CN111402952A - Method and system for detecting tumor heterogeneity degree - Google Patents

Method and system for detecting tumor heterogeneity degree Download PDF

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CN111402952A
CN111402952A CN202010231560.3A CN202010231560A CN111402952A CN 111402952 A CN111402952 A CN 111402952A CN 202010231560 A CN202010231560 A CN 202010231560A CN 111402952 A CN111402952 A CN 111402952A
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tumor
cluster
mutation
somatic
variation
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金皓玄
方文峰
陈龙昀
苏小凡
廖裕威
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Shenzhen Yuce Biotechnology Co ltd
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    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Abstract

The invention provides a method and a system for detecting tumor heterogeneity degree, wherein the method comprises the following steps: obtaining sequencing data of a tumor tissue sample and a control sample, and carrying out somatic mutation detection to obtain a somatic mutation site; clustering somatic cell mutation sites according to clusters; judging a main cloning mutant cluster and a sub-cloning mutant cluster according to the clustering result; and calculating the ratio of the number of somatic variations in the subclone mutation cluster in the tumor tissue sample to the number of all somatic variations, wherein the ratio is the tumor heterogeneity value. The invention fully utilizes the mutation frequency detected by each mutation site of the sample to estimate the cell proportion of the corresponding tumor clone, thereby calculating the specific numerical value of the heterogeneity degree of the tumor and providing a numerically referable basis for the subsequent prediction of the curative effect of the immunotherapy.

Description

Method and system for detecting tumor heterogeneity degree
Technical Field
The invention relates to the technical field of bioinformatics, in particular to a method and a system for detecting tumor heterogeneity degree.
Background
Cancer is one of the most major non-infectious diseases in the world and is a disease with a high mortality rate, and in China, nearly 430 thousands of people are diagnosed with cancer every year and over 280 thousands of people die from cancer.
The anti-tumor targeted drug and the immune checkpoint inhibitor are effective means for treating cancers at present, and currently, compared with accepted immune checkpoint inhibitor anti-PD- (L) 1 curative effect evaluation potential indexes, such as TMB (tumor mutation load), MSI (microsatellite instability) and the like, can not completely screen out patients who benefit the immune checkpoint inhibitor.
Currently, there are no authoritative methods for quantifying and calculating the ITH, and the ITH indexes calculated by the methods can not be verified or can be verified in a small data set to evaluate the curative effect of the immune checkpoint inhibitor anti-PD- (L) 1.
For example, Chinese patent application publication No. CN106676178A discloses a method and apparatus for assessing tumor heterogeneity, wherein the method comprises 1) sequencing cfDNA of patients (preferably high throughput sequencing), obtaining sequencing information, 2) using the sequencing information to determine ctDNA variation, determining the number of mutations in the region based on the sequencing information and the determined ctDNA variation, calculating the allele frequency of the variation, determining the actual total copy number of the region where the variation is located, calculating the ratio of ctDNA to cfDNA, 3) clustering the ctDNA variation based on the ratio determined in step 2) and the sequencing information and copy number information of the ctDNA variation, determining each cluster obtained by clustering as a molecular clone, obtaining a clustered clone level, 4) assessing tumor heterogeneity of the patients according to their clone level, the patients having more clone levels and having more tumor heterogeneity, the main defect that the amount of ctDNA in blood is lower, about 1% or even 0.01% of the whole cfDNA [1] DNA F, K, M, C, D, DNA of the amount of mutation detected by the expression of protein I, protein II, protein I, protein II, protein I, protein II, protein I, protein II, protein I, protein II, protein I, protein II, protein I, protein II, protein I, protein II, protein I, protein II, protein I, protein II, protein I, protein II, protein I, protein II, III, protein I, III, protein I, protein II, protein I, III, protein I, III, protein II, III.
Thus, the prior art fails to detect the degree of tumor heterogeneity.
Disclosure of Invention
The invention mainly solves the technical problems that the curative effect evaluation of an immune checkpoint inhibitor anti-PD- (L) 1 lacks enough potential indexes, and how to enable an ITH index to evaluate the curative effect of the immune checkpoint inhibitor PD-1 in a larger scale and a wider range.
According to a first aspect, there is provided in one embodiment a method of detecting the degree of tumor heterogeneity, comprising the steps of:
obtaining sequencing data of the same tumor tissue sample and a control sample from each subject, and carrying out somatic mutation detection on the tumor tissue sample and the control sample to obtain a somatic mutation site;
clustering the somatic cell variant sites according to clusters;
according to the clustering result, if the cluster with the highest tumor cell cluster proportion contains two or more variant abnormal results, judging the cluster with the highest tumor cell cluster proportion as a main clone mutation cluster, and judging the rest mutation clusters as sub clone mutation clusters; if the cluster with the highest tumor cell cluster proportion only contains one abnormal result of variation, judging the mutant cluster with the highest tumor cell cluster proportion and the second highest tumor cell cluster proportion as a main cloning mutant cluster at the same time, and judging the rest mutant clusters as sub-cloning mutant clusters;
and calculating the ratio of the number of somatic variations in the subclone mutation cluster in the tumor tissue sample to the number of all somatic variations, wherein the ratio is a tumor heterogeneity value.
As will be appreciated by those skilled in the art, a somatic mutation may also be referred to as a somatic mutation, and the site of the mutation may also be referred to as a mutation site.
It will be understood by those skilled in the art that the same tumor tissue sample and control sample refer to a tumor tissue sample and a control sample derived from the same subject.
In some embodiments, a variant allelic sequencing depth, a variant allelic frequency, a variant locus copy number, a tumor purity value is calculated from the somatic variant loci, and the somatic variant loci are clustered by performing cluster analysis based on the somatic variant loci and the variant allelic sequencing depth, the variant allelic frequency, the variant locus copy number, the tumor purity value.
