Method for determining methylation level of target cpg site of tumor tissue

Abstract

Provided are a method and system for determining a methylation level of a target CpG site of a tumor tissue, a computer device, and a medium. The method comprises: acquiring a first methylation measurement result of a mixed tumor tissue sample and a second methylation measurement result of a paired paracancerous tissue sample, wherein the mixed tumor tissue sample comprises a tumor tissue and a paracancerous tissue; on the basis of the first methylation measurement result and a third methylation measurement result of a reference paracancerous tissue sample, determining a distribution of a read, in the mixed tumor tissue sample, that comprises a target CpG site and derives from the tumor tissue or the paracancerous tissue; and on the basis of the determined distribution, the first methylation measurement result, and the second methylation measurement result, determining a methylation level of the target CpG site of the tumor tissue in the mixed tumor tissue sample.

Description

Method for determining the methylation level of target CpG sites in tumor tissue

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Chinese patent application No. 202410205480.9, filed on February 23, 2024, entitled “Method for determining the methylation level of a target CpG site in tumor tissue”, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present disclosure relates to the field of biological detection technology, and more particularly, to a method, system, computing device, computer-readable storage medium, and computer program product for determining the methylation level of a target CpG site of a tumor tissue. Background Art

[0004] Methylation is a chemical process in which methyl groups are added to certain molecules, such as CpG sites in DNA (the region between C and G genes in the genome). DNA methylation can affect how genes are expressed, altering gene expression. Therefore, DNA methylation is an important mechanism that regulates many biological processes, including development, cell proliferation, and tumorigenesis. Summary of the Invention

[0005] In view of this, the present disclosure provides a method, system, computing device, computer-readable storage medium, and computer program product for determining the methylation level of a target CpG site of a tumor tissue.

[0006] According to a first aspect of the present disclosure, a method for determining the methylation level of a target CpG site of a tumor tissue is provided, comprising: obtaining a first methylation measurement result of a mixed tumor tissue sample and a second methylation measurement result of a paired adjacent cancer tissue sample, the mixed tumor tissue sample comprising tumor tissue and adjacent cancer tissue; determining, based on the first methylation measurement result and a third methylation measurement result of a reference adjacent cancer tissue sample, a distribution of reads comprising the target CpG site in the mixed tumor tissue sample originating from the tumor tissue or adjacent cancer tissue; and determining, based on the determined distribution, the first methylation measurement result, and the second methylation measurement result, the methylation level of the target CpG site of the tumor tissue in the mixed tumor tissue sample.

[0007] According to a second aspect of the present disclosure, a system for determining the methylation level of a target CpG site of a tumor tissue is provided, comprising: a methylation measurement unit configured to obtain a first methylation measurement result of a mixed tumor tissue sample and a second methylation measurement result of a paired adjacent cancer tissue sample, wherein the mixed tumor tissue sample comprises tumor tissue and adjacent cancer tissue; a read distribution determination unit configured to determine, based on the first methylation measurement result and a third methylation measurement result of a reference adjacent cancer tissue sample, the distribution of reads comprising the target CpG site in the mixed tumor tissue sample originating from the tumor tissue or adjacent cancer tissue; and a methylation level determination unit configured to determine, based on the determined distribution, the first methylation measurement result, and the second methylation measurement result, the methylation level of the target CpG site of the tumor tissue in the mixed tumor tissue sample.

[0008] According to a third aspect of the present disclosure, a computing device is provided, comprising: at least one processing unit; and at least one memory, the at least one memory being coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions, when executed by the at least one processing unit, causing the computing device to execute the method as described in the first aspect of the present disclosure.

[0009] According to a fourth aspect of the present disclosure, a non-transitory computer storage medium is provided, comprising machine-executable instructions, which, when executed by a device, cause the device to perform the method according to the first aspect of the present disclosure.

[0010] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising machine-executable instructions, which, when executed by a device, cause the device to perform the method according to the first aspect of the present disclosure.

[0011] It should be understood that the summary of the invention is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and other objects, features and advantages of the embodiments of the present disclosure will become more readily understood through the following detailed description with reference to the accompanying drawings, in which several embodiments of the present disclosure are illustrated by way of example and not limitation, in which:

[0013] FIG1 illustrates a block diagram of a computing device capable of implementing various embodiments of the present disclosure;

[0014] FIG2 shows a schematic block diagram of a framework of a methylation level corrector according to an embodiment of the present disclosure;

[0015] FIG3 shows a schematic flow chart of a method for determining the methylation level of a target CpG site in a tumor tissue according to an embodiment of the present disclosure;

[0016] FIG4 shows a schematic diagram of a process for determining the methylation level of pure tumor tissue according to an embodiment of the present disclosure;

[0017] 5A-5C show the test results of detecting early screening markers using corrected methylation levels according to an embodiment of the present disclosure;

[0018] 6A-6C show experimental results of detecting tissue-tracing markers using corrected methylation levels according to an embodiment of the present disclosure; and

[0019] FIG7 shows a schematic block diagram of an apparatus for determining the methylation level of a target CpG site in a tumor tissue according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0020] The concepts of the present disclosure will now be described with reference to the various exemplary embodiments shown in the accompanying drawings. It should be understood that the description of these embodiments is merely to enable those skilled in the art to better understand and further implement the present disclosure, and is not intended to limit the scope of the present disclosure in any way. It should be noted that similar or identical reference numerals may be used in the figures where possible, and similar or identical reference numerals may represent similar or identical elements. It will be understood by those skilled in the art from the description below that alternative embodiments of the structures and / or methods described herein may be adopted without departing from the principles and concepts of the present disclosure described.

