Apparatus and method for providing information for diagnosing micro-residual diseases through quantification of single-nucleotide variants in cfdna

Abstract

The present disclosure relates to an apparatus and a method for providing information for diagnosing micro-residual diseases through quantification of single-nucleotide variants in cfDNA. The apparatus according to the present disclosure comprises: a memory in which at least one process for performing an operation for providing information for diagnosing micro-residual diseases is stored; and a processor for performing an operation according to the process, wherein the processor analyzes WGS of patient-derived cancer tissues using two or more pieces of somatic variant analysis software to obtain data on the positions of consensus single-nucleotide variants commonly identified in primary cancer samples, matches reference sequences at the positions of the single-nucleotide variants with cfDNA WGS of the patient to obtain supporting reads matching in which the variants match in overlapping regions of paired reads, filters the obtained supporting reads on the basis of predetermined conditions, and generates probability data related to micro-residual diseases on the basis of the filtered supporting reads.

Description

Information providing device and method for diagnosing minimal residual disease through quantification of single nucleotide variants in CFDNA

[0001] The present disclosure relates to an information-providing apparatus and method for quantifying single nucleotide variations in cfDNA (Cell-free DNA) samples derived from cancer patients and using this to sensitively diagnose Minimal Residual Disease (MRD) in cancer patients.

[0002] Next Generation Sequencing (NGS) is a high-speed genomic analysis method that divides the genome into countless fragments, reads them, and aligns the resulting sequence fragments to analyze the genome's sequence. Whole-Genome Sequencing (WGS) using NGS technology is useful for detecting almost all types of somatic variants; thanks to this utility, it is widely used in various fields and plays a particularly important role in cancer genomics.

[0003] Genome analysis businesses are rapidly developing worldwide, and these next-generation sequencing techniques are being actively utilized in the fields of clinical genomics, pharmaco-genomics, and translational medicine.

[0004] Single nucleotide variants (SNVs) occur with high frequency in cancer patients. It is known that cfDNA enters the bloodstream due to apoptosis in cancer cells, and if a single nucleotide variant specific to the primary cancer is detected in the cfDNA, it is expected that this can be used to diagnose minimal residual disease (MRD) remaining in the body after cancer treatment.

[0005] The purpose of the present disclosure is to provide an information-providing apparatus and method that quantifies single nucleotide variants using WGS of primary cancer and utilizes this for the diagnosis of minimal residual disease.

[0006] The problems that this disclosure aims to solve are not limited to those mentioned above, and other unmentioned problems will be clearly understood by a person skilled in the art from the description below.

[0007] An information providing device for diagnosing minimal residual disease through the quantification of single nucleotide mutations in cfDNA to achieve the aforementioned technical task includes a memory in which at least one process for performing an information providing operation for diagnosing minimal residual disease is stored, and a processor that performs an operation according to said process. The processor analyzes the WGS of patient-derived cancer tissue using two or more somatic mutation analysis software to obtain consensus single nucleotide mutation location data of primary cancer samples that are commonly identified, matches the reference sequence of said single nucleotide mutation location with the cfDNA WGS of the patient to obtain supporting reads in which the mutation matches in the overlapping region of the paired read, filters the obtained supporting reads based on predetermined conditions, and can generate probability data related to minimal residual disease based on the unfiltered supporting reads.

[0008] In one embodiment, the above-mentioned predetermined condition may be that the depth of a single variation location is at least twice the average plus standard deviation of the depth.

[0009] In one embodiment, the above-mentioned predetermined condition may be that there is at least 1% of supporting leads in the BAM for the patient's normal cells and in the BAM for the non-patient's normal cells.

[0010] In one embodiment, the above-mentioned predetermined condition may be that the ratio of leads where the CIGAR of the leads at a single variant position is not all Match is 10% or more.

[0011] In one embodiment, the above-mentioned predetermined condition may be that the ratio of reads in which there are two or more regions that match but do not exactly match the reference sequence of the single mutation location and the patient's cfDNA WGS is 25% or more.

[0012] In one embodiment, the predetermined condition may be when the number of support leads at the single variation position is an outlier from the high-frequency number value.

[0013] In one embodiment, the WGS analysis software may be two or more of Mutect2, Strelka2, and MuSE.

[0014] In one embodiment, the cfDNA may be detected in a cfDNA sample derived from a cancer patient, which is derived from the cerebrospinal fluid, pleural fluid, pericardial fluid, ascites, urine, whole blood, plasma, or serum of the cancer patient.

