of 176 columns of data information, each column representing the number of copy number fragments in a particular tumor sample featuring a certain copy number variation fragment;
step 4: calculating the copy number variation characteristic mode of each tumor sample; calculating a specific copy number variation characteristic mode of each tumor by using a nonnegative matrix factorization algorithm;
step 5: quantifying the activity of the pattern of copy number variation characteristics in each tumor; the relative activity of the characteristic pattern of copy number variation and absolute activity values; the relative activity indicates the proportion of contribution of a specific copy number variation characteristic pattern to other variation patterns in a tumor, and can be directly obtained after NMF; absolute activity represents the number of copy number variation records associated with each copy number variation signature pattern;
step 6: parting the tumor according to the copy number variation characteristic mode; classifying each tumor according to the activity of the copy number variation characteristic mode by utilizing cluster analysis;
step 7: prognosis prediction is carried out on tumor patients according to the activity of the copy number variation characteristic mode.
2. The method for identifying copy number variation patterns as claimed in claim 1, wherein: the conventional analysis method in the step 2 comprises the use of FACET and Sequencza software.
3. Use of a copy number variation pattern recognition method as claimed in any one of claims 1-2 for prognostic predictive assessment of a tumour patient.
4. Use of a copy number variation pattern recognition method as claimed in any of claims 1-2 for accurate typing of a patient suffering from a tumour.
5. Use of a copy number variation pattern recognition method as claimed in any one of claims 1-2 for the preparation of a test kit for prognosis prediction of a tumour patient.
6. Use of a copy number variation pattern recognition method as claimed in any one of claims 1-2 for the preparation of a detection kit for accurate typing of a patient suffering from a tumour.
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| CN110289047A (en) * | 2019-05-15 | 2019-09-27 | 西安电子科技大学 | Tumour purity and absolute copy number prediction technique and system based on sequencing data |
| CN112509636A (en) * | 2020-12-21 | 2021-03-16 | 上海科技大学 | Tumor genome copy number variation characteristic pattern recognition method and application thereof |
| CN112634987A (en) * | 2020-12-25 | 2021-04-09 | 北京吉因加医学检验实验室有限公司 | Method and device for detecting copy number variation of single-sample tumor DNA |
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