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

Abstract

Embodiments of the present disclosure provide methods, systems, computing devices and media for determining methylation levels of target CpG sites of tumor tissue. The method comprises: obtaining a first methylation measurement of a mixed tumor tissue sample and a second methylation measurement of a paired cancer adjacent tissue sample, the mixed tumor tissue sample comprising tumor tissue and cancer adjacent tissue; determining a distribution of reads comprising target CpG sites in the mixed tumor tissue sample originating from the tumor tissue or the cancer adjacent tissue based on the first methylation measurement and a third methylation measurement of a reference cancer adjacent tissue sample; and determining a methylation level of the target CpG sites of the tumor tissue in the mixed tumor tissue sample based on the determined distribution, the first methylation measurement and the second methylation measurement. In this way, the accuracy of the methylation level measurement can be improved, thereby effectively avoiding the problem of missed screening of early screening markers and false positive of tissue provenance markers.

Description

Technical Field

[0001] This disclosure relates to the field of biological detection technology, and more specifically, to a method, system, computing device, computer-readable storage medium, and computer program product for determining the methylation level of a target CpG site in tumor tissue. Background Technology

[0002] Methylation is a chemical process in which a methyl group is added to a molecule, such as the CpG site on DNA (the region between the 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.

[0003] In tumor tissue, the methylation pattern of CpG sites is often abnormal and differs from that in normal tissue. This difference can manifest as alterations in overall methylation levels, meaning increased or decreased methylation of many genes. In some cases, specific genes may be methylated, preventing their expression. This gene silencing can 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 tumor nature and treatment options.

[0004] In actual collected tissue samples, tumor tissue samples are generally mixed tumor tissues composed of pure tumor tissue (which can also be referred to as tumor tissue in this paper) and adjacent normal tissue. When the proportion of pure tumor tissue in the mixed tumor tissue is not high, the methylation level of the mixed tumor tissue differs significantly from that of pure tumor tissue, which greatly affects downstream analysis, especially the screening of differentially methylated sites. Existing methods usually use linear models to inversely solve for the unit site methylation level of pure tumor tissue. Due to the inherent errors in sequencing, the methylation level of mixed tumor tissue and adjacent normal tissue often deviates from the true methylation level. Using linear relationships to solve for the methylation level of pure tumor tissue will cause the methylation level to exceed the predetermined range, which contradicts the actual situation. Summary of the Invention

[0005] In view of this, this disclosure provides a method, system, computing device, computer-readable storage medium, and computer program product for determining the methylation level of target CpG sites in tumor tissue, which can be performed by...

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

[0007] According to a second aspect of this disclosure, a system for determining the methylation level of a target CpG site in tumor tissue is provided, comprising: a methylation measurement unit configured to acquire a first methylation measurement result of a mixed tumor tissue sample and a second methylation measurement result of a paired adjacent normal tissue sample, the mixed tumor tissue sample including tumor tissue and adjacent normal 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 normal tissue sample, the distribution of reads including the target CpG site in the mixed tumor tissue sample originating from tumor tissue or adjacent normal 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 in the tumor tissue of the mixed tumor tissue sample.

[0008] According to a third aspect of this disclosure, a computing device is provided, comprising: at least one processing unit; and 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 as described in the first aspect of this disclosure.

[0009] According to a fourth aspect of this disclosure, a non-transient computer storage medium is provided, including machine-executable instructions that, when executed by a device, cause the device to perform the method as described in the first aspect of this disclosure.

[0010] According to a fifth aspect of this disclosure, a computer program product is provided, including machine-executable instructions that, when executed by a device, cause the device to perform the method as described in the first aspect of this disclosure.

[0011] It should be understood that the summary section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0012] The above and other objects, features, and advantages of embodiments of the present disclosure will become more readily understood from the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the present disclosure will be described by way of example and non-limitation, wherein:

[0013] Figure 1 A block diagram of a computing device capable of implementing several embodiments of the present disclosure is shown;

[0014] Figure 2 A schematic block diagram of the framework of a methylation level corrector according to an embodiment of the present disclosure is shown;

[0015] Figure 3 A flowchart illustrating a method for determining the methylation level of a target CpG site in tumor tissue according to an embodiment of the present disclosure is shown.

[0016] Figure 4 A schematic flowchart illustrating the process for determining the methylation level of pure tumor tissue according to embodiments of the present disclosure is shown;

[0017] Figures 5A-5C The results of an experiment using corrected methylation levels to detect early screening markers according to embodiments of the present disclosure are shown.