[0015] In one embodiment, the cancer is gastric cancer, lung cancer, non-small cell lung cancer, breast cancer, ovarian cancer, liver cancer, bronchial cancer, nasopharyngeal cancer, laryngeal cancer, pancreatic cancer, bladder cancer, colorectal cancer, colon cancer, cervical cancer, bone cancer, non-small cell bone cancer, blood cancer, skin cancer (melanoma, etc.), head or neck cancer, uterine cancer, rectal cancer, anal cancer, colon cancer, fallopian tube cancer, endometrial cancer, vaginal cancer, vulvar cancer, Hodgkin's disease, esophageal cancer, small intestine cancer, endocrine gland cancer, thyroid cancer, parathyroid cancer, adrenal cancer, soft tissue sarcoma, urethral cancer, penile cancer, prostate cancer, chronic or acute leukemia, lymphocytic lymphoma, kidney or ureteral cancer, renal cell carcinoma, renal-pelvic carcinoma, polyploid carcinoma, salivary gland cancer, sarcoma, pseudomyxoma, hepatoblastoma, testicular cancer. It may be detected in cfDNA samples derived from cancer patients, which are glioblastoma, lip cancer, ovarian germ cell tumor, basal cell carcinoma, multiple myeloma, gallbladder cancer, choroidal melanoma, ampulla of Vater cancer, peritoneal cancer, adrenal cancer, tongue cancer, small cell carcinoma, pediatric lymphoma, neuroblastoma, duodenal cancer, ureteral cancer, astrocytoma, meningioma, renal pelvis cancer, vulvar cancer, thymic cancer, central nervous system (CNS) tumor, primary CNS lymphoma, spinal cord tumor, brainstem glioma or pituitary adenoma.

[0016] In one embodiment, the information regarding the minimal residual disease may be probability information regarding the possibility of cancer recurrence during or after treatment.

[0017] Additionally, the method for providing information for diagnosing minimal residual disease through the detection of single nucleotide mutations in cfDNA according to the present disclosure may include the steps of: a processor analyzing WGS of patient-derived cancer tissue with two or more somatic mutation analysis software to obtain consensus single nucleotide mutation location data of primary cancer samples that are commonly identified; the processor matching the reference sequence of the single nucleotide mutation location with the cfDNA WGS of the patient to obtain a supporting read in which the mutation matches in an overlapping region of a paired read; the processor filtering the obtained supporting read based on predetermined conditions; and the processor outputting probability data related to minimal residual disease based on the filtered supporting read.

[0018] The information providing device according to the present disclosure can detect single nucleotide mutations with high sensitivity even if cancer-derived cfDNA is present in blood or the like with low purity, and by utilizing single nucleotide mutations that are present in almost all cancers, it can be used universally for diagnosing minimal residual disease regardless of the type of cancer.

[0019] In addition, the information providing device according to the present disclosure can detect microscopic residual disease using only a sample from a cancer patient without the need to secure a large number of samples from other people to build a diagnostic model.

[0020] FIG. 1 is an overall system diagram of the present disclosure.

[0021] FIG. 2 is a block diagram of a server included in the information providing device of the present disclosure.

[0022] FIG. 3 is a block diagram of a terminal included in the information providing device of the present disclosure.

[0023] FIGS. 4 to 7 are flowcharts of an information provision method according to the present disclosure.

[0024] FIG. 8 is a table showing cell line experimental conditions according to the present disclosure.

[0025] Figure 9 is a graph showing an example of the results of an experiment conducted under 1:1600 mix conditions.

[0026] Figure 10 is a graph showing an example of the results of an experiment conducted under the 1:12800 mix condition.

[0027] Figure 11 is a graph showing the correlation between the Mix ratio and the ctDNA ratio.

[0028] Figures 12 to 15 show the results of the first clinical verification.

[0029] Figures 16 to 18 show the results of the second clinical verification.

[0030] Figures 19 and 20 are the results of the third clinical verification.

[0031] Figure 21 shows the experimental results for a blank sample.

[0032] Throughout this disclosure, the same reference numerals denote the same components. This disclosure does not describe all elements of the embodiments, and general content in the art to which this disclosure pertains or content that overlaps between embodiments is omitted. The terms 'part, module, component, block' as used in the specification may be implemented in software or hardware, and depending on the embodiments, a plurality of 'parts, modules, components, blocks' may be implemented as a single component, or a single 'part, module, component, block' may include a plurality of components.

[0033] Throughout the specification, when a part is described as being "connected" to another part, this includes not only cases where they are directly connected but also cases where they are indirectly connected, and indirect connections include connections made via a wireless communication network.

[0034] Furthermore, when it is stated that a part "includes" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0035] Throughout the specification, when it is stated that a component is located "on" another component, this includes not only cases where a component is in contact with another component, but also cases where another component exists between the two components.