[0021] In the context of this disclosure, the term "including" and its various variations can be understood as open-ended terms, meaning "including but not limited to," the term "based on" can be understood as "based, at least in part, on," the term "one embodiment" can be understood as "at least one embodiment," and the term "another embodiment" can be understood as "at least one other embodiment." Other terms that may appear but are not mentioned here should not be interpreted or limited in a manner that is inconsistent with the concepts underlying the embodiments of this disclosure, unless explicitly stated.

[0022] Methylation refers to a chemical process in which methyl groups are added to certain molecules, such as CpG sites in DNA (the region between C and G genes in the genome). DNA methylation can affect the way genes are expressed, changing how genes are expressed. In tumor tissue, the methylation pattern of CpG sites is often different compared to normal tissue. This difference can manifest as a change in overall methylation levels, with an increase or decrease in the degree of methylation of many genes. In some cases, specific genes may become methylated, rendering them unable to be expressed. This gene silencing can be a key factor in the development of tumors because it can affect the way cells grow and proliferate. Therefore, methylation in tumor tissue is an important biomarker that can provide information about the nature of the tumor and treatment options.

[0023] In tumor tissue, the methylation pattern of CpG sites is often abnormal and differs from that in normal tissue. This difference can manifest as changes in overall methylation levels, with increased or decreased methylation across many genes. In some cases, specific genes may become methylated, rendering them inoperable. This gene silencing may be a key factor in tumorigenesis because it can affect how cells grow and proliferate. Therefore, methylation in tumor tissue is an important biomarker that can provide information about the nature of the tumor and treatment options.

[0024] Among the tissue samples actually collected, tumor tissue samples are generally mixed tumor tissues composed of pure tumor tissue (also referred to as tumor tissue in this article) and adjacent tumor tissues. When the proportion of pure tumors in mixed tumor tissues is not high, the methylation level of the mixed tumor tissue is quite different from that of the pure tumor tissue, which has a great impact on downstream analysis, especially the screening of differential methylation sites. Existing schemes usually use linear models to inversely solve the single-point methylation level of pure tumors. Due to the errors in sequencing itself, the methylation level of mixed tumor tissues and the methylation level of adjacent tumors often deviate from the true methylation level. Using a linear relationship to solve will cause the methylation level of pure tumor tissue to exceed the predetermined value range, which is contrary to the actual situation.

[0025] In order to solve or alleviate the above-mentioned problems and / or other potential problems, an embodiment of the present disclosure proposes a method for correcting the methylation level of a target CpG site of a tumor tissue. The method obtains a mixed tumor tissue sample and a paired adjacent cancer tissue sample from the same source (patient), obtains a reference adjacent cancer tissue sample from multiple sources (patients), and combines the mixed tumor tissue sample and the reference adjacent cancer tissue sample to determine the distribution of reads including the target CpG site in the mixed tumor tissue sample from tumor tissue or adjacent cancer tissue, and finally determines the methylation level of the target CpG site of the tumor tissue in the mixed tumor tissue sample based on the distribution. In this way, the methylation level of the target CpG site of the tumor tissue can be detected more accurately, avoiding the deviation of the linear relationship calculation from the actual level.

[0026] The following describes the basic principles and implementations of the present disclosure with reference to the accompanying drawings. It should be understood that the exemplary embodiments provided are only intended to enable those skilled in the art to better understand and implement the embodiments of the present disclosure, and are not intended to limit the scope of the present disclosure in any way.

[0027] FIG1 illustrates a block diagram of a computing device 100 capable of implementing various embodiments of the present disclosure. It should be understood that the computing device 100 illustrated in FIG1 is merely exemplary and should not be construed as limiting the functionality and scope of the implementations described herein. As shown in FIG1 , the components of the computing device 100 may include, but are not limited to, one or more processors or processing units 110, a memory 120, a storage device 130, one or more communication units 140, one or more input devices 150, and one or more output devices 160.

[0028] In some implementations, the computing device 100 can be implemented as various user terminals or service terminals with computing capabilities. The service terminal can be a server, a large computing device, etc. provided by various service providers. The user terminal is such as a mobile terminal, a fixed terminal, or a portable terminal of any type, including a mobile phone, a site, a unit, a device, a multimedia computer, a multimedia tablet, an Internet node, a communicator, a desktop computer, a laptop computer, a notebook computer, a netbook computer, a tablet computer, a personal communication system (PCS) device, a personal navigation device, a personal digital assistant (PDA), an audio / video player, a digital camera / camcorder, a positioning device, a television receiver, a radio broadcast receiver, an e-book device, a gaming device, or any combination thereof, including accessories and peripherals of these devices, or any combination thereof. It is also foreseeable that the computing device 100 can support any type of interface for the user (such as a "wearable" circuit, etc.).