In some embodiments, the variant allelic sequencing depth ViThe number of variant sequences of somatic variation at corresponding sites in sequencing data is referred to;
the allelic frequency of the variation
Figure BDA0002429433690000031
RiRefers to the reference allelic sequencing depth, i.e., the number of normal sequences in the sequencing data in which the somatic variation did not occur at the corresponding site;
the tumor purity value refers to the ratio Pur of the number of tumor cells in the total number of the tumor tissue sample cells, the value range is (0, 1), and the tumor cells refer to the sum of all cells with somatic cell variation;
the calculation process of the copy number of the variant site is as follows: according to somatic mutation site variCopy number variation of the region CNViCalculating the somatic mutation site variReference copy number of the region in NCNiAnd actual total copy number NCNiWherein:
Figure BDA0002429433690000032
Figure BDA0002429433690000033
and obtaining somatic mutation sites variThe two chromosomes on which the copy number variation CNV is allele-specifici，major、CNVi，minorWherein CNVi，major≥CNVi，minor，
Thereby calculating the factThe number of actual allele-specific copies CNi，major、CNi，minor：
Figure BDA0002429433690000034
Figure BDA0002429433690000035
In some embodiments, the cells in the subject's tumor tissue sample are classified into three categories, normal cells (N), tumor cells that do not carry the variation (T), for any type of somatic variation at the time of cluster analysiswt) And tumor cells (T) carrying the mutationmut) Tumor cells (T) carrying said somatic variationsmut) Account for all tumor cells (T)mut+Twt) If the proportion of the variant tumor cells of two or more variant sites meets the requirement in the same distribution model, the variants in the same distribution model are endowed with the same cluster label and clustered into a cluster, which is called a clone;
each subject's per cluster tag Cj(j-1, …, c) all having a proportion of tumor cell clusters corresponding thereto
Figure BDA0002429433690000037
，
Figure BDA0002429433690000036
If the tumor cell cluster ratio is high
Figure BDA0002429433690000038
Abnormal results in which the highest cluster contains two or more mutations, the tumor cell cluster ratio is determined
Figure BDA0002429433690000039
The highest cluster is judged as the main clone mutation cluster Cmain(ii) a If the tumor cell cluster ratio is highExample (b)
Figure BDA00024294336900000310
If the highest cluster only contains one abnormal result of mutation, the cluster with the highest tumor cell cluster proportion and the next highest tumor cell cluster proportion are simultaneously judged as the main clone mutation cluster Cmain，
Figure BDA0002429433690000041
Wherein j is 1,.. multidot.c, k is 1,. multidot.c, and k is not equal to j;
simultaneously, the remaining clusters are judged as a subcloned mutant cluster Csub：
Csub＝Cl，l∈{1，...，c}，l≠j，l≠k；
Statistical master cloning of mutant Cluster CmainMiddle somatic mutation site variNumber n ofmainAnd subcloning of mutant cluster CsubMiddle somatic mutation site variNumber n ofsubCalculating a tumor heterogeneity value ITH, which is the ratio of the number of somatic variation sites in a subcloned mutation cluster to the number of all somatic variation sites:
Figure BDA0002429433690000042
as will be appreciated by those skilled in the art, the somatic mutation site variMay also be referred to as variant vari。
In some embodiments, further comprising: setting a threshold according to the ratio of the number of somatic variations in the subclone mutation cluster in the tumor tissue sample to the number of all somatic variations, judging the subject corresponding to the sample smaller than or equal to the threshold as a low-risk subject, and judging the case corresponding to the sample larger than the threshold as a high-risk subject.
In some embodiments, the median of the tumor heterogeneity values for all subjects is used as a threshold for determining high/low tumor heterogeneity for each subject, and subjects with clonal levels below this threshold have lower tumor heterogeneity, whereas subjects with higher tumor heterogeneity are considered.
In some embodiments, the subject is a solid tumor patient, preferably a lung cancer, nasopharyngeal cancer, or melanoma patient.
In some embodiments, the somatic variation is selected from at least one of a point mutation (SNV), an insertion/deletion (indel), a Structural Variation (SV), a Copy Number Variation (CNV). For example, in some embodiments, the reference signal may specifically be SNV, indel, in other embodiments, SNV, indel, SV, and in other embodiments, SNV, indel, SV, CNV may also be used.
In some embodiments, the sequencing method of the tumor tissue sample and the control sample is whole genome sequencing, whole exome sequencing or probe capture sequencing, preferably whole exome sequencing.
According to a second aspect, there is provided a system for detecting the degree of tumor heterogeneity, the system comprising:
a data acquisition module for acquiring sequencing data of the same tumor tissue sample and control sample from each subject;
the somatic mutation detection module is used for carrying out somatic mutation detection on the tumor tissue sample and the control sample to obtain a somatic mutation site;
the clustering module is used for clustering the somatic cell mutation sites according to clusters;
the main clone and sub-clone judging module is used for judging the cluster with the highest proportion of the tumor cell clusters as a main clone mutation cluster and the rest mutation clusters as sub-clone mutation clusters according to the clustering result if the cluster with the highest proportion of the tumor cell clusters contains two or more mutation abnormal results; if the cluster with the highest tumor cell cluster proportion only contains one abnormal result of variation, judging the mutant cluster with the highest tumor cell cluster proportion and the second highest tumor cell cluster proportion as a main cloning mutant cluster at the same time, and judging the rest mutant clusters as sub-cloning mutant clusters;
tumor heterogeneity degree calculation module: and calculating the ratio of the number of somatic variations in the subcloned mutant clusters in the tumor tissue sample to the number of all somatic variations, wherein the ratio is a tumor heterogeneity value.
According to a third aspect, there is provided an apparatus for detecting the degree of tumor heterogeneity, the apparatus comprising:
a memory for storing a program;
a processor for implementing the method as described in the first aspect by executing the program stored by the memory.
According to a fourth aspect, there is provided a computer readable storage medium comprising a program executable by a processor to implement the method of the first aspect.
The mutation detection software includes, but is not limited to VarScan and mutec, and specifically may be VarScan (v2.4.1), mutec (v4.0.12.0), and the like.
In some embodiments, the copy number detection software used includes, but is not limited to, CNVkit, ascatNgs, and specifically, CNVkit (v0.8.1), ascatNgs (v3.1.0).
In some embodiments, the clustering software is selected from PyClone software.
In some embodiments, other versions of PyClone software or other variant cluster analysis software, such as CloneSig (v0.1), may be employed.