[0036] The terms first, second, etc. are used to distinguish one component from another, and the components are not limited by the aforementioned terms.

[0037] Singular expressions include plural expressions unless there is an obvious exception in the context.

[0038] In each step, identification codes are used for convenience of explanation and do not describe the order of the steps; the steps may be performed differently from the specified order unless a specific order is clearly indicated in the context.

[0039] The operating principles and embodiments of the present disclosure will be described below with reference to the attached drawings.

[0040] In this specification, the term "device according to the present disclosure" includes all various devices capable of performing computational processing and providing results to a user. For example, the device according to the present disclosure may include all of a computer, a server device, and a portable terminal, or may be in the form of any one of these.

[0041] Here, the computer may include, for example, a notebook, desktop, laptop, tablet PC, slate PC, etc. equipped with a web browser.

[0042] The above server device is a server that processes information by communicating with an external device, and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, and a web server.

[0043] The above portable terminal may include, for example, all types of handheld-based wireless communication devices such as PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminals, smartphones, etc., as well as wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted devices (HMDs).

[0044] The information providing device according to the present disclosure may be implemented by at least one of a server and a terminal. Specifically, the device according to the present disclosure may be implemented by either a server or a terminal, or may be implemented as a system through data transmission and reception between a server and a terminal.

[0045] Hereinafter, an information providing device according to the present disclosure will be described.

[0046] Referring to FIG. 1, an information providing device according to the present disclosure may include a server (10) and a terminal (20).

[0047] The server (10) is connected to the terminal (20) via a network, and after receiving data necessary for diagnosis from the terminal (20), can transmit the diagnosis result to the terminal (20).

[0048] Meanwhile, it is obvious to a person skilled in the art that the above-described terminal (20) is not limited to the portable terminal described above and may include a laptop, desktop, laptop, tablet PC, slate PC, etc. equipped with a processor.

[0049] As described above, the information providing device according to the present disclosure can be implemented through data transmission and reception between a server (10) and a terminal (20).

[0050] Hereinafter, each of the server (10) and terminal (20) for implementing the information providing device according to the present disclosure will be described.

[0051] FIG. 2 is a block diagram of a server included in the information providing device of the present disclosure.

[0052] A server (100) according to the present disclosure may include at least one of a communication unit (110), a storage unit (120), and a processor (130).

[0053] The communication unit (110) can communicate with at least one of a terminal, an external storage (e.g., a database (140)), an external server, and a cloud server.

[0054] Meanwhile, an external server or cloud server may be configured to perform at least a part of the role of the processor (130). That is, the performance of data processing or data computation, etc., can be performed on an external server or cloud server, and the present invention does not impose any special restrictions on such a method.

[0055] Meanwhile, the communication unit (110) can support various communication methods according to the communication standards of the target being communicated (e.g., electronic device, external server, device, etc.).

[0056] For example, the communication unit (110) may be configured to communicate with a communication target using at least one of the following technologies: WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Wi-Fi (Wireless Fidelity) Direct, DLNA (Digital Living Network Alliance), WiBro (Wireless Broadband), WiMAX (World Interoperability for Microwave Access), HSDPA (High Speed ​​Downlink Packet Access), HSUPA (High Speed ​​Uplink Packet Access), LTE (Long Term Evolution), LTE-A (Long Term Evolution-Advanced), 5G (5th Generation Mobile Telecommunication), Bluetooth (Bluetooth™), RFID (Radio Frequency Identification), Infrared Data Association (IrDA), UWB (Ultra-Wideband), ZigBee, NFC (Near Field Communication), Wi-Fi Direct, and Wireless USB (Wireless Universal Serial Bus).

[0057] Next, the storage unit (120) may be configured to store various information related to the present invention. In the present invention, the storage unit (120) may be provided in the device itself according to the present invention. Alternatively, at least part of the storage unit (120) may refer to at least one of a database (DB, 140) or a cloud storage (or cloud server). That is, the storage unit (120) is sufficient as a space where information necessary for the device and method according to the present invention is stored, and it can be understood that there are no restrictions on physical space. Accordingly, below, the storage unit (120), database (140), external storage, and cloud storage (or cloud server) will not be distinguished separately and will all be referred to as the storage unit (120).

[0058] Next, the processor (130) may be configured to control the overall operation of the device related to the present invention. The processor (130) may process signals, data, information, etc. that are input or output through the components described above, or provide or process appropriate information or functions to the user.

[0059] The processor (130) includes at least one CPU (Central Processing Unit) and can perform the functions according to the present invention.

[0060] At least one component may be added or removed in response to the performance of the components illustrated in FIG. 2. Additionally, it will be readily understood by those skilled in the art that the relative positions of the components may be changed in response to the performance or structure of the device.

