CN116913380B - Method and device for judging dynamic change of ctDNA of advanced tumor - Google Patents
Method and device for judging dynamic change of ctDNA of advanced tumor Download PDFInfo
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Abstract
The application provides a method and a device for judging dynamic changes of ctDNA of advanced tumors, and belongs to the technical field of medical detection. The method comprises the steps of receiving sequencing data of plasma before treatment of the advanced tumor and paired blood cells, and constructing a personalized variation map of the plasma before treatment of a tumor patient; tracking the variation signal of the plasma after treatment according to the plasma variation map; based on mutation frequency of mutation sites before and after clinical treatment and quantitative weight of sequencing depth, the contribution degree of the mutation frequency change of the sites is estimated, the influence of fluctuation caused by sequencing and analysis is reduced, the change of the ctDNA of plasma before and after treatment of a late patient is more accurately quantified, and whether the patient responds to the treatment mode is estimated. The computing of the device, the storage medium and the equipment is realized based on the method. The application can more accurately judge the dynamic change of ctDNA of the advanced tumor, and can effectively improve the accuracy of curative effect evaluation on patients with advanced tumor.
Description
Technical Field
The application relates to the technical field of biomedicine, in particular to a method and a device for judging dynamic changes of ctDNA of advanced tumors.
Background
The circulating tumor DNA (ctDNA) mainly comes from apoptosis and necrosis of tumor cells, and in non-small cell lung cancer (NSCLC), the plasma ctDNA is generally highly matched with tumor tissues, can well reflect the tumor load of patients, has proved to be an alternative means of tissue detection and has clinical guidance value equivalent to the tissue detection. For early-stage operable patients, the postoperative ctDNA detection can sensitively capture tiny residual lesions, and compared with traditional imaging, the method can early warn disease recurrence in advance and provide effective reference information for later-stage treatment and intervention. However, because tissue samples of patients with advanced cancer are not readily available, identification of such ctDNA status suffers from various drawbacks, such as the detection of ctDNA is prone to false positives, resulting in poor accuracy in ctDNA detection.
The circulating tumor DNA (ctDNA) is widely applied to various aspects of early diagnosis, medication guidance, drug resistance monitoring and the like of tumor patients at present. The research data at home and abroad show that ctDNA detection can be used for individuation recurrence risk stratification of lung cancer patients, and more dimension reference information is provided for decision making of subsequent intervention measures. A large number of researches show that ctDNA dynamic monitoring can be used as an postoperative monitoring index of early patients, and is also expected to become an effective mode for evaluating curative effect of late patients during treatment. Thus, a method capable of efficiently analyzing ctDNA dynamic changes based on plasma mutation tracking before and after treatment is needed.
Disclosure of Invention
In order to solve the problems, the application provides a method and a device for judging ctDNA dynamic change of advanced tumors, which track plasma after treatment by using a mutation sequence obtained based on plasma before advanced tumor treatment and paired blood cells, so as to accurately identify the ctDNA dynamic change of an advanced tumor patient, and further predict the curative effect of treatment of the advanced tumor patient.
The technical scheme provided by the application is as follows:
in one aspect, the application provides a method for determining the dynamic change of ctDNA of a late tumor, comprising the following steps:
obtaining sequencing data of plasma of patients with advanced tumor after treatment;
tracking the sequencing data according to a pre-obtained mutation sequence to obtain a significant mutation sequence, wherein the pre-obtained mutation sequence is obtained by filtering sequencing data of plasma and paired blood cells of a patient with advanced tumor before treatment;
analyzing the dynamic change of the plasma ctDNA before and after treatment according to a pre-created differential weight quantitative ctDNA dynamic change model;
and judging the molecular response state of the patients with advanced tumors after treatment according to the model analysis result.
On the other hand, the application also provides a device for judging the dynamic change of ctDNA of the advanced tumor, which comprises the following steps:
the data receiving module is used for acquiring sequencing data of the plasma of the patients with advanced tumors after treatment;
the mutation tracking module is used for tracking the sequencing data according to a pre-obtained mutation sequence to obtain a significant mutation sequence, wherein the pre-obtained mutation sequence is obtained by filtering sequencing data of plasma and paired blood cells before treatment of a late tumor patient;
the dynamic analysis module is used for quantifying the dynamic change of the ctDNA according to a pre-created differential weight and analyzing the dynamic change of the plasma ctDNA before and after treatment;
and the judging module is used for judging the molecular response state of the late tumor patient after treatment according to the model analysis result.
On the other hand, the application also provides a terminal device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of the method for judging the dynamic change of the ctDNA of the advanced tumor when running the computer program.