According to the method and the system of the embodiment, the invention provides a specific method for calculating the tumor heterogeneity degree of a sample, which fully utilizes the mutation frequency detected at each mutation site of the sample to estimate the cell proportion of corresponding tumor clones, thereby calculating a specific numerical value of the tumor heterogeneity degree and providing a numerically referable basis for the subsequent prediction of the immunotherapy curative effect.
Drawings
FIG. 1 is a block diagram showing a flow chart of the detection of the degree of tumor heterogeneity in an embodiment of the present invention;
FIG. 2 shows the results of the measurement of tumor heterogeneity levels of the lung cancer cohort tissue samples in example 1 of the present invention.
FIG. 3 is a graph showing the prediction of the outcome of immunotherapy efficacy as a function of the degree of tumor heterogeneity in a lung cancer cohort tissue sample in example 1 of the present invention.
FIG. 4 shows the results of the detection of tumor heterogeneity of nasopharyngeal carcinoma cohort tissue samples in example 2.
FIG. 5 shows the prediction of the outcome of immunotherapy efficacy as a function of tumor heterogeneity in nasopharyngeal carcinoma cohort tissue samples according to example 2 of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following detailed description and accompanying drawings. Wherein like elements in different embodiments are numbered with like associated elements. In the following description, numerous details are set forth in order to provide a better understanding of the present application. However, those skilled in the art will readily recognize that some of the features may be omitted or replaced with other elements, materials, methods in different instances. In some instances, certain operations related to the present application have not been shown or described in detail in order to avoid obscuring the core of the present application from excessive description, and it is not necessary for those skilled in the art to describe these operations in detail, so that they may be fully understood from the description in the specification and the general knowledge in the art.
Furthermore, the features, operations, or characteristics described in the specification may be combined in any suitable manner to form various embodiments. Also, the various steps or actions in the method descriptions may be transposed or transposed in order, as will be apparent to one of ordinary skill in the art. Thus, the various sequences in the specification and drawings are for the purpose of describing certain embodiments only and are not intended to imply a required sequence unless otherwise indicated where such sequence must be followed.
The numbering of the components as such, e.g., "first", "second", etc., is used herein only to distinguish the objects as described, and does not have any sequential or technical meaning.
It must be noted that, as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the content clearly dictates otherwise.
As used herein, the terms "comprises," "comprising," "includes," "including," "contains," "containing," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or composition of matter that comprises, or contains an element or list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or composition of matter.
As used herein, the term "providing," as used in the context of a sample, is intended to encompass any and all means of obtaining the sample. The term encompasses all direct or indirect means of causing the presence of the sample in the practice of the claimed method.
As used herein, the term "patient" preferably refers to a human, but also encompasses other mammals. The terms "organism," "individual," "subject" or "patient" are used as synonyms for interchangeable use.
The invention is applicable to all cancer patients. The cancer may be a respiratory system cancer, or subtypes and stages thereof (phase), the respiratory system including the respiratory tract (nasal cavity, pharynx, larynx, trachea, bronchi) and lungs, and in some embodiments, the cancer includes, but is not limited to, lung cancer, nasopharyngeal cancer, laryngeal cancer, pharyngeal cancer, tracheal cancer, and the like. In some embodiments, the cancer may also include, but is not limited to, breast cancer, lung cancer, prostate cancer, colorectal cancer, brain cancer, esophageal cancer, gastric cancer, bladder cancer, pancreatic cancer, cervical cancer, head and neck cancer, ovarian cancer, melanoma, and multidrug resistant cancers; or its subtype and stage (phase).
In some embodiments, the subject may also be a solid tumor patient, including but not limited to a lung cancer, nasopharyngeal carcinoma, or melanoma patient.
As used herein, the term "tumor" refers to all tumor cell growth and proliferation, either malignant or benign, as well as all precancerous and cancerous cells and tissues. Such cancers include, but are not limited to, cancers of the respiratory tract including, but not limited to, lung cancer, nasopharyngeal cancer, laryngeal cancer, pharyngeal cancer, tracheal cancer, and the like; the cancer may also be other lymphoproliferative cancers, such as precursor B lymphoblastic leukemia/lymphoblastic lymphoma, follicular B cell non-Hodgkin's lymphoma, Hodgkin's lymphoma precursor T cell lymphoblastic leukemia/lymphoblastic lymphoma, neoplasms of immature T cells, neoplasms of T cells after peripheral thymus, T cell prolymphocytic leukemia, peripheral T cell lymphoma, undefined anaplastic large cell lymphoma, adult T cell leukemia/lymphoma, chronic lymphocytic leukemia, mantle cell lymphoma, follicular lymphoma, marginal zone lymphoma, hairy cell leukemia, diffuse large B cell lymphoma, Burkitt's lymphoma, lymphoplasmacytic lymphoma, precursor T lymphoblastic leukemia/lymphoblastic lymphoma, T cell prolymphocytic leukemia, angioimmunoblastic lymphoma or hodgkin's lymphoma mainly composed of nodular lymphocytes.
As used herein, variant allelic sequencing depth ViAlso called mutation depth, variant allelic frequency VAFiAlso referred to as mutation frequency.
The prior art can not quantitatively calculate the heterogeneity degree of the tumor, and only qualitatively distinguish the degree of the heterogeneity; or the degree of tumor heterogeneity, based on the number of cell subclones derived from the mutation frequency, and none or only a small number of data sets enable the index to be predictive of immunotherapy efficacy.
At present, the cost of second-generation sequencing is lower and larger, and the coverage area and the depth are larger and larger, the embodiment of the invention accurately detects the mutation site, mutation frequency and copy number variation condition in the coverage area by using the advantage of the large coverage area of the whole exome, and calculates the tumor purity value by using somatic variation in a tumor sample, thereby calculating the tumor heterogeneity condition.
In one embodiment, the degree of tumor heterogeneity assessment of the present invention is performed prior to patient administration, and the quality of the indicator ITH is used to assess whether a patient would likely benefit from treatment with the immune checkpoint inhibitor anti-PD- (L) 1. since the total effective rate of such immunotherapy for lung cancer is only 20-25%, the patient costs approximately 20 million each year, if not beneficial, not only would the general patient's home economy not allow it, but also the social medical insurance cannot afford it.