[0061] Hereinafter, a terminal included in the information providing device of the present disclosure will be described in detail.

[0062] FIG. 3 is a block diagram of a terminal included in the information providing device of the present disclosure.

[0063] Referring to FIG. 3, the terminal (200) according to the present disclosure may include a communication unit (210), an input unit (220), a display unit (230), and a processor (240), etc. Since the components illustrated in FIG. 3 are not essential for implementing the information providing device according to the present disclosure, the terminal described in this specification may have more or fewer components than those listed above.

[0064] Among the above components, the communication unit (210) may include one or more components that enable communication with an external device, and, for example, may include at least one of a broadcast receiving module, a wired communication module, a wireless communication module, a short-range communication module, and a location information module.

[0065] The wired communication module may include various wired communication modules such as a Local Area Network (LAN) module, a Wide Area Network (WAN) module, or a Value Added Network (VAN) module, as well as various cable communication modules such as USB (Universal Serial Bus), HDMI (High Definition Multimedia Interface), DVI (Digital Visual Interface), RS-1302 (recommended standard 1302), power line communication, or POTS (plain old telephone service).

[0066] In addition to Wi-Fi modules and WiBro (Wireless broadband) modules, the wireless communication module may include wireless communication modules that support various wireless communication methods such as GSM (global System for Mobile Communication), CDMA (Code Division Multiple Access), WCDMA (Wideband Code Division Multiple Access), UMTS (universal mobile telecommunications system), TDMA (Time Division Multiple Access), LTE (Long Term Evolution), 4G, 5G, and 6G.

[0067] The input unit (220) is for inputting video information (or signal), audio information (or signal), data, or information input from a user, and may include at least one of at least one camera, at least one microphone, and a user input unit. Voice data or image data collected from the input unit may be analyzed and processed into a user control command.

[0068] The display unit (230) is intended to generate outputs related to sight, hearing, or touch, and may include at least one of a display unit, an audio output unit, a haptic module, and an optical output unit. The display unit may form a layered structure with the touch sensor or be formed integrally to implement a touch screen. Such a touch screen functions as a user input unit that provides an input interface between the device and the user, and at the same time can provide an output interface between the device and the user.

[0069] The display unit displays (outputs) information processed by the device. For example, the display unit may display execution screen information of an application program (e.g., an application) running on the device, or UI (User Interface) and GUI (Graphic User Interface) information based on such execution screen information.

[0070] In addition to the components described above, the terminal described above may further include an interface unit and memory.

[0071] The interface section serves as a passage for various types of external devices connected to the present device. This interface section may include at least one of a wired / wireless headset port, an external charger port, a wired / wireless data port, a memory card port, a port for connecting a device equipped with a SIM card, an audio I / O (Input / Output) port, a video I / O (Input / Output) port, and an earphone port. The present device can perform appropriate control related to the external device connected to the interface section.

[0072] The memory can store data supporting various functions of the device and programs for the operation of the processor, and can store input / output data (e.g., music files, still images, videos, etc.), and can store multiple application programs (or applications) running on the device, data for the operation of the device, and instructions. At least some of these application programs may be downloaded from an external server via wireless communication.

[0073] Such memory may include at least one type of storage medium among flash memory type, hard disk type, SSD type (Solid State Disk type), SSD type (Silicon Disk Drive type), multimedia card micro type, card type memory (e.g., SD or XD memory, etc.), RAM (random access memory; RAM), SRAM (static random access memory), ROM (read-only memory; ROM), EEPROM (electrically erasable programmable read-only memory), PROM (programmable read-only memory), magnetic memory, magnetic disk, and optical disk. Additionally, the memory may be a database that is separate from the device but connected via wired or wireless connection.

[0074] Meanwhile, the above-described terminal includes a processor (240). The processor may be implemented as a memory that stores data for an algorithm or a program that reproduces the algorithm for controlling the operation of components within the device, and at least one processor (not shown) that performs the above-described operation using the data stored in the memory. In this case, the memory and the processor may each be implemented as separate chips. Alternatively, the memory and the processor may be implemented as a single chip.

[0075] In addition, the processor may control one or a combination of the components described above in order to implement various embodiments according to the present disclosure described in the drawings below on the device.

[0076] Meanwhile, at least one component may be added or removed in response to the performance of the components illustrated in FIGS. 1 to 3. In addition, it will be readily understood by those skilled in the art that the relative positions of the components may be changed in response to the performance or structure of the device.

[0077] Hereinafter, a method for providing information using the components described in FIGS. 1 to 3 will be described.

[0078] FIGS. 4 to 7 are flowcharts of an information provision method according to the present disclosure.