In another aspect, the present application also provides a computer readable storage medium storing a computer program which when executed by a processor implements the steps of the above-described late-stage tumor ctDNA dynamic change determination method.
According to the method and the device for judging the dynamic change of the ctDNA of the advanced tumor, firstly, a mutation sequence meeting a filtering standard is obtained according to sequencing data of plasma before the treatment of the advanced tumor; then follow up in plasma after advanced tumor blood cell treatment; and finally, the ctDNA dynamic change model is combined with the dynamic change of the blood plasma VAF before and after treatment by the differentiated weight to identify the ctDNA state of the blood plasma after the late treatment of the tumor. Compared with the prior art, the method has higher accuracy in judging the ctDNA state of the blood plasma after the treatment of the advanced tumor, can effectively avoid the problem of false positive in the prior art, can provide a guiding direction for further clinical diagnosis and treatment, is convenient, safe and effective, effectively assists in the treatment decision of the cancer, and improves the treatment efficiency. Meanwhile, the application obtains the original data based on the sequencing of the plasma DNA sample before the advanced tumor treatment, can make up the defect that the advanced tumor patient cannot take the tissue sample, and reduces the requirement on the sequencing sample.
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The above features, technical features, advantages and implementation thereof will be further described in the following detailed description of preferred embodiments with reference to the accompanying drawings in a clearly understandable manner.
FIG. 1 is a flow chart of a method for determining the dynamic change of ctDNA of a late stage tumor according to an embodiment of the present application;
FIG. 2 is a representation of the relationship between the matching VAF and std of the regression of the loss in the present application;
FIG. 3 is a representation of the normal distribution of VAF in duplicate samples according to the present application;
FIG. 4 is a plot of the standard deviation of VAF and the correlation of VAF and sequencing depth in the present application;
FIG. 5 shows different threshold values in an embodiment of the applicationMinverva-DeltaHazard ratio comparison;
FIG. 6 is a plot of sensitivity and specificity of mutation site detection in an example of the present application;
FIG. 7 shows an embodiment of the present applicationMinverva-DeltaConsistency with expected tumor purity changes;
FIG. 8 is a survival analysis chart for accurately judging the dynamic changes of the ctDNA of the plasma before and after the ctDNA treatment of the advanced tumor in the embodiment of the application, which can effectively predict the curative effect of the immunotherapy of the patients with the advanced tumor;
FIG. 9 is a diagram ofIn the embodiment of the application, the imaging evaluation effect in the treatment evaluation effect of the advanced patient is compared with the disputed SD patient,Minverva-Deltathere may also be further differentiated survival analysis plots;
FIG. 10 is a block diagram showing a device for determining the amount of change in ctDNA of a late stage tumor according to an embodiment of the present application;
fig. 11 is a block diagram of an electronic device in an embodiment of the application.
Reference numerals:
100-ctDNA dynamic change judging device, 110-data receiving module, 120-mutation tracking module, 130-dynamic analysis module and 140-judging module.
Detailed Description
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description will explain the specific embodiments of the present application with reference to the accompanying drawings. It is evident that the drawings in the following description are only examples of the application, from which other drawings and other embodiments can be obtained by a person skilled in the art without inventive effort.
English description of the application:
ctDNA: circulating tumor DNA circulating tumor DNA
De novo: new mutation detection method
Genogyping: detection method for tracking known mutation
VAF: variant Allele Frequency variation frequency
Support reads/vsm: mutant Gene reads support number of tumor samples
Depth: depth after deduplication of the mutation site
InDel: insertion deletion indel mutation
And (3) low regression: is a non-parametric method for local regression analysis, used to fit the relationship of two variables
Kolmogorov-Smirnov test: a non-parametric hypothesis test is used to test whether a set of samples is from a probability distribution or to compare whether the distributions of two sets of samples are identical
Fisher test: the fischer-tropsch accurate test is a non-parametric test method for calculating the correlation between two class variables, for checking if there is a significant correlation between the two class variables.
MinerVa-DeltaAnd (3) model: model for quantifying ctDNA dynamic change by differentiating weight
Molecular responder: molecular response
Molecular non-sensor: molecular unresponsiveness
SD: stable Disease and Stable Disease
Hazard ratio: refer to the risk ratio, mainly used for survival analysis of queue research
Progression-free survival probability of Progression-free survival (PFS)
Overall Survivin (OS) probability of survival
In one embodiment of the present application, a method for determining ctDNA dynamic change of advanced tumor is shown in fig. 1, which is a flowchart of the method. Referring to fig. 1, the method includes:
s10, acquiring sequencing data of plasma of patients with advanced tumors after treatment.