[0029] Processing unit 110 may be a real or virtual processor and is capable of performing various processes according to a program stored in memory 120. In a multi-processor system, multiple processing units execute computer-executable instructions in parallel to increase the parallel processing capabilities of computing device 100. Processing unit 110 may also be referred to as a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor, a controller, or a microcontroller.

[0030] The computing device 100 typically includes a plurality of computer storage media. Such media can be any available media accessible to the computing device 100, including but not limited to volatile and non-volatile media, removable and non-removable media. The memory 120 can be a volatile memory (e.g., a register, a cache, a random access memory (RAM)), a non-volatile memory (e.g., a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), flash memory) or some combination thereof. The memory 120 can include a methylation level corrector 122 implemented as a program module, and the methylation level corrector 122 can be configured to perform the program module of the methylation level detection function of the target CpG site of the tumor tissue in the mixed tumor tissue sample described herein. The methylation level corrector 122 can be accessed and run by the processing unit 110 to implement the corresponding function.

[0031] The storage device 130 may be a removable or non-removable medium and may include machine-readable media that can be used to store information and / or data and can be accessed within the computing device 100. The computing device 100 may further include additional removable / non-removable, volatile / non-volatile storage media. Although not shown in FIG1 , a magnetic disk drive for reading from or writing to a removable, non-volatile magnetic disk and an optical disk drive for reading from or writing to a removable, non-volatile optical disk may be provided. In these cases, each drive may be connected to a bus (not shown) by one or more data media interfaces.

[0032] The communication unit 140 enables communication with other computing devices via a communication medium. Additionally, the functionality of the components of the computing device 100 can be implemented as a single computing cluster or multiple computing machines that can communicate via a communication connection. Thus, the computing device 100 can operate in a networked environment using logical connections to one or more other servers, personal computers (PCs), or another general network node.

[0033] Input device 150 may be one or more of various input devices, such as a mouse, keyboard, trackball, touch screen, voice input device, etc. Output device 160 may be one or more output devices, such as a display, speaker, printer, etc. Computing device 100 may also communicate with one or more external devices (not shown) via communication unit 140 as needed, such as storage devices, display devices, etc., with one or more devices that allow a user to interact with computing device 100, or with any device that allows computing device 100 to communicate with one or more other computing devices (e.g., a network card, modem, etc.). Such communication may be performed via an input / output (I / O) interface (not shown).

[0034] In some implementations, in addition to being integrated on a single device, some or all of the various components of computing device 100 may be configured in the form of a cloud computing architecture. In a cloud computing architecture, these components may be remotely located and work together to implement the functionality described herein. In some implementations, cloud computing provides computing, software, data access, and storage services that do not require the end user to be aware of the physical location or configuration of the systems or hardware providing these services. In various implementations, cloud computing provides services over a wide area network (such as the Internet) using appropriate protocols. For example, a cloud computing provider provides applications over a wide area network, and these applications can be accessed through a web browser or any other computing component. The software or components of the cloud computing architecture and the corresponding data may be stored on servers at remote locations. Computing resources in a cloud computing environment may be consolidated at remote data center locations or they may be dispersed. Cloud computing infrastructure can provide services through shared data centers, even though they appear to be a single access point for users. Therefore, the components and functionality described herein can be provided from a service provider at a remote location using a cloud computing architecture. Alternatively, they can be provided from traditional servers, or they can be installed directly or otherwise on the client device.

[0035] The computing device 100 can detect the methylation level of the target CpG site of the tumor tissue in the mixed tumor tissue sample according to various implementations of the present disclosure. As shown in Figure 1, the computing device 100 can receive the methylation measurement results 170 of the mixed sample, the paired adjacent cancer sample, and the reference adjacent cancer sample through the input device 150. The methylation measurement results 170 can be the number of reads and the total number of reads of the target CpG site. Alternatively, the computing device 100 can also read the methylation measurement results 170 of the mixed sample, the paired adjacent cancer sample, and the reference adjacent cancer sample from the storage device 130, or receive the methylation measurement results 170 from other devices (e.g., mobile phones, tablets, personal computers, etc.) from the communication device 140. The computing device 100 can transmit the methylation measurement results 170 of the mixed sample, the paired adjacent cancer sample, and the reference adjacent cancer sample to the methylation level corrector 122. The methylation level corrector 122 obtains the corrected methylation level 180 based on the methylation measurement results 170 of the mixed sample, the paired adjacent cancer sample, and the reference adjacent cancer sample.

[0036] For example, the mixed tumor tissue sample can be liver cancer tissue from a liver cancer patient, the paired adjacent para-cancer tissue sample can be adjacent para-cancer tissue from the same patient, and the reference adjacent para-cancer tissue sample can be a cohort of adjacent para-cancer tissue from multiple liver cancer patients. Even when the mixed tumor tissue sample is composed of other tumor tissue types, the resulting corrected methylation level of 180 can still achieve accuracy comparable to linear calculations, regardless of specific tumor tissue types.