In one embodiment, the present invention provides a method of predicting the degree of tumor heterogeneity, the method comprising the steps of: s1, detecting somatic variation, depth and mutation frequency information of the sample in the whole exon range through variation detection software; s2, detecting somatic copy number variation of the sample in the whole exon range and estimated tumor purity information through copy number detection software; s3, substituting the calculation results of S1 and S2 into Pyclone software, and clustering the mutations according to clusters according to the somatic mutation of the sample, the sequencing depth of the corresponding mutation site, the somatic mutation support number, the copy number variation number and the tumor purity; s4, according to the calculation result of S3, the mutation cluster with the highest ratio of the corresponding somatic cell estimation is judged as a main clone mutation cluster, and the rest mutation clusters are judged as sub clone mutation clusters; s5, because of the abnormal result that the mutation cluster with the highest somatic cell ratio estimated by the PyClone only contains one mutation, when the abnormal result occurs, the mutation cluster with the highest and the next highest somatic cell ratio estimated by the corresponding somatic cell is simultaneously judged as a main clone mutation cluster, and the rest mutation clusters are judged as sub clone mutation clusters; and S6, calculating the heterogeneity degree of the tumor, wherein the calculation method is that the ratio of the somatic cell variation number in the subcloned mutant cluster to all the somatic cell variation numbers is calculated, and the final value range of the value is [0,1 ].
It will be appreciated by those skilled in the art that the sample referred to in step S1 may be sequenced by known sequencing techniques, including but not limited to high throughput sequencing techniques such as whole genome, or probe capture sequencing, and corresponding methods of informatics analysis.
Those skilled in the art will appreciate that all or part of the functions of the various methods in the above embodiments may be implemented by hardware, or may be implemented by computer programs. When all or part of the functions of the above embodiments are implemented by a computer program, the program may be stored in a computer-readable storage medium, and the storage medium may include: a read only memory, a random access memory, a magnetic disk, an optical disk, a hard disk, etc., and the program is executed by a computer to realize the above functions. For example, the program may be stored in a memory of the device, and when the program in the memory is executed by the processor, all or part of the functions described above may be implemented. In addition, when all or part of the functions in the above embodiments are implemented by a computer program, the program may be stored in a storage medium such as a server, another computer, a magnetic disk, an optical disk, a flash disk, or a removable hard disk, and may be downloaded or copied to a memory of a local device, or may be version-updated in a system of the local device, and when the program in the memory is executed by a processor, all or part of the functions in the above embodiments may be implemented.
In the present embodiment, it is desirable to simultaneously detect a tumor sample and a control sample from the same subject. The subject may, for example, be an individual who has been clinically diagnosed as a tumor patient. The tumor sample generally refers to a sample derived from the affected part or tissue of a tumor patient, such as a lung tissue sample of a lung cancer patient. The control sample is generally derived from a non-diseased part or tissue of the same tumor patient, such as a leukocyte sample isolated from peripheral blood.
In embodiments of the invention, the genome second-generation sequencing data of the tumor sample and the control sample are typically aligned first to the reference genome. Thus, in a preferred embodiment, the data acquisition step acquires alignment files of the genomic second generation sequencing data of the tumor sample and the control sample aligned to the reference genome. In a more preferred embodiment, the reference genome may specifically be human reference genome hg 19.
In some embodiments, as shown in fig. 1, the present invention provides a method and apparatus for predicting the degree of tumor heterogeneity, comprising the following:
1. high-throughput sequencing detection module for tumor collection sample gene variation of subject
Firstly, selecting a plurality of same cancer subjects as subjects, and performing variation detection and parameter calculation on each subject, wherein the specific steps are as follows:
1.1 sequencing the tumor tissue sample of the subject and the control sample by high-throughput sequencing technologies such as whole genome, whole exome or probe capture sequencing and the like and corresponding bioinformatics analysis methods to obtain the variation contained in the sample, including but not limited to at least one of SNV, indel, SV and CNV.
In some embodiments, the tumor tissue sample has a sequencing depth of > 200 ×, in other embodiments, the tumor tissue sample has a sequencing depth of > 300 ×, in other embodiments, the tumor tissue sample has a sequencing depth of > 400 ×, and in other embodiments, the tumor tissue sample has a sequencing depth of > 500 ×.
In some embodiments, the control sample has a sequencing depth of > 50 ×, in other embodiments, the control sample has a sequencing depth of > 100 ×, and in other embodiments, the control sample has a sequencing depth of > 200 ×.
1.2 obtaining a variant var (the variant var is selected from SNV, indel and SV) (var) from the sequencing result in step 1.1iReference allelic sequencing depth (R) for i-1, …, ni) And variant allelic sequencing depth (V)i) And calculating the Variant Allelic Frequency (VAF) of the mutation sitei)，
Figure BDA0002429433690000081
Wherein the reference allelic sequencing depth (R)i) The number of normal sequences in which the mutation does not occur at the corresponding site in the sequencing result, and the allelic sequencing depth (V) of the mutationi) The number of the variant sequences in which the mutation occurs at the corresponding site in the sequencing result is shown.
In some embodiments, the variant var may be obtained by existing variant detection software including, but not limited to, VarScan, mutec, and in some preferred embodiments, VarScan (v2.4.1) or mutec (v4.0.12.0).
1.3 Using variant variCNV (CNV) of the regioni,i＝1,…N) calculating the variance variReference copy number (NCN) of the regioni) And actual Total Copy Number (TCN)i) Wherein, in the step (A),
Figure BDA0002429433690000091
Figure BDA0002429433690000092
at the same time, the variant var can be obtained through the result of softwareiAllele-specific Copy Number Variation (CNV) on both chromosomesi，major，CNVi，minorWherein CNVi，major≥CNV i，minor1, …, n) to calculate the actual allele-specific Copy Number (CN)i，major，CNi，minor)，
Figure BDA0002429433690000093
Figure BDA0002429433690000094
In some embodiments, this step may be performed by existing detection software to calculate the variant variReference copy number (NCN) of the regioni) And actual Total Copy Number (TCN)i) The detection software includes, but is not limited to, CNVkit, ascatNgs, and in some preferred embodiments, CNVkit (v0.8.1) or ascatNgs (v3.1.0).