Patients with advanced tumors are non-operable patients, typically patients with stage III-IV and localized metastases, and the range of advanced tumors includes other carcinoma species such as squamous carcinoma of the lung, gastric carcinoma, colorectal carcinoma. In the application, the sequencing of the plasma after tumor treatment can be performed based on the first generation, second generation and third generation gene sequencing technologies, so as to obtain sequencing data.
S20, tracking sequencing data according to a pre-obtained mutation sequence to obtain a significant mutation sequence, wherein the pre-obtained mutation sequence is obtained by filtering sequencing data of plasma and paired blood cells of a patient with advanced tumor treatment.
Before determining the dynamic change of ctDNA of the advanced tumor, the sequencing data of the plasma and the paired blood cells (PBMC (peripheral blood mononuclear cell) peripheral blood mononuclear cells) before the treatment of the advanced tumor are required to be subjected to germ line mutation and background noise filtration to obtain a plasma personalized mutation map of a patient so as to obtain the mutation frequency VAF of a plasma sample after the treatment of a mutation site, so that the plasma sample after the treatment is conveniently tracked later. In the application, the sequencing data can be obtained by sequencing the plasma and paired blood cells before tumor treatment based on the first generation, second generation and third generation gene sequencing technologies. The filtering rules specifically include:
1) Germ line mutation points in the paired samples are filtered.
2) The mutation points with the allele frequency VAF of no less than 1% of a non-platinum list are filtered, the platinum list is the variation point list of 1A-2C type evidence grade related to solid tumor treatment in oncogene mutation, and oncogene (oncogene) mutation is screened by a cancer sequence variation explanation and report guide issued by the combined reference of 2017 ASCO (American society of clinical oncology), AMP (molecular pathology Association) and CAP (American society of pathologists).
3) The mutation points of the mutant base Support reads >5 were filtered. The Support reads are the number of mutant gene reads supports of tumor samples.
4) Mutation points with the depth of the filtering site being more than or equal to 500X.
5) Mutations with strand preference were filtered. Chain preference is embodied as: when short sequence sequencing is performed using an Illumina high throughput instrument, there are times when the types of variation exhibited by the positive and negative strands will vary significantly, e.g., one may exhibit homozygous and one may exhibit heterozygous mutations.
6) The mutation point with InDel gene type is filtered.
7) And filtering and annotating the mutation points of the blacklist, wherein the blacklist is obtained by counting the frequency of people with certain mutation points in a large number of samples, and if the frequency of people is high, adding the people into the blacklist.
S30, quantifying a ctDNA dynamic change model according to the pre-created differential weightMinerVa-DeltaModel) the dynamic changes of plasma ctDNA before and after treatment were analyzed.
MinerVa-DeltaThe model obtains a single sample by tracking the weighted summation of all mutation site changes to obtain quantitative values of ctDNA dynamic changes of plasma samples before and after sample layer treatmentA kind of electronic deviceMinerVa-DeltaThe values are as in formulas (1) - (2):
(1)
(2)
wherein,nrepresents the number of mutation points in the mutation sequence obtained in advance,represent the firstiSite weights of the individual mutation points, +.>Represent the firstiThe rate of VAF decrease (dynamic change at site level) after treatment at each mutation point, and (2)>Represent the firstiVAF value of plasma gene after treatment with individual mutation points,/->Represent the firstiVAF value of plasma gene after treatment with each mutation point.
For the firstiPoint weights of mutation pointsThe application provides the following 7 implementation modes:
the first method uses the loess regression to fit the association between standard deviation (std) and VAF based on the standard sample to obtainNormal distribution density function, and further calculate +.>Probability of falling on both ends of normal distribution +.>Obtaining the weight of the site according to a weight formula>。
From sequencing and analysisThere will be fluctuations and normal distribution, in this method, by fitting the distribution of VAF and std using the loess regression to the sequencing data of the standard, as shown in fig. 2, the horizontal axis is VAF, the vertical axis is std, and the curve is the relationship between VAF and std of the loess fit, and it can be seen from the figure that std is maximum at a VAF value of about 50%. Based on the fitting result, it can be used to predict the +.sub.f. of a single mutation site>Std of (2) to get->A normal distribution density function, thus calculating +.>Probability of falling on both ends of normal distribution +.>The weight of the site can be obtained according to the weight formula of the following formula：
(3)
Wherein,represent the firstkThe difference between the individual mutation points is significant p-value.
The second method, in which a single mutation point is calculated according to the standard deviation formula, considers that std is related to not only VAF but also the depth of site sequencing (depth)Standard deviation of>Normal distribution density function, and further calculate +.>Probability of falling on both ends of normal distribution +.>Obtaining the weight of the site according to the weight formula shown in formula (3)>。
(4)
Wherein,representation->Standard deviation of>Represents the depth of post-deduplication sequencing of the pre-treatment plasma mutation site,/->Represent the firstkThe difference between the individual mutation points is significant p-value.