[0037] The technical solution described above is intended to be illustrative only and is not intended to limit the present invention. To more clearly explain the principles of the above solution, the following describes in more detail the process of obtaining a corrected methylation level 180 based on the methylation measurement results 170 of the mixed sample, the paired adjacent normal sample, and the reference adjacent normal sample, with reference to FIG2 .

[0038] FIG2 shows a schematic block diagram of the framework of a methylation level corrector 200 according to an embodiment of the present disclosure. The methylation level corrector 200 is an example implementation of the methylation level corrector 122 of FIG1 . It should be noted that the methylation level corrector 200 shown in FIG2 is merely illustrative and can be implemented using different systems or frameworks. For example, some modules can be omitted or modified, and the framework is not limited to that shown in FIG2 .

[0039] As shown in Figure 2, the methylation level corrector 200 can receive a mixed sample measurement result 201, a paired adjacent cancer tissue measurement result 202, and a reference adjacent cancer tissue measurement result 203, which are a first methylation measurement result 170 from a mixed tumor tissue sample, a second methylation measurement result 180 from a paired adjacent cancer tissue sample, and a third methylation measurement result 210 from a reference adjacent cancer tissue sample. The mixed sample measurement result 201 and the paired adjacent cancer tissue measurement result 202 are from the same cancer patient, and the reference adjacent cancer tissue measurement result 203 is a collection of adjacent cancer tissues from multiple cancer patients of the same cancer type.

[0040] The mixed sample measurement result 201, the paired adjacent normal cell measurement result 202, and the reference adjacent normal cell measurement result 203 can be obtained by whole genome bisulfite sequencing (WGBS) and all indicate the methylation level of the same target CpG site. In some embodiments, the methylation measurement result 170 can be the number of methylated reads and the total number of reads for the target CpG site.

[0041] As shown in the figure, the mixed sample measurement result 201 and the reference adjacent tumor tissue measurement result 203 can be provided to a read distribution determination unit 204 to obtain a distribution 205 of reads including the target CpG site in the mixed tumor tissue sample originating from tumor tissue or adjacent tumor tissue.

[0042] In some embodiments, the read distribution determination unit 204 can implement the ichorCNA algorithm. The ichorCNA algorithm is used to detect tumor proportion and copy number variation in cfDNA samples with very low sequencing depth. The read distribution determination unit 204 first obtains mixed tissue characteristics from the mixed sample measurement results 201 and the reference adjacent tumor measurement results 203, including the tumor proportion, the subclonal proportion of the target CpG site, and the copy number in the corresponding clonal state. Then, based on the mixed tissue characteristics, the distribution 205 of the reads including the target CpG site in the mixed tumor tissue sample originating from the tumor tissue or adjacent tumor tissue is calculated.

[0043] As shown in the figure, the paired adjacent cancer measurement results 202 and the reference adjacent cancer measurement results 203 can be provided to the methylation level correction unit 206 to obtain the corrected paired adjacent cancer methylation level 207. In some embodiments, the methylation level correction unit 206 can implement the DSS algorithm (Dispersion shrinkage for sequencing data). The DSS algorithm is used to perform differential analysis on count-based sequencing data. It detects differential methylation sites or regions from bisulfite sequencing (BS-seq) and can be used to estimate the gamma-Poisson distribution or beta-binomial distribution. The methylation level correction unit 206 first obtains the methylation probability shrinkage rate of the target CpG site from the paired adjacent cancer measurement results 202 and the reference adjacent cancer measurement results 203, and then combines the average methylation level of the target CpG site to obtain the corrected number of methylation reads.

[0044] As shown in the figure, the read distribution 205 and the corrected paired adjacent-cancer methylation level 207 can be provided to a methylation level determination unit 208 to obtain a pure tumor tissue methylation level 209 and a pure adjacent-cancer tissue methylation level 210 in the mixed tumor tissue. In some embodiments, the methylation level determination unit 208 can continuously iterate and converge using an EM algorithm to ultimately obtain the pure tumor tissue methylation level 209 and the pure adjacent-cancer tissue methylation level 210 in the mixed tumor tissue.

[0045] FIG3 illustrates a flow diagram of a method 300 for determining the methylation level of a target CpG site in tumor tissue according to some embodiments of the present disclosure. In some embodiments, the method 300 may be implemented, for example, by the computing device 100 shown in FIG1 . More specifically, the method 300 may be implemented by the methylation level corrector 122 of FIG1 . It should be understood that the method 300 may also include additional actions not shown and / or may omit the actions shown, and the scope of the present disclosure is not limited in this respect. For ease of explanation, the method 300 will be described with reference to the framework shown in FIG2 .

