Gallbladder cancer prediction method and device based on plasma free DNA mutation detection

Abstract

The invention provides a gallbladder cancer prediction method and device based on plasma free DNA mutation detection. The gallbladder cancer prediction method comprises the steps that a gene mutation feature extraction module extracts sample gene mutation features from sample gene sequencing data through a gene mutation analysis algorithm; the gall bladder occupying benign and malignant identification model building module is used for screening key genes of gallbladder cancer from the target genes based on gene mutation characteristics so as to obtain a trained gall bladder occupying benign and malignant identification model; the detection module obtains gene mutation characteristics of key genes of a to-be-detected person from plasma free DNA of the to-be-detected person, and inputs the gene mutation characteristics of the key genes of the to-be-detected person into the gallbladder occupation benign and malignant identification model to obtain the malignant probability of gallbladder occupation lesion. According to the method, the peripheral blood of a patient can be collected in a non-invasive mode, free DNA is extracted, harm and pain to the patient are reduced, and a judgment result containing the prediction probability of the gallbladder cancer can be accurately and efficiently obtained through the gallbladder occupation benign and malignant identification model.

Description

Technical Field

[0001] The present invention relates to the field of gene detection, and in particular to a method and device for predicting gallbladder cancer based on plasma free DNA mutation detection. Background Art

[0002] Currently, non-invasive genetic testing has not been widely carried out in gallbladder cancer, and there are no suitable and recognized candidate genes and testing methods. Therefore, the positive rate of gene mutation detection is unstable; most gallbladder cancer patients still need to use tissue samples obtained after puncture or surgery for genetic testing. Summary of the invention

[0003] The purpose of the present invention is to provide a method and device for predicting gallbladder cancer based on plasma free DNA mutation detection.

[0004] To solve the above problems, the present invention provides a method for predicting gallbladder cancer based on plasma free DNA mutation detection, comprising:

[0005] The gene testing sample collection module obtains gene sequencing data of multiple preset targeted genes from the plasma free DNA of the sample patient;

[0006] The gene mutation feature extraction module uses the gene mutation analysis algorithm to extract the sample gene mutation features from the sample gene sequencing data;

[0007] The module for establishing a model for distinguishing benign and malignant gallbladder lesions is used to screen key genes for gallbladder cancer from targeted genes based on gene mutation characteristics to obtain a trained model for distinguishing benign and malignant gallbladder lesions.

[0008] The detection module obtains the gene mutation characteristics of the key genes of the subject to be detected from the plasma free DNA of the subject to be detected, and inputs the gene mutation characteristics of the key genes of the subject to be detected into the gallbladder space-occupying benign and malignant differentiation model to obtain the malignancy probability of the gallbladder space-occupying lesion.

[0009] Furthermore, in the above method, the preset multiple targeted genes include the following 111 targeted genes:

[0010] ABL1, BARD1 CDKN2A, ERBB3, IDH1, MEN1, NFE2L2, PRDM1, SMARCA4, VHL, ALK, BCL2, CEBPA, ERBB4, IDH2, MET, NOTCH1, PTCH1, SMARCB1, AMER1, BCOR, CHEK1, EZH2, JAK1, MLH1, NOTCH2, PTPN11, SMO,

[0011] APC, BLM, CHEK2, FANCE, JAK2, MPL, NPM1, RAD50, SOCS1,

[0012] AR, BRAF, CIC, FBXW7, JAK3, MRE11A, PALB2, RAD51C, SPOP,

[0013] ARID1A, BRCA1, CREBBP, FGFR2, KDM5C, MSH2, PAX5, RAD51D, STAG2,

[0014] ASXL1, BRCA2, CTNNB1, FGFR3, KDM6A, MSH6, PBRM1, RB1, STK11,

[0015] ATM, BRIP1, DAXX, FLT3, KIT, MUTYH, PDGFRA, RET, TET2

[0016] ATR, CARD11, DNMT3A, FOXL2, KMT2D, MYD88, PIK3CA, RNF43, TNFAIP3, ATRX, CBL, EGFR, GATA3, KRAS, NBN, PIK3R1, SETD2, TP53,

[0017] AXIN1, CDC73, EP300, GNAS, MAP3K1, NF1, POLG, SF3B1, TSC1,

[0018] BAP1, CDH1, ERBB2, HNF1A, MED12, NF2, PPP2R1A, SMAD2, U2AF1.