[0018] Figures 6A-6C The results of a trial using corrected methylation levels to detect tissue traceability markers according to embodiments of the present disclosure are shown; and

[0019] Figure 7 A schematic block diagram of an apparatus for determining the methylation level of a target CpG site in tumor tissue, according to an embodiment of the present disclosure, is shown. Detailed Implementation

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

[0021] In the context of this disclosure, the term "comprising" and its various variations may be understood as open-ended terms meaning "including but not limited to"; the term "based on" may be understood as "at least partially based on"; the term "one embodiment" may be understood as "at least one embodiment"; and the term "another embodiment" may be understood as "at least one other embodiment". Other terms that may appear but are not mentioned herein should not be interpreted or limited in a manner contrary to the concept on which the embodiments of this disclosure are based, unless expressly stated otherwise.

[0022] Methylation is a chemical process in which methyl groups are added to a molecule, such as a CpG site on DNA (the region between C and G genes in the genome). DNA methylation can affect how genes are expressed, altering gene expression. In tumor tissue, the methylation pattern of CpG sites often differs from that in normal tissue. This difference can manifest as alterations in overall methylation levels, meaning increased or decreased methylation of many genes. In some cases, specific genes may be methylated, preventing their expression. This gene silencing can 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 tumor nature and treatment options.

[0023] In actual collected tissue samples, tumor tissue samples are generally mixed tumor tissues composed of pure tumor tissue and adjacent normal tissue. When the proportion of pure tumor tissue in the mixed tumor tissue is not high, the methylation level of the mixed tumor tissue differs significantly from that of pure tumor tissue, which greatly affects downstream analysis, especially the screening of differentially methylated sites. Existing protocols typically use linear models to inversely solve for the unit site methylation level of pure tumor tissue. Due to the inherent errors in sequencing, the methylation levels of mixed tumor tissue and adjacent normal tissue often deviate from the true methylation levels. Using linear relationships to solve for these levels will cause the methylation level of pure tumor tissue to exceed the predetermined range, which contradicts the actual situation.

[0024] To address or mitigate the aforementioned problems and / or other potential issues, embodiments of this disclosure propose a method for correcting the methylation levels of target CpG sites in tumor tissue. This method involves obtaining mixed tumor tissue samples and paired adjacent normal tissue samples from the same source (patient), obtaining reference adjacent normal tissue samples from multiple sources (patients), and combining the mixed tumor tissue samples and reference adjacent normal tissue samples to determine the distribution of reads containing target CpG sites in the mixed tumor tissue samples as originating from tumor tissue or adjacent normal tissue. Finally, based on this distribution, the methylation level of the target CpG sites in the tumor tissue within the mixed tumor tissue samples is determined. In this manner, the methylation level of target CpG sites in tumor tissue can be detected more accurately, avoiding deviations between linear relationship calculations and actual levels.

[0025] The basic principles and implementation of this disclosure are illustrated below with reference to the accompanying drawings. It should be understood that the exemplary embodiments given are merely intended to enable those skilled in the art to better understand and implement the embodiments of this disclosure, and are not intended to limit the scope of this disclosure in any way.

[0026] Figure 1 A block diagram of a computing device 100 capable of implementing various embodiments of the present disclosure is shown. It should be understood that... Figure 1 The computing device 100 shown is merely exemplary and should not constitute any limitation on the functionality and scope of the implementation described in this disclosure. Figure 1 As shown, the components of computing device 100 may include, but are not limited to, one or more processors or processing units 110, memory 120, storage device 130, one or more communication units 140, one or more input devices 150, and one or more output devices 160.

[0027] In some implementations, computing device 100 can be implemented as various user terminals or service terminals with computing capabilities. Service terminals can be servers, large computing devices, etc., provided by various service providers. User terminals can be any type of mobile terminal, fixed terminal, or portable terminal, including mobile phones, sites, units, devices, multimedia computers, multimedia tablets, internet nodes, communicators, desktop computers, laptop computers, notebook computers, netbook computers, tablet computers, personal communication system (PCS) devices, personal navigation devices, personal digital assistants (PDAs), audio / video players, digital cameras / camcorders, positioning devices, television receivers, radio receivers, e-book devices, gaming devices, or any combination thereof, including accessories and peripherals of these devices, or any combination thereof. It is also foreseeable that computing device 100 can support any type of user-facing interface (such as "wearable" circuitry).