[0046] As shown in Figure 3, in box 310, the computing device 100 obtains a first methylation measurement result of a mixed tumor tissue sample and a second methylation measurement result of a paired adjacent cancer tissue sample. The first methylation measurement result can be, for example, the mixed sample measurement result 201 shown in Figure 2, and the second methylation measurement result can be, for example, the paired adjacent cancer measurement result 202 shown in Figure 2. The mixed tumor tissue sample includes tumor tissue and adjacent cancer tissue, and the mixed tumor tissue sample and the paired adjacent cancer tissue sample are from the same cancer patient. In some embodiments, the computing device 100 can be a local device, and the user can operate in an application (APP) to input the mixed sample measurement result 201 and the paired adjacent cancer measurement result 202. In some embodiments, the computing device 100 can be a server on the Internet, such as a cloud server, which receives the methylation measurement result transmitted from the user's mobile phone via the network.

[0047] In some embodiments, methylation measurements can be obtained by gene sequencing and all indicate the methylation level of the same target CpG site. Alternatively, the methylation measurement result can be the number of reads and the total number of reads at the target CpG site. Directly using the number of reads for calculation can assign higher weights to samples with higher sequencing depth, thereby making the final measurement result more robust.

[0048] Returning to FIG. 3 , at block 320 , the computing device 100 determines, based on the first methylation measurement result and a third methylation measurement result of a reference adjacent tissue sample, a distribution 205 of reads in the mixed tumor tissue sample that include the target CpG site originating from tumor tissue or adjacent tissue. The reference adjacent tissue sample is a collection of adjacent tissue samples from multiple cancer patients. The third methylation measurement result can be, for example, the reference adjacent tissue measurement result 203 shown in FIG. 2 .

[0049] The computing device 100 can use a read distribution determination unit 204 to obtain a read distribution 205 based on the mixed sample measurement results 201 and the reference adjacent tumor tissue measurement results 203. In some embodiments, the read distribution determination unit 204 can use the ichorCNA algorithm to obtain mixed tissue characteristics of the mixed tumor tissue, including the tumor fraction, the subclonal fraction of the target CpG site, and the copy number in the corresponding clonal state. Based on the mixed tissue characteristics, the unit can determine the distribution of reads containing the target CpG site in the mixed tumor tissue sample as being derived from tumor tissue or adjacent tumor tissue. Optionally, the distribution conforms to a mixed Bernoulli distribution.

[0050] Assume that the methylation levels of pure tumor tissue and pure adjacent tissue per site follow an independent and identically distributed Binomial distribution, and the distribution function is as follows:

[0051] Where i represents the i-th CpG site, t = 0 or 1, representing pure paracancerous tissue / pure tumor tissue, respectively; M it represents the methylation reads of the i-th CpG site, C it represents the total number of reads of the i-th CpG site, β it represents the methylation level of the i-th CpG site.

[0052] Because mixed tumor tissue is a mixture of pure tumor and pure adjacent tissue, for single-site methylation modeling, each read has a certain probability of originating from pure tumor or pure adjacent tissue. Given the source of a read, the methylation state of the read follows a Bernoulli distribution with parameters consistent with the parameters of the Binomial model for the corresponding source at the CpG site, satisfying the following distribution:

[0053] where x ij ∈{0,1} represents the methylation status of the jth read segment of the i-th CpG site in the mixed tumor tissue, Z ij =t∈{0,1} represents the source of the jth read segment of the i-th CpG site, that is, pure adjacent cancer tissue or pure tumor tissue.

[0054] Since normal tissues are diploid, tumor tissues may have copy number abnormalities in certain areas. The ratio of reads from pure tumors / pure adjacent tumors in mixed tumor tissues is not only related to the tumor proportion, but also to the copy number status of pure tumors at that site. Therefore, the copy number estimated by ichorCNA and the tumor proportion information are used to characterize the source of the reads Z ij Distribution:

[0055] where s i ,c i They represent the subclone ratio of the i-th CpG site and the corresponding copy number in this state, and TF represents the tumor ratio. The above parameters can be obtained by the ichorCNA algorithm.

[0056] Returning to FIG. 3 , at block 330 , the computing device 100 determines the methylation level of the target CpG site in the tumor tissue of the mixed tumor tissue sample based on the determined distribution 205 , the first methylation measurement result, and the second methylation measurement result. In some embodiments, the methylation level correction unit 206 may also determine the methylation level of the target CpG site in the paired adjacent tissue sample based on the second methylation measurement result, and correct the methylation level of the target CpG site in the paired adjacent tissue sample using the third methylation measurement result.

[0057] In some embodiments, the methylation level correction unit 206 can use the DSS algorithm to obtain the average methylation level and methylation probability shrinkage rate of the target CpG site based on the reference adjacent cancer tissue sample, and based on the average methylation level and methylation probability shrinkage rate, correct the methylation level of the target CpG site in the paired adjacent cancer tissue sample.