[0019] Furthermore, in the above method, the gene mutation analysis algorithm includes: gene sequence alignment, deduplication, mutation site detection and mutation annotation.

[0020] Furthermore, in the above method, a module for establishing a model for distinguishing benign and malignant gallbladder lesions is used to screen key genes for gallbladder cancer from targeted genes based on gene mutation characteristics to obtain a trained model for distinguishing benign and malignant gallbladder lesions, including:

[0021] The module for establishing a model for distinguishing benign and malignant gallbladder lesions screens key genes for gallbladder cancer from targeted genes based on the sample gene mutation characteristics, and uses the mutation abundance in key genes and their mutation characteristics, tumor markers of sample patients, and DNA concentration in plasma free DNA as detection indicators to obtain a trained model for distinguishing benign and malignant gallbladder lesions. The model for distinguishing benign and malignant gallbladder lesions can combine the patient's non-invasive genetic test results and medical record information to predict the malignancy probability of the gallbladder lesion.

[0022] Furthermore, in the above method, the key genes include: ERBB3, MEN1, PTCH1, NOTCH2, SMO, ARID1A, CTNNB1, PBRM1, DNMT3A and TP53.

[0023] Furthermore, in the above method, the tumor marker is CA19-9.

[0024] Furthermore, in the above method, the detection module obtains the gene mutation characteristics of the key genes of the subject to be detected from the plasma free DNA of the subject to be detected, and inputs the gene mutation characteristics of the key genes of the subject to be detected into the gallbladder space-occupying benign and malignant differentiation model to obtain the malignancy probability of the gallbladder space-occupying lesion, including:

[0025] The detection module obtains the mutation abundance in the gene mutation characteristics of the key genes of the person to be detected from the plasma free DNA of the person to be detected, and inputs the tumor markers of the person to be detected, the DNA concentration in the plasma free DNA and the mutation abundance in the gene mutation characteristics of the key genes into the gallbladder space-occupying lesion differentiation model to obtain the malignancy probability of the gallbladder space-occupying lesion.

[0026] Furthermore, in the above method, the gallbladder lesion-occupying lesion differentiation model is a linear fitting model of four algorithms: random forest, logistic regression, support vector machine and XgBoost.

[0027] According to another aspect of the present invention, there is further provided a computer-readable storage medium having computer-executable instructions stored thereon, wherein when the computer-executable instructions are executed by a processor, the processor is caused to: execute any of the methods described above.

[0028] According to another aspect of the present invention, there is also provided a computer device, comprising:

[0029] Processor; and

[0030] A memory arranged to store computer executable instructions, which when executed cause the processor to: perform any of the methods described above.

[0031] Compared with the prior art, the present invention obtains gene sequencing data of preset multiple targeted genes from plasma free DNA of sample patients through a gene detection sample collection module; a gene mutation feature extraction module extracts sample gene mutation features from sample gene sequencing data using a gene mutation analysis algorithm; a gallbladder mass lesion benign and malignant identification model establishment module screens key genes of gallbladder cancer from targeted genes based on gene mutation features to obtain a trained gallbladder mass lesion benign and malignant identification model; a detection module obtains gene mutation features of key genes of the subject to be detected from plasma free DNA of the subject to be detected, and inputs the gene mutation features of key genes of the subject to be detected into the gallbladder mass lesion benign and malignant identification model to obtain the malignant probability of gallbladder mass lesions. The patient's peripheral blood can be collected in a non-invasive manner to extract free DNA, reducing harm and pain to the patient. For example, peripheral blood of patients with pulmonary nodules can be extracted for targeted gene detection, and the obtained genetic detection results, namely the gene sequencing data of the targeted genes, can be accurately and efficiently obtained through the gallbladder mass lesion benign and malignant identification model. The present invention can make use of deep learning and machine learning algorithms. The system can discover implicit rules and patterns from a large amount of clinical information and sequencing data, and then provide accurate and comprehensive prediction results for gallbladder cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a flow chart of a gallbladder cancer prediction method based on plasma free DNA mutation detection according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0034] In a typical configuration of the present application, the terminal, the device of the service network and the trusted party all include one or more processors (CPU), input / output interfaces, network interfaces and memories.