1.4 obtaining the tumor purity of the subject, namely the ratio Pur of the tumor cells in the collected tumor sample, and the value range is (0, 1).
In some embodiments, the subject's tumor purity may be obtained by existing software, including but not limited to ascatNgs, which in some preferred embodiments may be ascatNgs (v3.1.0) software.
2. Tumor somatic variation clustering module
For each subject, the detected variation was subjected to cluster analysis and calculation based on the parameters obtained in module 1 (i.e., the genetic variation detection module).
For any type of variation (SNV/indel/SV), the cells in a sample tumor sample from a subject are classified into three categories, normal cells (N), tumor cells that do not carry the variation (T)wt) And tumor cells (T) carrying the mutationmut). Tumor cells (T) carrying this variationmut) Account for all tumor cells (T)mut+Twt) The ratio of (a) is referred to as the ratio of tumor cells at the mutation site, and if the ratios of the tumor cells at two or more mutation sites are similar, it is considered that they occur at similar times and are likely to occur in the same group of tumor cells, and the larger the ratio, the earlier the variation occurs in more tumor cells. A similar proportion of the variation will be assigned the same cluster label and clustered into a cluster, called a clone.
All the variants (SNV/indel/SV) detected in each subject were clustered in turn, and software to accomplish this step includes, but is not limited to PyClone, preferably PyClone (vO.13.0).
In one example, all of the variants (SNV/indel/SV) detected in each subject were clustered in turn using PyClone (vo.13.0) with the main parameters set as follows: setting a-prior parameter as major-copy-number, setting an-iterations parameter as 10,000, setting a-burn-in parameter as 1000, setting a-tumour contents parameter as the value of the tumor purity Pur of the subject, and arranging the variation result of the subject into a sample. Tsv takes a tab as a file of a divider, the head line is a header line, except the header line, each line contains information of a variation (SNV/indel/SV), and the six columns are included in sequence: variation number (var)i) Reference allelic sequencing depth (R)i) Variant allelic sequencing depth (V)i) Variation variReference copy number (NCN) of the regioni) Variation variActual allele-specific copy number CNi，minorAnd CNi，majorThe default parameters are adopted for the two parameters and the rest parameters.
PyClone estimates each variation var based on the input informationiThe ratio of the cells in the tumor to all tumor cells, and a cluster label is assigned to each variation (A)
Figure BDA0002429433690000101
i is 1, …, n, j is 1, …, c, c is the number of clusters).
In some embodiments, other versions of PyClone or other variant clustering software such as CloneSig (v0.1) may also be employed in variant clustering.
3. Tumor main clone and sub-clone judgment module
(ii) each cluster label (C) for each subject based on the calculation of the last modulejJ-1, …, c), all have an estimated tumor cell cluster fraction corresponding thereto
Figure BDA0002429433690000105
，
Figure BDA0002429433690000102
The cluster with the highest proportion of tumor cell clusters is judged as a main clone mutation cluster (C)main) (ii) a Due to the characteristics of the Pyclone software algorithm, the abnormal result that the cluster with the highest tumor cell cluster ratio only contains one mutation sometimes appears, and at the moment, the cluster with the highest tumor cell cluster ratio and the next highest tumor cell cluster ratio are simultaneously judged as the main clone mutation cluster Cmain，
Figure BDA0002429433690000103
Wherein j is 1,.. multidot.c, k is 1,.. multidot.c and k is not equal to j. At the same time, the remaining clusters were judged as subcloned mutant clusters (C)sub)，Csub＝Cl，l∈{1，...，c}，l≠j，l≠k。
4. Tumor heterogeneity degree calculation module
Calculating the swelling degree of each subject according to the judgment result of the last moduleDegree of tumor heterogeneity. Statistical master cloning of mutant Cluster CmainMedium variant variNumber n ofmainAnd subcloning of mutant cluster CsubMedium variant variNumber n ofsub. Tumor heterogeneity ITH refers to the ratio of the number of variations in a subcloned cluster of mutations to the number of all variations, specifically,
Figure BDA0002429433690000104
the invention will be further illustrated by means of specific embodiments in conjunction with the accompanying drawings. It should be understood that the examples are illustrative only and are not to be construed as limiting the scope of the invention.
Example 1
In this example, the samples used were a batch of tumor tissue samples from a lung cancer immunotherapy cohort of 69 patients and a control blood sample, specifically a leukocyte sample isolated from peripheral blood. The tumor tissue sample sampling method of this embodiment is to sample pathological puncture single points at lung lesions of each patient and prepare formalin-fixed paraffin-embedded (FFPE) samples, and the sampling unit is the tumor control center of the university of zhongshan.
The specific steps for detecting tumor heterogeneity in the paired samples in this example are as follows:
1) tumor tissue samples and control blood samples were obtained and whole exome sequencing was performed, this sequencing being provided by Nanjing and Gene Biotechnology, Inc. After obtaining the sequencing data, the variation detection software VarScan (v2.4.1) is used to detect the variation (SNV/indel) of the sample in the whole exon range, the minimum coverage rate is set to be equal to 20, the minimum support reading is set to be equal to 5, and the default parameters are adopted for other parameters. And obtaining the reference allelic sequencing depth and the variant allelic sequencing depth information of each mutation site. Detecting somatic copy number variation of a tested sample in a whole exon range by using copy number detection software ascatNgs (v3.1.0), setting a mode as allele _ count, and detecting and obtaining the copy number of each allele specificity and an estimated tumor purity value by adopting other parameters as default parameters;
2) and (3) performing cluster analysis on the mutation by clusters through PyClone (vO.13.0) software according to the related parameters obtained in the previous step, setting a primer parameter as major _ copy _ number, an events parameter as 10,000, a burn-in parameter as 1000, and a tumor contents parameter as the value of the tumor purity Pur of the subject. The variation number, the reference allelic sequencing depth, the variant allelic sequencing depth, the reference copy number of the region where the variation is located and the actual allelic specific copy number parameter of the variation are made into a file sample.tsv which is tabulated as a separator as an input of the-in _ files parameter, such as the sample.tsv file content input by one patient (patient number: F17120989277) in the group of lung cancer cohorts. Analysis of the somatic mutation clusters for each sample in the cohort was obtained after running PyClone software, as shown in table 1.