[0058] make is the methylation probability shrinkage rate of the i-th CpG site in the adjacent cancer tissue estimated by the DSS algorithm, is the methylation level of the ith CpG site in the sth adjacent cancer sample, N si is the total number of reads for the ith CpG site in the sth sample, is the average methylation level of the i-th CpG site in the adjacent adjacent cancer samples, then:

[0059] This is the posterior Bayesian estimate of the methylation level of the sth adjacent para-cancer sample. Assume that the total number of reads at the i-th CpG site of all adjacent para-cancer samples is The estimated posterior methylation read count can be obtained

[0060] In some embodiments, considering individual differences, single-site methylation level correction is performed on a single tumor tissue sample and its paired adjacent adjacent tumor sample, and tumor proportion correction is performed separately for different samples. Total number of read segments The tumor proportion p of mixed tumor tissue unit site estimated by ichrorCNA algorithm i , where i is the i-th CpG site; the number of adjacent methylated reads Total number of adjacent cancer segments The number of adjacent cancer methylation reads corrected by the DSS algorithm Total number of read segments Where i is the i-th CpG site. Assume x ij is the methylation status of the jth read segment at the i-th CpG site in the mixed tumor tissue sample, z ij is x ij The source of z (tumor tissue or adjacent tissue). Since z is an unknown latent variable, it can be solved using the EM algorithm. The EM algorithm, also known as the maximum expectation algorithm, is an iterative algorithm primarily used for maximum likelihood estimation of parameters in a probabilistic model containing latent variables. The process of determining the methylation level of a target CpG site in a mixed tumor tissue sample using the EM algorithm will be described in more detail below with reference to FIG4 .

[0061] FIG4 illustrates a flow chart 400 for determining the methylation level of pure tumor tissue according to an embodiment of the present disclosure. As shown in FIG4 , at block S1, the computing device 100 may select an initialization estimate of the methylation level of a target CpG site in a mixed tumor tissue sample. The initialization estimate of the methylation level may be any number between (0, 1).

[0062] In block S2, the computing device 100 may obtain an expected value of the probability distribution of the methylation level of the target CpG site of the tumor tissue based on the initialization estimate, the second methylation measurement result, and the distribution. Optionally, the second methylation measurement result may be the corrected paired adjacent cancer methylation level 207. The expected value is

[0063] in,

[0064] If x ij =1, Record If x ij =0, Record

[0065] In block S3, the computing device 100 may update the estimated value of the methylation level of the target CpG site of the tumor tissue in the mixed tumor tissue by maximizing the expected value.

[0066] and estimated methylation levels of target CpG sites in adjacent adjacent tissues within mixed tumor tissues

[0067] In block S4 , the computing device 100 may repeat steps S2 and S3 and iterate continuously until a convergence condition is met, thereby obtaining the final methylation level of the target CpG site of the tumor tissue.

[0068] In some embodiments, a gradient dilution experiment was performed using a cohort of 11 colorectal cancer tissues and their paired adjacent normal-cancer samples. The approximate mean original tumor percentage of the colorectal cancer cohort was 0.635, with a maximum of 1 and a minimum of 0.4. The original cancer tissue samples and adjacent normal-cancer samples were diluted at a gradient of -10%, -20%, and -30% to compare the reduction effect of the disclosed algorithm at different tumor percentages. The DSS algorithm was then used to identify differentially methylated sites (DMCs) between the tumor tissue and adjacent normal-cancer tissues in both the diluted and diluted samples.

[0069] Figures 5A-5C show the test results of using the corrected methylation level to detect early screening markers according to an embodiment of the present disclosure. Figure 5A shows a schematic diagram of the number of differentially methylated sites obtained under different tumor proportions according to some embodiments of the present disclosure. Among them, purified represents the number of differentially methylated sites between pure tumor tissue and adjacent cancer tissue restored by the tumor proportion correction algorithm, -k% represents the dilution gradient, p and delta are parameters of the DSS algorithm, representing the p-value of the test and the threshold value of the difference, respectively. As shown in Figure 5A, after gradient dilution, the number of differentially methylated sites decreases significantly with the increase of the dilution gradient. It can be seen from this that the proportion of pure tumor tissue in mixed tumor tissue greatly affects the number of differentially methylated sites, but after the tumor proportion correction is performed by the algorithm of the present disclosure, the number of differentially methylated sites remains relatively stable under each dilution gradient.

[0070] Figure 5B shows a schematic diagram of the inclusion relationship of differentially methylated sites in a cohort at different dilution ratios before correction, with parameters set at p = 0.01 and delta = 0.2, according to some embodiments of the present disclosure. As shown in Figure 5B, the differentially methylated sites obtained after tumor proportion correction for the original data substantially include the differentially methylated sites found in the original data, as well as the differentially methylated sites after gradient dilution.

[0071] Figure 5C shows a schematic diagram of the differential methylation site inclusion relationship of the cohort at different dilution ratios after correction, with the parameters p = 0.01 and delta = 0.2, according to some embodiments of the present disclosure. As shown in Figure 5C, the restored data after gradient dilution and correction using tumor proportion still has good correlation.

[0072] As can be seen from Figures 5A-5C, pure tumor tissue and adjacent cancer tissue corrected and restored by the algorithm disclosed in the present invention can find more differentially methylated sites, thereby finding more early screening (DOC) markers.