[0035] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0036] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory media such as modulated data signals and carrier waves.

[0037] The gallbladder cancer prediction method based on plasma free DNA mutation detection includes: steps S1 to S4. In a specific embodiment, step S1 includes a gene detection sample collection module, which obtains gene sequencing data of a preset plurality of target genes from the plasma free DNA of the sample patient;

[0038] Here, targeted testing means setting up probes that only contain, for example, 111 specific genes, so sequencing will only detect the genes in our list and not other genes. Because the test defines the scope, it will be more accurate than whole genome sequencing and reduce costs;

[0039] Obtaining gene sequencing data of multiple preset targeted genes means reading the exon region of each gene with a sequencer. What is read is the specific base sequence, which is the raw data "off the machine", i.e. gene sequencing data;

[0040] Preferably, the preset multiple targeted genes of the present application are 111 targeted genes as shown in the following table:

[0041]

[0042] Here, 111 genes with high mutation frequencies in gallbladder cancer tissues were screened as a list of exclusive gene tests for gallbladder cancer. On the basis of targeted exon sequencing, ultra-deep (30,000x) sequencing can be used to improve the sensitivity of mutation detection.

[0043] The targeted detection of the present application selects only 111 genes from all human genes for detection, instead of testing all of them. This can reduce costs on the one hand, because the 111 genes found are the genes that we have found to be easily mutated in gallbladder cancer; on the other hand, using probes to capture specific genes can improve the accuracy of sequencer readings, because it is equivalent to defining a clear range for it, which is less prone to errors.

[0044] Step S2, a gene mutation feature extraction module, using a gene mutation analysis algorithm to extract sample gene mutation features from sample gene sequencing data;

[0045] Preferably, the gene mutation analysis algorithm includes: gene sequence alignment, deduplication, mutation site detection, mutation annotation, etc.

[0046] Mutation analysis algorithm process: First, compare it with the normal human genome used worldwide, then find the different positions and types of bases (including deletions, substitutions, etc.), and finally annotate to obtain the mutation site, type and abundance.

[0047] Preferably, the gene mutation analysis algorithm of the present invention can efficiently process a large amount of gene sequencing data of targeted genes with the support of high computing power servers, obtain the gene mutation situation of the patient, and finally annotate to obtain the gene mutation characteristics, including: mutation abundance, mutation site, mutation type and total number of mutations of the gene. Here, abundance refers to how much of this gene region in the patient's blood is different from normal.

[0048] Step S3, a module for establishing a model for distinguishing benign and malignant gallbladder lesions, screening key genes for gallbladder cancer from targeted genes based on gene mutation characteristics, so as to obtain a trained model for distinguishing benign and malignant gallbladder lesions;

[0049] More preferably, the key genes include: ERBB3, MEN1, PTCH1, NOTCH2, SMO, ARID1A, CTNNB1, PBRM1, DNMT3A and TP53.

[0050] Preferably, the tumor marker is CA19-9.

[0051] Tumor markers of sample patients can be obtained from their medical records.

[0052] Here, the gallbladder mass identification model can be combined with a variety of machine learning algorithms, such as convolutional neural network (CNN), random forest, logistic regression, support vector machine (SVM) and XgBoost, etc. Through continuous iterative training and optimization, the diagnostic accuracy and generalization ability of the model can be improved.