[0028] Processing unit 110 can be a physical or virtual processor and is capable of performing various processes according to programs stored in memory 120. In a multiprocessor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capability of computing device 100. Processing unit 110 may also be referred to as a central processing unit (CPU), graphics processing unit (GPU), microprocessor, controller, or microcontroller.

[0029] Computing device 100 typically includes multiple computer storage media. Such media can be any available media accessible to computing device 100, including but not limited to volatile and non-volatile media, removable and non-removable media. Memory 120 can be volatile memory (e.g., registers, cache, random access memory (RAM)), non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. Memory 120 may include a methylation level corrector 122 implemented as a program module, which can be configured to perform the function of detecting the methylation level of target CpG sites in a mixed tumor tissue sample as described herein. Methylation level corrector 122 can be accessed and run by processing unit 110 to implement the corresponding function.

[0030] Storage device 130 may be a removable or non-removable medium and may include machine-readable media capable of storing information and / or data and accessible within computing device 100. Computing device 100 may further include additional removable / non-removable, volatile / non-volatile storage media. Although not explicitly stated... Figure 1 As shown, disk drives for reading from or writing to removable, non-volatile disks and optical disc drives for reading from or writing to removable, non-volatile optical discs can be provided. In these cases, each drive can be connected to a bus (not shown) via one or more data media interfaces.

[0031] 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 capable of communicating via communication connections. Therefore, 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.

[0032] Input device 150 can be one or more various input devices, such as a mouse, keyboard, trackball, touchscreen, voice input device, etc. Output device 160 can be one or more output devices, such as a monitor, speaker, printer, etc. Computing device 100 can also communicate as needed with one or more external devices (not shown) via communication unit 140. These external devices, such as storage devices, display devices, etc., can communicate with one or more devices that enable user interaction with computing device 100, or with any device (e.g., network card, modem, etc.) that enables computing device 100 to communicate with one or more other computing devices. Such communication can be performed via input / output (I / O) interfaces (not shown).

[0033] In some implementations, in addition to being integrated into a single device, some or all of the components of computing device 100 may be configured in the form of a cloud computing architecture. In a cloud computing architecture, these components can be remotely deployed and can work together to achieve the functionality described herein. In some implementations, cloud computing provides computing, software, data access, and storage services without requiring end users to know 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, cloud computing providers offer 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, along with 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 distributed. Cloud computing infrastructure can provide services through shared data centers, even if they appear as a single access point for users. Therefore, the components and functionality described herein can be provided from service providers at remote locations using a cloud computing architecture. Alternatively, they may also be provided from traditional servers, or they may be installed directly or otherwise on client devices.

[0034] The computing device 100 can detect the methylation level of target CpG sites in a mixed tumor tissue sample according to various implementations of this disclosure. For example... Figure 1As shown, the computing device 100 can receive methylation measurement results 170 of a mixed sample, paired adjacent normal samples, and a reference adjacent normal sample via an input device 150. The methylation measurement results 170 can be the number of reads at the target CpG site and the total number of reads. Alternatively, the computing device 100 can also read the methylation measurement results 170 of the mixed sample, paired adjacent normal samples, and reference adjacent normal samples from a storage device 130, or receive methylation measurement results 170 from other devices (e.g., mobile phones, tablets, personal computers, etc.) from a communication device 140. The computing device 100 can transmit the methylation measurement results 170 of the mixed sample, paired adjacent normal samples, and reference adjacent normal samples to a methylation level corrector 122. The methylation level corrector 122 obtains a corrected methylation level 180 based on the methylation measurement results 170 of the mixed sample, paired adjacent normal samples, and reference adjacent normal samples.

[0035] For example, a mixed tumor tissue sample could be liver cancer tissue taken from a liver cancer patient, and a paired adjacent normal tissue sample could be adjacent normal tissue also taken from the same patient. The reference adjacent normal tissue sample could be a cohort of adjacent normal tissues taken from multiple liver cancer patients. Even when the mixed tumor tissue sample is of other types of tumor tissue, the final corrected methylation level of 180 can still achieve the accuracy of linear calculations, and is not limited to a specific type of tumor tissue.

[0036] The technical solutions described above are for illustrative purposes only and are not intended to limit the invention. To more clearly explain the principles of the above solutions, the following will refer to... Figure 2 To describe in more detail the process of obtaining the corrected methylation level 180 based on methylation measurements 170 from mixed samples, paired adjacent normal samples, and reference adjacent normal samples.