[0079] Referring to FIGS. 4 and 5, a step is performed in which a processor analyzes the WGS of patient-derived cancer tissue using two or more somatic mutation analysis software to obtain common (consensus) single nucleotide mutation location data of the primary cancer sample that is commonly identified (S110).

[0080] The processor obtains common (consensus) single nucleotide variant data of the primary cancer sample (S210). At this time, the WGS of the patient-derived cancer tissue and the WGS of the normal control group can be analyzed as two or more single nucleotide variant analysis data.

[0081] In the above steps, two or more single nucleotide variant analysis software may be used to obtain more accurate single nucleotide variant data of the patient.

[0082] In the above step, the processor performs WGS and alignment using cancer tissue obtained from a cancer patient, i.e., a primary cancer sample and a normal control, and through this, mapping is performed to identify the location of the sequencing reads on the reference genome, determining which chromosome and which position the DNA is located at. Once mapping is completed, chromosome number and location information on the reference genome are provided for each sequencing read, and a BAM (binary alignment map) format, which is an aligned base sequence containing this information, can be obtained (S220).

[0083] In this specification, 'consensus single nucleotide variant location data' refers to information regarding single nucleotide variant locations identified as common across multiple single nucleotide variant analysis software programs executed simultaneously.

[0084] The above step may be used for the purpose of building a database of single nucleotide variants present in primary cancer samples of cancer patients, and the common single nucleotide variant location data obtained in this way can be repeatedly utilized for monitoring purposes in comparative analysis with cfDNA single nucleotide variants of the same cancer patient obtained at various points in time. To this end, the single nucleotide variant data may be stored and utilized in a separate cloud.

[0085] The single nucleotide sequence variant analysis software used in the present invention may use various types of software known in the field to detect somatic single nucleotide sequence variants through whole-genome analysis without limitation, and may use two or more types selected from the group consisting of Mutect2, Strelka2, and MuSE.

[0086] Next, referring to FIGS. 4 and FIGS. 6, a step of filtering the detected SNV by the processor is performed (S120).

[0087] Specifically, the processor matches the reference sequence of the single nucleotide variant location with the patient's cfDNA WGS to obtain a supporting read in which the variant matches in the overlapping region of the paired read (S320).

[0088] Afterwards, the processor filters the acquired support leads based on predetermined conditions (S330).

[0089] The above predetermined conditions are as follows:

[0090] 1) The depth of a single variation location must be at least twice the mean plus the standard deviation of the depths.

[0091] 2) There shall be at least 1% of supporting leads in the BAM of the patient's normal cells and the BAM of the non-patient's normal cells.

[0092] 3) The percentage of leads with CIGARs that are not all Matched among leads in a single variant position must be 10% or higher.

[0093] 4) The proportion of reads in which there are two or more regions that match the reference sequence of the single mutation site above but do not exactly match in the patient's cfDNA WGS is 25% or more.

[0094] 5) This may be the case when the number of support leads at the single variation position is an outlier in the high-frequency count value.

[0095] The processor excludes support leads satisfying the above conditions from the analysis target. The processor may exclude some of the secured support leads from the analysis target by utilizing at least one of the five conditions.

[0096] Finally, referring to Figures 4 and 7, a step is performed in which the processor generates probability data related to minimal residual disease based on filtered support leads.

[0097] PCR errors, which are a pre-sequencing step, cannot be distinguished from genuine mutations in the data. To overcome this, the processor creates a false-positive normal distribution (e.g., Shapiro test > 0.05) by repeatedly randomly selecting common single nucleotide polymorphisms (SNPs) that are not present in the patient (e.g., 10,000) and checking the number of error reads that occurred as false positives at those locations (S420), and can check the significance probability of the number of mutation reads at somatic mutation locations relative to this distribution (S430).

[0098] Based on the filtered support leads, the processor can generate and output probability information regarding the possibility of cancer recurrence during or after treatment based on information about the minimal residual disease.

[0099] The present disclosure is characterized by the ability to detect microscopic residual cancer with high sensitivity by quantifying low proportions of nucleotide sequence variations present in cfDNA samples universally, regardless of the type of cancer. In the present invention, the cfDNA may be, but is not limited to, cfDNA derived from the cerebrospinal fluid, pleural fluid, pericardial fluid, ascites, urine, whole blood, plasma, or serum of a cancer patient.

[0100] The detection sensitivity of the present invention is determined according to the error level of the corresponding sample.

[0101] Meanwhile, the cfDNA of the cancer patient to be analyzed in step S120 above may be obtained from the patient during or after cancer treatment. In the present invention, a database of common single nucleotide mutation sites present in the primary cancer tissue of the cancer patient is constructed in step S110 above, and subsequently, by comparing the sequence of single nucleotide mutation sites in the patient's cfDNA during or after cancer treatment, information on minute levels of residual cancer cells remaining in the patient after treatment can be provided.