[0053] The optimal model for distinguishing benign and malignant gallbladder space-occupying lesions can utilize a variety of machine learning algorithms to improve the diagnostic accuracy and generalization ability of the model through continuous iterative training and optimization. Finally, four algorithms including random forest, logistic regression, support vector machine and XgBoost can be selected for linear fitting, and finally a composite diagnostic algorithm can be constructed to predict the malignancy probability of gallbladder space-occupying lesions.

[0054] More preferably, step S3 comprises:

[0055] The module for establishing a model for distinguishing benign and malignant gallbladder lesions screens key genes for gallbladder cancer from targeted genes based on the sample gene mutation characteristics, and uses the mutation abundance in key genes and their mutation characteristics, tumor markers of sample patients, and DNA concentration in plasma free DNA as detection indicators to obtain a trained model for distinguishing benign and malignant gallbladder lesions. The model for distinguishing benign and malignant gallbladder lesions can combine the patient's non-invasive genetic test results and medical record information to predict the malignancy probability of the gallbladder lesion.

[0056] The gallbladder mass identification model of the present invention can also have the ability of self-learning and updating, and can adapt to the increasing detection indicators, such as imaging data, tumor marker results, etc.

[0057] Step S4, the detection module obtains the gene mutation characteristics of the key genes of the subject to be detected from the plasma free DNA of the subject to be detected, and inputs the gene mutation characteristics of the key genes of the subject to be detected into the gallbladder space-occupying lesion benign and malignant identification model to obtain the malignancy probability of the gallbladder space-occupying lesion.

[0058] Preferably, in step S4, the detection module obtains the mutation abundance in the gene mutation characteristics of the key genes of the person to be tested from the plasma free DNA of the person to be tested, and inputs the tumor markers of the person to be tested, the DNA concentration in the plasma free DNA and the mutation abundance in the gene mutation characteristics of the key genes into the gallbladder space-occupying lesion differentiation model to obtain the malignancy probability of the gallbladder space-occupying lesion.

[0059] Here, the gallbladder mass lesion differentiation model of the present invention is to screen the genes that are easy to mutate in gallbladder cancer again from the above 111 genes, and narrow the gene range from 111 to 10. Because gene mutation is a common biological behavior of cancer, and benign tumors are less likely to mutate, it can play a role in differentiation. At the same time, tumor marker CA19-9 and extracted DNA concentration (DNA content in 1ml of blood), two indicators that differ greatly in benign and malignant tumors, are added to construct the final differentiation model.

[0060] Optionally, the detection module can provide an intuitive and easy-to-use user interface in the form of a WeChat applet or an online webpage, so that users can quickly view the patient's prediction results and related data through simple operations. The interface also supports multiple forms of data display, such as charts, reports, etc., to facilitate users to conduct comprehensive analysis.

[0061] In addition, the present invention can also provide data anonymization and desensitization functions to protect patient privacy. The present invention can also adopt multiple encryption and security measures to ensure the storage and transmission security of data. At the same time, the present invention can also implement a strict access control policy, and only authorized users can access relevant data. In addition, the present invention can also provide data backup and recovery functions to ensure the reliability and stability of data.

[0062] Here, the present invention can collect peripheral blood of patients in a non-invasive manner to extract free DNA, reducing harm and pain to patients. For example, peripheral blood of patients with pulmonary nodules can be extracted for targeted gene testing. The obtained genetic test results, namely the gene sequencing data of the targeted gene, can be used to accurately and efficiently obtain a judgment result including the predicted probability of gallbladder cancer through the gallbladder mass lesion differentiation model. The present invention can use deep learning and machine learning algorithms. The system can discover implicit rules and patterns from a large amount of clinical information and sequencing data, and then provide accurate and comprehensive prediction results of gallbladder cancer.

[0063] Specifically, the present invention can adopt a modular design, including two parts: hardware and software.