[0073] In some embodiments, tissue-of-origin (TOO) marker screening was performed using the aforementioned colorectal cancer cohort and another liver cancer cohort with a sample size of 15. The average tumor percentage in the liver cancer cohort was approximately 0.644, the highest tumor percentage was 0.95, and the lowest tumor percentage was 0.41. Gradient dilutions were performed on liver cancer and its adjacent tissues, and colorectal cancer and its adjacent tissues, respectively, to obtain tissue-of-origin marker screening results based on the DSS algorithm.

[0074] Figures 6A-6C show the experimental results of using the corrected methylation levels to detect tissue traceability markers according to an embodiment of the present disclosure. Figure 6A shows a schematic diagram of the number of differential methylation sites when the colorectal cancer samples are fixed and the liver cancer samples are diluted, and a schematic diagram of the number of differential methylation sites when the liver cancer samples are fixed and the colorectal cancer samples are diluted according to some embodiments of the present disclosure. As shown in Figure 6A, since the diluted cancer samples are more similar to the adjacent cancer samples as the gradient dilution increases, the differential methylation sites are more similar to the difference between the fixed cancer and the diluted adjacent cancer, and the number of differential methylation sites before correction shows a monotonically increasing or monotonically decreasing trend as the dilution gradient increases.

[0075] Figure 6B shows a schematic diagram of the inclusion relationship of differentially methylated sites in a cohort at different dilution ratios before correction, with parameters p = 1e^-5, delta = 0.2, and colorectal cancer fixed and liver cancer diluted, according to some embodiments of the present disclosure. As shown in Figure 6B, as the tumor proportion decreases, the intersection of differentially methylated sites decreases, and the number of false positive sites increases.

[0076] Figure 6C shows a schematic diagram of the differential methylation site inclusion relationship of the cohort at different dilution ratios after correction according to some embodiments of the present disclosure, with the parameters p = 1e^-5, delta = 0.2, colorectal cancer fixed, and liver cancer diluted. As shown in Figure 6C, when the dilution gradient is small (the overall tumor proportion is high), the false positive rate is low and the detection rate is high; when the dilution gradient is high (the overall tumor proportion is low), although the false positive rate increases, it is still lower than before correction, and the detection rate remains high.

[0077] As can be seen from Figures 6A-6C, the pure tumor tissue corrected and restored by the algorithm disclosed in the present invention can effectively reduce the false positive problem of tissue traceability markers at any overall tumor ratio.

[0078] The above reference figures 2 to 6C describe exemplary embodiments of the present disclosure. Compared with the existing methylation level measurement scheme of the target CpG site of the tumor tissue in the mixed tumor tissue, the methylation level measurement scheme of the present disclosure can use paired para-cancer tissue and reference para-cancer tissue to increase the accuracy of the methylation level measurement of the tumor tissue, and effectively avoid the deviation of the linear model in solving the methylation level. In some implementations, the methylation level of the paired para-cancer tissue sample can also be corrected using the average methylation level of the reference para-cancer tissue sample, so that the measured methylation level of the tumor tissue is more accurate. In some implementations, the number of reads is directly used for subsequent calculations, which can give samples with higher sequencing depth a higher weight, thereby making the methylation level measurement results of the tumor tissue more robust. In some implementations, the final methylation level of the tumor tissue can also be obtained by iterative calculation based on the EM algorithm, which can effectively avoid the problem of missed screening of early screening markers and false positive problems of tissue tracing markers.

[0079] FIG7 shows a schematic block diagram of an apparatus 700 for determining the methylation level of a target CpG site in tumor tissue according to an embodiment of the present disclosure. Apparatus 700 can be implemented, for example, at the methylation level corrector 122 in the computing device 100 shown in FIG1 . As shown in FIG7 , apparatus 700 includes a methylation measurement unit 710 , a read distribution determination unit 720 , and a methylation level determination unit 730 .

[0080] In some embodiments, the methylation measurement unit 710 is configured to obtain a first methylation measurement result of a mixed tumor tissue sample and a second methylation measurement result of a paired adjacent cancer tissue sample, the mixed tumor tissue sample including tumor tissue and adjacent cancer tissue; the read distribution determination unit 720 is configured to determine the distribution of reads including the target CpG site in the mixed tumor tissue sample originating from the tumor tissue or adjacent cancer tissue based on the first methylation measurement result and the third methylation measurement result of the reference adjacent cancer tissue sample; and the methylation level determination unit 730 is configured to determine the methylation level of the target CpG site of the tumor tissue in the mixed tumor tissue sample based on the determined distribution, the first methylation measurement result, and the second methylation measurement result.

[0081] It should be noted that more actions or steps shown in Figures 2 to 4 can be implemented by the apparatus 700 shown in Figure 7. For example, the apparatus 700 may include more modules or units to implement the actions or steps described above, or some units or modules shown in Figure 7 may be further configured to implement the actions or steps described above. This will not be repeated here.

[0082] In some embodiments, the methods and processes described above may be implemented as a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present disclosure.