[0037] Figure 2 A schematic block diagram of the framework of a methylation level corrector 200 according to an embodiment of the present disclosure is shown. The methylation level corrector 200 is... Figure 1 An example implementation of the methylation level corrector 122. It should be noted that... Figure 2 The methylation level corrector 200 shown is merely illustrative; the methylation level corrector 200 can also be implemented using different systems or frameworks. For example, some modules can be omitted or changed, and it is not limited to this. Figure 2 The frame shown.

[0038] like Figure 2As shown, the methylation level corrector 200 can receive mixed sample measurement results 201, paired adjacent normal tissue measurement results 202, and reference adjacent normal tissue measurement results 203. The first methylation measurement result 170, the second methylation measurement result 180, and the third methylation measurement result 210 from the mixed tumor tissue sample are from the paired adjacent normal tissue sample. The mixed sample measurement result 201 and the paired adjacent normal tissue measurement result 202 are from the same cancer patient, and the reference adjacent normal tissue measurement result 203 is a collection of adjacent normal tissues from multiple cancer patients of the same cancer type.

[0039] The pooled sample measurement result 201, paired adjacent normal measurement result 202, and reference adjacent normal 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 may be the number of methylated reads and the total number of reads at the target CpG site.

[0040] As shown in the figure, the mixed sample measurement results 201 and the reference adjacent normal measurement results 203 can be provided to the read distribution determination unit 204 to obtain the distribution 205 of reads in the mixed tumor tissue sample that include the target CpG site originating from the tumor tissue or adjacent normal tissue.

[0041] In some embodiments, the read distribution determination unit 204 may implement the ichorCNA algorithm. The ichorCNA algorithm is used to detect the tumor proportion and copy number variation in cfDNA samples with very low depth sequencing. The read distribution determination unit 204 first obtains mixed tissue characteristics from the mixed sample measurement results 201 and the reference adjacent normal 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, it calculates the distribution 205 of reads in the mixed tumor tissue sample that include the target CpG site originating from tumor tissue or adjacent normal tissue.

[0042] As shown in the figure, paired adjacent normal tissue measurement results 202 and reference adjacent normal tissue measurement results 203 can be provided to the methylation level correction unit 206 to obtain the corrected paired adjacent normal tissue 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 for differential analysis of count-based sequencing data, which detects differentially methylated sites or regions from bisulfite sequencing (BS-seq) and can be used to estimate gamma-Poisson or beta-binomial distributions. The methylation level correction unit 206 first obtains the methylation probability shrinkage rate of the target CpG site from the paired adjacent normal tissue measurement results 202 and the reference adjacent normal tissue measurement results 203, and then combines it with the average methylation level of the target CpG site to obtain the corrected number of methylated reads.

[0043] As shown in the figure, the read distribution 205 and the corrected paired adjacent normal tissue methylation level 207 can be provided to the methylation level determination unit 208 to obtain the pure tumor tissue methylation level 209 and the pure adjacent normal tissue methylation level 210 in the mixed tumor tissue. In some embodiments, the methylation level determination unit 208 can iteratively converge using the EM algorithm to finally obtain the pure tumor tissue methylation level 209 and the pure adjacent normal tissue methylation level 210 in the mixed tumor tissue.

[0044] Figure 3 A flowchart illustrating a method 300 for determining the methylation level of a target CpG site in tumor tissue according to some embodiments of the present disclosure is shown. In some embodiments, method 300 may be, for example, Figure 1 The computing device 100 shown is used to implement this. More specifically, method 300 can be implemented by... Figure 1 The methylation level corrector 122 is used to achieve this. It should be understood that method 300 may also include additional actions not shown and / or the actions shown may be omitted, and the scope of this disclosure is not limited in this respect. For ease of explanation, reference will be made to... Figure 2 The framework shown illustrates method 300.

[0045] like Figure 3 As shown, in box 310, computing device 100 acquires a first methylation measurement result of a mixed tumor tissue sample and a second methylation measurement result of a paired adjacent normal tissue sample. The first methylation measurement result may be, for example... Figure 2 The mixed sample measurement result 201 shown can be, for example, the second methylation measurement result. Figure 2The paired adjacent normal tissue measurement result 202 is shown. The mixed tumor tissue sample includes tumor tissue and adjacent normal tissue, and the mixed tumor tissue sample and the paired adjacent normal tissue sample are from the same cancer patient. In some embodiments, the computing device 100 may be a local device, and the user may operate it in an application (APP) to input the mixed sample measurement result 201 and the paired adjacent normal tissue measurement result 202. In some embodiments, the computing device 100 may be a server on the Internet, such as a cloud server, receiving methylation measurement results transmitted from the user's mobile phone via the network.