TABLE 1
Figure BDA0002429433690000111
Figure BDA0002429433690000121
Figure BDA0002429433690000131
Figure BDA0002429433690000141
And operating PyClone software to obtain the analysis result of the mutation clustering of each sample in the queue. The PyClone run results for example patient F17120989277 are exemplified below. Wherein sample _ id is the sample number of the subject, cluster _ id is the number of each variation cluster clustered by PyClone, size is the number of variations contained in each variation cluster, mean represents the estimated proportion of the corresponding variation cluster in the tumor cells, std represents the standard deviation of the calculation result, and is shown in Table 2.
TABLE 2
Figure BDA0002429433690000142
3) And (3) judging the main clone mutation cluster and the sub clone mutation cluster of each sample in the queue according to the analysis result of the somatic mutation cluster in the last step by using a tumor main clone and sub clone module.
4) And (3) calculating a tumor heterogeneity value of each sample through the last step of results by using a tumor heterogeneity degree calculation module, wherein as shown in fig. 2, a histogram represents the tumor heterogeneity of each tested patient, an ordinate represents the tumor heterogeneity value of each tested patient, red represents a subclone mutation, blue represents a main clonal mutation, heights of different color columns represent clonal ratios, and higher subclone mutation ratios represent higher tumor heterogeneity of the tested lung cancer patients.
5) After calculating the tumor heterogeneity (ITH) value of each sample, the median of all tumor heterogeneity was taken, the ITH threshold was set to 0.45, samples with ITH of 0.45 or less were judged to be low ITH (ITH-L), samples with ITH of 0.45 or more were judged to be high ITH (ITH-H), and the therapeutic efficacy and progression-free survival (PFS) information of the patients were collected, as shown in table 3 below, this batch of samples was subjected to survival analysis (see fig. 3, time on the abscissa is day), and the tumor heterogeneity results evaluated by ITH were found to have a significant predictive effect on the PFS prognosis of the patients (p is 0.00011), and patients with high tumor heterogeneity had a higher risk of progression (HR is 2.7068).
TABLE 3
Figure BDA0002429433690000151
Figure BDA0002429433690000161
Example 2
In this example, the samples used were a batch of nasopharyngeal carcinoma immunotherapy cohort tumor tissue samples and a control blood sample, specifically a leukocyte sample isolated from peripheral blood, of 56 patients in total. The tumor tissue sample is sampled in a single point at the lung lesion of each patient. The tumor tissue sample sampling method of this example is single-point sampling of pathological puncture at lung lesion of each patient and preparation of formalin-fixed paraffin-embedded (FFPE) sample, and the sampling unit is the tumor control center of zhongshan university.
The specific steps for detecting tumor heterogeneity in the paired samples in this example are as follows:
1) tumor tissue samples and control blood samples were obtained and whole exome sequencing was performed, this sequencing being provided by Nanjing and Gene Biotechnology, Inc. After obtaining the sequenced data, the variation detection software VarScan (v2.4.1) was used to detect the variation (SNV/indel) of the test sample within the whole exon range, setting the minimum coverage equal to 20, the minimum support reading equal to 5, and the default parameters for the other parameters, as in example 1. And obtaining the reference allelic sequencing depth and the variant allelic sequencing depth information of each mutation site. And (3) detecting the somatic copy number variation of the tested sample in the whole exon range by using copy number detection software ascatNgs (v3.1.0), setting the mode as allele _ count, and detecting and obtaining the copy number of each allele specificity and estimated tumor purity information by adopting default parameters for other parameters.
2) And (3) performing cluster analysis on the mutation by clusters through PyClone (vO.13.0) software according to the related parameters obtained in the previous step, setting a primer parameter as major _ copy _ number, an events parameter as 10,000, a burn-in parameter as 1000, and a tumor contents parameter as the value of the tumor purity Pur of the subject. The file sample.tsv with the mutation number, the reference allele sequencing depth, the variant allele sequencing depth, the reference copy number of the region where the variant is located and the actual allele-specific copy number parameter of the variant as a separator is made as the input of the-in _ files parameter, such as the sample.tsv file content input by one tested patient (patient number: F17120989297) in the nasopharyngeal carcinoma cohort. Analysis of the somatic mutation clusters for each sample in the cohort was obtained after running PyClone software, as shown in table 4.
TABLE 4
Figure BDA0002429433690000171
Figure BDA0002429433690000181
Figure BDA0002429433690000191
And operating PyClone software to obtain the analysis result of the mutation clustering of each sample in the queue. The PyClone operating results for the exemplary patient are exemplified below. Wherein sample _ id is the sample number of the subject, cluster _ id is the number of each variation cluster clustered by PyClone, size is the number of variations contained in each variation cluster, mean represents the estimated proportion of the corresponding variation cluster in the tumor cells, std represents the standard deviation of the calculation result, and the analysis result of the mutation cluster of the sample is shown in Table 5.
TABLE 5
Figure BDA0002429433690000192
3) And (3) judging the main clone mutation cluster and the sub clone mutation cluster of each sample in the queue according to the analysis result of the somatic mutation cluster in the last step by using a tumor main clone and sub clone module.
4) And (3) calculating the tumor heterogeneity value of each sample by using a tumor heterogeneity degree calculation module through the result of the previous step, wherein as shown in fig. 4, a bar chart represents the tumor heterogeneity of each tested patient, red is subclone mutation, blue is main clone mutation, the heights of the columns with different colors represent clone ratios, and the higher the subclone mutation ratio is, the higher the tumor heterogeneity of the tested nasopharyngeal carcinoma patient is.