[0083] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0084] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0085] The computer program instructions for performing the disclosed operation can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state setting data or source code or the object code written in any combination of one or more programming languages, wherein the programming languages ​​include object-oriented programming languages, and conventional procedural programming languages.Computer-readable program instructions can be performed completely on the user's computer, partially on the user's computer, performed as an independent software package, partly on the user's computer and partly on the remote computer, or performed completely on the remote computer or server. In the case of relating to the remote computer, the remote computer can be connected to the user's computer by any type of network-comprising local area network (LAN) or wide area network (WAN), or can be connected to an external computer (such as utilizing an Internet service provider to connect by the Internet). In certain embodiments, by utilizing the state information of computer-readable program instructions to carry out personalized customization electronic circuit, such as programmable logic circuit, field programmable gate array (FPGA) or programmable logic array (PLA), this electronic circuit can perform computer-readable program instructions, thereby realizes various aspects of the present disclosure.

[0086] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine such that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0087] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0088] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and the part of the module, program segment or instruction contains one or more executable instructions for realizing the prescribed logical function. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart, can be implemented by a dedicated hardware-based system that performs the prescribed function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0089] While various embodiments of the present disclosure have been described above, the foregoing description is intended to be illustrative, non-exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or technical improvements to existing technologies, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for determining the methylation level of a target CpG site in a tumor tissue, comprising: Obtaining a first methylation measurement result of a mixed tumor tissue sample and a second methylation measurement result of a paired adjacent paracancerous tissue sample, wherein the mixed tumor tissue sample includes tumor tissue and adjacent paracancerous tissue; Determining, based on the first methylation measurement result and a third methylation measurement result of a reference adjacent tumor tissue sample, a distribution of reads including the target CpG site in the mixed tumor tissue sample originating from tumor tissue or adjacent tumor tissue; as well as Based on the determined distribution, the first methylation measurement result, and the second methylation measurement result, a methylation level of the target CpG site of the tumor tissue in the mixed tumor tissue sample is determined.

2. The method according to claim 1, wherein Also includes: determining the methylation level of the target CpG site in the paired adjacent cancer tissue sample based on the second methylation measurement result; as well as The methylation level of the target CpG site in the paired adjacent cancer tissue sample is corrected using the third methylation measurement result.

3. The method according to claim 2, wherein: Correcting the methylation level of the target CpG site in the paired adjacent cancer tissue sample comprises: Based on the reference adjacent cancer tissue sample, obtaining the average methylation level and methylation probability contraction rate of the target CpG site; and The methylation level of the target CpG site in the paired adjacent cancer tissue samples is corrected based on the average methylation level and the methylation probability shrinkage rate.

4. The method according to claim 1, wherein Determining the distribution of reads containing the target CpG site in the mixed tumor tissue sample originating from tumor tissue or adjacent tissue includes: Obtaining mixed tissue characteristics of the mixed tumor tissue, the mixed tissue characteristics including tumor proportion, subclone proportion of the target CpG site, and copy number in the corresponding clone state; and Based on the mixed tissue characteristics, it is determined that the reads including the target CpG site in the mixed tumor tissue sample originate from the tumor tissue or the adjacent tissue. The method according to claim 4 , wherein the distribution conforms to a mixed Bernoulli distribution.

6. The method according to claim 1, wherein Determining the methylation level of the tumor tissue in the mixed tumor tissue sample comprises: S1. selecting an initialized estimated value of the methylation level of the target CpG site of the tumor tissue in the mixed tumor tissue sample; S2. Obtaining an expected value of a probability distribution of the methylation level of the target CpG site of the tumor tissue based on the initialization estimate, the second methylation measurement result, and the distribution; S3. updating the estimated value of the methylation level of the target CpG site of the tumor tissue by maximizing the expected value; and S4. Repeat steps S2 and S3 until the convergence condition is met.

7. The method according to claim 1, wherein Any one of the first measurement result, the second measurement result, and the third measurement result indicates the number of methylated reads and the total number of reads of the target CpG site.

8. A system for determining the methylation level of a target CpG site in a tumor tissue, comprising: a methylation measurement unit configured to obtain a first methylation measurement result of a mixed tumor tissue sample and a second methylation measurement result of a paired adjacent paracancerous tissue sample, wherein the mixed tumor tissue sample includes tumor tissue and adjacent paracancerous tissue; a read distribution determining unit configured to determine, based on the first methylation measurement result and a third methylation measurement result of a reference adjacent tissue sample, a distribution of reads containing the target CpG site in the mixed tumor tissue sample originating from the tumor tissue or adjacent tissue; as well as A methylation level determination unit is configured to determine the methylation level of the target CpG site of the tumor tissue in the mixed tumor tissue sample based on the determined distribution, the first methylation measurement result, and the second methylation measurement result.

9. A computing device comprising: at least one processing unit; At least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions, when executed by the at least one processing unit, causing the computing device to perform the method of any one of claims 1 to 7.

10. A non-transitory computer storage medium comprising machine-executable instructions which, when executed by a device, cause the device to perform the method of any one of claims 1 to 7.

11. A computer program product comprising machine-executable instructions which, when executed by a device, cause the device to perform the method according to any one of claims 1 to 7.

Images