[0064] The hardware part may include sample processing equipment, gene detection equipment, and data storage equipment;

[0065] The software part adopts a layered architecture design mode, including: data layer, processing layer, algorithm layer and application layer. In a specific embodiment, the data layer, such as the gene detection sample collection module, is responsible for the storage and access of the original targeted gene sequencing data; the processing layer, such as the gene mutation feature extraction module, is responsible for the cleaning, annotation and enhancement of the gene sequencing data of the targeted gene; the algorithm layer, such as the gallbladder mass benign and malignant differentiation model establishment module, is responsible for training and optimizing the gallbladder mass benign and malignant differentiation model; the application layer, such as the detection module, provides users with an intuitive interactive interface.

[0066] According to another aspect of the present invention, a computer-readable storage medium is provided, on which computer-executable instructions are stored, wherein when the computer-executable instructions are executed by a processor, the processor is caused to execute the method as described in any of the above embodiments.

[0067] According to another aspect of the present invention, there is also provided a computer device, comprising:

[0068] Processor; and

[0069] A memory arranged to store computer executable instructions, which, when executed, cause the processor to: execute the method described in any one of the above embodiments.

[0070] The detailed contents of the device embodiments of the present invention can be found in the corresponding parts of the method embodiments, which will not be described in detail here.

[0071] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

[0072] It should be noted that the present invention can be implemented in software and / or a combination of software and hardware, for example, can be implemented using an application specific integrated circuit (ASIC), a general purpose computer or any other similar hardware device. In one embodiment, the software program of the present invention can be executed by a processor to implement the steps or functions described above. Similarly, the software program of the present invention (including related data structures) can be stored in a computer readable recording medium, for example, a RAM memory, a magnetic or optical drive or a floppy disk and the like. In addition, some steps or functions of the present invention can be implemented using hardware, for example, as a circuit that cooperates with a processor to perform each step or function.

[0073] In addition, a part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. The program instruction for calling the method of the present invention may be stored in a fixed or removable recording medium, and / or transmitted through a data stream in a broadcast or other signal-bearing medium, and / or stored in a working memory of a computer device that runs according to the program instruction. Here, according to an embodiment of the present invention, a device is included, the device including a memory for storing computer program instructions and a processor for executing program instructions, wherein, when the computer program instruction is executed by the processor, the device is triggered to run the method and / or technical solution based on the aforementioned multiple embodiments of the present invention.

[0074] It is obvious to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

Claims

1. A method for predicting gallbladder cancer based on plasma free DNA mutation detection, characterized in that: include: The gene testing sample collection module obtains gene sequencing data of multiple preset targeted genes from the plasma free DNA of the sample patient; The gene mutation feature extraction module uses the gene mutation analysis algorithm to extract the sample gene mutation features from the sample gene sequencing data; The module for establishing a model for distinguishing benign and malignant gallbladder lesions is used to screen key genes for gallbladder cancer from targeted genes based on gene mutation characteristics to obtain a trained model for distinguishing benign and malignant gallbladder lesions. The detection module obtains the gene mutation characteristics of the key genes of the subject to be detected from the plasma free DNA of the subject to be detected, and inputs the gene mutation characteristics of the key genes of the subject to be detected into the gallbladder space-occupying benign and malignant differentiation model to obtain the malignancy probability of the gallbladder space-occupying lesion.