5) After calculating the tumor heterogeneity (ITH) value of each sample, the median of all tumor heterogeneity was taken, ROC curve analysis was performed for correction, the ITH threshold was set to 0.33, samples with ITH of 0.33 or less were judged to be low ITH (ITH-L), samples with ITH of 0.33 or more were judged to be high ITH (ITH-H), and after collecting the efficacy and progression-free survival (PFS) information of the subject patients, as shown in table 6 below, survival analysis was performed on this batch of samples (see fig. 5, time unit on abscissa is day), and the tumor heterogeneity results evaluated using ITH were found to have a significant predictive effect on PFS of the patients (p ═ 0.7), patients with high tumor heterogeneity had a higher risk of progression (HR ═ 2.0501), and the results were re-validated successfully in another nasopharyngeal cancer cohort to evaluate the efficacy and accuracy of tumor heterogeneity using the ITH analysis technique, and also demonstrated that ITH could be used as a biomarker for predicting the efficacy of immunotherapy for nasopharyngeal cancer.
TABLE 6
Figure BDA0002429433690000201
Figure BDA0002429433690000211
In summary, the embodiments of the present invention provide a specific method for calculating the degree of tumor heterogeneity of a sample based on the data of Whole Exome Sequencing (WES), and fully utilize the mutation frequency detected at each mutation site of the sample to estimate the cell ratio of the corresponding tumor clone, thereby calculating a specific value of the degree of tumor heterogeneity, and providing a numerically referable basis for the subsequent prediction of the immunotherapy efficacy.
The present invention has been described in terms of specific examples, which are provided to aid understanding of the invention and are not intended to be limiting. For a person skilled in the art to which the invention pertains, several simple deductions, modifications or substitutions may be made according to the idea of the invention.

Claims (10)

1. A method for detecting the degree of tumor heterogeneity, comprising the steps of:
obtaining sequencing data of the same tumor tissue sample and a control sample from each subject, and carrying out somatic mutation detection on the tumor tissue sample and the control sample to obtain a somatic mutation site;
clustering the somatic cell variant sites according to clusters;
according to the clustering result, if the cluster with the highest tumor cell cluster proportion contains two or more variant abnormal results, judging the cluster with the highest tumor cell cluster proportion as a main clone mutation cluster, and judging the rest mutation clusters as sub clone mutation clusters; if the cluster with the highest tumor cell cluster proportion only contains one abnormal result of variation, judging the mutant cluster with the highest tumor cell cluster proportion and the second highest tumor cell cluster proportion as a main cloning mutant cluster at the same time, and judging the rest mutant clusters as sub-cloning mutant clusters;
and calculating the ratio of the number of somatic variations in the subclone mutation cluster in the tumor tissue sample to the number of all somatic variations, wherein the ratio is a tumor heterogeneity value.
2. The method of claim 1, wherein the somatic mutation sites are clustered by calculating mutation allele sequencing depth, mutation allele frequency, mutation site copy number and tumor purity value according to the somatic mutation sites, and performing cluster analysis according to the somatic mutation sites, the mutation allele sequencing depth, the mutation allele frequency, the mutation site copy number and the tumor purity value.
3. The method of claim 2, wherein the variant allelic sequencing depth ViThe number of variant sequences of somatic variation at corresponding sites in sequencing data is referred to;
the allelic frequency of the variation
Figure FDA0002429433680000011
RiRefers to the reference allelic sequencing depth, i.e. the number of normal sequences without somatic variation at the corresponding sites in the sequencing data;
the tumor purity value refers to the ratio Pur of the number of tumor cells in the total number of the tumor tissue sample cells, the value range is (0, 1), and the tumor cells refer to the sum of all cells with somatic cell variation;
the calculation process of the copy number of the variant site is as follows: according to somatic mutation site variCopy number variation of the region CNViCalculating the somatic mutation site variReference copy number of the region in NCNiAnd actual total copy number TCNiWherein:
Figure FDA0002429433680000012
Figure FDA0002429433680000013
and obtaining somatic mutation sites variThe two chromosomes on which the copy number variation CNV is allele-specifici，major、CNVi，minorWherein CNVi，major≥CNVi，minor，
Thereby calculating the actual allele-specific copy number CNi，major、CNi，minor：
Figure FDA0002429433680000014
Figure FDA0002429433680000021
4. The method of claim 1, wherein the cells in the tumor tissue sample of the subject are classified into three categories for any type of somatic variation in cluster analysis: normal cell N, tumor cell T not carrying said somatic variationwtAnd tumor cells T carrying said somatic variationsmuutTumor cells T carrying said somatic variationsmutAll tumor cells (T)mut+Twt) Is called the somatic cell changeIf the proportion of the variant tumor cells of two or more somatic variations meets the requirement in the same distribution model, the variations in the same distribution model are endowed with the same cluster label and are clustered into a cluster, namely a clone;
each subject's per cluster tag Cj(j-1, …, c) all having a proportion of tumor cell clusters corresponding thereto
Figure FDA0002429433680000022
Figure FDA0002429433680000023
If the tumor cell cluster ratio is high
Figure FDA0002429433680000024
Abnormal results of two or more variation contained in the highest cluster, and the ratio of the tumor cell clusters is determined
Figure FDA0002429433680000025
The highest cluster is judged as the main clone mutation cluster Cmain(ii) a If the tumor cell cluster ratio is high
Figure FDA0002429433680000026
If the highest cluster only contains one abnormal result of variation, the cluster with the highest tumor cell cluster proportion and the next highest tumor cell cluster proportion are simultaneously judged as the main clone mutant cluster Cmain，
Figure FDA0002429433680000027
Wherein j is 1,.. multidot.c, k is 1,. multidot.c, and k is not equal to j;
simultaneously, the remaining clusters are judged as a subcloned mutant cluster Csub：
Csub＝Cl，l∈{1，...，c}，l≠j，l≠k；
Statistical master cloning of mutant Cluster CmainMiddle somatic mutation site variNumber n ofmainAnd subcloning of mutant cluster CsubMiddle somatic mutation site variNumber n ofsubCalculating a tumor heterogeneity value ITH, which is the ratio of the number of variations in a subcloned cluster to the number of all variations:
Figure FDA0002429433680000028
5. the method of claim 1, further comprising: setting a threshold value according to the tumor heterogeneity value ITH, judging the subjects corresponding to the samples smaller than or equal to the threshold value as low-risk subjects, and judging the cases corresponding to the samples larger than the threshold value as high-risk subjects.