[0102] More specifically, the above step S120 is as follows: First, the processor obtains cfDNA mapping data, i.e., cfDNA BAM (binary alignment map) format in which chromosome number and location information on the standard genome is recorded for each sequencing read obtained by WGS and alignment of the cancer patient's cfDNA. Subsequently, the processor inputs the obtained cfDNA mapping data, the cfDNA BAM file, into single nucleotide variant quantification software along with the single nucleotide variant location data obtained in step S110, and performs a supporting read call to quantify the single nucleotide variant present in the cfDNA sample by detecting the supporting reads along with the counter reference sequence at the common single nucleotide variant location identified in step S110.

[0103] The method of the present invention can be universally applied to all cancers regardless of the type of cancer, and unlike existing imaging, blood tests, or targeted high-depth sequencing methods, it does not involve a separate process of preparing test items or pre-designs according to the type of cancer. Accordingly, the cancer subject to the present invention may include, without limitation, cancer types known in the art, such as gastric cancer, lung cancer, non-small cell lung cancer, breast cancer, ovarian cancer, liver cancer, bronchial cancer, nasopharyngeal cancer, laryngeal cancer, pancreatic cancer, bladder cancer, colorectal cancer, colon cancer, cervical cancer, bone cancer, non-small cell bone cancer, blood cancer, skin cancer (melanoma, etc.), head or neck cancer, uterine cancer, rectal cancer, prostatic cancer, colon cancer, fallopian tube cancer, endometrial cancer, vaginal cancer, vulvar cancer, Hodgkin's disease, esophageal cancer, small intestine cancer, endocrine gland cancer, thyroid cancer, parathyroid cancer, adrenal cancer, soft tissue sarcoma, urethral cancer, penile cancer, prostate cancer, chronic or acute leukemia, lymphocytic lymphoma, kidney or ureteral cancer, renal cell carcinoma, renal-pelvic carcinoma, polyploid carcinoma, salivary gland cancer, sarcoma, pseudomyxoma, It may be hepatoblastoma, testicular cancer, glioblastoma, lip cancer, ovarian germ cell tumor, basal cell carcinoma, multiple myeloma, gallbladder cancer, choroidal melanoma, ampulla of Vater cancer, peritoneal cancer, adrenal cancer, tongue cancer, small cell carcinoma, pediatric lymphoma, neuroblastoma, duodenal cancer, ureteral cancer, astrocytoma, meningioma, renal pelvis cancer, vulvar cancer, thymic cancer, central nervous system (CNS) tumor, primary CNS lymphoma, spinal cord tumor, brainstem glioma or pituitary adenoma.

[0104] The single nucleotide variant to be detected in the present disclosure is a somatic single nucleotide variant, and refers to a variant in which a single nucleotide shows a sequence-specific difference when compared to a corresponding reference sequence. In most cancers, hundreds to tens of thousands of single nucleotide variants occur in the early stages of cancer development, and these early single nucleotide variants are maintained during the progression of cancer. In cancer cells, cfDNA is released into the bloodstream through apoptosis, and if a single nucleotide variant sequence found in cancer cells is identified in the cfDNA present in the bloodstream, it can be diagnosed that cancer remains in the patient's body and that there is a possibility of causing minimal residual disease. Therefore, the single nucleotide variant sequence to be detected in the present invention is the sequence of a single nucleotide variant present in cfDNA among the single nucleotide variants confirmed to exist in the patient's cancer cells.

[0105] Example. Diagnosis of minimal residual disease through detection of single nucleotide variants in cfDNA

[0106] Cancer cells and standard materials were used as samples for the analysis verification experiment. In defining variants, NA12878 was designated as the standard material, and SNVs were quantified based on it. NA12878 was purchased from the Coriell Institute. Five cancer cell lines (WM2664, A375, SNU16, HCC1954, HCC95) were purchased from the Korean Cell Line Bank.

[0107] DNA from NA12878 and DNA collected from five cancer cell lines (WM2664, A375, SNU16, HCC1954, HCC95) were shared at a size of 150 to 170 bp, which is known as a typical cfDNA fragment size. Each of the five cancer cell lines was diluted to 10 ng / ul and mixed in equal volumes of 50 ul to prepare a final mixed sample of 10 ng / ul and 250 ul, which was prepared as the initial sample. The initial sample volume of NA 12878 was 50 ng / ul. Subsequently, the mixing ratios of these five types of cancer cell mixture samples and NA 12878 were varied as cell line: NA12878 = 1:100, 1:200, 1:400, 1:800, 1:1600, 1:3200, 1:6400, and 1:12800, and were used in experiments to confirm the sensitivity of the present invention, and all experiments were repeated three times.