2. The method for predicting gallbladder cancer based on plasma free DNA mutation detection according to claim 1, characterized in that: The preset multiple targeted genes include the following 111 targeted genes: ABL1, BARD1 CDKN2A, ERBB3, IDH1, MEN1, NFE2L2, PRDM1, SMARCA4, VHL, ALK, BCL2, CEBPA, ERBB4, IDH2, MET, NOTCH1, PTCH1, SMARCB1, AMER1, BCOR, CHEK1, EZH2, JAK1, MLH1, NOTCH2, PTPN11, SMO, APC, BLM, CHEK2, FANCE, JAK2, MPL, NPM1, RAD50, SOCS1, AR, BRAF, CIC, FBXW7, JAK3, MRE11A, PALB2, RAD51C, SPOP, ARID1A, BRCA1, CREBBP, FGFR2, KDM5C, MSH2, PAX5, RAD51D, STAG2, ASXL1, BRCA2, CTNNB1, FGFR3, KDM6A, MSH6, PBRM1, RB1, STK11, ATM, BRIP1, DAXX, FLT3, KIT, MUTYH, PDGFRA, RET, TET2 ATR, CARD11, DNMT3A, FOXL2, KMT2D, MYD88, PIK3CA, RNF43, TNFAIP3, ATRX, CBL, EGFR, GATA3, KRAS, NBN, PIK3R1, SETD2, TP53, AXIN1, CDC73, EP300, GNAS, MAP3K1, NF1, POLG, SF3B1, TSC1, BAP1, CDH1, ERBB2, HNF1A, MED12, NF2, PPP2R1A, SMAD2, U2AF1.

3. The method for predicting gallbladder cancer based on plasma free DNA mutation detection according to claim 1, characterized in that: The gene mutation analysis algorithm includes: gene sequence alignment, deduplication, mutation site detection and mutation annotation.

4. The method for predicting gallbladder cancer based on plasma free DNA mutation detection according to claim 1, characterized in that: The module for establishing a model for distinguishing benign and malignant gallbladder lesions is used to screen key genes for gallbladder cancer from targeted genes based on gene mutation characteristics to obtain a trained model for distinguishing benign and malignant gallbladder lesions, including: The module for establishing a model for distinguishing benign and malignant gallbladder lesions screens key genes for gallbladder cancer from targeted genes based on the sample gene mutation characteristics, and uses the mutation abundance in key genes and their mutation characteristics, tumor markers of sample patients, and DNA concentration in plasma free DNA as detection indicators to obtain a trained model for distinguishing benign and malignant gallbladder lesions. The model for distinguishing benign and malignant gallbladder lesions can combine the patient's non-invasive genetic test results and medical record information to predict the malignancy probability of the gallbladder lesion.

5. The method for predicting gallbladder cancer based on plasma free DNA mutation detection according to claim 1 or 4, characterized in that: The key genes include: ERBB3, MEN1, PTCH1, NOTCH2, SMO, ARID1A, CTNNB1, PBRM1, DNMT3A and TP53.

6. The method for predicting gallbladder cancer based on plasma free DNA mutation detection according to claim 4, characterized in that: The tumor marker is CA19-9.

7. The method for predicting gallbladder cancer based on plasma free DNA mutation detection according to claim 4, characterized in that: The detection module obtains the gene mutation characteristics of the key genes of the subject to be detected from the plasma free DNA of the subject to be detected, and inputs the gene mutation characteristics of the key genes of the subject to be detected into the gallbladder space-occupying benign and malignant differentiation model to obtain the malignancy probability of the gallbladder space-occupying lesion, including: The detection module obtains the mutation abundance in the gene mutation characteristics of the key genes of the person to be detected from the plasma free DNA of the person to be detected, and inputs the tumor markers of the person to be detected, the DNA concentration in the plasma free DNA and the mutation abundance in the gene mutation characteristics of the key genes into the gallbladder space-occupying lesion differentiation model to obtain the malignancy probability of the gallbladder space-occupying lesion.

8. The method for predicting gallbladder cancer based on plasma free DNA mutation detection according to claim 1, characterized in that: The model for distinguishing benign and malignant gallbladder lesions is a linear fitting model of four algorithms: random forest, logistic regression, support vector machine and XgBoost.

9. A computer-readable storage medium having computer-executable instructions stored thereon, wherein: When the computer executable instructions are executed by a processor, the processor is caused to: perform the method according to any one of claims 1 to 8.

10. A computer device, wherein: include: processor; as well as A memory arranged to store computer executable instructions which, when executed, cause the processor to: perform a method as claimed in any one of claims 1 to 8.

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