6. The method of claim 5, wherein the median of the tumor heterogeneity values of all subjects is used as a threshold for determining high/low tumor heterogeneity of each subject, and subjects with clone levels below the threshold have low tumor heterogeneity, whereas subjects with high tumor heterogeneity are selected.
7. The method of claim 1, wherein the subject is a patient with a solid tumor, preferably a lung cancer, nasopharyngeal cancer, or melanoma;
and/or, the somatic variation is selected from at least one of point mutation, insertion/deletion, structural variation, copy number variation;
and/or the sequencing method of the tumor tissue sample and the control sample is whole genome sequencing, whole exome sequencing or probe capture sequencing, preferably whole exome sequencing.
8. A system for detecting the degree of tumor heterogeneity, the system comprising:
a data acquisition module for acquiring sequencing data of the same tumor tissue sample and control sample from each subject;
the somatic mutation detection module is used for carrying out somatic mutation detection on the tumor tissue sample and the control sample to obtain a somatic mutation site;
the clustering module is used for clustering the somatic cell mutation sites according to clusters;
the main clone and sub-clone judging module is used for judging the cluster with the highest proportion of the tumor cell clusters as a main clone mutation cluster and the rest mutation clusters as sub-clone mutation clusters according to the clustering result if the cluster with the highest proportion of the tumor cell clusters contains two or more variant abnormal results; if the cluster with the highest tumor cell cluster proportion only contains one abnormal result of variation, judging the mutant cluster with the highest tumor cell cluster proportion and the second highest tumor cell cluster proportion as a main cloning mutant cluster at the same time, and judging the rest mutant clusters as sub-cloning mutant clusters;
tumor heterogeneity degree calculation module: and calculating the ratio of the number of somatic variations in the subcloned mutant clusters in the tumor tissue sample to the number of all somatic variations, wherein the ratio is a tumor heterogeneity value.
9. An apparatus for detecting the degree of tumor heterogeneity, the apparatus comprising:
a memory for storing a program;
a processor for implementing the method of any one of claims 1 to 7 by executing a program stored by the memory.
10. A computer-readable storage medium, characterized by comprising a program which is executable by a processor to implement the method of any one of claims 1-7.
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111968702A (en) * 2020-08-24 2020-11-20 西安时代基因健康科技股份有限公司 Early malignant tumor screening system based on circulating tumor DNA
CN111979323A (en) * 2020-08-28 2020-11-24 深圳裕策生物科技有限公司 Biomarker detection method and system for predicting tumor immunotherapy effect
CN113053458A (en) * 2021-01-19 2021-06-29 深圳裕康医学检验实验室 Prediction method and device for tumor neoantigen load
CN115954052A (en) * 2023-03-15 2023-04-11 广州迈景基因医学科技有限公司 Method and system for screening monitoring sites of tiny residual lesions of solid tumors
WO2024003004A1 (en) * 2022-06-30 2024-01-04 Koninklijke Philips N.V. Methods and systems for generating and visualizing patient information including tumor heterogeneity,therapy effectiveness, and survival statistics

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB201618485D0 (en) * 2016-11-02 2016-12-14 Ucl Business Plc Method of detecting tumour recurrence
CN106676178A (en) * 2017-01-19 2017-05-17 北京吉因加科技有限公司 System and method for tumor heterogeneity assessment
CN106778073A (en) * 2017-01-19 2017-05-31 北京吉因加科技有限公司 A kind of method and system for assessing tumor load change
US20190362808A1 (en) * 2017-02-01 2019-11-28 The Translational Genomics Research Institute Methods of detecting somatic and germline variants in impure tumors
WO2020023420A2 (en) * 2018-07-23 2020-01-30 Guardant Health, Inc. Methods and systems for adjusting tumor mutational burden by tumor fraction and coverage
CN110770838A (en) * 2017-12-01 2020-02-07 Illumina公司 Method and system for determining clonality of somatic mutations

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB201618485D0 (en) * 2016-11-02 2016-12-14 Ucl Business Plc Method of detecting tumour recurrence
CN110462065A (en) * 2016-11-02 2019-11-15 Ucl商业有限公司 The method for detecting tumor recurrence
CN106676178A (en) * 2017-01-19 2017-05-17 北京吉因加科技有限公司 System and method for tumor heterogeneity assessment
CN106778073A (en) * 2017-01-19 2017-05-31 北京吉因加科技有限公司 A kind of method and system for assessing tumor load change
US20190362808A1 (en) * 2017-02-01 2019-11-28 The Translational Genomics Research Institute Methods of detecting somatic and germline variants in impure tumors
CN110770838A (en) * 2017-12-01 2020-02-07 Illumina公司 Method and system for determining clonality of somatic mutations
WO2020023420A2 (en) * 2018-07-23 2020-01-30 Guardant Health, Inc. Methods and systems for adjusting tumor mutational burden by tumor fraction and coverage

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111968702A (en) * 2020-08-24 2020-11-20 西安时代基因健康科技股份有限公司 Early malignant tumor screening system based on circulating tumor DNA
CN111968702B (en) * 2020-08-24 2024-04-19 西安时代基因健康科技股份有限公司 Malignant tumor early screening system based on circulating tumor DNA
CN111979323A (en) * 2020-08-28 2020-11-24 深圳裕策生物科技有限公司 Biomarker detection method and system for predicting tumor immunotherapy effect
CN113053458A (en) * 2021-01-19 2021-06-29 深圳裕康医学检验实验室 Prediction method and device for tumor neoantigen load
CN113053458B (en) * 2021-01-19 2023-08-04 深圳裕康医学检验实验室 Method and device for predicting tumor neoantigen load
WO2024003004A1 (en) * 2022-06-30 2024-01-04 Koninklijke Philips N.V. Methods and systems for generating and visualizing patient information including tumor heterogeneity,therapy effectiveness, and survival statistics
CN115954052A (en) * 2023-03-15 2023-04-11 广州迈景基因医学科技有限公司 Method and system for screening monitoring sites of tiny residual lesions of solid tumors
CN115954052B (en) * 2023-03-15 2023-06-02 广州迈景基因医学科技有限公司 Screening method and system for monitoring sites of tiny residual focus of solid tumor

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