[0046] In some embodiments, methylation measurements can be obtained by gene sequencing and all indicate the methylation level of the same target CpG site. Optionally, the methylation measurement results can be the number of reads at the target CpG site and the total number of reads. Directly using the number of reads for calculation allows for assigning higher weight to samples with higher sequencing depth, thereby making the final measurement results more robust.

[0047] return Figure 3 In box 320, computing device 100, based on the first methylation measurement result and the third methylation measurement result of a reference adjacent normal tissue sample, determines the distribution 205 of reads including target CpG sites in the mixed tumor tissue sample originating from tumor tissue or adjacent normal tissue. The reference adjacent normal tissue sample is a collection of adjacent normal tissues from multiple cancer patients. The third methylation measurement result can be, for example... Figure 2 The reference adjacent normal measurement result 203 is shown.

[0048] The computing device 100 can use the read distribution determination unit 204 to obtain the read distribution 205 based on the mixed sample measurement results 201 and the reference adjacent normal measurement results 203. In some embodiments, the read distribution determination unit 204 can use the iChorCNA algorithm to obtain the mixed tissue characteristics of the mixed tumor tissue, including the tumor proportion, the subclonal proportion of the target CpG site, and the copy number in the corresponding clonal state, and based on the mixed tissue characteristics, determine the distribution of reads including the target CpG site in the mixed tumor tissue sample originating from tumor tissue or adjacent normal tissue. Optionally, this distribution conforms to a mixed Bernoulli distribution.

[0049] Assume that the methylation levels at unit sites in pure tumor tissue and pure adjacent normal tissue follow an independent and identically distributed Binomial distribution, with the distribution function as follows:

[0050]

[0051] Where i represents the i-th CpG site, t = 0 or 1, representing pure adjacent normal tissue / pure tumor tissue, respectively; M it Represents the methylation reading at the i-th CpG site, C itβ represents the total reading of the i-th CpG site. it This represents the methylation level of the i-th CpG site.

[0052] Since mixed tumor tissue is a mixture of pure tumor and pure adjacent normal tissue, for methylation modeling at a single site, each read originates from either pure tumor or pure adjacent normal tissue with a certain probability. Given the origin of a read, its methylation state follows a Bernoulli distribution with parameters consistent with the parameters of the corresponding Binomial model for that CpG site, satisfying the following distribution:

[0053]

[0054] Where x ij ∈{0,1} represents the methylation state of the j-th read at the i-th CpG site in mixed tumor tissue, Z ij =t∈{0,1} represents the source of the j-th read at the i-th CpG site, i.e., pure adjacent normal tissue or pure tumor tissue.

[0055] Because normal tissue is diploid, tumor tissue exhibits copy number abnormalities in certain regions. The ratio of reads originating from pure tumor / pure adjacent normal tissue in mixed tumor tissue is related not only to the tumor predominance but also to the copy number status of the pure tumor at that site. Therefore, copy number estimated using ichorCNA and tumor predominance information is used to characterize the read origin Z. ij Distribution:

[0056]

[0057]

[0058] Where s i ,c i These represent the subclonal percentage of the i-th CpG site and the corresponding copy number in that state, respectively. TF represents the tumor percentage. All of these parameters can be obtained by the ichorCNA algorithm.

[0059] return Figure 3 In block 330, computing device 100 determines the methylation level of the target CpG site in 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, methylation level correction unit 206 may also determine the methylation level of the target CpG site in the paired adjacent normal tissue sample based on the second methylation measurement result, and correct the methylation level of the target CpG site in the paired adjacent normal tissue sample using the third methylation measurement result.

[0060] In some embodiments, the methylation level correction unit 206 may employ the DSS algorithm to obtain the average methylation level and methylation probability shrinkage rate of the target CpG site based on a reference adjacent normal tissue sample, and correct the methylation level of the target CpG site in the paired adjacent normal tissue sample based on the average methylation level and methylation probability shrinkage rate.

[0061] make The methylation probability shrinkage rate of the i-th CpG site in adjacent normal tissue, estimated by the DSS algorithm. Let N be the methylation level of the i-th CpG site in the s-th adjacent normal sample. si The total number of reads for the i-th CpG site in the s-th sample. Let be the average methylation level of the i-th CpG site in the adjacent normal sample, then:

[0062]

[0063]

[0064] This represents the posterior Bayesian estimate of the methylation level of the s-th adjacent normal sample. It assumes the total number of reads adjacent to the i-th CpG site across all adjacent normal samples. The estimated number of posterior methylated reads can be obtained.