The following description is given of the origin of the standard deviation formula of the above formula (4), which is also used in several methods hereinafter, and will not be repeated here:
the VAF distribution at the mutation site in the repeated sample of the standard product is found to be normal due to the fluctuation of the VAF within a certain normal range caused by sequencing and analysis, and is shown in fig. 3, which is a histogram of the VAF distribution of nras.p.q61k mutation in 439 repeated samples, wherein the horizontal axis is VAF and the vertical axis is the corresponding VAFThe number of samples Count of VAF values. The standard deviation of the VAF is found to be related to factors such as VAF level and sequencing depth, etc. by simulation data and standard data, and as shown in fig. 4, the horizontal axis is VAF, the vertical axis is standard deviation std, wherein the solid curve represents sequencing depth of 2000, the dotted curve represents sequencing depth of 1000, and the dotted curve represents sequencing depth of 500. As can be seen from the figure, the standard deviation std maximum and the distribution of the actual data are met when the VAF value is close to 50% (corresponding to 0.5 in the figure), and the standard deviation std of the VAF is related to the sequencing depth, and the VAF standard deviation std corresponding to the position with high depth is smaller. Obtaining the compound shown as the formula (5) through mathematical derivationIs calculated according to the formula:
(5)
third method, due to sequencing and analysis methodsAnd->There will be fluctuations and a normal distribution. In this method, when->When the mutation point is not equal to 0, calculating according to a standard deviation formula to obtain a single mutation point +.>And->Standard deviations (as in formulae (4) and (6)) of +.>And->The normal distribution density function is then used by Kolmogorov-The Smirnov test (KS test) calculates the difference significance of two normal distributions +.>. When (when)When=0, calculate +.>Probability of falling on both ends of normal distribution +.>Further, the weight of the site is obtained according to the weight formula shown in formula (3)>。
(6)
Wherein,representation->Standard deviation of>Shows the depth of sequencing after deduplication of plasma mutation sites after treatment.
Fourth method, due to sequencing and analysis methodsAnd->There will be fluctuations and a normal distribution. In this method, when->When the mutation point is not equal to 0, calculating according to a standard deviation formula to obtain a single mutation point +.>Andstandard deviations (as in formulae (4) and (6)) to give +.>And->Normal distribution density function, calculating the proportion of intersection of two normal distributions +.>. When->When=0, calculate +.>Probability of falling on both ends of normal distribution +.>Further, the weight of the site is obtained according to the weight formula shown in formula (3)>。
Fifth method, according to the number of supported reads and depth of mutation sites before and after treatment, performing Fisher test on the significanceObtaining the weight of the site according to the weight formula shown in formula (3)>。
Sixth method, calculating mutation sites before and after treatmentIt is assumed that the number of supported reads vsm for the mutation site before and after treatment is obeyed with respect to +.>Is calculated as the poisson distribution of (2) two sides +.>And->Multiplying to obtain +.>Obtaining the weight of the site according to a weight formula>：
(7)
If it isThen: />Is less than +.>Is a function of the probability of (1),is greater than->Probability of (2);
if it isThen: />Is less than +.>Is a function of the probability of (1),is greater than->Is a probability of (2).
(8)
Wherein,p-value representing the left side of poisson distribution,/->P-value representing the right side of the poisson distribution; />The number of supported reads vsm, representing the site of mutation before treatment,>support reads number vsm, representing the mutation site after treatment,>represents the sequencing depth after duplication removal of plasma mutation sites before treatment,representing the sequencing depth after the duplication removal of the plasma mutation site after treatment; />Represent the firstkThe difference between the individual mutation points is significant p-value.
Seventh method, considerAnd->All are positiveDistribution of states, and->The test method is the same when the mutation point is equal to 0 and the mutation point is not equal to 0, and the single mutation point is calculated according to a standard deviation formula>And->The standard deviation of (e.g. formulas (4) and (6)) is calculated as weight +.>And carrying out weight standardization so that the sum of the weights of all sites in the sample is 1 to obtain the weight of the site +.>As in formulas (9) - (11):
(9)
(10)
(11)
wherein,indicating the amount of change of VAF at mutation site before and after treatment,/->Representation ofStandard deviation of>Indicating treatmentDepth of sequencing after de-duplication of plasma mutation sites before therapy,representation->Standard deviation of>Represents the depth of post-deduplication sequencing of plasma mutation sites after treatment,nthe number of mutation points in the mutation sequence obtained in advance is shown.