[0108] The overview of the cell line mix experiment is shown in Table 9. 1:3200, 1:6400, and 1:12800 mixes were also prepared using the same method as above.

[0109] Referring to Figures 9 through 11, it was confirmed that a statistically significant number of supporting reads could be identified in all samples, including 1:12,800, thereby identifying single nucleotide variants in patient cfDNA samples and providing information on minimal residual disease. Figure 9 is an example of results for a sample mixed with HCC1954 (CD_21_16910) and NA12878 at a ratio of 1:1600, and Figure 10 is an example of results for a sample mixed with A375SM (CD_21_16908) and NA12878 at a ratio of 1:12800. In each result, the shapiro test p-value, mean (mean_estimate), and standard deviation (sd_estimate) of the error read frequency are measured, and the frequency of supporting reads (prov_altvcf) and the probability of the frequency of supporting reads in the error distribution are presented. The upper figure of Fig. 11 shows the frequency of supporting leads detected at each dilution ratio, and in all cases, it was measured with a significant probability [p ​​value (1-probability) < 0.0001%]. The lower figure of Fig. 11 shows the dilution ratio measurement [ corrected dilution ratio = ( detection ratio - average error lead frequency) x 2 ] corrected by considering the average error of the sample in the frequency of supporting leads detected at each dilution ratio, which shows a high correlation (r=0.996) with the actual dilution ratio.

[0110] Meanwhile, the analysis method according to the present disclosure was applied to three cancer patients.

[0111] The results of the first clinical trial are as follows.

[0112] - Case 1

[0113] - Diagnosis: Brain metastatic adenocarcinoma of lung

[0114] - Date of plasma sampling: 06-11-07 [CD_20_17018_XP_WGS], 07-01-23 [CD_23_02063_PL_F_SRG_1], 07-04-12 [CD_20_17025_XP_WGS], 07-06-18[CD_23_02064_PL_F_SRG_1]

[0115] Referring to Figures 12 to 15, it can be seen that the rate at which somatic SNVs are observed in the blood of clinical patients is similar to the frequency of ctDNA support leads measured by this method.

[0116] The results of the second clinical trial are as follows.

[0117] - Case 2

[0118] - Diagnosis: Brain: Metastatic squamous cell carcinoma of lung

[0119] - Date of plasma sampling: 10-10-29 [CD_20_17017_XP_WGS], 11-01- 04 [CD_20_17024_XP_WGS]

[0120] Referring to Figures 16 to 18, it can be seen that the rate at which somatic SNVs are observed in the blood of clinical patients is similar to the frequency of ctDNA support leads measured by this method.

[0121] The results of the third clinical trial are as follows.

[0122] - Case 3

[0123] - Diagnosis: Brain metastatic adenocarcinoma of lung

[0124] - Date of plasma sampling: 10-07-13 [CD_20_17012_XP_WGS], 15-07-02 [CD_20_17019_XP_WGS]

[0125] Referring to Figures 19 and 20, it can be seen that the rate at which somatic SNVs are observed in the blood of clinical patients is similar to the frequency of ctDNA support leads measured by this method.

[0126] Meanwhile, referring to Figure 21, it was confirmed that in the case of the Blank sample, the probability of the support lead frequency was not significant.

[0127] As described above, the information providing device according to the present disclosure can quantify single nucleotide mutations with high sensitivity even if cancer-derived cfDNA is present in blood or the like with low purity, and can be used universally for the diagnosis of minimal residual disease regardless of the type of cancer. In addition, since WGS is sufficiently possible with a small amount of plasma, generally 2-5 ml, there is an advantage in that cancer cells remaining in a patient after cancer treatment can be detected with a small amount of sample.

[0128] In addition, the information providing device according to the present disclosure can detect microscopic residual disease using only a blood sample from a cancer patient without the need to secure a large number of samples from other people to build a diagnostic model.

[0129] Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium that stores instructions executable by a computer. The instructions may be stored in the form of program code and, when executed by a processor, may generate a program module to perform the operation of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

[0130] Computer-readable recording media include all types of recording media that store instructions that can be decoded by a computer. Examples include ROM (Read Only Memory), RAM (Random Access Memory), magnetic tape, magnetic disk, flash memory, optical data storage devices, etc.

[0131] As described above, the disclosed embodiments have been explained with reference to the attached drawings. Those skilled in the art will understand that the present disclosure may be practiced in forms different from the disclosed embodiments without changing the technical spirit or essential features of the present disclosure. The disclosed embodiments are illustrative and should not be interpreted restrictively.