[0065] In some embodiments, taking into account individual differences, unit-point methylation level correction is performed from a single tumor tissue sample and its paired adjacent normal samples, and tumor proportion correction is performed separately for different samples. Existing data are the number of methylation reads in mixed tumor tissues. Total number of segments read The tumor percentage p of mixed tumor tissue unit points estimated by the ichrorCNA algorithm i Where i is the i-th CpG site; the number of adjacent methylated reads Total number of segments read next to cancer Number of adjacent normal methylated reads corrected by DSS algorithm Total number of segments read Where i is the i-th CpG site. Assume x ij For the methylation state of the j-th read at the i-th CpG site in a mixed tumor tissue sample, z ij For x ij The source of z (tumor tissue or adjacent normal tissue). Since z is an unknown latent variable, it can be solved using the EM algorithm. The EM algorithm, also known as the expectation-maximization algorithm, is an iterative algorithm mainly used for maximum likelihood estimation of parameters in probabilistic models containing latent variables. The following will refer to... Figure 4To describe in more detail the process of determining the methylation level of target CpG sites in tumor tissues in mixed tumor tissue samples based on the EM algorithm.

[0066] Figure 4 A schematic flowchart 400 illustrating the determination of methylation levels in pure tumor tissue according to embodiments of the present disclosure is shown. Figure 4 As shown, in box S1, the computing device 100 can select an initial estimate of the methylation level of the target CpG site in the tumor tissue from the mixed tumor tissue sample. The initial estimate of the methylation level can be any number in (0,1).

[0067] In box S2, the computing device 100 can obtain the expected value of the probability distribution of the methylation level of the target CpG site in the tumor tissue based on the initial estimate, the second methylation measurement result, and the distribution. Optionally, the second methylation measurement result can be a corrected paired adjacent normal methylation level 207. Let... The expected value is

[0068]

[0069] in,

[0070] If x ij =1, Record If x ij =0, Record

[0071] In box S3, computing device 100 can update the estimated methylation level of target CpG sites in the mixed tumor tissue by maximizing this expected value.

[0072]

[0073] And estimates of the methylation levels of target CpG sites in adjacent normal tissues within mixed tumor tissues.

[0074]

[0075] In box S4, the computing device 100 can obtain the final methylation level of the target CpG site in the tumor tissue by repeating steps S2 and S3 iteratively until the convergence condition is met.

[0076] In some embodiments, a gradient dilution experiment was performed using a cohort of 11 colorectal cancer tissues and their paired adjacent normal samples. The mean original tumor percentage of the colorectal cancer tissues in the cohort was approximately 0.635, with a maximum of 1 and a minimum of 0.4. The original cancer tissue samples and adjacent normal samples were diluted with gradients of -10%, -20%, and -30% to compare the restoration effect of the algorithm disclosed herein under different tumor percentages. Simultaneously, the DSS algorithm was used to find differentially methylated sites (DMCs) between the tumor tissues and adjacent normal tissues for both the diluted samples and the restored samples.

[0077] Figures 5A-5C The results of an experiment using corrected methylation levels to detect early screening markers according to embodiments of the present disclosure are shown. Figure 5A This diagram illustrates the number of differentially methylated sites obtained under different tumor percentages according to some embodiments of this disclosure. Here, 'purified' represents the number of differentially methylated sites between pure tumor tissue and adjacent normal tissue as restored by the tumor percentage correction algorithm, '-k%' represents the dilution gradient, and 'p' and 'delta' are parameters of the DSS algorithm, representing the p-value and the threshold for difference, respectively. Figure 5A As shown, after gradient dilution, the number of differentially methylated sites decreases significantly with increasing dilution gradient. This indicates that the proportion of pure tumor tissue in mixed tumor tissue greatly affects the number of differentially methylated sites. However, after tumor proportion correction using the algorithm disclosed in this paper, the number of differentially methylated sites remains relatively stable at each dilution gradient.

[0078] Figure 5B A schematic diagram illustrating the cohort differential methylation site inclusion relationships at different dilution ratios before correction is shown, with settings p = 0.01 and delta = 0.2 according to some embodiments of this disclosure. Figure 5B As shown, the differentially methylated sites obtained after correcting for tumor proportion in the original data basically include the differentially methylated sites found in the original data, as well as the differentially methylated sites after gradient dilution.