S40, judging the molecular response state of the patients with advanced tumors after treatment according to the model analysis result.
Here, the molecular response state of the plasma sample after treatment of the late stage tumor patient is determined from quantitative values of ctDNA dynamic changes of the plasma sample before and after the sample-level treatment. In particular, when a single sampleMinerVa-DeltaThe value is smaller than or equal to a preset threshold value, and the molecular response group is judged; when a single sampleMinerVa-DeltaAnd the value is larger than a preset threshold value, and the molecular non-response group is judged. The preset threshold may be determined according to the actual application. Based on the graph as shown in FIG. 5 (represented by the abscissaMinerVa-DeltaThreshold, ordinate indicates risk ratio), it can be seen that in two dimensions, PFS (progression free lifetime) indicated by a dotted line and OS (total lifetime) indicated by a solid line,MinerVa-Delta<=30% packet ratioMinerVa-Delta>30% of Hazard ratio is the lowest, and therefore, it is determined thatMinerVa-Delta30% can be used as a threshold value for the obvious change of tumor purity, and Molecular responder is judgedMinerVa-Delta<=30%) is a Molecular non-response groupMinerVa-Delta>30%) is a molecular non-reacted group, wherein the molecular response group has a significantly longer progression free and overall survival than the molecular non-response group.
The effect of the above method is described below by way of an example:
experimental data of this example through analysis of performance verification, pairMinerva-DeltaPerforming performance verification analysis on the number of standard substancesWherein the known mutation site and the unknown mutation site are tracked, wherein the unknown mutation site is the mutation site of each standard, and the known mutation site is the common mutation obtained by repeating the sample, and the method comprises the steps ofMinerva-DeltaThe model calculates ctDNA dynamics at site level and sample level.
1.1 sensitivity and specificity of detection of mutation sites
The two standard substance data are used for respectively covering the distribution of the mutation frequencies of low frequency and high frequency, firstly, the normal diploid cell line GM12878 is used for carrying out gradient dilution on GW_OGTM006, the GW_OGTM006 obtains 6 dilution gradients in total, and the average value of mutation frequencies according to a hot spot mutation theory is respectively as follows: 1%, 0.5%, 0.3%, 0.1%, 0.05%, 0.03%. Secondly, reference standards tTMB-p1, tTMB-p2, tTMP-p3, tTMB-p4 and tTMB-p5 of Nanjac Biao Biotechnology Co., ltd were used and serial dilutions were made at dilution ratios of 100%, 30%, 25%, 20% and 10%.
Based on the known mutation site and the unknown mutation site mentioned in the present application, standard data are analyzed, as shown in fig. 6 (the abscissa is the VAF theoretical value, the ordinate is the VAF actual value), the ctDNA VAF value of absolute site quantification has high consistency with the theoretical reference value (r=0.94 p-value <2.2 e-16) and it can be observed that mutation information of lower frequency can be obtained by adopting a tracking mode (triangle point in the figure).
1.2Minerva-Delta accuracy verification
Calculation using samples of Kebaitm b standard and different dilution gradientsMinerva-DeltaComparing the value with the actual ctDNA concentration variation, as shown in FIG. 7, the abscissa represents the actual ctDNA concentration variation and the ordinate represents the ordinateMinerva-DeltaThe values, as can be seen from the graph, dynamically vary in plasma tumor burdenMinerVa-DeltaIn the verification, the ctDNA content change calculated by the model is highly linearly related to the expected tumor purity change, and meanwhile, the consistency of the dynamic change quantification and the expected tumor purity ratio is observed to be high.
The result of ctDNA quantification in plasma after advanced tumor treatment obtained by the provided method is shown in fig. 8, which shows the result of survival analysis according to whether the result is greater than or equal to 30% in a molecular response group and a non-response group, wherein (a) in fig. 8 shows a Progression Free Survival (PFS) result diagram, a dotted line shows a time-dependent curve of the number of patients in the molecular response group, and a solid line shows a time-dependent curve of the number of patients in the molecular non-response group; fig. 8 (b) shows a graph of total survival (OS) results, the dashed line shows the time-dependent number of patients in the molecular response group, and the solid line shows the time-dependent number of patients in the molecular non-response group. As can be seen from the figure, both the Progression Free Survival (PFS) and the total survival (OS) of the molecular response group were longer than those of the non-response group, and a good prediction of the efficacy of immunotherapy was seen.