Claims

1. An information providing device for diagnosing minimal residual disease through the detection of single nucleotide sequence variants in cfDNA, A memory storing at least one process for performing an information provision operation for diagnosing minimal residual disease; and It includes a processor that performs operations according to the above process, and The above processor is, By analyzing the WGS of patient-derived cancer tissue using two or more somatic mutation analysis software to obtain consensus single nucleotide variant location data of primary cancer samples that are commonly identified, and By matching the reference sequence of the above single nucleotide variant location with the patient's cfDNA WGS, supporting reads in which the variant matches are obtained from the overlapping region of the paired reads, and Filter the above-mentioned support leads based on predetermined conditions, and Information providing device that generates probability data related to minimal residual disease based on filtered support leads.

2. In Paragraph 1, The above predetermined conditions are, An information providing device characterized in that the depth of a single variation location is at least twice the average plus standard deviation of the depth.

3. In Paragraph 2, The above predetermined conditions are, An information providing device characterized by the presence of 1% or more of supporting leads in the BAM for the normal cells of the above-mentioned patient and the BAM for the normal cells of the non-patient.

4. In Paragraph 3, The above predetermined conditions are, An information providing device characterized by the fact that the ratio of leads in which the CIGAR of a single variant location is not all Match is 10% or more.

5. In Paragraph 4, The above predetermined conditions are, An information providing device characterized by having a ratio of 25% or more of reads in which there are two or more regions that match but do not exactly match the reference sequence of the single mutation location in the patient's cfDNA WGS.

6. In Paragraph 5, The above predetermined conditions are, An information providing device characterized in that the number of support leads at the above single variation position is an outlier in the high-frequency count value.

7. In Paragraph 5, The above WGS analysis software is an information providing device characterized by being at least two of Mutect2, Strelka2, and MuSE.

8. In Paragraph 6, An information providing device characterized by the fact that the above cfDNA is detected in a cancer patient-derived cfDNA sample, which is derived from the cancer patient's cerebrospinal fluid, pleural fluid, pericardial fluid, ascites, urine, whole blood, plasma, or serum.

9. In Paragraph 7, The above cancers include gastric cancer, lung cancer, non-small cell lung cancer, breast cancer, ovarian cancer, liver cancer, bronchial cancer, nasopharyngeal cancer, laryngeal cancer, pancreatic cancer, bladder cancer, colorectal cancer, colon cancer, cervical cancer, bone cancer, non-small cell bone cancer, blood cancer, skin cancer (melanoma, etc.), head or neck cancer, uterine cancer, rectal cancer, anal cancer, colon cancer, fallopian tube cancer, endometrial cancer, vaginal cancer, vulvar cancer, Hodgkin's disease, esophageal cancer, small intestine cancer, endocrine gland cancer, thyroid cancer, parathyroid cancer, adrenal cancer, soft tissue sarcoma, urethral cancer, penile cancer, prostate cancer, chronic or acute leukemia, lymphocytic lymphoma, kidney or ureteral cancer, renal cell carcinoma, renal-pelvic carcinoma, polyploid carcinoma, salivary gland cancer, sarcoma, pseudomyxoma, hepatoblastoma, testicular cancer, glioblastoma, lip cancer. An information providing device characterized by detecting cfDNA samples derived from cancer patients, which are ovarian germ cell tumors, basal cell carcinoma, multiple myeloma, gallbladder cancer, choroidal melanoma, ampulla of Vater cancer, peritoneal cancer, adrenal cancer, tongue cancer, small cell carcinoma, pediatric lymphoma, neuroblastoma, duodenal cancer, ureteral cancer, astrocytoma, meningioma, renal pelvis cancer, vulvar cancer, thymic cancer, central nervous system (CNS) tumors, primary CNS lymphoma, spinal cord tumors, brainstem gliomas, or pituitary adenomas.

10. In Paragraph 8, An information providing device characterized in that the information regarding the above-mentioned minimal residual disease is probability information regarding the possibility of cancer recurrence during or after treatment.

11. A method for providing information to diagnose minimal residual disease through the detection of single nucleotide variants in cfDNA, A step in which a processor analyzes the WGS of patient-derived cancer tissue using two or more somatic mutation analysis software to obtain common (consensus) single nucleotide mutation location data of the primary cancer samples that are commonly identified; The processor matches the reference sequence of the single nucleotide variant location with the cfDNA WGS of the patient to obtain a supporting read in which the variant matches in the overlapping region of the paired read; The step of the processor filtering the acquired support leads based on predetermined conditions; and A method for providing information comprising the step of the above processor outputting probability data related to minimal residual disease based on filtered support leads.

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