[0079] Figure 5C A schematic diagram illustrating the cohort differential methylation site inclusion relationships at different dilution ratios after correction, with parameters p = 0.01 and delta = 0.2 according to some embodiments of this disclosure. Figure 5C As shown, the data restored after gradient dilution and tumor proportion correction still have good correlation.

[0080] Depend on Figures 5A-5C As can be seen, the pure tumor tissue and adjacent normal tissue corrected and restored by the algorithm disclosed herein can find more differentially methylated sites, thereby enabling the identification of more early screening (DOC) markers.

[0081] In some embodiments, tissue origination (TOO) biomarker screening was performed using the aforementioned colorectal cancer cohort and another liver cancer cohort with a sample size of 15. The approximate average tumor percentage in the liver cancer cohort was 0.644, with the highest tumor percentage at 0.95 and the lowest at 0.41. Serial dilutions were performed on liver cancer and its adjacent normal tissues, and on colorectal cancer and its adjacent normal tissues, respectively, to obtain the tissue origination biomarker screening results based on the DSS algorithm.

[0082] Figures 6A-6C The results of an experiment using corrected methylation levels to detect tissue traceability markers according to embodiments of the present disclosure are shown. Figure 6A Schematic diagrams illustrating the number of differentially methylated sites in a fixed colorectal cancer sample and a diluted liver cancer sample, and vice versa, are shown, according to some embodiments of the present disclosure. Figure 6A As shown, as the gradient dilution increases, the diluted cancer sample tends to be more like the adjacent normal sample, and the differential methylation sites tend to be more like the difference between the fixed cancer and the diluted adjacent normal. The number of differential methylation sites before correction shows a monotonically increasing or monotonically decreasing trend with the increase of the dilution gradient.

[0083] Figure 6B A schematic diagram illustrating the cohort differential methylation site inclusion relationships at different dilution ratios before correction, with settings p = 1e^-5, delta = 0.2, for colorectal cancer fixation and liver cancer dilution, according to some embodiments of this disclosure, is shown. Figure 6B As shown, as the proportion of tumors decreases, the overlap of differentially methylated sites decreases, and the number of false positive sites increases.

[0084] Figure 6C A schematic diagram illustrating the cohort differential methylation site inclusion relationships at different dilution ratios after correction, with parameters p = 1e^-5, delta = 0.2, for colorectal cancer fixation and liver cancer dilution, is shown according to some embodiments of this disclosure. Figure 6C As shown, when the dilution gradient is small (the overall proportion of tumors is high), the false positive rate is low and the detection rate is high; when the dilution gradient is large (the overall proportion of tumors is low), although the false positive rate increases, it is still smaller than before correction, and the detection rate is still very high.

[0085] Depend on Figures 6A-6C It is evident that the pure tumor tissue corrected and restored by the algorithm disclosed herein can effectively reduce the false positive problem of tissue traceability markers under any overall tumor proportion.

[0086] The above is for reference only. Figures 2 to 6CExemplary embodiments of this disclosure are described. Compared to existing schemes for measuring the methylation level of target CpG sites in mixed tumor tissues, the methylation level measurement scheme of this disclosure can utilize paired adjacent normal tissues and reference adjacent normal tissues to increase the accuracy of tumor tissue methylation level measurement and effectively avoid the bias of linear models in solving methylation levels. In some implementations, the methylation level of paired adjacent normal tissue samples can also be corrected using the average methylation level of the reference adjacent normal tissue sample, thereby making the measured methylation level of tumor tissue more accurate. In some implementations, the number of reads is directly used for subsequent calculations, which can assign higher weights to samples with higher sequencing depth, thereby making the tumor tissue methylation level measurement results more robust. In some implementations, the final methylation level of tumor tissue can also be calculated iteratively based on the EM algorithm, thereby effectively avoiding the problems of missed screening of early screening biomarkers and false positives of tissue traceability biomarkers.

[0087] Figure 7 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, is shown. The apparatus 700 can be implemented in, for example... Figure 1 The methylation level corrector 122 is located in the computing device 100 shown. For example... Figure 7 As shown, the device 700 includes: a methylation measurement unit 710, a read distribution determination unit 720, and a methylation level determination unit 730.