In addition, the results of survival analysis of patients with advanced cancer who have compared with the disputed imaging evaluation as SD are shown in fig. 9, where (a) in fig. 9 shows a progression-free survival (PFS) result chart, the dashed line shows the time-dependent number of patients in the molecular response group, and the solid line shows the time-dependent number of patients in the molecular non-response group; fig. 9 (b) shows a graph of total survival (OS) results, the dashed line shows the time-dependent number of patients in the molecular response group, and the solid line shows the time-dependent number of patients in the molecular non-response group. As can be seen from the figure, the survival time of the group of molecular responses is longer than that of the group of molecular non-responses, i.eMinerVa-DeltaFurther differentiation is also possible. As can be seen in this subgroup of the group,MinerVa-Deltathe method of the method can further refine and distinguish the progression risk of the SD patient, and can enable the treatment decision of the patient to be more accurately and individually supported.
Corresponding to the foregoing method, the present application also provides a device 100 for determining ctDNA dynamic change of advanced tumor, fig. 10, comprising: a data receiving module 110 for obtaining sequencing data of plasma of patients with advanced tumor after treatment; the mutation tracking module 120 is configured to track the sequencing data according to a mutation sequence obtained in advance to obtain a significant mutation sequence, where the mutation sequence obtained in advance is obtained by filtering sequencing data of plasma and paired blood cells of a patient with advanced tumor before treatment; a dynamic analysis module 130, configured to quantify ctDNA dynamic change models according to pre-created differential weights, and analyze dynamic changes of plasma ctDNA before and after treatment; and the judging module 140 is used for judging the molecular response state of the patients with advanced tumors after treatment according to the model analysis result. The operation of each module is the same as that of the previous method, and a detailed description is omitted here.
It will be apparent to those skilled in the art that the above-described program modules are merely illustrative of the division of each program module for convenience and brevity of description, and that in practical application, the above-described functional allocation may be performed by different program modules, i.e. the internal structure of the apparatus is divided into different program units or modules, to perform all or part of the above-described functions. The program modules in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one processing unit, where the integrated units may be implemented in a form of hardware or in a form of a software program unit. In addition, the specific names of the program modules are also only for distinguishing from each other, and are not used to limit the protection scope of the present application.
Fig. 11 is a schematic structural diagram of a terminal device according to an embodiment of the present application, and as shown in the drawing, the terminal device 210 includes: the memory 211, the processor 213, and the steps stored in the memory 211 and capable of implementing the steps in the embodiment of the method for determining the dynamic change of ctDNA based on advanced tumor described above when the processor 213 executes the computer program 212, or the processor 213 executes the computer program 212 to implement the functions of the modules in the embodiment of the apparatus for determining the dynamic change of ctDNA based on advanced tumor described above.
The terminal device 210 may be a notebook, tablet, mobile phone, etc. But are not limited to, processor 213, memory 211. It will be appreciated by those skilled in the art that fig. 11 is merely an example of a terminal device 210 and does not constitute a limitation of the terminal device 210 and may include more or fewer components than shown, or may combine certain components, or different components, such as: terminal device 210 can also include input and output devices, display devices, network access devices, buses, and the like.
The processor 213 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), etc. The general purpose processor 213 may be a microprocessor or the processor may be any conventional processor or the like.
The memory 211 may be an internal storage unit of the terminal device 210, for example: the hard disk or memory of the terminal device 210. The memory 211 may also be an external storage device of the terminal device 210, such as: a plug-in hard disk provided on the terminal device 210, a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 211 may also include both an internal storage unit and an external storage device of the terminal device 210. The memory 211 is used to store a computer program 212 and other programs and data required by the terminal device 210. The memory 211 may also be used to temporarily store data that has been output or is to be output.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and the parts of a certain embodiment that are not described or depicted in detail may be referred to in the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above-described embodiments, or may be implemented by sending instructions to related hardware by the computer program 212, where the computer program 212 may be stored in a computer readable storage medium, and where the computer program 212, when executed by the processor 213, may implement the steps of the method embodiments described above. Wherein the computer program 212 comprises: computer program code, which may be in the form of source code, executable files, or in some intermediate form, etc. The computer readable storage medium may include: any entity or device capable of carrying the computer program 212 code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
It should be noted that the above embodiments can be freely combined as needed. The foregoing is merely a preferred embodiment of the application, and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application, which are intended to be comprehended within the scope of the application.
Claims (13)
1. A method for determining the dynamic change of ctDNA of a late tumor, comprising:
obtaining sequencing data of plasma of patients with advanced tumor after treatment;
tracking the sequencing data according to a pre-obtained mutation sequence to obtain a significant mutation sequence, wherein the pre-obtained mutation sequence is obtained by filtering sequencing data of plasma and paired blood cells of a patient with advanced tumor before treatment;
analyzing the dynamic change of the plasma ctDNA before and after treatment according to a pre-created differential weight quantitative ctDNA dynamic change model;
judging the molecular response state of the patients with advanced tumors after treatment according to the model analysis result;
quantifying the dynamic change of ctDNA in the plasma before and after treatment according to a pre-created differential weight dynamic change model, and obtaining a single sampleMinerVa-DeltaThe values are:
，
wherein,nrepresents the number of mutation points in the mutation sequence obtained in advance,represent the firstiThe site weights of the individual mutation points,represent the firstiRatio of VAF decrease after treatment of individual mutation points,/->Represent the firstiVAF value of plasma gene after treatment with individual mutation points,/->Represent the firstiVAF values of plasma genes before point-of-mutation treatment.
2. The method for determining the dynamic change of ctDNA of an advanced tumor according to claim 1, wherein the filtering rule comprises:
filtering germ line mutation points in the paired samples;
filtering mutation points with non-platinum list allele frequency VAF more than or equal to 1%, wherein the platinum list is a 1A-2C type evidence grade change point list related to solid tumor treatment in oncogene mutation;
filtering mutation points of the mutation base Support reads > 5;
mutation points with the depth of the filtering site being more than or equal to 500X;
filtering the mutation with strand preference;
filtering mutation points with InDel gene type;
and filtering and annotating the mutation points to a blacklist, wherein the blacklist is a mutation point list with the occurrence frequency higher than 10% in the crowd obtained through statistics.
3. The method for determining the dynamic change of ctDNA of late stage tumor according to claim 1, wherein the step of calculating the th calculation in the analysis of the dynamic change of ctDNA of plasma before and after the treatment according to the pre-created differential weight quantitative ctDNA dynamic change modeliThe step of weighting the mutation points comprises the following steps:
fitting the association relationship between the standard deviation and the VAF by using the loess regression based on the standard sample to obtainNormal distribution density function and meterCalculate->Probability of falling on both ends of normal distribution +.>Obtaining the weight of the site according to a weight formula>：
，
Wherein,represent the firstkThe difference between the individual mutation points is significant p-value.
4. The method for determining the dynamic change of ctDNA of late stage tumor according to claim 1, wherein the step of calculating the th calculation in the analysis of the dynamic change of ctDNA of plasma before and after the treatment according to the pre-created differential weight quantitative ctDNA dynamic change modeliThe step of weighting the mutation points comprises the following steps:
calculating according to standard deviation formula to obtain single mutation pointStandard deviation of>Normal distribution density function, and further calculate +.>Probability of falling on both ends of normal distribution +.>Obtaining the weight of the site according to a weight formula：
，
Wherein,representation->Standard deviation of>Represents the depth of sequencing after duplication removal of the mutation site in pre-treatment blood,/->Represent the firstkThe difference between the individual mutation points is significant p-value.
5. The method for determining the dynamic change of ctDNA of late stage tumor according to claim 1, wherein the step of calculating the th calculation in the analysis of the dynamic change of ctDNA of plasma before and after the treatment according to the pre-created differential weight quantitative ctDNA dynamic change modeliThe step of weighting the mutation points comprises the following steps:
calculating according to standard deviation formula to obtain single mutation pointAnd->Standard deviation of (2) to obtainAnd->Normal distribution density function, in turn using Kolmogorov-Smirnov test calculates the difference significance of two normal distributions +.>Calculate->Probability of falling on both ends of normal distribution +.>Obtaining the weight of the site according to a weight formula>：
，
Wherein,representation->Standard deviation of>Represents the depth of sequencing after duplication removal of plasma mutation sites before treatment,/->Representation->Standard deviation of>Represents the sequencing depth after duplication removal of plasma gene mutation site after treatment,/->Represent the firstkAt a mutation pointDifference significance p-value;represent the firstkThe difference between the individual mutation points is significant p-value.
6. The method for determining the dynamic change of ctDNA of late stage tumor according to claim 1, wherein the step of calculating the th calculation in the analysis of the dynamic change of ctDNA of plasma before and after the treatment according to the pre-created differential weight quantitative ctDNA dynamic change modeliThe step of weighting the mutation points comprises the following steps:
calculating according to standard deviation formula to obtain single mutation pointAnd->Standard deviation of (2) to obtainAnd->The normal distribution density function, and further calculate the ratio of the intersection of two normal distributions +.>Calculate->Probability of falling on both ends of normal distribution +.>Obtaining the weight of the site according to a weight formula>：
，
Wherein,representation->Standard deviation of>Represents the depth of sequencing after duplication removal of the mutation site of the plasma gene before treatment,/->Representation->Standard deviation of>Represents the sequencing depth after duplication removal of plasma gene mutation site after treatment,/->Represent the firstkThe difference between the individual mutation points is significant p-value.