[0088] In some embodiments, the methylation measurement unit 710 is configured to acquire a first methylation measurement result of a mixed tumor tissue sample and a second methylation measurement result of a paired adjacent normal tissue sample, the mixed tumor tissue sample including tumor tissue and adjacent normal tissue; the read distribution determination unit 720 is configured to determine, based on the first methylation measurement result and a third methylation measurement result of a reference adjacent normal tissue sample, the distribution of reads including target CpG sites in the mixed tumor tissue sample originating from tumor tissue or adjacent normal tissue; and the methylation level determination unit 730 is 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 in the tumor tissue of the mixed tumor tissue sample.

[0089] It should be noted that the reference Figures 2 to 4 More actions or steps can be shown through Figure 7 The illustrated device 700 is used to implement this. For example, device 700 may include more modules or units to implement the actions or steps described above, or Figure 7 Some of the units or modules shown can be further configured to implement the actions or steps described above. This will not be repeated here.

[0090] In some embodiments, the methods and processes described above can be implemented as a computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of this disclosure.

[0091] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0092] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper cables, fiber optic cables, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to computer-readable storage media within the respective computing / processing device.

[0093] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​and conventional procedural programming languages. The computer-readable program instructions may execute entirely on a user's computer, partially on a user's computer, as a standalone software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0094] 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 apparatus to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0095] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be 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 perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0096] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0097] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they 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 chosen to best explain the principles, practical application, or technical improvements to the technology in the market, 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 tumor tissue, comprising: Obtain the first methylation measurement results of a mixed tumor tissue sample and the second methylation measurement results of a paired adjacent normal tissue sample, wherein the mixed tumor tissue sample includes tumor tissue and adjacent normal tissue from the same patient; Based on the first methylation measurement result and the third methylation measurement result of the reference adjacent normal tissue sample, the distribution of the reads including the target CpG site in the mixed tumor tissue sample originating from tumor tissue or adjacent normal tissue is determined, wherein the reference adjacent normal tissue sample is a collection of adjacent normal tissues from multiple patients. as well as Based on the determined distribution, the first methylation measurement result, and the second methylation measurement result, the methylation level of the target CpG site in the tumor tissue of the mixed tumor tissue sample is determined.

2. The method according to claim 1, wherein, Also includes: Based on the second methylation measurement results, the methylation level of the target CpG site in the paired adjacent normal tissue sample was determined; as well as The methylation level of the target CpG site in the paired adjacent normal tissue samples was corrected using the third methylation measurement results.

3. The method according to claim 2, wherein, The correction of the methylation level of the target CpG site in the paired adjacent normal tissue sample includes: Based on the reference adjacent normal tissue sample, the average methylation level and methylation probability shrinkage rate of the target CpG site were obtained; and Based on the average methylation level and the methylation probability shrinkage rate, the methylation level of the target CpG site in the paired adjacent normal tissue sample is corrected.

4. The method according to claim 1, wherein, The determination of the distribution of reads containing the target CpG site in the mixed tumor tissue sample originating from tumor tissue or adjacent normal tissue includes: Acquire the mixed tissue characteristics of the mixed tumor tissue, including the tumor percentage, the subclonal percentage of the target CpG site, and the copy number in the corresponding clonal state; and Based on the characteristics of the mixed tissue, it is determined that the reads including the target CpG site in the mixed tumor tissue sample originate from the tumor tissue or adjacent normal tissue.

5. The method of 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 includes: S1. Select an initial estimate of the methylation level of the target CpG site in the tumor tissue of the mixed tumor tissue sample; S2. Based on the initial estimate, the second methylation measurement result, and the distribution, obtain the expected value of the probability distribution of the methylation level of the target CpG site in the tumor tissue; S3. By maximizing the expected value, update the estimated methylation level of the target CpG site in the tumor tissue; and S4. Repeat steps S2 and S3 until the convergence condition is met.

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

8. A system for determining the methylation level of a target CpG site in tumor tissue, comprising: A methylation measurement unit is configured to acquire a first methylation measurement result of a mixed tumor tissue sample and a second methylation measurement result of a paired adjacent normal tissue sample, wherein the mixed tumor tissue sample includes tumor tissue and adjacent normal tissue from the same patient; The read distribution determination unit is configured to determine, based on the first methylation measurement result and the third methylation measurement result of the reference adjacent normal tissue sample, the distribution of reads including the target CpG site in the mixed tumor tissue sample originating from tumor tissue or adjacent normal tissue, wherein the reference adjacent normal tissue sample is a collection of adjacent normal tissues from multiple patients. as well as A methylation level determination unit is configured to determine the methylation level of the target CpG site 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 as described in any one of claims 1 to 7.

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

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

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