7. The method for determining the dynamic change of ctDNA of late stage tumor according to claim 1, wherein the step of calculating the th calculation in the analysis of the dynamic change of ctDNA of plasma before and after the treatment according to the pre-created differential weight quantitative ctDNA dynamic change modeliThe step of weighting the mutation points comprises the following steps:
according to the number of supported reads and depth of mutation sites before and after treatment, performing Fisher test on the significanceObtaining the weight of the site according to a weight formula>：
，
Wherein,represent the firstkThe difference significance p-value, depth of each mutation point vaf represents the sequencing depth.
8. The method for determining the dynamic change of ctDNA of late stage tumor according to claim 1, wherein the step of calculating the th calculation in the analysis of the dynamic change of ctDNA of plasma before and after the treatment according to the pre-created differential weight quantitative ctDNA dynamic change modeliThe step of weighting the mutation points comprises the following steps:
calculation of mutation sites before and after treatmentIt is assumed that the number of supported reads vsm for the mutation site before and after treatment is obeyed with respect to +.>Is calculated according to the maximum likelihood estimation Method (MLE)>Obtaining the weight of the site according to a weight formula>：
，
If it isThen: />Is less than +.>Is a function of the probability of (1),is greater than->Probability of (2);
if it isThen: />Is less than +.>Is a function of the probability of (1),is greater than->Probability of (2);
，
wherein,p-value representing the left side of poisson distribution,/->P-value representing the right side of the poisson distribution; />Representing a pre-treatment mutation siteDot support reads number vsm, < >>Support reads number vsm, representing the mutation site after treatment,>represents the sequencing depth after duplication removal of plasma mutation sites before treatment,representing the sequencing depth after the duplication removal of the plasma mutation site after treatment; />Represent the firstkThe difference between the individual mutation points is significant p-value.
9. The method for determining the dynamic change of ctDNA of late stage tumor according to claim 1, wherein the step of calculating the th calculation in the analysis of the dynamic change of ctDNA of plasma before and after the treatment according to the pre-created differential weight quantitative ctDNA dynamic change modeliThe step of weighting the mutation points comprises the following steps:
calculating according to standard deviation formula to obtain single mutation pointAnd->Calculating weights by standard deviation of (2)And carrying out weight standardization so that the sum of the weights of all sites in the sample is 1 to obtain the weight of the site +.>：
，
Wherein,indicating the amount of change of VAF at mutation site before and after treatment,/->Representation ofStandard deviation of>Represents the sequencing depth after the duplication removal of the plasma gene mutation site before treatment,representation->Standard deviation of>Represents the sequencing depth of the plasma gene before treatment,nthe number of mutation points in the mutation sequence obtained in advance is shown.
10. The method for determining the dynamic change of ctDNA of an advanced tumor according to claim 1, wherein the determining the molecular response state of the patient after the treatment of the advanced tumor according to the model analysis result comprises:
when a single sampleMinerVa-DeltaThe value is smaller than or equal to a preset threshold value, and the molecular response group is judged;
when a single sampleMinerVa-DeltaAnd the value is larger than a preset threshold value, and the molecular non-response group is judged.
11. An advanced-tumor ctDNA dynamic change determination device, which is applied to the advanced-tumor ctDNA dynamic change determination method according to any one of claims 1 to 10, comprising:
the data receiving module is used for acquiring sequencing data of the plasma of the patients with advanced tumors after treatment;
the mutation tracking module is used for tracking the sequencing data according to a pre-obtained mutation sequence to obtain a significant mutation sequence, wherein the pre-obtained mutation sequence is obtained by filtering sequencing data of plasma and paired blood cells before treatment of a late tumor patient;
the dynamic analysis module is used for analyzing the dynamic change of the plasma ctDNA before and after treatment according to a pre-created differential weight quantitative ctDNA dynamic change model to obtain a single sampleMinerVa-DeltaThe values are:
，
wherein,nrepresents the number of mutation points in the mutation sequence obtained in advance,represent the firstiThe site weights of the individual mutation points,represent the firstiRatio of VAF decrease after treatment of individual mutation points,/->Represent the firstiVAF value of plasma gene after treatment with individual mutation points,/->Represent the firstiVAF values of plasma genes before point-of-mutation treatment;
and the judging module is used for judging the molecular response state of the late tumor patient after treatment according to the model analysis result.
12. Terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor, when running the computer program, implements the steps of the advanced tumour ctDNA dynamic change determination method according to any of claims 1-10.
13. A computer-readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the advanced tumor ctDNA dynamic change determination method according to any one of claims 1-10.
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